diff --git a/CRC_velocity.png b/CRC_velocity.png new file mode 100644 index 0000000..94e1d80 Binary files /dev/null and b/CRC_velocity.png differ diff --git a/Cell_Cell_interaction.r b/Cell_Cell_interaction.r new file mode 100644 index 0000000..6184b69 --- /dev/null +++ b/Cell_Cell_interaction.r @@ -0,0 +1,386 @@ + + +library(Seurat) +dat <- readRDS("/public/workspace/lily/Lung2Brain/Version5/CSOmap/LCBM_tumor_myeloid.RDS") +dat@active.ident <- factor(dat$celltype) +tmp <- AverageExpression(dat,assay="RNA")$RNA +tmp$M.T.FC <- tmp$MDM/tmp$tumor +tmp$T.M.FC <- tmp$tumor/tmp$MDM +# tmp <- tmp[order(tmp$M.T.FC,decreasing=T),] +tmp.f <- tmp[-which(is.infinite(tmp$T.M.FC)|is.infinite(tmp$M.T.FC)),] +MDM.h <- rownames(tmp.f)[which(tmp.f$M.T.FC>2)] +tumor.f <- rownames(tmp.f)[which(tmp.f$T.M.FC>100)] +# 2021-6-10 +library(CellChat) +CellChatDB <- CellChatDB.human + +info <- data.frame(ligand=sapply(strsplit(as.vector(CellChatDB$interaction$interaction_name_2)," - "),function(x){x[1]}), +receport=sapply(strsplit(as.vector(CellChatDB$interaction$interaction_name_2)," - "),function(x){x[2]}),stringsAsFactors=F) + +for(i in 1:nrow(info)){ + if(length(grep("\\)|\\(",info[i,2]))>0){ + tmp <- gsub("\\(|\\)|^ ","",info[i,2]) + pair <- strsplit(tmp,"\\+")[[1]] + for(j in 1:length(pair)){ + value <- c(info[i,1],pair[j]) + info <- rbind(info,value) + } + } +} +info.f <- info[-grep("\\+",info$receport),] # get gene pair +colnames(info.f) <- c("ligand","receptor") +saveRDS(info.f,file="/public/workspace/lily/Lung2Brain/Version5/CellChat/gene_pair.RDS") +# get gene expression + +library(Seurat) +dat <- readRDS("/public/workspace/lily/Lung2Brain/Version5/CSOmap/LCBM_tumor_myeloid.RDS") +dat@active.ident <- factor(dat$celltype) +tmp <- AverageExpression(dat,assay="RNA")$RNA +tmp$M.T <- tmp$MDM-tmp$tumor +tmp$T.M <- tmp$tumor-tmp$MDM +tmp$gene <- rownames(tmp) + +# LUAD +dat <- readRDS("/public/workspace/lily/Lung2Brain/Version5/CSOmap/LUAD_tumor_myeloid.RDS") +dat@active.ident <- factor(dat$celltype) +tmp <- AverageExpression(dat,assay="RNA")$RNA +tmp$M.T <- tmp$MDM-tmp$tumor +tmp$T.M <- tmp$tumor-tmp$MDM +tmp$gene <- rownames(tmp) + + +# GBM +dat <- readRDS("/public/workspace/lily/Lung2Brain/Version5/CSOmap/GBM_tumor_myeloid.RDS") +dat@active.ident <- factor(dat$celltype) +tmp <- AverageExpression(dat,assay="RNA")$RNA +tmp$M.T <- tmp$MDM-tmp$GBM_tumor +tmp$T.M <- tmp$GBM_tumor-tmp$MDM +tmp$gene <- rownames(tmp) + + +genepair <- readRDS("/public/workspace/lily/Lung2Brain/Version5/CellChat/gene_pair.RDS") +# genepair$ligand <- gsub(" $","",genepair$ligand) +# genepair$receptor <- gsub("^ ","",genepair$receptor) +# saveRDS(genepair,file="/public/workspace/lily/Lung2Brain/Version5/CellChat/gene_pair.RDS") +tmp1 <- merge(tmp[,c("M.T","gene")],genepair,by.x="gene",by.y="ligand",all.y=T) +colnames(tmp1)[1] <- "ligand" +tmp2 <- merge(tmp[,c("T.M","gene")],tmp1,by.x="gene",by.y="receptor",all.y=T) +colnames(tmp2)[1] <- "receptor" +tmp2[which(tmp2$M.T>0.5&tmp2$T.M>0.5),] + + + + + + + + + + +################################################################################################################### +# 2021-6-11 +# +library(Seurat) +dat <- readRDS("/public/workspace/lily/Lung2Brain/Version5/CSOmap/LCBM_tumor_myeloid.RDS") +dat@active.ident <- factor(dat$celltype) +tmp <- AverageExpression(dat,assay="RNA")$RNA +tmp$M.T <- tmp$MDM-tmp$tumor +tmp$T.M <- tmp$tumor-tmp$MDM +tmp$gene <- rownames(tmp) + +dat$class1 <- as.vector(dat$celltype) +dat$class1[which(dat$celltype%in%c("MG","Monocyte","tumor"))] <- "other" +dat@active.ident <- factor(dat$class1) +tmp1 <- AverageExpression(dat,assay="RNA")$RNA +tmp1$MDM.h <- tmp1$MDM -tmp1$other + +dat$class2 <- as.vector(dat$celltype) +dat$class2[which(dat$celltype%in%c("MG","Monocyte","MDM"))] <- "other" +dat@active.ident <- factor(dat$class2) +tmp2 <- AverageExpression(dat,assay="RNA")$RNA +tmp2$tumor.h <- tmp2$tumor -tmp2$other + +tmp <- cbind(tmp1$MDM.h,tmp2$tumor.h) +colnames(tmp) <- c("M.T","T.M") +rownames(tmp) <- rownames(tmp1) +tmp <- data.frame(tmp) +tmp$gene <- rownames(tmp) + + +genepair <- readRDS("/public/workspace/lily/Lung2Brain/Version5/CellChat/gene_pair.RDS") +# genepair$ligand <- gsub(" $","",genepair$ligand) +# genepair$receptor <- gsub("^ ","",genepair$receptor) +# saveRDS(genepair,file="/public/workspace/lily/Lung2Brain/Version5/CellChat/gene_pair.RDS") +tmp1 <- merge(tmp[,c("M.T","gene")],genepair,by.x="gene",by.y="receptor",all.y=T) +colnames(tmp1)[1] <- "receptor" +tmp2 <- merge(tmp[,c("T.M","gene")],tmp1,by.x="gene",by.y="ligand",all.y=T) +colnames(tmp2)[1] <- "ligand" + +tmp2[which(tmp2$M.T>0.5&tmp2$T.M>0.5),] + + + +tmp1 <- merge(tmp[,c("M.T","gene")],genepair,by.x="gene",by.y="ligand",all.y=T) +colnames(tmp1)[1] <- "ligand" +tmp2 <- merge(tmp[,c("T.M","gene")],tmp1,by.x="gene",by.y="receptor",all.y=T) +colnames(tmp2)[1] <- "receptor" +tmp2[which(tmp2$M.T>0.5&tmp2$T.M>0.5),] + + +# get gene pair names + +tmp.log <- log2(tmp[,c("MDM","tumor")]+1) +tmp.log$group <- "no" +tmp.log$group[which(rownames(tmp.log)%in%c("SDC4","OSMR","EGFR","ITGA3","IL1R1","SDC1"))] <- "receptor" +tmp.log$group[which(rownames(tmp.log)%in%c("FN1","THBS1","OSM","IL1B","HBEGF"))] <- "ligand" +tmp.log$group <- factor(tmp.log$group,levels=c("receptor","ligand","no")) + +tmp.log$gene <- "" +tmp.log$gene[which(rownames(tmp.log)%in%c("SDC4","OSMR","EGFR","ITGA3","IL1R1","SDC1"))] <- rownames(tmp.log)[which(rownames(tmp.log)%in%c("SDC4","OSMR","EGFR","ITGA3","IL1R1","SDC1"))] +tmp.log$gene[which(rownames(tmp.log)%in%c("FN1","THBS1","OSM","IL1B","HBEGF"))] <- rownames(tmp.log)[which(rownames(tmp.log)%in%c("FN1","THBS1","OSM","IL1B","HBEGF"))] + + +pdf("/public/workspace/lily/Lung2Brain/Version5/Myeloid/MDM2Tumor.interaction.pdf",useDingbats=F) +ggplot(tmp.log,aes(x=MDM,y=tumor,colour=group,group=group)) + geom_point() + theme_classic() + + scale_colour_manual(values=c("#007cc0","#ffb310","grey")) + + geom_text(aes(label = gene), size = 3) + +dev.off() + + + + + + + + + + +####################################################################################################################################### +# 2021-6-17 +# CellChat result check +#====================================================================================================================================== +# 1. MDM2Tumor +library(Seurat) +library(CellChat) + + +cellchat1 <- readRDS("/public/workspace/lily/Lung2Brain/Version5/CellChat/LCBM.CellChat.RDS") +cellchat2 <- readRDS("/public/workspace/lily/Lung2Brain/Version5/CellChat/GBM.CellChat.RDS") +cellchat3 <- readRDS("/public/workspace/lily/Lung2Brain/Version5/CellChat/LUAD.CellChat.RDS") + +# LCBM +tmp <- netVisual_bubble(cellchat1, sources.use = 3, targets.use = 4, remove.isolate = FALSE,return.data=T)$communication +tmp.res <- tmp[,c("prob","interaction_name_2")] +tmp.res$group <- "LCBM" +tmp1 <- tmp.res +# LUAD +tmp <- netVisual_bubble(cellchat3, sources.use = 1, targets.use = 4, remove.isolate = FALSE,return.data=T)$communication +tmp.res <- tmp[,c("prob","interaction_name_2")] +tmp.res$group <- "LUAD" +tmp3 <- tmp.res +# GBM +tmp <- netVisual_bubble(cellchat2, sources.use = 2, targets.use = 1, remove.isolate = FALSE,return.data=T)$communication +tmp.res <- tmp[,c("prob","interaction_name_2")] +tmp.res$group <- "GBM" +tmp2 <- tmp.res + +# merge interaction data +tmp.f <- merge(tmp1,tmp2,all.x=T,all.y=T,by="interaction_name_2") +colnames(tmp.f)[2:5] <- c("prob.LCBM","group.LCBM","prob.GBM","group.GBM") +tmp.rs <- merge(tmp.f,tmp3,all.x=T,all.y=T,by="interaction_name_2") + +sub.rs <- tmp.rs[,c("interaction_name_2","prob.LCBM","prob.GBM","prob")] +colnames(sub.rs)[4] <- "prob.LUAD" + + + +tmp.lung <- sub.rs[which(sub.rs$prob.LUAD>0&sub.rs$prob.LCBM>0&is.na(sub.rs$prob.GBM)),] +rownames(tmp.lung) <- tmp.lung$interaction_name_2 +tmp.lung <- tmp.lung[order(tmp.lung$prob.LCBM,decreasing=T),] +pdf("/public/workspace/lily/Lung2Brain/Version5/Myeloid/LCBM.LUAD.CellChat.MDM.tumor.pdf",useDingbats=F) +pheatmap::pheatmap(tmp.lung[,2:4],na_col="grey",cluster_rows=F,cluster_cols=F) +dev.off() + +tmp.brain <- sub.rs[which(sub.rs$prob.GBM>0&sub.rs$prob.LCBM>0&is.na(sub.rs$prob.LUAD)),] +rownames(tmp.brain) <- tmp.brain$interaction_name_2 +tmp.brain <- tmp.brain[order(tmp.brain$prob.LCBM,decreasing=T),] +pdf("/public/workspace/lily/Lung2Brain/Version5/Myeloid/LCBM.GBM.CellChat.MDM.tumor.pdf",useDingbats=F) +pheatmap::pheatmap(tmp.brain[,2:4],na_col="grey",cluster_rows=F,cluster_cols=F) +dev.off() + + + + + + + +# 2021-7-6 +# 2. Tumor2MDM +##################################################################################################################################### +library(Seurat) +library(CellChat) + + +cellchat1 <- readRDS("/public/workspace/lily/Lung2Brain/Version5/CellChat/LCBM.CellChat.RDS") +cellchat2 <- readRDS("/public/workspace/lily/Lung2Brain/Version5/CellChat/GBM.CellChat.RDS") +cellchat3 <- readRDS("/public/workspace/lily/Lung2Brain/Version5/CellChat/LUAD.CellChat.RDS") + +# LCBM +tmp <- netVisual_bubble(cellchat1, sources.use = 4, targets.use = 1, remove.isolate = FALSE,return.data=T)$communication +tmp.res <- tmp[,c("prob","interaction_name_2")] +tmp.res$group <- "LCBM" +tmp1 <- tmp.res +# LUAD +tmp <- netVisual_bubble(cellchat3, sources.use = 4, targets.use = 1, remove.isolate = FALSE,return.data=T)$communication +tmp.res <- tmp[,c("prob","interaction_name_2")] +tmp.res$group <- "LUAD" +tmp3 <- tmp.res +# GBM +tmp <- netVisual_bubble(cellchat2, sources.use = 1, targets.use = 2, remove.isolate = FALSE,return.data=T)$communication +tmp.res <- tmp[,c("prob","interaction_name_2")] +tmp.res$group <- "GBM" +tmp2 <- tmp.res + +# merge interaction data +tmp.f <- merge(tmp1,tmp2,all.x=T,all.y=T,by="interaction_name_2") +colnames(tmp.f)[2:5] <- c("prob.LCBM","group.LCBM","prob.GBM","group.GBM") +tmp.rs <- merge(tmp.f,tmp3,all.x=T,all.y=T,by="interaction_name_2") + +sub.rs <- tmp.rs[,c("interaction_name_2","prob.LCBM","prob.GBM","prob")] +colnames(sub.rs)[4] <- "prob.LUAD" + + +tmp.lung <- sub.rs[which(sub.rs$prob.LUAD>0&sub.rs$prob.LCBM>0&is.na(sub.rs$prob.GBM)),] +rownames(tmp.lung) <- tmp.lung$interaction_name_2 +tmp.lung <- tmp.lung[order(tmp.lung$prob.LCBM,decreasing=T),] +pdf("/public/workspace/lily/Lung2Brain/Version5/Myeloid/LCBM.LUAD.CellChat.Tumor.mdm.pdf",useDingbats=F) +pheatmap::pheatmap(tmp.lung[,2:4],na_col="grey",cluster_rows=F,cluster_cols=F) +dev.off() + +tmp.brain <- sub.rs[which(sub.rs$prob.GBM>0&sub.rs$prob.LCBM>0&is.na(sub.rs$prob.LUAD)),] +rownames(tmp.brain) <- tmp.brain$interaction_name_2 +tmp.brain <- tmp.brain[order(tmp.brain$prob.LCBM,decreasing=T),] +pdf("/public/workspace/lily/Lung2Brain/Version5/Myeloid/LCBM.GBM.CellChat.Tumor.mdm.pdf",useDingbats=F) +pheatmap::pheatmap(tmp.brain[,2:4],na_col="grey",cluster_rows=F,cluster_cols=F) +dev.off() + + + + + + + + + + + + + +# 2021-7-6 +# calculate LCBM samples Tumor with Myeloid cell cell communication +#################################################################################################################################### +library(Seurat) +library(CellChat) +# make a data +dat <- readRDS("/public/workspace/lily/Lung2Brain/Version5/Myeloid/inte9_myeloid.RDS") +sub.dat <- subset(dat,cells=which(dat$type_group=="LCBM")) +tumor <- readRDS("/public/workspace/lily/Lung2Brain/Version5/Fig4/Data/LCBM_sub.tumor.RDS") +tumor$celltype <- tumor$group +tmp.res <- merge(sub.dat,tumor) +sub.dat.f <- subset(tmp.res,cells=which(tmp.res$celltype%in%c("MDM","MG","Monocyte","group09","group16","group47"))) +saveRDS(sub.dat.f,file="/public/workspace/lily/Lung2Brain/Version5/Myeloid/SubTumor/LCBM_subtumor_myeloid.RDS") + +# by the way ,do cell chat analysis +dat <- readRDS("/public/workspace/lily/Lung2Brain/Version5/Myeloid/SubTumor/LCBM_subtumor_myeloid.RDS") +data.input <- GetAssayData(dat, assay = "RNA", slot = "data") # normalized data matrix +meta <- data.frame(dat@meta.data) # create a dataframe of the cell labels +cellchat <- createCellChat(object = data.input, meta = meta, group.by = "celltype") +cellchat <- setIdent(cellchat, ident.use = "celltype") # set "labels" as default cell identity +levels(cellchat@idents) + +CellChatDB <- CellChatDB.human +# use all DB +CellChatDB.use <- CellChatDB +# set the used database in the object +cellchat@DB <- CellChatDB.use +cellchat <- subsetData(cellchat) # This step is necessary even if using the whole database +future::plan("multiprocess", workers = 8) # do parallel + +##################################################################################################### +cellchat <- identifyOverExpressedGenes(cellchat) +cellchat <- identifyOverExpressedInteractions(cellchat) +# project gene expression data onto PPI network (should use) +cellchat <- projectData(cellchat, PPI.human) + +cellchat <- computeCommunProb(cellchat) # 1W cells 5min +# Filter out the cell-cell communication if there are only few number of cells in certain cell groups +cellchat <- filterCommunication(cellchat, min.cells = 10) +cellchat <- computeCommunProbPathway(cellchat) +# Calculate the aggregated cell-cell communication network +cellchat <- aggregateNet(cellchat) + +saveRDS(cellchat,file="/public/workspace/lily/Lung2Brain/Version5/Myeloid/SubTumor/LCBM_subtumor_myeloid.cellchat.RDS") + + + + + +#=================================================================================================================================== +# check result +# 0. interaction number differneces +library(CellChat) +cellchat <- readRDS("/public/workspace/lily/Lung2Brain/Version5/Myeloid/SubTumor/LCBM_subtumor_myeloid.cellchat.RDS") +groupSize <- table(cellchat@idents) +netVisual_circle(cellchat@net$count, vertex.weight = groupSize, weight.scale = T, label.edge= F, title.name = "Number of interactions") + +netVisual_bubble(cellchat, sources.use = 1, targets.use = 2, remove.isolate = FALSE,return.data=F) + + +cellchat@net$count -> tmp +tmp[1:3,1:3] +tmp[1:3,1:3] <- 0 +tmp[4:6,4:6] <- 0 + +rownames(tmp) <- paste0(rownames(tmp),".source") +colnames(tmp) <- paste0(colnames(tmp),".target") +groups <- rep(c("group09","group16","group47","MDM","MG","Monocyte"),2) +names(groups) <- c(rownames(tmp),colnames(tmp)) +cols <- c("#fbbc05","#fbbc05","#34a853","#34a853","#ea4335","#ea4335") +names(cols) <-c("Myeloid.source","Myeloid.target","T_cell.source","T_cell.target","Tumor_cell.source","Tumor_cell.target") + +netVisual_chord_cell_internal(tmp,color.use=cols,sources.use=rownames(tmp),targets.use=colnames(tmp),group=groups,big.gap=20,small.gap=0) + + + +# 1. tumor interaction with Myeloids +library(CellChat) +cellchat <- readRDS("/public/workspace/lily/Lung2Brain/Version5/Myeloid/SubTumor/LCBM_subtumor_myeloid.cellchat.RDS") +groupSize <- table(cellchat@idents) +tmp <- netVisual_bubble(cellchat, sources.use = 4, targets.use = 1:3, remove.isolate = FALSE,return.data=T)$communication +tmp.res <- tmp[,c("prob","source.target","interaction_name_2")] +rs <- reshape2::dcast(tmp.res,source.target~interaction_name_2,value.var="prob") +rs[is.na(rs)] <- 0 +rownames(rs) <- rs[,1] +rs <- rs[,-1] +rs <- data.frame(t(rs)) + + + + + + + + + + + + + + + + + + + + diff --git a/CellphoneDB.sh b/CellphoneDB.sh new file mode 100644 index 0000000..4be1cf7 --- /dev/null +++ b/CellphoneDB.sh @@ -0,0 +1,108 @@ + +# cellphone DB +# run in R +library(Seurat) +cellphoneDB_input <- function(mat, clusterInfo, expr_outfile, cellinfo_outfile){ + # mat: Seurat RDS + # clusterInfo: group in mat@meta.data + count = as.data.frame(mat@assays$RNA@counts) + Gene = rownames(mat@assays$RNA@counts) + genes = as.data.frame(Gene) + count1 <- cbind(genes, count) + write.table(count1, expr_outfile, + row.names = FALSE, quote = FALSE, sep = "\t") + info <- data.frame(Cell = colnames(mat), + cell_type = mat@meta.data[, clusterInfo]) + write.table(info, cellinfo_outfile, + row.names = FALSE, quote = FALSE, sep = "\t") +} + +outpath="/public/workspace/lily/Lung2Brain/Version5/Cellphone/Data/LCBM/" +tmp <- readRDS("/public/workspace/lily/Lung2Brain/Version5/CSOmap/LCBM_tumor_myeloid.RDS") +cellphoneDB_input(tmp,"celltype",paste0(outpath,"expr.txt"),paste0(outpath,"cellinfo.txt")) + +outpath="/public/workspace/lily/Lung2Brain/Version5/Cellphone/Data/LUAD/" +tmp <- readRDS("/public/workspace/lily/Lung2Brain/Version5/CSOmap/LUAD_tumor_myeloid.RDS") +cellphoneDB_input(tmp,"celltype",paste0(outpath,"expr.txt"),paste0(outpath,"cellinfo.txt")) + +outpath="/public/workspace/lily/Lung2Brain/Version5/Cellphone/Data/GBM/" +tmp <- readRDS("/public/workspace/lily/Lung2Brain/Version5/CSOmap/GBM_tumor_myeloid.RDS") +cellphoneDB_input(tmp,"celltype",paste0(outpath,"expr.txt"),paste0(outpath,"cellinfo.txt")) + +# run in shell +for i in `ls /public/workspace/lily/Lung2Brain/Version5/Cellphone/Data/` +do + bytlib load lib64.pool + bytlib load sqlite3-snapshot + bytlib load python-3.6.6 + bytlib load cellphonedb-2.1.1 + + mkdir -p /public/workspace/lily/Lung2Brain/Version5/Cellphone/Res/${i}/ + RESPATH="/public/workspace/lily/Lung2Brain/Version5/Cellphone/Res/${i}/" + # run CellphoneDB + cellphonedb method statistical_analysis /public/workspace/lily/Lung2Brain/Version5/Cellphone/Data/${i}/cellinfo.txt /public/workspace/lily/Lung2Brain/Version5/Cellphone/Data/${i}/expr.txt --threads 8 \ + --output-path=${RESPATH} --counts-data gene_name + +done + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/E-MTAB_8659_analysis.r b/E-MTAB_8659_analysis.r new file mode 100644 index 0000000..bfc8897 --- /dev/null +++ b/E-MTAB_8659_analysis.r @@ -0,0 +1,113 @@ + +# deal with E-MTAB-8659 +# 2021-9-8 +#=========================================================================================================================================== +samplelist <- dir("./") +tmp.res <- matrix(NA,nrow=48107) +for(i in 1:length(samplelist)){ + tmp <- read.table(paste0("./",samplelist[i]),header=T) + tmp.res <- cbind(tmp.res,tmp[,c(1,2)]) +} + +tmp.res <- tmp.res[,-1] +apply(tmp.res,2,function(x){all(as.vector(tmp.res[,1])==as.vector(x))}) # to check probe ID names + +res <- tmp.res[,grep("mean",colnames(tmp.res))] +res$probe <- tmp.res[,1] + +tmp.ann <- data.table::fread("/public/workspace/lily/metastasis/data/verify/E-MTAB-8659/A-GEOD-10558_comments.txt") +ann.f <- as.data.frame(tmp.ann[,c("Comment[Symbol]","Comment[Probe_Id]")]) +colnames(ann.f) <- c("Symbol","Probe") + +# filter some gene +res.f <- res[which(res$probe%in%ann.f$Probe),] +rs.dat <- merge(res.f,ann.f,by.x="probe",by.y="Probe") +rs.dat$probe <- NULL + +rs.f <- aggregate(.~Symbol,data=rs.dat,FUN=median) + +# trans date to gene name +tmp.date <- read.table("/public/workspace/lily/software/date_gene.txt") +for(i in 1:nrow(tmp.date)){ + rs.f[grep(paste0("^",tmp.date[i,2],"$"),rs.f$Symbol),"Symbol"] <- as.vector(tmp.date[i,1]) +} + +rs.ff <- rs.f[-1,] +rownames(rs.ff) <- as.vector(rs.ff$Symbol) +rs.ff$Symbol <- NULL + +saveRDS(rs.ff,file="/public/workspace/lily/metastasis/data/verify/E-MTAB-8659/LCBM_S63_data.RDS") + + + + +############################################################################################################################################ +# 2021-9-8 +# analysis in a new data ,what need to do +# 1. MDM and MG in sample +# 2. BMS Lung and Brain like tumor score + +dat <- readRDS("/public/workspace/lily/metastasis/data/verify/E-MTAB-8659/LCBM_S63_data.RDS") +source('/public/workspace/lily/software/ssGSEA/ssgseaMOD.r') +mod <- mod.analyze2(as.matrix(dat),c("BMS_update","Brain_gene","Lung_gene","BMDM_marker","MG_marker","RES","SEN","immunecheck"),"/public/workspace/lily/MOD_file/",permN=0) +mod <- as.data.frame(mod) + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/Fig1_add.r b/Fig1_add.r new file mode 100644 index 0000000..bafdef5 --- /dev/null +++ b/Fig1_add.r @@ -0,0 +1,284 @@ +#!/usr/bin/Rscript +# 2021-6-28 +# this program is used to add some plot for Figure1 +# the main body for figure1 is in Inte7_Figure1.R + + +# 0. LUAD gene signature https://www.nature.com/articles/1209442 +# come from nature medicine +library(Seurat) +dat <-readRDS("/public/workspace/lily/Lung2Brain/inte7/Data/inte7_ann.RDS") +# tumor <- subset(dat,cells=which(dat$type=="maliganant")) + +# calculate +source('/public/workspace/lily/software/ssGSEA/ssgseaMOD.r') +mod <- mod.analyze2(as.matrix(dat[['RNA']]@data),c("LUAD_tumor","Lung_gene"),"/public/workspace/lily/MOD_file/",permN=0) +mod <- as.data.frame(mod) +saveRDS(mod,file="/public/workspace/lily/Lung2Brain/Version5/Fig1/LUAD_sig_mod.RDS") + + +# plot result +mod$group <- dat$maliganant +pdf("/public/workspace/lily/Lung2Brain/Version5/Fig1/Res/LUAD_tumor_sig.pdf",useDingbats=F) +boxplot(LUAD_tumor_norm~group,data=mod,FUN=median,outline=F,ylims=c(0,1)) +dev.off() + + + + + + + + +# replot one result +dat <- readRDS("/public/workspace/lily/Lung2Brain/inte7/Data/inte7_ann.RDS") +data <- dat[['RNA']]@data + +tumor_gene <- apply(data[,which(dat$maliganant=="tumor")],2,function(x){length(which(x[]>0))}) +ntumor_gene <- apply(data[,which(dat$maliganant=="non-tumor")],2,function(x){length(which(x[]>0))}) + +pdf("/public/workspace/lily/Lung2Brain/Version5/Fig1/Res/tumor_ntumor_gene.pdf") +boxplot(tumor_gene,ntumor_gene,names=c('malignant','non-malignant'),main="express gene numbers",col=c("#E41A1C","#A6CEE3"),outline=F,ylim=c(0,8000)) +legend('topright',legend=paste('p =',round(wilcox.test(tumor_gene,ntumor_gene)$p.value,5)),bty='n') +dev.off() + + + + + + + + + + + + +# 0. change a idea ,use all cells to calculate inferCNV +#================================================================================================================================================ +library(infercnv) +#================================================================================================================================================ + +dat <- readRDS("/public/workspace/lily/Lung2Brain/inte7/Data/inte7_ann.RDS") +dat$maliganant[which(dat$maliganant=="non-tumor")] <- "non_tumor" +sample <- unique(dat$orig.ident) + +for(i in 1:length(sample)){ + tmp <- subset(dat,cells=which(dat$orig.ident==sample[i])) + dir.create(paste0("/public/workspace/lily/Lung2Brain/Version5/infercnv/",sample[i])) + setwd(paste0("/public/workspace/lily/Lung2Brain/Version5/infercnv/",sample[i])) + + write.table(tmp$maliganant,paste(sample[i],"cell_info.txt",sep='_'),sep="\t",col.names=F,quote=F) + count<-as.matrix(tmp@assays$RNA@counts) + write.table(count,paste(sample[i],"count_exp.txt",sep='_'),sep="\t",quote=F) + + infercnv_obj = CreateInfercnvObject(raw_counts_matrix=paste(sample[i],"count_exp.txt",sep='_'), + annotations_file=paste(sample[i],"cell_info.txt",sep='_'), + delim="\t", + gene_order_file="/public/workspace/lily/REF/INDEX-hg19/anno/gencode_hg19_pos.txt", + ref_group_names=c("non_tumor")) + + + infercnv_obj = infercnv::run(infercnv_obj, + cutoff=0.1, # cutoff=1 works well for Smart-seq2, and cutoff=0.1 works well for 10x Genomics + out_dir="./", + cluster_by_groups=T, + plot_steps=F, + no_prelim_plot = TRUE, + num_threads=10, #big + no_plot=F , + denoise=TRUE, + output_format = "pdf" # maybe can more quick + # used for final scaling to fit range (0,2) centered at 1. + ) + + + plot_cnv(infercnv_obj, out_dir = "./",output_filename = paste0("infercnv",sample[i]), output_format = "pdf",color_safe_pal=F) + + +} + + +# check result +# get sample expr data +dat.list <- list() +file <- dir("/public/workspace/lily/Lung2Brain/Version5/infercnv/") +for(i in 1:length(file)){ + tmp <- readRDS(paste0("/public/workspace/lily/Lung2Brain/Version5/infercnv/",file[i],"/run.final.infercnv_obj")) + tmp.exp <- tmp@expr.data + tmp.a <- apply(tmp.exp,2,function(x){sqrt(sum((x-1)^2))}) + tmp.rs <- data.frame(row.names=names(tmp.a),CNV.score=unname(tmp.a),sample=rep(file[i],length(tmp.a))) + dat.list[[i]] <- tmp.rs + names(dat.list)[i] <- file[i] +} + +# then combine all cells +rs <- dat.list[[1]] +rs <- rbind(rs,dat.list[[2]]) +rs <- rbind(rs,dat.list[[3]]) +rs <- rbind(rs,dat.list[[4]]) +rs <- rbind(rs,dat.list[[5]]) +rs <- rbind(rs,dat.list[[6]]) +rs <- rbind(rs,dat.list[[7]]) +dat <- readRDS("/public/workspace/lily/Lung2Brain/inte7/Data/inte7_ann.RDS") +rs <- rs[colnames(dat),] +rs$type <- dat$type +saveRDS(rs,file="/public/workspace/lily/Lung2Brain/Version5/Fig1/inte7_CNVscore.RDS") + +# get Seurat object andplot result +rs <- readRDS("/public/workspace/lily/Lung2Brain/Version5/Fig1/inte7_CNVscore.RDS") +aggregate(CNV.score~type,data=rs,FUN=median) + +# Plot +library(ggplot2) +library(dplyr) +library(hrbrthemes) +library(viridis) +library(gg.gap) + +rs$type <- factor(rs$type,levels=c("maliganant","Oligodendrocyte","Fibroblast","Endothelial","Myeloid","T_cell","B_cell")) +p1 <- ggplot(data=rs,aes(x=type, y=CNV.score, fill=type)) + + geom_violin(width=1.4) + + geom_boxplot(width=0.2, color="black", alpha=0.2,outlier.shape=NA) + + scale_fill_viridis(discrete = TRUE) + + theme( legend.position="none") + ylim(0,10) + + +p2 <- gg.gap(plot = p1, + segments = c(10, 20), + tick_width = 10, # 坐标间隔 + rel_heights = c(0.5,0, 0.25),# 设置分隔为的三个部分的宽度 + ylim = c(0,30) +) + +pdf("/public/workspace/lily/Lung2Brain/Version5/Fig1/Res/CNV.score.celltype.pdf",useDingbats=F) +print(p1) + +ggplot(data=rs,aes(x=type, y=CNV.score, fill=type)) + + geom_violin(width=1.4) + + geom_boxplot(width=0.2, color="black", alpha=0.2,outlier.shape=NA) + + scale_fill_viridis(discrete = TRUE) + + theme( legend.position="none") + +dev.off() + + + + + + + + + + + + + + +# 1. calculate Tumor between LCBM and LC for cytotrace +# 2021 -6- 29 +library(Seurat) +load("~/Lung2Brain/inte7/res_Data/cytotrace_result.RData") +dat <- readRDS("/public/workspace/lily/Lung2Brain/inte7/Data/inte7_ann.RDS") + +all(names(results$CytoTRACE)==colnames(tumor)) +pdf("/public/workspace/lily/Lung2Brain/Version5/Fig1/Res/Cytotrace_Tumor.pdf",useDingbats=F) +boxplot(CytoTrace~type_group,data=tumor@meta.data,FUN=median,outline=F,main="Stemness") +dev.off() + + + + + +# 2. Caculate entropy +# not very good ,myedloid show highest ocuupy +library(Seurat) +dat <- readRDS("/public/workspace/lily/Lung2Brain/inte7/Data/inte7_ann.RDS") + +# calculate occpuicy +clusters <- as.numeric(as.vector(unique(dat$seurat_clusters))) +dat$orig.ident <- factor(dat$orig.ident,levels=unique(dat$orig.ident)) +tmp <- sapply(clusters,function(i){ + + idx <- unname(which.max(table(dat$orig.ident[which(dat$seurat_clusters==i)])/table(dat$orig.ident))) + rs <- unname(table(dat$orig.ident[which(dat$seurat_clusters==i)])[idx])/length(which(dat$seurat_clusters==i)) + return(rs) +}) + +tmp.res <- data.frame(occupy= tmp,cluster= clusters) + +tmp.res$type <- "unknow" +tmp.res$type[which(tmp.res$cluster%in%c(1,2,3,18,39,36,9,30,19,34,40,43,15,28,35,38))] <- "T_cell" +tmp.res$type[which(tmp.res$cluster%in%c(4,6,12,13,16,7,26,25,14))] <- "Myeloid" +tmp.res$type[which(tmp.res$cluster%in%c(11,23))] <- "B_cell" +tmp.res$type[which(tmp.res$cluster%in%c(29,27))] <- "Oligodendrocyte" +tmp.res$type[which(tmp.res$cluster%in%c(22,33))] <- "Fibroblast" +tmp.res$type[which(tmp.res$cluster%in%c(32))] <- "Endothelial" +tmp.res$type[which(tmp.res$cluster%in%c(0,5,10,20,21,37,24,17,8,31))] <- "malignant" + + + +plot(density(tmp.res[which(tmp.res$type=="malignant"),"occupy"]),col="red") +lines(density(tmp.res[which(tmp.res$type=="T_cell"),"occupy"]),col="green") +lines(density(tmp.res[which(tmp.res$type=="Myeloid"),"occupy"]),col="orange") + + + + + + + + + + +#============================================================================================================================================ +# TCGA LUAD chr8 verify +# 2021-12-17 + +tmp.dat <- read.table("/public/workspace/lily/Lung2Brain/Data/luad_tcga_pub_segments.seg",header=T,sep="\t") + +dat <- tmp.dat[which(tmp.dat$chrom==8&tmp.dat$loc.start>46000123),] +dat$length <- dat$loc.end - dat$loc.start +dat$value <- dat$seg.mean * dat$length +tmp.res <- aggregate(.~ID,data=dat[,c(1,7,8)],FUN=sum) +tmp.res$res <- tmp.res$value / tmp.res$length +tmp.res$ID <- gsub("-",".",tmp.res$ID) +tmp.res$status <- ifelse(tmp.res$res>0,"gain","loss") +# load BMS update score +mod <- readRDS("/public/workspace/lily/Lung2Brain/Version5/Data/TCGA_LUAD_mod.RDS") +mod$ID <- rownames(mod) +res <- merge(tmp.res,mod[,c(2,4)],by="ID") +# + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/Fig2.r b/Fig2.r new file mode 100644 index 0000000..5fbaf9f --- /dev/null +++ b/Fig2.r @@ -0,0 +1,718 @@ +#!/usr/bin/Rscript + +# 2021-4-30 +# this program is used to plot Fig2 +#======================================================================================== +# 0. use sig_test.r change +# + +source("/public/workspace/lily/Lung2Brain/Version5/Fig2/sig_test.r") +sigtest(name="BMS_update") + + + + + + +######################################################################################### +# other some validate + +# 1. GSE8894 +#================================================================================================================== +dat <- readRDS("/public/workspace/lily/metastasis/data/verify/GSE8894/GSE8894_expr.RDS") +ann <- readRDS("/public/workspace/lily/metastasis/data/verify/GSE8894/ann.RDS") +ann.f <- ann[which(ann$Type=="Adenocarcinoma"),] +dat.f <- dat[,which(colnames(dat)%in%rownames(ann.f))] +source('~/software/ssGSEA/ssgseaMOD.r') + +mod <- mod.analyze2(as.matrix(dat.f),c("BMS_update"),'/public/workspace/lily/MOD_file/',permN=0) +mod <- data.frame(mod) +mod$Recurrent <- ann.f$Recurrent + +pdf("/public/workspace/lily/Lung2Brain/Version5/Fig2/GSE8894_recurrent.pdf",useDingbats=F) +boxplot(BMS_update_norm~Recurrent,data=mod,FUN=median,outline=F,ylim=c(0,1)) +legend("topright",legend=paste0("P.value=",wilcox.test(BMS_update_norm~Recurrent,data=mod)$p.value)) +dev.off() + + + + +# 2. TCGA LUAD early stage verify +#========================================================================================================================= +dat <- readRDS("/public/workspace/lily/metastasis/data/verify/TCGA_LUAD/TCGA_LUAD_exp.RDS") +tmp <- read.delim("/public/workspace/lily/metastasis/data/verify/TCGA_LUAD/LUAD_clinicalMatrix.txt",sep="\t",header=T) +tcga.clin <- tmp[,c("sampleID","pathologic_M","pathologic_N","pathologic_T","pathologic_stage","RFS.time","RFS","OS.time","OS", +"new_neoplasm_event_type","new_tumor_event_after_initial_treatment","days_to_new_tumor_event_after_initial_treatment")] +rownames(tcga.clin) <- gsub("-",".",tcga.clin$sampleID) +########################################################################################################################## +tcga.f <- dat[,which(colnames(dat)%in%rownames(tcga.clin)[which(tcga.clin$pathologic_stage%in%c("Stage I","Stage IA","Stage IB","Stage II","Stage IIA","Stage IIB"))])] + +# calculate BMS score +########################################################################################################################### +source('/public/workspace/lily/software/ssGSEA/ssgseaMOD.r') +mod <- mod.analyze2(as.matrix(tcga.f),c("BMS_update"),'/public/workspace/lily/MOD_file/',permN=0) +mod <- data.frame(mod) + +sample.high <- rownames(mod)[which(mod$BMS_update_norm>=quantile(mod$BMS_update_norm,0.67))] +sample.low <- rownames(mod)[which(mod$BMS_update_norm<=quantile(mod$BMS_update_norm,0.33))] + +tcga.clin.f <- tcga.clin[c(sample.high,sample.low),] +tcga.clin.f$group <- "High" +tcga.clin.f$group[which(rownames(tcga.clin.f)%in%sample.low)] <- "Low" + +library(survminer) +library(survival) + +# OS time +pdf("/public/workspace/lily/Lung2Brain/Version5/Fig2/TCGA_early_stage_surv.pdf",useDingbats=F) +surv <- Surv(tcga.clin.f$OS.time,tcga.clin.f$OS) +km <- survfit(surv~group,data=tcga.clin.f) +ggsurvplot(km,pval=T,palette=c(rgb(251,176,59,maxColorValue = 255),rgb(140,198,63,maxColorValue = 255),'grey'), +surv.median.line='hv',xlim=c(0,3000),xscale='d_m',break.time.by =304.37,xlab=" Overall survival (months)")+labs(title="Early stage LUAD") + +# RFS time +surv <- Surv(tcga.clin.f$RFS.time,tcga.clin.f$RFS) +km <- survfit(surv~group,data=tcga.clin.f) +ggsurvplot(km,pval=T,palette=c(rgb(251,176,59,maxColorValue = 255),rgb(140,198,63,maxColorValue = 255),'grey'), +surv.median.line='hv',xlim=c(0,3000),xscale='d_m',break.time.by =304.37,xlab=" Progression free survival (months)")+labs(title="Early stage LUAD") +dev.off() + + + + + +# 2021-8-11 +# 2021-8-12 +# try to use BMS to calculate Distant Metastasis and non-Metastasis sample +#============================================================================================================================================ +dat <- readRDS("/public/workspace/lily/metastasis/data/verify/TCGA_LUAD/TCGA_LUAD_exp.RDS") +tmp <- read.delim("/public/workspace/lily/metastasis/data/verify/TCGA_LUAD/LUAD_clinicalMatrix.txt",sep="\t",header=T) +tcga.clin <- tmp[,c("sampleID","pathologic_M","pathologic_N","pathologic_T","pathologic_stage","RFS.time","RFS","OS.time","OS", +"new_neoplasm_event_type","new_tumor_event_after_initial_treatment","days_to_new_tumor_event_after_initial_treatment")] +rownames(tcga.clin) <- gsub("-",".",tcga.clin$sampleID) +# get sample names +early <- rownames(tcga.clin)[which(tcga.clin$new_tumor_event_after_initial_treatment=="NO"&tcga.clin$pathologic_stage%in%c("Stage I","Stage IA","Stage IB","Stage II","Stage IIA","Stage IIB"))] +earlyM <- rownames(tcga.clin)[which(tcga.clin$new_neoplasm_event_type=="Distant Metastasis"&tcga.clin$pathologic_stage%in%c("Stage I","Stage IA","Stage IB","Stage II","Stage IIA","Stage IIB"))] +# get TCGA data and clinical data +tcga.f <- dat[,which(colnames(dat)%in%c(early,earlyM))] +tcga.clin.sub <- tcga.clin[which(rownames(tcga.clin)%in%c(early,earlyM)),] + + +# get sample names +# nMet <- rownames(tcga.clin)[which(tcga.clin$new_tumor_event_after_initial_treatment=="NO")] +# Met <- rownames(tcga.clin)[which(tcga.clin$new_neoplasm_event_type=="Distant Metastasis")] +# # get TCGA data and clinical data +# tcga.f <- dat[,which(colnames(dat)%in%c(nMet,Met))] +# tcga.clin.sub <- tcga.clin[which(rownames(tcga.clin)%in%c(nMet,Met)),] + +# tranlation Metastasis info +tcga.clin.sub$Met <- 0 +tcga.clin.sub$Met[which(tcga.clin.sub$new_tumor_event_after_initial_treatment=="YES")] <- 1 + +#=================================================================================================================================== +# calculate BMS and plot survival +source('/public/workspace/lily/software/ssGSEA/ssgseaMOD.r') +mod <- mod.analyze2(as.matrix(tcga.f),c("BMS_update"),'/public/workspace/lily/MOD_file/',permN=0) +mod <- data.frame(mod) + +# mod.rs <- readRDS("/public/workspace/lily/Lung2Brain/Version5/Data/TCGA_LUAD_mod.RDS") +# mod <- mod.rs[which(rownames(mod.rs)%in%rownames(mod)),] +# all(rownames(mod)==colnames(tcga.f)) + +sample.high <- rownames(mod)[which(mod$BMS_update_norm>=quantile(mod$BMS_update_norm,0.75))] +sample.low <- rownames(mod)[which(mod$BMS_update_norm<=quantile(mod$BMS_update_norm,0.25))] + +tcga.clin.f <- tcga.clin.sub[c(sample.high,sample.low),] +tcga.clin.f$group <- "High" +tcga.clin.f$group[which(rownames(tcga.clin.f)%in%sample.low)] <- "Low" + +library(survminer) +library(survival) + +pdf("/public/workspace/lily/Lung2Brain/Version5/Fig2/early_TCGA_Metastasis_surv_25p.pdf",useDingbats=F) +surv <- Surv(tcga.clin.f$RFS.time,tcga.clin.f$Met) +km <- survfit(surv~group,data=tcga.clin.f) +ggsurvplot(km,pval=T,palette=c(rgb(251,176,59,maxColorValue = 255),rgb(140,198,63,maxColorValue = 255),'grey'), +surv.median.line='hv',xlim=c(0,3000),xscale='d_m',break.time.by =304.37,xlab=" Progression free survival (months)")+labs(title="Early stage LUAD") +dev.off() + + +# try to use boxplot to check result +# however not significant +tcga_clin <- tcga.clin.sub[which(rownames(tcga.clin.sub)%in%rownames(mod)),] +tcga_clin <- tcga_clin[rownames(mod),] +all(rownames(tcga_clin)==rownames(mod)) + +mod$group <- tcga_clin$Met + + + + + + + + + + + +# 3. other survival validate +# GSE68465 +# 2021-4-30 seem this just have trend but not signifcant +#========================================================================================================================= + +dat <- readRDS("/public/workspace/lily/metastasis/data/verify/GSE68465/GSE68465_LUAD_expr.RDS") +info <- readRDS('/public/workspace/lily/metastasis/data/verify/GSE68465/info.RDS') + +source('/public/workspace/lily/software/ssGSEA/ssgseaMOD.r') +mod <- mod.analyze2(as.matrix(dat),c("BMS_update"),"/public/workspace/lily/MOD_file/",permN=0) +mod <- as.data.frame(mod) +res.ff <- merge(mod,info,by="row.names") + +#================================================ +# survival +#================================================ +library(survminer) +library(survival) + +res.ff$type <- "MID" +res.ff$type[which(res.ff$BMS_update_normunname(quantile(res.ff$BMS_update_norm,0.67)))] <- "High" +res.ff$OS_status <- 0 #change type info +res.ff$OS_status[which(res.ff$Status=="Dead")] <- 1 + +res.ff <- res.ff[-which(res.ff$type=="MID"),] +res.ff$OS.time <- as.numeric(as.vector(res.ff$months_to_last_contact_or_death)) +surv <- Surv(res.ff$OS.time,res.ff$OS_status) +km <- survfit(surv~type,data=res.ff) +pairwise_survdiff(Surv(res.ff$OS.time,res.ff$OS_status)~type,data=res.ff) +# pdf("./GSE68465.pdf") +ggsurvplot(km,pval=T,surv.median.line='hv')$plot+scale_colour_manual(values=c(rgb(251,176,59,maxColorValue = 255),rgb(140,198,63,maxColorValue = 255),'grey')) + +dev.off() + + + + + + +# 2021-9-26 +# add CCL20 to analysis +library(survminer) +library(survival) + +dat <- readRDS("/public/workspace/lily/metastasis/data/verify/GSE68465/GSE68465_LUAD_expr.RDS") +info <- readRDS('/public/workspace/lily/metastasis/data/verify/GSE68465/info.RDS') +survinfo <- info[colnames(dat),] + +survinfo$CCL20 <- as.numeric(dat["CCL20",]) +survinfo$type <- "MID" +survinfo$type[which(survinfo$CCL20unname(quantile(survinfo$CCL20,0.5)))] <- "High" +survinfo$OS_status <- 0 #change type info +survinfo$OS_status[which(survinfo$Status=="Dead")] <- 1 + +info.f <- survinfo[-which(survinfo$type=="MID"),] +info.f$OS.time <- as.numeric(as.vector(info.f$months_to_last_contact_or_death)) +surv <- Surv(info.f$OS.time,info.f$OS_status) +km <- survfit(surv~type,data=info.f) +pairwise_survdiff(Surv(info.f$OS.time,info.f$OS_status)~type,data=info.f) +# pdf("./GSE68465.pdf") +ggsurvplot(km,pval=T,surv.median.line='hv')$plot+scale_colour_manual(values=c(rgb(251,176,59,maxColorValue = 255),rgb(140,198,63,maxColorValue = 255),'grey')) + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +# 4. make sure this signature can be find in LCBM group +# 2021-5-10 +#============================================================================================================ +library(Seurat) +recluster <- function(tmp_dat){ +# seurat object + tmp_dat <- FindVariableFeatures(object = tmp_dat) + # scaling + all.genes <- rownames(x = tmp_dat) + tmp_dat <- ScaleData(object = tmp_dat, features = all.genes) + # PCA + tmp_dat <- RunPCA(object = tmp_dat, features = VariableFeatures(object = tmp_dat)) + # clustering + tmp_dat <- FindNeighbors(object = tmp_dat) + # select proper resolution + tmp_dat <- FindClusters(object = tmp_dat,resolution=0.5) + + tmp_dat <- RunTSNE(object = tmp_dat,dims=1:10) + return(tmp_dat) +} + +tumor.d <- readRDS("/public/workspace/lily/Lung2Brain/Multiple_LungBrain/tumor.D.RDS") +tumor.e <- readRDS("/public/workspace/lily/Lung2Brain/Multiple_LungBrain/tumor.E.RDS") +tumor.d <- recluster(tumor.d) +tumor.e <- recluster(tumor.e) + +# calculate BMS +source('~/software/ssGSEA/ssgseaMOD.r') +mod.d <- mod.analyze2(as.matrix(tumor.d[["RNA"]]@data),c("BMS_update"),'/public/workspace/lily/MOD_file/',permN=0) +mod.d <- data.frame(mod.d) + +mod.e <- mod.analyze2(as.matrix(tumor.e[["RNA"]]@data),c("BMS_update"),'/public/workspace/lily/MOD_file/',permN=0) +mod.e <- data.frame(mod.e) + +# +tumor.d$BMS_update <- mod.d[,2] +tumor.e$BMS_update <- mod.e[,2] + + +pdf("./tmp.pdf") +DimPlot(tumor.d,label=T,reduction="tsne") +FeaturePlot(tumor.d,features="BMS_update",reduction="tsne",min.cutoff=0.5) +DimPlot(tumor.e,label=T,reduction="tsne") +FeaturePlot(tumor.e,features="BMS_update",reduction="tsne",min.cutoff=0.5) + +DimPlot(tumor.d,label=T) +FeaturePlot(tumor.d,features="BMS_update",min.cutoff=0.6) +DimPlot(tumor.e,label=T) +FeaturePlot(tumor.e,features="BMS_update",min.cutoff=0.6) +dev.off() + + + + + + + + + +# 5. why choose C4 and C7 +mod <- readRDS("/public/workspace/lily/Lung2Brain/Version5/Signature/LCBM_hallmark.RDS") +# mod <- readRDS("/public/workspace/lily/Lung2Brain/Version5/Signature/LCBM_metastasis_pathway.RDS") +dat <- readRDS("/public/workspace/lily/Lung2Brain/Version5/Signature/pur_LCBM_clust.RDS") + +# mod.f <- mod[,22:42] +mod.f <- mod[,51:100] +mod.f$Cluster <- paste0("C",dat$seurat_clusters) +tmp.res <- aggregate(.~Cluster,data=mod.f,FUN=median) +rownames(tmp.res) <- tmp.res$Cluster +tmp.res$Cluster <- NULL +res.f <- t(tmp.res) + +table(apply(res.f,1,function(x){colnames(res.f)[order(x,decreasing=T)]})[1:2,]) +# calculate hallmark and top2 cluster +# > table(apply(res.f,1,function(x){colnames(res.f)[order(x,decreasing=T)]})[1:2,]) + +# C0 C1 C10 C2 C4 C6 C7 C8 C9 +# 16 1 16 1 29 1 21 7 8 + + + + + +# add a asian data +# GSE31210 +# use shell to get sample info +library(GEOquery) +gset <- getGEO( "GSE31210", getGPL = F ) +Gset <- gset[[1]] +pdata<-pData(Gset) +sampleinfo <- pdata[,c(2,11,62,64,52,53,54,57)] +colnames(sampleinfo) <- c("GEO","Age","Stage","RFS","OS.time","RFS.time","OS","Gene_fusion") +sampleinfo$Age <- as.numeric(gsub("age \\(years\\): |age: ","",sampleinfo$Age)) +sampleinfo$RFS[which(sampleinfo$RFS=="not relapsed")] <- 0 +sampleinfo$RFS[which(sampleinfo$RFS=="relapsed")] <- 1 +sampleinfo$OS[which(sampleinfo$OS=="alive")] <- 0 +sampleinfo$OS[which(sampleinfo$OS=="dead")] <- 1 +info.f <- sampleinfo[-c(227:246),] +saveRDS(info.f,file="~/metastasis/data/verify/GSE31210/sampleinfo.RDS") + +# data +dat <- read.table("~/metastasis/data/verify/GSE31210/GSE31210_series_matrix.txt",sep="\t",header=T,comment.char="!") +tmp <- read.table("/public/workspace/lily/metastasis/data/verify/GPL570.txt",sep="\t",header=T) +tmp.dat <- merge(tmp,dat,by.x="ID",by.y="ID_REF") +tmp.dat.f <- tmp.dat[-grep("//",tmp.dat$Gene.Symbol),-1] +tmp.res <- aggregate(.~Gene.Symbol,data=tmp.dat.f,FUN=mean) +res.f <- tmp.res[-c(1:25),] +rownames(res.f) <- res.f$Gene.Symbol +res.f$Gene.Symbol <- NULL + +saveRDS(res.f,file="~/metastasis/data/verify/GSE31210/GSE31210_expr.RDS") + + +################################################################################################################################### +dat <- readRDS("~/metastasis/data/verify/GSE31210/GSE31210_expr.RDS") +info <- readRDS("~/metastasis/data/verify/GSE31210/sampleinfo.RDS") +# dat.f <- dat[,which(colnames(dat)%in%rownames(info))] +# dat.f <- dat.f[,rownames(info)] +# saveRDS(dat.f,file="~/metastasis/data/verify/GSE31210/GSE31210_expr.RDS") + +source('/public/workspace/lily/software/ssGSEA/ssgseaMOD.r') +mod <- mod.analyze2(as.matrix(dat),c("BMS_update"),"/public/workspace/lily/MOD_file/",permN=0) +mod <- as.data.frame(mod) +all(rownames(mod)==rownames(info)) +info$BMS <- mod[,2] +# make survival +library(survminer) +library(survival) + +info$type <- "MID" # a bit question is MID is the best +info$type[which(info$BMSunname(quantile(info$BMS,0.67)))] <- "High" +# change type +info$OS.time <- as.numeric(as.vector(info$OS.time)) +info$RFS.time <- as.numeric(as.vector(info$RFS.time)) +info$OS <- as.numeric(as.vector(info$OS)) +info$RFS <- as.numeric(as.vector(info$RFS)) + + +info <- info[-which(info$type=="MID"),] +surv <- Surv(info$OS.time,info$OS) +km <- survfit(surv~type,data=info) +pairwise_survdiff(Surv(info$OS.time,info$OS)~type,data=info) + +pdf("/public/workspace/lily/Lung2Brain/Version5/Fig2/GSE31210_surv.pdf",useDingbats=F) +ggsurvplot(km,pval=T,xlim=c(0,3000),xscale='d_m',break.time.by =304.37,xlab=" Overall survival (months)")$plot+ +scale_colour_manual(values=c(rgb(251,176,59,maxColorValue = 255),rgb(140,198,63,maxColorValue = 255),'grey'))+labs(title="Early stage LUAD") + +surv <- Surv(info$RFS.time,info$RFS) +km <- survfit(surv~type,data=info) +pairwise_survdiff(Surv(info$RFS.time,info$RFS)~type,data=info) +# pdf("./GSE68465.pdf") +ggsurvplot(km,pval=T,xlim=c(0,3000),xscale='d_m',break.time.by =304.37,xlab=" RFS (months)")$plot+ +scale_colour_manual(values=c(rgb(251,176,59,maxColorValue = 255),rgb(140,198,63,maxColorValue = 255),'grey'))+labs(title="Early stage LUAD") + +dev.off() + + + + + + + + +#================================================================================================================================================ +# 2021-9-26 +# data analysis for CCL20 +#================================================================================================================================================ +dat <- readRDS("~/metastasis/data/verify/GSE31210/GSE31210_expr.RDS") +info <- readRDS("~/metastasis/data/verify/GSE31210/sampleinfo.RDS") + +# make survival +library(survminer) +library(survival) + +info$CCL20 <- as.numeric(dat["CCL20",]) +info$type <- "MID" # a bit question is MID is the best +info$type[which(info$CCL20unname(quantile(info$CCL20,0.67)))] <- "High" +# change type +info$OS.time <- as.numeric(as.vector(info$OS.time)) +info$RFS.time <- as.numeric(as.vector(info$RFS.time)) +info$OS <- as.numeric(as.vector(info$OS)) +info$RFS <- as.numeric(as.vector(info$RFS)) + +info.f <- info[-which(info$type=="MID"),] +surv <- Surv(info.f$OS.time,info.f$OS) +km <- survfit(surv~type,data=info.f) +pairwise_survdiff(Surv(info.f$OS.time,info.f$OS)~type,data=info.f) + +ggsurvplot(km,pval=T,xlim=c(0,3000),xscale='d_m',break.time.by =304.37,xlab=" Overall survival (months)")$plot+ +scale_colour_manual(values=c(rgb(251,176,59,maxColorValue = 255),rgb(140,198,63,maxColorValue = 255),'grey'))+labs(title="Early stage LUAD") + + +surv <- Surv(info.f$RFS.time,info.f$RFS) +km <- survfit(surv~type,data=info.f) +pairwise_survdiff(Surv(info.f$RFS.time,info.f$RFS)~type,data=info.f) +# pdf("./GSE68465.pdf") +ggsurvplot(km,pval=T,xlim=c(0,3000),xscale='d_m',break.time.by =304.37,xlab=" RFS (months)")$plot+ +scale_colour_manual(values=c(rgb(251,176,59,maxColorValue = 255),rgb(140,198,63,maxColorValue = 255),'grey'))+labs(title="Early stage LUAD") + + + + + + + + + + + + + + + + + + + + + +########################################################################################################################### +# COX analysis for TCGA LUAD +# 2021-5-17 +#========================================================================================================================== +luad_mod <- readRDS("/public/workspace/lily/Lung2Brain/Version5/Data/TCGA_LUAD_mod.RDS") +tmp <- read.delim("/public/workspace/lily/metastasis/data/verify/TCGA_LUAD/LUAD_clinicalMatrix.txt",sep="\t",header=T) +tcga.clin <- tmp[,c("sampleID","EGFR","KRAS","MET","ALK_translocation","ROS1_translocation","ERBB2","BRAF", + "age_at_initial_pathologic_diagnosis","OS.time","OS","RFS.time","RFS","gender","pathologic_stage","vital_status", + "Cnncl_mt_n_KRAS_EGFR_ALK_RET_ROS1_BRAF_ERBB2_HRAS_NRAS_AKT1_MAP2")] +rownames(tcga.clin) <- gsub("-",".",tcga.clin$sampleID) +tcga.clin.f <- merge(tcga.clin,luad_mod,by="row.names") + +# BMS +# tcga.clin.f$BMS_type <- "MID" +# tcga.clin.f$BMS_type[which(tcga.clin.f$BMS_test_norm>quantile(tcga.clin.f$BMS_test_norm,0.67))] <- "High" +# tcga.clin.f$BMS_type[which(tcga.clin.f$BMS_test_norm tcga.clin.f$Age +tcga.clin.f$age_at_initial_pathologic_diagnosis <- NULL + +# mutation use 0 1 2 to subtitued +tcga.clin.f$EGFR_mut <- 1 +tcga.clin.f$EGFR_mut[which(tcga.clin.f$EGFR=="none")] <- 0 +tcga.clin.f$EGFR_mut[which(tcga.clin.f$EGFR=="")] <- NA + + +# mutation use 0 1 2 to subtitued +tcga.clin.f$KRAS_mut <- 1 +tcga.clin.f$KRAS_mut[which(tcga.clin.f$KRAS=="none")] <- 0 +tcga.clin.f$KRAS_mut[which(tcga.clin.f$KRAS=="")] <- NA + + +# mutation use 0 1 2 to subtitued +tcga.clin.f$MET_mut <- 1 +tcga.clin.f$MET_mut[which(tcga.clin.f$MET=="none")] <- 0 +tcga.clin.f$MET_mut[which(tcga.clin.f$MET=="")] <- NA + +tcga.clin.f$ALK_trans <- 1 +tcga.clin.f$ALK_trans[which(tcga.clin.f$ALK_translocation=="none")] <- 0 +tcga.clin.f$ALK_trans[which(tcga.clin.f$ALK_translocation=="")] <- NA + +# ROS1 +tcga.clin.f$ROS1_trans <- 1 +tcga.clin.f$ROS1_trans[which(tcga.clin.f$ROS1_translocation=="none")] <- 0 +tcga.clin.f$ROS1_trans[which(tcga.clin.f$ROS1_translocation=="")] <- NA + +# BRAF +tcga.clin.f$BRAF_mut <- 1 +tcga.clin.f$BRAF_mut[which(tcga.clin.f$BRAF=="none")] <- 0 +tcga.clin.f$BRAF_mut[which(tcga.clin.f$BRAF=="")] <- NA + +# ERBB2 +tcga.clin.f$ERBB2_mut <- 1 +tcga.clin.f$ERBB2_mut[which(tcga.clin.f$ERBB2=="none")] <- 0 +tcga.clin.f$ERBB2_mut[which(tcga.clin.f$ERBB2=="")] <- NA + +# gender use 0 1 to change +tcga.clin.f$sex <- 0 +tcga.clin.f$sex[which(tcga.clin.f$gender=="MALE")] <- 1 + + +# Stage use 0 1 2 3 4 to change + +tcga.clin.f$stage <- NA +tcga.clin.f$stage[which(tcga.clin.f$pathologic_stage%in%c("Stage I","Stage IA","Stage IB","Stage II","Stage IIA","Stage IIB","Stage IIIA","Stage IIIB"))] <- "Low stage" +tcga.clin.f$stage[which(tcga.clin.f$pathologic_stage%in%c("Stage IV"))] <- "High stage" +tcga.clin.f$stage <- factor(tcga.clin.f$stage,levels=c("Low stage","High stage")) + + +library(ggplot2) +library(survival) +library(survminer) +pdf("/public/workspace/lily/Lung2Brain/Version5/Fig2/TCGA_Coxph.pdf",useDingbats=F) + +model <- coxph(Surv(OS.time, OS) ~ BMS_update_norm+Age+sex+EGFR_mut+KRAS_mut+MET_mut+BRAF_mut+ERBB2_mut+stage, data = tcga.clin.f) +ggforest(model, data = tcga.clin.f) + +dev.off() + + + + + + + + + + + +##################################################################################################################################################### +# 2021-5-17 +# TCGA LUAD +# early stage and late stage +#==================================================================================================================================================== +luad_mod <- readRDS("/public/workspace/lily/Lung2Brain/Version5/Data/TCGA_LUAD_mod.RDS") +tmp <- read.delim("/public/workspace/lily/metastasis/data/verify/TCGA_LUAD/LUAD_clinicalMatrix.txt",sep="\t",header=T) +tcga.clin <- tmp[,c("sampleID","EGFR","KRAS","MET","ALK_translocation","ROS1_translocation","ERBB2","BRAF", + "age_at_initial_pathologic_diagnosis","OS.time","OS","RFS.time","RFS","gender","pathologic_stage")] +rownames(tcga.clin) <- gsub("-",".",tcga.clin$sampleID) +tcga.clin.f <- merge(tcga.clin,luad_mod,by="row.names") + +########################################################################################################################## +tcga.clin.f$stage <- NA +tcga.clin.f$stage[which(tcga.clin.f$pathologic_stage%in%c("Stage I","Stage IA","Stage IB","Stage II","Stage IIA","Stage IIB"))] <- "Early stage" +tcga.clin.f$stage[which(tcga.clin.f$pathologic_stage%in%c("Stage IIIA","Stage IIIB","Stage IV"))] <- "Advanced stage" +res.f <- tcga.clin.f[-which(is.na(tcga.clin.f$stage)),] +# plot result +library(ggplot2) +pdf("/public/workspace/lily/Lung2Brain/Version5/Fig2/TCGA_LUAD_early_late.pdf",useDingbats=F) +ggplot(dat=res.f ,aes(x=stage,y=BMS_update_norm,fill=stage))+stat_boxplot(geom = "errorbar",width=0.15)+geom_boxplot(notch = T,outlier.shape = NA) + theme_classic()+ + annotate("text",x=2,y=1,label=wilcox.test(BMS_update_norm~stage,data=res.f,FUN=median)$p.value,parse=T,color="blue") +dev.off() + + + + + + +# GSE123902 +# 2021-5-18 +#==================================================================================================================================================== +source('/public/workspace/lily/software/ssGSEA/ssgseaMOD.r') +GSE123902 <- readRDS("/public/workspace/lily/Lung2Brain/inte7/Pyscenic/GSE123902/primary_LUAD.RDS") +GSE123902$group <- "Early" +GSE123902$group[which(GSE123902$sample=="LX675")] <- "Advanced" +gse123902_mod <- mod.analyze2(as.matrix(GSE123902[['RNA']]@data),"BMS_update","/public/workspace/lily/MOD_file/",permN=0) # calculate mod +gse123902_mod <- data.frame(gse123902_mod) +gse123902_mod$group <- GSE123902$group + +library(ggridges) +library(ggplot2) + +# boxplot + + +# TCGA extravasion +#=================================================================================================================================================== +tmp1 <- readRDS("/public/workspace/lily/Lung2Brain/Version5/Fig2/TCGA_LUAD_invasion_mod.RDS") +tmp2 <- readRDS("/public/workspace/lily/Lung2Brain/Version5/Data/TCGA_LUAD_mod.RDS") +res <- data.frame(BMS=tmp2$BMS_update_norm,localvasion=tmp1$localInvasion_cp_norm,extra=tmp1$intraInvasion_cp_norm) + +# plot result +library(ggExtra) +library(ggplot2) +library(ggpubr) +pdf("/public/workspace/lily/Lung2Brain/Version5/Fig2/BMS_extravasion_cor.pdf",useDingbats=F) +p<-ggplot(res,aes(x=BMS,y=extra)) + + stat_density2d(aes(alpha = 1,fill = ..density..),geom = "raster", contour = FALSE) + + scale_fill_gradient (low = "#FFFFFF", high = "#377EB8") + + ylab("Extravasion score") + xlab('BMS score') + stat_smooth(method="lm",se=T) + + stat_cor(method = "spearman",size=2,label.x = 0.1,label.y =0.1) + + geom_point(colour='#377EB8',size=0.5) + theme_bw() + + theme(panel.grid.major=element_blank(),panel.grid.minor=element_blank(),legend.position="none",axis.line = element_line(colour = "black"), axis.text=element_text(colour="black")) +p1 <- ggMarginal(p, type="density", margins = "both", fill = "#BBDFFB") +print(p1) +dev.off() + + +pdf("/public/workspace/lily/Lung2Brain/Version5/Fig2/BMS_localvasion_cor.pdf",useDingbats=F) +p<-ggplot(res,aes(x=BMS,y=localvasion)) + + stat_density2d(aes(alpha = 1,fill = ..density..),geom = "raster", contour = FALSE) + + scale_fill_gradient (low = "#FFFFFF", high = "#377EB8") + + ylab("localvasion score") + xlab('BMS score') + stat_smooth(method="lm",se=T) + + stat_cor(method = "spearman",size=2,label.x = 0.1,label.y =0.1) + + geom_point(colour='#377EB8',size=0.5) + theme_bw() + + theme(panel.grid.major=element_blank(),panel.grid.minor=element_blank(),legend.position="none",axis.line = element_line(colour = "black"), axis.text=element_text(colour="black")) +ggMarginal(p, type="density", margins = "both", fill = "#BBDFFB") +dev.off() + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +# 2021-9-9 +# calculate in GSE131907 data by sample +#=================================================================================================================================== +library(Seurat) +dat <- readRDS("/public/workspace/lily/Lung2Brain/GSE131907/useTumor.RDS") +tmp.res <- list() +# calculate by each sample +samplelist <- as.vector(unique(dat$Sample)) +for(i in 1:length(samplelist)){ + tmp.sub <- subset(dat,cells=which(dat$Sample==samplelist[i])) + DefaultAssay(tmp.sub) <- "RNA" + # calculate BMS score + source('/public/workspace/lily/software/ssGSEA/ssgseaMOD.r') + mod <- mod.analyze2(as.matrix(tmp.sub[['RNA']]@data),c("BMS_update","Brain_gene","Lung_gene"),"/public/workspace/lily/MOD_file/",permN=0) + mod <- as.data.frame(mod) + tmp.res[[i]] <- mod + names(tmp.res)[i] <- samplelist[i] +} + +saveRDS(tmp.res,file="/public/workspace/lily/Lung2Brain/GSE131907/GSE131907_Sig.RDS") + + + + +# tmp +tmp.res <- readRDS("/public/workspace/lily/Lung2Brain/GSE131907/GSE131907_Sig.RDS") +res <- sapply(tmp.res,function(x){median(x[,6])}) + + + + + + + + + + + + + + + + + diff --git a/Fig3.r b/Fig3.r new file mode 100644 index 0000000..41babed --- /dev/null +++ b/Fig3.r @@ -0,0 +1,763 @@ + +#!/usr/bin/Rscript +# 2021-5-5 +#============================================================================================================= +# 1. check TFs +# 2. check High plascity +# 3. trajecoty analysis + + + +############################################################################################################### +library(Seurat) +dat <- readRDS("/public/workspace/lily/Lung2Brain/Version5/Data/Tumor_pur/pur_LCBM_clust.RDS") + +# 0. update BMS score and High Plascity +source('/public/workspace/lily/software/ssGSEA/ssgseaMOD.r') +mod <- mod.analyze2(as.matrix(dat[['RNA']]@data),c("BMS_update","HPSC_C5"),"/public/workspace/lily/MOD_file/",permN=0) +mod <- as.data.frame(mod) +dat$BMS <- NULL +dat$BMS_update <- mod[,3] +all(rownames(mod)==rownames(dat@meta.data)) +dat$HPSC <- mod[,4] +saveRDS(dat,file="/public/workspace/lily/Lung2Brain/Version5/Data/Tumor_pur/pur_LCBM_clust.RDS") + +# plot result +############################### +library(Seurat) +library(ggplot2) + +dat <- readRDS("/public/workspace/lily/Lung2Brain/Version5/Data/Tumor_pur/pur_LCBM_clust.RDS") +pdf("/public/workspace/lily/Lung2Brain/Version5/Fig3/BMS_score_FeaturePlot.pdf",useDingbats=F) +DimPlot(dat,label=T) +FeaturePlot(dat,features="BMS_update") + + scale_colour_gradientn(colours = c("#598c14","#b5c327","#edd812","#faae40","#ff3c41","red"),values = c(0,0.4,0.5,0.8,1.0)) +dev.off() + +# FeaturePlot(pbmc_small, c("LYZ", "MS4A1")) & +# scale_colour_gradientn(colours = rev(brewer.pal(n = 11, name = "RdBu"))) + + + + +#================================================================================================================================ +# Calculate Stmeness use different siganture +# 2021-7-1 +#================================================================================================================================ +library(Seurat) +library(pheatmap) + +dat <- readRDS("/public/workspace/lily/Lung2Brain/Version5/Data/Tumor_pur/pur_LCBM_clust.RDS") +# calculate stemness signature +source('/public/workspace/lily/software/ssGSEA/ssgseaMOD.r') +mod <- mod.analyze2(as.matrix(dat[['RNA']]@data),c("PNAS_stem","stem_cell"),"/public/workspace/lily/MOD_file/NatureMedicine/",permN=0) +mod <- as.data.frame(mod) +saveRDS(mod,file="/public/workspace/lily/Lung2Brain/Version5/Fig4/Data/LCBM_pur_stemness.RDS") + + +# result output +library(pheatmap) +library(Seurat) +library(ggplot2) + +dat <- readRDS("/public/workspace/lily/Lung2Brain/Version5/Data/Tumor_pur/pur_LCBM_clust.RDS") +mod <- readRDS("/public/workspace/lily/Lung2Brain/Version5/Fig4/Data/LCBM_pur_stemness.RDS") +mod$BMS <- dat$BMS_update +dat.f <- mod[,c(3,4,7)] +dat.f$cytoTrace <- dat$cytoTrace + +dat$PNAS_stem <- mod$PNAS_stem_norm +dat$cell_stem <- mod$stem_cell_norm + +pdf("/public/workspace/lily/Lung2Brain/Version5/Fig4/LCBM_pur_Stem.pdf",useDingbats=F) +FeaturePlot(dat,features=c("BMS_update"))+ +scale_colour_gradientn(colours = c("#598c14","#b5c327","#edd812","#faae40","#ff3c41","red"),values = c(0,0.4,0.5,0.8,1.0)) + +FeaturePlot(dat,features=c("cell_stem"))+ +scale_colour_gradientn(colours = c("#598c14","#b5c327","#edd812","#faae40","#ff3c41","red"),values = c(0,0.45,0.5,0.65,1.0)) + +pheatmap(dat.f,cluster_rows=F,cluster_cols=F,show_rownames=F, + annotation_col=data.frame(row.names=colnames(dat.f), + correlation=apply(dat.f,2,function(x){cor.test(x,dat.f$BMS,method="spearman")$estimate}) + ), + color=colorRampPalette(c('steelblue','white','red'))(8000) +) + +dev.off() + + + + + + + + + + + + + + + + + + + + + +# 1. Find high expression and activity TFs +dat <- readRDS("/public/workspace/lily/Lung2Brain/Version5/Data/Tumor_pur/pur_LCBM_clust.RDS") +dat$type.tumor <- "tumor.l" +dat$type.tumor[which(dat$seurat_clusters%in%c(4,7))] <- "tumor.h" # identiy tumor subgroup +# +tmp.data <- as.matrix(dat[['RNA']]@data) +tf.dat <- read.csv("/public/workspace/lily/Lung2Brain/Version5/Pyscenic/LCBM/step3.auc_mtx.csv") +rownames(tf.dat) <- tf.dat$Cell +tf.dat$Cell <- NULL +colnames(tf.dat) <- gsub("\\.\\.\\.$","",colnames(tf.dat)) + +# identify gene expression +data.f <- tmp.data[which(rownames(tmp.data)%in%colnames(tf.dat)),] +index.h <- unname(which(dat$type.tumor=="tumor.h")) +tmp.res <- data.frame(t(apply(data.f,1,function(x){ + exp.h <- mean(x[index.h]) + exp.l <- mean(x[-index.h]) + log2fc <- log2(exp.h/exp.l) + p <- wilcox.test(x[index.h],x[-index.h])$p.value + c(exp.h,exp.l,log2fc,p) +}))) +colnames(tmp.res) <- c("Exp.H","Exp.L","Log2FC","pvalue") +tmp.res$p.adj <- p.adjust(tmp.res$pvalue) + +tmp.res <- tmp.res[order(tmp.res$Log2FC,decreasing=T),] +rs <- tmp.res[which(tmp.res$p.adj<0.05),] + + + +# identify TF activity difference +# all(rownames(tf.dat)==colnames(dat)) +# tmp.tf <- data.frame(t(apply(tf.dat,2,function(x){ +# exp.h <- mean(as.numeric(as.vector(x[index.h]))) +# exp.l <- mean(as.numeric(as.vector(x[-index.h]))) +# log2fc <- log2(exp.h/exp.l) +# p <- wilcox.test(as.numeric(as.vector(x[index.h])),as.numeric(as.vector(x[-index.h])))$p.value +# c(exp.h,exp.l,log2fc,p) +# }))) +# colnames(tmp.tf) <- c("Exp.H","Exp.L","Log2FC","pvalue") +# tmp.tf$p.adj <- p.adjust(tmp.tf$pvalue) +# tmp.tf <- tmp.tf[order(tmp.tf$Log2FC,decreasing=T),] +# tf.rs <- tmp.tf[which(tmp.tf$p.adj<0.05),] + + +tf.dat$type.tumor <- dat$type.tumor +tmp <- aggregate(.~type.tumor,data=tf.dat,FUN=mean) +rownames(tmp) <- tmp$type.tumor +tmp$type.tumor <- NULL +tf.rs <- data.frame(t(tmp)) +tf.rs$log2FC <- log2(tf.rs$tumor.h/tf.rs$tumor.l) +tf.rs <- tf.rs[order(tf.rs$log2FC,decreasing=T),] +# pheatmap::pheatmap(tmp) + +# use Log2FC and log2FC to choose gene +rs.f <- merge(rs,tf.rs,by="row.names") +rownames(rs.f) <- rs.f[,1] +colnames(rs.f)[1] <- "Gene" +rs.f$name <- rs.f$Gene +rs.f$name[-which(rs.f$Log2FC>0.5&rs.f$log2FC>0.5)] <- "" +# change Pvalue +rs.f$pvalue[which(rs.f$pvalue==0)] <- min(rs.f$pvalue[which(rs.f$pvalue>0)]) +rs.f$log10Pvalue <- -log10(rs.f$pvalue) + +library(ggplot2) +pdf("/public/workspace/lily/Lung2Brain/Version5/Fig3/TFs.pdf",useDingbats=F) +ggplot(rs.f,aes(y=log10Pvalue,x=Log2FC,color=log2FC)) + geom_point(aes(size=2))+ geom_text(aes(label = name),nudge_x=0,nudge_y=0)+ + geom_vline(aes(xintercept=3.45)) + geom_vline(aes(xintercept=5.09)) + + scale_colour_gradientn(colours=c("steelblue","steelblue","white","#ff3c41","red"),values=c(0,0.8,0.8,1.0))+ + theme_bw() +dev.off() + + + + + + + +# 2. plot E2F8 expression in LCBM clusters and in LC +library(Seurat) +dat <- readRDS("/public/workspace/lily/Lung2Brain/Version5/Data/Tumor_pur/pur_LCBM_clust.RDS") +# add a new meta data info +dat$type.tumor <- "tumor.l" +dat$type.tumor[which(dat$seurat_clusters%in%c(4,7))] <- "tumor.h" # identiy tumor subgroup +dat@active.ident <- factor(dat$type.tumor) +tmp <- AverageExpression(dat,assays="RNA",features="E2F8") + + + +# LC <- readRDS("/public/workspace/lily/Lung2Brain/Version5/Data/Tumor_pur/pur_LUAD_clust.RDS") +# LC@active.ident <- factor(LC$type_group) +# source('/public/workspace/lily/software/ssGSEA/ssgseaMOD.r') +# mod <- mod.analyze2(as.matrix(LC[['RNA']]@data),c("BMS_update"),"/public/workspace/lily/MOD_file/",permN=0) +# mod <- as.data.frame(mod) +# median(mod[,2]) +# AverageExpression(LC,assays="RNA",features="E2F8") + + +tmp.dat <- data.frame(Cluster=c("Tumor.H","Tumor.L"),stringsAsFactors=F, + E2F8=as.numeric(unname(as.numeric(tmp$RNA))), + BMS_update=as.numeric(aggregate(BMS_update~type.tumor,data=dat@meta.data,FUN=median)[,2])) +tmp.dat <- rbind(tmp.dat,c("LC",0.09190325,0.2979875)) +tmp.dat$E2F8 <- as.numeric(tmp.dat$E2F8) +tmp.dat$BMS_update <- as.numeric(tmp.dat$BMS_update) + +pdf("/public/workspace/lily/Lung2Brain/Version5/Fig3/E2F8_expression.pdf",useDingbats=F) +ggplot(tmp.dat,aes(x=Cluster,y=E2F8))+geom_bar(aes(fill=Cluster),stat="identity",position="dodge") + + theme_bw() + scale_fill_manual(values=c("blue","red","green")) +dev.off() + + + +# 1. add a verify for E2F8 +# GSE14995 +library(beeswarm) +GSE14995 <- readRDS("/public/workspace/lily/metastasis/data/verify/GSE14995/GSE14995_dat.RDS") +tmp.dat <- data.frame(E2F8 = as.numeric(GSE14995["E2F8",]),group=c(rep("control",3),rep("invasion",3))) +pdf("/public/workspace/lily/Lung2Brain/Version5/Fig3/E2F8_GSE14995.pdf",useDingbats=F) +boxplot(E2F8~group,data=tmp.dat,main="E2F8 expression",names=c("Low invasion","High invasion"),ylim=c(0.9,1.2)) +legend("topright",legend=(paste0("p=",wilcox.test(as.numeric(GSE14995["E2F8",1:3]),as.numeric(GSE14995["E2F8",4:6]))$p.value))) +beeswarm::beeswarm(E2F8~group,data=tmp.dat,col = 4, pch = 16,main = 'beeswarm + bxplot',add = TRUE) +dev.off() + + + + + + + + + + + + + + +# 2. calculate high Plastic score +dat <- readRDS("/public/workspace/lily/Lung2Brain/Version5/Data/Tumor_pur/pur_LCBM_clust.RDS") +dat$type.tumor <- "tumor.l" +dat$type.tumor[which(dat$seurat_clusters%in%c(4,7))] <- "tumor.h" # identiy tumor subgroup +aggregate(HPSC~type.tumor,data=dat@meta.data,FUN=median) + +pdf("/public/workspace/lily/Lung2Brain/Version5/Fig3/LCBM_HPSC.pdf",useDingbats=F) +boxplot(HPSC~type.tumor,data=dat@meta.data,FUN=median,outline=F) +dev.off() + + + + + + + + + + +# 2021-5-14 +# maybe do trajectory is not a good way to show result +# 2021-8-12 +# try to use trajectory to find specific genes +# # 3. do a trajectory to show +#======================================================================================================================================= +library(Seurat) +library(monocle) +dat <- readRDS("/public/workspace/lily/Lung2Brain/Version5/Fig4/Data/LCBM_sub.tumor.RDS") +# make a monocle object +DefaultAssay(dat) <- "RNA" +tmp.dat <- Seurat::as.CellDataSet(dat) +tmp.dat <- estimateSizeFactors(tmp.dat) +tmp.dat <- detectGenes(tmp.dat, min_expr = 1) +# fData(dat)$use_for_ordering <-fData(dat)$num_cells_expressed > 0.05 * ncol(dat) +expressed_genes <- row.names(subset(fData(tmp.dat),num_cells_expressed >= 10)) + +# find integration gene +inte.list <- list() +samplelist <- unique(dat$orig.ident) +for(i in 1:length(samplelist)){ + tmp <- subset(dat,cells=which(dat$orig.ident==samplelist[i])) + DefaultAssay(tmp) <- "RNA" + inte.list[[i]] <- tmp +} +integration.anchors <- FindIntegrationAnchors(object.list = inte.list) +inte <- IntegrateData(anchorset = integration.anchors) +#FindVariableFeatures +inte <- FindVariableFeatures(inte) + +# run monocle +DefaultAssay(inte) <- "integrated" +genes <- VariableFeatures(inte)[1:1000] +ordering_genes <- genes +tmp.dat <- setOrderingFilter(tmp.dat, ordering_genes = ordering_genes) +tmp.dat <- reduceDimension(tmp.dat, method = 'DDRTree') +tmp.dat <- orderCells(tmp.dat) + +pdf("/public/workspace/lily/Lung2Brain/Version5/Trajectory/monocle/LCBM_subtumor_1000_monocle.pdf",useDingbats=F) +plot_cell_trajectory(tmp.dat,color_by="group") +plot_cell_trajectory(tmp.dat,color_by="BMS_update") +plot_cell_trajectory(tmp.dat,color_by="HPSC") +plot_cell_trajectory(tmp.dat,color_by="Lung.gene") +plot_cell_trajectory(tmp.dat,color_by="Brain.gene") +plot_cell_trajectory(tmp.dat,color_by="Pseudotime") +plot_cell_trajectory(tmp.dat,color_by="seurat_clusters") + facet_wrap(~seurat_clusters) +dev.off() + +# saveRDS(tmp.dat,file="/public/workspace/lily/Lung2Brain/Version5/Trajectory/monocle/LCBM_tumor_monocle.RDS") + +#=========================================================================================================================================== +# try to find DEG for state +# 2021-8-12 +# use Seurat to find DEG of different State # because monocle DEG program result do not know how to check +#=========================================================================================================================================== +dat$State <- tmp.dat$State +dat@active.ident <- factor(dat$State) +gene <- FindAllMarkers(dat,min.pct=0,logfc.threshold=0) +gene <- gene[order(gene$avg_logFC,decreasing=T),] + + + + + + + + + + + + + + + + + + + + + + + + +# # 4. veolocity to run +# # bytlib load hdf5 +# library(Seurat) +# library(loomR) + +# dat <- readRDS("/public/workspace/lily/Lung2Brain/Version5/Data/Tumor_pur/pur_LCBM_clust.RDS") +# A05 <- subset(dat,cells=which(dat$orig.ident=="A20190305")) +# A12 <- subset(dat,cells=which(dat$orig.ident=="A20190312")) +# TB <- subset(dat,cells=which(dat$orig.ident=="T-Bsc1")) +# DefaultAssay(A05) <- "RNA" +# DefaultAssay(A12) <- "RNA" +# DefaultAssay(TB) <- "RNA" +# # Github say this to as.loom +# A05 <- FindVariableFeatures(A05) +# A12 <- FindVariableFeatures(A12) +# TB <- FindVariableFeatures(TB) + +# A05$RNA_snn_res.2 <- NULL # debug for erro https://github.com/mojaveazure/loomR/issues/40 +# A12$RNA_snn_res.2 <- NULL +# TB$RNA_snn_res.2 <- NULL +# TB$RNA_snn_res.1 <- NULL + +# # tansform into loom file +# as.loom(A05,filename = "/public/workspace/lily/Lung2Brain/Version5/Data/LCBM_tumor_veolcyto/A05.loom",verbose = T) +# as.loom(A12,filename = "/public/workspace/lily/Lung2Brain/Version5/Data/LCBM_tumor_veolcyto/A12.loom",verbose = T) +# as.loom(TB,filename = "/public/workspace/lily/Lung2Brain/Version5/Data/LCBM_tumor_veolcyto/TB.loom",verbose = T) + + + + + + + + + +# 5. cytoTrace +# R - 4.0.2 + +library(Seurat) +library(CytoTRACE) +dat <- readRDS("/public/workspace/lily/Lung2Brain/Version5/Data/Tumor_pur/pur_LCBM_clust.RDS") + +as_matrix <- function(mat){ + + tmp <- matrix(data=0L, nrow = mat@Dim[1], ncol = mat@Dim[2]) + + row_pos <- mat@i+1 + col_pos <- findInterval(seq(mat@x)-1,mat@p[-1])+1 + val <- mat@x + + for (i in seq_along(val)){ + tmp[row_pos[i],col_pos[i]] <- val[i] + } + + row.names(tmp) <- mat@Dimnames[[1]] + colnames(tmp) <- mat@Dimnames[[2]] + return(tmp) +} + +tmp.dat <- as_matrix(dat[['RNA']]@data) +# calculate +results <- CytoTRACE(tmp.dat,ncores = 6,subsamplesize = 1000) +all(names(results$CytoTRACE) == colnames(dat)) +dat$cytoTrace <- results$CytoTRACE +dat$type.tumor <- "tumor.l" +dat$type.tumor[which(dat$seurat_clusters%in%c(4,7))] <- "tumor.h" +saveRDS(dat,file="/public/workspace/lily/Lung2Brain/Version5/Data/Tumor_pur/pur_LCBM_clust.RDS") +saveRDS(results,file="/public/workspace/lily/Lung2Brain/Version5/Data/cytotarce_LCBM_tumor.RDS") + + + + + +# # GSE110495 data verfiy +# # 2021-5-14 +# # however do not know the exactlt meaning of BMIT LT BT BMIC +# load("~/metastasis/data/verify/GSE110495/GSE110495.RData") +# load("~/metastasis/data/verify/GSE110495/GSE110495_anno.RData") + +# source('/public/workspace/lily/software/ssGSEA/ssgseaMOD.r') +# mod <- mod.analyze2(as.matrix(gse110495),c("BMS_update"),"/public/workspace/lily/MOD_file/",permN=0) +# mod <- as.data.frame(mod) + +# mod$type <- anno$stage.of.the.metastatic.cascade + + + + + +# 6. plasticy and stemness result +# plot Brain.gene signature and LC.gene signature +library(Seurat) +dat <- readRDS("/public/workspace/lily/Lung2Brain/Version5/Data/Tumor_pur/pur_LCBM_clust.RDS") +tmp <- aggregate(cytoTrace~seurat_clusters,data=dat@meta.data,FUN=median) +tmp <- tmp[order(tmp$cytoTrace,decreasing=F),] +dat$seurat_clusters <- factor(dat$seurat_clusters,levels=as.vector(tmp$seurat_clusters)) + +pdf("/public/workspace/lily/Lung2Brain/Version5/Fig3/Cytotrace_cluster.pdf",useDingbats=F) +boxplot(cytoTrace~seurat_clusters,data=dat@meta.data,FUN=median,outline=F) +dev.off() + + + +# 7. plot Brain.gene and LC.gene signature +# 2021-5-19 +# use circlize bar plot +#========================================================================================================================= +dat <- readRDS("/public/workspace/lily/Lung2Brain/Version5/Data/Tumor_pur/pur_LCBM_clust.RDS") +tmp <- aggregate(Lung.gene~seurat_clusters,data=dat@meta.data,FUN=median) +tmp <- tmp[-11,] # filter Cluster 10 +colnames(tmp) <- c("Cluster","Lung.gene") +tmp$Cluster <- paste0("C",tmp$Cluster) +tmp <- tmp[order(tmp$Lung.gene,decreasing=T),] +tmp$Cluster <- factor(tmp$Cluster,levels=tmp$Cluster) + +# Get the name and the y position of each label +label_data <- tmp +# calculate the ANGLE of the labels +number_of_bar <- nrow(label_data) +angle <- 90 - 360 * (as.numeric(label_data$Cluster)-0.5) /number_of_bar +# I substract 0.5 because the letter must have the angle of the center of the bars. Not extreme right(1) or extreme left (0) +# calculate the alignment of labels: right or left +# If I am on the left part of the plot, my labels have currently an angle < -90 +label_data$hjust<-ifelse( angle < -90, 1, 0) + +# flip angle BY to make them readable +label_data$angle<-ifelse(angle < -90, angle+180, angle) + +pdf("/public/workspace/lily/Lung2Brain/Version5/Fig3/Tumor.Lung.gene.pdf",useDingbats=F) +library(ggplot2) +ggplot(data=tmp,aes(x=Cluster,y=Lung.gene)) + geom_bar(stat="identity", fill=alpha("blue", 0.3)) + + theme_minimal() + + theme( + axis.text = element_blank(), + axis.title = element_blank(), + panel.grid = element_blank(), + plot.margin = unit(rep(-2,4), "cm") # This remove unnecessary margin around plot + ) + coord_polar(start = 0) + +geom_text(data=label_data, aes(x=Cluster, y=Lung.gene+0.05, label=Cluster, hjust=hjust), color="black", fontface="bold", size=3) +dev.off() + + + + + + + + + + + + + +# Brain.gene +################################################################################################################################## +library(Seurat) +dat <- readRDS("/public/workspace/lily/Lung2Brain/Version5/Data/Tumor_pur/pur_LCBM_clust.RDS") +tmp <- aggregate(Brain.gene~seurat_clusters,data=dat@meta.data,FUN=median) +tmp <- tmp[-11,] # filter Cluster 10 +colnames(tmp) <- c("Cluster","Brain.gene") +tmp$Cluster <- paste0("C",tmp$Cluster) +tmp <- tmp[order(tmp$Brain.gene,decreasing=T),] +tmp$Cluster <- factor(tmp$Cluster,levels=tmp$Cluster) + +# Get the name and the y position of each label +label_data <- tmp +# calculate the ANGLE of the labels +number_of_bar <- nrow(label_data) +angle <- 90 - 360 * (as.numeric(label_data$Cluster)-0.5) /number_of_bar +# I substract 0.5 because the letter must have the angle of the center of the bars. Not extreme right(1) or extreme left (0) +# calculate the alignment of labels: right or left +# If I am on the left part of the plot, my labels have currently an angle < -90 +label_data$hjust<-ifelse( angle < -90, 1, 0) + +# flip angle BY to make them readable +label_data$angle<-ifelse(angle < -90, angle+180, angle) + +pdf("/public/workspace/lily/Lung2Brain/Version5/Fig3/Tumor.Brain.gene.pdf",useDingbats=F) +library(ggplot2) +ggplot(data=tmp,aes(x=Cluster,y=Brain.gene)) + geom_bar(stat="identity", fill=alpha("blue", 0.3)) + + theme_minimal() + + theme( + axis.text = element_blank(), + axis.title = element_blank(), + panel.grid = element_blank(), + plot.margin = unit(rep(-2,4), "cm") # This remove unnecessary margin around plot + ) + coord_polar(start = 0) + +geom_text(data=label_data, aes(x=Cluster, y=Brain.gene+0.05, label=Cluster, hjust=hjust), color="black", fontface="bold", size=3) +dev.off() + + + + + +# maybe add a feature plot is more good +# 2021-5-19 +#======================================================================================================= +library(Seurat) +dat <- readRDS("/public/workspace/lily/Lung2Brain/Version5/Data/Tumor_pur/pur_LCBM_clust.RDS") + +pdf("/public/workspace/lily/Lung2Brain/Version5/Fig3/Lung.Brain.FeaturePlot.pdf",useDingbats=F) +FeaturePlot(dat,features="Lung.gene",min.cutoff=median(dat$Lung.gene),order=T) +FeaturePlot(dat,features="Brain.gene",min.cutoff=median(dat$Brain.gene),order=T) +dev.off() + + + + + +# 2021-5-19 +# maybe stemness need to calculate ? + +# 2021-5-19 +# use Lung to Brain double lesion to verify +#======================================================================================================== +# D0927 +library(Seurat) +dat <- readRDS("/public/workspace/lily/Lung2Brain/Multiple_LungBrain/tumor.D.RDS") +# calculate signature +source('/public/workspace/lily/software/ssGSEA/ssgseaMOD.r') +mod <- mod.analyze2(as.matrix(dat[['RNA']]@data),c("BMS_update","Brain_gene","Lung_gene"),"/public/workspace/lily/MOD_file/",permN=0) +mod <- as.data.frame(mod) +dat$BMS.update <- mod[,4] +dat$Brain.gene <- mod[,5] +dat$Lung.gene <- mod[,6] +saveRDS(dat,file="/public/workspace/lily/Lung2Brain/Multiple_LungBrain/tumor.D.RDS") + +library(ggplot2) + +pdf("/public/workspace/lily/Lung2Brain/Version5/Fig3/Fig3_D0927_Tumor.pdf",useDingbats=F) +DimPlot(dat) +FeaturePlot(dat,features="BMS.update",order=T)+ + scale_colour_gradientn(colours=c("steelblue","steelblue","white","#ff3c41","red"),values=c(0,0.7,0.7,1.0)) +FeaturePlot(dat,features="Lung.gene",order=T) + + scale_colour_gradientn(colours=c("steelblue","steelblue","white","#ff3c41","red"),values=c(0,0.6,0.6,1.0)) +FeaturePlot(dat,features="Brain.gene",order=T) + + scale_colour_gradientn(colours=c("steelblue","steelblue","white","#ff3c41","red"),values=c(0,0.45,0.45,1.0)) +# DimPlot(dat,reduction="tsne") +# FeaturePlot(dat,features="BMS.update",min.cutoff=median(dat$BMS.update),reduction="tsne",order=T) +# FeaturePlot(dat,features="Lung.gene",min.cutoff=median(dat$Lung.gene),reduction="tsne",order=T) +# FeaturePlot(dat,features="Brain.gene",min.cutoff=median(dat$Brain.gene),reduction="tsne",order=T) +dev.off() + + + + +# E0927 +library(Seurat) +dat <- readRDS("/public/workspace/lily/Lung2Brain/Multiple_LungBrain/tumor.E.RDS") +# calculate signature +source('/public/workspace/lily/software/ssGSEA/ssgseaMOD.r') +mod <- mod.analyze2(as.matrix(dat[['RNA']]@data),c("BMS_update","Brain_gene","Lung_gene"),"/public/workspace/lily/MOD_file/",permN=0) +mod <- as.data.frame(mod) +dat$BMS.update <- mod[,4] +dat$Brain.gene <- mod[,5] +dat$Lung.gene <- mod[,6] +saveRDS(dat,file="/public/workspace/lily/Lung2Brain/Multiple_LungBrain/tumor.E.RDS") +# then recluster +recluster <- function(tmp_dat){ + +# seurat object + tmp_dat <- FindVariableFeatures(object = tmp_dat) + # scaling + all.genes <- rownames(x = tmp_dat) + tmp_dat <- ScaleData(object = tmp_dat, features = all.genes) + # PCA + tmp_dat <- RunPCA(object = tmp_dat, features = VariableFeatures(object = tmp_dat)) + # clustering + tmp_dat <- FindNeighbors(object = tmp_dat,dims=1:10) + # select proper resolution + tmp_dat <- FindClusters(object = tmp_dat,resolution=1) + # T-SNE + tmp_dat <- RunTSNE(object = tmp_dat,dims=1:10,check_duplicates = FALSE) + tmp_dat <- RunUMAP(tmp_dat,dims=1:10) + + return(tmp_dat) +} + + + +library(ggplot2) + +pdf("/public/workspace/lily/Lung2Brain/Version5/Fig3/Fig3_E0927_Tumor.pdf",useDingbats=F) +DimPlot(dat) +FeaturePlot(dat,features="BMS.update",order=T)+ + scale_colour_gradientn(colours=c("steelblue","steelblue","white","#ff3c41","red"),values=c(0,0.6,0.6,1.0)) +FeaturePlot(dat,features="Lung.gene",order=T) + + scale_colour_gradientn(colours=c("steelblue","steelblue","white","#ff3c41","red"),values=c(0,0.6,0.6,1.0)) +FeaturePlot(dat,features="Brain.gene",order=T) + + scale_colour_gradientn(colours=c("steelblue","steelblue","white","#ff3c41","red"),values=c(0,0.55,0.55,1.0)) +# DimPlot(dat,reduction="tsne") +# FeaturePlot(dat,features="BMS.update",min.cutoff=median(dat$BMS.update),reduction="tsne",order=T) +# FeaturePlot(dat,features="Lung.gene",min.cutoff=median(dat$Lung.gene),reduction="tsne",order=T) +# FeaturePlot(dat,features="Brain.gene",min.cutoff=median(dat$Brain.gene),reduction="tsne",order=T) +dev.off() + + + + + + + +# use time data to veirfy if E2F8 is increas +# GSE136935 +# not oK + +library(reshape2) +library(dplyr) +library(GEOquery) +library(idmap1) +gset<-getGEO('GSE136935',destdir='/public/workspace/wangzy/work/LungMet/Data/') +gset=gset[[1]] +Edata=exprs(gset) +map = idmap1::getIDs('GPL13497') +rownames(map)=map[,1] +Edata = merge(map,Edata,by='row.names') +Edata = as.data.frame(Edata[,-c(1:2,4)]%>%group_by(symbol)%>%summarise_all(mean)) +Edata = Edata[!is.na(Edata[,1]),] +rownames(Edata)=Edata[,1] +Edata=Edata[,-1] +pd=pData(gset) +pd = pd[pd$`treatmnet:ch1`=='PBS',] +pd = pd[pd$`cell line:ch1`=='A549',] + +#################################################################################################################### +info <- pd[,c(33,34,35)] +colnames(info) <- c("cell_line","treattime","treatment") +all(rownames(info)==colnames(Edata)) +dat <- Edata[,which(info$treatment=="PBS"&info$cell_line=="A549")] + +# calculate BMS +source('/public/workspace/lily/software/ssGSEA/ssgseaMOD.r') +mod <- mod.analyze2(as.matrix(dat),c("BMS_update"),"/public/workspace/lily/MOD_file/",permN=0) +mod <- as.data.frame(mod) + + +info.f <- info[which(info$treatment=="PBS"&info$cell_line=="A549"),] +tmp.res <- data.frame(E2F8=as.numeric(as.vector(dat["E2F8",])),time=as.vector(info.f$treattime)) + + + + + + + +# GSE58355 +# plot E2F8 expression and BMS score +tmp.dat <- read.table("/public/workspace/lily/metastasis/data/verify/GSE58355/GSE58355_series_matrix.txt",comment.char="!",header=T,sep="\t") +ann <- read.table("/public/workspace/lily/metastasis/data/verify/GSE58355/gene_ann.txt",sep="\t",header=T) +tmp.res <- merge(tmp.dat,ann,by.x="ID_REF",by.y="ID") + +tmp.res.f <- tmp.res[-grep("\\|",tmp.res$Gene_symbol),] +tmp.res.f$ID_REF <- NULL +res.f <- aggregate(.~Gene_symbol,data=tmp.res.f,FUN=median) +rs <- res.f[-c(1,2),] + +saveRDS(rs,file="/public/workspace/lily/metastasis/data/verify/GSE58355/GSE58355_exp.RDS") + +#====================================================================================================================== +# caculate BMS score +# 2021-6-3 + +dat <- readRDS("/public/workspace/lily/metastasis/data/verify/GSE58355/GSE58355_exp.RDS") +source('/public/workspace/lily/software/ssGSEA/ssgseaMOD.r') +mod <- mod.analyze2(as.matrix(dat[,c(1:6)]),c("BMS_update"),"/public/workspace/lily/MOD_file/",permN=0) +mod <- as.data.frame(mod) + +library(beeswarm) +tmp.dat <- data.frame(BMS=mod[,2],group=c(rep("day2",3),rep("day25",3))) +pdf("/public/workspace/lily/Lung2Brain/Version5/Fig2/GSE58355_primary_BMS.pdf",useDingbats=F) +boxplot(BMS~group,data=tmp.dat,main="BMS score in primary tumor ",names=c("day2","day25"),ylim=c(0,1)) +legend("topright",legend=(paste0("p=",wilcox.test(BMS~group,data=tmp.dat)$p.value))) +beeswarm::beeswarm(BMS~group,data=tmp.dat,col = 4, pch = 16,main = 'beeswarm + bxplot',add = TRUE) +dev.off() + + + + +#================================================================================================================= +# calculate E2F8 +# 2021-6-3 + +dat <- readRDS("/public/workspace/lily/metastasis/data/verify/GSE58355/GSE58355_exp.RDS") + +library(beeswarm) +tmp.dat <- data.frame(E2F8=as.numeric(as.vector(dat["E2F8",13:18])),group=c(rep("day10",3),rep("day25",3))) +pdf("/public/workspace/lily/Lung2Brain/Version5/Fig3/GSE58355_metastasis_E2F8.pdf",useDingbats=F) +boxplot(E2F8~group,data=tmp.dat,main="E2F8 in metastasis tumor ",names=c("day10","day25")) +legend("topright",legend=(paste0("p=",wilcox.test(E2F8~group,data=tmp.dat)$p.value))) +beeswarm::beeswarm(E2F8~group,data=tmp.dat,col = 4, pch = 16,main = 'beeswarm + bxplot',add = TRUE) +dev.off() + + + + + + +#================================================================================================================= +# calculate E2F8 in BM- and BM+ +# GSE + + + + + + + + + + + + + + + + + + + diff --git a/Fig3_TFs.r b/Fig3_TFs.r new file mode 100644 index 0000000..4c04ad8 --- /dev/null +++ b/Fig3_TFs.r @@ -0,0 +1,423 @@ + +# 2021-11-17 +# this program is used to get CEBPD and CCL20 +# 0. BMS hallmark analysis +# 1. maoding genes hallmark analysis +# 2. 4 TFs activated genes and gene enrichment analysis +# 3. how about 脂代谢 and immune +# maybe need scRNA seq data ? +# 4. cellchat for BMS high and BMS low interaction with TME +#======================================================================================================================================== + +# 0. get maoding and plot +# map gene +dat <- readRDS("~/metastasis/data/verify/GSE14108/GSE14108_res.RDS") +gene <- readRDS("/public/workspace/lily/Lung2Brain/Version5/Signature/BMS_update_gene.RDS") +source('/public/workspace/lily/software/ssGSEA/ssgseaMOD.r') +mod <- mod.analyze2(as.matrix(dat),c("BMS_update"),"/public/workspace/lily/MOD_file/",permN=0) +mod <- as.data.frame(mod) + +tmp <- sort(apply(dat,1,function(x){cor.test(as.numeric(x),mod$BMS_update_norm,method="spearman")$estimate})) +tmp.f <- tmp[-which(names(tmp)%in%gene)] # do not get signature gene + +write.table(names(tail(tmp.f,20)),file="~/tmp.txt",quote=F,col.names=F,row.names=F) + +# for another data +# dat <- readRDS("/public/workspace/lily/metastasis/data/verify/E-MTAB-8659/LCBM_S63_data.RDS") + +# now plot result +library(pheatmap) +mod <- mod[order(mod$BMS_update_norm,decreasing=T),] +dat.f <- dat[which(rownames(dat)%in%names(tail(tmp.f,20))),rownames(mod)] +dat.f <- dat.f[rev(names(tail(tmp.f,20))),] + +# add row and column annotation +ann_row <- data.frame(row.names=rev(names(tail(tmp.f,20))),correlation=rev(unname(tail(tmp.f,20)))) +ann_col <- data.frame(row.names=rownames(mod),BMS=mod$BMS_update_norm) + +pdf("/public/workspace/lily/Lung2Brain/Version5/Fig3_TFs/GSE14108_maoding20gene.pdf",useDingbats=F) +pheatmap(dat.f,cluster_cols=F,cluster_rows=F,scale="row",annotation_row=ann_row , + annotation_col=ann_col, + color = colorRampPalette(c("steelblue","#B9D0E2", "white", "#FF5252","red"))(100)) +dev.off() + + + + +# 2021-11-22 +# use BMS signature +dat <- read.table("/public/workspace/lily/Lung2Brain/Version5/Data/EnrichR/MSigDB_Hallmark_2020_BMS.txt",sep="\t",header=T) +library(ggplot2) +dat$logP <- -log2(dat$Adjusted.P.value) +sub.dat <- dat[which(dat$Adjusted.P.value<0.05),] +sub.dat <- sub.dat[order(sub.dat$logP,decreasing=T),] +sub.dat$Term <- factor(sub.dat$Term,levels=rev(sub.dat$Term)) + +pdf("/public/workspace/lily/Lung2Brain/Version5/Fig3_TFs/BMS_hallmark.pdf",useDingbats=F) +ggplot(sub.dat,aes(x=Term,y=Odds.Ratio,fill=logP))+ + geom_bar(stat="identity",position = position_dodge(0.5),width=0.5) + coord_flip() + + scale_fill_gradientn(colors=c("lightgrey","steelblue"),limits=c(2, ceiling(max(sub.dat$logP)))) +dev.off() + + + + + + + + +#============================================================================================================================================ +# 1. plot GSE14108 hallmark result +# Gene enrichment analysis + +dat <- read.table("/public/workspace/lily/Lung2Brain/Version5/Data/EnrichR/MSigDB_Hallmark_2020_GSE14108_20maodinggene.txt",sep="\t",header=T) +library(ggplot2) +dat$logP <- -log2(dat$Adjusted.P.value) +sub.dat <- head(dat,10) +sub.dat <- sub.dat[order(sub.dat$logP,decreasing=T),] +sub.dat$Term <- factor(sub.dat$Term,levels=rev(sub.dat$Term)) + +pdf("/public/workspace/lily/Lung2Brain/Version5/Fig3_TFs/GSE14108_maoding20gene_hallmark.pdf",useDingbats=F) +ggplot(sub.dat,aes(x=Term,y=Odds.Ratio,fill=logP))+ + geom_bar(stat="identity",position = position_dodge(0.5),width=0.5) + coord_flip() + + scale_fill_gradientn(colors=c("lightgrey","steelblue")) +dev.off() + + + + + + + + +#============================================================================================================================================= +# 2. plot sangji plot +#============================================================================================================================================= +require(igraph) +require(rCharts) +require(rjson) +require(plyr) +require(reshape2) +require(networkD3) +options(stringsAsFactors=F) +library(ggalluvial) +library(ggplot2) +library(RColorBrewer) +#============================================================================================================================================= +gene <- readRDS("/public/workspace/lily/Lung2Brain/Version5/Signature/BMS_update_gene.RDS") +trrust.dat <- read.table('/public/workspace/lily/metastasis/data/TRRUST/trrust_rawdata.human.tsv',sep="\t") +colnames(trrust.dat) <- c("source","traget","effect","id") +# head(trrust.dat[which(trrust.dat$source%in%up.tf),]) # check TFs +tmp.res <- trrust.dat[which(trrust.dat$source%in%gene&trrust.dat$effect=="Activation"),] + +# links +links <- tmp.res[,c("source","traget","effect")] +links$effect <- 1 +tmp <- to_lodes_form(links,axes = 1:2,id = "Cohort") + + +# plot +pdf("/public/workspace/lily/Lung2Brain/Version5/Fig3_TFs/Sangjiplot_TF.pdf",useDingbats=F,height=10) +ggplot(tmp,aes(x =factor(x,level = c("source","traget")),y=effect,stratum = stratum, alluvium = Cohort,fill = stratum, label =stratum)) + +geom_flow( width = 1/4) + # flow +geom_stratum( width = 1/4,linetype=0,size=0.5,alpha =0.5,color = "black")+ +geom_text(stat ="stratum" , size =3) + #添加名字 +scale_x_discrete(limits = c() )+ #去掉横坐标轴 +theme_bw() + +theme(legend.position="none") + +scale_fill_manual(values = colorSpace) +dev.off() + + + + + + + + +#============================================================================================================================================= +# 3. GSEA plot for gene set +# +#============================================================================================================================================= +# for CEBPD + +dat <- read.table("/public/workspace/lily/Lung2Brain/Version5/Data/EnrichR/MSigDB_Hallmark_2020_CEBPD_act_gene.txt",sep="\t",header=T) +library(ggplot2) +dat$logP <- -log2(dat$Adjusted.P.value) +sub.dat <- dat[which(dat$Adjusted.P.value<0.05),] +sub.dat <- sub.dat[order(sub.dat$logP,decreasing=T),] +sub.dat$Term <- factor(sub.dat$Term,levels=rev(sub.dat$Term)) + +pdf("/public/workspace/lily/Lung2Brain/Version5/Fig3_TFs/CEBPD_act_.pdf",useDingbats=F) +ggplot(sub.dat,aes(x=Term,y=Odds.Ratio,fill=logP))+ + geom_bar(stat="identity",position = position_dodge(0.5),width=0.5) + coord_flip() + + scale_fill_gradientn(colors=c("lightgrey","steelblue","steelblue"),limits=c(2, ceiling(max(sub.dat$logP)))) +dev.off() + + + +# and for KLF5 +dat <- read.table("/public/workspace/lily/Lung2Brain/Version5/Data/EnrichR/MSigDB_Hallmark_2020_KLF5_act_gene.txt",sep="\t",header=T) +library(ggplot2) +dat$logP <- -log2(dat$Adjusted.P.value) +sub.dat <- dat[which(dat$Adjusted.P.value<0.05),] +sub.dat <- sub.dat[order(sub.dat$logP,decreasing=T),] +sub.dat$Term <- factor(sub.dat$Term,levels=rev(sub.dat$Term)) + +pdf("/public/workspace/lily/Lung2Brain/Version5/Fig3_TFs/KLF5_act_.pdf",useDingbats=F) +ggplot(sub.dat,aes(x=Term,y=Odds.Ratio,fill=logP))+ + geom_bar(stat="identity",position = position_dodge(0.5),width=0.5) + coord_flip() + + scale_fill_gradientn(colors=c("lightgrey","#F2BED7","#F2BED7"),limits=c(2, ceiling(max(sub.dat$logP)))) +dev.off() + + + + + + + + + + +#============================================================================================================================================= +# 4. check LCBM have higher expression of CEBPD and KLF5 ? +# +#============================================================================================================================================= +library(Seurat) +dat <- readRDS("/public/workspace/lily/Lung2Brain/Version5/Data/Tumor_pur/pur_LCBM_clust.RDS") +dat@active.ident <- dat$seurat_clusters + + +dat <- readRDS("/public/workspace/lily/metastasis/data/verify/GSE123904/GSE123902.RDS") +sub.dat <- subset(dat,cells=which(dat$seurat_clusters==6)) +sub.dat$group[which(sub.dat$group=="PRIMARY"&sub.dat$sample=="LX675")] <- "ADVANCED" + +sub.dat@active.ident <- factor(sub.dat$group) + + + + + +#============================================================================================================================================ +# Caculate for scRNA-seq data +# 0.25 percentage and 0.75 percentage for BMS group +# 2021-11-18 +#============================================================================================================================================ +library(Seurat) +dat <- readRDS("/public/workspace/lily/Lung2Brain/Version5/Data/Tumor_pur/pur_LCBM_clust.RDS") + +datname <- "pur_LCBM" +filepath <- "/public/workspace/lily/Lung2Brain/Version5/Fig3_TFs/" +# 0. define celltype +dat$BMS.group <- "inte" +dat$BMS.group[which(dat$BMS_updatequantile(dat$BMS_update,0.75))] <- "high" +DefaultAssay(dat) <- "RNA" +saveRDS(dat,file="/public/workspace/lily/Lung2Brain/Version5/Data/Tumor_pur/pur_LCBM_clust.RDS") + +# 1. plot CEBPD expression percentage +pdf(paste0(filepath,datname,"_CEBPD_KLF5_featureplot.pdf"),useDingbats=F,width=18) +FeaturePlot(dat,features=c("CEBPD"),split.by="BMS.group") +FeaturePlot(dat,features=c("KLF5"),split.by="BMS.group") +dev.off() + + + +# 2. plot metabolism +# first run metabolism in APP +tmp.dat <- read.table("/public/workspace/lily/Lung2Brain/Version5/Metabolism/final_metabolism_pathways_activity_pur_LCBM_BMS.group.txt",header=T,sep="\t") +dat <- tmp.dat[c(59,75,1,3,16,69),c(2,3)] +dat$pathway <- rownames(dat) +library(ggplot2) +p.dat <- reshape2::melt(dat) + +pdf("/public/workspace/lily/Lung2Brain/Version5/Fig3_TFs/pur_LCBM_metabolism.pdf",useDingbats=F) +ggplot(p.dat,aes(x=variable,y=pathway))+geom_point(aes(size=value,color=variable)) +dev.off() + + + + + + + + + + + + + + + + + +#========================================================================================================================================== +# bulik data verify +# CEBPD with apido,purity,and BMS +# GSE14108 and E-MTAB is ok +#========================================================================================================================================== +dat <- readRDS("~/metastasis/data/verify/GSE14108/GSE14108_res.RDS") +scores <- read.table("/public/workspace/lily/Lung2Brain/Version5/Fig4/Data/GSE14108_estimate_score.gct",skip = 2,header = T) + +# dat <- readRDS("/public/workspace/lily/metastasis/data/verify/E-MTAB-8659/LCBM_S63_data.RDS") +# scores <- read.table("/public/workspace/lily/Lung2Brain/Version5/Fig4/Data/E_MTAB_estimate_score.gct",skip = 2,header = T) + +source('/public/workspace/lily/software/ssGSEA/ssgseaMOD.r') +mod <- mod.analyze2(as.matrix(dat),c("BMS_update"),"/public/workspace/lily/MOD_file/",permN=0) +mod <- as.data.frame(mod) +mod$purity <- as.numeric(as.vector(scores[4,-c(1,2)])) +mod$immune <- as.numeric(as.vector(scores[2,-c(1,2)])) + +source('/public/workspace/lily/software/ssGSEA/ssgseaMOD.r') +mod.rs <- mod.analyze2(as.matrix(dat),c("HALLMARK_ADIPOGENESIS","HALLMARK_FATTY_ACID_METABOLISM"),"/public/workspace/lily/MOD_file/HALLMARK/",permN=0) +mod.rs <- as.data.frame(mod.rs) + +cor.test(as.numeric(dat["CEBPD",]),mod$purity,method="spearman") +cor.test(as.numeric(dat["CEBPD",]),mod$BMS_update_norm,method="spearman") +cor.test(as.numeric(dat["CEBPD",]),mod.rs$HALLMARK_ADIPOGENESIS_norm,method="spearman") +cor.test(as.numeric(dat["CEBPD",]),mod.rs$HALLMARK_FATTY_ACID_METABOLISM_norm,method="spearman") + +# cor.test(mod$BMS_update_norm,mod.rs$HALLMARK_ADIPOGENESIS_norm,method="spearman") +# cor.test(mod$BMS_update_norm,mod.rs$HALLMARK_FATTY_ACID_METABOLISM_norm,method="spearman") + +############################################################################################################################################ +# plot result + +pdf("/public/workspace/lily/Lung2Brain/Version5/Fig3_TFs/GSE14108_CEBPD_BMS_Fatty.pdf",useDingbats=F) +plot(as.numeric(dat["CEBPD",]),mod$purity,main="CEBPD with purity",xlab="CEBPD expression",ylab="estimate purity score",pch=19) +abline(lm(mod$purity~as.numeric(dat["CEBPD",])),col="red") +# cor.tes() +legend("topright",legend=paste0("rho= ",-0.41," pvalue=",0.031)) + +plot(as.numeric(dat["CEBPD",]),mod$BMS_update_norm,main="CEBPD with BMS",xlab="CEBPD expression",ylab="BMS score",pch=19) +abline(lm(mod$BMS_update_norm~as.numeric(dat["CEBPD",])),col="red") +# cor.tes() +legend("topright",legend=paste0("rho= ",0.34," pvalue=",0.08)) + +plot(as.numeric(dat["CEBPD",]),mod.rs$HALLMARK_ADIPOGENESIS_norm,main="CEBPD expression",xlab="HALLMARK_ADIPOGENESIS",ylab="estimate immune score",pch=19) +abline(lm(mod.rs$HALLMARK_ADIPOGENESIS_norm~as.numeric(dat["CEBPD",])),col="red") +# cor.tes() +legend("topright",legend=paste0("rho= ",0.67," pvalue<",0.001)) + +plot(as.numeric(dat["CEBPD",]),mod.rs$HALLMARK_FATTY_ACID_METABOLISM_norm,main="CEBPD expression",xlab="HALLMARK_FATTY_ACID_METABOLISM",ylab="estimate immune score",pch=19) +abline(lm(mod.rs$HALLMARK_FATTY_ACID_METABOLISM_norm~as.numeric(dat["CEBPD",])),col="red") +# cor.tes() +legend("topright",legend=paste0("rho= ",0.59," pvalue=",0.001)) + +dev.off() + + + + + + + + + + + + + + +#========================================================================================================================================== +# 4. run Cellchat for BMS high and BMS low +library(Seurat) +library(CellChat) + +LCBM.tumor <- readRDS("/public/workspace/lily/Lung2Brain/Version5/Data/Tumor_pur/pur_LCBM_clust.RDS") +LCBM.tumor <- subset(LCBM.tumor,cells=which(LCBM.tumor$BMS.group%in%c("high","low"))) +ntumor <- readRDS("/public/workspace/lily/Lung2Brain/Version5/Data/inte9_ntumor.RDS") +LCBM.ntumor <- subset(ntumor,cells=which(ntumor$type_group=="LCBM")) +LCBM.ntumor <- subset(LCBM.ntumor,cells=-which(LCBM.ntumor$type=="unknow")) +LCBM.ntumor$BMS.group <- LCBM.ntumor$type +dat <- merge(LCBM.tumor,LCBM.ntumor) + +# Run CellChat +data.input <- GetAssayData(dat,assay = "RNA", slot = "data") # normalized data matrix +meta <- data.frame(dat@meta.data) # create a dataframe of the cell labels +cellchat <- createCellChat(object = data.input, meta = meta, group.by = "BMS.group") +cellchat <- setIdent(cellchat, ident.use = "BMS.group") # set "labels" as default cell identity +levels(cellchat@idents) + +CellChatDB <- CellChatDB.human +# use all DB +CellChatDB.use <- CellChatDB +# set the used database in the object +cellchat@DB <- CellChatDB.use +cellchat <- subsetData(cellchat) # This step is necessary even if using the whole database +future::plan("multiprocess", workers = 4) # do parallel + +##################################################################################################### +cellchat <- identifyOverExpressedGenes(cellchat) +cellchat <- identifyOverExpressedInteractions(cellchat) +# project gene expression data onto PPI network (should use) +cellchat <- projectData(cellchat, PPI.human) + +cellchat <- computeCommunProb(cellchat) # 1W cells 5min +# Filter out the cell-cell communication if there are only few number of cells in certain cell groups +cellchat <- filterCommunication(cellchat, min.cells = 10) +cellchat <- computeCommunProbPathway(cellchat) +# Calculate the aggregated cell-cell communication network +cellchat <- aggregateNet(cellchat) + +groupSize <- as.numeric(table(cellchat@idents)) + +# saveRDS(cellchat,file="/public/workspace/lily/Lung2Brain/Version5/CellChat/LCBM.subtumor.CellChat.RDS") + +pdf("/public/workspace/lily/Lung2Brain/Version5/Fig3_TFs/cellchat_LCBM_interaction.pdf",useDingbats=F) +groupSize <- table(cellchat@idents) +netVisual_circle(cellchat@net$count, sources.use = c(4,5),targets.use=c(1:3,6:8),vertex.weight = groupSize, weight.scale = T, label.edge= F, title.name = "Number of interactions") +netVisual_circle(cellchat@net$count, sources.use = 4,targets.use=c(1:3,6:8),vertex.weight = groupSize, weight.scale = T, label.edge= F, title.name = "Number of interactions") +netVisual_circle(cellchat@net$count, sources.use = 5,targets.use=c(1:3,6:8),vertex.weight = groupSize, weight.scale = T, label.edge= F, title.name = "Number of interactions") +dev.off() + + + +pdf("/public/workspace/lily/Lung2Brain/Version5/Fig3_TFs/cellchat_LCBM_interaction_num.pdf",useDingbats=F) +barplot(c(1915,440),names=c("BMS.high","BMS.low"),main="BMS.interaction.num",ylim=c(0,2000)) +dev.off() + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/Fig4.r b/Fig4.r new file mode 100644 index 0000000..89ff7b4 --- /dev/null +++ b/Fig4.r @@ -0,0 +1,783 @@ + + +# this program is used to analysis different tumor cell group function +# C0,C9 is Lung gene high +# C4,C7 is BMS high +# C1,C6 is Brain gene high +# 2021-5-14 +# 0. BMS high cells is a group High Plascity cells. +# 1. hallmarks +# 2. metabolism +# 3. localinvasion and extravasion + + +# 0.0 calculate Brain specific genes # "NEFL" ,"NP22", +gene <- c("GFAP","STMN2","GAP43","PLP1","PMP2", + "GABBR2","INA","DNM1","SH3GL2","SNAP25", + "MBP","TPPP","S100B","NEFM","NCAN","KIF3C","SCG3","STMN4") + +dat <- readRDS("/public/workspace/lily/Lung2Brain/Version5/Fig4/Data/LCBM_sub.tumor.RDS") +DefaultAssay(dat) <- "RNA" +dat@active.ident <- factor(dat$group) + +# this show that group16 is like brain +FeaturePlot(dat,features=c("PLP1","MBP","GAP43","GFAP"),order=T) + + + + + + +############################################################################################################### +# 0. High Plascity cells +library(Seurat) +dat <- readRDS("/public/workspace/lily/Lung2Brain/Version5/Data/Tumor_pur/pur_LCBM_clust.RDS") +sub.dat <- subset(dat,cells=which(dat$seurat_clusters%in%c(0,1,4,6,7,9))) + +# calculate HPSC signature +source('/public/workspace/lily/software/ssGSEA/ssgseaMOD.r') +mod <- mod.analyze2(as.matrix(sub.dat[['RNA']]@data),c("HPSC_C5"),"/public/workspace/lily/MOD_file/",permN=0) +mod <- as.data.frame(mod) +saveRDS(mod,file="/public/workspace/lily/Lung2Brain/Version5/Data/sub.Tumor.HPSC.RDS") +sub.dat$HPSC <- mod[,2] + +saveRDS(sub.dat,file="/public/workspace/lily/Lung2Brain/Version5/Fig4/Data/LCBM_sub.tumor.RDS") +# 2021-5-19 +# both Lung gene and BMS is high plascity +library(Seurat) +dat <- readRDS("/public/workspace/lily/Lung2Brain/Version5/Fig4/Data/LCBM_sub.tumor.RDS") +dat$seurat_clusters <- factor(as.vector(dat$seurat_clusters),levels=c("4","7","0","9","1","6")) + +pdf("/public/workspace/lily/Lung2Brain/Version5/Fig4/subtumor_HPSC.pdf",useDingbats=F) +boxplot(HPSC~seurat_clusters,data=dat@meta.data,FUN=median,outline=F) +dev.off() + + + +################################################################################################################################## +# 2021-5-19 +# 1. metabolism +# ~/Lung2Brain/Version5/Metabolism/metabolic_tumor.r.exec +# run sub dat metabolism +# ~/Lung2Brain/Version5/Fig4 + +# check result +#========================================================= +# tmp.dat <- read.table("/public/workspace/lily/Lung2Brain/Version5/Metabolism/KEGGpathway_activity_shuffle.txt",header=T,sep="\t") +# colnames(tmp.dat) <- gsub("X","C",colnames(tmp.dat)) + +tmp.dat <- read.table("/public/workspace/lily/Lung2Brain/Version5/Fig4/Data/KEGGpathway_activity_shuffle.txt",header=T,sep="\t") +colnames(tmp.dat) <- gsub("X","C",colnames(tmp.dat)) +tmp.dat[is.na(tmp.dat)] <- 1 + +pdf("/public/workspace/lily/Lung2Brain/Version5/Fig4/subtumor_metabolism.pdf",useDingbats=F,height=10) +pheatmap::pheatmap(tmp.dat,scale="row") +dev.off() + + + + + +# 2. localvasion and angiogenesis signature +# looks not every good +# localinvasion socre calculate +library(Seurat) +dat <- readRDS("/public/workspace/lily/Lung2Brain/Version5/Fig4/Data/LCBM_sub.tumor.RDS") + +# calculate localinvasion and agogensis +source('/public/workspace/lily/software/ssGSEA/ssgseaMOD.r') +mod <- mod.analyze2(as.matrix(dat[['RNA']]@data),c("localInvasion_cp","intraInvasion_cp","blood_signature"),"/public/workspace/lily/MOD_file/",permN=0) +mod <- as.data.frame(mod) + +tmp.res <- mod[,c(4:6)] +tmp.res$Cluster <- paste0("C",dat$seurat_clusters) + + + + +# 3. hallmark calculate +# 2021-5-20 +#============================================================================================ +dat <- readRDS("/public/workspace/lily/Lung2Brain/Version5/Fig4/Data/LCBM_sub.tumor.RDS") +source('~/software/ssGSEA/ssgseaMOD.r') +modlist <- gsub("\\.mod","",dir("/public/workspace/lily/MOD_file/HALLMARK/")) +mod <- mod.analyze2(as.matrix(dat[["RNA"]]@data),modlist,'/public/workspace/lily/MOD_file/HALLMARK/',permN=0) +mod <- data.frame(mod) +saveRDS(mod,file="/public/workspace/lily/Lung2Brain/Version5/Fig4/Data/subdat.tumor.hallmark.RDS") + +tmp.res <- mod[,51:100] +tmp.res$Cluster <- paste0("C",dat$seurat_clusters) +res.f <- aggregate(.~Cluster,data=tmp.res,FUN=median) +rownames(res.f) <- res.f$Cluster +res.f$Cluster <- NULL +res.f <- t(res.f) + +pdf("/public/workspace/lily/Lung2Brain/Version5/Fig4/subtumor_hallmark.pdf",useDingbats=F) +pheatmap::pheatmap(res.f,scale="row") +dev.off() + + + + + + +# 4. MKI67 gene check +# 2021-5-31 +# test if sub tumor +# 2021-7-1 add other genes +#================================================================================================ +library(Seurat) +dat <- readRDS("/public/workspace/lily/Lung2Brain/Version5/Data/Tumor_pur/pur_LCBM_clust.RDS") + +pdf("/public/workspace/lily/Lung2Brain/Version5/Fig4/MKI67_LCBM.pdf",useDingbats=F) +FeaturePlot(dat,feature="MKI67",min.cutoff=0) +FeaturePlot(dat,feature="TOP2A",min.cutoff=0) +FeaturePlot(dat,feature="STMN1",min.cutoff=0) +dev.off() + + +# use this three gene to calculate a proliferation score +source('/public/workspace/lily/software/ssGSEA/ssgseaMOD.r') +gene <- c("MKI67","TOP2A","STMN1") +name <- "Proliferation" +respath <- "/public/workspace/lily/MOD_file/NatureMedicine/" +mod.generate(gene,name,out=paste0(respath,name,".mod")) # make a mod file + +# use sub tumor to calculate +dat <- readRDS("/public/workspace/lily/Lung2Brain/Version5/Fig4/Data/LCBM_sub.tumor.RDS") +source('/public/workspace/lily/software/ssGSEA/ssgseaMOD.r') +mod <- mod.analyze2(as.matrix(dat[['RNA']]@data),c("Proliferation"),"/public/workspace/lily/MOD_file/NatureMedicine/",permN=0) +mod <- as.data.frame(mod) + +mod$group <- dat$group +pdf("/public/workspace/lily/Lung2Brain/Version5/Fig4/porliferation_LCBM_sub.pdf",useDingbats=F) +boxplot(Proliferation_norm~group,data=mod,outline=F,ylim=c(0,1)) +dev.off() + + + +# 5. pySCENIC +# 2021-5-31 +#================================================================================================================================= +library(Seurat) +dat <- readRDS("/public/workspace/lily/Lung2Brain/Version5/Data/Tumor_pur/pur_LCBM_clust.RDS") +tf.dat <- read.table("/public/workspace/lily/Lung2Brain/Version5/Pyscenic/LCBM/step3.auc_mtx.csv",sep=",",header=T) +rownames(tf.dat) <- tf.dat$Cell +tf.dat$Cell <- NULL +colnames(tf.dat) <- gsub("\\.\\.\\.$","",colnames(tf.dat)) +all(rownames(tf.dat)==rownames(dat@meta.data)) + +# tf data +tf.dat$Cluster <- dat$seurat_clusters +tmp.res <- aggregate(.~Cluster,data=tf.dat,FUN=mean) +rownames(tmp.res ) <- paste0("C",tmp.res$Cluster) +tmp.res$Cluster <- NULL + +# caculate which TFs +pdf("/public/workspace/lily/Lung2Brain/Version5/Fig4/TFs_LCBM.pdf",useDingbats=F,width=30,height=10) +pheatmap::pheatmap(tmp.res,scale="column",color=colorRampPalette(c('steelblue','white','red'))(50)) +dev.off() + + +# try to use C0 C9 C1 C6 C4 C7 +#================================================================================================================================ +library(Seurat) +dat <- readRDS("/public/workspace/lily/Lung2Brain/Version5/Data/Tumor_pur/pur_LCBM_clust.RDS") +tf.dat <- read.table("/public/workspace/lily/Lung2Brain/Version5/Pyscenic/LCBM/step3.auc_mtx.csv",sep=",",header=T) +rownames(tf.dat) <- tf.dat$Cell +tf.dat$Cell <- NULL +colnames(tf.dat) <- gsub("\\.\\.\\.$","",colnames(tf.dat)) +all(rownames(tf.dat)==rownames(dat@meta.data)) + +sub.dat <- subset(dat,cells=which(dat$seurat_clusters%in%c(0,9,1,6,4,7))) +sub.dat$group <- "group09" +sub.dat$group[which(sub.dat$seurat_clusters%in%c(1,6))] <- "group16" +sub.dat$group[which(sub.dat$seurat_clusters%in%c(4,7))] <- "group47" + +tf.dat.f <- tf.dat[which(rownames(tf.dat)%in%colnames(sub.dat)),] +all(rownames(tf.dat.f)==colnames(sub.dat)) + +# tf.dat.f$group <- sub.dat$group +# tmp.res <- aggregate(.~group,data=tf.dat.f,FUN=mean) +# rownames(tmp.res ) <- tmp.res$group +# tmp.res$group <- NULL + +# caculate score and pvalue +# 2021-6-1 +#=================================================================================================== +idx.09 <- which(sub.dat$group=="group09") +idx.16 <- which(sub.dat$group=="group16") +idx.47 <- which(sub.dat$group=="group47") + +tmp.res <- t(apply(tf.dat.f,2,function(x){ + fc09 <- mean(x[idx.09])/mean(x[-idx.09]) + pvalue09 <- wilcox.test(x[idx.09],x[-idx.09])$p.value + + fc16 <- mean(x[idx.16])/mean(x[-idx.16]) + pvalue16 <- wilcox.test(x[idx.16],x[-idx.16])$p.value + + fc47 <- mean(x[idx.47])/mean(x[-idx.47]) + pvalue47 <- wilcox.test(x[idx.47],x[-idx.47])$p.value + + c(fc09,pvalue09,fc16,pvalue16,fc47,pvalue47) + +})) +tmp.res <- data.frame(tmp.res) +colnames(tmp.res) <- c("FC09","pvalue09","FC16","pvalue16","FC47","pvalue47") + + + +# caculate DEG +#==================================================================================================== +DefaultAssay(sub.dat) <- "RNA" +sub.dat@active.ident <- factor(sub.dat$group) +tmp.gene <- FindAllMarkers(sub.dat,assay="RNA",logfc.threshold=0.1) +tmp.gene <- tmp.gene[order(tmp.gene$avg_logFC,decreasing=T),] + +deg <- tmp.gene[which(tmp.gene$avg_logFC>0&tmp.gene$p_val_adj<0.01),] + +deg09 <- deg[which(deg$cluster=="group09"°$avg_logFC>1),] +write.table(as.vector(deg09$gene),file="/public/workspace/lily/Lung2Brain/Version5/Fig4/Group09_DEG.txt",sep="\t",row.names=F,col.names=F,quote=F) + +# the same cut off do not have DEGs +deg16 <- deg[which(deg$cluster=="group16"°$avg_logFC>1),] +write.table(as.vector(deg16$gene),file="./tmp.txt",sep="\t",row.names=F,col.names=F,quote=F) + +deg47 <- deg[which(deg$cluster=="group47"°$avg_logFC>1),] +write.table(as.vector(deg47$gene),file="./tmp.txt",sep="\t",row.names=F,col.names=F,quote=F) + + + + + + + + + + + +################################################################################################################################### +# 2021-6-1 +# plot result +#================================================================================================================================== +# 0. TSNE plot show different group +library(Seurat) +dat <- readRDS("/public/workspace/lily/Lung2Brain/Version5/Data/Tumor_pur/pur_LCBM_clust.RDS") +dat$group <- "non" +dat$group[which(dat$seurat_clusters%in%c(0,9))] <- "group09" +dat$group[which(dat$seurat_clusters%in%c(1,6))] <- "group16" +dat$group[which(dat$seurat_clusters%in%c(4,7))] <- "group47" +pdf("/public/workspace/lily/Lung2Brain/Version5/Fig4/sub.Tumor.DimPlot.pdf",useDingbats=F) +DimPlot(dat,group.by="group",cols=c("#2baf2b","#00a5dc","#ffc719","#ced7df")) +dev.off() + + + + +# 1. HPSC +library(Seurat) +dat <- readRDS("/public/workspace/lily/Lung2Brain/Version5/Fig4/Data/LCBM_sub.tumor.RDS") +# dat$group <- "group09" +# dat$group[which(dat$seurat_clusters%in%c(1,6))] <- "group16" +# dat$group[which(dat$seurat_clusters%in%c(4,7))] <- "group47" +# saveRDS(dat,file="/public/workspace/lily/Lung2Brain/Version5/Fig4/Data/LCBM_sub.tumor.RDS") + +mod <- readRDS("/public/workspace/lily/Lung2Brain/Version5/Data/sub.Tumor.HPSC.RDS") +all(colnames(dat)==rownames(mod)) + +mod$group <- factor(dat$group,levels=c("group47","group09","group16")) +pdf("/public/workspace/lily/Lung2Brain/Version5/Fig4/subtumor_HPSC.pdf",useDingbats=F) +boxplot(HPSC_C5_norm~group,data=mod,FUN=median,outline=F,ylim=c(0,1)) +dev.off() + + + + + + +# 2.DEG analysis +# find Markers to get DEG +# then use enrichr to do hallmarks analysis +#============================================================================================================================== +dat <- read.table("/public/workspace/lily/Lung2Brain/Version5/Fig4/MSigDB_Hallmark_2020_group09.txt",sep="\t",header=T) + +dat$logOR <- log2(dat$Odds.Ratio) +dat <- dat[order(dat$Odds.Ratio,decreasing=T),] +dat <- dat[1:10,] +dat$Term <- factor(dat$Term,levels=rev(as.vector(dat$Term))) +library(ggplot2) + +pdf("/public/workspace/lily/Lung2Brain/Version5/Fig4/sub.Tumor.Top10.hallmark.group09.pdf",useDingbats=F) +ggplot(dat,aes(x=logOR,y=Term)) + geom_point(aes(size= -log10(Adjusted.P.value),color= logOR))+ + scale_colour_gradientn(colours = c("#598c14","#b5c327","#edd812","#faae40","#ff3c41","red"),values = c(0,0.4,0.5,0.8,1.0))+ + theme_bw() +dev.off() + + + + + + + + + +# 3. heatmap show Immune gene +library(Seurat) +dat <- readRDS("/public/workspace/lily/Lung2Brain/Version5/Fig4/Data/LCBM_sub.tumor.RDS") +dat@active.ident <- factor(dat$group) +tmp <- AverageExpression(dat,assay="RNA",feature=c("IDO1","CD274","KYNU","CD47","CD96","LGALS9"))$RNA +pdf("/public/workspace/lily/Lung2Brain/Version5/Fig4/sub.Tumor.immune_heatmap.pdf",useDingbats=F) +pheatmap::pheatmap(tmp,scale="row",color=colorRampPalette(c('steelblue','white','red'))(50)) +dev.off() + + + + + + + +# 4. PySCENIC show group16 expression Brain tissue TFs +#=========================================================================================================================== +library(Seurat) +dat <- readRDS("/public/workspace/lily/Lung2Brain/Version5/Fig4/Data/LCBM_sub.tumor.RDS") +################################################################################################################## +# 2021-8-9 +# use LCBM sub tumor RDS to calculate TFs +# tf.dat <- read.table("/public/workspace/lily/Lung2Brain/Version5/Pyscenic/LCBM_subtumor/step3.auc_mtx.csv",sep=",",header=T) # do not OK +tf.dat <- read.table("/public/workspace/lily/Lung2Brain/Version5/Pyscenic/LCBM/step3.auc_mtx.csv",sep=",",header=T) +rownames(tf.dat) <- tf.dat$Cell +tf.dat$Cell <- NULL +colnames(tf.dat) <- gsub("\\.\\.\\.$","",colnames(tf.dat)) +tf.dat.f <- tf.dat[which(rownames(tf.dat)%in%colnames(dat)),] + +all(rownames(tf.dat.f)==rownames(dat@meta.data)) + +# analysis +#=========================================================================================================================== +idx.09 <- which(dat$group=="group09") +idx.16 <- which(dat$group=="group16") +idx.47 <- which(dat$group=="group47") + +tmp.res <- t(apply(tf.dat.f,2,function(x){ + fc09 <- mean(x[idx.09])/mean(x[-idx.09]) + pvalue09 <- wilcox.test(x[idx.09],x[-idx.09])$p.value + + fc16 <- mean(x[idx.16])/mean(x[-idx.16]) + pvalue16 <- wilcox.test(x[idx.16],x[-idx.16])$p.value + + fc47 <- mean(x[idx.47])/mean(x[-idx.47]) + pvalue47 <- wilcox.test(x[idx.47],x[-idx.47])$p.value + + c(fc09,pvalue09,fc16,pvalue16,fc47,pvalue47) + +})) +tmp.res <- data.frame(tmp.res) +colnames(tmp.res) <- c("FC09","pvalue09","FC16","pvalue16","FC47","pvalue47") + +# caculate C1 and C6 TFs and plot result +################################################################################## +tmp <- tmp.res[,c("FC16","pvalue16"),drop=F] +rs.f <- tmp[which(tmp$pvalue16<0.05),] +rs.f <- rs.f[order(rs.f$FC16,decreasing=T),] +plot.rs <- rs.f[1:10,] +plot.rs$gene <- rownames(plot.rs) +plot.rs$log10p <- -log10(plot.rs$pvalue16) +plot.rs$log10p[which(is.infinite(plot.rs$log10p))] <- max(plot.rs$log10p[-which(is.infinite(plot.rs$log10p))]) +plot.rs$gene <- factor(plot.rs$gene,levels=rownames(plot.rs)) +library(ggplot2) + +pdf("/public/workspace/lily/Lung2Brain/Version5/Fig4/sub.Tumor.group16.TF.pdf",useDingbats=F) +ggplot(plot.rs,aes(x= gene ,y= FC16 ,fill= log10p)) + geom_bar(stat="identity",position="dodge") + theme_bw() + + labs(x="Transcription factors",y="Fold change") + +dev.off() + + + + + + +# tmp.res$group <- factor(tmp.res$group,levels=c("group09","group16","group47","Unknow")) + + + + + + + + + + + + + + + + + + + + +################################################################################# +# USE https://www.proteinatlas.org/ENSG00000125820-NKX2-2/tissue +# show NKX2.2 is high expression in Brain +#================================================================================ + + + + +################################################################################## +# metabolism +#================================================================================= + +tmp <- data.table::fread("/public/workspace/lily/metastasis/data/GTEx/GTEx_Analysis_2017-06-05_v8_RNASeQCv1.1.9_gene_tpm.gct",) +tmp <- as.data.frame(tmp) +tmp.f <- tmp[,as.numeric(c(1,2,which(colnames(tmp)%in%as.vector(ann[which(ann$SMTS%in%c("Brain","Lung")),"SAMPID"]))))] +tmp.f$Name <- NULL +tmp.res <- aggregate(.~Description,data=tmp.f,FUN=median) # aggregate gene expression + +rownames(tmp.res) <- tmp.res$Description +tmp.res$Description <- NULL + +ann.f <- ann[which(ann$SAMPID%in%colnames(tmp.res)),] +rownames(ann.f) <- ann.f$SAMPID +saveRDS(tmp.res,file="/public/workspace/lily/metastasis/data/GTEx/GTEx_brain_lung.RDS") +saveRDS(ann.f,file="/public/workspace/lily/metastasis/data/GTEx/GTEx_brain_lung_ann.RDS") + + +#================================================================================== +library(GSEABase) +library(GSVA) +library(parallel) +genelist <- getGmt("/public/workspace/lily/software/SingleCellMetabolic/Data/KEGG_metabolism.gmt") +dat <- readRDS("/public/workspace/lily/metastasis/data/GTEx/GTEx_brain_lung.RDS") +dat <- log2(dat+1) +result_gsva <- gsva(as.matrix(dat), genelist, kcdf="Gaussian", method="ssgsea",ssgsea.norm=TRUE, parallel.sz=10) + +saveRDS(result_gsva,file="/public/workspace/lily/Lung2Brain/Version5/Fig4/Data/GTEx_Lung_Brain_metabolism_GSVA.RDS") + + +################################################################################### +# 2021-6-3 +# 2021-6-4 use ssGSEA to calculate metabolism mod +#================================================================================== +# dat <- readRDS("/public/workspace/lily/metastasis/data/GTEx/GTEx_brain_lung.RDS") +mod <- readRDS("/public/workspace/lily/Lung2Brain/Version5/Fig4/Data/GTEx_Lung_Brain_metabolism_ssGSEA.RDS") +ann <- readRDS("/public/workspace/lily/metastasis/data/GTEx/GTEx_brain_lung_ann.RDS") +# source('/public/workspace/lily/software/ssGSEA/ssgseaMOD.r') +# filename <- gsub("\\.mod","",dir("/public/workspace/zhumy/CRC2Liver/ref/mod/metabolism")) +# mod.rs <- mod.analyze2(as.matrix(dat),filename,"/public/workspace/zhumy/CRC2Liver/ref/mod/metabolism/",permN=0) +# mod.rs <- as.data.frame(mod.rs) + +# mod <- data.frame(mod.rs[,86:170]) +# saveRDS(mod,file="/public/workspace/lily/Lung2Brain/Version5/Fig4/Data/GTEx_Lung_Brain_metabolism_ssGSEA.RDS") + + +rownames(mod) <- gsub("\\.","-",rownames(mod)) +all(rownames(mod)==rownames(ann)) +mod$type <- ann$SMTS +tmp.res <- aggregate(.~type,data=mod,FUN=median) +rownames(tmp.res) <- tmp.res$type +tmp.res$type <- NULL +tmp.res <- data.frame(t(tmp.res)) +tmp.res$diff <- tmp.res$Brain -tmp.res$Lung + +tmp.res <- tmp.res[order(tmp.res$diff,decreasing=T),] + +############################################################################################################################################################### +# use GTEX Brain and Lung data to show Brain specific metabolism pathway +# Alanine__aspartate_and_glutamate_metabolism_norm +# Mannose_type_O.glycan_biosynthesis_norm +# Taurine_and_hypotaurine_metabolism_norm +# Butanoate_metabolism_norm +# Arginine_and_proline_metabolism_norm +# Oxidative_phosphorylation_norm +# and so on + + +#============================================================================================================================ +# 2021-6-4 +# calculate metabolism for sub.Tumor +#============================================================================================================================ +library(Seurat) +dat <- readRDS("/public/workspace/lily/Lung2Brain/Version5/Fig4/Data/LCBM_sub.tumor.RDS") + +source('/public/workspace/lily/software/ssGSEA/ssgseaMOD.r') +filename <- gsub("\\.mod","",dir("/public/workspace/zhumy/CRC2Liver/ref/mod/metabolism")) +mod.rs <- mod.analyze2(as.matrix(dat[["RNA"]]@data),filename,"/public/workspace/zhumy/CRC2Liver/ref/mod/metabolism/",permN=0) +mod.rs <- as.data.frame(mod.rs) +saveRDS(mod.rs,file="/public/workspace/lily/Lung2Brain/Version5/Fig4/Data/sub.Tumor.metabolism.RDS") + + +#====================================================================================================================== +# analysis result +# 2021-6-5 +####################################################################################################################### +library(Seurat) +dat <- readRDS("/public/workspace/lily/Lung2Brain/Version5/Fig4/Data/LCBM_sub.tumor.RDS") +tmp.dat <- readRDS("/public/workspace/lily/Lung2Brain/Version5/Fig4/Data/sub.Tumor.metabolism.RDS") +tmp.f <- data.frame(tmp.dat[,86:170]) +tmp.f$group <- dat$group + +tmp.res <- aggregate(.~group,data=tmp.f,FUN=median) +rownames(tmp.res) <- tmp.res$group +tmp.res$group <- NULL + +tmp.res <- t(tmp.res) +res.f <- tmp.res[-which(rowSums(tmp.res)==0),] + +pdf("/public/workspace/lily/Lung2Brain/Version5/Fig4/sub.Tumor.metabolism.pdf",useDingbats=F,height=20,width=10) +pheatmap::pheatmap(res.f,scale="row") +dev.off() + + + + + + + + +############################################################################################################################# +# 2021-6-16 +# calculate Nature Medicine signature +#============================================================================================================================ +library(Seurat) +dat <- readRDS("/public/workspace/lily/Lung2Brain/Version5/Data/Tumor_pur/pur_LCBM_clust.RDS") +source('~/software/ssGSEA/ssgseaMOD.r') +modlist <- gsub("\\.mod","",dir("/public/workspace/lily/MOD_file/NatureMedicine/")) +mod <- mod.analyze2(as.matrix(dat[["RNA"]]@data),modlist,'/public/workspace/lily/MOD_file/NatureMedicine/',permN=0) +mod <- data.frame(mod) + + + + + + + + + + + +# 2021-8-6 +#=========================================================================================================== +# run Pyscience to analysis subtype specific TFs +# just use these cells +#=========================================================================================================== +library(Seurat) +dat <- readRDS("/public/workspace/lily/Lung2Brain/Version5/Fig4/Data/LCBM_sub.tumor.RDS") +################################################################################################################## +# 2021-8-9 +# use LCBM sub tumor RDS to calculate TFs +# tf.dat <- read.table("/public/workspace/lily/Lung2Brain/Version5/Pyscenic/LCBM_subtumor/step3.auc_mtx.csv",sep=",",header=T) # do not OK +tf.dat <- read.table("/public/workspace/lily/Lung2Brain/Version5/Pyscenic/LCBM/step3.auc_mtx.csv",sep=",",header=T) +rownames(tf.dat) <- tf.dat$Cell +tf.dat$Cell <- NULL +colnames(tf.dat) <- gsub("\\.\\.\\.$","",colnames(tf.dat)) +tf.dat.f <- tf.dat[which(rownames(tf.dat)%in%colnames(dat)),] + +all(rownames(tf.dat.f)==rownames(dat@meta.data)) + +# analysis +#=========================================================================================================================== +idx.09 <- which(dat$group=="group09") +idx.16 <- which(dat$group=="group16") +idx.47 <- which(dat$group=="group47") + +tmp.res <- t(apply(tf.dat.f,2,function(x){ + fc09 <- mean(x[idx.09])/mean(x[-idx.09]) + pvalue09 <- wilcox.test(x[idx.09],x[-idx.09])$p.value + + fc16 <- mean(x[idx.16])/mean(x[-idx.16]) + pvalue16 <- wilcox.test(x[idx.16],x[-idx.16])$p.value + + fc47 <- mean(x[idx.47])/mean(x[-idx.47]) + pvalue47 <- wilcox.test(x[idx.47],x[-idx.47])$p.value + + c(fc09,pvalue09,fc16,pvalue16,fc47,pvalue47) + +})) +tmp.res <- data.frame(tmp.res) +colnames(tmp.res) <- c("FC09","pvalue09","FC16","pvalue16","FC47","pvalue47") + +################################################################################################################# +# 2021-8-9 +# plot 3D plot to show result group specific TFs +#================================================================================================================ +# another way to show TFs +# add some info to plot result +tmp.res$group <- "Unknow" +tmp.res$group[which(rownames(tmp.res) %in% rownames(head(tmp.res[order(tmp.res$FC16,decreasing=T),],2)))] <- "group16" +tmp.res$group[which(rownames(tmp.res) %in% rownames(head(tmp.res[order(tmp.res$FC47,decreasing=T),],2)))] <- "group47" +tmp.res$group[which(rownames(tmp.res) %in% rownames(head(tmp.res[order(tmp.res$FC09,decreasing=T),],2)))] <- "group09" +tmp.res$group[which(rownames(tmp.res)=="CEBPD")] <- "specifc" +# set color +colss <- forcats::fct_recode(as.factor(tmp.res$group), + "#2baf2b" = "group09","#00a5dc" = "group16", # new <- old + "#ffc719" = "group47","#ced7df" = "Unknow","red" = "specifc" + ) + + +tmp.res$gene <- "" +tmp.res$gene[which(rownames(tmp.res) %in% rownames(head(tmp.res[order(tmp.res$FC16,decreasing=T),],2)))] <- c("NKX2.2","SALL1") +# c("BHLHE41","NKX2.2","SALL1","ZNF384","ZNF589","ZNF770") +tmp.res$gene[which(rownames(tmp.res) %in% rownames(head(tmp.res[order(tmp.res$FC47,decreasing=T),],2)))] <- c("E2F2","MYBL1") +# c("E2F2","E2F8","ETV4","MYBL1","NFYA","TEAD4") +tmp.res$gene[which(rownames(tmp.res) %in% rownames(head(tmp.res[order(tmp.res$FC09,decreasing=T),],2)))] <- c("NR4A2","ZNF880") +# c("ERG","NFIA","NR2F1","NR4A2","RUNX1","ZNF880") +tmp.res$gene[which(rownames(tmp.res)=="CEBPD")] <- "CEBPD" + +library(plot3D) +pdf("/public/workspace/lily/Lung2Brain/Version5/Fig4/TF_specific_add.pdf",useDingbats=F) +with(tmp.res, scatter3D(x = FC09, y = FC47, z = FC16, + pch = 21, cex = 1.5,col="black",bg=colss, + xlab = "Fold change group09", + ylab = "Fold change group47", + zlab = "Fold change group16", + ticktype = "detailed",bty = "f",box = TRUE, + theta = -30, phi = 20, d=3, + colkey = FALSE)) + +with(tmp.res,text3D(x = FC09, y = FC47, z = FC16,col="black",bg=colss, + colkey = FALSE, add = TRUE, + labels = tmp.res$gene)) + +dev.off() + + + + + + + + + + + +############################################################################################################################################################### +# 2021-8-9 +# try to use GSE110495 to analysis +# 2021-9-15 use GSE110495 to plot results +# check origin LT and BMIT +#============================================================================================================================================================== +load("~/metastasis/data/verify/GSE110495/GSE110495.RData") +load("~/metastasis/data/verify/GSE110495/GSE110495_anno.RData") +source('/public/workspace/lily/software/ssGSEA/ssgseaMOD.r') +mod <- mod.analyze2(as.matrix(gse110495),c("Lung_gene","Brain_gene","BMS_update"),"/public/workspace/lily/MOD_file/",permN=0) +mod <- as.data.frame(mod) +mod$group <- anno[,2] +mod.f <- mod[which(mod$group%in%c("original","LT","BMIT")),] + +# reshape +library(reshape2) +library(ggplot2) +tmp.res <- reshape2::melt(mod.f[,c(4:6,10)]) +colnames(tmp.res) <- c("group","type","score") +tmp.res$group <- factor(tmp.res$group,levels=c("original","LT","BMIT")) +tmp.res$type <- factor(tmp.res$type,levels=c("BMS_update_norm","Lung_gene_norm","Brain_gene_norm")) + + +pdf("/public/workspace/lily/Lung2Brain/Version5/Fig4/GSE110495_3score_boxplot.pdf",useDingbats=F) +ggplot(tmp.res,aes(x=group,y=score,fill=type)) + geom_boxplot(outlier.color=NA) + scale_fill_manual(values=c("#ffc719","#2baf2b","#00a5dc")) +dev.off() + + + + + + + + + + + + + + + +#============================================================================================================================================================== +# 2021-8-12 +# need to make sure cell numbers of different cell type +############################################################################################################################################### +# 0. self data +libray(Seurat) +dat <- readRDS("/public/workspace/lily/Lung2Brain/Version5/Data/LCBM_celltype_type.RDS") +table(dat$celltype,dat$orig.ident) + + +# 1. D0927 and E927 +dat <- readRDS("/public/workspace/lily/Lung2Brain/Multiple_LungBrain/D0927.RDS") +#check <- function(dat){ + + DefaultAssay(dat) <- "RNA" + pdf("~/tmp.pdf",width=10,height=10) + DimPlot(dat,label=T) + FeaturePlot(dat,features=c('CD3D','CD3E','CD2','PTPRC'),label=T,order=F) # T cell + FeaturePlot(dat,features=c('CD19','CD68','FCGR3A','FCGR1A'),label=T,order=F) # Myeloid + FeaturePlot(dat,features=c('MS4A1',"CD79A",'PTPRC'),label=T,order=T) # B cell + FeaturePlot(dat,features=c('MAG','MOG','CNDP1','PTPRC'),label=T,order=T) # Oligodendrocyte + FeaturePlot(dat,features=c('COL1A1','COL1A2','DCN','CD248'),label=T,order=T) # Fibroblast/Vascular + FeaturePlot(dat,features=c('CLDN5','VWF','ABCG2','CDH5'),label=T,order=T) # Endothelial + FeaturePlot(dat,features=c("EGFR","EPCAM","PTPRC"),label=T,order=T,cols=c("lightgrey", "red")) + + VlnPlot(dat,features=c('CD3D','CD3E','CD2','PTPRC'),pt.size=0) + VlnPlot(dat,features=c('CD19','CD68','FCGR3A','FCGR1A'),pt.size=0) + VlnPlot(dat,features=c('MS4A1',"CD79A",'PTPRC'),pt.size=0) + VlnPlot(dat,features=c('MAG','MOG','CNDP1','PTPRC'),pt.size=0) + VlnPlot(dat,features=c('COL1A1','COL1A2','DCN','CD248'),pt.size=0) + VlnPlot(dat,features=c('CLDN5','VWF','ABCG2','CDH5'),pt.size=0) + VlnPlot(dat,features=c("EGFR","EPCAM","PTPRC"),pt.size=0) + dev.off() + +#} +# rm(list=ls()) +# run again +dat <- readRDS("/public/workspace/lily/Lung2Brain/Multiple_LungBrain/E0927.RDS") + + +tmp <- matrix(c(58,37,136,83,192,256,488,1543,1021,90,770,421,101,32,1631,492,161,122,991,350,157,49,708,136),ncol=4,byrow=T) +colnames(tmp) <- c("A20190305","A20190312","T-Bsc1","E0927") +rownames(tmp) <- c("Endothelial","Fibroblast","group09","group16","group47","Oligodendrocyte") + + +1021 90 770 421 +101 32 1631 492 +161 122 991 350 +157 49 708 136 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/Fig5.r b/Fig5.r new file mode 100644 index 0000000..42811ea --- /dev/null +++ b/Fig5.r @@ -0,0 +1,1693 @@ + +# this program is used to calculate Figure 5 which calculate Mutation and BMS . therapy +# 2021-5-10 +#================================================================================================ +# 0. claulate BMS score associate mutation +# LUAD <- readRDS("/public/workspace/lily/metastasis/data/verify/TCGA_LUAD/TCGA_LUAD_exp.RDS") +# source('~/software/ssGSEA/ssgseaMOD.r') +# mod <- mod.analyze2(as.matrix(LUAD),c("BMS_update"),'/public/workspace/lily/MOD_file/',permN=0) +# mod <- data.frame(mod) +# saveRDS(mod,file="/public/workspace/lily/Lung2Brain/Version5/Data/TCGA_LUAD_mod.RDS") + + +#=============================================================================================== +luad_mod <- readRDS("/public/workspace/lily/Lung2Brain/Version5/Data/TCGA_LUAD_mod.RDS") +a <- read.table('~/metastasis/data/verify/TCGA_LUAD/mutation_broad_LUAD',sep='\t',header=T) +a$sample <- gsub('-','.',a$sample) +mut <- a[which(a$sample%in%rownames(luad_mod)),] +mod <- luad_mod[which(rownames(luad_mod)%in%mut$sample),] + +gene <- unique(mut$gene) +res <- data.frame(gene) +p_value <- c() +mut_mean <- c() +no_mut_mean <- c() +percent <- c() +for(i in 1:length(gene)){ + as.vector(unique(mut[which(mut$gene==gene[i]),]$sample)) -> samp + per <- length(unique(mut[which(mut$gene==gene[i]),]$sample))/length(unique(mut$sample)) + which(rownames(mod)%in%as.vector(samp)) -> y + p <- wilcox.test(mod[y,2],mod[-y,2])$p.value + y_mean <- mean(mod[y,2]) + n_mean <- mean(mod[-y,2]) + p_value <- c(p_value,p/2) + mut_mean <- c(mut_mean,y_mean) + no_mut_mean<- c(no_mut_mean,n_mean) + percent <- c(percent,per) +} +cbind(res,mut_mean,no_mut_mean,percent,p_value) -> res +res$p.adj <- p.adjust(res$p_value) +res-> luad_res1 +# filter some mutation and rank by varation +res.f <- luad_res1[which(luad_res1$p_value<0.01),] +res.ff <- res.f[which(res.f$percent>0.15),] +res.ff$varation <- (res.ff$mut_mean-res.ff$no_mut_mean) +res.ff <- res.ff[order(res.ff$varation,decreasing=T),] +res.ff$gene <- factor(res.ff$gene,levels=res.ff$gene) + + + +#========================================================================================================================== +# +library(ggplot2) +# ggplot(res.ff,aes(x=varation,y=percent)) + geom_point() +pdf("/public/workspace/lily/Lung2Brain/Version5/Fig5/Mutation_KRAS.pdf",useDingbats=F) +ggplot(res.ff,aes(x=gene,y=varation,fill= percent)) + geom_bar(stat="identity",position="dodge") + theme_bw() +dev.off() + + + + + +# 0.1 maftools check mutation point +#========================================================================================================================== +#########KRAS########## +#library(g3viz) +library(ggplot2) +library(maftools) +#########LUAD-C1########## +luad_mod <- readRDS("/public/workspace/lily/Lung2Brain/Version5/Data/TCGA_LUAD_mod.RDS") +luad_mod <- luad_mod[order(luad_mod[,2],decreasing=T),] +a <- luad_mod[,2] +nM <- luad_mod[which(luad_mod[,2]unname(quantile(a,0.67))),] +rownames(nM) <- gsub("\\.",'-',rownames(nM)) +rownames(M) <- gsub("\\.","-",rownames(M)) + +maffile <- read.table('~/metastasis/data/verify/TCGA_LUAD/mutation_LUAD.maf',sep='\t',header=T) +maffile[which(maffile$Tumor_Sample_Barcode%in%rownames(nM)),]-> maf_nM +maffile[which(maffile$Tumor_Sample_Barcode%in%rownames(M)),]-> maf_M +maf_nM$Tumor_Sample_Barcode <- factor(maf_nM$Tumor_Sample_Barcode) +maf_M$Tumor_Sample_Barcode <- factor(maf_M$Tumor_Sample_Barcode) + +maf_n <- read.maf(maf_nM) +maf_y <- read.maf(maf_M) + + +pdf("/public/workspace/lily/Lung2Brain/Version5/Fig5/KRAS_maftools.pdf",useDingbats=F) +lollipopPlot(maf_n,gene = "KRAS",labelPos = "all",AACol = "Amino_Acid_Change",labPosSize = 0.9, +printCount=T,showMutationRate = TRUE,showDomainLabel=F) + +lollipopPlot(maf_y,gene = "KRAS",labelPos = "all",AACol = "Amino_Acid_Change",labPosSize = 0.9,printCount=T, +showMutationRate = TRUE,showDomainLabel=F) +dev.off() + + + + +# pdf("/public/workspace/lily/Lung2Brain/inte7/Fig/TP53_maftools.pdf",useDingbats=F) +# lollipopPlot(maf_n,gene = "TP53",labelPos = "all",AACol = "Amino_Acid_Change",labPosSize = 0.9, +# printCount=T,showMutationRate = TRUE,showDomainLabel=F) + +# lollipopPlot(maf_y,gene = "TP53",labelPos = "all",AACol = "Amino_Acid_Change",labPosSize = 0.9,printCount=T,repel=T, +# showMutationRate = TRUE,showDomainLabel=F) +# dev.off() + + + + + + + + +# 1. use CCLE data to verfiy [KRAS Mutation sample ] +#======================================================================================================================= +tmp <- read.table("/public/workspace/lily/metastasis/data/verify/CCLE/CCLE_KRAS_mut_sample.txt",sep="\t",header=F) +tmp.dat <- read.table("/public/workspace/lily/metastasis/data/verify/CCLE/CCLE_ann.txt",header=T,sep="\t") +tmp.f <- tmp.dat[which(tmp.dat$tcga_code=="LUAD"&tmp.dat$Pathology=="primary"),] +# tmp.f <- tmp.dat[which(tmp.dat$tcga_code=="LUAD"),] +tmp.f$KRAS_Mut <- "N" +tmp.f$KRAS_Mut[which(tmp.f$depMapID%in%tmp$V1)] <- "Y" + + +dat <- readRDS("/public/workspace/lily/metastasis/data/verify/CCLE/CCLE_RPKM.RDS") +dat.f <- dat[,which(colnames(dat)%in%tmp.f$CCLE_ID)] + +# calculate BMS score +source('~/software/ssGSEA/ssgseaMOD.r') +mod <- mod.analyze2(as.matrix(dat.f),c("BMS_update"),'/public/workspace/lily/MOD_file/',permN=0) +mod <- data.frame(mod) +rownames(tmp.f) <- tmp.f$CCLE_ID +tmp.f <- tmp.f[rownames(mod),] +mod$KRAS_Mut <- tmp.f$KRAS_Mut +boxplot(BMS_update_norm~KRAS_Mut,data=mod,FUN=median) + +# have trend but not significant + + + +# 2. use cell line data to verify +# have trend but not significant +#========================================================================================================== +# GSE63882 +# 40+ cell line +#========================================================================================================== +dat <- readRDS("/public/workspace/lily/metastasis/data/verify/GSE63882/GSE63882_expr.RDS") +ann <- read.table("/public/workspace/lily/metastasis/data/verify/GSE63882/GSE63882_ann.txt",sep="\t",header=T) +colnames(ann)[4] <- "KRAS_type" +rownames(ann) <- ann$GSM +dat <- dat[,rownames(ann)] +dat.f <- dat[,grep("Adenocarcinoma",ann$Cellline)] +ann.f <- ann[grep("Adenocarcinoma",ann$Cellline),] +#res.f <- res[which(res$Somker=="Y"),] +source('~/software/ssGSEA/ssgseaMOD.r') +mod <- mod.analyze2(as.matrix(dat.f),c("BMS_update","RAF_activate"),'/public/workspace/lily/MOD_file/',permN=0) +mod <- data.frame(mod) +mod$KRAS_type <- ann.f$KRAS_type + + + + +# 3. use BULK data to verfiy ,signifcant +#=========================================================================================================== +# 400 + LUAD samples +# GSE72094 +#=========================================================================================================== +dat <- readRDS("/public/workspace/lily/metastasis/data/verify/GSE72094/GSE72094_expr.RDS") +source('~/software/ssGSEA/ssgseaMOD.r') +# tmp <- mod.analyze2(as.matrix(dat),c("RAF_activate"),'/public/workspace/lily/MOD_file/',permN=0) +mod <- mod.analyze2(as.matrix(dat),c("BMS_update"),'/public/workspace/lily/MOD_file/',permN=0) +mod <- data.frame(mod) +ann <- t(read.table("/public/workspace/lily/metastasis/data/verify/GSE72094/KRAS_info.txt",header=T)) +ann <- ann[-1,,drop=F] +colnames(ann) <- "KRAS_type" +all(rownames(mod)==rownames(ann)) +tmp.res <- cbind(mod,ann) + +pdf("/public/workspace/lily/Lung2Brain/Version5/Fig5/GSE72094_verfiy_KRAS.pdf",useDingbats=F) +boxplot(BMS_update_norm~KRAS_type,data=tmp.res,FUN=median,outline=F) +dev.off() + + + +# 4.GSE15326 +# trend but not significant +dat <- readRDS("/public/workspace/lily/metastasis/data/verify/GSE15326/GSE15326_expr.RDS") +dat.f <- dat[,c(1,2,9,10,5,6,7,8)] +source('/public/workspace/lily/software/ssGSEA/ssgseaMOD.r') +mod <- mod.analyze2(as.matrix(dat.f),c("BMS_update.m"),"/public/workspace/lily/MOD_file/",permN=0) +mod <- as.data.frame(mod) +wilcox.test(mod[1:4,2],mod[5:8,2]) + + + +# 5. GDSC +#============================= +cellline.ann <- readRDS("/public/workspace/lily/Lung2Brain/inte7/GDSC/GDSC_Cellline_ann.RDS") +dat <- readRDS("/public/workspace/lily/Lung2Brain/inte7/GDSC/GDSC_Cellline_expr.RDS") +dat.f <- dat[,which(colnames(dat)%in%paste0("DATA.",cellline.ann$COSMIC.identifier))] # just use LUAD cell line expression data + +# calculate BMS score +#============================= +source('~/software/ssGSEA/ssgseaMOD.r') +mod <- mod.analyze2(as.matrix(dat.f),c("BMS_update"),'/public/workspace/lily/MOD_file/',permN=0) +mod <- data.frame(mod) + +#============================= +# GDSC1 data +gdsc1 <- readRDS("/public/workspace/lily/Lung2Brain/inte7/GDSC/GDSC1_LUAD_ann.RDS") +mod$ID <- gsub("DATA\\.","",rownames(mod)) +res <- merge(gdsc1,mod,by.x="COSMIC_ID",by.y="ID") + +res.f <- matrix(ncol=3) +drug <- unique(res$DRUG_NAME) +for(i in 1:length(drug)){ + tmp <- res[which(res$DRUG_NAME==drug[i]),] + tmp.res <- c(drug[i],cor.test(tmp$BMS_update_norm,tmp$LN_IC50)$estimate,cor.test(tmp$BMS_update_norm,tmp$LN_IC50)$p.value) + res.f <- rbind(res.f,tmp.res) +} +res.f <- res.f[-1,] +res.f <- data.frame(res.f) +rownames(res.f) <- res.f[,1] +res.f$V1 <- NULL +res.final <- apply(res.f,2,function(x){as.numeric(as.vector(x))}) +rownames(res.final) <- rownames(res.f) +colnames(res.final) <- c("correlation","pvalue") +res.final <- data.frame(res.final) +res.final$log2p <- -log2(res.final$pvalue) +# rs <- res.final[which(res.final$pvalue<0.05),] +res.final$text <- "" +res.final$text[which(res.final$log2p>5)] <- rownames(res.final)[which(res.final$log2p>5)] + + +# plot result +# +library(ggplot2) + +pdf("/public/workspace/lily/Lung2Brain/Version5/Fig5/GDSC1.pdf",useDingbats=F) +ggplot(res.final,aes(x=correlation,y=log2p,color=log2p))+ geom_point(size=3) + geom_text(aes(label = text), size = 3) + + scale_colour_gradientn(colours=c("#007cc0","#ffb310","#ed1c24")) + + geom_hline(yintercept=5 ,linetype=4) + + theme_classic() +dev.off() + + + + + + +########## GDSC2 +# 2021-5-19 +# GDSC2 result is more good +cellline.ann <- readRDS("/public/workspace/lily/Lung2Brain/inte7/GDSC/GDSC_Cellline_ann.RDS") +dat <- readRDS("/public/workspace/lily/Lung2Brain/inte7/GDSC/GDSC_Cellline_expr.RDS") +dat.f <- dat[,which(colnames(dat)%in%paste0("DATA.",cellline.ann$COSMIC.identifier))] # just use LUAD cell line expression data + +# calculate BMS score +#============================= +source('~/software/ssGSEA/ssgseaMOD.r') +mod <- mod.analyze2(as.matrix(dat.f),c("BMS_update"),'/public/workspace/lily/MOD_file/',permN=0) +mod <- data.frame(mod) +gdsc2 <- readRDS("/public/workspace/lily/Lung2Brain/inte7/GDSC/GDSC2_LUAD_ann.RDS") +mod$ID <- gsub("DATA\\.","",rownames(mod)) +res <- merge(gdsc2,mod,by.x="COSMIC_ID",by.y="ID") + +res.f <- matrix(ncol=3) +drug <- unique(res$DRUG_NAME) +for(i in 1:length(drug)){ + tmp <- res[which(res$DRUG_NAME==drug[i]),] + tmp.res <- c(drug[i],cor.test(tmp$BMS_update_norm,tmp$LN_IC50)$estimate,cor.test(tmp$BMS_update_norm,tmp$LN_IC50)$p.value) + res.f <- rbind(res.f,tmp.res) +} +res.f <- res.f[-1,] +res.f <- data.frame(res.f) +rownames(res.f) <- res.f[,1] +res.f$V1 <- NULL +res.final <- apply(res.f,2,function(x){as.numeric(as.vector(x))}) +rownames(res.final) <- rownames(res.f) +colnames(res.final) <- c("correlation","pvalue") +res.final <- data.frame(res.final) +res.final$log2p <- -log2(res.final$pvalue) +# rs <- res.final[which(res.final$pvalue<0.05),] +res.final$text <- "" +res.final$text[which(res.final$log2p>5)] <- rownames(res.final)[which(res.final$log2p>5)] + +############################################################################################ +# plot result +# +library(ggplot2) + +pdf("/public/workspace/lily/Lung2Brain/Version5/Fig5/GDSC2.pdf",useDingbats=F) +ggplot(res.final,aes(x=correlation,y=log2p,color=log2p))+ geom_point(size=3) + geom_text(aes(label = text), size = 3) + + scale_colour_gradientn(colours=c("#007cc0","#ffb310","#ed1c24")) + + geom_hline(yintercept=5 ,linetype=4) + + theme_classic() +dev.off() + + + + + + + + +# 6. MEK inhibitor result +# 2021-5-19 +#=========================================================================================== +dat <- readRDS("~/metastasis/data/verify/GSE79235/GSE79235_expr.RDS") +dat.f <- dat[,5:8] +source('~/software/ssGSEA/ssgseaMOD.r') +mod <- mod.analyze2(as.matrix(dat.f),c("BMS_test"),'/public/workspace/lily/Lung2Brain/inte7/',permN=0) +mod <- data.frame(mod) + + +pdf("/public/workspace/lily/Lung2Brain/Version5/Fig5/GSE79235_MEK_inhibitor.pdf",useDingbats=F) +barplot(c(median(mod[1:2,2]),median(mod[3:4,2])),names=c("Control","Treat"),ylim=c(0,1)) +dev.off() + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +########################################################################################################################################### +# 2021-7-1 +# use this program to analysis difference of three subtype tumor cells in LCBM +#========================================================================================================================================== +# 0. pheatmap show DEGs of three group +# dose percentage have differences in LCBMs? +# 1. metabolism difference in three group +# 2. three subtype in DTC to macrometastasis +# 3. differnece for immunotherapy +# 4. + + + + + +# 1. pheatmap show DEGs +library(Seurat) +dat <- readRDS("/public/workspace/lily/Lung2Brain/Version5/Fig4/Data/LCBM_sub.tumor.RDS") +DefaultAssay(dat) <- "RNA" +dat@active.ident <- factor(dat$group) +gene <- FindAllMarkers(dat,assay="RNA",logfc.threshold=0,only.pos=T) +gene <- gene[order(gene$avg_logFC,decreasing=T),] +gene.f <- gene[which(gene$p_val_adj<0.05),] +gene.f <- gene.f[-grep("^RPL|^RPS",gene.f$gene),] +# get genes +gene.f <- gene.f[order(gene.f$avg_logFC,decreasing=T),] +gene.use <- c() +top <- 10 +for(i in unique(gene.f$cluster)){ + tmp.gene <- gene.f[which(gene.f$cluster==i),"gene"] + gene.use <- c(gene.use,tmp.gene[1:top]) +} + + +# plot data @data[heatmap.gene,] +# data <- dat[["RNA"]]@data[gene.use,] +# library(pheatmap) +# ann <- dat@meta.data[,"group",drop=F] +# ann <- ann[order(ann$group),,drop=F] +# pheatmap(data[,rownames(ann)],annotation_col=ann, +# cluster_cols=F,scale="row",show_colnames=F,color=colorRampPalette(c('steelblue',"steelblue",'white',"red",'red'))(100)) +# data <- data[,rownames(ann)] + +# group by cluster +data <- dat[["RNA"]]@data[gene.use,] +data <- t(as.matrix(data)) +data <- data.frame(data) +data$group <- dat$group +tmp <- aggregate(.~group,data=data,FUN=mean) +rownames(tmp) <- tmp$group +tmp$group <- NULL +tmp.res <- t(tmp) + + +pdf("/public/workspace/lily/Lung2Brain/Version5/Fig5/LCBM_sub_tumor_heatmap_DEG.pdf",useDingbats=F) +pheatmap::pheatmap(tmp.res,cluster_cols=F,scale="row", + show_colnames=T,color=colorRampPalette(c('steelblue',"steelblue",'white',"red",'red'))(100)) +dev.off() + + + + + + +# 2. try radioresistance signature +# this siganture come from MSGDB +# HP_INCREASED_SENSITIVITY_TO_IONIZING_RADIATION +source('/public/workspace/lily/software/ssGSEA/ssgseaMOD.r') +mod <- mod.analyze2(as.matrix(dat[['RNA']]@data),c("Radio_sens","Radio_sen_test","Radio_sen_BMC","Radio_sen_CCR"),"/public/workspace/lily/MOD_file/",permN=0) +mod <- as.data.frame(mod) +mod$group <- dat$group +mod$group <- factor(mod$group,levels=c("group16","group09","group47")) +pdf("/public/workspace/lily/Lung2Brain/Version5/Fig5/Radio_sensetivity.pdf",useDingbats=F) +boxplot(Radio_sens_norm~group,data=mod,outline=F,ylim=c(0,1)) +dev.off() + +# 2021-10-14 +pdf("/public/workspace/lily/Lung2Brain/Version5/Fig5/Radio_sensetivity_new.pdf",useDingbats=F) +boxplot(Radio_sen_test_norm~group,data=mod,outline=F,ylim=c(0,1)) +dev.off() + + +# calculate in Bulk +# 2021-10-14 +dat.BM <- readRDS("/public/workspace/lily/metastasis/data/verify/GSE14108/GSE14108_res.RDS") +source('/public/workspace/lily/software/ssGSEA/ssgseaMOD.r') +mod <- mod.analyze2(as.matrix(dat.BM),c("Lung_gene","Brain_gene","BMS_update","Radio_sen_test"),"/public/workspace/lily/MOD_file/",permN=0) +mod <- as.data.frame(mod) + + +# plot result +gene <- c("AR","PRKCA","RELA","SUMO1","CDK1","HDAC1","IRF1") +dat <- dat.BM[gene,] +tmp.ann <- t(sapply(gene,function(x){ + c(cor.test(mod$Brain_gene_norm,as.numeric(dat[x,]),method="spearman")$estimate, + cor.test(mod$Lung_gene_norm,as.numeric(dat[x,]),method="spearman")$estimate, + cor.test(mod$BMS_update_norm,as.numeric(dat[x,]),method="spearman")$estimate + ) +})) +colnames(tmp.ann) <- c("Brain","Lung","Stem") +ann_row <- data.frame(apply(tmp.ann,2,function(x){ifelse(x>0,"pos","neg")})) +ann_colors = list( + Brain = c(pos = "steelblue", neg = "red"), + Lung = c(pos = "steelblue", neg = "red"), + Stem = c(pos = "steelblue", neg = "red"), + Lung_gene_norm = colorRampPalette(c('white',"#44AC48"))(100), + Brain_gene_norm = colorRampPalette(c('white',"#269DCB"))(100), + BMS_update_norm = colorRampPalette(c('white',"#F6BB3D"))(100) +) + +ann_colors = list( + Brain = c(pos = "steelblue", neg = "red"), + Lung = c(pos = "steelblue", neg = "red"), + Stem = c(pos = "steelblue", neg = "red"), + Lung_gene_norm = c(High = "red", Medium = "white",Low = "green"), + Brain_gene_norm = c(High = "red", Medium = "white",Low = "green"), + BMS_update_norm = c(High = "red", Medium = "white",Low = "green") +) + +ann_col <- data.frame(mod[,c(5:7)]) +ann_col <- data.frame(apply(ann_col,2,function(x){ifelse(xquantile(x,0.67),"High","Medium"))})) +library(pheatmap) +pdf("/public/workspace/lily/Lung2Brain/Version5/Fig5/GSE14108_sentive_gene_heatmap.pdf",useDingbats=F,height=10,width=10) +pheatmap(dat,scale="row",annotation_col=ann_col,annotation_row=ann_row,annotation_colors=ann_colors, + color=colorRampPalette(c('steelblue','white',"#E41A1C"))(100),cellwidth=8,cellheight=15 +) +dev.off() + + +# E-MTAB +# 2021-10-14 +dat.BM <- readRDS("/public/workspace/lily/metastasis/data/verify/E-MTAB-8659/LCBM_S63_data.RDS") +source('/public/workspace/lily/software/ssGSEA/ssgseaMOD.r') +mod <- mod.analyze2(as.matrix(dat.BM),c("Lung_gene","Brain_gene","BMS_update","Radio_sens","Radio_sen_test"),"/public/workspace/lily/MOD_file/",permN=0) +mod <- as.data.frame(mod) + +# plot result +gene <- c("AR","PRKCA","RELA","SUMO1","CDC2","HDAC1","IRF1") +dat <- dat.BM[gene,] +tmp.ann <- t(sapply(gene,function(x){ + c(cor.test(mod$Brain_gene_norm,as.numeric(dat[x,]))$estimate, + cor.test(mod$Lung_gene_norm,as.numeric(dat[x,]))$estimate, + cor.test(mod$BMS_update_norm,as.numeric(dat[x,]))$estimate + ) +})) +colnames(tmp.ann) <- c("Brain","Lung","Stem") +ann_row <- data.frame(apply(tmp.ann,2,function(x){ifelse(x>0,"pos","neg")})) +ann_colors = list( + Brain = c(pos = "steelblue", neg = "red"), + Lung = c(pos = "steelblue", neg = "red"), + Stem = c(pos = "steelblue", neg = "red"), + Lung_gene_norm = colorRampPalette(c('white',"#44AC48"))(100), + Brain_gene_norm = colorRampPalette(c('white',"#269DCB"))(100), + BMS_update_norm = colorRampPalette(c('white',"#F6BB3D"))(100) +) +ann_col <- data.frame(mod[,c(6:8)]) +library(pheatmap) +pdf("/public/workspace/lily/Lung2Brain/Version5/Fig5/E_MTAB_sentive_gene_heatmap.pdf",useDingbats=F,height=10,width=10) +pheatmap(dat,scale="row",annotation_col=ann_col,annotation_row=ann_row,annotation_colors=ann_colors, + color=colorRampPalette(c('steelblue','white',"#E41A1C"))(100),cellwidth=8,cellheight=15 +) +dev.off() + + + + + +# RRRS +library(Seurat) +dat <- readRDS("/public/workspace/lily/Lung2Brain/Version5/Fig4/Data/LCBM_sub.tumor.RDS") +DefaultAssay(dat) <- "RNA" +source('/public/workspace/lily/software/ssGSEA/ssgseaMOD.r') +mod <- mod.analyze2(as.matrix(dat[['RNA']]@data),c("RES","SEN","blood_signature"),"/public/workspace/lily/MOD_file/",permN=0) +mod <- as.data.frame(mod) +mod$group <- dat$group +mod$group <- factor(mod$group,levels=c("group16","group09","group47")) +saveRDS(mod,file="/public/workspace/lily/Lung2Brain/Version5/Fig5/LCBM_suntumor_RRRS_mod.RDS") + + + + + +mod <- readRDS("/public/workspace/lily/Lung2Brain/Version5/Fig5/LCBM_suntumor_RRRS_mod.RDS") +mod$Radio <- mod$RES_norm - mod$SEN_norm +mod$group <- factor(mod$group,levels=c("group09","group16","group47")) +pdf("/public/workspace/lily/Lung2Brain/Version5/Fig5/Radio_response.pdf",useDingbats=F) +boxplot(Radio~group,data=mod,outline=F) +dev.off() + + + + + + + + + + + + + + +# 3. DTC 2 Macrometastases verify +# this was used in Figure4 +# however these samples do not have brain metastasis,maybe not every good +#==================================================================================================================================== + + + + + +#==================================================================================================================================== +# 4. metabolism pathway analysis +# one is metabolism pathway activation numbers + +#==================================================================================================================================== +library(Seurat) +dat <- readRDS("/public/workspace/lily/Lung2Brain/Version5/Fig4/Data/LCBM_sub.tumor.RDS") +tmp.dat <- readRDS("/public/workspace/lily/Lung2Brain/Version5/Fig4/Data/sub.Tumor.metabolism.RDS") +tmp.f <- data.frame(tmp.dat[,86:170]) +tmp.f$group <- dat$group + +tmp.res <- aggregate(.~group,data=tmp.f,FUN=median) +rownames(tmp.res) <- tmp.res$group +tmp.res$group <- NULL + +tmp.res <- t(tmp.res) +res.f <- tmp.res[-which(rowSums(tmp.res)==0),] + +# prepare for plot result +rownames(res.f) <- gsub("_norm$","",rownames(res.f)) +rownames(res.f) <- gsub("_",".",rownames(res.f)) + +# circle plot +library(pheatmap) +pdf("/public/workspace/lily/Lung2Brain/Version5/Fig5/LCBM_subTumor_metabolism.pdf",useDingbats=F,height=15) +plot_Circ_heatmap(t(res.f)) +dev.off() + + +# calculate pathway numbers +tmp <- unlist(apply(res.f,1,function(x){ + colnames(res.f)[which(x[]>0.5)] +})) + + + + + +# Find the Brain specific metabolism pathway and do GSEA +library(Seurat) +dat <- readRDS("/public/workspace/lily/Lung2Brain/Version5/Fig4/Data/LCBM_sub.tumor.RDS") +dat@active.ident <- factor(dat$group) +gene <- FindMarkers(dat,assays="RNA",ident.1="group16",min.pct=0.05,logfc.threshold=0) +gene <- gene[order(gene$avg_logFC,decreasing=T),] +write.table(gene[,2,drop=F],file="/public/workspace/lily/Lung2Brain/Version5/Fig5/Group16_DEG.txt",row.names=T,col.names=T,quote=F,sep="\t") + +gene.f <- rownames(gene[which(gene$p_val_adj<0.05&gene$avg_logFC>0.25),]) +write.table(gene.f,file="/public/workspace/lily/Lung2Brain/Version5/Fig5/Group16_DEG_tmp.txt",row.names=F,col.names=F,quote=F,sep="\t") + + +gene <- gene[order(gene$p_val,decreasing=F),] +gene$logp <- -log() +write.table(gene[,1,drop=F],file="/public/workspace/lily/Lung2Brain/Version5/Fig5/Group16_DEG.txt",row.names=T,col.names=T,quote=F,sep="\t") + + + + + +# 2021-7-14 +# GSE LCBM data show Brain signature correlation with Ketone metabolism +#======================================================================================================================================= +dat.BM <- readRDS("/public/workspace/lily/metastasis/data/verify/GSE14108/GSE14108_res.RDS") +source('/public/workspace/lily/software/ssGSEA/ssgseaMOD.r') +mod <- mod.analyze2(as.matrix(dat.BM),c("Synthesis_and_degradation_of_ketone_bodies","Lung_gene","Brain_gene","BMS_update"),"/public/workspace/lily/MOD_file/",permN=0) +mod <- as.data.frame(mod) + + +pdf("/public/workspace/lily/Lung2Brain/Version5/Fig5/GSE14108_Brain_ketone.pdf",useDingbats=F) +plot(mod$Brain_gene_norm,mod$Synthesis_and_degradation_of_ketone_bodies_norm,mian="Brain signature with VIM") +abline(lm(mod$Synthesis_and_degradation_of_ketone_bodies_norm~mod$Brain_gene_norm),col="red") +legend("topright",legend=paste0("rho=",0.51," pvalue=",0.007)) +dev.off() + + + +# self data, E0927 and GSE123902 data use to verify +############################################################################################################################################# +dat <- readRDS("/public/workspace/lily/Lung2Brain/Version5/Fig4/Data/LCBM_sub.tumor.RDS") +source('/public/workspace/lily/software/ssGSEA/ssgseaMOD.r') +mod.rs <- mod.analyze2(as.matrix(dat[['RNA']]@data),c("Synthesis_and_degradation_of_ketone_bodies"),"/public/workspace/zhumy/CRC2Liver/ref/mod/metabolism/",permN=0) +mod.rs <- as.data.frame(mod.rs) +mod.rs$group <- factor(dat$group,levels=c("group16","group47","group09")) + +# plot result +pdf("/public/workspace/lily/Lung2Brain/Version5/Fig5/LCBM_subTumor_ketone_metabolism.pdf",useDingbats=F) +boxplot(Synthesis_and_degradation_of_ketone_bodies_norm~group,data=mod.rs,FUN=median,outline=F,ylim=c(0,1)) +#wilcox.test(mod.rs$Synthesis_and_degradation_of_ketone_bodies_norm[which(mod.rs$group=="group16")],mod.rs$Synthesis_and_degradation_of_ketone_bodies_norm[which(mod.rs$group=="group47")]) +dev.off() + + +# E0927 data +pdf("/public/workspace/lily/Lung2Brain/Version5/Fig5/E0927_subTumor_ketone_metabolism.pdf",useDingbats=F) +boxplot(Synthesis_and_degradation_of_ketone_bodies_norm~group,data=mod.rs,FUN=median,outline=F,ylim=c(0,1)) +#wilcox.test(mod.rs$Synthesis_and_degradation_of_ketone_bodies_norm[which(mod.rs$group=="group16")],mod.rs$Synthesis_and_degradation_of_ketone_bodies_norm[which(mod.rs$group=="group47")]) +dev.off() + + +# GSE123902 data +mod.rs$group <- dat$tmp.group +pdf("/public/workspace/lily/Lung2Brain/Version5/Fig5/GSE123902_subTumor_ketone_metabolism.pdf",useDingbats=F) +boxplot(Synthesis_and_degradation_of_ketone_bodies_norm~group,data=mod.rs,FUN=median,outline=F,ylim=c(0,1)) +#wilcox.test(mod.rs$Synthesis_and_degradation_of_ketone_bodies_norm[which(mod.rs$group=="group16")],mod.rs$Synthesis_and_degradation_of_ketone_bodies_norm[which(mod.rs$group=="group47")]) +dev.off() + + + + + + + +############################################################################################################################################################## +# try to find group16 expression gene and do network +# 2021-7-8 +# verify by other samples show Group16 is low gene expression +#============================================================================================================================================================= +# 2021-7-9 +# do metabolism PCA +# however , maybe should try to use raw data +#============================================================================================================================================================= +dat <- readRDS("/public/workspace/lily/metastasis/data/GTEx/GTEx_brain_lung.RDS") +source('/public/workspace/lily/software/ssGSEA/ssgseaMOD.r') +filename <- gsub("\\.mod","",dir("/public/workspace/zhumy/CRC2Liver/ref/mod/metabolism")) +mod.rs <- mod.analyze2(as.matrix(dat),filename,"/public/workspace/zhumy/CRC2Liver/ref/mod/metabolism/",permN=0) +mod.rs <- as.data.frame(mod.rs) + +saveRDS(mod.rs,file="/public/workspace/lily/Lung2Brain/Version5/Fig4/Data/GTEx_Lung_Brain_metabolism_mod.RDS") + +#============================================================================================================================================================= +# check result +# GTEx result +gtex <- readRDS("/public/workspace/lily/Lung2Brain/Version5/Fig4/Data/GTEx_Lung_Brain_metabolism_mod.RDS")[,1:85] +dat <- readRDS("/public/workspace/lily/Lung2Brain/Version5/Fig4/Data/LCBM_sub.tumor.RDS") +tmp.dat <- readRDS("/public/workspace/lily/Lung2Brain/Version5/Fig4/Data/sub.Tumor.metabolism.RDS")[,1:85] + + + + + + + + + + + + + +# Analysis Therapy induced lung cancer +# seem not vevry intersting +# 2021-7-16 +#========================================================================================================================================================== +library(Seurat) +dat <- readRDS("~/metastasis/data/verify/TKI_multiple_Lung/multiple_LB_tumor.RDS") +dat@meta.data <- dat@meta.data[,c(1:14,19:21,26,40,41,45:46)] +saveRDS(dat,file="/public/workspace/lily/metastasis/data/Cell_TKI_lung/Tumor.RDS") + +#========================================================================================================================================================== +library(Seurat) +dat <- readRDS("/public/workspace/lily/metastasis/data/Cell_TKI_lung/Tumor.RDS") +source('/public/workspace/lily/software/ssGSEA/ssgseaMOD.r') +mod <- mod.analyze2(as.matrix(dat[['RNA']]@data),c("BMS_update","HPSC_C5","Brain_gene","Lung_gene"),"/public/workspace/lily/MOD_file/",permN=0) +mod <- as.data.frame(mod) + + + + + + + + + +########################################################################################################################################################### +# 2021-7-27 +# CSOmap to run LCBM sub tumor +#========================================================================================================================================================== +# bytlib load languages/R-4.0.2 +# load package +library(Seurat) +library(CSOmapR) +library(CSOmapR.demo) + + +# function 1 +as_matrix <- function(mat){ + tmp <- matrix(data=0L, nrow = mat@Dim[1], ncol = mat@Dim[2]) + row_pos <- mat@i+1 + col_pos <- findInterval(seq(mat@x)-1,mat@p[-1])+1 + val <- mat@x + + for (i in seq_along(val)){ + tmp[row_pos[i],col_pos[i]] <- val[i] + } + row.names(tmp) <- mat@Dimnames[[1]] + colnames(tmp) <- mat@Dimnames[[2]] + return(tmp) +} + +# function 2 +run_CSOmap <- function(labelData, TPM, sampling=0, seed=315) { + if (sampling) { + TPM_ <- TPM + labelData_ <- labelData + + set.seed(seed) + index <- unlist( + apply(as.data.frame(table(labelData_$labels) * sampling/ncol(TPM_)), 1, function(x) { + sample(which(labelData_$labels==x[1]), x[2]) + }) + ) + + TPM <- TPM_[,index] + labelData <- labelData_[index,] + } + + # Calculate optimized 3D coordinates + affinityMat = getAffinityMat(TPM, LR, verbose = T) + + coords_res = runExactTSNE_R( + X = affinityMat, + no_dims = 3, + max_iter = 1000, + verbose = T + ) + coords = coords_res$Y + rownames(coords) <- colnames(TPM) + colnames(coords) <- c('x', 'y', 'z') + + # Visualization(by 3D density) + require(dplyr) + # arrange data + coords_tbl = bind_cols(cellName = rownames(coords), as.data.frame(coords)) + + join_vec = setNames(colnames(labelData)[1], nm = colnames(coords_tbl)[1]) + cellinfo_tbl = left_join(coords_tbl, labelData, by = join_vec) + + density_obj = getDensity3D(cellinfo_tbl$x, cellinfo_tbl$y, cellinfo_tbl$z) + cellinfo_tbl = cellinfo_tbl %>% mutate(density = density_obj) + + # p_3Ddensity = plot3D(cellinfo_tbl, color_by = "density", title = "3D density") + + # Get significance + signif_results = getSignificance(coords, labels = cellinfo_tbl$labels, verbose = T) + contribution_list = getContribution(TPM, LR, signif_results$detailed_connections) + + return(list( + labelData=labelData, + TPM=TPM, + cellinfo_tbl=cellinfo_tbl, + signif_results=signif_results, + contribution_list=contribution_list + )) + +} + + +tmp.res <- readRDS("/public/workspace/lily/Lung2Brain/Version5/Fig4/Data/LCBM_sub.tumor.RDS") +TPM <- as.matrix(tmp.res$RNA@counts) + +labelData <- data.frame( + cells = colnames(tmp.res), + labels = as.vector(tmp.res$group) +) + +results = run_CSOmap(labelData, TPM, sampling=0) + + +saveRDS(results, file="/public/workspace/lily/Lung2Brain/Version5/CSOmap/LCBM_subtumor_CSOmap.results.RDS") + + +# download to local and plot +plot_ly(result$cellinfo_tbl, x = ~x, y = ~y, z = ~z, color = ~labels , text = ~labels,colors = c("#2BAF2B","#00A5DC","#FFC719"),size=0.3) + + + + + +#============================================================================================================= +# caculate different cell type distance with sub tumor +# 2021-8-4 +#-============================================================================================================ +# bytlib load languages/R-4.0.2 +# load package +library(Seurat) +library(CSOmapR) +library(CSOmapR.demo) + +dat <- readRDS("/public/workspace/lily/Lung2Brain/Version5/Data/LCBM_celltype_type.RDS") +# load function1 and function2 +TPM <- as.matrix(dat$RNA@counts) + +labelData <- data.frame( + cells = colnames(dat), + labels = as.vector(dat$celltype) +) + +results = run_CSOmap(labelData, TPM, sampling=0) + + +saveRDS(results, file="/public/workspace/lily/Lung2Brain/Version5/CSOmap/LCBM_subtumor_celltype_CSOmap.results.RDS") +# caculate distance +tmp <- aggregate(.~labels,data=results$cellinfo_tbl[,c(2:5)],FUN=mean) +rownames(tmp) <- tmp$labels +tmp$labels <- NULL +# download to local and plot +plot_ly(result$cellinfo_tbl, x = ~x, y = ~y, z = ~z, color = ~labels , text = ~labels,colors = c("#2BAF2B","#00A5DC","#FFC719"),size=0.3) + + + + + + + + + + + + + + +########################################################################################################################################### +# 2021-7-27 +# calculate LCBM sub tumor metabolism with GBM and Lung cancer +#========================================================================================================================================== +# LCBM sub tumor has run metabolism +# now run GBM and LUAD + +library(Seurat) + +# GBM tumor +tmp.dat <- readRDS("/public/workspace/lily/Lung2Brain/Version5/CSOmap/GBM_tumor_myeloid.RDS") +tumor <- subset(tmp.dat,cells=which(tmp.dat$type=="malignant")) + +source('/public/workspace/lily/software/ssGSEA/ssgseaMOD.r') +filename <- gsub("\\.mod","",dir("/public/workspace/zhumy/CRC2Liver/ref/mod/metabolism")) +mod.rs <- mod.analyze2(as.matrix(tumor[["RNA"]]@data),filename,"/public/workspace/zhumy/CRC2Liver/ref/mod/metabolism/",permN=0) +mod.rs <- as.data.frame(mod.rs) +saveRDS(mod.rs,file="/public/workspace/lily/Lung2Brain/Version5/Fig5/GBM_tumor_metabolism_ssGSEA.RDS") + + +###### LUAD +tumor <- readRDS("/public/workspace/lily/Lung2Brain/Version5/Data/Tumor_pur/pur_LUAD_clust.RDS") + +source('/public/workspace/lily/software/ssGSEA/ssgseaMOD.r') +filename <- gsub("\\.mod","",dir("/public/workspace/zhumy/CRC2Liver/ref/mod/metabolism")) +mod.rs <- mod.analyze2(as.matrix(tumor[["RNA"]]@data),filename,"/public/workspace/zhumy/CRC2Liver/ref/mod/metabolism/",permN=0) +mod.rs <- as.data.frame(mod.rs) +saveRDS(mod.rs,file="/public/workspace/lily/Lung2Brain/Version5/Fig5/LUAD_tumor_metabolism_ssGSEA.RDS") + + +#================================================================================================================================================ +# get result +dat1 <- readRDS("/public/workspace/lily/Lung2Brain/Version5/Fig5/GBM_tumor_metabolism_ssGSEA.RDS") +res1 <- apply(dat1[,86:170],2,function(x){median(x)}) + +dat2 <- readRDS("/public/workspace/lily/Lung2Brain/Version5/Fig5/LUAD_tumor_metabolism_ssGSEA.RDS") +res2 <- apply(dat2[,86:170],2,function(x){median(x)}) + +dat <- readRDS("/public/workspace/lily/Lung2Brain/Version5/Fig4/Data/sub.Tumor.metabolism.RDS") +dat <- dat[,86:170] +tmp.dat <- readRDS("/public/workspace/lily/Lung2Brain/Version5/Fig4/Data/LCBM_sub.tumor.RDS") +dat$group <- tmp.dat$group +aggregate(.~group,data=dat,FUN=median)-> tmp.res +rownames(tmp.res) <- tmp.res$group +tmp.res$group <- NULL +res <- data.frame(t(tmp.res)) +all(rownames(res)==names(res1)) +res$GBM <- unname(res1) +res$LUAD <- unname(res2) + +saveRDS(res,file="/public/workspace/lily/Lung2Brain/Version5/Fig5/GBM.LCBM.LUAD.tumor.metabolism.RDS") + + +# cluster show not good result +# try to use PCA, also do not show good result +dat <- readRDS("/public/workspace/lily/Lung2Brain/Version5/Fig5/GBM.LCBM.LUAD.tumor.metabolism.RDS") +pheatmap::pheatmap(dat[-15,],scale="row") + + + + + + + + + + + + + + + + + + +#================================================================================================================================================ +# 2021-7-30 +# NKX2.2 in normal brain devlopment +#================================================================================================================================================ +tmp.dat <- data.table::fread("/public/workspace/lily/metastasis/data/verify/E-MTAB-6814/E-MTAB-6814-query-results.tpms.tsv",sep="\t",header=T) +tmp.f <- as.data.frame(tmp.dat)[,c(2,grep("brain",colnames(tmp.dat)))] +colnames(tmp.f) <- sapply(strsplit(colnames(tmp.f)," "),function(x){paste0(x[length(x)],"_",x[1])}) +res <- tmp.f[grep("NKX2-2",tmp.f$Name_Gene),-1] +# set order +res.f <- as.numeric(res[,c(11,1:8,12,9,10,13:31)]) +pdf("~/NKX2.2_brain_dev.pdf") +plot(res.f[1:20],type="o",main="forebrain") +plot(res.f[21:31],type="o",main="hindbrain") +dev.off() + + + + + + + + + + + + + + + + + + + + + + + + + + + + +##################################################################################################################################################### +# 2021-8-14 +# Lung like tumor analysis +# use tumor inflamation score verify in GSE14108 +#==================================================================================================================================================== +# make a new mod +# https://jitc.bmj.com/content/6/1/63 +gene <- c("PSMB10","HLA-DQA1","HLA-DRB1","CMKLR1","HLA-E","NKG7","CD8A", + "CCL5","CXCL9","CD27","CXCR6","IDO1","STAT1","TIGIT","LAG3","CD274","PDCD1LG2","CD276") + + +source('/public/workspace/lily/software/ssGSEA/ssgseaMOD.r') +mod.generate(gene,"TIS",out="/public/workspace/lily/MOD_file/TIS.mod") # make a mod file + + + +dat.BM <- readRDS("/public/workspace/lily/metastasis/data/verify/GSE14108/GSE14108_res.RDS") +source('/public/workspace/lily/software/ssGSEA/ssgseaMOD.r') +mod <- mod.analyze2(as.matrix(dat.BM),c("TIS","Lung_gene","Brain_gene","BMS_update","Angiogenesis","RES","SEN"),"/public/workspace/lily/MOD_file/",permN=0) +mod <- as.data.frame(mod) + + +tmp <- read.table("~/metastasis/data/verify/GSE14108/ImmuCellAI_abundance_result_GSE14108.txt",sep="\t",header=T) +all(rownames(mod)==tmp$X) +tmp.res <- data.frame(t(apply(tmp[,-1],2,function(x){ + c(cor.test(x,mod$Brain_gene_norm,method="spearman")$estimate,cor.test(x,mod$Brain_gene_norm,method="spearman")$p.value, + cor.test(x,mod$Lung_gene_norm,method="spearman")$estimate,cor.test(x,mod$Lung_gene_norm,method="spearman")$p.value, + cor.test(x,mod$BMS_update_norm,method="spearman")$estimate,cor.test(x,mod$BMS_update_norm,method="spearman")$p.value) +}))) +colnames(tmp.res) <- c("B.cor","B.p","L.cor","L.p","BMS.c","BMS.p") + + + + + + + + + + + + + + + + + + + + +#==================================================================================================================================================== +# 2021-8-14 +# cancer cell 6A +# use LCBM +#==================================================================================================================================================== +dat <- readRDS("/public/workspace/lily/metastasis/data/verify/GSE14108/GSE14108_res.RDS") +tcell <- c("CD2","CD3D","CD3E","CD3G") +source('/public/workspace/lily/software/ssGSEA/ssgseaMOD.r') +mod.generate(tcell,"Tcell",out="/public/workspace/lily/MOD_file/Tcell.mod") # make a mod file + +# load T cell specifc +tcell <- as.vector(read.table("/public/workspace/lily/MOD_file/Tcell_specific.txt")[,1]) +dat.tcell <- t(dat[which(rownames(dat)%in% tcell ),]) +# calculate score +source('/public/workspace/lily/software/ssGSEA/ssgseaMOD.r') +mod <- mod.analyze2(as.matrix(dat),c("Lung_gene","Brain_gene","BMS_update","Tcell"),"/public/workspace/lily/MOD_file/",permN=0) +mod <- as.data.frame(mod) +res <- cbind(mod[,c(5,6,7,8)],t(dat[which(rownames(dat)%in% tcell ),])) + +#calculate correlation +tmp <- t(apply(res,2,function(x){ + c( + cor.test(x,res[,1])$estimate,cor.test(x,res[,1])$p.value, + cor.test(x,res[,2])$estimate,cor.test(x,res[,2])$p.value, + cor.test(x,res[,3])$estimate,cor.test(x,res[,3])$p.value, + cor.test(x,res[,4])$estimate,cor.test(x,res[,4])$p.value + ) +})) + +colnames(tmp) <- c("Lung_gene.cor","Lung_gene.pval","Brain_gene.cor","Brain_gene.pval","BMS.cor","BMS.pval","Tcell.cor","Tcell.pval") +tmp <- data.frame(tmp) + + + + + + + + + +#=========================================================================================================================================== +# 2021-8-15 +# make lung sig and brain sig +#=========================================================================================================================================== +library(Seurat) +dat <- readRDS("/public/workspace/lily/Lung2Brain/Version5/Fig4/Data/LCBM_sub.tumor.RDS") +DefaultAssay(dat) <- "RNA" +dat@active.ident <- factor(dat$group) +gene <- FindAllMarkers(dat,min.pct=0.1,logfc.threshold=0.1) +gene <- gene[order(gene$avg_logFC,decreasing=T),] +# filter RP gene +gene.f <- gene[-grep("^RP",rownames(gene)),] + +# get lung.gene +lung.gene <- head(gene.f[which(gene.f$cluster=="group09"),"gene"],10) +source('/public/workspace/lily/software/ssGSEA/ssgseaMOD.r') +mod.generate(lung.gene,"Lung.sig",out="/public/workspace/lily/MOD_file/Lung.sig.mod") # make a mod file + +# get brain.gene +brain.gene <- head(gene.f[which(gene.f$cluster=="group16"),"gene"],10) +source('/public/workspace/lily/software/ssGSEA/ssgseaMOD.r') +mod.generate(brain.gene,"Brain.sig",out="/public/workspace/lily/MOD_file/Brain.sig.mod") # make a mod file + + + + +dat <- readRDS("/public/workspace/lily/metastasis/data/verify/GSE14108/GSE14108_res.RDS") +tcell <- c("CD2","CD3D","CD3E","CD3G") +source('/public/workspace/lily/software/ssGSEA/ssgseaMOD.r') +mod.generate(tcell,"Tcell",out="/public/workspace/lily/MOD_file/Tcell.mod") # make a mod file + +# load T cell specifc +tcell <- as.vector(read.table("/public/workspace/lily/MOD_file/Tcell_specific.txt")[,1]) +dat.tcell <- t(dat[which(rownames(dat)%in% tcell ),]) +# calculate score +source('/public/workspace/lily/software/ssGSEA/ssgseaMOD.r') +mod <- mod.analyze2(as.matrix(dat),c("Lung_gene","Brain_gene","BMS_update","TIS","BMDM_marker","MG_marker","Treg","Tcell_tumor"),"/public/workspace/lily/MOD_file/",permN=0) +mod <- as.data.frame(mod) +res <- cbind(mod[,c(7:12)],t(dat[which(rownames(dat)%in% tcell ),])) + + + + + + + + + + + +#========================================================================================================================================================== +# 2021-8-16 +# try to use immunosuppression signature to analysis [get OK result] +# now try to plot results +#========================================================================================================================================================== +# immunegene <- c("ADORA2A","ARHGEF5","BTLA","CD160","CD244","CD27","CD274","CD276","CD47","CD80","CEACAM1","CTLA4", +# "GEM","HAVCR2","ICOS","IDO1","LAG3","PDCD1","TNFSF4","VISTA","VTCN1") + +# source('/public/workspace/lily/software/ssGSEA/ssgseaMOD.r') +# mod.generate(immunegene,"immunecheck",out="/public/workspace/lily/MOD_file/immunecheck.mod") # make a mod file + +dat <- readRDS("/public/workspace/lily/metastasis/data/verify/GSE14108/GSE14108_res.RDS") +source('/public/workspace/lily/software/ssGSEA/ssgseaMOD.r') +mod <- mod.analyze2(as.matrix(dat),c("Lung_gene","RES","SEN","immunecheck","Brain_gene","BMS_update"),"/public/workspace/lily/MOD_file/",permN=0) +mod <- as.data.frame(mod) + +mod$RRS <- mod$RES_norm-mod$SEN_norm + + +# plot result for therapy resistance +pdf("/public/workspace/lily/Lung2Brain/Version5/Fig5/GSE14108_RRRS_lung.pdf",useDingbats=F) +plot(mod$Lung_gene_norm,mod$RRS,main="Lung like tumor with RRRS",xlab="Lung cancer signature",ylab="RRS signature",pch=19) +abline(lm(mod$RRS~mod$Lung_gene_norm),col="red") +# cor.tes() +legend("topright",legend=paste0("rho=",0.70," pvalue<",0.001)) +dev.off() + + +# plot result for immunecheckpoint + +pdf("/public/workspace/lily/Lung2Brain/Version5/Fig5/GSE14108_immunecheck_lung.pdf",useDingbats=F) +plot(mod$Lung_gene_norm,mod$immunecheck_norm,main="Lung like tumor with immunecheckpoint",xlab="Lung cancer signature",ylab="immunocheckpoint signature",pch=19) +abline(lm(mod$immunecheck_norm~mod$Lung_gene_norm),col="red") +# cor.tes() +legend("topright",legend=paste0("rho=",0.45," pvalue=",0.017)) +dev.off() + + +# use cibersort inflammation or immuncell AI result to do correlation +# cibersort do not have inflatrate score, so use ImmuncellAI +source("~/software/Cibersort_R.R") +tmp <- read.table("~/metastasis/data/verify/GSE14108/ImmuCellAI_abundance_result_GSE14108.txt",sep="\t",header=T) +all(rownames(tmp$X)==rownames(mod)) + +pdf("/public/workspace/lily/Lung2Brain/Version5/Fig5/GSE14108_inflammation_lung.pdf",useDingbats=F) +plot(mod$Lung_gene_norm,tmp$InfiltrationScore,main="Lung like tumor with inflammation",xlab="Lung cancer signature",ylab="inflammation signature",pch=19) +abline(lm(tmp$InfiltrationScore~mod$Lung_gene_norm),col="red") +# cor.tes() +legend("topright",legend=paste0("rho=",0.49," pvalue=",0.01)) +dev.off() + + + + + +#=================================================================================================================================== +# check immune checkpoint in bulk data +# 2021-10-14 +#################################################################################################################################### +library(pheatmap) +BM.dat <- readRDS("/public/workspace/lily/metastasis/data/verify/GSE14108/GSE14108_res.RDS") +BM.dat <- readRDS("/public/workspace/lily/metastasis/data/verify/E-MTAB-8659/LCBM_S63_data.RDS") +source('/public/workspace/lily/software/ssGSEA/ssgseaMOD.r') +mod <- mod.analyze2(as.matrix(BM.dat),c("Lung_gene","Brain_gene","BMS_update","immunecheck","immune_act","immunsupp"),"/public/workspace/lily/MOD_file/",permN=0) +mod <- as.data.frame(mod) + +# make a annotation +ann_col <- mod[,c(6:8)] +ann_col <- data.frame(apply(ann_col,2,function(x){ifelse(xquantile(x,0.67),"High","Medium"))})) +# immunegene <- c("ADORA2A","ARHGEF5","BTLA","CD160","CD244","CD27","CD274","CD276","CD47","CD80","CEACAM1","CTLA4", +# "GEM","HAVCR2","ICOS","IDO1","LAG3","PDCD1","TNFSF4","VISTA","VTCN1") +# gene.f <- immunegene[which(immunegene%in%rownames(BM.dat))] +gene.f <- c("LGALS9","ARG1","CD47","IDO1","VEGFA","IL10","TGFB1","PVR") # CD274 not found +gene.f <- c("LGALS9","ARG1","CD47","IDO1","VEGFA","IL10","CD274","TGFB1","PVR") # CD274 should in E-MTAB +dat <- BM.dat[gene.f,] + + +tmp.ann <- t(sapply(gene.f,function(x){ + c(cor.test(mod$Brain_gene_norm,as.numeric(dat[x,]),method="spearman")$p.value, + cor.test(mod$Lung_gene_norm,as.numeric(dat[x,]),method="spearman")$p.value, + cor.test(mod$BMS_update_norm,as.numeric(dat[x,]),method="spearman")$p.value + ) +})) +ann_row <- data.frame(apply(tmp.ann,2,function(x){ifelse(x>0,"pos","neg")}))[,,drop=F] +colnames(ann_row) <- c("Brain.cor","Lung.cor","Stem.cor") + +ann_colors = list( + Brain.cor = c(pos = "steelblue", neg = "red"), + Lung.cor = c(pos = "steelblue", neg = "red"), + Stem.cor = c(pos = "steelblue", neg = "red"), + Lung_gene_norm = colorRampPalette(c('white',"#44AC48"))(100), + Brain_gene_norm = colorRampPalette(c('white',"#269DCB"))(100), + BMS_update_norm = colorRampPalette(c('white',"#F6BB3D"))(100) +) + +pdf("/public/workspace/lily/Lung2Brain/Version5/Fig5/GSE14108_immunesuppression_gene_heatmap.pdf",useDingbats=F,width=15,height=12) +pheatmap(dat[],annotation_col=ann_col,scale="row",breaks=seq(-5,5,0.1),annotation_row=ann_row, + annotation_colors=ann_colors,color=colorRampPalette(c('steelblue',"steelblue",'white',"red",'red'))(100), + cellwidth=8,cellheight=12) +dev.off() + + + + + + + + + + + +#===================================================================================================================================================== +# 2021-8-17 +# MSGDB GO BP immune response activation signature +# do this signature with Lung_like +#===================================================================================================================================================== +# this signature seems not good +# tmp <- as.vector(read.table("/public/workspace/lily/tmp/GOBP_immune_act.txt")[,1]) +# source('/public/workspace/lily/software/ssGSEA/ssgseaMOD.r') +# mod.generate(tmp,"immune_act",out="/public/workspace/lily/MOD_file/immune_act.mod") # make a mod file +# calculate signature +dat <- readRDS("/public/workspace/lily/metastasis/data/verify/GSE14108/GSE14108_res.RDS") +source('/public/workspace/lily/software/ssGSEA/ssgseaMOD.r') +mod <- mod.analyze2(as.matrix(dat),c("Lung_gene","Brain_gene","BMS_update","immunecheck","immune_act"),"/public/workspace/lily/MOD_file/",permN=0) +mod <- as.data.frame(mod) + + +# E-MTAB +dat <- readRDS("/public/workspace/lily/metastasis/data/verify/E-MTAB-8659/LCBM_S63_data.RDS") +source('/public/workspace/lily/software/ssGSEA/ssgseaMOD.r') +mod <- mod.analyze2(as.matrix(dat),c("Lung_gene","Brain_gene","BMS_update","immunecheck","immune_act"),"/public/workspace/lily/MOD_file/",permN=0) +mod <- as.data.frame(mod) + + + + + + + +#=================================================================================================================================================== +# 2021-8-18 +# 2021-10-14 2021-10-15 +# use GSE14108 to plot heatmap +# T cell marker and Myeloid marker +#=================================================================================================================================================== +library(pheatmap) +Tgene <- c("CD3D","CD3E","CD2","CD96") +Mgene <- c("ITGAM","CD14","CD163","CCR2") + + +ann_row <- mod[,6:8,drop=F] +ann_row <- data.frame(apply(ann_row,2,function(x){ifelse(xquantile(x,0.67),"High","Medium"))})) +#ann_row <- ann_row[order(ann_row$Lung_gene_norm),,drop=F] + +tmp.dat <- dat[c(Tgene,Mgene),] +tmp.dat <- t(tmp.dat) + +dat.BM <- dat +gene <- c(Tgene,Mgene) +dat <- dat.BM[gene,] +tmp.ann <- t(sapply(gene,function(x){ + c(cor.test(mod$Brain_gene_norm,as.numeric(dat[x,]),method="spearman")$estimate, + cor.test(mod$Lung_gene_norm,as.numeric(dat[x,]),method="spearman")$estimate, + cor.test(mod$BMS_update_norm,as.numeric(dat[x,]),method="spearman")$estimate + ) +})) +ann_col <- data.frame(apply(tmp.ann,2,function(x){ifelse(x>0,"pos","neg")}))[,,drop=F] +colnames(ann_col) <- c("Brain.cor","Lung.cor","Stem.cor") +ann_colors = list( + Brain.cor = c(pos = "steelblue", neg = "red"), + Lung.cor = c(pos = "steelblue", neg = "red"), + Stem.cor = c(pos = "steelblue", neg = "red"), + Lung_gene_norm = colorRampPalette(c('white',"#44AC48"))(100), + Brain_gene_norm = colorRampPalette(c('white',"#269DCB"))(100), + BMS_update_norm = colorRampPalette(c('white',"#F6BB3D"))(100) +) + + +ann_colors = list( + Brain = c(pos = "steelblue", neg = "red"), + Lung = c(pos = "steelblue", neg = "red"), + Stem = c(pos = "steelblue", neg = "red"), + Lung_gene_norm = c(High = "red", Medium="white",Low = "green"), + Brain_gene_norm = c(High = "red", Medium="white",Low = "green"), + BMS_update_norm = c(High = "red", Medium="white",Low = "green") +) + + + + + + +pdf("/public/workspace/lily/Lung2Brain/Version5/Fig5/GSE14108_myeloid_Tcell_heatmap.pdf",useDingbats=F) +pheatmap(tmp.dat,annotation_row=ann_row,scale="column", + breaks=seq(-3,2,0.1),cellwidth=8,cellheight=8,annotation_col=ann_col,annotation_colors=ann_colors, + color=colorRampPalette(c('steelblue',"steelblue",'white',"red",'red'))(60)) +dev.off() + + +pheatmap(tmp.dat[,rownames(ann_col)],annotation_col=ann_col,scale="row",cluster_cols=F,cluster_rows=F,breaks=seq(-3,2,0.1), + color=colorRampPalette(c('green',"green",'black',"red",'red'))(60)) + + + + + + + + + + + + + + + + +#============================================================================================================================================== +# 2021-8-20 +# plot immune activation cytokine result +# not siginificant +#============================================================================================================================================= +dat <- readRDS("/public/workspace/lily/metastasis/data/verify/GSE14108/GSE14108_res.RDS") +source('/public/workspace/lily/software/ssGSEA/ssgseaMOD.r') +mod <- mod.analyze2(as.matrix(dat),c("Lung_gene","Brain_gene","BMS_update","Treg","Cytokine_act","immunecheck"),"/public/workspace/lily/MOD_file/",permN=0) +mod <- as.data.frame(mod) + +# plot result in Supplementary +pdf("/public/workspace/lily/Lung2Brain/Version5/Fig5/GSE14108_Lung.Cytokine.pdf",useDingbats=F) +plot(mod$Lung_gene_norm,mod$Cytokine_act_norm,main="Lung like tumor with act Cytokine",xlab="Lung cancer signature",ylab="act Cytokine signature",pch=19) +dev.off() + + + + + + + + + + + + + + +#============================================================================================================================================= +# 2021-10-5 +# LCBM check immune supressive gene expression percentage +#============================================================================================================================================= +library(Seurat) +gene <- c("CD274","CD47","IDO1","LGALS9","IL10","TGFB1","VEGFA","ARG1","PVR") + +dat <- readRDS("/public/workspace/lily/Lung2Brain/Version5/Fig4/Data/LCBM_sub.tumor.RDS") +DefaultAssay(dat) <- "RNA" +dat@active.ident <- factor(dat$group) + +percent_feature <- function(dat,genelist,group){ + res.list <- c() + for(i in 1:length(genelist)){ + dat$tmp_gene <- ifelse(dat[["RNA"]]@data[genelist[i],]>0,"Y","N") + if(all(dat$tmp_gene=="N")){ + res.list[[i]] <- c(0,0,0,0) + }else{ + res.list[[i]] <- apply(table(dat$tmp_gene,dat@meta.data[,group]),2,function(x){x/sum(x)})[2,] + } + + names(res.list)[i] <- genelist[i] + } + return(res.list) +} + +res.dat.list <- percent_feature(dat,gene,group="group") + +list2mat <- function(res.list){ + res.dat <- matrix(unlist(res.list),ncol=length(res.list[[1]]),byrow=T) + rownames(res.dat) <- unique(sapply(strsplit(as.vector(names(unlist(res.list))),"\\."),function(x){x[[1]]})) + colnames(res.dat) <- unique(sapply(strsplit(as.vector(names(unlist(res.list))),"\\."),function(x){x[[2]]})) + return(res.dat) +} + +mat <- list2mat(res.dat.list) + +# plot result +library(pheatmap) +pdf("/public/workspace/lily/Lung2Brain/Version5/Fig5/LCBM_subtumor_immunesupressive_percent.pdf",useDingbats=F) +pheatmap::pheatmap(mat,scale="row",show_colnames=T,color=colorRampPalette(c("steelblue",'white',"#E41A1C"))(100),cellwidth=10,cellheight=10) +dev.off() + +# by the way plot other samples +library(pheatmap) +pdf("/public/workspace/lily/Lung2Brain/Version5/Fig5/LCBM_all_immunesuppressive_percent_heatmap.pdf",useDingbats=F) +pheatmap(mat.e[,1:3],scale="row",cluster_rows=F,cluster_cols=F,cellwidth=10,cellheight=10,color=colorRampPalette(c('steelblue','white',"#E41A1C"))(100),main="mat.e") +pheatmap(mat.d[,1:3],scale="row",cluster_rows=F,cluster_cols=F,cellwidth=10,cellheight=10,color=colorRampPalette(c('steelblue','white',"#E41A1C"))(100),main="mat.d") +# pheatmap(mat.lx701[,1:3],scale="row",cluster_rows=F,cluster_cols=F) +# pheatmap(mat.lx681[,1:3],scale="row",cluster_rows=F,cluster_cols=F) +# pheatmap(mat.lx255b[,1:3],scale="row",cluster_rows=F,cluster_cols=F) +pheatmap(mat.lcbm[,c(3,1,2)],scale="row",cluster_rows=F,cluster_cols=F,cellwidth=10,cellheight=10,color=colorRampPalette(c('steelblue','white',"#E41A1C"))(100),main="mat.lcbm") +pheatmap(mat.gse123902[,1:3],scale="row",cluster_rows=F,cluster_cols=F,cellwidth=10,cellheight=10,color=colorRampPalette(c('steelblue','white',"#E41A1C"))(100),main="mat.gse123902") +dev.off() + + + + + + + + + + + + + + + + + +#============================================================================================================================================ +# 2021-11-23 +# percentage of BMS.group tumor for 4 samples +#============================================================================================================================================ + +percent_feature <- function(dat,genelist,group){ + res.list <- c() + for(i in 1:length(genelist)){ + dat$tmp_gene <- ifelse(dat[["RNA"]]@data[genelist[i],]>0,"Y","N") + if(all(dat$tmp_gene=="N")){ + res.list[[i]] <- rep(0,length=length(table(dat@meta.data[,group]))) + }else{ + res.list[[i]] <- apply(table(dat$tmp_gene,dat@meta.data[,group]),2,function(x){x/sum(x)})[2,] + } + + names(res.list)[i] <- genelist[i] + } + return(res.list) +} + +library(Seurat) +dat <- readRDS("/public/workspace/lily/Lung2Brain/Multiple_LungBrain/tumor.D.RDS") +tumor.d.per <- percent_feature(dat,c("CCL2","CSF1"),"BMS.group") +dat@active.ident <- factor(dat$BMS.group) +tumor.d.exp <- AverageExpression(dat,assays="RNA",features=c("CCL2","CSF1"))$RNA + +tumor.e <- readRDS("/public/workspace/lily/Lung2Brain/Multiple_LungBrain/tumor.E.RDS") +tumor.e.per <- percent_feature(tumor.e,c("CCL2","CSF1"),"BMS.group") +tumor.e@active.ident <- factor(tumor.e$BMS.group) +tumor.e.exp <- AverageExpression(tumor.e,assays="RNA",features=c("CCL2","CSF1"))$RNA + +gse123902 <- readRDS("/public/workspace/lily/Lung2Brain/Version5/Fig5/GSE123902_BM_tumor.RDS") +DefaultAssay(gse123902) <- "RNA" +gse123902.per <- percent_feature(gse123902,c("CCL2","CSF1"),"BMS.group") +gse123902@active.ident <- factor(gse123902$BMS.group) +gse123902.exp <- AverageExpression(gse123902,assays="RNA",features=c("CCL2","CSF1"))$RNA + +lcbm <- readRDS("/public/workspace/lily/Lung2Brain/Version5/Data/Tumor_pur/pur_LCBM_clust.RDS") +DefaultAssay(lcbm) <- "RNA" +lcbm.per <- percent_feature(lcbm,c("CCL2","CSF1"),"BMS.group") +lcbm@active.ident <- factor(lcbm$BMS.group) +lcbm.exp <- AverageExpression(lcbm,assays="RNA",features=c("CCL2","CSF1"))$RNA + + +tmp.res <- data.frame( +sample = rep(c("tumor.e","tumor.d","gse123902","lcbm"),each=2), +CCL2.exp = c(as.numeric(tumor.e.exp["CCL2",c(1,3)]),as.numeric(tumor.d.exp["CCL2",c(1,3)]),as.numeric(gse123902.exp["CCL2",c(1,3)]),as.numeric(lcbm.exp["CCL2",c(1,3)])), +CCL2.per = c(as.numeric(tumor.e.per$CCL2[c(1,3)]),as.numeric(tumor.d.per$CCL2[c(1,3)]),as.numeric(gse123902.per$CCL2[c(1,3)]),as.numeric(lcbm.per$CCL2[c(1,3)])), +CSF1.exp = c(as.numeric(tumor.e.exp["CSF1",c(1,3)]),as.numeric(tumor.d.exp["CSF1",c(1,3)]),as.numeric(gse123902.exp["CSF1",c(1,3)]),as.numeric(lcbm.exp["CSF1",c(1,3)])), +CSF1.per = c(as.numeric(tumor.e.per$CSF1[c(1,3)]),as.numeric(tumor.d.per$CSF1[c(1,3)]),as.numeric(gse123902.per$CSF1[c(1,3)]),as.numeric(lcbm.per$CSF1[c(1,3)])), +group = rep(c("high","low"),times=4) +) + +tmp.res <- reshape2::melt(tmp.res) +res <- data.table::rbindlist( + list( cbind(tmp.res[1:8,],tmp.res[9:16,4]) , cbind(tmp.res[17:24,],tmp.res[25:32,4]) ), + use.names=FALSE +) +colnames(res) <- c("sample","group","gene","exp","percent") +res$gene <- sapply(strsplit(as.vector(res$gene),"\\."),function(x){x[[1]]}) +res <- as.data.frame(res,stringsAsFactors=F) + + + +# plot result +library(ggplot2) +res$class <- paste0(res$sample,"-",res$gene) + +tmp.percent <- c() +for(i in c(1,3,5,7,9,11,13,15)){ + tmp.percent <- c(tmp.percent,as.numeric(scale(res$percent[c(i,i+1)],center=F))) +} +res$scale.percent <- tmp.percent + +pdf("/public/workspace/lily/Lung2Brain/Version5/Fig5/CCL2_CSF1_BMS_high_low.pdf",useDingbats=F) +ggplot(res,aes(x=group,y=(class))) + geom_point(aes(size=scale.percent,color=exp)) + scale_colour_gradientn(colours=c("steelblue","#F1AB3E")) +dev.off() + + + + + + +#============================================================================================================================================ +# 2021-11-23 +# GSE14108 use CCL2 and CSF1 and MDM-MG +#============================================================================================================================================ +dat <- readRDS("/public/workspace/lily/metastasis/data/verify/GSE14108/GSE14108_res.RDS") +# dat <- readRDS("/public/workspace/lily/metastasis/data/verify/E-MTAB-8659/LCBM_S63_data.RDS") +source('/public/workspace/lily/software/ssGSEA/ssgseaMOD.r') +mod <- mod.analyze2(as.matrix(dat),c("BMS_update","BMDM_marker","MG_marker"),"/public/workspace/lily/MOD_file/",permN=0) +mod <- as.data.frame(mod) +mod$MDM.MG <- mod$BMDM_marker_norm - mod$MG_marker_norm +mod$CCL2 <- as.numeric(dat["CCL2",]) +mod$CSF1 <- as.numeric(dat["CSF1",]) + +cor.test(mod$CCL2,mod$BMS_update_norm,method="spearman") +cor.test(mod$CCL2,mod$BMDM_marker_norm,method="spearman") +cor.test(mod$CCL2,mod$MG_marker_norm,method="spearman") +cor.test(mod$CCL2,mod$MDM.MG,method="spearman") +cor.test(mod$CSF1,mod$MDM.MG,method="spearman") + + + + +# plot result +pdf("/public/workspace/lily/Lung2Brain/Version5/Fig5/CCL2_BMS_GSE14108.pdf",useDingbats=F) +par(mfrow=c(2,2)) +plot(mod$BMS_update_norm,mod$CCL2,main="CCL2 with BMS",pch = 20, cex = 2.5) +abline(lm(mod$CCL2~mod$BMS_update_norm),col="red") + +plot(mod$BMDM_marker_norm,mod$CCL2,main="CCL2 with BMDM",pch = 20, cex = 2.5) +abline(lm(mod$CCL2~mod$BMDM_marker_norm),col="red") + +plot(mod$MG_marker_norm,mod$CCL2,main="CCL2 with MG",pch = 20, cex = 2.5) +abline(lm(mod$CCL2~mod$MG_marker_norm),col="red") + +plot(mod$MDM.MG,mod$CCL2,main="CCL2 with MDM.MG",pch = 20, cex = 2.5) +abline(lm(mod$CCL2~mod$MDM.MG),col="red") +dev.off() + +pdf("/public/workspace/lily/Lung2Brain/Version5/Fig5/CSF1_BMS_GSE14108.pdf",useDingbats=F) +par(mfrow=c(2,2)) +plot(mod$BMS_update_norm,mod$CSF1,main="CSF1 with BMS",pch = 20, cex = 2.5) +abline(lm(mod$CSF1~mod$BMS_update_norm),col="red") + +plot(mod$BMDM_marker_norm,mod$CSF1,main="CSF1 with BMDM",pch = 20, cex = 2.5) +abline(lm(mod$CSF1~mod$BMDM_marker_norm),col="red") + +plot(mod$MG_marker_norm,mod$CSF1,main="CSF1 with MG",pch = 20, cex = 2.5) +abline(lm(mod$CSF1~mod$MG_marker_norm),col="red") + +plot(mod$MDM.MG,mod$CSF1,main="CSF1 with MDM.MG",pch = 20, cex = 2.5) +abline(lm(mod$CSF1~mod$MDM.MG),col="red") +dev.off() + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/Fig6_Myeloid.r b/Fig6_Myeloid.r new file mode 100644 index 0000000..8539d6c --- /dev/null +++ b/Fig6_Myeloid.r @@ -0,0 +1,1123 @@ + +# 2021-6-7 +# analysis Myeloid cells +#====================================================================================== +library(Seurat) +dat <- readRDS("/public/workspace/lily/Lung2Brain/Version5/Fig6/Data/inte9_myeloid.RDS") +inte.list <- list() +samplelist <- unique(dat$orig.ident) +for(i in 1: length(samplelist)){ + tmp <- subset(dat,cells=which(dat$orig.ident==samplelist[i])) + DefaultAssay(tmp) <- "RNA" + inte.list[[i]] <- tmp +} +integration.anchors <- FindIntegrationAnchors(object.list = inte.list,k.filter=20) +inte <- IntegrateData(anchorset = integration.anchors) +#FindVariableFeatures +inte <- FindVariableFeatures(inte) +##Scaling the integrateda +all.genes <- rownames(inte) +inte <- ScaleData(inte, features = all.genes) +#PCA +inte <- RunPCA(inte) +#cluster +inte <- FindNeighbors(inte) +inte <- FindClusters(inte) +#TSNE +# if Umap can not use +inte <- RunTSNE(inte) + +saveRDS(inte,file="/public/workspace/lily/Lung2Brain/Version5/Fig6/Data/inte9_myeloid.RDS") + + + + + +########################################################################################################################################## +# 2021-6-7 +# define cell type +#========================================================================================================================================= +library(Seurat) +library(SingleR) +dat <- readRDS("/public/workspace/lily/Lung2Brain/Version5/Fig6/Data/inte9_myeloid.RDS") +ref <- readRDS("/public/workspace/lily/software/SingleRData/SingleR_BluePrint.RDS") +res.cluster<-SingleR(test=as.matrix(dat@assays$RNA@data),ref=ref,labels=ref$label.fine,clusters=dat$seurat_clusters,method="cluster") + + +# VlnPlot show result +#======================================================================================================================================== +# define MDM and MG and Monocyte +# 2021-6-7 +library(Seurat) +dat <- readRDS("/public/workspace/lily/Lung2Brain/Version5/Fig6/Data/inte9_myeloid.RDS") +dat$hbmarker <- NULL +dat$llymarker <- NULL +dat$celltype <- NULL +dat$putativeTumor3 <- NULL +dat$res.2 <- NULL +dat$Phase <- NULL +dat$G2M.Score <- NULL +dat$S.Score <- NULL +dat$percent.mito <- NULL +dat$nUMI <- NULL +dat$nGene <- NULL +dat$RNA_snn_res.2 <- NULL + +dat$celltype <- "Myeloid" +dat$celltype[which(dat$seurat_clusters%in%c(14))] <- "Undefined" +dat$celltype[which(dat$seurat_clusters%in%c(1,7))] <- "Monocyte" +sub.dat <- subset(dat,cells=which(dat$celltype=="Myeloid")) +# calculate BMS score +source('/public/workspace/lily/software/ssGSEA/ssgseaMOD.r') +mod <- mod.analyze2(as.matrix(sub.dat[['RNA']]@data),c("BMDM_marker","MG_marker"),"/public/workspace/lily/MOD_file/",permN=0) +mod <- as.data.frame(mod) + +saveRDS(mod,file="/public/workspace/lily/Lung2Brain/Version5/Myeloid/Myeloid_MDM_MG_mod.RDS") + + + + +#=========================================================================================================================== +# define cell type +mod$type <- "MDM" +mod$type[which(mod$BMDM_marker_norm% mutate(density = density_obj) + + # p_3Ddensity = plot3D(cellinfo_tbl, color_by = "density", title = "3D density") + + # Get significance + signif_results = getSignificance(coords, labels = cellinfo_tbl$labels, verbose = T) + contribution_list = getContribution(TPM, LR, signif_results$detailed_connections) + + return(list( + labelData=labelData, + TPM=TPM, + cellinfo_tbl=cellinfo_tbl, + signif_results=signif_results, + contribution_list=contribution_list + )) + +} + +TPM <- as.matrix(tmp.res$RNA@counts) + +labelData <- data.frame( + cells = colnames(tmp.res), + labels = as.vector(tmp.res$celltype) +) + +results = run_CSOmap(labelData, TPM, sampling=0) +saveRDS(results, file="/public/workspace/lily/Lung2Brain/Version5/CSOmap/LUAD_CSOmap.results.RDS") + +saveRDS(results, file="/public/workspace/lily/Lung2Brain/Version5/CSOmap/GBM_CSOmap.results.RDS") + +saveRDS(results, file="/public/workspace/lily/Lung2Brain/Version5/CSOmap/LCBM_CSOmap.results.RDS") + + +# plot LCBM result +#======================================================================================================================== +library(plot3D) +result <- readRDS("/public/workspace/lily/Lung2Brain/Version5/CSOmap/LCBM_CSOmap.results.RDS") +cellinfo_tbl <- result$cellinfo_tbl + +stat = aggregate(cbind(x,y,z)~labels, cellinfo_tbl,mean) +rownames(stat) = stat[,1] +stat=stat[,-1] + +cex=0.8 +phi=30 +theta=0 +tmp = cellinfo_tbl[which(cellinfo_tbl $labels=='MDM'),] +scatter3D(tmp$x, tmp$y, tmp$z,pch=16,cex=cex, + phi= phi,theta= theta,col="#f2af00", + xlim=range(cellinfo_tbl $x),ylim=range(cellinfo_tbl $y), + zlim=range(cellinfo_tbl $z)) +tmp = cellinfo_tbl[which(cellinfo_tbl $labels=='tumor'),] +scatter3D(tmp$x, tmp$y, tmp$z,pch=16,cex=cex, + phi= phi,theta= theta,col="#ce1126",add=T) +tmp = cellinfo_tbl[which(cellinfo_tbl $labels=='MG'),] +scatter3D(tmp$x, tmp$y, tmp$z,pch=16,cex=cex, + phi= phi,theta= theta,col="#5482ab",add=T) +tmp = cellinfo_tbl[which(cellinfo_tbl $labels=='Monocyte'),] +scatter3D(tmp$x, tmp$y, tmp$z,pch=16,cex=cex, + phi= phi,theta= theta,col="#7ab800",add=T) + +# plotly +library(plotly) +set.seed(12345) +plot_ly(cellinfo_tbl, x = ~x, y = ~y, z = ~z, color = ~labels , text = ~labels,colors = c("#c9c9c9","#2db928","#fcd000","#d13814"),size=0.3) + + + +#=================================================================================================================================== +# plot Brplot to show distance +result <- readRDS("/public/workspace/lily/Lung2Brain/Version5/CSOmap/LCBM_CSOmap.results.RDS") +cellinfo_tbl <- result$cellinfo_tbl + +stat = aggregate(cbind(x,y,z)~labels, cellinfo_tbl,mean) +rownames(stat) = stat[,1] +stat=stat[,-1] +dist(stat) +pdf("/public/workspace/lily/Lung2Brain/Version5/Myeloid/LCBM_Myeloid_tumor_distance.pdf",useDingbats=F) +barplot(c(0.08978695,0.09794600,0.12931052),names=c("MDM","Monocyte","MG"),ylim=c(0,0.15)) +dev.off() + +##################################################################### +# GBM +result <- readRDS("/public/workspace/lily/Lung2Brain/Version5/CSOmap/GBM_CSOmap.results.RDS") +cellinfo_tbl <- result$cellinfo_tbl + +stat = aggregate(cbind(x,y,z)~labels, cellinfo_tbl,mean) +rownames(stat) = stat[,1] +stat=stat[,-1] +dist(stat) +pdf("/public/workspace/lily/Lung2Brain/Version5/Myeloid/GBM_Myeloid_tumor_distance.pdf",useDingbats=F) +barplot(c(0.27407694,0.32412684,0.22030794),names=c("MDM","Monocyte","MG"),ylim=c(0,0.5)) +dev.off() +############################################################################ +# LUAD +result <- readRDS("/public/workspace/lily/Lung2Brain/Version5/CSOmap/LUAD_CSOmap.results.RDS") +cellinfo_tbl <- result$cellinfo_tbl + +stat = aggregate(cbind(x,y,z)~labels, cellinfo_tbl,mean) +rownames(stat) = stat[,1] +stat=stat[,-1] +dist(stat) +pdf("/public/workspace/lily/Lung2Brain/Version5/Myeloid/LUAD_Myeloid_tumor_distance.pdf",useDingbats=F) +barplot(c(0.4435861,0.5051479,0.6597093),names=c("MDM","Monocyte","MG"),ylim=c(0,0.8)) +dev.off() + + + + +# plot together +#============================================================================= +dat <- data.frame(percent=c(0.08978695,0.09794600,0.12931052,0.27407694,0.32412684,0.22030794,0.4435861,0.5051479,0.6597093), + celltype=c(rep(c("MDM","Monocyte","MG"),3)),group=c(rep(c("LCBM","GBM","LUAD"),each=3))) + + +dat <- matrix(c(0.08978695,0.09794600,0.12931052,0.27407694,0.32412684,0.22030794,0.4435861,0.5051479,0.6597093), + ncol=3,byrow=T) + +colnames(dat) <- c("MDM","Monocyte","MG") +rownames(dat) <- c("LCBM","GBM","LUAD") + +# radar chat is not good to use ; not suitable +# library(fmsb) +# radarchart( data.frame(dat) , axistype=1 , +# #custom polygon +# # pcol=colors_border , pfcol=colors_in , plwd=4 , plty=1, +# #custom the grid +# cglcol="grey", cglty=1, axislabcol="grey", caxislabels=seq(0,1,0.25), cglwd=0.8, +# #custom labels +# vlcex=0.8 +# ) + +library(ggplot2) +dat <- data.frame(distance=c(0.08978695,0.09794600,0.12931052,0.27407694,0.32412684,0.22030794,0.4435861,0.5051479,0.6597093), + celltype=c(rep(c("MDM","Monocyte","MG"),3)),group=c(rep(c("LCBM","GBM","LUAD"),each=3))) + +dat$celltype <- factor(dat$celltype,levels=c("MDM","Monocyte","MG")) + +pdf("/public/workspace/lily/Lung2Brain/Version5/Myeloid/Myeloid_Tumor_distance.pdf",useDingbats=F) +ggplot(dat,aes(x=group,y=distance,fill=celltype,group=celltype)) + geom_bar(stat="identity",position="dodge")+ theme_classic()+ + scale_fill_manual(values=c("#fbb034","#89ba16","#00a4e4"))+ + geom_text(aes(label=round(distance,2),y = distance + 0.05) , position = position_dodge(0.9),size =3) +dev.off() + + + + + + + + + + + + + +#################################################################################################################################### +# run cell cell communication +# maybe need to focus on BMDM ? +# 2021-6-8 +library(Seurat) +library(CellChat) +# tmp.res <- readRDS("/public/workspace/lily/Lung2Brain/Version5/CSOmap/LUAD_tumor_myeloid.RDS") +# tmp.res <- readRDS("/public/workspace/lily/Lung2Brain/Version5/CSOmap/GBM_tumor_myeloid.RDS") +tmp.res <- readRDS("/public/workspace/lily/Lung2Brain/Version5/CSOmap/LCBM_tumor_myeloid.RDS") + +#================================================================================================================================== +# Run CellChat +data.input <- GetAssayData(tmp.res, assay = "RNA", slot = "data") # normalized data matrix +meta <- data.frame(tmp.res@meta.data) # create a dataframe of the cell labels +cellchat <- createCellChat(object = data.input, meta = meta, group.by = "celltype") +cellchat <- setIdent(cellchat, ident.use = "celltype") # set "labels" as default cell identity +levels(cellchat@idents) + +CellChatDB <- CellChatDB.human +# use all DB +CellChatDB.use <- CellChatDB +# set the used database in the object +cellchat@DB <- CellChatDB.use +cellchat <- subsetData(cellchat) # This step is necessary even if using the whole database +future::plan("multiprocess", workers = 4) # do parallel + +##################################################################################################### +cellchat <- identifyOverExpressedGenes(cellchat) +cellchat <- identifyOverExpressedInteractions(cellchat) +# project gene expression data onto PPI network (should use) +cellchat <- projectData(cellchat, PPI.human) + +cellchat <- computeCommunProb(cellchat) # 1W cells 5min +# Filter out the cell-cell communication if there are only few number of cells in certain cell groups +cellchat <- filterCommunication(cellchat, min.cells = 10) +cellchat <- computeCommunProbPathway(cellchat) +# Calculate the aggregated cell-cell communication network +cellchat <- aggregateNet(cellchat) + +groupSize <- as.numeric(table(cellchat@idents)) +saveRDS(cellchat,file="/public/workspace/lily/Lung2Brain/Version5/CellChat/GBM.CellChat.RDS") + +saveRDS(cellchat,file="/public/workspace/lily/Lung2Brain/Version5/CellChat/LCBM.CellChat.RDS") + +saveRDS(cellchat,file="/public/workspace/lily/Lung2Brain/Version5/CellChat/LUAD.CellChat.RDS") + + + +################################################################################################################################## +# get cellchat +# 2021-6-10 +library(Seurat) +library(CellChat) + +cellchat <- readRDS("/public/workspace/lily/Lung2Brain/Version5/CellChat/LCBM.CellChat.RDS") +groupSize <- as.numeric(table(cellchat@idents)) + +# found LCBM MDM2tumor specific gene pair +#=============================================================================================================================== +# pdf("./tmp.pdf") +# netVisual_circle(cellchat@net$count, vertex.weight = groupSize, weight.scale = T, label.edge= F, title.name = "Number of interactions") +# dev.off() +# MDM MG Monocyte tumor +# values <- c(1, 2, 3) +# names(values) <- c("p > 0.05", "0.01 < p < 0.05", "p < 0.01") +tmp <- netVisual_bubble(cellchat, sources.use = 1:3, targets.use = 4, remove.isolate = FALSE,return.data=T)$communication +# df <- tmp[which(tmp$prob>0.05&tmp$pval>=3),] +tmp.res <- tmp[,c("prob","source.target","interaction_name_2")] +rs <- reshape2::dcast(tmp.res,source.target~interaction_name_2,value.var="prob") +#rs[is.na(rs)] <- 0 +rownames(rs) <- rs[,1] +rs <- rs[,-1] +rs <- data.frame(t(rs)) +colnames(rs) <- c("MDM.tumor","MG.tumor","Monocyte.tumor") + +# check MDM specific interaction +a <- rs[which(rs$MG.tumor==0&rs$Monocyte.tumor==0),] +all(tmp[which(tmp$interaction_name_2%in%rownames(a)),"interaction_name_2"]==rownames(a)) +a$pathway <- tmp[which(tmp$interaction_name_2%in%rownames(a)),"pathway_name"] + +saveRDS(a,file="/public/workspace/lily/Lung2Brain/Version5/CellChat/Pair/LCBM_MDM2tumor.specifc.RDS") + + + + +# Check GBM to have more speicfic +cellchat <- readRDS("/public/workspace/lily/Lung2Brain/Version5/CellChat/GBM.CellChat.RDS") +groupSize <- as.numeric(table(cellchat@idents)) +tmp <- netVisual_bubble(cellchat, sources.use = 2:4, targets.use = 1, remove.isolate = FALSE,return.data=T)$communication +tmp.res <- tmp[,c("prob","source.target","interaction_name_2")] +rs <- reshape2::dcast(tmp.res,source.target~interaction_name_2,value.var="prob") +rs[is.na(rs)] <- 0 +rownames(rs) <- rs[,1] +rs <- rs[,-1] +rs <- data.frame(t(rs)) +colnames(rs) <- c("MDM.tumor","MG.tumor","Monocyte.tumor") +saveRDS(rs,file="/public/workspace/lily/Lung2Brain/Version5/CellChat/Pair/GBM_MDM2tumor.specifc.RDS") + + + + + +# check LUAD to have more specific +cellchat <- readRDS("/public/workspace/lily/Lung2Brain/Version5/CellChat/LUAD.CellChat.RDS") +groupSize <- as.numeric(table(cellchat@idents)) +tmp <- netVisual_bubble(cellchat, sources.use = 1:3, targets.use = 4, remove.isolate = FALSE,return.data=T)$communication +tmp.res <- tmp[,c("prob","source.target","interaction_name_2")] +rs <- reshape2::dcast(tmp.res,source.target~interaction_name_2,value.var="prob") +rs[is.na(rs)] <- 0 +rownames(rs) <- rs[,1] +rs <- rs[,-1] +rs <- data.frame(t(rs)) +colnames(rs) <- c("MDM.tumor","MG.tumor","Monocyte.tumor") + +a <- rs[which(rs$MG.tumor==0&rs$Monocyte.tumor==0),] +saveRDS(a,file="/public/workspace/lily/Lung2Brain/Version5/CellChat/Pair/LUAD_MDM2tumor.specifc.RDS") + + + + + + + + + + + + +#==================================================================================================================================== +# 2021-6-17 +# plot gene pair calculate by Cell_Cell_interaction.r +#==================================================================================================================================== +library(Seurat) +# merge cells and define cell group +dat1 <- readRDS("/public/workspace/lily/Lung2Brain/Version5/CSOmap/LCBM_tumor_myeloid.RDS") +dat1$cell.group <- paste0(dat1$type_group,"_",dat1$celltype) +# all(names(dat1$cell.group[grep("LCBM_tumor",dat1$cell.group)])==rownames(dat1@meta.data[which(dat1$cell.group=="LCBM_tumor"),])) +dat1$cell.group[which(dat1$cell.group=="LCBM_tumor")] <- paste0("LCBM_tumor",dat1@meta.data[which(dat1$cell.group=="LCBM_tumor"),]$seurat_clusters) + +dat2 <- readRDS("/public/workspace/lily/Lung2Brain/Version5/CSOmap/GBM_tumor_myeloid.RDS") +dat2$cell.group <- paste0(dat2$type_group,"_",dat2$celltype) + +dat3 <- readRDS("/public/workspace/lily/Lung2Brain/Version5/CSOmap/LUAD_tumor_myeloid.RDS") +dat3$cell.group <- paste0(dat3$type_group,"_",dat3$celltype) + +dat <- merge(dat1,y=c(dat2,dat3)) + + +rece <- c("SDC1","SDC4","ITGA3","IL1R1","OSMR","EGFR") +ligand <- c("IL1B","OSM","THBS1","FN1","HBEGF") + +# calculate +dat@active.ident <- factor(dat$cell.group) + +tumor <- AverageExpression(dat,assays="RNA",features=rece)$RNA +tumor <- tumor[,grep("tumor",colnames(tumor))] + +mye <- AverageExpression(dat,assays="RNA",features=ligand)$RNA +mye <- mye[,grep("MDM",colnames(mye))] + + + + + + + + + + + + +# 2021-6-17 +# GSE 137762 verify HBEGF +#======================================================================================================================= +dat <- readRDS("/public/workspace/lily/metastasis/data/verify/GSE137762/GSE137762_expr.RDS") +Hbegf.res <- as.data.frame(dat["Ccl20",]) +Hbegf.res$type <- "unknow" +colnames(Hbegf.res)[1] <- "exp" +Hbegf.res$type <- gsub("[0-9]$","",rownames(Hbegf.res)) + +#======================================================================================================================= +# just show Hbegf not show chemotherapy result +#=========================================================== +tmp.f <- Hbegf.res[grep("BMDM|Blood",rownames(Hbegf.res)),] +tmp.f <- tmp.f[1:14,] +tmp.f$type <- factor(tmp.f$type,levels=c("Control_BloodMonocyte","small_BMDM","large_BMDM")) + +pdf("/public/workspace/lily/Lung2Brain/Version5/Myeloid/HBEGF.BMDM.exp.pdf",useDingbats=F) +boxplot(exp~type,data=tmp.f,FUN=median) +beeswarm::beeswarm(exp~type,data=tmp.f,col = 4, pch = 16,main = 'beeswarm + bxplot',add = TRUE) +dev.off() + + + + +# 2021-7-12 +# MDM result change into IL1B and IL1R1 +#======================================================================================================================== +dat <- readRDS("/public/workspace/lily/metastasis/data/verify/GSE137762/GSE137762_expr.RDS") +Il1b.res <- as.data.frame(dat["Ccl20",]) +Il1b.res$type <- "unknow" +colnames(Il1b.res)[1] <- "exp" +Il1b.res$type <- gsub("[0-9]$","",rownames(Il1b.res)) + +#======================================================================================================================= +# just show Il1b not show chemotherapy result +#=========================================================== +tmp.f <- Il1b.res[grep("BMDM|Blood",rownames(Il1b.res)),] +tmp.f <- tmp.f[1:14,] +tmp.f$type <- factor(tmp.f$type,levels=c("Control_BloodMonocyte","small_BMDM","large_BMDM")) + +pdf("/public/workspace/lily/Lung2Brain/Version5/Myeloid/IL1B.BMDM.exp.pdf",useDingbats=F) +boxplot(exp~type,data=tmp.f,FUN=median) +beeswarm::beeswarm(exp~type,data=tmp.f,col = 4, pch = 16,main = 'beeswarm + bxplot',add = TRUE) +dev.off() + + + +#============================================================================================================================================ +# 2021-12-4 +# CEBPD verify + +dat <- readRDS("/public/workspace/lily/metastasis/data/verify/GSE137762/GSE137762_expr.RDS") +Cebpd.res <- as.data.frame(dat["Cebpd",]) +Cebpd.res$type <- "unknow" +colnames(Cebpd.res)[1] <- "exp" +Cebpd.res$type <- gsub("[0-9]$","",rownames(Cebpd.res)) + +#======================================================================================================================= +# just show Cebpd not show chemotherapy result +#=========================================================== +tmp.f <- Cebpd.res[grep("BMDM|Blood",rownames(Cebpd.res)),] +tmp.f <- tmp.f[1:14,] +tmp.f$type <- factor(tmp.f$type,levels=c("Control_BloodMonocyte","small_BMDM","large_BMDM")) + +pdf("/public/workspace/lily/Lung2Brain/Version5/Myeloid/Cebpd.BMDM.exp.pdf",useDingbats=F) +boxplot(exp~type,data=tmp.f,FUN=median,ylim=c(0,200)) +beeswarm::beeswarm(exp~type,data=tmp.f,col = 4, pch = 16,main = 'beeswarm + bxplot',add = TRUE) +dev.off() + + + + + + + + + + + + + + + + + + +################################################################################################################################ +library(Seurat) +dat <- readRDS("/public/workspace/lily/Lung2Brain/Version5/Fig4/Data/LCBM_sub.tumor.RDS") +DefaultAssay(dat)<- "RNA" +dat@active.ident <- factor(dat$group) + +AverageExpression(dat,assays="RNA",features="IL1R1")$RNA + +pdf("/public/workspace/lily/Lung2Brain/Version5/Myeloid/LCBM_subtumor_IL1R1.pdf",useDingbats=F) +barplot(c(3.336415,0.3584679,0.7695442),names=c("group09","group16","group47"),ylim=c(0,3.5)) +dev.off() + + + + +# try to use GSE14108 to analysis +dat.BM <- readRDS("/public/workspace/lily/metastasis/data/verify/GSE14108/GSE14108_res.RDS") +source('/public/workspace/lily/software/ssGSEA/ssgseaMOD.r') +mod <- mod.analyze2(as.matrix(dat.BM),c("Lung_gene"),"/public/workspace/lily/MOD_file/",permN=0) +mod <- as.data.frame(mod) + + + + + + + + + +#============================================================================================================================ +# TCGA and GSE126548 to verify SDC4 +# 1. TCGA mod with SDC4 expression +#============================================================================================================================ +dat <- readRDS("~/metastasis/data/verify/TCGA_LUAD/TCGA_LUAD_exp.RDS") +mod <- readRDS("/public/workspace/lily/Lung2Brain/Version5/Data/TCGA_LUAD_mod.RDS") +mod$SDC4 <- as.numeric(as.vector(dat["SDC4",])) + +pdf("/public/workspace/lily/Lung2Brain/Version5/Myeloid/TCGA.BMS.SDC4.pdf",useDingbats=F) +plot(mod$SDC4,mod$BMS_update_norm) +abline(lm(mod$BMS_update_norm~mod$SDC4),col="red") +legend("topright",legend=paste0("rho=",0.28)) +dev.off() + +# 2. GSE126548 to analysis show not significant result + + + + + + + + + + + + + + + + + + +######################################################################################################################### +# Analysis about Microgila +# 2021-6-21 +#======================================================================================================================== +library(Seurat) +library(CellChat) + +cellchat1 <- readRDS("/public/workspace/lily/Lung2Brain/Version5/CellChat/LCBM.CellChat.RDS") +cellchat2 <- readRDS("/public/workspace/lily/Lung2Brain/Version5/CellChat/GBM.CellChat.RDS") +# cellchat3 <- readRDS("/public/workspace/lily/Lung2Brain/Version5/CellChat/LUAD.CellChat.RDS") # microglia of LUAD is not OK + + +# LCBM +tmp <- netVisual_bubble(cellchat1, sources.use = 2, targets.use = 4, remove.isolate = FALSE,return.data=T)$communication +tmp.res <- tmp[,c("prob","interaction_name_2")] +tmp.res$group <- "LCBM" +tmp1 <- tmp.res + +# GBM +tmp <- netVisual_bubble(cellchat2, sources.use = 3, targets.use = 1, remove.isolate = FALSE,return.data=T)$communication +tmp.res <- tmp[,c("prob","interaction_name_2")] +tmp.res$group <- "GBM" +tmp2 <- tmp.res + +# merge data +#=========================================================================== +tmp.f <- merge(tmp1,tmp2,all.x=T,all.y=T,by="interaction_name_2") +colnames(tmp.f)[2:5] <- c("prob.LCBM","group.LCBM","prob.GBM","group.GBM") + + + + + + + + + + + + + +#======================================================================================================================================================= +# 2021-9-23 +# analysis about MDM and MG +#======================================================================================================================================================= +# 1. use single cell data to analysis CCR6 and ITGB2 expression in MDM/MG +library(Seurat) +dat <- readRDS("/public/workspace/lily/Lung2Brain/Version5/CSOmap/LCBM_tumor_myeloid.RDS") +DefaultAssay(dat) <- "RNA" +dat@active.ident <- factor(dat$celltype) + +AverageExpression(dat,assay="RNA",features="CCR6") + + +# 2. use bulk data to verify CCR6 with MDM/MG signature score +# GSE14108 +dat.BM <- readRDS("/public/workspace/lily/metastasis/data/verify/GSE14108/GSE14108_res.RDS") +source('/public/workspace/lily/software/ssGSEA/ssgseaMOD.r') +mod <- mod.analyze2(as.matrix(dat.BM),c("Lung_gene","Brain_gene","BMS_update","BMDM_marker","MG_marker"),"/public/workspace/lily/MOD_file/",permN=0) +mod <- as.data.frame(mod) + + +# E-MTAB +# use this because have CCL3 +dat.BM <- readRDS("/public/workspace/lily/metastasis/data/verify/E-MTAB-8659/LCBM_S63_data.RDS") +source('/public/workspace/lily/software/ssGSEA/ssgseaMOD.r') +mod <- mod.analyze2(as.matrix(dat.BM),c("Lung_gene","Brain_gene","BMS_update","BMDM_marker","MG_marker"),"/public/workspace/lily/MOD_file/",permN=0) +mod <- as.data.frame(mod) + + + + + + + + + + + + + + + + +# 2021-12-3 +# use GSE161116 to do some verify +# however GSE161116 is not OK +#============================================================================================================================================== +dat <- read.table("~/metastasis/data/verify/GSE161116/GSE161116_series_matrix.txt",sep="\t",header=T,comment.char="!") +rownames(dat) <- dat$ID_REF +dat$ID_REF <- NULL +info <- as.vector(read.table("~/metastasis/data/verify/GSE161116/sampleinfo.txt"))[-1] +names(info) <- NULL +ann <- data.frame(sample=colnames(dat),patient=sapply(sapply(info,function(x){strsplit(as.vector(x)," ")}),function(y){paste0(y[1],y[2])}), + group=sapply(sapply(info,function(x){strsplit(as.vector(x)," ")}),function(y){paste0(y[3])}) +) + +source('/public/workspace/lily/software/ssGSEA/ssgseaMOD.r') +mod <- mod.analyze2(as.matrix(dat),c("BMS_update","Treg"),"/public/workspace/lily/MOD_file/",permN=0) +mod <- as.data.frame(mod) + + + + +#============================================================================================================================================= +# 2021-12-3 +# use GSE14108 to calculate hallmark and IL1B ,IL1R1 +dat <- readRDS("~/metastasis/data/verify/GSE14108/GSE14108_res.RDS") +source('~/software/ssGSEA/ssgseaMOD.r') +modlist <- gsub("\\.mod","",dir("/public/workspace/lily/MOD_file/HALLMARK/")) +mod <- mod.analyze2(as.matrix(dat),modlist,'/public/workspace/lily/MOD_file/HALLMARK/',permN=0) +mod <- data.frame(mod) + +tmp.res <- mod[,51:100] +tmp.res$IL1B <- as.numeric(dat["IL1B",]) +tmp.res$IL1R1 <- as.numeric(dat["IL1R1",]) + +# calculate spearman +res.f <- data.frame(t(apply(tmp.res,2,function(x){ + c( + cor.test(as.numeric(x),as.numeric(tmp.res[,"IL1B"]),method="spearman")$estimate, + cor.test(as.numeric(x),as.numeric(tmp.res[,"IL1B"]),method="spearman")$p.value + ) +}))) +colnames(res.f) <- c("Rho","pvalue") +res.final <- res.f[which(res.f$pvalue<0.05),] +res.final <- res.final[-c(22,23),] + +p.data <- res.final[order(res.final$Rho,decreasing=T),] + + +#============================================================================================================================================ +library(ggplot2) +p.data$Pathway <- gsub("^HALLMARK_|_norm$","",rownames(p.data)) +p.data$Pathway <- factor(p.data$Pathway,levels=p.data$Pathway) +p.data$logP <- -log2(p.data$pvalue) + +pdf("/public/workspace/lily/Lung2Brain/Version5/Myeloid/GSE14108_IL1B_hallmark.pdf",useDingbats=F) +ggplot(p.data, aes(x = Pathway, y = Rho)) + + geom_hline(yintercept = 0, color = "grey", size = 1) + # 添加y=0的辅助线 + geom_point(aes(color = Pathway,size=logP)) + # 将点的size设置大一些比较好看 + geom_bar(aes(fill = Pathway), stat = "identity", width = 0.2) + # 注意将width宽度设小 + theme(panel.grid.minor = element_blank(), + panel.grid.major.x = element_blank(), # 消除竖条的背景线 + axis.text.x = element_text(angle = 90), + legend.position = "None", + panel.border = element_blank(), + # text = element_text(family = "STHeiti"), # Mac 电脑上绘图展现中文需要此行命令 + plot.title = element_text(hjust = 0.5)) +dev.off() + + + + + + +# 2021-12-3 +# use GSE142620 to do verify +#============================================================================================================================================= +filelist <- dir()[-1] +tmp.res <- read.table(paste0("./",filelist[1]),header=F,sep="\t") +rownames(tmp.res) <- tmp.res$V1 +colnames(tmp.res) <- c("ENSG","GSM4232997") +for(i in 2:length(filelist)){ + tmp <- read.table(paste0("./",filelist[i]),header=F,sep="\t") + if(all(rownames(tmp.res)==tmp$V1)){ + tmp.res <- cbind(tmp.res,tmp[,2]) + colnames(tmp.res)[ncol(tmp.res)] <- strsplit(filelist[i],"_")[[1]][1] + } +} + +# now transform into TPM +res.final <- TCount2TPM(tmp.res,"ENSG",36) + +saveRDS(res.final,file="/public/workspace/lily/metastasis/data/verify/GSE142620/GSE142620_exp.RDS") + +ann <- data.frame(samplename=colnames(res.final),treat=c( + "Ctrl_6d_repA","IL1b_6d_repA","Ctrl_15d_repA","IL1b_15d_repA","Ctrl_21d_repA","IL1b_21d_repA","Ctrl_w6d_repA","IL1b_w6d_repA","Ctrl_w15d_repA","IL1b_w15d_repA","Ctrl_w30d_repA","IL1b_w30d_repA", + "Ctrl_6d_repB","IL1b_6d_repB","Ctrl_15d_repB","IL1b_15d_repB","Ctrl_21d_repB","IL1b_21d_repB","Ctrl_w6d_repB","IL1b_w6d_repB","Ctrl_w15d_repB","IL1b_w15d_repB","Ctrl_w30d_repB","IL1b_w30d_repB", + "Ctrl_6d_repC","IL1b_6d_repC","Ctrl_15d_repC","IL1b_15d_repC","Ctrl_21d_repC","IL1b_21d_repC","Ctrl_w6d_repC","IL1b_w6d_repC","Ctrl_w15d_repC","IL1b_w15d_repC","Ctrl_w30d_repC","IL1b_w30d_repC" +)) + +ann$group <- sapply(strsplit(as.vector(ann$treat),"_"),function(x){x[[1]]}) +ann$time <- sapply(strsplit(as.vector(ann$treat),"_"),function(x){x[[2]]}) +ann$rep <- sapply(strsplit(as.vector(ann$treat),"_"),function(x){x[[3]]}) + +saveRDS(ann,file="/public/workspace/lily/metastasis/data/verify/GSE142620/GSE142620_sampleinfo.RDS") + +#================================================================================================================================================= +# now do analysis +# 2021-12-3 +dat <- readRDS("/public/workspace/lily/metastasis/data/verify/GSE142620/GSE142620_exp.RDS") +ann <- readRDS("/public/workspace/lily/metastasis/data/verify/GSE142620/GSE142620_sampleinfo.RDS") + +source('~/software/ssGSEA/ssgseaMOD.r') +# modlist <- gsub("\\.mod","",dir("/public/workspace/lily/MOD_file/HALLMARK/")) +mod <- mod.analyze2(as.matrix(dat),"BMS_update",'/public/workspace/lily/MOD_file/',permN=0) +mod <- data.frame(mod) + +tmp.res <- mod[,2,drop=F] +tmp.res$group <- ann$group +tmp.res$time<- ann$time +tmp.res$rep <- ann$rep + +#=========================================================================================================================================== +tmp.res$CCL20 <- as.numeric(dat["CCL20",]) +tmp.res$IL1R1 <- as.numeric(dat["IL1R1",]) +tmp.res$IL1B <- as.numeric(dat["IL1B",]) +tmp.res$CEBPD <- as.numeric(dat["CEBPD",]) +tmp.res$class <- "before" +tmp.res$class[grep("w",tmp.res$time)] <- "after" + +# 1. type : +tmp.res$type <- paste0(tmp.res$group,"_",tmp.res$class) +tmp.res$type[grep("Ctrl",tmp.res$type)] <- "Ctrl" + + +# plot result +#=========================================================================================================================================== +pdf("/public/workspace/lily/Lung2Brain/Version5/Myeloid/GSE142620_IL1B_CCL20.pdf",useDingbats=F) +boxplot(CCL20~type,data=tmp.res,FUN=median) +beeswarm::beeswarm(CCL20~type,data=tmp.res,col = 4, pch = 16,main = 'beeswarm + bxplot',add = TRUE) + + +boxplot(BMS_update_norm~type,data=tmp.res,FUN=median) +beeswarm::beeswarm(BMS_update_norm~type,data=tmp.res,col = 4, pch = 16,main = 'beeswarm + bxplot',add = TRUE) + + +dev.off() + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/Fig7_Tcell.r b/Fig7_Tcell.r new file mode 100644 index 0000000..c9f66d3 --- /dev/null +++ b/Fig7_Tcell.r @@ -0,0 +1,1132 @@ + +# 2021-6-23 +# this program is used to analysis T cell +# result plot should be in /public/workspace/lily/Lung2Brain/Version5/T_cell/plot +# 1. T cell TSNE +# 2. bar plot show cell percentage change in different group +# 3. T cell exhausted signature calculate ? + + + + + +#1. T cell subgroup +library(Seurat) +dat <- readRDS("/public/workspace/lily/Lung2Brain/Version5/T_cell/inte9_lympho.RDS") +# Bcell CD4 naive CD8 cytotoxic +# 1189 2744 2697 +# CD8 exhausted/cytotoxic NKcell Plasma +# 3035 702 680 +# Tfh Treg Undefine +# 581 1567 609 + +cols <- c("#54B0E4","#4DAF4A","#F29403","#c6af92","#B3DE69","#00CDD1","#BC9DCC","#5E4FA2","#999999") +DimPlot(dat,group.by="celltype",cols=cols) +pdf("/public/workspace/lily/Lung2Brain/Version5/T_cell/plot/Tcell_tsne.pdf",useDingbats=F) +DimPlot(dat,group.by="celltype",cols=cols) +dev.off() + + + +# 2.cell percentage +library(reshape) +library(ggplot2) +library(ggalluvial) + +dat <- readRDS("/public/workspace/lily/Lung2Brain/Version5/T_cell/inte9_lympho.RDS") +tmp <- table(dat$type_group,dat$celltype) +tmp.f <- tmp[,-grep("Undefine",colnames(tmp))] +res.f <- apply(tmp.f,1,function(x){x/sum(x)}) # + +tmp.dat <- melt(res.f,id="col.names") +colnames(tmp.dat) <- c("Cell_type","Samples","value") +tmp.dat$Samples <- factor(tmp.dat$Samples,level=c("GBM","LCBM","LC")) +pdf("/public/workspace/lily/Lung2Brain/Version5/T_cell/plot/Tcell_celltype.pdf",useDingbats=F) +cols <- c("#54B0E4","#4DAF4A","#F29403","#c6af92","#B3DE69","#00CDD1","#BC9DCC","#5E4FA2") +ggplot(tmp.dat, aes(x = Samples, y = value, fill = Cell_type,stratum = Cell_type, alluvium = Cell_type)) + +geom_stratum(width=0.45) + +geom_flow(alpha = 0.5) + +scale_fill_manual(values = cols)+ +theme_bw() + theme(panel.grid.major = element_blank()) +dev.off() + + + + + + + +# 3. TCGA and GSE161116 to verify +# GSE161116 show converse trend (brain metastasis have less Treg expression [FOXP3,IL2RA]) +# so maybe use GSE126548 to verify +#======================================================================================================== +# Treg signature +# https://genomebiology.biomedcentral.com/articles/10.1186/gb-2006-7-7-r54/figures/2 +# gene[-which(gene%in%rownames(dat))] # change some gene alias +# [1] "BHLHB2" "CEB1" "GPR2" "HLA-DRB3" "G1P2" +# BHLHE40 HERC5 CCR10 no ISG15 +# gene <- c("FOXP3","SDC4","NINJ2",'PTTG1','TIAF1','TRIB1','S100A10','GBP2','GATA3','IL2RA', +# 'BHLHE40','HERC5','CTLA4','TFRC','HLA-DMA','AKAP2','TNFRSF1B','CCR5','CCR10','IL2RB', +# 'SHMT2','HLA-DRB1','HLA-DRB3','TP53INP1','GBP5','EPSTI1','LGALS3','SLAMF1','TRAF1', +# 'LGALS1','S100A4',"ISG15") + +# source('/public/workspace/lily/software/ssGSEA/ssgseaMOD.r') +# mod.generate(gene,"Treg",out="/public/workspace/lily/MOD_file/Treg.mod") # make a mod file + + + +# TCGA data calculate BMS and Treg signature +dat <-readRDS("~/metastasis/data/verify/TCGA_LUAD/TCGA_LUAD_exp.RDS") +source('/public/workspace/lily/software/ssGSEA/ssgseaMOD.r') +mod <- mod.analyze2(as.matrix(dat),c("BMS_update","Treg"),"/public/workspace/lily/MOD_file/",permN=0) +mod <- as.data.frame(mod) + +# plot result +pdf("/public/workspace/lily/Lung2Brain/Version5/T_cell/plot/TCGA_BMS_Treg.pdf",useDingbats=F) +plot(mod$BMS_update_norm,mod$Treg_norm) +abline(lm(mod$BMS_update_norm~mod$Treg_norm),col="red") +# cor.tes() +legend("topright",legend=paste0("rho=",0.37)) +dev.off() + + + + + +# GSE161116 +# GSE161116 show not significant +#=========================================================================================================================================== +# dat <- read.table("~/metastasis/data/verify/GSE161116/GSE161116_series_matrix.txt",sep="\t",header=T,comment.char="!") +# rownames(dat) <- dat$ID_REF +# dat$ID_REF <- NULL +# info <- as.vector(read.table("~/metastasis/data/verify/GSE161116/sampleinfo.txt"))[-1] +# names(info) <- NULL + +# ann <- data.frame(sample=colnames(dat),patient=sapply(sapply(info,function(x){strsplit(as.vector(x)," ")}),function(y){paste0(y[1],y[2])}), +# group=sapply(sapply(info,function(x){strsplit(as.vector(x)," ")}),function(y){paste0(y[3])}) +# ) + +# # calculate BMS +# source('/public/workspace/lily/software/ssGSEA/ssgseaMOD.r') +# mod <- mod.analyze2(as.matrix(dat),c("BMS_update","Treg"),"/public/workspace/lily/MOD_file/",permN=0) +# mod <- as.data.frame(mod) + + +# GSE126548 to verify result +# show correlation positive but not significant +#=========================================================================================================================================== +# load("~/metastasis/data/verify/GSE126548/GSE126548_rpkm.RData") +# dat <- rpkm +# source('/public/workspace/lily/software/ssGSEA/ssgseaMOD.r') +# mod <- mod.analyze2(as.matrix(dat),c("BMS_update","Treg"),"/public/workspace/lily/MOD_file/",permN=0) +# mod <- as.data.frame(mod) + + + + + + + + + + + + + + +# 4. Tumor and T cell analysis +# use CellChat to analysis Tumor cells and Tcells +#=============================================================================================================================== +library(Seurat) +library(CellChat) + +tumor <- readRDS("/public/workspace/lily/Lung2Brain/Version5/Fig4/Data/LCBM_sub.tumor.RDS") +tumor$celltype <- tumor$group +tmp <- readRDS("/public/workspace/lily/Lung2Brain/Version5/T_cell/inte9_lympho.RDS") +tcell <- subset(tmp,cells=which(tmp$celltype%in%c("CD4 naive","CD8 cytotoxic","CD8 exhausted/cytotoxic","NKcell","Tfh","Treg")&tmp$type_group=="LCBM")) +dat <- merge(tumor,tcell) + +# set a CellChat object +# Run CellChat +data.input <- GetAssayData(dat, assay = "RNA", slot = "data") # normalized data matrix +meta <- data.frame(dat@meta.data) # create a dataframe of the cell labels +cellchat <- createCellChat(object = data.input, meta = meta, group.by = "celltype") +cellchat <- setIdent(cellchat, ident.use = "celltype") # set "labels" as default cell identity +levels(cellchat@idents) + +CellChatDB <- CellChatDB.human +# use all DB +CellChatDB.use <- CellChatDB +# set the used database in the object +cellchat@DB <- CellChatDB.use +cellchat <- subsetData(cellchat) # This step is necessary even if using the whole database +future::plan("multiprocess", workers = 8) # do parallel + +##################################################################################################### +cellchat <- identifyOverExpressedGenes(cellchat) +cellchat <- identifyOverExpressedInteractions(cellchat) +# project gene expression data onto PPI network (should use) +cellchat <- projectData(cellchat, PPI.human) + +cellchat <- computeCommunProb(cellchat) # 1W cells 5min +# Filter out the cell-cell communication if there are only few number of cells in certain cell groups +cellchat <- filterCommunication(cellchat, min.cells = 10) +cellchat <- computeCommunProbPathway(cellchat) +# Calculate the aggregated cell-cell communication network +cellchat <- aggregateNet(cellchat) + +saveRDS(cellchat,file="/public/workspace/lily/Lung2Brain/Version5/T_cell/LCBM_tumor_tcell.cellchat.RDS") + +#=================================================================================================== + + + + + + + + + +################################################################################################################################ +# 5. another way to analysis T cell and Tumor cell +# 2021-6-25 +# dat <- read.table("/public/workspace/lily/Lung2Brain/Version5/PairsLigRec.txt",sep="\t",header=T) +library(Seurat) + +tumor <- readRDS("/public/workspace/lily/Lung2Brain/Version5/Fig4/Data/LCBM_sub.tumor.RDS") +tumor$celltype <- tumor$group +tmp <- readRDS("/public/workspace/lily/Lung2Brain/Version5/T_cell/inte9_lympho.RDS") +tcell <- subset(tmp,cells=which(tmp$celltype%in%c("CD4 naive","CD8 cytotoxic","CD8 exhausted/cytotoxic","NKcell","Tfh","Treg")&tmp$type_group=="LCBM")) +dat <- merge(tumor,tcell) + +gene <- list() +group <- names(table(dat$celltype)) +for(i in 1: length(group)){ + tmp <- NULL + if(i %in% c(1:3,7:9)){ + tmp <- FindMarkers(tcell,assay="RNA",ident.1=group[i],min.pct=0.1,only.pos=T,group.by="celltype",logfc.threshold=0.1) + } + + if(i %in% c(4:6)){ + tmp <- FindMarkers(tumor,assay="RNA",ident.1=group[i],min.pct=0.1,only.pos=T,group.by="celltype",logfc.threshold=0.1) + } + + gene[[i]] <- tmp + names(gene)[i] <- gsub(" |/",".",group[i]) + +} + +ann <- read.table("/public/workspace/lily/Lung2Brain/Version5/PairsLigRec.txt",sep="\t",header=T) + + +ann.f <- ann[which(ann$Receptor.ApprovedSymbol%in%rownames(gene$Treg)&ann$Ligand.ApprovedSymbol%in%rownames(gene$group09)),] +ann.f[,c(1,2,4)] + + + +ann.f <- ann[which(ann$Receptor.ApprovedSymbol%in%rownames(gene$Treg)&ann$Ligand.ApprovedSymbol%in%rownames(gene$CD8.cytotoxic)),] + + + + + + + + + + + + +############################################################################################################################################### +# plot T cell results +# 1. scatter plot show correlation in GSE14108 Treg and BMS update +# 2. scatter plot show correlation in GSE14108 Trrg and Lung_gene signature +# 3. circle plot show subgroup interation number with Treg +# 4. heatmap show in group47 and grou09 interaction with +# 5. correlation for LGAS9 and Treg +#================================================================================================================================= +# 2021-7-7 +# 1 and 2 GSE 14108 data to verify +dat.BM <- readRDS("/public/workspace/lily/metastasis/data/verify/GSE14108/GSE14108_res.RDS") +source('/public/workspace/lily/software/ssGSEA/ssgseaMOD.r') +mod <- mod.analyze2(as.matrix(dat.BM),c("Treg","Lung_gene","Brain_gene","BMS_update"),"/public/workspace/lily/MOD_file/",permN=0) +mod <- as.data.frame(mod) + +pdf("/public/workspace/lily/Lung2Brain/Version5/T_cell/plot/GSE14108_LCBM_treg_lung_BMS.pdf",useDingbats=F) +plot(mod$BMS_update_norm,mod$Treg_norm,mian="BMS with Treg") +abline(lm(mod$BMS_update_norm~mod$Treg_norm),col="red") +# cor.tes() +legend("topright",legend=paste0("rho=",0.455," pvalue=",0.016)) +############################################################# +plot(mod$Lung_gene_norm,mod$Treg_norm,mian="Lung with Treg") +abline(lm(mod$Lung_gene_norm~mod$Treg_norm),col="red") +# cor.tes() +legend("topright",legend=paste0("rho=",0.574," pvalue=",0.001)) + +dev.off() + + + + +# 3. and 4. CellChat result show circle plot +library(Seurat) +library(Cellchat) + +# analysis result and check +# 2021-7-7 +library(Seurat) +library(CellChat) + +dat <- readRDS("/public/workspace/lily/Lung2Brain/Version5/T_cell/LCBM_tumor_tcell.cellchat.RDS") +groupSize <- table(dat@idents) + +# 3. circle plot show cell chat interaction +netVisual_circle(dat@net$count, vertex.weight = groupSize, weight.scale = T, label.edge= F, title.name = "Number of interactions") + +tmp <- dat@net$count +tmp.f <- tmp[c(4,6,9),c(4,6,9)] +tmp.f[c(1:2),c(1:2)] <- 0 +tmp.f[3,3] <- 0 + +rownames(tmp.f) <- paste0(rownames(tmp.f),".source") +colnames(tmp.f) <- paste0(colnames(tmp.f),".target") +groups <- rep(c("group09","group47","Treg"),2) +names(groups) <- c(rownames(tmp.f),colnames(tmp.f)) + + +netVisual_chord_cell_internal(tmp.f,sources.use=rownames(tmp.f),targets.use=colnames(tmp.f),group=groups,big.gap=20,small.gap=0) + + + + +# 4. pheatmap show result +#=================================================================================================================================== +# netVisual_bubble(dat, sources.use = c(4,5,6), targets.use = c(9), remove.isolate = FALSE) +tmp <- netVisual_bubble(dat, sources.use = c(4,5,6), targets.use = c(9), remove.isolate = FALSE,return.data=T)$communication +tmp.res <- tmp[,c("prob","source.target","interaction_name_2")] +rs <- reshape2::dcast(tmp.res,source.target~interaction_name_2,value.var="prob") +rownames(rs) <- rs$source.target +rs$source.target <- NULL +rs <- t(rs) +rs <- data.frame(rs) + +tmp09 <- rs[which(rs$group09....Treg>0&is.na(rs$group47....Treg)),] +tmp09 <- tmp09[order(tmp09$group09....Treg,decreasing=T),] + +tmp47 <- rs[which(rs$group47....Treg>0&is.na(rs$group09....Treg)),] +tmp47 <- tmp47[order(tmp47$group47....Treg,decreasing=T),] + + +pdf("/public/workspace/lily/Lung2Brain/Version5/T_cell/plot/LCBM_subTumor_Treg_heatmap.pdf",useDingbats=F) +pheatmap::pheatmap(tmp47,na_col="grey",cluster_rows=F,cluster_cols=F) +pheatmap::pheatmap(tmp09,na_col="grey",cluster_rows=F,cluster_cols=F) +dev.off() + + + +# 4.1 add some data verify +library(Seurat) +dat <- readRDS("/public/workspace/lily/Lung2Brain/Version5/Fig4/Data/LCBM_sub.tumor.RDS") +DefaultAssay(dat) <- "RNA" + +pdf("/public/workspace/lily/Lung2Brain/Version5/T_cell/plot/LCBM_subTumor_LGALS9.pdf",useDingbats=F) +barplot(c(0.2554715,0.2045145,0.08139221),ylim=c(0,0.3),names=c("group09","group16","group47"),main="LGALS9 expression") +dev.off() + + + + + + + +# 5. LGALS9 correlation with Treg signature +# gene show no significant with Gene +# however gene correlation with gene show signifiacant +dat.BM <- readRDS("/public/workspace/lily/metastasis/data/verify/GSE14108/GSE14108_res.RDS") +source('/public/workspace/lily/software/ssGSEA/ssgseaMOD.r') +mod <- mod.analyze2(as.matrix(dat.BM),c("Treg","Lung_gene","Brain_gene","BMS_update"),"/public/workspace/lily/MOD_file/",permN=0) +mod <- as.data.frame(mod) +mod$LGALS9 <- as.numeric(as.vector(dat.BM["LGALS9",])) + +# purity +scores <- read.table("/public/workspace/lily/Lung2Brain/Version5/Fig4/Data/GSE14108_estimate_score.gct",skip = 2,header = T) +mod$purity <- as.numeric(as.vector(scores[4,-c(1,2)])) + + + +pdf("/public/workspace/lily/Lung2Brain/Version5/T_cell/plot/GSE14108_LCBM_FOXP3.pdf",useDingbats=F) +plot(mod$LGALS9,mod$FOXP3,mian="LGALS9 with FOXP3") +abline(lm(mod$FOXP3~mod$LGALS9),col="red") +# cor.tes() +legend("topright",legend=paste0("rho=",0.39," pvalue=",0.042)) + + +plot(mod$LIF,mod$FOXP3,mian="LGALS9 with FOXP3") +abline(lm(mod$FOXP3~mod$LIF),col="red") +# cor.tes() +legend("topright",legend=paste0("rho=",0.53," pvalue=",0.004)) + +dev.off() + + + + + + +#================================================================================================================================================== +# 2021-8-15 +# treg verify in TCGA and BM data (GSE14108) +# https://www.jianshu.com/p/0baac4c52ac8 +#================================================================================================================================================== +source("~/software/Cibersort_R.R") +result1 <- CIBERSORT('~/software/LM22.txt','~/software/GSE14108_exp.txt', perm = 100, QN = T) +result2 <- CIBERSORT('~/software/LM22.txt','~/metastasis/data/verify/TCGA_LUAD/LUAD_RNAseq_Exp.txt', perm = 100, QN = T) +saveRDS(result2,file="~/metastasis/data/verify/TCGA_LUAD/TCGA_LUAD_cibersort.RDS") + +# cibersort result +# 2021-8-18 use cibersortx result +lcbm <- read.table("~/tmp/CIBERSORTx_GSE14108_Results.txt",sep="\t",header=T) +luad <- read.table("~/tmp/CIBERSORTx_TCGA_LUAD_Results.txt",header=T,sep="\t") +pdf("/public/workspace/lily/Lung2Brain/Version5/T_cell/plot/GSE14108_TCGA_treg.cibersort.pdf",useDingbats=F) +boxplot(lcbm[,10],luad[,10],outline=F,names=c("LCBM","TCGA_LUAD")) +legend("topright",legend=paste0(" pvalue<",0.001)) +dev.off() + + + +# Treg signature +luad <- readRDS("~/metastasis/data/verify/TCGA_LUAD/TCGA_LUAD_exp.RDS") +source('/public/workspace/lily/software/ssGSEA/ssgseaMOD.r') +mod.luad <- mod.analyze2(as.matrix(luad),c("Treg"),"/public/workspace/lily/MOD_file/",permN=0) +mod.luad <- as.data.frame(mod.luad) + +lcbm <- readRDS("/public/workspace/lily/metastasis/data/verify/GSE14108/GSE14108_res.RDS") +source('/public/workspace/lily/software/ssGSEA/ssgseaMOD.r') +mod.lcbm <- mod.analyze2(as.matrix(lcbm),c("Treg"),"/public/workspace/lily/MOD_file/",permN=0) +mod.lcbm <- as.data.frame(mod.lcbm) + + +dat.BM <- readRDS("/public/workspace/lily/metastasis/data/verify/E-MTAB-8659/LCBM_S63_data.RDS") +source('/public/workspace/lily/software/ssGSEA/ssgseaMOD.r') +mod.mtab <- mod.analyze2(as.matrix(dat.BM),c("Treg"),"/public/workspace/lily/MOD_file/",permN=0) +mod.mtab <- as.data.frame(mod.mtab) + +# cibersort result + +pdf("/public/workspace/lily/Lung2Brain/Version5/T_cell/plot/GSE14108_TCGA_treg.sig.pdf",useDingbats=F) +boxplot(mod.lcbm[,2],mod.luad[,2],outline=F,names=c("LCBM","TCGA_LUAD")) +legend("topright",legend=paste0(" pvalue= ",0.076)) +dev.off() + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +############################################################################################################################################################# +# 2021-9-14 +# analysis T cells cytokines +# 2021-9-23 +# 2021-12-9 use BMS.group new classify +# analysis +#============================================================================================================================================================ +library(Seurat) +lcbm <- readRDS("/public/workspace/lily/Lung2Brain/Version5/Data/Tumor_pur/pur_LCBM_clust.RDS") +DefaultAssay(lcbm) <- "RNA" +lcbm.sub <- subset(lcbm,cells=which(lcbm$BMS.group%in%c("high","low"))) +lcbm.sub$cell.type <- lcbm.sub$BMS.group + +# myeloid +mye <- readRDS("/public/workspace/lily/Lung2Brain/Version5/Myeloid/inte9_myeloid.RDS") +DefaultAssay(mye) <- "RNA" +mye.lcbm <- subset(mye,cells=which(mye$type_group=="LCBM"&mye$celltype%in%c("MDM","MG","Monocyte"))) +mye.lcbm$cell.type <- mye.lcbm$celltype + +# lymphy +lym <- readRDS("/public/workspace/lily/Lung2Brain/Version5/T_cell/inte9_lympho.RDS") +DefaultAssay(lym) <- "RNA" +lym.lcbm <- subset(lym,cells=which(lym$type_group=="LCBM"&lym$celltype%in%c("Bcell","NKcell","Plasma"))) +lym.lcbm$cell.type <- lym.lcbm$celltype + +# MES +tmp <- readRDS("/public/workspace/lily/Lung2Brain/inte7/Data/inte7_ann.RDS") +DefaultAssay(tmp) <- "RNA" +tmp.lcbm <- subset(tmp,cells=which(tmp$type_group=="LCBM"&tmp$type%in%c("Endothelial","Fibroblast","Oligodendrocyte"))) +tmp.lcbm$cell.type <- tmp.lcbm$type + +dat.res <- merge(x=lcbm.sub,y=c(mye.lcbm,lym.lcbm,tmp.lcbm)) + +# also analysis MDM and MG +# dat <- readRDS("/public/workspace/lily/Lung2Brain/Version5/Myeloid/SubTumor/LCBM_subtumor_myeloid.RDS") +# tmp <- readRDS("/public/workspace/lily/Lung2Brain/Version5/Data/LCBM_celltype_type.RDS") # use all cell type # 2021-12-9 think this is not ok +# dat <- subset(tmp,cells=which(tmp$celltype%in%c("Endothelial","Bcell","Fibroblast","MDM","MG","Monocyte","NKcell", +# "group09","group16","group47","Oligodendrocyte","Plasma"))) +# DefaultAssay(dat) <- "RNA" +# dat@active.ident <- factor(dat$celltype) + +dat <- dat.res +DefaultAssay(dat) <- "RNA" +dat@active.ident <- factor(dat$cell.type) + +features <- c("CD274","LGALS9","PVR","IDO1","CCL28","CCL5","CCL8","CCL22","CCL20", +"IL6","CXCL10","CXCL9","CCL3","IFNG","CXCL11","CCL4","CCL11","IL22","CCL17","IL4","IL17F","IL5", +"IL13","IL2","IL17A","IL21","CCL2","CXCL5","CXCL1") + +features <- c("LGALS9","IDO1","CCL28","CCL8","CCL22","CCL20", +"IL6","CXCL10","CXCL9","IFNG","CXCL11","IL22","IL4","IL17F","IL5", +"IL13","IL2","IL17A","IL21","CCL2","CXCL5","CXCL1") + +features <- c("IL2","IFNG", +"CXCL9","CXCL10","CXCL11", +"CCL22","CCL20","CCL28", +"IDO1","LGALS9", +"CXCL1","IL6","CCL8","CCL2" +) + + + + +percent_feature <- function(dat,genelist,group){ + res.list <- c() + for(i in 1:length(genelist)){ + dat$tmp_gene <- ifelse(dat[["RNA"]]@data[genelist[i],]>0,"Y","N") + if(all(dat$tmp_gene=="N")){ + res.list[[i]] <- rep(0,length=length(table(dat@meta.data[,group]))) + }else{ + res.list[[i]] <- apply(table(dat$tmp_gene,dat@meta.data[,group]),2,function(x){x/sum(x)})[2,] + } + + names(res.list)[i] <- genelist[i] + } + return(res.list) +} + +res.dat.list <- percent_feature(dat,features,group="cell.type") + +list2mat <- function(res.list){ + res.dat <- matrix(unlist(res.list),ncol=length(res.list[[1]]),byrow=T) + rownames(res.dat) <- names(res.list) + colnames(res.dat) <- names(res.list[[1]]) + return(res.dat) +} + +mat.lcbm <- list2mat(res.dat.list) +# mat.f <- mat.lcbm[-which(rowSums(mat.lcbm)==0),] +# # do some prepare work +# mat.f[which(mat.f[]<0.01)] <- 0 +# mat <- mat.f[-which(rowSums(mat.f)==0),] + +mat <- mat.lcbm[,c("NKcell","Bcell","Plasma","Fibroblast","Endothelial","Oligodendrocyte","high","low","MDM","MG","Monocyte")] + +pdf("/public/workspace/lily/Lung2Brain/Version5/T_cell/LCBM_Myeloid_cytokine.pdf",useDingbats=F) +library(pheatmap) +pheatmap(mat,scale="row",cluster_rows=F,cluster_cols=F,color=colorRampPalette(c('steelblue','white',"#E41A1C"))(100),gaps_col=c(3,6,8)) +dev.off() + +# library(pheatmap) +# pdf("/public/workspace/lily/Lung2Brain/Version5/T_cell/LCBM_Myeloid_cytokine.pdf",useDingbats=F) +# pheatmap(mat.lcbm[,1:5],scale="row",cellwidth=10,cellheight=10,color=colorRampPalette(c('steelblue','white',"#E41A1C"))(100)) +# dev.off() + + +# use other data to verify +# 1. GSE14108 +dat.BM <- readRDS("/public/workspace/lily/metastasis/data/verify/GSE14108/GSE14108_res.RDS") +#dat.BM <- dat.BM[,20:28] +source('/public/workspace/lily/software/ssGSEA/ssgseaMOD.r') +mod <- mod.analyze2(as.matrix(dat.BM),c("BMS_update","BMDM_marker","MG_marker","Treg"),"/public/workspace/lily/MOD_file/",permN=0) +mod <- as.data.frame(mod) + +# features <- c("CD274","LGALS9","PVR","VTCN1","IDO1","IDO2","CCL28","CCL5","CCL8","CCL22","CCL20") + +features <- c("IL2","IFNG", +"CXCL9","CXCL10","CXCL11", +"CCL22","CCL20", +"IDO1","LGALS9", +"CXCL1","IL6","CCL8","CCL2","FOXP3","CCL28","CEBPD" +) + + + +res.list <- list() +for(i in 1:length(features)){ + if(features[i]%in%rownames(dat.BM)){ + gene_exp <- as.numeric(dat.BM[features[i],]) + tmp <- apply(mod[,5:8],2,function(x){ + c(cor.test(x,gene_exp,method="spearman")$estimate, cor.test(x,gene_exp,method="spearman")$p.value) + }) + }else{ + tmp <- c(0,0) + } + res.list[[i]] <- tmp + names(res.list)[i] <- features[i] +} + + +# 2. E-MTAB +# use this because have CCL3 + +dat.BM <- readRDS("/public/workspace/lily/metastasis/data/verify/E-MTAB-8659/LCBM_S63_data.RDS") +source('/public/workspace/lily/software/ssGSEA/ssgseaMOD.r') +mod <- mod.analyze2(as.matrix(dat.BM),c("BMS_update","BMDM_marker","MG_marker","Treg"),"/public/workspace/lily/MOD_file/",permN=0) +mod <- as.data.frame(mod) +# features <- c("CD274","LGALS9","PVR","VTCN1","IDO1","IDO2","CCL28","CCL5","CCL8","CCL22","CCL20") +features <- c("IL2","IFNG","CEBPD", +"CXCL9","CXCL10","CXCL11", +"CCL22","CCL20", +"IDO1","LGALS9", +"CXCL1","IL6","CCL8","CCL2","FOXP3","PDCD1","CTLA4","LAG3","CCL28" +) + +res.list <- list() +for(i in 1:length(features)){ + if(features[i]%in%rownames(dat.BM)){ + gene_exp <- as.numeric(dat.BM[features[i],]) + tmp <- apply(mod[,5:8],2,function(x){ + c(cor.test(x,gene_exp,method="spearman")$estimate, cor.test(x,gene_exp,method="spearman")$p.value) + }) + }else{ + tmp <- c(0,0) + } + res.list[[i]] <- tmp + names(res.list)[i] <- features[i] +} + + + + + + + +#=================================================================================================================================================== +# 2021-9-24 +# use immunity Treg signature +# seem not every good +#=================================================================================================================================================== +# tmp <- as.vector(read.table("/public/workspace/lily/tmp/tumor.Treg")[,1]) +# source('/public/workspace/lily/software/ssGSEA/ssgseaMOD.r') +# mod.generate(tmp,"tumor.Treg",out="/public/workspace/lily/MOD_file/tumor.Treg.mod") # make a mod file + + + +# 2021-9-24 +# plot CCL20 result +LUAD <- readRDS("/public/workspace/lily/metastasis/data/verify/TCGA_LUAD/TCGA_LUAD_exp.RDS") +luad_ann <- readRDS("/public/workspace/lily/metastasis/data/verify/TCGA_LUAD/TCGA_LUAD_anno.RDS") + +rownames(luad_ann) <- gsub("-",".",rownames(luad_ann)) +luad_ann <- luad_ann[colnames(LUAD),] + +# LUAD three +luad_ann$type <- rep("Unknow",nrow(luad_ann)) +luad_ann[which(LUAD["CCL20",] < as.numeric(quantile(LUAD["CCL20",],0.33))),"type"] <- "CCL20.Low" +luad_ann[which(LUAD["CCL20",] > as.numeric(quantile(LUAD["CCL20",],0.67))),"type"] <- "CCL20.High" + +library(ggplot2) +library(survminer) +library(survival) + + +pdf("/public/workspace/lily/Lung2Brain/Version5/T_cell/CCL20_surv.pdf",useDingbats=F) +surv <- Surv(luad_ann$OS.time,luad_ann$OS) +km <- survminer::surv_fit(surv~type,data=luad_ann) +ggsurvplot(km,pval=T,palette=c(rgb(251,176,59,maxColorValue = 255),rgb(140,198,63,maxColorValue = 255),'grey'),surv.median.line='hv',xlim=c(0,2000),xscale='d_m',break.time.by =304.37, + title="LUAD_sig_three",xlab=" Overall survival (months)")+labs(title="LUAD_sig_three") + + +surv <- Surv(luad_ann$RFS_time,luad_ann$RFS) +km <- survminer::surv_fit(surv~type,data=luad_ann) +ggsurvplot(km,pval=T,palette=c(rgb(251,176,59,maxColorValue = 255),rgb(140,198,63,maxColorValue = 255),'grey'),surv.median.line='hv',xlim=c(0,2000),xscale='d_m',break.time.by =304.37, + title="LUAD_sig_three",xlab=" RF survival (months)")+labs(title="LUAD_sig_three") + +dev.off() + +# calculate P-value +pairwise_survdiff(Surv(OS.time,OS)~type,data=luad_ann) # 0.043 +pairwise_survdiff(Surv(RFS_time,RFS)~type,data=luad_ann) # 0.061 + + + + +# CCL20 expression with Lung_signature +dat.BM <- readRDS("/public/workspace/lily/metastasis/data/verify/E-MTAB-8659/LCBM_S63_data.RDS") +source('/public/workspace/lily/software/ssGSEA/ssgseaMOD.r') +mod <- mod.analyze2(as.matrix(dat.BM),c("Lung_gene","Brain_gene","BMS_update","BMDM_marker","MG_marker","Treg"),"/public/workspace/lily/MOD_file/",permN=0) +mod <- as.data.frame(mod) +mod$CCL20 <- as.numeric(dat.BM["CCL20",]) + +pdf("/public/workspace/lily/Lung2Brain/Version5/T_cell/plot/E_MTAB_LCBM_CCL20.pdf",useDingbats=F) +plot(mod$CCL20,mod$Lung_gene_norm,mian="CCL20 with Lung sig.") +abline(lm(mod$Lung_gene_norm~mod$CCL20),col="red") +# cor.test(mod$CCL20,mod$Lung_gene_norm,method="spearman") +legend("topright",legend=paste0("rho=",0.30," pvalue=",0.019)) + + +plot(mod$CCL20,mod$BMS_update_norm,mian="CCL20 with BMS sig.") +abline(lm(mod$BMS_update_norm~mod$CCL20),col="red") +# cor.test(mod$CCL20,mod$Brain_gene_norm,method="spearman") +legend("topright",legend=paste0("rho=",0.26," pvalue=",0.037)) + + +plot(mod$CCL20,mod$Treg_norm,mian="CCL20 with BMS sig.") +abline(lm(mod$Treg_norm~mod$CCL20),col="red") +# cor.test(mod$CCL20,mod$Treg_norm,method="spearman") +legend("topright",legend=paste0("rho=",0.13," pvalue=",0.30)) + + +dev.off() + + + + + +#==================================================================================================================================================== +# CCL20 in LUAD and LCBM +# 2021-9-26 + +dat <- readRDS("~/metastasis/data/verify/GSE14108/GSE14108_res.RDS") +tcga <- readRDS("~/metastasis/data/verify/TCGA_LUAD/TCGA_LUAD_exp.RDS") + +# housing-keeping +grep("ACTB",rownames(dat),value=T) +grep("ACTB",rownames(tcga),value=T) + +dat.f <- apply(dat,2,function(x){x/x[149]}) +tcga.f <- apply(tcga,2,function(x){x/x[8176]}) + + +pdf("/public/workspace/lily/Lung2Brain/Version5/T_cell/CCL20_LUAD_LCBM.pdf",useDingbats=F) +boxplot(dat.f["CCL20",],tcga.f["CCL20",],outline=F,names=c("LCBM","LUAD"),main="CCL20 (ACTB adjust)") +dev.off() + + + + + + + + + + + + + + + + + + + + +#============================================================================================================================================= +# CCR6 in T cell subset expression percentage +library(Seurat) +tmp <- readRDS("/public/workspace/lily/Lung2Brain/Version5/Data/LCBM_celltype_type.RDS") # use all cell type +dat <- subset(tmp,cells=which(tmp$celltype%in%c("CD4 naive","CD8 cytotoxic","CD8 exhausted/cytotoxic","Tfh","Treg"))) +DefaultAssay(dat) <- "RNA" +dat@active.ident <- factor(dat$celltype) + +features <- "CCR4" +percent_feature <- function(dat,genelist,group){ + res.list <- c() + for(i in 1:length(genelist)){ + dat$tmp_gene <- ifelse(dat[["RNA"]]@data[genelist[i],]>0,"Y","N") + if(all(dat$tmp_gene=="N")){ + res.list[[i]] <- rep(0,length=length(table(dat@meta.data[,group]))) + }else{ + res.list[[i]] <- apply(table(dat$tmp_gene,dat@meta.data[,group]),2,function(x){x/sum(x)})[2,] + } + + names(res.list)[i] <- genelist[i] + } + return(res.list) +} + +res.dat.list <- percent_feature(dat,features,group="celltype") + +pdf("/public/workspace/lily/Lung2Brain/Version5/T_cell/plot/Tcell_subset_CCR6.pdf",useDingbats=F) +barplot(sort(res.dat.list[[1]]),las=2) +dev.off() + + + + + +#======================================================================================================= +# TFs activity in different group cells. +# 2021-9-24 +# plot in Fig4.R +# 2021-12-4 +# use new cell type group to calculate +library(Seurat) +dat <- readRDS("/public/workspace/lily/Lung2Brain/Version5/Data/Tumor_pur/pur_LCBM_clust.RDS") +################################################################################################################## +# 2021-8-9 +# use LCBM sub tumor RDS to calculate TFs +# tf.dat <- read.table("/public/workspace/lily/Lung2Brain/Version5/Pyscenic/LCBM_subtumor/step3.auc_mtx.csv",sep=",",header=T) # do not OK +tf.dat <- read.table("/public/workspace/lily/Lung2Brain/Version5/Pyscenic/LCBM/step3.auc_mtx.csv",sep=",",header=T) +rownames(tf.dat) <- tf.dat$Cell +tf.dat$Cell <- NULL +colnames(tf.dat) <- gsub("\\.\\.\\.$","",colnames(tf.dat)) +tf.dat.f <- tf.dat[which(rownames(tf.dat)%in%colnames(dat)),] + +all(rownames(tf.dat.f)==rownames(dat@meta.data)) + +tf.dat.f$BMS.group <- dat$BMS.group +tmp <- aggregate(CEBPD~BMS.group,data=tf.dat.f,FUN=mean) + +pdf("/public/workspace/lily/Lung2Brain/Version5/T_cell/plot/CEBPD_TFs.pdf",useDingbats=F) +barplot(c(0.11435307,0.08096245),names=c("BMS-high","BMS-low"),ylim=c(0,0.2),main="CEBPD activity") +dev.off() + + + + + + + + + +########################################################################################################################################################## +# 2021-9-26 +# do some verify for Treg enrichment +# 0. check by GSE131907 data # 2021-9-26 result show Treg percentage is not high in BM +# 1. check in 123902 +#========================================================================================================================================================= +# 0. GSE131907 +library(Seurat) +tmp <- readRDS("/public/workspace/lily/Lung2Brain/GSE131907/GSE131907_all_cell_v12_3.RDS") +dat <- subset(tmp,cells=which(tmp$Cell_type=="T lymphocytes")) + + + + + + + + + +library(Seurat) +tmp <- readRDS("/public/workspace/lily/metastasis/data/verify/GSE123904/GSE123902.RDS") +DefaultAssay(tmp) <- "RNA" +FeaturePlot(tmp,features=c("CD3D","CD3E"),label=T) +VlnPlot(tmp,features=c("CD3D","CD3E"),pt.size=0) +tmp.dat <- subset(tmp,cells=which(tmp$seurat_clusters%in%c(0,1,3,5,8,9,12,17))) + +# re-integration +inte.list <- list() +samples <- unique(tmp.dat$sample) +for(i in 1:length(samples)){ + tmp <- subset(tmp.dat,cells=which(tmp.dat$sample==samples[i])) + DefaultAssay(tmp) <- "RNA" + inte.list[[i]] <- tmp +} + +integration.anchors <- FindIntegrationAnchors(object.list = inte.list,k.filter=50,k.score=50) +inte <- IntegrateData(anchorset = integration.anchors) +#FindVariableFeatures +inte <- FindVariableFeatures(inte) +##Scaling the integrateda +all.genes <- rownames(inte) +inte <- ScaleData(inte, features = all.genes) +#PCA +inte <- RunPCA(inte) +#cluster +inte <- FindNeighbors(inte) +inte <- FindClusters(inte) +#TSNE +# if Umap can not use +inte <- RunTSNE(inte) + +DefaultAssay(inte) <- "RNA" +FeaturePlot(inte,features=c("FOXP3","IL2RA")) +VlnPlot(inte,features=c("FOXP3","IL2RA"),pt.size=0) + + + +# get Tregs +FeaturePlot(tmp.dat,features=c("FOXP3","IL2RA"),label=T) + + + + + + + + + + + + + + + + + + + + + +#============================================================================================================================================== +# 2021-10-4 +# GSE74639 analysis CTC with CCL20 +library(readxl) +tmp <- read_excel("/public/workspace/lily/metastasis/data/verify/GSE74639/GSE74639_readCounts.xls") +ann <- read_excel("/public/workspace/lily/metastasis/data/verify/GSE74639/GSE74639_annotation_for_readCounts.xls") +colnames(tmp)[1] <- "ID" +tmp.dat <- merge(tmp,ann[,c(1,2,4)],by="ID") +colnames(tmp.dat)[18] <- "Entrez.ID" +# tmp.dat$ID <- NULL + +# counts to expreesion translation +anno <- read.table("~/REF/hg19_gene_ann.txt",sep="\t",header=T) +anno$length <- anno$Gene.end..bp. -anno$Gene.start..bp. +tmp.res <- merge(anno[,c(6,7)],tmp.dat,by.x="NCBI.gene..formerly.Entrezgene..ID",by.y="Entrez.ID") +tmp.res$NCBI.gene..formerly.Entrezgene..ID <- NULL +tmp.res$ID <- NULL +# transform into expression data +################################################################################################################ +# tmp.res[,2] <- NULL +# +tmp <- t(apply(tmp.res[,-18],1,function(x){x/x[1]}))*10^3 +res.f <- apply(tmp,2,function(x){x/sum(x)})*10^6 +res.f <- data.frame(res.f) +res.f$gene_name <- tmp.res$symbol +res.data <- aggregate(.~gene_name,data=res.f,FUN=median) +rownames(res.data) <- res.data$gene_name +res.data[,c(1,2)] <- NULL +saveRDS(as.matrix(res.data),file="/public/workspace/lily/metastasis/data/verify/GSE74639/GSE74639_exp.RDS") + +#============================================================================================================================================= +dat <- readRDS("/public/workspace/lily/metastasis/data/verify/GSE74639/GSE74639_exp.RDS") + + + +############################################################################################################################ +############################################################################################################################ +############################################################################################################################ +# 1. use GSE123902 DTC data +# 2. use GSE123902 early parimary and metastasis + + +# 1. GSE123902 DTC data +dat <- readRDS("/public/workspace/lily/Lung2Brain/Version5/Fig5/GSE123904_DTC2Macro.RDS") +DefaultAssay(dat) <- "RNA" +dat$CCL20 <- ifelse(dat[["RNA"]]@data["CCL20",]>0,"Y","N") + +tmp <- apply(table(dat$CCL20,dat$group),2,function(x){x/sum(x)}) + +pdf("/public/workspace/lily/Lung2Brain/Version5/T_cell/CCL20_GSE123902_DTC_percent.pdf",useDingbats=F) +barplot(tmp[2,],main="GSE123902.DTC",ylab="CCL20 percentage") +dev.off() + + + +# 2. GSE123902 early primary and advanced +# 2021-10-5 +GSE123902 <- readRDS("/public/workspace/lily/Lung2Brain/inte7/Pyscenic/GSE123902/primary_LUAD.RDS") +GSE123902$group <- "Early" +GSE123902$group[which(GSE123902$sample=="LX675")] <- "Advanced" + +GSE123902$CCL20 <- ifelse(GSE123902[["RNA"]]@data["CCL20",]>0,"Y","N") + +tmp <- apply(table(GSE123902$CCL20,GSE123902$group),2,function(x){x/sum(x)}) + +pdf("/public/workspace/lily/Lung2Brain/Version5/T_cell/CCL20_GSE123902_Primary_percent.pdf",useDingbats=F) +barplot(tmp[2,],main="GSE123902.Primary",ylab="CCL20 percentage") +dev.off() + + + + + + + + + + + + + + + + + + + + + + +#============================================================================================================================================ +# 2021-12-16 +# use E-MTAB calculate CCL20 with BMS + +plot(mod$BMS_update_norm,as.numeric(dat.BM["CCL20",]),mian="CCL20 with BMS sig.",xlab="BMS score",ylab="Expression value of CCL20") +abline(lm(as.numeric(dat.BM["CCL20",])~mod$BMS_update_norm),col="red") +legend("topleft",legend=paste0("rho=",0.26," pvalue=",0.037),bty="n") + + + + + + + + + + + + + + + + +#=========================================================================================================================================== +# 2021-12-16 +# check CCL20 expression in malignant cells and Myeloid cells +#=========================================================================================================================================== +library(Seurat) +dat <- readRDS("/public/workspace/lily/Lung2Brain/inte7/Data/inte7_ann.RDS") +dat.sub <- subset(dat,cells=which(dat$type_group=="LCBM"&dat$type=="maliganant")) + +mye <- readRDS("/public/workspace/lily/Lung2Brain/Version5/Myeloid/inte9_myeloid.RDS") +mye.sub <- subset(mye,cells=names(which(mye$type_group=="LCBM"&mye$celltype%in%c("MDM","Monocyte","MG")))) + +tmp.dat <- merge(dat.sub,mye.sub) +# re-integration +inte.list <- list() +samples <- unique(tmp.dat$orig.ident) +for(i in 1:length(samples)){ + tmp <- subset(tmp.dat,cells=which(tmp.dat$orig.ident==samples[i])) + DefaultAssay(tmp) <- "RNA" + inte.list[[i]] <- tmp +} + +integration.anchors <- FindIntegrationAnchors(inte.list) +inte <- IntegrateData(anchorset = integration.anchors) +#FindVariableFeatures +inte <- FindVariableFeatures(inte) +##Scaling the integrateda +all.genes <- rownames(inte) +inte <- ScaleData(inte, features = all.genes) +#PCA +inte <- RunPCA(inte) +#cluster +inte <- FindNeighbors(inte) +inte <- FindClusters(inte) +#TSNE +# if Umap can not use +inte <- RunTSNE(inte) + +######################################################################################## +# check result +inte$CCL20 <- ifelse(inte[["RNA"]]@data["CCL20",]>0,"Y","N") +FeaturePlot(inte,features="RNA_CCL20",order=T) +DimPlot(inte,group.by="type") + +barplot(c(0.536752,0.4453288),names=c("malignant","Myeloid"),ylab="Number of CCL20+ cells",ylim=c(0,0.6)) + + + + + + + + + + + + + + + + +#============================================================================================================================================= +# 2021-12-17 +# GSE59831 analysis to verify MDM expression CCL20 +#============================================================================================================================================= +tmp.dat <- read.table("/public/workspace/lily/metastasis/data/verify/GSE59831/GSE59831_processed_data_FPKM.txt",sep="\t",header=T) + +tmp.sample <- read.table("/public/workspace/lily/metastasis/data/verify/GSE59831/sampleinfo.txt",sep="\t") +tmp.sample$type <- sapply(strsplit(as.vector(tmp.sample$V3),": "),function(x){x[[2]]}) + +# use macrophage and monocyte +gene <- "CCL20" +exp1 <- as.numeric(tmp.dat[which(tmp.dat$human_gene_symbol==gene),c("Tum1","Tum2","Tum3","Tum4","Tum5")]) +exp2 <- as.numeric(tmp.dat[which(tmp.dat$human_gene_symbol==gene),c("WT1","WT2","WT3","WT4")]) +exp3 <- as.numeric(tmp.dat[which(tmp.dat$human_gene_symbol==gene),c("Tum9","Tum10","Tum11")]) +exp4 <- as.numeric(tmp.dat[which(tmp.dat$human_gene_symbol==gene),c("WT8","WT9","WT10")]) +# exp5 <- as.numeric(tmp.dat[which(tmp.dat$human_gene_symbol==gene),c("Tum12","Tum13","Tum14")]) +# exp6 <- as.numeric(tmp.dat[which(tmp.dat$human_gene_symbol==gene),c("Tum11","Tum12","Tum13")]) + + +boxplot(exp1,exp2, + names=c("Myeloid with tumor","Control Myeloid"),main="Myeloid CCL20" +) + +boxplot(exp1,exp2,exp3,exp4,main="CCL20 expression",ylab="Expression",xlab="group", + names=c("T.Mye.","C.Mye.","T.Epi.","C.Epi.") +) + + +# plot result + +tmp.sample$CCL20 <- as.numeric(tmp.dat[which(tmp.dat$human_gene_symbol=="CCL20"),-c(1,2)]) + +pdf("/public/workspace/lily/Lung2Brain/Version5/T_cell/plot/GSE59831_CCL20_myeloid.pdf",useDingbats=F) +boxplot(CCL20~V2,data=tmp.sample[c(1:9),],names=c("Myeloid with tumor","Control Myeloid")) +beeswarm::beeswarm(CCL20~V2,data=tmp.sample[c(1:9),],col ="black", pch = 19,main = 'beeswarm + bxplot',add = TRUE) +legend("topright",legend="P = 0.11") +dev.off() + + + +pdf("/public/workspace/lily/Lung2Brain/Version5/T_cell/plot/GSE59831_CCL20_Epithelial.pdf",useDingbats=F) +boxplot(CCL20~V2,data=tmp.sample[c(16:21),],names=c("Tumor epithelial","Control Epithelial")) +beeswarm::beeswarm(CCL20~V2,data=tmp.sample[c(16:21),],col ="black", pch = 19,main = 'beeswarm + bxplot',add = TRUE) +legend("topright",legend="P = 0.1") +dev.off() + + + + + + + + + + + + + + + + + + + + + + diff --git a/Figure4_add.r b/Figure4_add.r new file mode 100644 index 0000000..452cc97 --- /dev/null +++ b/Figure4_add.r @@ -0,0 +1,615 @@ + +# this program is used to verify three subtype tumor cell is exist +# 2021-7-2 +# 2021-11-23 use BMS to get high BMS and low BMS tumor cells +# 1. use a self data D and E +# 2. use GSE 123904 data to verify +#============================================================================================================= + +# 1. use a self data to verify +# D0927 +library(Seurat) +# dat <- readRDS("/public/workspace/lily/Lung2Brain/Multiple_LungBrain/tumor.D.RDS") +# calculate signature +# source('/public/workspace/lily/software/ssGSEA/ssgseaMOD.r') +# mod <- mod.analyze2(as.matrix(dat[['RNA']]@data),c("BMS_update","Brain_gene","Lung_gene"),"/public/workspace/lily/MOD_file/",permN=0) +# mod <- as.data.frame(mod) +# dat$BMS.update <- mod[,4] +# dat$Brain.gene <- mod[,5] +# dat$Lung.gene <- mod[,6] +# saveRDS(dat,file="/public/workspace/lily/Lung2Brain/Multiple_LungBrain/tumor.D.RDS") + +# library(ggplot2) +# pdf("/public/workspace/lily/Lung2Brain/Version5/Fig3/Fig3_D0927_Tumor.pdf",useDingbats=F) +# DimPlot(dat) +# FeaturePlot(dat,features="BMS.update",order=T)+ +# scale_colour_gradientn(colours=c("steelblue","steelblue","white","#ff3c41","red"),values=c(0,0.7,0.7,1.0)) +# FeaturePlot(dat,features="Lung.gene",order=T) + +# scale_colour_gradientn(colours=c("steelblue","steelblue","white","#ff3c41","red"),values=c(0,0.6,0.6,1.0)) +# FeaturePlot(dat,features="Brain.gene",order=T) + +# scale_colour_gradientn(colours=c("steelblue","steelblue","white","#ff3c41","red"),values=c(0,0.45,0.45,1.0)) +# # DimPlot(dat,reduction="tsne") +# dev.off() + + +#################################################################################################### +# 2021-7-3 recluster then plot result +library(Seurat) +dat <- readRDS("/public/workspace/lily/Lung2Brain/Multiple_LungBrain/tumor.D.RDS") + +recluster <- function(tmp_dat){ +# seurat object + tmp_dat <- FindVariableFeatures(object = tmp_dat) + all.genes <- rownames(x = tmp_dat) + tmp_dat <- ScaleData(object = tmp_dat, features = all.genes) + tmp_dat <- RunPCA(object = tmp_dat, features = VariableFeatures(object = tmp_dat)) + tmp_dat <- FindNeighbors(object = tmp_dat,dims=1:10) + tmp_dat <- FindClusters(object = tmp_dat,resolution=1) + tmp_dat <- RunTSNE(object = tmp_dat,dims=1:10,check_duplicates = FALSE) + return(tmp_dat) +} +dat <- recluster(dat) + +# define cell sub group +tmp.group <- apply(dat@meta.data[,c("Brain.gene","Lung.gene","BMS.update")],1,function(x){ + tmp <- c() + res <- c() + if(x[1]>quantile(dat$Brain.gene,0.75)){ + tmp <- c(tmp,x[1]) + res <- c(res,"group16") + } + if(x[2]>quantile(dat$Lung.gene,0.75)){ + tmp <- c(tmp,x[2]) + res <- c(res,"group09") + } + if(x[3]>quantile(dat$BMS.update,0.75)){ + tmp <- c(tmp,x[1]) + res <- c(res,"BMS.update") + } + + if(length(res)>0){ + res.f <- res[which.max(tmp)] + }else{ + res.f <- "Unclassify" + } + + return(res.f) +}) +dat$tmp.group <- tmp.group +saveRDS(dat,file="/public/workspace/lily/Lung2Brain/Multiple_LungBrain/tumor.D.RDS") + + +pdf("/public/workspace/lily/Lung2Brain/Version5/Fig4/D0927_tumor_subgroup.pdf",useDingbats=F) +DimPlot(dat,group.by="tmp.group",cols=c("#ffc719","#2baf2b","#00a5dc","#ced7df"),reduction="tsne") +dev.off() + + + +# 2021-11-23 +dat$BMS.group <- "inte" +dat$BMS.group[which(dat$BMS.updatequantile(dat$BMS.update,0.75))] <- "high" +DefaultAssay(dat) <- "RNA" +saveRDS(dat,file="/public/workspace/lily/Lung2Brain/Multiple_LungBrain/tumor.D.RDS") + + + + + + + + + + + + + + + +############################################################################################################################## +# another sample E0927 +library(Seurat) + +dat <- readRDS("/public/workspace/lily/Lung2Brain/Multiple_LungBrain/tumor.E.RDS") +recluster <- function(tmp_dat){ +# seurat object + tmp_dat <- FindVariableFeatures(object = tmp_dat) + all.genes <- rownames(x = tmp_dat) + tmp_dat <- ScaleData(object = tmp_dat, features = all.genes) + tmp_dat <- RunPCA(object = tmp_dat, features = VariableFeatures(object = tmp_dat)) + tmp_dat <- FindNeighbors(object = tmp_dat,dims=1:10) + tmp_dat <- FindClusters(object = tmp_dat,resolution=0.5) + tmp_dat <- RunTSNE(object = tmp_dat,dims=1:10,check_duplicates = FALSE) + return(tmp_dat) +} +dat <- recluster(dat) + +# define cell sub group +tmp.group <- apply(dat@meta.data[,c("Brain.gene","Lung.gene","BMS.update")],1,function(x){ + tmp <- c() + res <- c() + if(x[1]>quantile(dat$Brain.gene,0.75)){ + tmp <- c(tmp,x[1]) + res <- c(res,"group16") + } + if(x[2]>quantile(dat$Lung.gene,0.75)){ + tmp <- c(tmp,x[2]) + res <- c(res,"group09") + } + if(x[3]>quantile(dat$BMS.update,0.75)){ + tmp <- c(tmp,x[1]) + res <- c(res,"BMS.update") + } + + if(length(res)>0){ + res.f <- res[which.max(tmp)] + }else{ + res.f <- "Unclassify" + } + + return(res.f) +}) +dat$tmp.group <- tmp.group +saveRDS(dat,file="/public/workspace/lily/Lung2Brain/Multiple_LungBrain/tumor.E.RDS") + + + + + +pdf("/public/workspace/lily/Lung2Brain/Version5/Fig4/E0927_tumor_subgroup.pdf",useDingbats=F) +DimPlot(dat,group.by="tmp.group",cols=c("#ffc719","#2baf2b","#00a5dc","#ced7df"),reduction="tsne") +dev.off() + + +# 2021-11-23 +dat$BMS.group <- "inte" +dat$BMS.group[which(dat$BMS.updatequantile(dat$BMS.update,0.75))] <- "high" +DefaultAssay(dat) <- "RNA" +saveRDS(dat,file="/public/workspace/lily/Lung2Brain/Multiple_LungBrain/tumor.E.RDS") + + + + + + + + + + + + + +###################################################################################################################### +# MSK data show result +# use GSE123902.RDS which inteagrate primary normal and brain metastasis samples. +#===================================================================================================================== +library(Seurat) +dat <- readRDS("/public/workspace/lily/metastasis/data/verify/GSE123904/GSE123902.RDS") + +sub.dat <- subset(dat,cells=which(dat$seurat_clusters==6&dat$group=="METASTASIS")) +inte.list <- list() +samples <- unique(sub.dat$sample) +for(i in 1:length(samples)){ + tmp <- subset(sub.dat,cells=which(sub.dat$sample==samples[i])) + DefaultAssay(tmp) <- "RNA" + inte.list[[i]] <- tmp +} + +# re - integration +integration.anchors <- FindIntegrationAnchors(object.list = inte.list,k.filter=50,k.score=50) +inte <- IntegrateData(anchorset = integration.anchors) +#FindVariableFeatures +inte <- FindVariableFeatures(inte) +##Scaling the integrateda +all.genes <- rownames(inte) +inte <- ScaleData(inte, features = all.genes) +#PCA +inte <- RunPCA(inte) +#cluster +inte <- FindNeighbors(inte) +inte <- FindClusters(inte) +#TSNE +# if Umap can not use +inte <- RunTSNE(inte) +saveRDS(inte,file="/public/workspace/lily/Lung2Brain/Version5/Fig5/GSE123902_BM_tumor.RDS") + +# calculate BMS score and Brain/Lung signature +#=============================================================================================================================== +library(Seurat) +dat <- readRDS("/public/workspace/lily/Lung2Brain/Version5/Fig5/GSE123902_BM_tumor.RDS") +source('/public/workspace/lily/software/ssGSEA/ssgseaMOD.r') +mod <- mod.analyze2(as.matrix(dat[['RNA']]@data),c("BMS_update","Brain_gene","Lung_gene"),"/public/workspace/lily/MOD_file/",permN=0) +mod <- as.data.frame(mod) +dat$BMS.update <- mod[,4] +dat$Brain.gene <- mod[,5] +dat$Lung.gene <- mod[,6] + +# define cell sub group +tmp.group <- apply(dat@meta.data[,c("Brain.gene","Lung.gene","BMS.update")],1,function(x){ + tmp <- c() + res <- c() + if(x[1]>quantile(dat$Brain.gene,0.8)){ + tmp <- c(tmp,x[1]) + res <- c(res,"group16") + } + if(x[2]>quantile(dat$Lung.gene,0.8)){ + tmp <- c(tmp,x[2]) + res <- c(res,"group09") + } + if(x[3]>quantile(dat$BMS.update,0.8)){ + tmp <- c(tmp,x[1]) + res <- c(res,"BMS.update") + } + + if(length(res)>0){ + res.f <- res[which.max(tmp)] + }else{ + res.f <- "Unclassify" + } + + return(res.f) +}) +dat$tmp.group <- tmp.group +saveRDS(dat,file="/public/workspace/lily/Lung2Brain/Version5/Fig5/GSE123902_BM_tumor.RDS") + + +pdf("/public/workspace/lily/Lung2Brain/Version5/Fig4/MSK123902_tumor_subgroup.pdf",useDingbats=F) +DimPlot(dat,group.by="tmp.group",cols=c("#ffc719","#2baf2b","#00a5dc","#ced7df"),reduction="tsne") +dev.off() + + + + +# 2021-11-23 +dat$BMS.group <- "inte" +dat$BMS.group[which(dat$BMS.updatequantile(dat$BMS.update,0.75))] <- "high" +DefaultAssay(dat) <- "RNA" +saveRDS(dat,file="/public/workspace/lily/Lung2Brain/Version5/Fig5/GSE123902_BM_tumor.RDS") + + + + + + + + + + + + +############################################################################################################################### +# MSK data GSE123902 +# DTC Incipent and Macrometastasis + +library(Seurat) +file <- dir("~/metastasis/data/verify/GSE123904/DTC2Macro/") +inte.list <- list() +for(i in 1:length(file)){ + tmp <- read.csv(paste0("~/metastasis/data/verify/GSE123904/DTC2Macro/",file[i]),stringsAsFactors=F,colClasses="character") + + if(i %in%c(3,4)){ + name <- strsplit(file[i],"_")[[1]][4] + }else{ + name <- strsplit(file[i],"_")[[1]][3] + } + rownames(tmp) <- paste0("Cell_",tmp$X) + tmp$X <- NULL + tmp <- t(tmp) + tmp.dat<- CreateSeuratObject(counts = tmp, project = name ,min.cells = 3, min.features = 200) + + # MT gene + tmp.dat[["percent.mt"]] <- PercentageFeatureSet(object = tmp.dat, pattern = "^MT.") + + # filter some cells + dat = subset(x=tmp.dat,subset=nFeature_RNA > 200 & nFeature_RNA < 5000 & percent.mt < 20) + dat = NormalizeData(object = dat) + dat$group <- name + dat$sample <- paste0(name,i) + # add into inte.list + inte.list[[i]] <- dat +} + +###################################################################################################################################### +# integration samples +integration.anchors <- FindIntegrationAnchors(object.list = inte.list) +inte <- IntegrateData(anchorset = integration.anchors) +#FindVariableFeatures +inte <- FindVariableFeatures(inte) +##Scaling the integrateda +all.genes <- rownames(inte) +inte <- ScaleData(inte, features = all.genes) +#PCA +inte <- RunPCA(inte) +#cluster +inte <- FindNeighbors(inte) +inte <- FindClusters(inte) +#TSNE +# if Umap can not use +inte <- RunTSNE(inte) +saveRDS(inte,file="/public/workspace/lily/Lung2Brain/Version5/Fig5/GSE123904_DTC2Macro.RDS") +# calculate sssGSEA for BMS and Brain and Lung +dat <- readRDS("/public/workspace/lily/Lung2Brain/Version5/Fig5/GSE123904_DTC2Macro.RDS") + +source('/public/workspace/lily/software/ssGSEA/ssgseaMOD.r') +mod <- mod.analyze2(as.matrix(dat[['RNA']]@data),c("BMS_update","Lung_gene","Brain_gene"),"/public/workspace/lily/MOD_file/",permN=0) +mod <- as.data.frame(mod) +saveRDS(mod,file="/public/workspace/lily/Lung2Brain/Version5/Fig5/GSE123904_DTC2Macro_mod.RDS") + + + +# check result +################################################################################################################ +library(Seurat) +dat <- readRDS("/public/workspace/lily/Lung2Brain/Version5/Fig5/GSE123904_DTC2Macro.RDS") +mod <- readRDS("/public/workspace/lily/Lung2Brain/Version5/Fig5/GSE123904_DTC2Macro_mod.RDS") + +dat$BMS.update <- mod$BMS_update_norm +dat$Lung.gene <- mod$Lung_gene_norm +dat$Brain.gene <- mod$Brain_gene_norm + + +# define cell sub group +tmp.group <- apply(dat@meta.data[,c("Brain.gene","Lung.gene","BMS.update")],1,function(x){ + tmp <- c() + res <- c() + if(x[1]>quantile(dat$Brain.gene,0.75)){ + tmp <- c(tmp,x[1]) + res <- c(res,"group16") + } + if(x[2]>quantile(dat$Lung.gene,0.75)){ + tmp <- c(tmp,x[2]) + res <- c(res,"group09") + } + if(x[3]>quantile(dat$BMS.update,0.75)){ + tmp <- c(tmp,x[1]) + res <- c(res,"BMS.update") + } + + if(length(res)>0){ + res.f <- res[which.max(tmp)] + }else{ + res.f <- "Unclassify" + } + + return(res.f) +}) +dat$tmp.group <- tmp.group + +tmp.res <- reshape2::melt(apply(table(dat$tmp.group,dat$group),1,function(x){x/sum(x)})[,c(1:3)]) +colnames(tmp.res) <- c("group1","group2","percentage") + +library(ggplot2) + +cols <- c("#e9e8dd","#5ba4e5","#9fbb58") +pdf("/public/workspace/lily/Lung2Brain/Version5/Fig4/MSKDTC2Macro.pdf",useDingbats=F) +ggplot(tmp.res,aes(x=group2,y=percentage,fill=group1,group=group1))+ geom_bar(stat="identity",position="dodge")+ + geom_text(aes(label=round(percentage,2), y=percentage+0.01), position=position_dodge(0.9), vjust=0)+ + scale_fill_manual(values=cols)+theme_bw()+ labs(y="percentage") +dev.off() + + + +# 2021-11-23 +dat$BMS.group <- "inte" +dat$BMS.group[which(dat$BMS.updatequantile(dat$BMS.update,0.75))] <- "high" +DefaultAssay(dat) <- "RNA" +saveRDS(dat,file="/public/workspace/lily/Lung2Brain/Version5/Fig5/GSE123904_DTC2Macro.RDS") + + + + + + + + + +# GSE14108 data analysis +# 2021-10-15 +# estimate calculate purity +############################################################################################################################### +library(estimate) +# calculate Brain metastasis sample purity +#============================================================================================================================== +dat <- readRDS("~/metastasis/data/verify/GSE14108/GSE14108_res.RDS") +# write.table(dat,file="GSE14108_exp.txt",sep="\t",row.names=T,col.names=T,quote=F) +# filterCommonGenes(input.f="GSE14108_exp.txt",output.f="BM_10412genes.gct",id="GeneSymbol") +# estimateScore("BM_10412genes.gct", "/public/workspace/lily/Lung2Brain/Version5/Fig4/Data/GSE14108_estimate_score.gct", platform="affymetrix") + +scores <- read.table("/public/workspace/lily/Lung2Brain/Version5/Fig4/Data/GSE14108_estimate_score.gct",skip = 2,header = T) + + +# ssGSEA +source('/public/workspace/lily/software/ssGSEA/ssgseaMOD.r') +mod <- mod.analyze2(as.matrix(dat),c("BMS_update","Brain_gene","Lung_gene","BMDM_marker","MG_marker"),"/public/workspace/lily/MOD_file/",permN=0) +mod <- as.data.frame(mod) + +mod$purity <- as.numeric(as.vector(scores[4,-c(1,2)])) + +mod$immune <- as.numeric(as.vector(scores[2,-c(1,2)])) + + +# pdf("/public/workspace/lily/Lung2Brain/Version5/Fig5/GSE14108_purity_lungsig.pdf",useDingbats=F) +# plot(mod$Lung_gene_norm,mod$purity,main="Lung like tumor with purity",xlab="Lung cancer signature",ylab="estimate purity score",pch=19) +# abline(lm(mod$purity~mod$Lung_gene_norm),col="red") +# # cor.tes() +# legend("topright",legend=paste0("rho= -",0.49," pvalue=",0.001)) +# dev.off() + + +pdf("/public/workspace/lily/Lung2Brain/Version5/Fig5/GSE14108_estimate_immune_lungsig.pdf",useDingbats=F) +plot(mod$Lung_gene_norm,mod$immune,main="Lung like tumor with purity",xlab="Lung cancer signature",ylab="estimate immune score",pch=19) +abline(lm(mod$immune~mod$Lung_gene_norm),col="red") +# cor.tes() +legend("topright",legend=paste0("rho= ",0.58," pvalue<",0.001)) + +plot(mod$Brain_gene_norm,mod$immune,main="Brain like tumor with purity",xlab="Brain cancer signature",ylab="estimate immune score",pch=19) +abline(lm(mod$immune~mod$Brain_gene_norm),col="red") +# cor.tes() +legend("topright",legend=paste0("rho= ",-0.05," pvalue=",0.76)) + +plot(mod$BMS_update_norm,mod$immune,main="BMS with purity",xlab="BMS signature",ylab="estimate immune score",pch=19) +abline(lm(mod$immune~mod$BMS_update_norm),col="red") +# cor.tes() +legend("topright",legend=paste0("rho= ",0.47," pvalue=",0.01)) + +dev.off() + + + + + +#================================================================================================================================ +# 2021-10-15 +# E-MTAB data analysi +dat <- readRDS("/public/workspace/lily/metastasis/data/verify/E-MTAB-8659/LCBM_S63_data.RDS") +# write.table(dat,file="E_MTAB_exp.txt",sep="\t",row.names=T,col.names=T,quote=F) +# filterCommonGenes(input.f="E_MTAB_exp.txt",output.f="E_MTAB_genes.gct",id="GeneSymbol") +# estimateScore("E_MTAB_genes.gct", "/public/workspace/lily/Lung2Brain/Version5/Fig4/Data/E_MTAB_estimate_score.gct", platform="affymetrix") + +# calcualte +scores <- read.table("/public/workspace/lily/Lung2Brain/Version5/Fig4/Data/E_MTAB_estimate_score.gct",skip = 2,header = T) +# ssGSEA +source('/public/workspace/lily/software/ssGSEA/ssgseaMOD.r') +mod <- mod.analyze2(as.matrix(dat),c("BMS_update","Brain_gene","Lung_gene","BMDM_marker","MG_marker"),"/public/workspace/lily/MOD_file/",permN=0) +mod <- as.data.frame(mod) +mod$purity <- as.numeric(as.vector(scores[4,-c(1,2)])) +mod$immune <- as.numeric(as.vector(scores[2,-c(1,2)])) + +pdf("/public/workspace/lily/Lung2Brain/Version5/Fig5/E_MTAB_estimate_immune_lungsig.pdf",useDingbats=F) +plot(mod$Lung_gene_norm,mod$immune,main="Lung like tumor with purity",xlab="Lung cancer signature",ylab="estimate immune score",pch=19) +abline(lm(mod$immune~mod$Lung_gene_norm),col="red") +# cor.tes() +legend("topright",legend=paste0("rho= ",0.29," pvalue=",0.022)) + +plot(mod$Brain_gene_norm,mod$immune,main="Brain like tumor with purity",xlab="Brain cancer signature",ylab="estimate immune score",pch=19) +abline(lm(mod$immune~mod$Brain_gene_norm),col="red") +# cor.tes() +legend("topright",legend=paste0("rho= ",-0.05," pvalue=",0.65)) + +plot(mod$BMS_update_norm,mod$immune,main="BMS with purity",xlab="BMS signature",ylab="estimate immune score",pch=19) +abline(lm(mod$immune~mod$BMS_update_norm),col="red") +# cor.tes() +legend("topright",legend=paste0("rho= ",0.46," pvalue<",0.001)) + +dev.off() + + + + + + +############################################################################################################# +# 2021-8-18 +# to check gene in samples +#============================================================================================================ +library(Seurat) + +tumor.e <- readRDS("/public/workspace/lily/Lung2Brain/Multiple_LungBrain/tumor.E.RDS") +tumor.e@active.ident <- factor(tumor.e$tmp.group) + +tumor.d <- readRDS("/public/workspace/lily/Lung2Brain/Multiple_LungBrain/tumor.D.RDS") +tumor.d@active.ident <- factor(tumor.d$tmp.group) + +gse123902 <- readRDS("/public/workspace/lily/Lung2Brain/Version5/Fig5/GSE123902_BM_tumor.RDS") + +lcbm <- readRDS("/public/workspace/lily/Lung2Brain/Version5/Fig4/Data/LCBM_sub.tumor.RDS") +DefaultAssay(lcbm) <- "RNA" +lcbm@active.ident <- factor(lcbm$group) + +features <- c("CSF1","CCL20") + +AverageExpression(tumor.e,assay="RNA",features=features) +AverageExpression(tumor.d,assay="RNA",features=features) +AverageExpression(gse123902,assay="RNA",features=features) +AverageExpression(lcbm,assay="RNA",features=features) + + + + + + +# 2021-9-9 +# divide GSE123902 into 3 sample +#============================================================================================================================================ +library(Seurat) +dat <- readRDS("/public/workspace/lily/Lung2Brain/Version5/Fig5/GSE123902_BM_tumor.RDS") +sub.dat <- dat # subset(dat,cells=which(dat$sample=="LX681")) # LX255B LX701 LX681 +source('/public/workspace/lily/software/ssGSEA/ssgseaMOD.r') +mod <- mod.analyze2(as.matrix(sub.dat[['RNA']]@data),c("BMS_update","Brain_gene","Lung_gene"),"/public/workspace/lily/MOD_file/",permN=0) +mod <- as.data.frame(mod) +sub.dat$BMS.update <- mod[,4] +sub.dat$Brain.gene <- mod[,5] +sub.dat$Lung.gene <- mod[,6] + +# define cell sub group +tmp.group <- apply(sub.dat@meta.data[,c("Brain.gene","Lung.gene","BMS.update")],1,function(x){ + tmp <- c() + res <- c() + if(x[1]>quantile(sub.dat$Brain.gene,0.75)){ + tmp <- c(tmp,x[1]) + res <- c(res,"group16") + } + if(x[2]>quantile(sub.dat$Lung.gene,0.75)){ + tmp <- c(tmp,x[2]) + res <- c(res,"group09") + } + if(x[3]>quantile(sub.dat$BMS.update,0.75)){ + tmp <- c(tmp,x[1]) + res <- c(res,"BMS.update") + } + + if(length(res)>0){ + res.f <- res[which.max(tmp)] + }else{ + res.f <- "Unclassify" + } + + return(res.f) +}) +sub.dat$tmp.group <- tmp.group + +saveRDS(sub.dat,file="/public/workspace/lily/Lung2Brain/Version5/Fig5/GSE123902_BM_tumor.RDS") + +sub.dat@active.ident <- factor(sub.dat$tmp.group) +AverageExpression(sub.dat,assays="RNA",features=c("CSF1","CCL2","SAA1")) + + +# saveRDS(sub.dat,file="/public/workspace/lily/Lung2Brain/Version5/Fig5/GSE123902_BM_tumor_LX681.RDS") + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/LCBM_fastq_data.txt b/LCBM_fastq_data.txt new file mode 100644 index 0000000..a426346 --- /dev/null +++ b/LCBM_fastq_data.txt @@ -0,0 +1,10 @@ +T_Bsc1 : /public/workspace/wulx/ANNUO/data/ANCJS190333_PM-JS190333-01_AHYJWHCCXY_2019-02-26/Cleandata/T-Bsc1/ +A20190305 : /public/workspace/wulx/ANNUO/data/ANCJS190370_PM-JS190370-03_BHYTHJCCXY_2019-04-13/Cleandata/1-4/ +A20190312 : /public/workspace/wulx/ANNUO/data/ANCJS190370_PM-JS190370-03_BHYTHJCCXY_2019-04-13/Cleandata/5-8/ + +D0927 : /data/202.195.187.5/public/workspace/DATA/sorted_by_date/2020-11-13/Lnj202010003/LPL2020101002/Rawdata/ +E0927 : /data/202.195.187.5/public/workspace/DATA/sorted_by_date/2020-11-13/Lnj202010003/LPL2020101005/Data/ + + +paired : LCBM : /public/workspace/DATA/sorted_by_date/2021-11-20/10X-WSY +LUAD : \ No newline at end of file diff --git a/LCBM_subtumor_cellchat.r b/LCBM_subtumor_cellchat.r new file mode 100644 index 0000000..e05cf8f --- /dev/null +++ b/LCBM_subtumor_cellchat.r @@ -0,0 +1,619 @@ + +# 2021-7-12 +# this program is used to analysis LCBM different group cell cell communication with different cell type +#============================================================================================================================================ +############################################################################################################################################################## +# 2021-7-10 +# check if Oligodendrocyte expression +library(Seurat) +dat <- readRDS("/public/workspace/lily/Lung2Brain/Version5/Fig4/Data/LCBM_sub.tumor.RDS") +myeloid <- readRDS("/public/workspace/lily/Lung2Brain/Version5/Myeloid/inte9_myeloid.RDS") +tcell <- readRDS("/public/workspace/lily/Lung2Brain/Version5/T_cell/inte9_lympho.RDS") +tmp.dat <- readRDS("/public/workspace/lily/Lung2Brain/Version5/Data/inte9_ntumor.RDS") + + +myeloid.sub <- subset(myeloid,cells=which(myeloid$type_group=="LCBM")) +myeloid.sub$type <- "Myeloid" +tcell.sub <- subset(tcell,cells=which(tcell$type_group=="LCBM")) +tcell.sub$type <- "Tcell" +tmp.sub <- subset(tmp.dat,cells=which(tmp.dat$type%in%c("Endothelial","Fibroblast","Oligodendrocyte")&tmp.dat$type_group=="LCBM")) +dat$celltype <- dat$group +dat$type <- dat$group + + +tmp.sub$hbmarker <- NULL +tmp.sub$llymarker <- NULL +tmp.sub$celltype <- NULL +tmp.sub$putativeTumor3 <- NULL +tmp.sub$res.2 <- NULL +tmp.sub$Phase <- NULL +tmp.sub$G2M.Score <- NULL +tmp.sub$S.Score <- NULL +tmp.sub$percent.mito <- NULL +tmp.sub$nUMI <- NULL +tmp.sub$nGene <- NULL +tmp.sub$RNA_snn_res.2 <- NULL + +# ready to +tmp.sub$celltype <- tmp.sub$type +dat.res <- merge(dat,c(myeloid.sub,tcell.sub,tmp.sub)) + + saveRDS(dat.res,file="/public/workspace/lily/Lung2Brain/Version5/Data/LCBM_celltype_type.RDS") + +########################################################################################################################################################### +# run cellchat +# use type is three group and big celltype +# celltype is small celltype and type_group is cell type +#========================================================================================================================================================== +library(Seurat) +library(CellChat) + +dat <- readRDS("/public/workspace/lily/Lung2Brain/Version5/Data/LCBM_celltype_type.RDS") +data.input <- GetAssayData(dat, assay = "RNA", slot = "data") # normalized data matrix +meta <- data.frame(dat@meta.data) # create a dataframe of the cell labels +cellchat <- createCellChat(object = data.input, meta = meta, group.by = "celltype") +cellchat <- setIdent(cellchat, ident.use = "celltype") # set "labels" as default cell identity +levels(cellchat@idents) + +CellChatDB <- CellChatDB.human +# use all DB +CellChatDB.use <- CellChatDB +# set the used database in the object +cellchat@DB <- CellChatDB.use +cellchat <- subsetData(cellchat) # This step is necessary even if using the whole database +future::plan("multiprocess", workers = 8) # do parallel + +##################################################################################################### +cellchat <- identifyOverExpressedGenes(cellchat) +cellchat <- identifyOverExpressedInteractions(cellchat) +# project gene expression data onto PPI network (should use) +cellchat <- projectData(cellchat, PPI.human) + +cellchat <- computeCommunProb(cellchat) # 1W cells 5min +# Filter out the cell-cell communication if there are only few number of cells in certain cell groups +cellchat <- filterCommunication(cellchat, min.cells = 10) +cellchat <- computeCommunProbPathway(cellchat) +# Calculate the aggregated cell-cell communication network +cellchat <- aggregateNet(cellchat) + +saveRDS(cellchat,file="/public/workspace/lily/Lung2Brain/Version5/Fig5/LCBM_subTumor_celltype.cellchat.RDS") + + + + + + + + + + + +# 2021-7-12 +# check result +############################################################################################################################################## +library(Seurat) +library(CellChat) + +cellchat <- readRDS("/public/workspace/lily/Lung2Brain/Version5/Fig5/LCBM_subTumor_type.cellchat.RDS") +groupSize <- table(cellchat@idents) +netVisual_circle(cellchat@net$count, sources.use=4,targets.use = c(1,2,6,7,8),vertex.weight = groupSize, weight.scale = T, label.edge= F, title.name = "Number of interactions") + +tmp <- netVisual_bubble(cellchat, sources.use = 4, targets.use = c(1,2,7), remove.isolate = FALSE,return.data=T)$communication +tmp.res <- tmp[,c("prob","source.target","interaction_name_2")] +rs <- reshape2::dcast(tmp.res,source.target~interaction_name_2,value.var="prob") +#rs[is.na(rs)] <- 0 +rownames(rs) <- rs[,1] +rs <- rs[,-1] +rs <- data.frame(t(rs)) + + + + + + + +#====================================================================================================================================== +# check Treg communication +# 2021-7-15 +#====================================================================================================================================== +library(Seurat) +library(CellChat) + +cellchat <- readRDS("/public/workspace/lily/Lung2Brain/Version5/Fig5/LCBM_subTumor_celltype.cellchat.RDS") +groupSize <- table(cellchat@idents) +netVisual_circle(cellchat@net$count, vertex.weight = groupSize, weight.scale = T, label.edge= F, title.name = "Number of interactions") + +tmp <- netVisual_bubble(cellchat, sources.use =c(7,9), targets.use = 8, remove.isolate = FALSE,return.data=T)$communication +tmp.res <- tmp[,c("prob","source.target","interaction_name_2")] +rs <- reshape2::dcast(tmp.res,source.target~interaction_name_2,value.var="prob") +#rs[is.na(rs)] <- 0 +rownames(rs) <- rs[,1] +rs <- rs[,-1] +rs <- data.frame(t(rs)) + + + + + + + + + + + + + + +###################################################################################################################################################################### +# 2021-7-29 +# use gene expression specifity to check interaction +#===================================================================================================================================================================== +library(Seurat) +source("/public/workspace/lily/software/specInt.R") +dat <- readRDS("/public/workspace/lily/Lung2Brain/Version5/Data/LCBM_celltype_type.RDS") +subdat <- subset(dat,cells=which(dat$celltype%in%c("group09","group16","group47","Oligodendrocyte","Endothelial","Fibroblast"))) +# use Function +rs = specInt(as.matrix(subdat[["RNA"]]@data),subdat$celltype,celldb[,c('Ligand.ApprovedSymbol','Receptor.ApprovedSymbol')], + c("group16"),c("Fibroblast") +) +rs$pairname <- celldb[rownames(rs),"Pair.Name"] +rs.f = rs[which(rs$L.mean>0.1 & rs$R.mean>0.1),] +rs.f$L.fc = rs.f$L.mean/rs.f$nL.mean +rs.f$R.fc = rs.f$R.mean/rs.f$nR.mean +head(rs.f[order(apply(rs.f[,c('L.fc','R.fc')],1,min),decreasing=T),],10) + + + + + + + + + + + +#======================================================================================================================================== +# 2021-8-18 +# 2021-9-8 +library(Seurat) +# source("/public/workspace/lily/software/specInt.R") +# dat <- readRDS("/public/workspace/lily/Lung2Brain/Version5/Data/LCBM_celltype_type.RDS") +# subdat <- subset(dat,cells=which(dat$celltype%in%c("group09","group16","group47","MDM","Treg"))) +# # use Function +# rs = specInt(as.matrix(subdat[["RNA"]]@data),subdat$celltype,celldb[,c('Ligand.ApprovedSymbol','Receptor.ApprovedSymbol')], +# c("group09"),c("MDM") +# ) + +source("/public/workspace/lily/software/specInt.R") +dat <- readRDS("/public/workspace/lily/Lung2Brain/Version5/Data/LCBM_celltype_type.RDS") +subdat <- subset(dat,cells=which(dat$celltype%in%c("group09","group16","group47","Treg"))) +genepair <- readRDS("/public/workspace/lily/Lung2Brain/Version5/CellChat/gene_pair.RDS") +genepair$Pair.Name <- paste0(genepair$ligand,"_",genepair$receptor) +rs = specInt(as.matrix(subdat[["RNA"]]@data),subdat$celltype,genepair, + c("group09"),c("Treg") +) + +rs$pairname <- genepair[rownames(rs),"Pair.Name"] +rs.f = rs[which(rs$L.mean>0.1 & rs$R.mean>0.1),] +rs.f$L.fc = rs.f$L.mean/rs.f$nL.mean +rs.f$R.fc = rs.f$R.mean/rs.f$nR.mean +head(rs.f[order(apply(rs.f[,c('L.fc','R.fc')],1,min),decreasing=T),],10) + + + + + + + + + + + + + + + + +############################################################################################################################################ +# 2021-9-13 +# analysis macrophage factor +#=========================================================================================================================================== +library(Seurat) + +tumor.e <- readRDS("/public/workspace/lily/Lung2Brain/Multiple_LungBrain/tumor.E.RDS") +tumor.e@active.ident <- factor(tumor.e$tmp.group) + +tumor.d <- readRDS("/public/workspace/lily/Lung2Brain/Multiple_LungBrain/tumor.D.RDS") +tumor.d@active.ident <- factor(tumor.d$tmp.group) + +# LX701 <- readRDS("/public/workspace/lily/Lung2Brain/Version5/Fig5/GSE123902_BM_tumor_LX701.RDS") +# DefaultAssay(LX701) <- "RNA" +# LX701@active.ident <- factor(LX701$tmp.group) + +# LX255B <- readRDS("/public/workspace/lily/Lung2Brain/Version5/Fig5/GSE123902_BM_tumor_LX255B.RDS") +# DefaultAssay(LX255B) <- "RNA" +# LX255B@active.ident <- factor(LX255B$tmp.group) + +# LX681 <- readRDS("/public/workspace/lily/Lung2Brain/Version5/Fig5/GSE123902_BM_tumor_LX681.RDS") +# DefaultAssay(LX681) <- "RNA" +# LX681@active.ident <- factor(LX681$tmp.group) + +lcbm <- readRDS("/public/workspace/lily/Lung2Brain/Version5/Fig4/Data/LCBM_sub.tumor.RDS") +DefaultAssay(lcbm) <- "RNA" +lcbm@active.ident <- factor(lcbm$group) + +gse123902 <- readRDS("/public/workspace/lily/Lung2Brain/Version5/Fig5/GSE123902_BM_tumor.RDS") +DefaultAssay(gse123902) <- "RNA" +gse123902@active.ident <- factor(gse123902$tmp.group) + +features <- c("CSF1","CCL2","CCL20","CCL5","CCL3","ICAM1") +# do not have CXCL12 and LGALS3 not each sample have this gene expression + +features <- c("CD274","LGALS9","PVR","VTCN1","IDO1") +tmp.e <- AverageExpression(tumor.e,assay="RNA",features=features)$RNA +tmp.d <- AverageExpression(tumor.d,assay="RNA",features=features)$RNA +tmp.lx701 <- AverageExpression(LX701,assay="RNA",features=features)$RNA +tmp.lx255b <- AverageExpression(LX255B,assay="RNA",features=features)$RNA +tmp.lx681 <- AverageExpression(LX681,assay="RNA",features=features)$RNA +tmp.lcbm <- AverageExpression(lcbm,assay="RNA",features=features)$RNA + + + +# remove unclassify and do scale +tmp.e <- tmp.e[,-4] +tmp.d <- tmp.d[,-4] +tmp.lx701 <- tmp.lx701[,-4] +tmp.lx255b <- tmp.lx255b[,-4] +tmp.lx681 <- tmp.lx681[,-4] + +exp.e <- t(apply(tmp.e,1,function(x){scale(as.numeric(x))})) +colnames(exp.e) <- colnames(tmp.e) +exp.d <- t(apply(tmp.d,1,function(x){scale(as.numeric(x))})) +colnames(exp.d) <- colnames(tmp.d) +exp.lx701 <- t(apply(tmp.lx701,1,function(x){scale(as.numeric(x))})) +colnames(exp.lx701) <- colnames(tmp.lx701) +exp.lx255b <- t(apply(tmp.lx255b,1,function(x){scale(as.numeric(x))})) +colnames(exp.lx255b) <- colnames(tmp.lx255b) +exp.lx681 <- t(apply(tmp.lx681,1,function(x){scale(as.numeric(x))})) +colnames(exp.lx681) <- colnames(tmp.lx681) +exp.lcbm <- t(apply(tmp.lcbm,1,function(x){scale(as.numeric(x))})) +colnames(exp.lcbm) <- colnames(tmp.lcbm) + + +#==================================================================================================================================== +# plot heatmap for each sample +library(pheatmap) + +# pdf("/public/workspace/lily/Lung2Brain/Version5/Myeloid/LCBM_recruit_mac.pdf",useDingbats=F) +# pheatmap(tmp.e[,1:3],scale="row",cluster_rows=F,cluster_cols=F,cellwidth=10,cellheight=10,main="tmp.e") +# pheatmap(tmp.d[,1:3],scale="row",cluster_rows=F,cluster_cols=F,cellwidth=10,cellheight=10,main="tmp.d") +# pheatmap(tmp.lx701[,1:3],scale="row",cluster_rows=F,cluster_cols=F,cellwidth=10,cellheight=10,main="tmp.lx701") +# pheatmap(tmp.lx255b[,1:3],scale="row",cluster_rows=F,cluster_cols=F,cellwidth=10,cellheight=10,main="tmp.255b") +# pheatmap(tmp.lx681[,1:3],scale="row",cluster_rows=F,cluster_cols=F,cellwidth=10,cellheight=10,main="tmp.lx681") +# pheatmap(tmp.lcbm,scale="row",cluster_rows=F,cluster_cols=F,cellwidth=10,cellheight=10,main="tmp.lcbm") +# dev.off() + +#gse123902 <- readRDS("/public/workspace/lily/Lung2Brain/Version5/Fig5/GSE123902_BM_tumor.RDS") +# add percentage info +percent_feature <- function(dat,genelist,group){ + res.list <- c() + for(i in 1:length(genelist)){ + dat$tmp_gene <- ifelse(dat[["RNA"]]@data[genelist[i],]>0,"Y","N") + if(all(dat$tmp_gene=="N")){ + tmpp <- c(0,0,0,0) + names(tmpp) <- names(table(dat@meta.data[,group])) + res.list[[i]] <- tmpp + }else{ + res.list[[i]] <- apply(table(dat$tmp_gene,dat@meta.data[,group]),2,function(x){x/sum(x)})[2,] + } + + names(res.list)[i] <- genelist[i] + } + return(res.list) +} + +res.e.list <- percent_feature(tumor.e,features,group="tmp.group") +res.d.list <- percent_feature(tumor.d,features,group="tmp.group") +# res.lx681.list <- percent_feature(LX681,features,group="tmp.group") +# res.lx701.list <- percent_feature(LX701,features,group="tmp.group") +# res.lx255b.list <- percent_feature(LX255B,features,group="tmp.group") +res.lcbm.list <- percent_feature(lcbm,features,group="group") +res.gse123902.list <- percent_feature(gse123902,features,group="tmp.group") + + + + + + +#=================================================================================================================================== +# just use percentage to plot result +# 2021-9-13 +#=================================================================================================================================== +library(Seurat) +list2mat <- function(res.list){ + res.dat <- matrix(unlist(res.list),ncol=length(res.list[[1]]),byrow=T) + rownames(res.dat) <- unique(sapply(strsplit(as.vector(names(unlist(res.list))),"\\."),function(x){x[[1]]})) + colnames(res.dat) <- unique(sapply(strsplit(as.vector(names(unlist(res.list))),"\\."),function(x){x[[2]]})) + return(res.dat) +} +mat.e <- list2mat(res.e.list) +mat.d <- list2mat(res.d.list) +# mat.lx701 <- list2mat(res.lx701.list) +# mat.lx681 <- list2mat(res.lx681.list) +# mat.lx255b <- list2mat(res.lx255b.list) +mat.lcbm <- list2mat(res.lcbm.list) +mat.gse123902 <- list2mat(res.gse123902.list) + +pdf("/public/workspace/lily/Lung2Brain/Version5/Myeloid/LCBM_recruit_mac_percent_heatmap.pdf",useDingbats=F) +pheatmap(mat.e[,1:3],scale="row",cluster_rows=F,cluster_cols=F,cellwidth=10,cellheight=10,color=colorRampPalette(c('steelblue','white',"#E41A1C"))(100),main="mat.e") +pheatmap(mat.d[,1:3],scale="row",cluster_rows=F,cluster_cols=F,cellwidth=10,cellheight=10,color=colorRampPalette(c('steelblue','white',"#E41A1C"))(100),main="mat.d") +# pheatmap(mat.lx701[,1:3],scale="row",cluster_rows=F,cluster_cols=F) +# pheatmap(mat.lx681[,1:3],scale="row",cluster_rows=F,cluster_cols=F) +# pheatmap(mat.lx255b[,1:3],scale="row",cluster_rows=F,cluster_cols=F) +pheatmap(mat.lcbm[,c(3,1,2)],scale="row",cluster_rows=F,cluster_cols=F,cellwidth=10,cellheight=10,color=colorRampPalette(c('steelblue','white',"#E41A1C"))(100),main="mat.lcbm") +pheatmap(mat.gse123902[,1:3],scale="row",cluster_rows=F,cluster_cols=F,cellwidth=10,cellheight=10,color=colorRampPalette(c('steelblue','white',"#E41A1C"))(100),main="mat.gse123902") +dev.off() + + + + + + + + + + + + + + + + + + + +# combine data +combine_data <- function(res.list,tmp.dat){ + library(reshape2) + tmp.dat <- data.frame(tmp.dat) + res.dat <- data.frame(percent=unname(unlist(res.list)),name=names(unlist(res.list))) + tmp.dat$gene <- rownames(tmp.dat) + tmp.res.dat <- melt(tmp.dat,id="gene") + tmp.res.dat$name <- paste0(tmp.res.dat$gene,".",tmp.res.dat$variable) + colnames(tmp.res.dat)[3] <- ("Exp") + res.e <- merge(res.dat,tmp.res.dat[,c("Exp","name")]) + + # split + res.e$gene <- sapply(strsplit(as.vector(res.e$name),"\\."),function(x){x[[1]]}) + res.e$group <- sapply(strsplit(as.vector(res.e$name),"\\."),function(x){x[[2]]}) + return(res.e) +} + +res.e <- combine_data(res.e.list,exp.e) +res.d <- combine_data(res.d.list,exp.d) +res.lx255b <- combine_data(res.lx255b.list,exp.lx255b) +res.lx701 <- combine_data(res.lx701.list,exp.lx701) +res.lx681 <- combine_data(res.lx681.list,exp.lx681) +res.lcbm <- combine_data(res.lcbm.list,exp.lcbm) + + + + + + + + +# plot result + +pdf("/public/workspace/lily/Lung2Brain/Version5/Myeloid/LCBM_recruit_mac_bubble.pdf",useDingbats=F) +ggplot(res.e,aes(x=group,y=gene,size=percent,color=Exp))+geom_point() + scale_size_continuous(range = c(0,8)) + + scale_colour_gradient2(low="steelblue",mid="white",high="#E41A1C") +theme_bw() + ggtitle("tmp.e") + +ggplot(res.d,aes(x=group,y=gene,size=percent,color=Exp))+geom_point() + scale_size_continuous(range = c(0,8)) + + scale_colour_gradient2(low="steelblue",mid="white",high="#E41A1C") + theme_bw() + ggtitle("tmp.d") + +ggplot(res.lx701,aes(x=group,y=gene,size=percent,color=Exp))+geom_point() + scale_size_continuous(range = c(0,8)) + + scale_colour_gradient2(low="steelblue",mid="white",high="#E41A1C") +theme_bw() + ggtitle("tmp.lx701") + +ggplot(res.lx681,aes(x=group,y=gene,size=percent,color=Exp))+geom_point() + scale_size_continuous(range = c(0,8)) + + scale_colour_gradient2(low="steelblue",mid="white",high="#E41A1C") +theme_bw() + ggtitle("tmp.lx681") + +ggplot(res.lx255b,aes(x=group,y=gene,size=percent,color=Exp))+geom_point() + scale_size_continuous(range = c(0,8)) + + scale_colour_gradient2(low="steelblue",mid="white",high="#E41A1C") +theme_bw() + ggtitle("tmp.lx255b") + +res.lcbm$group <- factor(res.lcbm$group,levels=c("group47","group09","group16")) +ggplot(res.lcbm,aes(x=group,y=gene,size=percent,color=Exp))+geom_point() + scale_size_continuous(range = c(0,8)) + + scale_colour_gradient2(low="steelblue",mid="white",high="#E41A1C") +theme_bw() + ggtitle("tmp.lcbm") +dev.off() + + + +library(ggpubr) +p1 <- ggplot(res.d,aes(x=group,y=gene,size=percent,color=Exp))+geom_point() + scale_size_continuous(range = c(0,8)) + + scale_colour_gradient2(low="steelblue",mid="white",high="#E41A1C") + theme_bw() + ggtitle("tmp.d") + +res.lcbm$group <- factor(res.lcbm$group,levels=c("group47","group09","group16")) +p2 <- ggplot(res.lcbm,aes(x=group,y=gene,size=percent,color=Exp))+geom_point() + scale_size_continuous(range = c(0,8)) + + scale_colour_gradient2(low="steelblue",mid="white",high="#E41A1C") +theme_bw() + ggtitle("tmp.lcbm") + +p3 <- ggplot(res.lx681,aes(x=group,y=gene,size=percent,color=Exp))+geom_point() + scale_size_continuous(range = c(0,8)) + + scale_colour_gradient2(low="steelblue",mid="white",high="#E41A1C") +theme_bw() + ggtitle("tmp.lx681") + +p4 <- ggplot(res.lx255b,aes(x=group,y=gene,size=percent,color=Exp))+geom_point() + scale_size_continuous(range = c(0,8)) + + scale_colour_gradient2(low="steelblue",mid="white",high="#E41A1C") +theme_bw() + ggtitle("tmp.lx255b") + +pdf("/public/workspace/lily/Lung2Brain/Version5/Myeloid/LCBM_recruit_mac_bubble_select.pdf",useDingbats=F) +ggarrange(p1,p2,p3,p4,ncol = 2,nrow=2, + common.legend = T) +dev.off() + + + + + + + + + +#======================================================================================================================================================= +# 2021-9-14 +# verify lung brain MDM MG signature +#======================================================================================================================================================= +# 1. GSE14108 +dat.BM <- readRDS("/public/workspace/lily/metastasis/data/verify/GSE14108/GSE14108_res.RDS") +source('/public/workspace/lily/software/ssGSEA/ssgseaMOD.r') +mod <- mod.analyze2(as.matrix(dat.BM),c("Lung_gene","Brain_gene","BMS_update","BMDM_marker","MG_marker"),"/public/workspace/lily/MOD_file/",permN=0) +mod <- as.data.frame(mod) + +features <- c("CSF1","CCL2","CCL20","CCL5","CCL3","ICAM1") +res.list <- list() +for(i in 1:length(features)){ + if(features[i]%in%rownames(dat.BM)){ + gene_exp <- as.numeric(dat.BM[features[i],]) + tmp <- apply(mod[,6:10],2,function(x){ + c(cor.test(x,gene_exp,method="spearman")$estimate, cor.test(x,gene_exp,method="spearman")$p.value) + }) + }else{ + tmp <- c(0,0) + } + res.list[[i]] <- tmp + names(res.list)[i] <- features[i] +} +# do not detec CCL3 ,remove +res.list$CCL3 <- NULL +tmp <- sapply(res.list,function(x){as.matrix(x)}) +rownames(tmp) <- c("Lung.cor","Lung.p","Brain.cor","Brain.p","BMS.cor","BMS.p","BMDM.cor","BMDM.p","MG.cor","MG.p") +tmp <- data.frame(t(tmp)) +tmp$gene <- rownames(tmp) + + +# tmp$color <- "NS." +# tmp$color[which(rownames(tmp)%in%c("CCL20","ICAM1"))] <- "Lung.BMS" +# tmp$color[which(rownames(tmp)%in%c("CCL3"))] <- "Brain" +# tmp$color[which(rownames(tmp)%in%c("CCL2"))] <- "Brain.BMS" +# tmp$color[which(rownames(tmp)%in%c("CCL5","CSF1"))] <- "BMS" + + +tmp$shape <- "NS." +tmp$shape[which(rownames(tmp)%in%c("CSF1","CCL2","CCL20","CCL5"))] <- "MDM" + +library(ggplot2) +pdf("/public/workspace/lily/Lung2Brain/Version5/Myeloid/GSE14108_factor_BMS.pdf",useDingbats=F) +ggplot(tmp,aes(x=Lung.cor,y=BMS.cor,shape=shape,size=I(5)))+ geom_point() + geom_text(aes(label=gene, y=BMS.cor+0.05)) + + scale_shape_manual(values=c(19,15)) +dev.off() + + + + +# 2. E-MTAB +# use this because have CCL3 + +dat.BM <- readRDS("/public/workspace/lily/metastasis/data/verify/E-MTAB-8659/LCBM_S63_data.RDS") +source('/public/workspace/lily/software/ssGSEA/ssgseaMOD.r') +mod <- mod.analyze2(as.matrix(dat.BM),c("Lung_gene","Brain_gene","BMS_update","BMDM_marker","MG_marker"),"/public/workspace/lily/MOD_file/",permN=0) +mod <- as.data.frame(mod) + +features <- c("CSF1","CCL2","CCL20","CCL5","CCL3","ICAM1") +res.list <- list() +for(i in 1:length(features)){ + if(features[i]%in%rownames(dat.BM)){ + gene_exp <- as.numeric(dat.BM[features[i],]) + tmp <- apply(mod[,6:10],2,function(x){ + c(cor.test(x,gene_exp,method="spearman")$estimate, cor.test(x,gene_exp,method="spearman")$p.value) + }) + }else{ + tmp <- c(0,0) + } + res.list[[i]] <- tmp + names(res.list)[i] <- features[i] +} + +# plot dot plot to show result +tmp <- sapply(res.list,function(x){as.matrix(x)}) +rownames(tmp) <- c("Lung.cor","Lung.p","Brain.cor","Brain.p","BMS.cor","BMS.p","BMDM.cor","BMDM.p","MG.cor","MG.p") +tmp <- data.frame(t(tmp)) +tmp$gene <- rownames(tmp) + +tmp$color <- "NS." +tmp$color[which(rownames(tmp)%in%c("CCL20","ICAM1"))] <- "Lung.BMS" +tmp$color[which(rownames(tmp)%in%c("CCL3"))] <- "Brain" +tmp$color[which(rownames(tmp)%in%c("CCL2"))] <- "Brain.BMS" +tmp$color[which(rownames(tmp)%in%c("CCL5","CSF1"))] <- "BMS" + + +tmp$shape <- "NS." +tmp$shape[which(rownames(tmp)%in%c("CCL2","CCL5","CCL3"))] <- "MDM.MG" +tmp$shape[which(rownames(tmp)%in%c("CSF1","ICAM1","CCL20"))] <- "MDM" + + +######################################################################################################################################################### +# ggplot show result +library(ggplot2) +pdf("/public/workspace/lily/Lung2Brain/Version5/Myeloid/E-MATB_factor_BMS.pdf",useDingbats=F) +ggplot(tmp,aes(x=Lung.cor,y=BMS.cor,shape=shape,color=color,size=I(5)))+ geom_point() + geom_text(aes(label=gene, y=BMS.cor+0.05)) + + scale_colour_manual(values=c("#FFC719","#00A5DC","#00b2a9","#a0ac48")) +dev.off() + + + +# try to plot heatmap +#===================================================================================================================================================== +library(pheatmap) +dat <- dat.BM[features,] +ann <- data.frame(row.names=colnames(dat),Lungsig= mod[,6],Brainsig=mod[,7],BMS=mod[,8],BMDM=mod[,9],MG=mod[,10]) +ann <- ann[order(ann$BMS,decreasing=T),] + +pheatmap(dat[,rownames(ann)],annotation_col=ann,scale="column") + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/LCBM_subtumor_net.r b/LCBM_subtumor_net.r new file mode 100644 index 0000000..8407491 --- /dev/null +++ b/LCBM_subtumor_net.r @@ -0,0 +1,242 @@ + +# 2021-10-13 +# also calculate three groups gene expression +library(Seurat) +dat <- readRDS("/public/workspace/lily/Lung2Brain/Version5/Fig4/Data/LCBM_sub.tumor.RDS") +dat@active.ident <- factor(dat$group) + +pdf("/public/workspace/lily/Lung2Brain/Version5/Brain_like/LCBM_gene_number.pdf",useDingbats=F) +boxplot(nFeature_RNA~group,data=dat@meta.data,FUN=median,main="expressed gene numbers",outline=F) +dev.off() + + +# GSE123902 data +rm(list=ls()) +dat <- readRDS("/public/workspace/lily/Lung2Brain/Version5/Fig5/GSE123902_BM_tumor.RDS") +pdf("/public/workspace/lily/Lung2Brain/Version5/Brain_like/GSE123902_gene_number.pdf",useDingbats=F) +boxplot(nFeature_RNA~tmp.group,data=dat@meta.data,FUN=median,main="expressed gene numbers",outline=F) +dev.off() + + + + +# tumor.d and tumor.e data +rm(list=ls()) +dat <- readRDS("/public/workspace/lily/Lung2Brain/Multiple_LungBrain/tumor.E.RDS") +pdf("/public/workspace/lily/Lung2Brain/Version5/Brain_like/Tumor_E_gene_number.pdf",useDingbats=F) +boxplot(nFeature_RNA~tmp.group,data=dat@meta.data,FUN=median,main="expressed gene numbers",outline=F) +dev.off() + +rm(list=ls()) +dat <- readRDS("/public/workspace/lily/Lung2Brain/Multiple_LungBrain/tumor.D.RDS") +pdf("/public/workspace/lily/Lung2Brain/Version5/Brain_like/Tumor_D_gene_number.pdf",useDingbats=F) +boxplot(nFeature_RNA~tmp.group,data=dat@meta.data,FUN=median,main="expressed gene numbers",outline=F) +dev.off() + + + + + + +#============================================================================================================================================================ +# calculate gene permutation for LCBM subtumor +library(Seurat) +dat <- readRDS("/public/workspace/lily/Lung2Brain/Version5/Fig4/Data/LCBM_sub.tumor.RDS") + +# calculate sub group gene correlation +lung <- subset(dat,cells=which(dat$group=="group09")) +lung.dat <- lung[["RNA"]]@data +tmp.percent <- apply(as.matrix(lung.dat),1,function(x){length(which(x>0))/ncol(lung.dat)}) +lung.dat.f <- lung.dat[-which(tmp.percent<0.05),] # filter a lot of gene +# then calculate +library(Hmisc) +tmp.res <- rcorr(t(as.matrix(lung.dat.f))) +res.f <- tmp.res$r +res.f[!upper.tri(res.f, diag = FALSE)]=0 # tri matrix +saveRDS(res.f,file="/public/workspace/lily/Lung2Brain/Version5/Brain_like/Lung.cor.mat.RDS") +#====================================================================================================== +brain <- subset(dat,cells=which(dat$group=="group16")) +brain.dat <- brain[["RNA"]]@data +tmp.percent <- apply(as.matrix(brain.dat),1,function(x){length(which(x>0))/ncol(brain.dat)}) +brain.dat.f <- brain.dat[-which(tmp.percent<0.05),] # filter a lot of gene +# then calculate +library(Hmisc) +tmp.res <- rcorr(t(as.matrix(brain.dat.f))) +res.f <- tmp.res$r +res.f[!upper.tri(res.f, diag = FALSE)]=0 # tri matrix +saveRDS(res.f,file="/public/workspace/lily/Lung2Brain/Version5/Brain_like/Brain.cor.mat.RDS") +#====================================================================================================== +stem <- subset(dat,cells=which(dat$group=="group47")) +stem.dat <- stem[["RNA"]]@data +tmp.percent <- apply(as.matrix(stem.dat),1,function(x){length(which(x>0))/ncol(stem.dat)}) +stem.dat.f <- stem.dat[-which(tmp.percent<0.05),] # filter a lot of gene +# then calculate +library(Hmisc) +tmp.res <- rcorr(t(as.matrix(stem.dat.f))) +res.f <- tmp.res$r +res.f[!upper.tri(res.f, diag = FALSE)]=0 # tri matrix +saveRDS(res.f,file="/public/workspace/lily/Lung2Brain/Version5/Brain_like/Stem.cor.mat.RDS") + + + + +#============================================================================================================================================ +# now random select cells to construct net work +#============================================================================================================================================ +library(Seurat) +dat <- readRDS("/public/workspace/lily/Lung2Brain/Version5/Fig4/Data/LCBM_sub.tumor.RDS") +stem <- subset(dat,cells=which(dat$group=="group47")) + + + + + + + + + + +#============================================================================================================================================= +# get gene and construct net +# lung +mat <- readRDS("/public/workspace/lily/Lung2Brain/Version5/Brain_like/Lung.cor.mat.RDS") +lung.res <- data.frame(gene1="A",gene2="B",correlation=1,stringsAsFactors=F) +threshold <- 0.3 +for(i in 1:nrow(mat)){ + for(j in 1:ncol(mat)){ + if(mat[i,j]>threshold){ + lung.res <- rbind(lung.res,c(rownames(mat)[i],colnames(mat)[j],mat[i,j])) + } + + } +} + +# set network +net.dat <- lung.res[-1,] +colnames(net.dat) <- c("from","to","correlation") +net <- graph_from_data_frame(net.dat, directed=TRUE) +write.table(net.dat,file=paste0("/public/workspace/lily/Lung2Brain/Version5/Brain_like/","Lung_cor_",threshold,"_table.txt"),sep="\t",row.names=F,col.names=T,quote=F) + +library(igraph) +# degree(net, mode="in") +centr_degree(net, mode="in", normalized=T) + + + + + + +# brain +mat <- readRDS("/public/workspace/lily/Lung2Brain/Version5/Brain_like/Brain.cor.mat.RDS") +#length(grep("^RPL|^RPS",colnames(mat)))/ncol(mat) +brain.res <- data.frame(gene1="A",gene2="B",correlation=1,stringsAsFactors=F) +threshold <- 0.3 +for(i in 1:nrow(mat)){ + for(j in 1:ncol(mat)){ + if(mat[i,j]>threshold){ + brain.res <- rbind(brain.res,c(rownames(mat)[i],colnames(mat)[j],mat[i,j])) + } + + } +} + +# set network +library(igraph) +net.dat <- brain.res[-1,] +dim(net.dat) +colnames(net.dat) <- c("from","to","correlation") +net <- graph_from_data_frame(net.dat, directed=TRUE) +write.table(net.dat,file=paste0("/public/workspace/lily/Lung2Brain/Version5/Brain_like/","Brain_cor_",threshold,"_table.txt"),sep="\t",row.names=F,col.names=T,quote=F) + + +# degree(net, mode="in") +centr_degree(net, mode="in", normalized=T) + + + + + + +# stem like +mat <- readRDS("/public/workspace/lily/Lung2Brain/Version5/Brain_like/Stem.cor.mat.RDS") +stem.res <- data.frame(gene1="A",gene2="B",correlation=1,stringsAsFactors=F) +threshold <- 0.3 +for(i in 1:nrow(mat)){ + for(j in 1:ncol(mat)){ + if(mat[i,j]>threshold){ + stem.res <- rbind(stem.res,c(rownames(mat)[i],colnames(mat)[j],mat[i,j])) + } + + } +} + +# set network +library(igraph) +net.dat <- stem.res[-1,] +dim(net.dat) +colnames(net.dat) <- c("from","to","correlation") +net <- graph_from_data_frame(net.dat, directed=TRUE) +write.table(net.dat,file=paste0("/public/workspace/lily/Lung2Brain/Version5/Brain_like/","Stem_cor_",threshold,"_table.txt"),sep="\t",row.names=F,col.names=T,quote=F) + + +# degree(net, mode="in") +centr_degree(net, mode="in", normalized=T) +transitivity(net, type = "average") + + + + + + + + + + + + + + + +#=========================================================================================================================================== +# random gene numbers to construct net work +# Lung like tumor cells +lung.mat <- readRDS("/public/workspace/lily/Lung2Brain/Version5/Brain_like/Lung.cor.mat.RDS") +idx <- sample(1:ncol(lung.mat),size=4315,replace=F) +mat <- lung.mat[idx,idx] + +# calculate net work +lung.res <- data.frame(gene1="A",gene2="B",correlation=1,stringsAsFactors=F) +threshold <- 0.5 +for(i in 1:nrow(mat)){ + for(j in 1:ncol(mat)){ + if(mat[i,j]>threshold){ + lung.res <- rbind(lung.res,c(rownames(mat)[i],colnames(mat)[j],mat[i,j])) + } + + } +} + +# set network +library(igraph) +net.dat <- lung.res[-1,] +colnames(net.dat) <- c("from","to","correlation") +net <- graph_from_data_frame(net.dat, directed=TRUE) +transitivity(net, type = "average") + + + + + + + + + + + + + + + + + + diff --git a/LCBM_subtumor_trajectory.r b/LCBM_subtumor_trajectory.r new file mode 100644 index 0000000..ef6f944 --- /dev/null +++ b/LCBM_subtumor_trajectory.r @@ -0,0 +1,88 @@ + +# 2021-8-6 +# do trajectory analysis +#========================================================================================================= +# do trajectory for LCBM sub tumor +# re-integration and do analysis +library(Seurat) +dat <- readRDS("/public/workspace/lily/Lung2Brain/Version5/Fig4/Data/LCBM_sub.tumor.RDS") + +# re-integration +DefaultAssay(dat) <- "RNA" +inte.list <- list() +samplelist <- unique(dat$orig.ident) +for(i in 1:length(samplelist)){ + tmp <- subset(dat,cells=which(dat$orig.ident==samplelist[i])) + DefaultAssay(tmp) <- "RNA" + inte.list[[i]] <- tmp +} +integration.anchors <- FindIntegrationAnchors(object.list = inte.list) +inte <- IntegrateData(anchorset = integration.anchors) +#FindVariableFeatures +inte <- FindVariableFeatures(inte) +##Scaling the integrateda +all.genes <- rownames(inte) +inte <- ScaleData(inte, features = all.genes) +#PCA +inte <- RunPCA(inte) +#cluster +inte <- FindNeighbors(inte) +inte <- FindClusters(inte,resolution=0.5) +#TSNE +# if Umap can not use +inte <- RunTSNE(inte) + +#===================================================================================================== +# run monocle +library(monocle) +DefaultAssay(inte) <- "RNA" +tmp.dat <- Seurat::as.CellDataSet(inte) +tmp.dat <- estimateSizeFactors(tmp.dat) +tmp.dat <- detectGenes(tmp.dat, min_expr = 1) +# fData(dat)$use_for_ordering <-fData(dat)$num_cells_expressed > 0.05 * ncol(dat) +expressed_genes <- row.names(subset(fData(tmp.dat),num_cells_expressed >= 10)) + +DefaultAssay(inte) <- "integrated" +genes <- VariableFeatures(inte)[1:800] +ordering_genes <- genes +tmp.dat <- setOrderingFilter(tmp.dat, ordering_genes = ordering_genes) +tmp.dat <- reduceDimension(tmp.dat, method = 'DDRTree') +tmp.dat <- orderCells(tmp.dat) + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/MSK_scRNA.r b/MSK_scRNA.r new file mode 100644 index 0000000..118c112 --- /dev/null +++ b/MSK_scRNA.r @@ -0,0 +1,89 @@ + +# this program is used to analysis MSK GSE123902 data +# 2021-6-23 +#=========================================================================================================================== +library(Seurat) +dat <- readRDS("/public/workspace/lily/Lung2Brain/Version5/MSK_scRNA/GSE123902.RDS") +# FeaturePlot(inte,features=c("EPCAM","EGFR"),label=T) +# show cluster 6 maybe tumor cell beacuse of EPCAM expression +tumor <- subset(dat,cells=which(dat$seurat_clusters==6)) +saveRDS(tumor,file="/public/workspace/lily/Lung2Brain/Version5/MSK_scRNA/GSE123902_tumor.RDS") +ntumor <- subset(dat,cells=colnames(dat)[-which(dat$seurat_clusters==6)]) +saveRDS(ntumor,file="/public/workspace/lily/Lung2Brain/Version5/MSK_scRNA/GSE123902_ntumor.RDS") + + + +#=========================================================================================================================== +# Now classify T cell and NK cell + +dat <- readRDS("/public/workspace/lily/Lung2Brain/Version5/MSK_scRNA/GSE123902_ntumor.RDS") +DefaultAssay(dat) <- "RNA" +pdf("/public/workspace/lily/Lung2Brain/Version5/MSK_scRNA/T_cell_vlnplot.pdf",width=10) +DefaultAssay(dat) <- "RNA" +VlnPlot(dat,features=c("MS4A1","CD79A","CD79B"),pt.size=0) # B cell +VlnPlot(dat,features=c("PTPRC","CD3D","CD3E"),pt.size=0) # CD4 T +VlnPlot(dat,features=c("GZMA","IL2","GZMK","GNLY","GZMB","IFNG"),pt.size=0) # cytotoxic T +VlnPlot(dat,features=c("FOXP3","IL2RA","TGFB1","IKZF2"),pt.size=0) # Treg +VlnPlot(dat,features=c("TIGIT","PDCD1","LAG3","HAVCR2"),pt.size=0) # exhausted +VlnPlot(dat,features=c("TCF7","SELL","LEF1","CCR7"),pt.size=0) # naive T +VlnPlot(dat,features=c("IRF4", "CREM", "NR4A2"),pt.size=0) # Th17 +VlnPlot(dat,features=c("MAF", "CXCR5", "CXCL13"),pt.size=0) # Th +dev.off() + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/Pair_LCBM_analysis.r b/Pair_LCBM_analysis.r new file mode 100644 index 0000000..619ce13 --- /dev/null +++ b/Pair_LCBM_analysis.r @@ -0,0 +1,156 @@ + +# 2021-12-18 +# analysis about paired-LCBM +#=========================================================================================================================================== +library(Seurat) +dat <- readRDS("/public/workspace/lily/Lung2Brain/Pair_LCBM/Pair_BM.RDS") +# Paired_LCBM +# get T cells and Epithelial to run infercnv +sub.dat <- subset(dat,cells=which(dat$seurat_clusters%in%c(16,19,1,9,10))) +# saveRDS(sub.dat,file="/public/workspace/lily/Lung2Brain/Pair_LCBM/Pair_BM_infercnv.RDS") +setwd('/public/workspace/lily/Lung2Brain/Pair_LCBM/inferCNV/') +dir.create('./Pair_BM') +sub.dat$infercnv.type <- paste0("T",sub.dat$seurat_clusters) +sub.dat$infercnv.type[which(sub.dat$seurat_clusters%in%c(16,19))] <- "Tcell" +write.table(sub.dat$infercnv.type,file="Pair_BM_cell_info.txt",sep="\t",col.names=F,quote=F) +count<-as.matrix(sub.dat@assays$RNA@counts) +write.table(count,file="Pair_BM_count_exp.txt",sep="\t",quote=F) + +infercnv_obj = CreateInfercnvObject(raw_counts_matrix="/public/workspace/lily/Lung2Brain/Pair_LCBM/inferCNV/Pair_BM_count_exp.txt", + annotations_file="/public/workspace/lily/Lung2Brain/Pair_LCBM/inferCNV/Pair_BM_cell_info.txt", + delim="\t", + gene_order_file="/public/workspace/lily/REF/INDEX-hg19/anno/gencode_hg19_pos.txt", + ref_group_names="Tcell") + + +setwd('/public/workspace/lily/Lung2Brain/Pair_LCBM/inferCNV/Pair_BM') +infercnv_obj = infercnv::run(infercnv_obj, + cutoff=0.1, # cutoff=1 works well for Smart-seq2, and cutoff=0.1 works well for 10x Genomics + out_dir="./", + cluster_by_groups=T, + plot_steps=F, + no_prelim_plot = TRUE, + num_threads=10, #big + no_plot=F , + output_format = "pdf" # maybe can more quick + # used for final scaling to fit range (0,2) centered at 1. + ) + + + + + +#============================================================================================================================================== +library(Seurat) +dat <- readRDS("/public/workspace/lily/Lung2Brain/Pair_LCBM/Pair_Lung.RDS") +# Paired_Lung +# get T cells and Epithelial to run infercnv +sub.dat <- subset(dat,cells=which(dat$seurat_clusters%in%c(12,20,7,15,19,22))) +# saveRDS(sub.dat,file="/public/workspace/lily/Lung2Brain/Pair_LCBM/Pair_BM_infercnv.RDS") +setwd('/public/workspace/lily/Lung2Brain/Pair_LCBM/inferCNV/') +dir.create('./Pair_Lung') +sub.dat$infercnv.type <- paste0("T",sub.dat$seurat_clusters) +sub.dat$infercnv.type[which(sub.dat$seurat_clusters%in%c(12,20))] <- "Tcell" +write.table(sub.dat$infercnv.type,file="Pair_Lung_cell_info.txt",sep="\t",col.names=F,quote=F) +count<-as.matrix(sub.dat@assays$RNA@counts) +write.table(count,file="Pair_Lung_count_exp.txt",sep="\t",quote=F) + +infercnv_obj = CreateInfercnvObject(raw_counts_matrix="/public/workspace/lily/Lung2Brain/Pair_LCBM/inferCNV/Pair_Lung_count_exp.txt", + annotations_file="/public/workspace/lily/Lung2Brain/Pair_LCBM/inferCNV/Pair_Lung_cell_info.txt", + delim="\t", + gene_order_file="/public/workspace/lily/REF/INDEX-hg19/anno/gencode_hg19_pos.txt", + ref_group_names="Tcell") + + +setwd('/public/workspace/lily/Lung2Brain/Pair_LCBM/inferCNV/Pair_Lung') +infercnv_obj = infercnv::run(infercnv_obj, + cutoff=0.1, # cutoff=1 works well for Smart-seq2, and cutoff=0.1 works well for 10x Genomics + out_dir="./", + cluster_by_groups=T, + plot_steps=F, + no_prelim_plot = TRUE, + num_threads=10, #big + no_plot=F , + output_format = "pdf" # maybe can more quick + # used for final scaling to fit range (0,2) centered at 1. + ) + + + + + + + + + + + + +#============================================================================================================================================= +# DimPlot +#============================================================================================================================================= +library(Seurat) +# LCBM data +dat <- readRDS("/public/workspace/lily/Lung2Brain/Pair_LCBM/Pair_BM.RDS") +dat$celltype <- "Undefined" +dat$celltype[which(dat$seurat_clusters%in%c(16,19))] <- "T&NK" +dat$celltype[which(dat$seurat_clusters%in%c(3))] <- "B cell" +dat$celltype[which(dat$seurat_clusters%in%c(4,7,12,15))] <- "Myeloid" +dat$celltype[which(dat$seurat_clusters%in%c(0,2,5,6,8))] <- "Fibro." +dat$celltype[which(dat$seurat_clusters%in%c(11))] <- "Endothelial" +dat$celltype[which(dat$seurat_clusters%in%c(9,10,1))] <- "Tumor" +dat$celltype[which(dat$seurat_clusters%in%c(18))] <- "Mast" + + +cols <- c('#377EB8','#910241','#984EA3',"#E7298A",'#F29403',"#B2DF8A",'#E41A1C','#999999') +DimPlot(dat,group.by="celltype",cols=cols) + + + + + + + + +# Lung data +dat <- readRDS("/public/workspace/lily/Lung2Brain/Pair_LCBM/Pair_Lung.RDS") +dat$celltype <- "Undefined" +dat$celltype[which(dat$seurat_clusters%in%c(12,20))] <- "T&NK" +dat$celltype[which(dat$seurat_clusters%in%c(14))] <- "B cell" +dat$celltype[which(dat$seurat_clusters%in%c(0,1,2,3,4,5,6,7,8,9,10,11,18))] <- "Myeloid" +dat$celltype[which(dat$seurat_clusters%in%c(13,16,19))] <- "Fibro." +dat$celltype[which(dat$seurat_clusters%in%c(21))] <- "Endothelial" +dat$celltype[which(dat$seurat_clusters%in%c(7,15))] <- "Tumor" +dat$celltype[which(dat$seurat_clusters%in%c(19,22))] <- "Epithelial" +dat$celltype[which(dat$seurat_clusters%in%c(17))] <- "Mast" + + +cols <- c('#377EB8','#910241',"#FB9A99",'#984EA3',"#E7298A",'#F29403',"#B2DF8A",'#E41A1C') +DimPlot(dat,group.by="celltype",cols=cols) + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/Paired_LCBM.R b/Paired_LCBM.R new file mode 100644 index 0000000..f045cf9 --- /dev/null +++ b/Paired_LCBM.R @@ -0,0 +1,77 @@ + +# 2021-12-15 +# use this program to analysis paired LCBM data +# this data run in .3 +# and RDS object will trans to .5 +#=============================================================================================================================================== +library(Seurat) + +# LCBM data +tmp <- Read10X(data.dir = "/public/workspace/lily/LCBM_pair/R21125541/outs/filtered_feature_bc_matrix/") +tmp.dat <- CreateSeuratObject(counts = tmp, project = "Pair_BM",min.cells = 3, min.features = 200) + +prepare <- function(tmp_dat){ + tmp_dat[["percent.mt"]] <- PercentageFeatureSet(object = tmp_dat, pattern = "^MT-") + tmp_dat = subset(x=tmp_dat,subset=nFeature_RNA > 200 & nFeature_RNA < 7500 & percent.mt < 20) +# seurat object + tmp_dat = NormalizeData(object = tmp_dat) + tmp_dat <- FindVariableFeatures(object = tmp_dat) + # scaling + all.genes <- rownames(x = tmp_dat) + tmp_dat <- ScaleData(object = tmp_dat, features = all.genes) + # PCA + tmp_dat <- RunPCA(object = tmp_dat, features = VariableFeatures(object = tmp_dat)) + # clustering + tmp_dat <- FindNeighbors(object = tmp_dat,dims=1:10) + # select proper resolution + tmp_dat <- FindClusters(object = tmp_dat,resolution=0.8) + # T-SNE + tmp_dat <- RunTSNE(object = tmp_dat,dims=1:10,check_duplicates = FALSE) + tmp_dat <- RunUMAP(tmp_dat,dims=1:10) + return(tmp_dat) +} + +dat <- prepare(tmp.dat) + +saveRDS(dat,file=) + + + + +# Lung cancer data +tmp <- Read10X(data.dir = "/public/workspace/lily/LCBM_pair/R21136163/outs/filtered_feature_bc_matrix/") +tmp.dat <- CreateSeuratObject(counts = tmp, project = "Pair_Lung",min.cells = 3, min.features = 200) +dat <- prepare(tmp.dat) + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/Signature.r b/Signature.r new file mode 100644 index 0000000..d96cf35 --- /dev/null +++ b/Signature.r @@ -0,0 +1,355 @@ +# nature medicine stem cell signature +gene <- c("VSNL1","AKR1B1","NOTCH4","TMEM237","SAMD5","PKD2", +"NAP1L1","PTTG1","CDK6","CDCA7","ACSL4","HELLS", +"IKBIP","PLTP","TMEM201","CACHD1","ILF3","DNMT1", +"USP31","FAM216A","SLC41A1","PFKM","KANK1","SUPT16H", +"ADCY3","FGD1","PTPN14","C20orf27","LGR6","SLC16A7", +"JAM3","FBL","NASP","RANBP1","PRNP","DSE","GPX7", +"KDELC1","FCHSD2","SLCO3A1","CCNB1IP1", +"LOC284023","NOL9","NKRF","NUP107","RCC2","ARHGAP25","DDX46","TCOF1","GMPS") +name="stem_cell" +respath="/public/workspace/lily/MOD_file/NatureMedicine/" + + +# hallmark EMT +gene <- c("ABI3BP","ACTA2","ADAM12","ANPEP", +"APLP1","AREG","BASP1","BDNF", +"BGN","BMP1","CADM1","CALD1", +"CALU","CAP2","CAPG","CD44","CD59", +"CDH11","CDH2","CDH6","COL11A1","COL12A1", +"COL16A1","COL1A1","COL1A2","COL3A1","COL4A1", +"COL4A2","COL5A1","COL5A2","COL5A3","COL6A2", +"COL6A3","COL7A1","COL8A2","COMP","COPA", +"CRLF1","CTGF","CTHRC1","CXCL1","CXCL12", +"CXCL6","CYR61","DAB2","DCN","DKK1", +"DPYSL3","DST","ECM1","ECM2","EDIL3", +"EFEMP2","ELN","EMP3","ENO2","FAP", +"FAS","FBLN1","FBLN2","FBLN5","FBN1", +"FBN2","FERMT2","FGF2","FLNA","FMOD", +"FN1","FOXC2","FSTL1","FSTL3","FUCA1", +"FZD8","GADD45A","GADD45B","GAS1","GEM", +"GJA1","GLIPR1","GLT25D1","GPC1","GPX7", +"GREM1","HTRA1","ID2","IGFBP2","IGFBP3", +"IGFBP4","IL15","IL32","IL6","IL8","INHBA", +"ITGA2","ITGA5","ITGAV","ITGB1","ITGB3", +"ITGB5","JUN","LAMA1","LAMA2","LAMA3","LAMC1", +"LAMC2","LEPRE1","LGALS1","LOX","LOXL1", +"LOXL2","LRP1","LRRC15","LUM","MAGEE1", +"MATN2","MATN3","MCM7","MEST","MFAP5","MGP", +"MMP1","MMP14","MMP2","MMP3","MSX1", +"MXRA5","MYL9","MYLK","NID2","NNMT", +"NOTCH2","NT5E","NTM","OXTR","PCOLCE", +"PCOLCE2","PDGFRB","PDLIM4","PFN2","PLAUR", +"PLOD1","PLOD2","PLOD3","PMEPA1","PMP22", +"POSTN","PPIB","PRRX1","PRSS2","PTHLH","PTX3", +"PVR","QSOX1","RGS4","RHOB","SAT1","SCG2", +"SDC1","SDC4","SERPINE1","SERPINE2", +"SERPINH1","SFRP1","SFRP4","SGCB","SGCD", +"SGCG","SLC6A8","SLIT2","SLIT3","SNAI2", +"SNTB1","SPARC","SPOCK1","SPP1","TAGLN", +"TFPI2","TGFB1","TGFBI","TGFBR3","TGM2", +"THBS1","THBS2","THY1","TIMP1","TIMP3","TNC", +"TNFAIP3","TNFRSF11B","TNFRSF12A","TPM1", +"TPM2","TPM4","VCAM1","VCAN","VEGFA","VEGFC","VIM","WIPF1","WNT5A") +name="Hallmark_EMT" +respath="/public/workspace/lily/MOD_file/NatureMedicine/" + + + + + + + +# Nature medicine CELLULAR_MORPHOGENESIS_DURING_DIFFERENTIATION +gene <- c("ALS2","AMIGO1","APOE","BAI1","BAIAP2", +"CDK5R1","CEP290","CNTN4","CYFIP1", +"DPYSL5","FEZ1","FEZ2","GLI2","KAL1", +"KLK8","LRRC4C","MAP1S","MAPT","NRL", +"NRP1","NRP2","NRXN1","NRXN3","NTNG1", +"NTNG2","OPHN1","OTX2","PARD3","PARD6B", +"PAX2","POU4F1","ROBO1","ROBO2","RORB", +"RTN4","RTN4RL1","RTN4RL2","S100B", +"SEMA3B","SEMA4F","SHH","SIAH1","SLIT1", +"SLIT2","SPON2","THY1","UBB","UNC5C","YWHAH") +name="MORPHOGENESIS" +respath="/public/workspace/lily/MOD_file/NatureMedicine/" + + + + +# Nature Medicine AEP +gene <- c("SFTPC","TECTA","POPDC3","PGC","RIMS4", +"AGTR2","SFTPA1","LHFPL3","NAPSA","PTPRD", +"PDPN","LAMP3","SORCS2","AARD","RP11-22C11.2", +"PLA2G1B","MAPT","BCL2L15","MYBPHL","GGTLC1", +"RP3-467L1.6","ABCA3","SEZ6L2","WFDC12","SFTPD", +"HOXD-AS1","ODAM","NBPF3","BMP2","GPR133", +"DPP4","PARM1","C4BPA","SDR16C5","KRT121P","CLDN18", +"CEACAM5","VWA2","SLC34A2","NPR1","SYTL5", +"SMPDL3B","HOPX","RGS16","ABCC6P2","AC090616.2", +"SPNS2","CHIA","EPHA1","SHE","HSD17B6","S100A2", +"TACC2","KRT16P1","PEBP4","CRTAC1","DUOX1","LAD1", +"AC008268.1","NTN4","GKN2","SFTA2","ARSJ","ABCC13", +"FMO5","VEPH1","SEC14L6","AXIN2","C1orf210","SGPP2", +"MAOA","PDZK1IP1","ITGB6","GPRIN2","GSTA4","PLLP", +"DUOXA1","SFN","C14orf132","FLRT3","SLC22A31", +"MFSD2A","C1R","AC034193.5","CREB3L1","MMP28", +"SMAGP","CDH3","SLC22A3","KANK2","SFTA3","COL12A1", +"GPR116","SLC6A14","PLS3","RAB17","PTK7","SEPP1","YAP1", +"AMN","AK1","CCT8P1","LIMCH1","SLC39A8","SEMA3B","CHI3L1", +"SUSD2","DOK4","STXBP1","AQP4","TMPRSS2","S100A14","SCEL", +"BEX2","MUC21","LRRC36","SELENBP1","SULT1A2","LAPTM4B", +"TMEM125","CFI","HNF1B","CFTR","ADAMTS1","LMO3","MUC1", +"SLAIN1","SLC44A3","MYO1B","TTC39A","PTPN13","PEG10", +"CADM1","GRB7","SNX25","NPNT","RAI2","SLC46A2","PRSS8", +"PPP1R9A","PID1","ALS2CL","EMP2","PON3","SFTA1P","CA12", +"CTSE","C4A","LPCAT1","RNF128","VSIG2","FERMT2","NEO1", +"ASS1","EFNB2","ID1","GPC4","CAV1","VSIG10","MECOM", +"PCP4L1","LURAP1L","SMARCA1","MALL","CRB3","PRR15L", +"ELF5","NCKAP1","HIP1R","DSG2","NFIX","FGFR2","ESRP2", +"RND1","KCNS3","PARD3","EPCAM","RNF180","OCLN","SLC25A23", +"CKB","RHOBTB2","CIT","SLC25A4","ARHGAP29","SDC1","LRP5","KRT7", +"COBLL1","NGFRAP1","C8orf4","ATP13A4","MAL2","MBIP","DAPK2", +"TM7SF2","IL20RA","RNF43","PRKCZ","SH3D19","EFNA1","FAM84B", +"AP1M2","CYBRD1","C16orf89","F3","AHCYL2","TMEM97","USP54", +"MSLN","CDH1","MAGI3","LSR","FXYD3","NAB2","GTF2IRD1", +"RAB25","PARD6B","NR1D1","IGFBP4","CGN","GPRC5C","C3","ARMC9", +"OSMR","MMP7","PPARGC1A","ATP2C2","EPS8","CXCL17","PKP3", +"MYO5C","FNBP1L","CNN3","LMO7","C1orf116","MTUS1","DDAH1", +"LGALSL","TMEM30B","SH2D4A","EVPL","MET","ANKRD29","GPR125", +"TM4SF1","ESRP1","LNX2","GPRC5A","SCNN1A","ALOX15B","LIPH", +"CEP70","CYP2B7P1","FADS2","TEF","LRIG3","AMMECR1","PMM1", +"MYH14","RASEF","PCDH1","NNMT","ANKRD65","SDHAP3","RAB40C", +"PDIA5","RNF145","NR3C2","CHI3L2","TTC3P1","RAB3IP","MAP3K13", +"GPD1L","EFEMP1","PXMP4","MTND4P12","FAM20A","TANC1","SQLE", +"TBCEL","NEBL","ERBB3","PPA1","ST3GAL5","GATA6","INADL","CTTN", +"FOLR1","KRT8","BNIP3","DHCR24","FGFR1","TFPI","DLC1","LDLR", +"ABLIM1","CYR61","KLF5","PON2","AMOTL2","MYC","CDS1","ELF3", +"CLIC5","FZD6","CDC42EP1","IFT57","KRT18","PLS1","TSPAN12","CTNNBIP1") + +name="AEP" +respath="/public/workspace/lily/MOD_file/NatureMedicine/" + + + + + + + + + + + + + + + +# PNAS stemnness signature +# https://www.pnas.org/content/pnas/116/18/9020.full.pdf +gene <- c("DNMT3B","PFAS","XRCC5","HAUS6","TET1","IGF2BP1","PLAA", +"TEX10","MSH6","DLGAP5","SKIV2L2","SOHLH2","RRAS2","PAICS","CPSF3", +"LIN28B","IPO5","BMPR1A","ZNF788","ASCC3","FANCB","HMGA2","TRIM24", +"ORC1","HDAC2","HESX1","INHBE","MIS18A","DCUN1D5","MRPL3","CENPH", +"MYCN","HAUS1","GDF3","TBCE","RIOK2","BCKDHB","RAD1","NREP","ADH5", +"PLRG1","ROR1","RAB3B","DIAPH3","GNL2","FGF2","NMNAT2","KIF20A", +"CENPI","DDX1","XXYLT1","GPR176","BBS9","C14orf166","BOD1","CDC123", +"SNRPD3","FAM118B","DPH3","EIF2B3","RPF2","APLP1","DACT1","PDHB", +"C14orf119","DTD1","SAMM50","CCL26","MED20","UTP6","RARS2","ARMCX2", +"RARS","MTHFD2","DHX15","HTR7","MTHFD1L","ARMC9","XPOT","IARS","HDX", +"ACTRT3","ERCC2","TBC1D16","GARS","KIF7","UBE2K","SLC25A3","ICMT", +"UGGT2","ATP11C","SLC24A1","EIF2AK4","GPX8","ALX1","OSTC","TRPC4", +"HAS2","FZD2","TRNT1","MMADHC","SNX8","CDH6","HAT1","SEC11A","DIMT1","TM2D2","FST","GBE1") + +name="PNAS_stem" +respath="/public/workspace/lily/MOD_file/NatureMedicine/" + + +source('/public/workspace/lily/software/ssGSEA/ssgseaMOD.r') +mod.generate(gene,name,out=paste0(respath,name,".mod")) # make a mod file + + + + + + + + + + + +# MSGDB signature +gene <- c("AKT1","BAP1","MRE11","NF2","NSMCE3","PDGFB","PIK3CA","RNF168","SMARCB1","SMARCE1","SMO","SUFU","TERT","TRAF7") + +name="Radio_sens" +respath="/public/workspace/lily/MOD_file/NatureMedicine/" + + +source('/public/workspace/lily/software/ssGSEA/ssgseaMOD.r') +mod.generate(gene,name,out=paste0(respath,name,".mod")) # make a mod file + + + + + +gene.f <- c("LGALS9","ARG1","CD47","IDO1","VEGFA","IL10","TGFB1","PVR") +source('/public/workspace/lily/software/ssGSEA/ssgseaMOD.r') +mod.generate(gene.f,"immunsupp",out="/public/workspace/lily/MOD_file/immunsupp.mod") # make a mod file + + + + + + + + + + + + +# 2021-9-28 +# Brain gene and Lung gene signature +# come form https://bioinfo.uth.edu/TissGDB/download_dir/g_ctype_tissue.txt?csrt=17039100935693777611 +# Tissue-specific genes (TissGenes) with cancer type and tissue information + +tmp <- read.table("~/tmp/g_ctype_tissue.txt") +unique(as.vector(tmp[which(tmp$V2%in%c("GBM","LGG")),1])) +unique(as.vector(tmp[which(tmp$V2%in%c("LUAD")),1])) + + + + + + + +# 2021-10-14 +# another radio signature +# https://bmcgenomics.biomedcentral.com/articles/10.1186/1471-2164-13-348/tables/1 +ACTN1 Down (0.42) +ANXA2 Down (0.36) +ANXA5 Down (0.42) +ARHGDIB Up (−0.49) +CAPNS1 Down (0.48) +CBR1 Down (0.41) +CCND1 Down (0.54) +CD63 Down (0.51) +CORO1A Up (−0.46) +CXCR4 Up (−0.46) +DAG1 Down (0.60) +EMP2 Down (0.41) +HCLS1 Up (−0.58) +HTRA1 Down (0.52) +ITGB5 Down (0.47) +LAPTM5 Up (−0.50) +LRMP Up (−0.49) +MYB Up (−0.59) +PFN2 Down (0.61) +PIR Down (0.43) +PKM2 Down (0.44) +PTMS Down (0.48) +PTPRC Up (−0.55) +PTPRCAP Up (−0.49) +PYGB Down (0.35) +RAB13 Down (0.43) +RALB Down (0.47) +SCRN1 Down (0.40) +SQSTM1 Down (0.48) +TWF1 Down (0.43) +WAS Up (−0.60) + +tmp <- read.table("/public/workspace/lily/tmp/tmp.txt",sep="\t") +gene <- as.numeric(gsub("−","-",(gsub("\\)","",sapply(strsplit(as.character(tmp$V2),"\\("),function(x){x[[2]]}))))) +names(gene) <- as.character(tmp$V1) +gene <- ifelse(gene<0,1,(-1)) + +gene <- gene[which(gene>0)] +source('/public/workspace/lily/software/ssGSEA/ssgseaMOD.r') +mod.generate(gene,"Radio_sen_BMC",out="/public/workspace/lily/MOD_file/Radio_sen_BMC.mod") # make a mod file + + + + + + + + + + + +ATP4A -2.03 +COLQ -0.42 +AMDHD1 0.42 +PER1 -0.3 +KPNA2 0.34 +TAP2 0.39 +MSMO1 0.29 +BTG3 0.12 +TUBB2A 0.64 +CMPK2 -0.04 +NEK2 0.91 +ZFYVE1 -0.01 +ATP5H -0.43 +LOC283398 0.07 +E2F8 -0.63 +GGA2 -0.01 +DDIT3 0.97 +TXNIP -0.33 +LINS -1.85 +ZNF787 0.38 +PLEK2 -0.06 +PLA2G4A 0.6 +SARS2 -0.4 +MMD 0.58 +BBS9 -0.34 +FGFR1OP -0.33 +ANO3 -0.33 +NUDT15 0.47 +COX7B 1.2 +H2AFJ 5.5 +PYGB -1.12 +USP7 -0.58 +PHEX 0.39 +FAM114A2 -0.43 + +tmp <- read.table("/public/workspace/lily/tmp/tmp.txt",sep="\t") +gene <- as.numeric(tmp$V2) +names(gene) <- as.character(tmp$V1) +gene <- ifelse(gene<0,1,(-1)) + +# gene <- gene[which(gene>0)] +source('/public/workspace/lily/software/ssGSEA/ssgseaMOD.r') +mod.generate(gene,"Radio_sen_CCR",out="/public/workspace/lily/MOD_file/Radio_sen_CCR.mod") # make a mod file + + + + + + + + + +#====================================================================================== +# 2021-10-14 +# make this +# use this https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3038688/ +gene <- c("AR","PRKCA","RELA","SUMO1","CDK1","HDAC1","IRF1") +source('/public/workspace/lily/software/ssGSEA/ssgseaMOD.r') +mod.generate(gene,"Radio_sen_test",out="/public/workspace/lily/MOD_file/Radio_sen_test.mod") # make a mod file + +sapply(gene,function(x){ + c(cor.test(mod$Brain_gene_norm,as.numeric(dat.BM[x,]),method="spearman")$estimate, + cor.test(mod$Lung_gene_norm,as.numeric(dat.BM[x,]),method="spearman")$estimate, + cor.test(mod$BMS_update_norm,as.numeric(dat.BM[x,]),method="spearman")$estimate) + }) + +# gene <- c("KIAA0586","C14orf135","TRMT5","GLMN","MUDENG","ZMYM6","SFRS5") +# source('/public/workspace/lily/software/ssGSEA/ssgseaMOD.r') +# mod.generate(gene,"Radio_sen_test",out="/public/workspace/lily/MOD_file/Radio_sen_test.mod") # make a mod file + + + +gene <- c("RPIA","RBBP4","RGS19") +source('/public/workspace/lily/software/ssGSEA/ssgseaMOD.r') +mod.generate(gene,"Radio_sen_test",out="/public/workspace/lily/MOD_file/Radio_sen_test.mod") # make a mod file + + + diff --git a/TME_prepare.r b/TME_prepare.r new file mode 100644 index 0000000..ee8f5db --- /dev/null +++ b/TME_prepare.r @@ -0,0 +1,229 @@ + +# analysis TME and Myeloid cells +# 2021-5-20 +############################################################################################### +# 0. some annoatation about inte TME # use merge is also OK,because we do not use this assay +# library(Seurat) +# dat <- readRDS("/public/workspace/lily/Lung2Brain/inte7/inte7_ann.RDS") +# GBM1 <- readRDS("/public/workspace/lily/PS/Final_716/lesion1.RDS") +# GBM2 <- readRDS("/public/workspace/lily/PS/Final_716/lesion2.RDS") +# # integration samples +# integration.anchors <- FindIntegrationAnchors(object.list = c(dat,GBM1,GBM2)) +# inte <- IntegrateData(anchorset = integration.anchors) +# #FindVariableFeatures +# inte <- FindVariableFeatures(inte) +# ##Scaling the integrateda +# all.genes <- rownames(inte) +# inte <- ScaleData(inte, features = all.genes) +# #PCA +# inte <- RunPCA(inte) +# #cluster +# inte <- FindNeighbors(inte) +# inte <- FindClusters(inte) +# #TSNE +# # if Umap can not use +# inte <- RunTSNE(inte) + +# #================================================================================================================================= +# # add some information +# # +# #================================================================================================================================= +# inte$type_group[which(inte$orig.ident%in%c('RD-20180817-001-SR18271','RD-20180817-002-SR18271'))] <- "GBM" # sample info +# # cell type info +# inte$type[which(inte$orig.ident%in%c('RD-20180817-001-SR18271','RD-20180817-002-SR18271'))] <- inte$celltype[which(inte$orig.ident%in%c('RD-20180817-001-SR18271','RD-20180817-002-SR18271'))] +# inte$type[which(inte$type%in%c("BMDM","MG"))] <- "Myeloid" +# inte$type[which(inte$type=="Tumor_cell")] <- "maliganant" + +# saveRDS(inte,file="/public/workspace/lily/Lung2Brain/TME/inte_TME.RDS") + +# 1. subset non-tumor samples and re-inte +library(Seurat) +dat <- readRDS("/public/workspace/lily/Lung2Brain/TME/inte_TME.RDS") +sub.dat <- subset(dat,cells=which(dat$malignant=="non-tumor")) +saveRDS(sub.dat,file="/public/workspace/lily/Lung2Brain/Version5/Fig6/Data/inte9_ntumor.RDS") + +# re - inte +#=================================================================================================================================== +# +library(Seurat) +dat <- readRDS("/public/workspace/lily/Lung2Brain/Version5/Fig6/Data/inte9_ntumor.RDS") +inte.list <- list() +samplelist <- unique(dat$orig.ident) +for(i in 1: length(samplelist)){ + tmp <- subset(dat,cells=which(dat$orig.ident==samplelist[i])) + DefaultAssay(tmp) <- "RNA" + inte.list[[i]] <- tmp +} +integration.anchors <- FindIntegrationAnchors(object.list = inte.list) +inte <- IntegrateData(anchorset = integration.anchors) +#FindVariableFeatures +inte <- FindVariableFeatures(inte) +##Scaling the integrateda +all.genes <- rownames(inte) +inte <- ScaleData(inte, features = all.genes) +#PCA +inte <- RunPCA(inte) +#cluster +inte <- FindNeighbors(inte) +inte <- FindClusters(inte) +#TSNE +# if Umap can not use +inte <- RunTSNE(inte) + +saveRDS(inte,file="/public/workspace/lily/Lung2Brain/Version5/Fig6/Data/inte9_ntumor.RDS") + +# subset T,B cell and Myeloid cells +dat <- readRDS("/public/workspace/lily/Lung2Brain/Version5/Fig6/Data/inte9_ntumor.RDS") +sub.lym <- subset(dat,cells=which(dat$type%in%c("B_cell","T_cell"))) +sub.mye <- subset(dat,cells=which(dat$type%in%c("Myeloid"))) + +saveRDS(sub.lym,file="/public/workspace/lily/Lung2Brain/Version5/Fig6/Data/inte9_lympho.RDS") + +saveRDS(sub.mye,file="/public/workspace/lily/Lung2Brain/Version5/Fig6/Data/inte9_myeloid.RDS") + + + + +######################################################################################################################################## +# 2021-6-2 +# analysis lymphocyte +#======================================================================================================================================= +library(Seurat) +dat <- readRDS("/public/workspace/lily/Lung2Brain/Version5/Fig6/Data/inte9_lympho.RDS") +DefaultAssay(dat) <- "RNA" + +inte.list <- list() +samplelist <- unique(dat$orig.ident) +for(i in 1: length(samplelist)){ + tmp <- subset(dat,cells=which(dat$orig.ident==samplelist[i])) + DefaultAssay(tmp) <- "RNA" + inte.list[[i]] <- tmp +} +integration.anchors <- FindIntegrationAnchors(object.list = inte.list,k.filter=20) +inte <- IntegrateData(anchorset = integration.anchors) +#FindVariableFeatures +inte <- FindVariableFeatures(inte) +##Scaling the integrateda +all.genes <- rownames(inte) +inte <- ScaleData(inte, features = all.genes) +#PCA +inte <- RunPCA(inte) +#cluster +inte <- FindNeighbors(inte) +inte <- FindClusters(inte) +#TSNE +# if Umap can not use +inte <- RunTSNE(inte) + +saveRDS(inte,file="/public/workspace/lily/Lung2Brain/Version5/Fig6/Data/inte9_lympho.RDS") + + + +#========================================================================================================================= +library(Seurat) + +dat <- readRDS("/public/workspace/lily/Lung2Brain/Version5/Fig6/Data/inte9_lympho.RDS") + +pdf("/public/workspace/lily/Lung2Brain/Version5/T_cell/T_cell_featureplot.pdf") +DefaultAssay(dat) <- "RNA" +DimPlot(dat,label=T) +DimPlot(dat,group.by="type_group") +FeaturePlot(dat,features=c("MS4A1","CD79A","CD79B"),label=T,order=F) # B cell +FeaturePlot(dat,features=c("PTPRC","CD3D","CD3E"),label=T,order=F) # CD4 T +FeaturePlot(dat,features=c("CD8A","CD8B","CD3D"),label=T,order=F) # CD8 T +FeaturePlot(dat,features=c("FOXP3","IL2RA","TGFB1","IKZF2"),label=T,order=F) # Treg +FeaturePlot(dat,features=c("TIGIT","PDCD1","LAG3","HAVCR2"),label=T,order=F) # exhausted +FeaturePlot(dat,features=c("TCF7","SELL","LEF1","CCR7"),label=T,order=F) # naive T +FeaturePlot(dat,features=c("IRF4", "CREM", "NR4A2"),label=T,order=F) # Th17 +dev.off() + + +pdf("/public/workspace/lily/Lung2Brain/Version5/T_cell/T_cell_vlnplot.pdf",width=10) +DefaultAssay(dat) <- "RNA" +VlnPlot(dat,features=c("MS4A1","CD79A","CD79B"),pt.size=0) # B cell +VlnPlot(dat,features=c("PTPRC","CD3D","CD3E"),pt.size=0) # CD4 T +VlnPlot(dat,features=c("GZMA","IL2","GZMK","GNLY","GZMB","IFNG"),pt.size=0) # cytotoxic T +VlnPlot(dat,features=c("FOXP3","IL2RA","TGFB1","IKZF2"),pt.size=0) # Treg +VlnPlot(dat,features=c("TIGIT","PDCD1","LAG3","HAVCR2"),pt.size=0) # exhausted +VlnPlot(dat,features=c("TCF7","SELL","LEF1","CCR7"),pt.size=0) # naive T +VlnPlot(dat,features=c("IRF4", "CREM", "NR4A2"),pt.size=0) # Th17 +VlnPlot(dat,features=c("MAF", "CXCR5", "CXCL13"),pt.size=0) # Th +dev.off() + +# combine with singleR +library(Seurat) +library(SingleR) +dat <- readRDS("/public/workspace/lily/Lung2Brain/Version5/Fig6/Data/inte9_lympho.RDS") +ref <- readRDS("/public/workspace/lily/software/SingleRData/SingleR_BluePrint.RDS") +res.cluster<-SingleR(test=as.matrix(dat@assays$RNA@data),ref=ref,labels=ref$label.fine,clusters=dat$seurat_clusters,method="cluster") + +# ref <- readRDS("/public/workspace/lily/software/SingleRData/SingleR_MonacoImmuneData.RDS") +# res.cluster<-SingleR(test=as.matrix(dat@assays$RNA@data),ref=ref,labels=ref$label.fine,clusters=dat$seurat_clusters,method="cluster") + + +# 2021-6-7 +#============================================================================================================ +# define cell +dat$hbmarker <- NULL +dat$llymarker <- NULL +dat$celltype <- NULL +dat$putativeTumor3 <- NULL +dat$res.2 <- NULL +dat$Phase <- NULL +dat$G2M.Score <- NULL +dat$S.Score <- NULL +dat$percent.mito <- NULL +dat$nUMI <- NULL +dat$nGene <- NULL +dat$RNA_snn_res.2 <- NULL + +dat$celltype <- "Undefine" +dat$celltype[which(dat$seurat_clusters%in%c(12,14))] <- "Plasma" +dat$celltype[which(dat$seurat_clusters%in%c(4))] <- "Bcell" #CD79A CD79B +dat$celltype[which(dat$seurat_clusters%in%c(11,16))] <- "NKcell" # KLRD +dat$celltype[which(dat$seurat_clusters%in%c(6,7))] <- "Treg" # IL2RA FOXP3 +dat$celltype[which(dat$seurat_clusters%in%c(0,8,10))] <- "CD8 exhausted/cytotoxic" # PDCD1 LAG3 CTLA4 +dat$celltype[which(dat$seurat_clusters%in%c(1,5))] <- "CD8 cytotoxic" # GZMA +dat$celltype[which(dat$seurat_clusters%in%c(2,3))] <- "CD4 naive" # IL7R CCR7 +dat$celltype[which(dat$seurat_clusters%in%c(13,15))] <- "Tfh" # CXCL13 +dat$celltype[which(dat$seurat_clusters%in%c(9))] <- "Undefine" # CD3D and CD3E no violin + +saveRDS(dat,file="/public/workspace/lily/Lung2Brain/Version5/Fig6/Data/inte9_lympho.RDS") +# mv into Tcell file + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/brain_like_DEG.txt b/brain_like_DEG.txt new file mode 100644 index 0000000..552221e --- /dev/null +++ b/brain_like_DEG.txt @@ -0,0 +1,2541 @@ +"p_val" "avg_logFC" "pct.1" "pct.2" "p_val_adj" +"7SK.2" 0.000534071147719249 1.64071682651202 0.367 0.489 1 +"SPP1" 0.00712308893811064 1.18880834608515 0.437 0.675 1 +"APOE" 0.283155917754549 1.09415269578383 0.365 0.532 1 +"LYZ" 0.652759748914131 0.993691055542605 0.261 0.358 1 +"S100A8" 2.31146944386124e-14 0.928630243057206 0.102 0.203 5.66286899051564e-10 +"MTRNR2L12" 2.46294170476968e-06 0.804801872054447 0.332 0.559 0.0603396088251524 +"RPL38" 0 0.781484478972347 0.981 0.992 0 +"IGFBP7" 0.0790811171874246 0.77738161069587 0.174 0.185 1 +"RPL27A" 0 0.765755001814199 0.977 0.997 0 +"RPL13A" 1.29786255931195e-289 0.753783271020512 0.957 0.993 3.17963348405835e-285 +"MTRNR2L8" 3.61299364176141e-36 0.750920432561941 0.165 0.41 8.85147312295127e-32 +"RPL35" 4.69960008250442e-249 0.749736163942597 0.96 0.986 1.15135502421276e-244 +"C1QA" 0.78499816135058 0.747447630556761 0.169 0.199 1 +"APOC1" 5.38085835045941e-18 0.743343251780212 0.185 0.373 1.31825648727905e-13 +"C1QB" 0.640260571445739 0.718451885524847 0.17 0.211 1 +"PLP1" 0.0658581684131405 0.706450938792981 0.152 0.204 1 +"FTL" 6.87998411630217e-153 0.702189967258495 0.979 0.999 1.68552730865287e-148 +"MBP" 1.024999735375e-68 0.67256517325035 0.104 0.38 2.51114685169522e-64 +"RPL37A" 0 0.653910145981478 0.997 0.999 0 +"C6orf48" 2.27265821993727e-12 0.636620619602914 0.401 0.819 5.56778537302431e-08 +"RPS18" 0 0.622734063771504 0.995 0.999 0 +"RPS19" 5.70403419682573e-217 0.607822193495497 0.973 0.995 1.39743133788034e-212 +"SRGN" 7.94500578463556e-13 0.594923824553827 0.151 0.284 1.94644696717786e-08 +"RPS24" 8.59138101559148e-268 0.593710295297792 0.993 0.998 2.10480243500976e-263 +"RPS29" 2.43661569095444e-191 0.581638736211224 0.985 0.993 5.96946478126928e-187 +"RPL31" 1.81702594592238e-133 0.580532859849308 0.83 0.989 4.45153186491525e-129 +"RPL10A" 1.13105417075361e-182 0.557164931637203 0.954 0.99 2.77096961292927e-178 +"VIM" 2.12644347693685e-66 0.551280676781269 0.771 0.807 5.2095738741476e-62 +"UQCRB" 1.77051644225369e-115 0.544412511952121 0.866 0.984 4.33758823187733e-111 +"RPL37" 0 0.542214492916336 0.998 0.999 0 +"USMG5" 5.37244454321226e-40 0.534020740420874 0.711 0.947 1.31619518864157e-35 +"RPL18A" 6.1141311659067e-187 0.5324892682547 0.978 0.996 1.49790099433548e-182 +"FAM3C" 2.07776203033306e-14 0.524514349390941 0.646 0.911 5.09030919811296e-10 +"RPS27" 5.82692222828208e-243 0.516997468932857 0.997 0.999 1.42753767670683e-238 +"RPL27" 2.57285120180939e-125 0.51562506739078 0.91 0.988 6.30322815931281e-121 +"RPS3A" 4.99970226867573e-252 0.510010938849208 0.997 0.998 1.22487705880287e-247 +"RPL3" 1.09381876009845e-158 0.505794403476562 0.969 0.994 2.67974658036519e-154 +"RPL7" 3.23228351152918e-168 0.502699034037866 0.968 0.998 7.91877137489534e-164 +"BCYRN1" 6.51522854429145e-14 0.498997073489448 0.258 0.481 1.59616584106596e-09 +"HLA-DPB1" 5.1553740810707e-41 0.496621969087486 0.162 0.417 1.26301509612151e-36 +"MTRNR2L2" 3.18754972983693e-43 0.492761148107703 0.132 0.366 7.80917808312749e-39 +"RPL32" 1.67934352867501e-171 0.491099781049557 0.981 0.998 4.11422371090091e-167 +"UBA52" 6.04200667452192e-172 0.488007414892773 0.94 0.998 1.48023121519112e-167 +"CLU" 1.13412976763368e-15 0.484603479104152 0.101 0.212 2.77850451772575e-11 +"TIMP1" 0.00161803218533349 0.478886598279542 0.482 0.842 1 +"TYROBP" 0.295912217892601 0.477670802615736 0.095 0.116 1 +"RPL14" 1.94637314086843e-171 0.47545368865255 0.98 0.996 4.76841955781358e-167 +"RPL28" 1.75493144176029e-164 0.470855765414034 0.985 0.999 4.29940653916854e-160 +"TMSB10" 1.34350100918555e-219 0.458783654212577 0.999 0.999 3.29144312240368e-215 +"PSAP" 0.00015300329236261 0.456824938463398 0.484 0.906 1 +"RPL36" 2.27990416915876e-157 0.45396462358438 0.963 0.999 5.58553722402204e-153 +"RPLP0" 1.11923966211514e-147 0.45026565611461 0.971 0.997 2.74202524821588e-143 +"RPS20" 2.66827893065573e-170 0.447466702398689 0.974 0.998 6.53701655221347e-166 +"C19orf33" 1.16613776214127e-17 0.444712152771541 0.399 0.843 2.85692090346989e-13 +"ATP5I" 0.00876569655323148 0.443389809239223 0.57 0.957 1 +"RPS11" 3.88987087689058e-92 0.44218418004431 0.867 0.997 9.52979466129424e-88 +"RPL41" 4.49098315051224e-215 0.438947955370661 0.998 1 1.10024596204399e-210 +"EIF3E" 5.94320215687206e-40 0.438542785646512 0.768 0.955 1.45602509641209e-35 +"S100A4" 1.32430406917889e-73 0.43828394763891 0.854 0.697 3.24441253908137e-69 +"SPARC" 8.15302946546158e-39 0.437364064630653 0.111 0.311 1.99741068874343e-34 +"LGALS1" 5.29673408301563e-45 0.434093615867666 0.762 0.747 1.297646882998e-40 +"EEF1B2" 1.69145927822861e-40 0.433104678220388 0.77 0.937 4.14390608573227e-36 +"RPS6" 1.01503781268895e-164 0.431416369113623 0.979 0.999 2.48674113730666e-160 +"CTSD" 1.98182979722605e-15 0.431021854531848 0.453 0.939 4.85528482022411e-11 +"RPL35A" 4.43132185137401e-143 0.43054406586221 0.985 0.997 1.08562954036812e-138 +"PGLS" 2.88915429427919e-09 0.428325645650554 0.58 0.785 7.07813910555459e-05 +"RPLP2" 1.57487439535013e-175 0.425971028799141 0.988 1 3.85828478116828e-171 +"RPL21" 1.33146168900046e-129 0.42523918185759 0.95 0.995 3.26194799188223e-125 +"RPL6" 1.07493471466049e-136 0.424474193283425 0.982 0.998 2.63348255744673e-132 +"EEF1A1" 2.88850626938368e-174 0.423008823046756 0.999 1 7.07655150936307e-170 +"RPS2" 4.29409222292082e-160 0.419615123479617 0.994 0.997 1.05200965369337e-155 +"RPL29" 6.93140442965642e-122 0.419349432697525 0.983 0.995 1.69812477122153e-117 +"EIF3L" 2.06981472573415e-27 0.41645741636845 0.376 0.83 5.07083909657608e-23 +"SLC25A6" 2.80888970123183e-10 0.415251343574003 0.625 0.914 6.88149887904787e-06 +"PFDN5" 4.38573201590949e-69 0.413289954644914 0.804 0.992 1.07446048657767e-64 +"RPL4" 5.13443743303333e-21 0.413241234106904 0.698 0.953 1.25788582671884e-16 +"RPL23A" 7.50698790878672e-156 0.410907563117514 0.978 0.998 1.83913696777366e-151 +"ATPIF1" 8.50578894636237e-26 0.408795123403316 0.371 0.792 2.08383323396932e-21 +"COX17" 0.879016020076242 0.40790065288633 0.541 0.941 1 +"NPM1" 8.04767661601538e-93 0.407399363777597 0.965 0.994 1.97160029415761e-88 +"TOMM7" 2.07813197829745e-52 0.402709217891422 0.821 0.989 5.09121553363092e-48 +"NSA2" 3.00668034299562e-05 0.399273053642264 0.599 0.95 0.736606617230496 +"ALDOA" 1.38417719746236e-48 0.394015082890202 0.858 0.957 3.39109571606303e-44 +"EIF3H" 0.000480299320268748 0.390976530907852 0.575 0.921 1 +"ATP5L" 5.44067422170652e-45 0.390818633929459 0.737 0.985 1.33291077757588e-40 +"FCER1G" 0.00012329505889553 0.387349143632734 0.083 0.129 1 +"RPL30" 2.95610479542808e-150 0.387087079254833 0.998 1 7.24216113831926e-146 +"ATHL1" 5.14420934547935e-42 0.386842560555721 0.105 0.312 1.26027984754899e-37 +"RPL8" 1.50480291184295e-119 0.385248179457784 0.996 0.998 3.68661665372405e-115 +"RPS4X" 5.67917679446641e-143 0.381987091980569 0.995 0.998 1.39134152287633e-138 +"C1QC" 0.00483119132816541 0.38170661286697 0.078 0.112 1 +"EEF1A2" 6.29685881693603e-10 0.380429415195518 0.315 0.562 1.54266744156116e-05 +"HLA-C" 3.75490769345776e-07 0.378549265032496 0.653 0.909 0.00919914835820217 +"NACA" 1.80259047258507e-102 0.375666156508755 0.975 0.997 4.41616639878615e-98 +"TKT" 1.06364891025785e-20 0.372784576901441 0.732 0.93 2.60583346524071e-16 +"RPS28" 2.2004761524467e-106 0.366536807967832 0.953 0.999 5.39094652587917e-102 +"ATP5H" 0.0460391924160401 0.3652899702033 0.508 0.892 1 +"GNB2L1" 6.82397449259047e-101 0.362429372523663 0.985 0.997 1.67180551093974e-96 +"NBEAL1" 0.000494952757413302 0.361433963840676 0.467 0.839 1 +"MYL6" 1.22533927915072e-64 0.359967227076362 0.908 0.995 3.00195869999135e-60 +"SNRPD2" 1.74594500859073e-34 0.359802594978547 0.359 0.818 4.27739067654643e-30 +"HMGN2" 0.012473232336169 0.356869513977309 0.592 0.888 1 +"EIF4A2" 1.26902715291263e-59 0.356429474510525 0.326 0.843 3.10898962192065e-55 +"RPL11" 4.69504049183288e-131 0.353482362042419 0.992 0.999 1.15023797009414e-126 +"SFTA2" 3.48800866191396e-22 0.353457007203363 0.74 0.943 8.54527242082301e-18 +"POLR2L" 0.928312615901106 0.353383155089424 0.549 0.919 1 +"RPL13" 7.73334144175825e-136 0.352593930762026 0.992 1 1.89459131981635e-131 +"C7orf50" 2.50843873275332e-08 0.35243674913815 0.409 0.751 0.000614542405137236 +"RPL19" 9.04965914839026e-104 0.352227504807813 0.988 0.998 2.21707599476413e-99 +"EDIL3" 6.35886648951036e-36 0.349741993024504 0.232 0.53 1.55785870126514e-31 +"SNRPC" 8.31696948450238e-37 0.349430161109048 0.341 0.777 2.03757435400824e-32 +"CHMP2A" 1.07750553905317e-58 0.346657485653807 0.302 0.773 2.63978082012635e-54 +"RPS5" 6.95998349056682e-56 0.346036033446311 0.884 0.99 1.70512635535396e-51 +"IMPDH2" 2.42500493436039e-37 0.344750545286898 0.328 0.742 5.94101958868953e-33 +"SSBP1" 0.196395562779187 0.344748822752394 0.562 0.893 1 +"NUPR1" 4.56383777711069e-19 0.342735905564444 0.087 0.202 1.11809461701435e-14 +"RPL22" 7.60516934090952e-58 0.341135698974443 0.884 0.998 1.86319043682942e-53 +"SSR4" 4.12437811011783e-15 0.340559257162888 0.7 0.965 1.01043139319777e-10 +"PRDX1" 2.08405155685832e-28 0.339846311625589 0.831 0.962 5.10571790914719e-24 +"CCL4" 1.38931802455265e-36 0.339467495853902 0.095 0.274 3.40369022835155e-32 +"NOL7" 1.61723399160669e-41 0.337232755564863 0.327 0.76 3.96206155603724e-37 +"GSTO1" 8.8051094583395e-47 0.336137051385945 0.291 0.695 2.15716376619859e-42 +"PSMB1" 0.000464045108148111 0.336005808814148 0.478 0.827 1 +"COPE" 2.84268596164109e-23 0.334623432072336 0.389 0.816 6.9642963374245e-19 +"C4orf48" 0.00368111796675612 0.334575820292687 0.594 0.958 1 +"RPL24" 5.27992211762975e-102 0.334489507551524 0.975 0.998 1.29352811959811e-97 +"ARPC3" 1.07497512812901e-16 0.334488159034508 0.722 0.97 2.63358156640327e-12 +"FXR1" 3.28438877533006e-42 0.334121185931016 0.351 0.832 8.04642406068112e-38 +"S100A6" 2.06685588632669e-118 0.333615405704609 0.999 0.997 5.06359023591176e-114 +"TBCA" 1.46479928862607e-08 0.33228450368665 0.647 0.978 0.000358861177720502 +"RPS27L" 1.01608237963449e-50 0.331344170013866 0.341 0.843 2.48930022186653e-46 +"CAPG" 1.60263189674332e-20 0.330345830934608 0.342 0.684 3.92628788383145e-16 +"NDUFA4" 5.63789270883066e-17 0.330335119067961 0.768 0.971 1.38122733473642e-12 +"COX7C" 2.72697937619714e-70 0.329214384777016 0.947 0.995 6.68082677374536e-66 +"RPS25" 9.71672452767027e-101 0.329173625583357 0.971 0.997 2.38050034203394e-96 +"KDELR2" 0.0237512972925365 0.326312553790512 0.609 0.926 1 +"RPL12" 1.97794900920925e-84 0.326225212578254 0.981 0.999 4.84577727766173e-80 +"RSL24D1" 2.3168083819551e-85 0.326109257014416 0.266 0.771 5.67594885495179e-81 +"EIF1" 1.04665081234724e-47 0.325899954291979 0.897 0.998 2.56418982516951e-43 +"RGS10" 1.37546383862699e-44 0.325307910168564 0.315 0.752 3.36974885825225e-40 +"ROMO1" 1.54881866924059e-13 0.324127718923412 0.459 0.947 3.79445085777253e-09 +"RPA3" 0.0319423027631079 0.323967018595438 0.475 0.726 1 +"RPS12" 3.1764805881922e-126 0.32343620534437 0.991 0.999 7.78205979301206e-122 +"EIF3I" 1.51041592566653e-49 0.322706618086562 0.308 0.756 3.70036797629043e-45 +"UBB" 1.29977161103603e-06 0.321391152031352 0.662 0.973 0.0318431046987716 +"XIST" 1.05563804887266e-43 0.321129690994918 0.236 0.561 2.58620765593314e-39 +"RPL26" 6.31861517855997e-59 0.319656616073677 0.94 0.997 1.54799753259541e-54 +"RPL5" 4.71369512320402e-45 0.319133285242612 0.896 0.996 1.15480816823375e-40 +"EPB41L4A-AS1" 1.13222859627175e-100 0.319067631461316 0.193 0.648 2.77384683800615e-96 +"RPL39" 2.67916491142053e-100 0.317789656565096 0.987 0.999 6.56368611648916e-96 +"CHCHD1" 1.20622985212896e-110 0.316810545761222 0.221 0.732 2.95514251473074e-106 +"EEF1D" 9.57175250933533e-31 0.316758674672855 0.788 0.988 2.34498364726206e-26 +"MRPL33" 9.63223647146435e-06 0.316179380170763 0.505 0.924 0.235980161314405 +"GLTSCR2" 2.62187630126606e-84 0.316006380606172 0.217 0.659 6.42333475047171e-80 +"RPLP1" 2.36523282010423e-102 0.313870777193181 0.993 0.999 5.79458388597334e-98 +"HLA-DRA" 2.2274632906043e-51 0.312753795341993 0.351 0.84 5.45706231565149e-47 +"LAPTM5" 3.47943106183353e-08 0.311707477965215 0.075 0.137 0.000852425815838598 +"PRR13" 3.4539547080779e-77 0.310764837645813 0.296 0.824 8.46184363932006e-73 +"RPS7" 8.4759078334816e-76 0.310550796786916 0.975 0.998 2.07651266012466e-71 +"CHCHD10" 5.74826884082221e-26 0.309847350148253 0.357 0.744 1.40826838331303e-21 +"S100A14" 0.00847455444875282 0.309486237058094 0.514 0.901 1 +"RPS14" 4.15886262554768e-141 0.309386097440421 0.998 1 1.01887975463293e-136 +"CYCS" 0.762617620620752 0.307386214253888 0.586 0.928 1 +"RPS27A" 9.28594453532275e-96 0.307018579059204 0.99 0.999 2.27496355170872e-91 +"ADIRF" 4.02269105929316e-60 0.305959011848269 0.194 0.533 9.8551908261623e-56 +"ERAP2" 2.94259938821694e-91 0.305629134078933 0.134 0.499 7.20907424119269e-87 +"C14orf2" 0.000161561121918694 0.30543995025742 0.517 0.961 1 +"ATP5J2" 0.657519764426348 0.305185834697887 0.566 0.932 1 +"ARPC1B" 2.03116013761561e-16 0.304364513826969 0.777 0.96 4.97613922114447e-12 +"GAPDH" 3.43136795592723e-43 0.304244272453441 0.986 0.984 8.40650835522612e-39 +"UPP1" 7.15360543592078e-39 0.304086827567435 0.366 0.839 1.75256179574623e-34 +"RPL22L1" 0.000134194923256999 0.302993779152357 0.627 0.775 1 +"TUBA1A" 2.30481701638824e-42 0.299168317961773 0.104 0.307 5.64657120844955e-38 +"GRN" 7.47917014886801e-99 0.298815472892142 0.237 0.738 1.83232189477117e-94 +"EIF3M" 2.27551720756063e-19 0.298537994258118 0.411 0.841 5.5747896068028e-15 +"METAP2" 9.4083064197869e-41 0.298401269104104 0.378 0.881 2.30494098978359e-36 +"ISG15" 2.5391401972155e-66 0.298072975051536 0.276 0.722 6.22063956915825e-62 +"CCT7" 3.37093939524618e-52 0.297677499030234 0.308 0.758 8.25846442441362e-48 +"H2AFJ" 3.09593994274339e-86 0.29728854208455 0.29 0.826 7.58474326572703e-82 +"CHCHD2" 1.74546596263003e-27 0.296951244879817 0.868 0.985 4.27621706184731e-23 +"SF3B14" 4.09728298737232e-06 0.29594464988516 0.498 0.929 0.100379335907634 +"PPA1" 2.38760686023761e-15 0.293380325905827 0.798 0.969 5.84939804689613e-11 +"ATP5B" 5.28729170336661e-05 0.291901063299492 0.503 0.895 1 +"PSMC3" 2.16054196174085e-67 0.291576591148132 0.257 0.686 5.29311175206891e-63 +"PSME1" 0.228139260554517 0.291333114536535 0.556 0.905 1 +"EIF3D" 1.73647452548274e-39 0.289784518297306 0.31 0.707 4.25418893998016e-35 +"RPS15" 5.15021700736013e-64 0.289390500153158 0.955 0.998 1.26175166463316e-59 +"PLD3" 2.09876194144227e-105 0.288856679704414 0.162 0.585 5.14175688033941e-101 +"COX7B" 2.20212981723169e-34 0.288740076809297 0.405 0.912 5.39499783923591e-30 +"FSCN1" 1.10662517638825e-21 0.287909188046193 0.315 0.616 2.71112101963358e-17 +"CD68" 4.60833164069598e-81 0.287801876561906 0.117 0.439 1.12899516865411e-76 +"RPS21" 2.46398849439789e-51 0.287641860264609 0.927 0.996 6.0365254124254e-47 +"POLR2F" 1.90814975280735e-91 0.286449679428742 0.252 0.753 4.67477607940273e-87 +"PSMB8" 2.29627309290115e-37 0.286382436525016 0.354 0.793 5.62563945029853e-33 +"CCL3" 4.09615513639329e-21 0.285253033216529 0.066 0.173 1.00351704686499e-16 +"UQCC2" 3.11718210738096e-26 0.285007099440968 0.408 0.871 7.6367844448726e-22 +"PSMB3" 5.15190907938842e-13 0.284952117320395 0.419 0.793 1.26216620535937e-08 +"RPL15" 1.87844920336683e-61 0.284816734270952 0.969 0.998 4.60201270332839e-57 +"RPL23" 1.80143632432268e-52 0.284142942721612 0.947 0.998 4.41333885095814e-48 +"ZFAND1" 1.56063885993608e-84 0.283600241838447 0.219 0.661 3.8234091429574e-80 +"SOD1" 4.98657536654679e-08 0.28354257931508 0.489 0.945 0.0012216610990503 +"YBX1" 1.80105014345654e-19 0.283308205225738 0.84 0.976 4.41239274645417e-15 +"HLA-DRB1" 1.85577096757061e-83 0.282664138447754 0.167 0.544 4.54645329345123e-79 +"HN1" 1.65378457341201e-08 0.282454149535419 0.707 0.854 0.000405160682640209 +"MYL12B" 0.238348000182983 0.281840421937688 0.557 0.951 1 +"PSMC5" 1.39155826851091e-55 0.280822082342081 0.302 0.751 3.40917860202488e-51 +"SNCG" 2.06793294109721e-27 0.279956474701093 0.281 0.589 5.06622891239405e-23 +"EEF2" 3.63247808969093e-05 0.279912861759536 0.629 0.989 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0.273644130287301 0.982 0.999 8.62192007641963e-68 +"RPL10" 2.16891715127587e-77 0.273147616508306 0.998 1 5.31363012891077e-73 +"PPIF" 4.03231091155589e-106 0.272509827958647 0.175 0.616 9.87875850222076e-102 +"CCDC85B" 5.75657261579186e-34 0.27192466847016 0.294 0.644 1.41030272514285e-29 +"MRPL13" 3.52846488405344e-73 0.271920564401634 0.286 0.771 8.64438611944252e-69 +"GUK1" 0.000213677314035913 0.270771634415128 0.635 0.974 1 +"EIF5A" 7.28791184679865e-60 0.270767514942671 0.333 0.845 1.7854655233472e-55 +"TIMP2" 5.96254167626259e-116 0.268824840957819 0.154 0.595 1.46076308526757e-111 +"MIF" 3.71779973345499e-05 0.268737926895506 0.49 0.887 0.910823756699137 +"ZFAS1" 0.00170660254012996 0.267875311887381 0.681 0.99 1 +"NDUFAF2" 4.84418032327433e-77 0.267733297890677 0.278 0.771 1.18677573739898e-72 +"MTCH1" 1.91367082702136e-83 0.267359401992991 0.272 0.776 4.68830215911962e-79 +"PSMB2" 1.20950974447678e-10 0.267184733556996 0.451 0.83 2.96317792299366e-06 +"RNF181" 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0.217122612612601 0.144 0.617 2.22357334327221e-134 +"ATP5O" 4.56786587109961e-88 0.217004703782004 0.317 0.887 1.11908145976069e-83 +"NQO1" 7.72860947915287e-62 0.216963202803172 0.311 0.775 1.89343203629766e-57 +"VEGFB" 4.75886176610859e-128 0.21669187418773 0.238 0.803 1.16587354407894e-123 +"LLPH" 5.42532976854043e-157 0.216454571766323 0.16 0.689 1.32915153999472e-152 +"SEC61G" 0.0055239607043258 0.216352660826663 0.726 0.978 1 +"HSP90AB1" 8.94718678305032e-06 0.216101240384421 0.797 0.984 0.21919712899795 +"NDUFB2" 8.21457328683713e-34 0.215971839826687 0.422 0.941 2.01248830954223e-29 +"MYL6B" 2.58350121113185e-121 0.215871793155097 0.231 0.774 6.32931961715193e-117 +"EVA1B" 1.01052666018007e-115 0.215840853849872 0.139 0.559 2.47568926477515e-111 +"NSUN5" 1.04021716852011e-117 0.215490488898585 0.139 0.563 2.54842804115742e-113 +"RARRES2" 6.51392088688015e-35 0.215365943737529 0.324 0.694 1.59584547807677e-30 +"PPP1R14B" 0.563392609891294 0.215040535492371 0.611 0.947 1 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0.209682450521597 0.131 0.504 2.53628237220102e-94 +"ORMDL2" 7.11514129599112e-116 0.209483774032515 0.21 0.709 1.74313846610487e-111 +"ATP6V1F" 1.85318745253012e-70 0.209437817006724 0.316 0.833 4.54012393995354e-66 +"CKB" 6.68711280869807e-85 0.209331373025738 0.185 0.582 1.63827576700294e-80 +"STRAP" 3.62000340771931e-98 0.209173046800485 0.278 0.822 8.86864634857154e-94 +"SARS" 2.71314925299365e-127 0.208786533959086 0.156 0.618 6.64694435490915e-123 +"DYNLL1" 0.0198317864724912 0.208692180244628 0.664 0.983 1 +"PHB" 2.32066015668785e-43 0.208295642593655 0.334 0.761 5.68538531786956e-39 +"VDAC1" 9.26882629750189e-17 0.208175345684652 0.472 0.964 2.27076975462499e-12 +"CIR1" 6.46464283983733e-177 0.208167143973342 0.14 0.677 1.58377284933175e-172 +"TUFM" 2.80157503424228e-65 0.20814443053112 0.321 0.815 6.86357867639015e-61 +"SIL1" 3.06369304413781e-159 0.208036804850552 0.162 0.697 7.50574158883321e-155 +"SNHG12" 5.08689487752481e-73 0.207344643251473 0.092 0.364 1.2462383760448e-68 +"HADHB" 1.80182421093325e-173 0.207264636652016 0.15 0.696 4.41428913436537e-169 +"GLRX3" 2.97479167445126e-114 0.207236309730957 0.21 0.706 7.28794212323813e-110 +"NT5C3A" 3.46800510959154e-151 0.206744137600252 0.158 0.674 8.49626571798832e-147 +"GPX3" 3.33314713112983e-104 0.206573363707025 0.118 0.492 8.16587715655498e-100 +"RBM8A" 1.70177776969192e-91 0.20616595531303 0.313 0.883 4.16918535796824e-87 +"TATDN1" 6.57290988994954e-125 0.205427577657079 0.188 0.684 1.61029719393874e-120 +"PDAP1" 1.01168028753061e-85 0.204630025952666 0.297 0.835 2.47851553642125e-81 +"LGALS3" 0.490819037610962 0.204323381254169 0.62 0.953 1 +"LY6D" 3.9855482710902e-49 0.203966588339818 0.257 0.617 9.76419470934388e-45 +"MRPL40" 5.55445398535748e-125 0.20392348989719 0.171 0.649 1.36078568187273e-120 +"ANAPC11" 1.24821655483664e-15 0.203867161757127 0.472 0.935 3.05800573769428e-11 +"MGMT" 2.37795202009126e-120 0.203731069701427 0.163 0.616 5.82574465402157e-116 +"PSMD8" 2.064644787496e-43 0.203452287432305 0.376 0.876 5.05817326488644e-39 +"UBXN1" 5.11217999067621e-84 0.203300260226448 0.267 0.764 1.25243297591577e-79 +"SSB" 6.82824806051528e-99 0.202867823019553 0.293 0.854 1.67285249234564e-94 +"C17orf89" 6.09458767173444e-129 0.2028565573394 0.242 0.816 1.49311303369822e-124 +"NDUFA1" 2.93331640058902e-107 0.202818347632678 0.298 0.897 7.18633184980304e-103 +"ATP5G2" 0.0144015905088369 0.20266034003637 0.642 0.984 1 +"BOLA3" 8.73089695761946e-110 0.202504228594858 0.265 0.825 2.13898244564719e-105 +"HMOX2" 8.43857188609656e-138 0.202495406538879 0.146 0.622 2.0673657263748e-133 +"SRP9" 8.9977195094158e-42 0.202329064820078 0.401 0.934 2.20435130261178e-37 +"CTSA" 2.56300461740225e-119 0.20231936633009 0.12 0.525 6.27910501217377e-115 +"ARHGDIA" 8.44243174061171e-156 0.202291983481826 0.194 0.758 2.06831135213246e-151 +"CWC15" 5.61225892809706e-153 0.20223501851253 0.205 0.782 1.3749473147945e-148 +"SH3YL1" 8.75210182190823e-107 0.202211258809637 0.21 0.695 2.1441774253493e-102 +"RAB34" 3.41089480758617e-116 0.202064377821123 0.217 0.728 8.35635118910535e-112 +"HLA-DQB1" 3.53354416964373e-99 0.202048511072119 0.115 0.471 8.65682986121018e-95 +"HMGA1" 4.44754109927989e-41 0.201857894746698 0.376 0.861 1.08960309391258e-36 +"MRPS36" 1.17739524168353e-116 0.20112817737635 0.275 0.856 2.88450060260047e-112 +"BLOC1S2" 8.97336303646287e-127 0.201120737143676 0.121 0.542 2.19838421030304e-122 +"CASP4" 4.6254457053192e-153 0.200825846582503 0.19 0.749 1.13318794334615e-148 +"LAMTOR1" 9.53347955618316e-112 0.200378373244413 0.277 0.861 2.33560715646931e-107 +"HTATIP2" 8.19516189509366e-132 0.200274403109817 0.201 0.731 2.007732712679e-127 +"FIS1" 8.73750919420169e-26 0.200158899556299 0.436 0.921 2.14060237748747e-21 +"CWF19L2" 1.84144809733627e-97 0.200158279648679 0.079 0.393 4.51136369366414e-93 +"SYNC" 6.63973754435721e-80 0.199924844704529 0.072 0.34 1.62666930099207e-75 +"RPS10" 9.29011541985994e-12 0.199741399157781 0.768 0.995 2.27598537671149e-07 +"UBE2L3" 6.90331788440905e-141 0.199698195505527 0.231 0.816 1.69124384850137e-136 +"CFD" 1.91647512757041e-39 0.199432554697423 0.054 0.206 4.69517241503475e-35 +"MYO1G" 2.46082206691696e-92 0.199333924031446 0.155 0.539 6.02876798173985e-88 +"FBL" 1.35834156156338e-110 0.198645275545744 0.199 0.674 3.32780099167412e-106 +"TOMM5" 2.15153441257779e-141 0.197985663411591 0.126 0.582 5.27104415737433e-137 +"NDUFA6" 2.80821590474349e-108 0.197928061803134 0.259 0.809 6.87984814503107e-104 +"IFI44" 4.55647170192858e-132 0.197755675404138 0.135 0.584 1.11629000225548e-127 +"CST6" 2.49297656372429e-111 0.197504258121957 0.105 0.478 6.10754328346815e-107 +"CCDC90B" 2.36026239424922e-133 0.197309359828766 0.137 0.59 5.78240683967116e-129 +"ZNHIT1" 8.4874966608225e-50 0.197265150094238 0.361 0.853 2.07935180693491e-45 +"DNAJC19" 2.07926583019565e-139 0.196617678041969 0.239 0.825 5.09399335739632e-135 +"GPNMB" 1.8384530699624e-38 0.196277880815777 0.06 0.213 4.50402617610088e-34 +"MDM2" 4.91265395436136e-123 0.195435843953989 0.224 0.76 1.20355109227899e-118 +"LAMTOR4" 3.82719667909272e-106 0.195221917953245 0.289 0.873 9.37624914410925e-102 +"BCAT1" 3.99690926721257e-107 0.195162193192003 0.108 0.473 9.79202801374407e-103 +"TCEA1" 1.35671477397308e-95 0.195139141888742 0.326 0.929 3.32381552475665e-91 +"MAGOH" 4.24122747693002e-105 0.194093761721112 0.265 0.814 1.03905831957309e-100 +"CHMP5" 4.00091773747489e-91 0.194042223986189 0.295 0.845 9.80184836503973e-87 +"EDF1" 5.2431178089079e-49 0.19376629039351 0.383 0.929 1.28451143200435e-44 +"SNRPF" 1.12959201826048e-14 0.193639191326902 0.494 0.951 2.76738748553635e-10 +"DYNC1I2" 2.27639285680064e-154 0.193177653306329 0.236 0.852 5.57693485987588e-150 +"PHPT1" 1.57021241091275e-65 0.193013902208338 0.342 0.891 3.84686338549514e-61 +"ANAPC16" 4.05583359133505e-149 0.192892830290356 0.23 0.832 9.93638671541175e-145 +"SSSCA1" 2.31808613846526e-151 0.191679064302233 0.146 0.646 5.67907923062604e-147 +"MZT2B" 3.12274677609709e-17 0.191414962164053 0.464 0.947 7.65041732676027e-13 +"ENY2" 7.65704352700295e-34 0.191321732560098 0.431 0.949 1.87589909368045e-29 +"LCP1" 8.62166809798969e-32 0.191317876389954 0.044 0.166 2.11222246732649e-27 +"DBNL" 6.15044884361231e-154 0.191240023541485 0.127 0.607 1.50679846219658e-149 +"TALDO1" 5.46739560958772e-57 0.190735136093216 0.351 0.865 1.3394572503929e-52 +"ABHD11" 8.5592565460559e-109 0.190378402667702 0.168 0.609 2.09693226121823e-104 +"LSM4" 3.62475013700834e-70 0.190242867489751 0.313 0.814 8.88027536065674e-66 +"RPSA" 8.25278291305437e-09 0.190045919622305 0.803 0.993 0.000202184928586919 +"ACTG1" 1.42785394620816e-21 0.189926021098057 0.96 0.998 3.49809938281537e-17 +"PSMA5" 6.19426724961095e-152 0.189919337797439 0.195 0.757 1.51753353348219e-147 +"NMT1" 2.75394454964686e-145 0.189445337874228 0.17 0.69 6.74688875217984e-141 +"FKBP4" 9.03762237640937e-106 0.189184377225363 0.214 0.696 2.21412710599653e-101 +"TMED9" 5.1891678873042e-118 0.189099409295812 0.273 0.866 1.27129424071066e-113 +"IDH3G" 1.31397390252838e-119 0.188951775720469 0.166 0.624 3.21910466380427e-115 +"SYF2" 1.57148935600928e-165 0.188773104711917 0.223 0.845 3.84999177328715e-161 +"PSMC4" 8.03352071562951e-120 0.188392413407028 0.155 0.603 1.96813224012207e-115 +"LAPTM4B" 2.10851739370502e-41 0.188369026751017 0.383 0.852 5.16565676283794e-37 +"MRPL52" 1.39062685422793e-71 0.187778934153483 0.317 0.836 3.406896730173e-67 +"HSPE1" 0.440580251828526 0.187686686994446 0.627 0.969 1 +"EIF4H" 3.53633848387149e-143 0.18749697842724 0.243 0.842 8.66367565163677e-139 +"TUBA1B" 0.0737896195315494 0.187253883764311 0.673 0.92 1 +"MRPL51" 2.53019889034024e-11 0.187207716052141 0.496 0.942 6.19873426144456e-07 +"PSME2" 0.00323861244083597 0.186947648701137 0.539 0.857 1 +"FILIP1L" 7.06964103893135e-81 0.186923141317715 0.158 0.513 1.73199135812779e-76 +"TMEM258" 0.00102952977629122 0.186545614962035 0.545 0.968 1 +"UFD1L" 1.00828897952974e-141 0.186245670234401 0.138 0.61 2.47020717094991e-137 +"EXOSC1" 1.32823141668606e-130 0.185602620567556 0.142 0.595 3.25403414773917e-126 +"MT1E" 7.18878623831415e-20 0.185334070811043 0.837 0.815 1.76118074052458e-15 +"C12orf45" 5.0682834702721e-155 0.184993630277059 0.133 0.624 1.24167876738196e-150 +"COPZ1" 1.98692727292251e-104 0.184803431595418 0.284 0.853 4.86777312593286e-100 +"MGST3" 1.14998528974018e-108 0.184763274482312 0.24 0.761 2.81734896133446e-104 +"FASTK" 5.45032341002509e-146 0.184721146089458 0.15 0.644 1.33527473222205e-141 +"MRPL12" 6.18204635207462e-86 0.184411009313547 0.267 0.757 1.51453953579476e-81 +"MXRA7" 3.80416350584475e-109 0.183990579766328 0.252 0.797 9.31982017296905e-105 +"ITGB2" 1.16283894129369e-65 0.183873102360885 0.058 0.28 2.84883912227542e-61 +"SH3BGRL" 1.17123284768686e-171 0.183842707879137 0.134 0.657 2.86940335354803e-167 +"HEXA" 3.07291952172945e-101 0.18378626700197 0.084 0.411 7.52834553628499e-97 +"ECHS1" 9.87228858775045e-112 0.183538497321336 0.227 0.742 2.41861198111298e-107 +"ST13" 4.21118660605223e-26 0.183493293489277 0.436 0.922 1.03169860661674e-21 +"AP2S1" 1.14845324121712e-107 0.18345724797988 0.239 0.755 2.81359559565781e-103 +"YWHAG" 9.99069752157205e-144 0.18343024448111 0.25 0.868 2.44762098580994e-139 +"TMPRSS11E" 2.15618239330609e-144 0.183382969987344 0.151 0.643 5.28243124536059e-140 +"C6orf106" 1.2229415769248e-142 0.183242631175378 0.119 0.57 2.99608456930806e-138 +"GPM6B" 6.28201995612639e-25 0.18309368272894 0.043 0.144 1.5390320690514e-20 +"MTHFD2" 1.94962319647756e-119 0.182931909808386 0.223 0.749 4.77638186905038e-115 +"NDUFV3" 6.47228947030278e-137 0.18265095796661 0.177 0.686 1.58564619732948e-132 +"RPS23" 9.7076268294398e-58 0.182474850289257 0.999 1 2.37827149694446e-53 +"APRT" 5.58747750829711e-35 0.182438733008128 0.409 0.905 1.36887611475771e-30 +"RP11-345L23.1" 3.83128687261234e-51 0.182210236197561 0.069 0.264 9.38626970921297e-47 +"SDSL" 3.77656451711587e-106 0.182054589229196 0.086 0.426 9.25220541048218e-102 +"DNAJC30" 2.81495248373133e-119 0.181968427616856 0.099 0.481 6.89635208989338e-115 +"VAT1" 6.34499018451863e-111 0.181614673021147 0.099 0.464 1.55445914530522e-106 +"SNRPE" 9.23204605234836e-18 0.181602374308295 0.47 0.927 2.26175896236482e-13 +"HLA-A" 0.484606939788137 0.181508449384242 0.673 0.985 1 +"NDUFB8.1" 5.38899793073213e-110 0.181479455875381 0.234 0.748 1.32025060305006e-105 +"C1orf35" 1.09383122030617e-146 0.18134389718167 0.13 0.598 2.67977710662809e-142 +"DPY30" 1.71766603280415e-97 0.181178412565925 0.296 0.864 4.20811001376689e-93 +"COMMD4" 1.10443675177697e-117 0.181171782969512 0.096 0.471 2.70575959817841e-113 +"VAMP8" 7.81810062039352e-43 0.180959958530484 0.407 0.947 1.91535647099021e-38 +"UQCRC2" 3.2880278998498e-139 0.18046455219702 0.193 0.726 8.05533955184204e-135 +"DCAF13" 4.57372258622794e-107 0.18043915230729 0.2 0.668 1.12051629639998e-102 +"COL18A1" 7.62845830631399e-127 0.180357231734897 0.117 0.535 1.86889600046386e-122 +"RABGGTB" 1.10837711268833e-133 0.18014966676165 0.173 0.672 2.71541308837514e-129 +"GSTO2" 2.57813061509226e-99 0.180122488475777 0.107 0.455 6.31616219391452e-95 +"NDUFAB1" 4.05248935139893e-24 0.179867594271413 0.454 0.943 9.92819366199225e-20 +"MCTS1" 9.88785071608221e-154 0.179795361999454 0.175 0.714 2.42242454693298e-149 +"LAMTOR2" 2.64031149065142e-147 0.179687861063102 0.217 0.796 6.46849912094691e-143 +"RFXANK" 1.52073067446985e-135 0.179478582269771 0.121 0.561 3.72563807938368e-131 +"EIF3B" 1.60969931866884e-79 0.17930530531713 0.281 0.777 3.94360236080679e-75 +"SF3B5" 7.34343727391495e-163 0.179249140618479 0.179 0.741 1.79906869773642e-158 +"CD63" 9.81859794921495e-06 0.178829994736416 0.862 0.995 0.240545831157817 +"EPCAM" 0.902719396700989 0.178457892238506 0.651 0.981 1 +"PRMT1" 2.61998917712984e-128 0.178419889617187 0.164 0.636 6.41871148505039e-124 +"RPS26" 0.00539325778708431 0.178384782339447 0.771 0.983 1 +"EIF6" 6.66629560613965e-128 0.178236335884972 0.203 0.723 1.63317576054815e-123 +"SNORA76" 2.1757185729017e-78 0.178158706683677 0.075 0.343 5.33029293175187e-74 +"TMEM176B" 1.02232613296029e-88 0.178071018533102 0.12 0.457 2.50459679313941e-84 +"DDOST" 2.59034681084826e-151 0.177972643307918 0.173 0.704 6.34609065189715e-147 +"EPN1" 2.5947344608778e-133 0.177440983782474 0.117 0.547 6.35683995570452e-129 +"SERPINB6" 3.67293387286787e-146 0.176987345358111 0.169 0.685 8.99832069513899e-142 +"P4HA1" 2.03640939744075e-82 0.176867776187317 0.24 0.692 4.98899938279008e-78 +"PMPCB" 1.34293846062736e-143 0.176770611671051 0.179 0.7 3.29006493469098e-139 +"CSTA" 1.40834092539443e-54 0.176044129507925 0.068 0.272 3.45029443312381e-50 +"NT5C3B" 2.30246103193562e-134 0.175980801159523 0.138 0.595 5.64079928213907e-130 +"TMPRSS13" 1.92646756845695e-87 0.175905779111014 0.076 0.366 4.71965289596268e-83 +"SLIRP" 2.02289273172463e-123 0.175854286246927 0.24 0.806 4.95588490345217e-119 +"FAM32A" 4.90706209577603e-141 0.175779456808572 0.111 0.549 1.20218114284417e-136 +"HINT2" 3.18297258556278e-150 0.175589182846555 0.165 0.681 7.79796453737025e-146 +"HNRNPA1" 0.00698236364114627 0.175434172253472 0.684 0.993 1 +"EIF4A1" 0.195459279363679 0.175205347401367 0.599 0.977 1 +"SYNGR2" 1.71440546353579e-162 0.174770560380105 0.178 0.739 4.20012194511633e-158 +"PRDX3" 5.8435664007022e-92 0.174415929575711 0.3 0.849 1.43161533250803e-87 +"ATP5C1" 5.86938502611673e-118 0.174304359690634 0.244 0.786 1.43794063754834e-113 +"POLR2G" 6.63313239090203e-157 0.174290617147856 0.125 0.61 1.62505110444709e-152 +"CCDC104" 3.03350823746241e-159 0.1741700022546 0.179 0.731 7.43179183095917e-155 +"ERP29" 9.42478813899989e-57 0.173982746134141 0.372 0.909 2.30897884617358e-52 +"SEPW1" 2.80551676263563e-94 0.17370430892614 0.311 0.883 6.87323551678102e-90 +"C16orf13" 3.00812532113213e-62 0.173088790979816 0.36 0.931 7.36960622424161e-58 +"NFU1" 4.61844283692634e-152 0.172745245230319 0.13 0.611 1.13147231061858e-147 +"ATP5G3" 2.94056566852523e-15 0.172730481078226 0.493 0.936 7.20409183131997e-11 +"SYT12" 6.0064674649384e-115 0.172695261986185 0.109 0.493 1.47152446423526e-110 +"AL592183.1" 1.32612878778731e-77 0.172532491365567 0.07 0.331 3.24888291720012e-73 +"SEPT7" 1.41125107991225e-45 0.172498322110129 0.398 0.915 3.45742402067703e-41 +"GRHPR" 6.70084948063909e-106 0.172195153031481 0.2 0.666 1.64164111426177e-101 +"GARS" 2.85912696289016e-64 0.172128383645684 0.288 0.736 7.0045751463846e-60 +"CCT4" 9.69285071000537e-93 0.172060199821469 0.292 0.833 2.37465149544421e-88 +"LAMTOR5" 2.10287796526011e-146 0.171823255038619 0.247 0.856 5.15184072709073e-142 +"PCNA" 1.87370149140804e-114 0.171795817739379 0.159 0.595 4.59038128380055e-110 +"FAM96B" 6.56000370548488e-62 0.171702739495577 0.352 0.891 1.60713530780674e-57 +"CCT3" 3.65487231060421e-94 0.171568119664478 0.281 0.805 8.95407167374925e-90 +"EIF2AK1" 2.72404504351765e-114 0.171537759629271 0.238 0.768 6.6736379521139e-110 +"TSR3" 9.05246830008581e-165 0.171498986081639 0.153 0.686 2.21776420883802e-160 +"LIMK1" 9.04946316585515e-128 0.171318667495646 0.108 0.518 2.21702798100285e-123 +"PTMA" 7.42345807692426e-22 0.171014468700372 0.964 0.999 1.81867299426567e-17 +"POLE4" 8.91075549774477e-106 0.170983266362192 0.23 0.729 2.18304598939249e-101 +"SERF2" 0.165347439745561 0.170767920772968 0.696 0.996 1 +"PSMB6" 1.17119209249104e-108 0.17071401341548 0.241 0.762 2.86930350739381e-104 +"GNL3" 2.10151530397776e-143 0.170594015815092 0.194 0.731 5.14850234321513e-139 +"PSPH" 6.51062608299673e-99 0.170518032124263 0.081 0.401 1.59503828407337e-94 +"ADSL" 2.46347732413059e-129 0.170409095732679 0.109 0.523 6.03527309638752e-125 +"TXNDC17" 1.81955272686625e-06 0.170266875710215 0.571 0.971 0.0445772222554963 +"RPL36A" 7.40283516130755e-37 0.170133829542113 0.421 0.965 1.81362058616874e-32 +"IRAK1" 8.00995036751587e-142 0.170110592256599 0.142 0.618 1.96235774053771e-137 +"SHFM1" 0.302380698069433 0.17009613765993 0.594 0.982 1 +"CD99" 1.8829051188541e-97 0.170041320573903 0.292 0.848 4.61292925068065e-93 +"MRPS18C" 4.81899224309569e-175 0.169816523054152 0.179 0.763 1.18060490963601e-170 +"CCT5" 4.15581894572435e-86 0.169708025451723 0.325 0.889 1.01813408351301e-81 +"VBP1" 2.90741723016974e-133 0.169688540700624 0.206 0.744 7.12288147219284e-129 +"GPC1" 4.96186813458286e-120 0.169615573866412 0.09 0.465 1.21560807429146e-115 +"COMT" 1.83195250650462e-110 0.169514086010671 0.236 0.753 4.48810044568568e-106 +"PNPLA4" 2.32984519354484e-129 0.169454693582563 0.129 0.565 5.7078877396655e-125 +"C12orf73" 3.55587886106489e-116 0.169389101166405 0.087 0.448 8.71154762172288e-112 +"OGFRL1" 2.9734368303435e-109 0.169148550124359 0.079 0.417 7.28462289065854e-105 +"GADD45GIP1" 2.02914887896326e-109 0.168905973543742 0.263 0.816 4.97121183857209e-105 +"TRAPPC4" 1.14816449230706e-149 0.16827182286358 0.143 0.633 2.81288818970308e-145 +"APEH" 1.11236602857219e-128 0.167879887203838 0.101 0.504 2.725185533399e-124 +"MRFAP1" 2.06724487366551e-135 0.167752230931825 0.264 0.879 5.06454321599314e-131 +"TPM2" 3.57656092459726e-44 0.167663234026429 0.044 0.198 8.76221660917083e-40 +"GSTP1" 4.29905459813308e-05 0.167629158486027 0.863 0.993 1 +"PAICS" 2.31701614253064e-108 0.167495489927915 0.187 0.64 5.67645784758581e-104 +"COX16" 3.59603484809755e-189 0.167288982818138 0.189 0.807 8.80992577435419e-185 +"EBNA1BP2" 5.98248198785278e-77 0.166752899391172 0.263 0.721 1.46564826220405e-72 +"AIP" 7.86122431348827e-121 0.166532551902743 0.1 0.487 1.92592134456149e-116 +"KIAA0101" 3.01629457848045e-59 0.166171138339273 0.151 0.441 7.38962008781924e-55 +"C19orf60" 2.5170408146638e-140 0.166061508817014 0.194 0.727 6.16649829184483e-136 +"PMVK" 1.40137899301322e-122 0.165879092572937 0.104 0.498 3.4332383949831e-118 +"LAGE3" 8.98438145761491e-115 0.165801991820979 0.236 0.768 2.20108361330108e-110 +"NDUFV2" 4.46214256998736e-55 0.165604720408904 0.38 0.908 1.0931803082212e-50 +"CUTA" 1.84716151058402e-146 0.165569543537967 0.202 0.762 4.52536098477979e-142 +"SEC11A" 1.07696776930507e-80 0.165565759496149 0.312 0.844 2.63846333802049e-76 +"NCBP2" 6.2681849693299e-168 0.165502432397741 0.156 0.695 1.53564263563613e-163 +"LCMT1" 9.92601411585226e-158 0.165448911862494 0.12 0.6 2.43177419824264e-153 +"BAX" 1.06554031017013e-151 0.1654089923456 0.167 0.687 2.6104672058858e-147 +"GTF2H2" 2.67907477916892e-131 0.165238002934096 0.107 0.521 6.56346530148594e-127 +"CDC37" 3.81079867655188e-124 0.165030373440639 0.164 0.627 9.33607567768444e-120 +"WDR46" 4.95624036006826e-134 0.165025774571244 0.107 0.526 1.21422932581312e-129 +"ANXA5" 2.56493927482066e-110 0.164894273719979 0.3 0.899 6.28384472938313e-106 +"MRPL1" 5.17143261843862e-168 0.163995246376637 0.157 0.697 1.26694927719128e-163 +"LSMD1" 3.00523658292938e-149 0.163901887712983 0.244 0.86 7.36252910451869e-145 +"SEPT9" 3.21792084318837e-154 0.163369080588438 0.229 0.834 7.88358427372719e-150 +"RAB4A" 9.04828445737602e-138 0.163239126109926 0.118 0.559 2.21673920921255e-133 +"MZT2A" 2.87082412888637e-90 0.162762605211567 0.299 0.857 7.03323203335872e-86 +"IRF7" 1.73788082023576e-129 0.162701402086165 0.099 0.502 4.25763422149558e-125 +"TP53I3" 5.41199067849196e-94 0.162513049494324 0.183 0.597 1.32588359632374e-89 +"TM7SF2" 4.22479023699188e-142 0.162490811529955 0.147 0.631 1.03503136016064e-137 +"ATP5J" 1.44148156302413e-40 0.162141784010214 0.424 0.952 3.53148568125282e-36 +"ZMIZ2" 1.58450232906287e-123 0.161947482484446 0.091 0.472 3.88187225597113e-119 +"HMGN1" 0.0183193369365976 0.161811409315075 0.579 0.981 1 +"SLC40A1" 3.433638614907e-133 0.161554501858899 0.188 0.7 8.41207124266067e-129 +"AIMP1" 4.57706253726028e-140 0.16136522245249 0.22 0.784 1.1213345510034e-135 +"HSPB11" 8.16502341345009e-114 0.161081305990997 0.255 0.8 2.00034908606114e-109 +"WIPI2" 1.0416418854654e-104 0.161048853347073 0.257 0.788 2.55191845520167e-100 +"NME1-NME2" 2.40300608939457e-110 0.161031851677846 0.094 0.449 5.88712461840776e-106 +"CCNB1" 1.60944845357553e-47 0.161014165301171 0.172 0.442 3.94298776641468e-43 +"FABP5" 7.08974424196414e-98 0.161004186294469 0.152 0.54 1.7369164418388e-93 +"KIAA0020" 2.37295118034284e-139 0.160957602526427 0.116 0.557 5.81349309672193e-135 +"SNX8" 2.16657758418428e-115 0.160755073372428 0.086 0.445 5.30789842349307e-111 +"KPNA2" 2.94013307079796e-114 0.160415504374251 0.158 0.593 7.20303201014793e-110 +"NIFK" 4.81176060455918e-159 0.16019955887916 0.147 0.66 1.17883323051095e-154 +"OLA1" 1.38426189142955e-118 0.160125825724249 0.218 0.739 3.39130320781324e-114 +"MRPS21" 6.80039366249216e-117 0.1600208521577 0.29 0.9 1.66602844337395e-112 +"SNF8" 1.5512682423944e-89 0.159928710330754 0.279 0.792 3.80045206704203e-85 +"FLII" 8.2951633100853e-145 0.159745316278493 0.12 0.574 2.0322320593378e-140 +"TCEA3" 9.12880528244596e-111 0.159718449580376 0.197 0.666 2.23646600614644e-106 +"FLNC" 2.07748687059299e-43 0.159473399018936 0.052 0.211 5.08963508426576e-39 +"PYGL" 3.43588708574878e-94 0.159199558892563 0.073 0.373 8.41757977137594e-90 +"NDUFB1" 1.40464236561444e-133 0.158954890368338 0.278 0.9 3.44123333151882e-129 +"PSMA1" 2.21742804473767e-83 0.158838943884396 0.329 0.908 5.43247696680281e-79 +"DCTN2" 3.72899772046931e-142 0.158706847763527 0.168 0.676 9.13567151537776e-138 +"MRPS7" 7.44823404736276e-133 0.158576362782978 0.192 0.705 1.8247428592634e-128 +"RARS" 2.82519630225728e-154 0.158389997342503 0.181 0.728 6.9214484209001e-150 +"ACAT1" 9.18207552587565e-164 0.158332309800448 0.116 0.604 2.24951668308428e-159 +"TMEM141" 7.8440115284346e-129 0.157564240646876 0.2 0.717 1.92170438435119e-124 +"ABHD12" 5.04780569102812e-174 0.157493970826867 0.136 0.661 1.23666191624498e-169 +"CEBPZ-AS1" 1.044234833873e-166 0.15743410607427 0.141 0.661 2.55827091950546e-162 +"TMEM54" 5.23032595257676e-127 0.157397900478315 0.197 0.704 1.28137755512178e-122 +"CCM2" 9.19121090317791e-112 0.157347161204043 0.192 0.66 2.25175475916956e-107 +"BASP1" 2.22225454409299e-88 0.157140572256499 0.094 0.402 5.44430140757342e-84 +"TAF9" 2.44072988354446e-75 0.15686775055271 0.34 0.902 5.97954414169557e-71 +"CAPNS1" 1.50551567023133e-129 0.156840773868052 0.238 0.799 3.68836284049973e-125 +"EIF1AX" 1.20280264096842e-28 0.155962742694978 0.46 0.953 2.94674619010853e-24 +"RNH1" 2.76776376803091e-127 0.155918027143362 0.209 0.733 6.78074445529892e-123 +"SUB1" 1.87874399460798e-06 0.155786866537831 0.556 0.982 0.046027349123901 +"EIF4A3" 3.59914835569152e-128 0.155732293254606 0.185 0.681 8.81755355660865e-124 +"PPA2" 1.63775211356774e-171 0.155593814768649 0.134 0.658 4.0123289030296e-167 +"BOD1" 4.44936220032588e-170 0.155477135766232 0.143 0.672 1.09004924545784e-165 +"METTL23" 5.58665198054584e-142 0.155424127355922 0.149 0.631 1.36867386871392e-137 +"C11orf48" 3.3826037886833e-147 0.155372611178172 0.18 0.71 8.28704102189523e-143 +"FUNDC1" 2.1666866349396e-172 0.155317027496938 0.145 0.681 5.30816558693853e-168 +"MTIF3" 6.74556871423987e-188 0.155313620376755 0.128 0.668 1.65259687930163e-183 +"MRPL15" 2.643086559893e-115 0.155224890368856 0.195 0.675 6.47529776308187e-111 +"TUBA1C" 2.56639053894621e-39 0.1549187891556 0.426 0.921 6.28740018136432e-35 +"UROD" 9.20068235015048e-139 0.154877527596595 0.105 0.533 2.25407516896337e-134 +"C21orf33" 3.28823528405522e-106 0.154474845044742 0.079 0.413 8.05584762240688e-102 +"SYTL1" 8.54400169873076e-115 0.154171140604723 0.093 0.458 2.09319497617205e-110 +"METRN" 1.28799712292059e-131 0.154060609588965 0.114 0.538 3.15546415144316e-127 +"MPC2" 7.71827382426213e-151 0.154044795720923 0.235 0.843 1.89089990420598e-146 +"CREB3" 1.43103693803853e-129 0.153897132870477 0.095 0.493 3.50589739450059e-125 +"STK16" 4.00025986365109e-124 0.153582057701761 0.091 0.471 9.80023663995879e-120 +"NDRG1" 1.28438887446763e-82 0.153169879552315 0.228 0.664 3.14662430355824e-78 +"PARL" 1.49657402354856e-156 0.153169230495809 0.111 0.578 3.66645670029162e-152 +"SCP2" 5.6901601926738e-96 0.152893502953247 0.337 0.95 1.39403234560315e-91 +"E2F4" 5.88047432435862e-156 0.152821996991118 0.098 0.548 1.44065740472462e-151 +"P2RX4" 6.60740037444852e-88 0.152683208404741 0.071 0.356 1.61874701773614e-83 +"NDUFB6" 3.91045666915554e-180 0.152635971790212 0.172 0.753 9.58022779376415e-176 +"UXT" 5.57779495554021e-138 0.152483695448533 0.249 0.855 1.3665039861578e-133 +"DDT" 4.26296243237627e-163 0.152354817463664 0.176 0.733 1.04438316630786e-158 +"TMEM208" 4.09664809842012e-153 0.152021644758006 0.189 0.74 1.00363781763195e-148 +"NUDT1" 3.47184464929108e-103 0.152007996480615 0.168 0.587 8.50567220629822e-99 +"FAM204A" 1.02127940534263e-185 0.15199924689864 0.125 0.661 2.50203241514891e-181 +"CISD1" 2.07020280567805e-152 0.151906282695367 0.132 0.616 5.07178985363065e-148 +"HPCAL1" 3.26066660581702e-137 0.151756231154356 0.181 0.692 7.98830711759112e-133 +"BAD" 3.78295321804589e-136 0.151619503046222 0.113 0.544 9.26785708889062e-132 +"EIF3K" 1.93664747309125e-33 0.151541973082823 0.445 0.967 4.74459264432625e-29 +"TIMM8B" 2.2893572319913e-147 0.151437125600084 0.24 0.857 5.60869628265548e-143 +"IFI35" 2.60469562898238e-119 0.151430185363576 0.155 0.595 6.38124382144394e-115 +"HMGB1" 2.36496628555274e-07 0.151409013584116 0.592 0.975 0.00579393090297566 +"PRKRIP1" 4.52346228852215e-127 0.151307789978291 0.086 0.469 1.10820302606504e-122 +"PSAT1" 1.43018019148504e-96 0.150988347061536 0.105 0.446 3.50379845111919e-92 +"CPNE3" 9.81125363974657e-172 0.150966023122552 0.194 0.788 2.40365902920151e-167 +"TBRG4" 1.29379114661606e-131 0.150573221937051 0.087 0.479 3.1696589300947e-127 +"EIF3F" 8.38825387127142e-73 0.150469129235145 0.343 0.91 2.05503831592278e-68 +"CNBP" 1.31219080436942e-67 0.150281808267179 0.366 0.935 3.21473625162463e-63 +"TXNRD1" 1.01485701344343e-05 0.150046059655347 0.565 0.922 0.248629819723505 +"TOM1L1" 3.20668510765162e-177 0.150036033398405 0.124 0.641 7.8560578452357e-173 +"U2AF1" 2.38540601878588e-115 0.149977646193256 0.259 0.809 5.84400620542353e-111 +"SYT1" 7.70101710713495e-122 0.149816891977535 0.106 0.501 1.88667218107699e-117 +"MRPS23" 8.09272207111245e-127 0.149762057473043 0.207 0.729 1.98263598020184e-122 +"CRIP1" 1.00298945159694e-69 0.149707517141783 0.065 0.303 2.45722385746733e-65 +"ST6GALNAC2" 8.54905662857772e-84 0.149473026859152 0.057 0.318 2.09443338343526e-79 +"NOP10" 7.37459793354973e-150 0.149442658566574 0.234 0.835 1.80670274774035e-145 +"CYB5R3" 4.15704663299006e-166 0.149295117064292 0.124 0.623 1.01843485461623e-161 +"INTS1" 1.49825794156461e-134 0.149248897285031 0.086 0.482 3.67058213103913e-130 +"RTCB" 4.5955822920394e-154 0.149211373050115 0.101 0.554 1.12587170572673e-149 +"TMUB1" 3.14640567470354e-138 0.149106938605063 0.113 0.549 7.7083792624562e-134 +"TAX1BP1" 4.60191125835279e-35 0.14904835974625 0.427 0.936 1.12742223918385e-30 +"ATP5F1" 4.24670855041353e-177 0.148957900840777 0.188 0.786 1.04040112776581e-172 +"TMEM59" 3.89902277887911e-45 0.148825287583871 0.418 0.967 9.55221590597593e-41 +"RAP1B" 6.9170065914346e-111 0.148046991820637 0.295 0.891 1.69459744483556e-106 +"COMMD9" 2.72738204044537e-106 0.147862173751812 0.077 0.407 6.6818132608871e-102 +"RPS6KB2" 2.88501199189029e-136 0.147805547712936 0.098 0.514 7.06799087893203e-132 +"FKBP11" 9.48319963828909e-125 0.147587373032887 0.084 0.457 2.32328907938444e-120 +"MRPL34" 1.39755921324321e-159 0.147499963108135 0.133 0.629 3.42388031652454e-155 +"RPL26L1" 5.29549569594309e-105 0.147407008821353 0.292 0.87 1.2973434905491e-100 +"RAD51C" 1.67336719339593e-136 0.147337900968809 0.095 0.505 4.09958228710069e-132 +"HMGCL" 2.97077197539863e-75 0.147063746024503 0.066 0.317 7.27809426252909e-71 +"LSM10" 4.10936596858104e-162 0.146892724409866 0.113 0.595 1.00675356864267e-157 +"MED8" 1.19828793769563e-157 0.146789930054296 0.146 0.656 2.93568561856051e-153 +"RGS14" 2.70516692800548e-85 0.146583031930797 0.069 0.345 6.62738845692064e-81 +"TYMP" 4.07113698344166e-161 0.146074435227039 0.142 0.652 9.97387849573372e-157 +"IFI27" 4.9551039421805e-07 0.145936981297918 0.827 0.983 0.012139509147948 +"LINC00152" 2.32020940682188e-141 0.145905972198786 0.116 0.558 5.68428102577293e-137 +"MRPL21" 4.07485410781267e-164 0.145732090645293 0.186 0.759 9.98298507873026e-160 +"CETN2" 1.1264680131621e-166 0.14558835422216 0.189 0.766 2.75973398544583e-162 +"CLDN7" 3.95042178027847e-51 0.145462240478955 0.358 0.826 9.67813831950422e-47 +"SUMO2" 0.0925952198395694 0.145301092290785 0.717 0.988 1 +"STK40" 1.19369035389987e-130 0.145278591014247 0.08 0.463 2.9244219980193e-126 +"NDUFS3" 8.35037299567231e-146 0.1451844533781 0.18 0.708 2.04575788020976e-141 +"AARS" 2.56671086907707e-142 0.14480687696553 0.113 0.556 6.28818495815191e-138 +"C19orf70" 8.65686566288578e-158 0.144742610317854 0.228 0.842 2.12084551875039e-153 +"TRAPPC6A" 7.55963296369494e-114 0.144682948241456 0.077 0.423 1.85203447977562e-109 +"ZBTB8OS" 2.99302271313294e-152 0.144642006386116 0.133 0.617 7.33260634490439e-148 +"PFDN2" 2.13180519626065e-88 0.144211645989043 0.318 0.883 5.22270955031897e-84 +"IL18" 6.41645870540763e-139 0.144143079148381 0.157 0.642 1.57196821823781e-134 +"TRAP1" 5.78418087476493e-144 0.143997946489234 0.113 0.559 1.41706647250866e-139 +"UFC1" 2.28129507990236e-144 0.143977184057188 0.244 0.853 5.5889448162528e-140 +"CHMP4A" 6.23343566203813e-150 0.143847667073001 0.164 0.677 1.52712940284272e-145 +"LGALS3BP" 3.48979209762554e-41 0.143712102344254 0.406 0.881 8.54964165997282e-37 +"NIT2" 5.39332536792993e-156 0.143471373784809 0.121 0.597 1.32131078188915e-151 +"SNRPG" 5.13077656985166e-15 0.143419253158322 0.493 0.95 1.25698895184796e-10 +"HCFC1R1" 7.38876872925923e-139 0.143305688284868 0.124 0.571 1.81017445098122e-134 +"RNASET2" 2.0492802006351e-143 0.143305533171701 0.091 0.51 5.02053156353593e-139 +"CD44" 1.54634648583642e-100 0.142830624367307 0.311 0.891 3.78839425565065e-96 +"GSDMD" 2.44727563819338e-152 0.14278980771433 0.146 0.644 5.99558058600995e-148 +"MCAM" 7.92375457964118e-117 0.142774045745738 0.102 0.482 1.94124063446629e-112 +"CACYBP" 4.07081202971859e-104 0.142685582814705 0.264 0.79 9.97308239160757e-100 +"TAF12" 1.90988433889456e-143 0.142661803097571 0.102 0.535 4.67902564185778e-139 +"CISD3" 1.05307997956611e-135 0.142646514019051 0.194 0.717 2.57994064193902e-131 +"KCNMB4" 3.44131041933198e-97 0.142646332253519 0.078 0.39 8.43086639632143e-93 +"NDUFV1" 1.87406178057546e-145 0.142645450410378 0.175 0.696 4.59126395623183e-141 +"COX20" 1.97785726999638e-126 0.142530413455465 0.281 0.889 4.84555252576414e-122 +"AP2M1" 6.97745066667782e-66 0.142493946320832 0.294 0.749 1.7094056388294e-61 +"NUDT22" 5.09251411760037e-161 0.142446666294847 0.117 0.601 1.24761503367091e-156 +"HDLBP" 2.72393138946093e-128 0.142416370247253 0.27 0.87 6.67335951104033e-124 +"YWHAH" 7.6291387754949e-108 0.14241166724181 0.24 0.748 1.86906270860849e-103 +"ITGB1BP1" 3.06884367224018e-138 0.142333280725906 0.123 0.569 7.51836011262121e-134 +"IFI27L2" 5.74042574126e-55 0.142287326715424 0.057 0.25 1.40634690235129e-50 +"MRPL19" 6.53545530924094e-172 0.14221765711137 0.162 0.715 1.60112119621094e-167 +"RNASEH2C" 5.5234120755112e-149 0.142208789781784 0.17 0.687 1.35318072437949e-144 +"TNFRSF12A" 1.25253116149174e-51 0.142148936419029 0.279 0.66 3.06857609253862e-47 +"IGBP1" 1.45881525932451e-171 0.142054807649709 0.157 0.705 3.57395150381912e-167 +"P4HB" 1.29189348169793e-48 0.142046488973253 0.41 0.951 3.16500984081176e-44 +"MT1A" 1.30697113811353e-82 0.141964583604375 0.098 0.397 3.20194859126433e-78 +"MVP" 1.18254342900562e-157 0.141903416695706 0.17 0.71 2.89711314672087e-153 +"MDH2" 4.96476144132272e-57 0.141857664497688 0.357 0.863 1.21631690550965e-52 +"EIF2B3" 2.52555206900555e-129 0.141775045037344 0.08 0.461 6.18735001385671e-125 +"ALKBH7" 3.62781939614389e-166 0.141581859937624 0.177 0.74 8.88779473861292e-162 +"ZNF581" 1.65420576405969e-64 0.141533607751516 0.069 0.297 4.05263870136984e-60 +"ENO1" 0.104553621556693 0.14144117136778 0.632 0.988 1 +"P4HTM" 1.15982910467007e-133 0.14130336948448 0.09 0.487 2.84146532353121e-129 +"DECR1" 5.41206071853744e-166 0.141270270925714 0.126 0.627 1.32590075543449e-161 +"C2orf49" 2.92598816079674e-142 0.141216859601702 0.07 0.462 7.16837839513594e-138 +"PSMA4" 2.12184129804824e-139 0.141012482570383 0.232 0.806 5.19829899608837e-135 +"NFKBIL1" 3.22730704181953e-102 0.141004241955863 0.093 0.432 7.90657952175367e-98 +"GTF2F1" 4.3396471708038e-138 0.140977435830385 0.088 0.493 1.06317016037522e-133 +"CDV3" 3.8451585255196e-106 0.140760167887511 0.303 0.893 9.42025387167046e-102 +"FH" 2.45167318679186e-150 0.14074362126553 0.121 0.588 6.00635414032138e-146 +"IFIT1" 6.94205544389786e-73 0.140742436810098 0.096 0.371 1.70073416320054e-68 +"PACSIN3" 3.60194091394024e-89 0.140673149956523 0.069 0.355 8.82439504506221e-85 +"TMEM160" 2.11257428587372e-137 0.140594004966573 0.112 0.543 5.17559574296203e-133 +"C19orf48" 4.57877099274726e-102 0.140544745917639 0.077 0.398 1.12175310551315e-97 +"ATP6V0B" 3.99056923609611e-68 0.140509638336138 0.354 0.891 9.77649557151185e-64 +"STUB1" 3.31535270368071e-112 0.140496642450594 0.281 0.861 8.12228258874738e-108 +"NDUFB10" 1.97397313655846e-54 0.140439794987775 0.39 0.936 4.83603678725457e-50 +"COX8A" 6.1776376395111e-26 0.140211975552162 0.451 0.945 1.51345944530382e-21 +"DRAP1" 9.64115422373842e-86 0.139991604184017 0.309 0.858 2.36198637327368e-81 +"PSMB7" 9.99743629166352e-158 0.139965907214593 0.18 0.729 2.44927191709465e-153 +"CBX3" 4.75046273901035e-06 0.139711606337049 0.574 0.966 0.116381586643014 +"GEMIN6" 9.53178700984489e-144 0.139682005320234 0.105 0.542 2.3351924995419e-139 +"GFER" 2.60981235775548e-123 0.139611072871965 0.101 0.493 6.39377929526514e-119 +"TPI1" 0.000139599049959782 0.139341983337907 0.793 0.986 1 +"FLOT1" 1.86279884136307e-150 0.13908397063874 0.226 0.821 4.5636708814554e-146 +"PUF60" 4.14781494503516e-120 0.1389552578782 0.206 0.71 1.01617318338416e-115 +"RNPEPL1" 4.31856953131597e-113 0.138934477013034 0.071 0.408 1.0580063494771e-108 +"AAMP" 5.81065849285805e-142 0.138878772779874 0.099 0.526 1.42355322416529e-137 +"PTTG1" 1.4040686545319e-43 0.138788776069688 0.333 0.723 3.4398277967377e-39 +"MRPS18B" 1.15425037947817e-157 0.138668239213709 0.17 0.707 2.82779800468356e-153 +"MGST2" 4.46581001054887e-162 0.13847627512081 0.094 0.55 1.09407879448437e-157 +"IK" 5.0620462506642e-97 0.138446573320069 0.276 0.807 1.24015071095022e-92 +"ATP6V0E1" 2.74297740824786e-12 0.138396795017412 0.529 0.984 6.72002035246643e-08 +"SLC25A1" 4.99379334024904e-134 0.138385760416775 0.09 0.49 1.22342943042761e-129 +"ACP1" 1.22946950814498e-128 0.1383338515537 0.249 0.826 3.01207734800438e-124 +"KIFC2" 7.27904361165985e-48 0.138225555631344 0.046 0.212 1.78329289442055e-43 +"QDPR" 6.28785821593078e-133 0.138131239633592 0.079 0.465 1.54046238432088e-128 +"GS1-251I9.4" 1.81675998937526e-182 0.138049179894305 0.125 0.653 4.45088029797044e-178 +"OST4" 8.70833763342553e-05 0.137987575860227 0.555 0.989 1 +"SF3B2" 4.19606234112907e-146 0.137928765139101 0.214 0.785 1.02799331295321e-141 +"CST7" 1.77266779838188e-53 0.137737514936336 0.046 0.225 4.34285883925578e-49 +"NAA20" 2.58879564470416e-175 0.13768734444784 0.187 0.771 6.34229044996073e-171 +"GRB2" 5.67406755295002e-168 0.137668583911217 0.164 0.712 1.39008980979723e-163 +"IFI6" 6.27840535633733e-13 0.137491892074837 0.47 0.82 1.53814652824908e-08 +"DDX18" 3.14594273296828e-180 0.137286964102831 0.208 0.831 7.707245101499e-176 +"PSMB5" 1.13441003612295e-80 0.137233488705236 0.312 0.85 2.77919114749762e-76 +"LTA4H" 1.9705933241596e-110 0.137224818409224 0.276 0.841 4.8277565848586e-106 +"ATP6V1E1" 2.61154079938614e-163 0.137216853413898 0.15 0.675 6.39801380441611e-159 +"MRPL28" 2.96137206511762e-160 0.137065942149968 0.136 0.64 7.25506542233166e-156 +"TXNIP" 0.000277419350539085 0.137049172197968 0.571 0.904 1 +"POLR2K" 2.54607769852426e-90 0.137016190014986 0.325 0.906 6.23763575361459e-86 +"SDCBP" 7.52950169365823e-155 0.136854292891162 0.259 0.899 1.84465261992933e-150 +"UBXN6" 2.23367567237408e-90 0.136709084693162 0.07 0.359 5.47228202974926e-86 +"FHL3" 4.55754075236528e-87 0.136306905346766 0.074 0.361 1.11655190892197e-82 +"NAP1L1" 0.301117207041707 0.136271328463867 0.696 0.994 1 +"CFI" 6.57076319681556e-170 0.135931510967887 0.117 0.614 1.60977127558784e-165 +"RHEB" 1.54253241929121e-39 0.135877071040211 0.431 0.957 3.77905017402153e-35 +"TRADD" 1.60356541004366e-148 0.135341138558077 0.113 0.566 3.92857489806595e-144 +"KRT18" 8.49069176339224e-10 0.135176913849809 0.91 0.986 2.08013457511346e-05 +"COA4" 5.63787117464814e-163 0.135102686528495 0.209 0.809 1.38122205907705e-158 +"MYL9" 7.85943797471135e-51 0.135062679128676 0.045 0.217 1.92548370942453e-46 +"ETFB" 7.1749045014186e-157 0.135012803233796 0.201 0.776 1.75777985380254e-152 +"CALU" 8.53968146676106e-88 0.134588065341268 0.323 0.887 2.09213656254179e-83 +"GPS1" 6.26418111171739e-143 0.13424010152077 0.102 0.532 1.53466173055964e-138 +"NPM3" 1.7184176152867e-140 0.134084573631429 0.085 0.491 4.20995131569089e-136 +"FAM136A" 5.05828956928729e-166 0.134006645996847 0.155 0.69 1.23923036157969e-161 +"AP1S2" 1.61284230138778e-69 0.133939954803712 0.044 0.257 3.95130235416992e-65 +"RPF1" 4.30384616232166e-124 0.133562919722541 0.071 0.43 1.05439927130718e-119 +"MET" 9.67395047330835e-83 0.133522190323284 0.34 0.914 2.37002112645581e-78 +"MXI1" 4.64324533395266e-81 0.133450517371911 0.074 0.346 1.13754867436506e-76 +"FOLR1" 2.32000712247447e-101 0.13315819043661 0.113 0.471 5.68378544935021e-97 +"FRA10AC1" 4.50810004314074e-138 0.133073634910662 0.078 0.472 1.10443942956905e-133 +"GPX8" 3.13903681125254e-116 0.132982674587561 0.095 0.462 7.6903262838876e-112 +"C8orf59" 2.42112553458143e-45 0.132792923356732 0.415 0.951 5.93151544717105e-41 +"TGFBI" 3.3677790577191e-89 0.132773155095754 0.263 0.756 8.25072191350602e-85 +"SSR2" 4.26570385548275e-107 0.132588023694303 0.3 0.899 1.04505478755472e-102 +"HIST1H1D" 2.36276079843266e-43 0.132531820851338 0.063 0.232 5.78852768008018e-39 +"COPS5" 5.89125292001924e-175 0.132164112375579 0.138 0.667 1.44329805287551e-170 +"TSC22D4" 2.53184601475799e-120 0.132086372550796 0.066 0.412 6.20276955155561e-116 +"RAB5C" 7.4320608157814e-182 0.132036843788966 0.201 0.818 1.82078057925828e-177 +"SCOC" 1.93648440700922e-139 0.131905327422421 0.278 0.914 4.74419314873189e-135 +"RP11-841O20.2" 1.16183010231349e-66 0.131881349051859 0.052 0.267 2.84636756765783e-62 +"GPX4" 0.8457332442408 0.131854319286756 0.646 0.99 1 +"FAM89B" 7.76857382518154e-167 0.131754110245689 0.139 0.66 1.90322290143122e-162 +"MRPL35" 9.95073340662771e-129 0.131735026340427 0.071 0.439 2.43783017728972e-124 +"ATP5D" 1.70732953979851e-91 0.131733591377805 0.32 0.897 4.18278663955237e-87 +"ZRANB2" 5.65685633910796e-181 0.131495678070368 0.162 0.733 1.38587323451806e-176 +"RAN" 0.0473138792290764 0.131480810370278 0.652 0.964 1 +"METTL5" 5.29188320136872e-163 0.131462305182842 0.198 0.781 1.29645846550332e-158 +"MRPS15" 1.22840146236081e-93 0.131235440653106 0.325 0.908 3.00946074263774e-89 +"CUEDC2" 8.42981058439726e-138 0.131195959577718 0.13 0.58 2.06521929507148e-133 +"ADAP1" 4.68898851019323e-71 0.131176069096255 0.057 0.29 1.14875529511224e-66 +"PRKAG1" 8.77463815576014e-121 0.131019541464977 0.076 0.433 2.14969860177968e-116 +"DNAJB2" 7.34299890411194e-59 0.130758405729977 0.054 0.255 1.79896130151838e-54 +"DDX56" 5.36628990833478e-150 0.130724050599843 0.106 0.553 1.31468736464294e-145 +"RRAS" 1.50907746878265e-133 0.130691783820532 0.12 0.554 3.69708889077062e-129 +"CARHSP1" 9.39807256767977e-119 0.130623402634964 0.265 0.833 2.30243379835587e-114 +"PFKP" 9.12792380803226e-135 0.130607312507886 0.162 0.642 2.23625005372982e-130 +"C12orf10" 9.70449783969866e-157 0.130551409491875 0.133 0.624 2.37750492574778e-152 +"MRPS22" 1.14932023270212e-135 0.130533028064166 0.09 0.493 2.81571963809693e-131 +"COX14" 1.02816335075133e-118 0.130512248218729 0.306 0.933 2.51889739300567e-114 +"ZCCHC17" 1.06408346762568e-155 0.13049331003907 0.098 0.548 2.60689808733614e-151 +"CTBS" 1.69634761485842e-177 0.130405834279187 0.106 0.604 4.15588202164165e-173 +"KHDRBS3" 1.48909701989892e-80 0.130255719669513 0.064 0.325 3.64813878905036e-76 +"BCL7C" 2.97558840949173e-147 0.130141149277102 0.178 0.704 7.28989404441378e-143 +"TMEM126B" 1.57787247725295e-171 0.130139511335233 0.124 0.633 3.86562978202201e-167 +"RHOD" 4.58740269384393e-132 0.130098027213616 0.141 0.594 1.12386778596482e-127 +"PHB2" 5.58657666516832e-37 0.12953825663793 0.396 0.868 1.36865541719959e-32 +"AP1B1" 5.84679394878293e-107 0.129527891403438 0.07 0.394 1.43240604951233e-102 +"GALM" 1.11866582240176e-132 0.129271931226557 0.111 0.532 2.74061939830208e-128 +"EMP3" 9.29013172259555e-86 0.129188331083571 0.109 0.424 2.27598937071868e-81 +"STK25" 6.69546907393703e-143 0.129157000038642 0.109 0.551 1.64032296842383e-138 +"DKC1" 1.14189276342599e-151 0.129153898902684 0.134 0.619 2.79752308111734e-147 +"DNAJC4" 5.40283433785424e-133 0.129074466562686 0.107 0.525 1.32364038443091e-128 +"AP1S1" 9.00327653051403e-168 0.128996105632641 0.161 0.712 2.20571271721063e-163 +"MRPL22" 2.63526797809335e-158 0.128859656836626 0.197 0.766 6.4561430195309e-154 +"NDUFS8" 2.81051432413643e-65 0.128670063945939 0.349 0.873 6.88547904270183e-61 +"PKP3" 3.21747829041009e-170 0.128651185394296 0.12 0.621 7.88250006367567e-166 +"NSMCE1" 9.54667451264048e-157 0.128646001808429 0.131 0.623 2.33883978885179e-152 +"CNIH1" 2.50953564568454e-151 0.128631786431143 0.108 0.56 6.14811137836256e-147 +"UBE2Z" 1.24250312562565e-172 0.128612604112888 0.135 0.657 3.04400840747028e-168 +"GLO1" 4.12669750190728e-100 0.12858094610294 0.31 0.894 1.01099962099227e-95 +"NME1" 3.74814017621501e-35 0.128562097153732 0.406 0.849 9.18256861770916e-31 +"PRELID1" 3.63965584601787e-22 0.128553313275947 0.47 0.95 8.91679285715919e-18 +"ANAPC5" 1.04123450879235e-169 0.128489433638396 0.162 0.715 2.55092042309038e-165 +"AES" 1.62498787987067e-179 0.128273544900197 0.211 0.839 3.98105780689516e-175 +"S100A11" 0.709827652023265 0.128242849511411 0.85 0.997 1 +"CLEC2B" 1.49972049913296e-76 0.128201233072419 0.067 0.322 3.67416525082583e-72 +"TCEAL8" 7.90610747778114e-202 0.128110007717048 0.13 0.696 1.9369172709816e-197 +"VPS29" 1.8317777151171e-147 0.128069083999439 0.253 0.868 4.48767222426538e-143 +"HSPA4" 1.70105156635307e-169 0.128009575444375 0.204 0.806 4.1674062324084e-165 +"TSPAN8" 2.82048425694604e-112 0.12782413692112 0.134 0.535 6.90990438109211e-108 +"AKAP8L" 1.91409832362449e-123 0.127757403436597 0.078 0.444 4.68934948304765e-119 +"ABCF1" 4.14002326846239e-159 0.127737938895239 0.145 0.656 1.0142643005406e-154 +"OAT" 4.8141457311167e-162 0.127737359094989 0.112 0.587 1.17941756266628e-157 +"CHCHD3" 3.70791479247132e-154 0.127704289814276 0.17 0.694 9.08402045007548e-150 +"AHCY" 1.26874772549544e-128 0.127556763305614 0.097 0.494 3.10830505269127e-124 +"PPP1CC" 8.04115661300243e-126 0.12751593092555 0.279 0.894 1.97000295861947e-121 +"GBA" 5.9134838276239e-58 0.127445340928987 0.044 0.232 1.44874440292958e-53 +"ABCA7" 1.05636223185205e-112 0.127288023162043 0.078 0.422 2.58798183181435e-108 +"GNAI2" 7.05025386988446e-99 0.127250503657202 0.271 0.794 1.72724169558299e-94 +"ACTA2" 1.43420804898146e-73 0.127237610975978 0.057 0.295 3.51366629919967e-69 +"ZNF259" 1.08523045344574e-149 0.127232825646164 0.103 0.545 2.65870608789673e-145 +"RAB9A" 9.93095271263221e-195 0.126938537778312 0.142 0.711 2.43298410506776e-190 +"GULP1" 3.45397860415447e-63 0.126809639853703 0.057 0.27 8.46190218231803e-59 +"EIF5B" 4.79155000075703e-139 0.126617366273469 0.261 0.868 1.17388183468546e-134 +"C11orf31" 4.73663547024633e-82 0.126613753914008 0.344 0.929 1.16042832385565e-77 +"COA6" 9.79178020827935e-176 0.126594430547339 0.188 0.782 2.39888823322636e-171 +"NTMT1" 2.23457106925464e-156 0.126453189480928 0.097 0.544 5.47447566256694e-152 +"PGAM1" 3.32506390243343e-87 0.126414711989301 0.282 0.79 8.14607405457167e-83 +"COPS3" 4.31977421389824e-150 0.126278992299926 0.107 0.555 1.05830148466293e-145 +"MCMBP" 9.63222125501885e-150 0.126170452986648 0.075 0.488 2.35979788526707e-145 +"NDUFA13" 3.1456018074868e-129 0.126166592968256 0.285 0.905 7.7064098681619e-125 +"FAM207A" 1.5055174118102e-145 0.126077045785612 0.128 0.59 3.6883671071938e-141 +"PPP1R35" 1.12514830299197e-154 0.126049269705785 0.151 0.661 2.75650082750002e-150 +"RN7SL1" 5.94103529311097e-77 0.126032073718979 0.055 0.297 1.45549423645926e-72 +"HIPK3" 5.97820367161885e-163 0.126005058253044 0.224 0.836 1.4646001175099e-158 +"ATP6AP1" 7.37406321363048e-158 0.125958290380359 0.098 0.551 1.80657174670733e-153 +"PDCD2" 5.99180453805175e-172 0.125862346885653 0.134 0.655 1.4679321937773e-167 +"NINJ1" 4.94701928977647e-134 0.125643366244533 0.091 0.492 1.21197025580234e-129 +"CDC20" 9.99135483242196e-51 0.125638302115758 0.121 0.359 2.44778202039506e-46 +"WDR45" 6.48526548356562e-136 0.125606038384493 0.1 0.514 1.58882519081874e-131 +"POLD4" 4.56919742511353e-168 0.12560352432913 0.197 0.792 1.11940767717856e-163 +"EPS8L2" 6.45642279482401e-155 0.125474337096416 0.107 0.566 1.58175902050393e-150 +"KIAA0319L" 5.37032724077903e-140 0.125390140009243 0.068 0.454 1.31567647071845e-135 +"TRIM27" 7.47729904023203e-148 0.125320184825205 0.093 0.524 1.83186349186644e-143 +"COMMD6" 3.63012344207774e-57 0.125241250427387 0.402 0.971 8.89343942074626e-53 +"YBEY" 3.00420768045739e-139 0.125095140286126 0.107 0.534 7.36000839635256e-135 +"CKS1B" 3.13504099116327e-82 0.125013604135005 0.183 0.565 7.6805369242509e-78 +"CD109" 5.08214577158093e-133 0.124911949576192 0.107 0.522 1.24507489257961e-128 +"PARP12" 1.06063006614572e-103 0.12485923191038 0.066 0.378 2.5984375990504e-99 +"SNHG7" 1.10657539544492e-162 0.124812080016267 0.152 0.677 2.71099906130052e-158 +"PLP2" 1.08452675461631e-42 0.124647869900136 0.377 0.842 2.65698209613451e-38 +"TAF11" 1.01345647720104e-162 0.124642527069472 0.096 0.555 2.48286702349482e-158 +"UBE2L6" 2.01497488435859e-159 0.124604551636966 0.193 0.76 4.93648696919011e-155 +"YKT6" 6.11645962003649e-152 0.124529409971709 0.105 0.555 1.49847144231274e-147 +"RRS1" 3.04380614778548e-114 0.124492921557745 0.063 0.394 7.45702068145966e-110 +"BAG2" 7.26349482052522e-131 0.124267842495891 0.078 0.459 1.77948359608047e-126 +"FTSJ2" 8.20657495681629e-136 0.124031158848489 0.09 0.494 2.01052879867042e-131 +"HLA-E" 2.67392215786156e-138 0.123974907819615 0.262 0.882 6.55084189454503e-134 +"SNX17" 7.19155754836536e-168 0.123927280861548 0.14 0.66 1.76185968377403e-163 +"YARS" 2.86704433562647e-150 0.123775781823036 0.115 0.573 7.02397191785129e-146 +"ELP2" 2.33928457566753e-106 0.123218059300847 0.062 0.377 5.73101328192789e-102 +"RAB11A" 7.35300132065706e-187 0.123174613909666 0.146 0.709 1.80141179354777e-182 +"TMEM134" 1.74876073220129e-168 0.123122384539162 0.187 0.764 4.28428891781993e-164 +"ZNF688" 1.15128777307316e-84 0.123111530780175 0.049 0.301 2.82053991525194e-80 +"RNF7" 1.0811191768238e-137 0.123074733544637 0.264 0.878 2.64863387130064e-133 +"UBL5" 1.53311654149961e-20 0.123006147864214 0.478 0.982 3.7559822150199e-16 +"TXN2" 1.43905607377543e-166 0.122943720934481 0.128 0.631 3.52554347514242e-162 +"CPSF4" 6.20165214283149e-98 0.122798016435821 0.065 0.364 1.51934275847229e-93 +"ABHD17A" 1.08736046825822e-145 0.122772990152636 0.086 0.502 2.66392441118582e-141 +"GNG5" 9.64448603803253e-25 0.12273025537147 0.454 0.942 2.36280263445759e-20 +"MAP4" 6.31684656888623e-140 0.122300547617674 0.184 0.698 1.54756424091144e-135 +"ATIC" 2.73875593052946e-164 0.122274163537819 0.107 0.582 6.70967815420411e-160 +"TTC3" 4.29864919070138e-162 0.122121707793031 0.226 0.838 1.05312606522993e-157 +"MAGED1" 4.85301613933914e-146 0.121911393081307 0.126 0.592 1.18894042397669e-141 +"SPCS1" 6.4503354518498e-50 0.12190464539302 0.403 0.938 1.58026768234868e-45 +"TMEM125" 2.80898413341325e-147 0.121898771307049 0.093 0.522 6.88173022844911e-143 +"NAGK" 1.03242714969081e-108 0.121871301504448 0.065 0.386 2.52934327402752e-104 +"ADRM1" 2.6446639425098e-155 0.121775092180445 0.176 0.713 6.47916219275477e-151 +"NDUFA11" 1.42341148863021e-159 0.121584184905725 0.251 0.895 3.48721580599516e-155 +"GPAA1" 7.41214453992625e-140 0.121566564850318 0.186 0.707 1.81590129083653e-135 +"VMA21" 4.51445919326293e-193 0.121476782767605 0.152 0.733 1.10599735775749e-188 +"MRPL24" 1.70792057402956e-147 0.121256852126399 0.119 0.577 4.18423461431501e-143 +"IDH2" 7.69692992446643e-150 0.12124097056283 0.159 0.667 1.88567086219503e-145 +"SEP15" 9.44916034136647e-150 0.121235912450233 0.232 0.832 2.31494979203137e-145 +"SIPA1" 8.11850100666264e-145 0.121171747358574 0.097 0.524 1.98895156162228e-140 +"DNAJC12" 1.41494146738982e-146 0.120983549595459 0.118 0.573 3.46646510095832e-142 +"TMA16" 7.42489531725905e-176 0.120879333105332 0.161 0.718 1.8190251037753e-171 +"STX8" 4.62419514205383e-116 0.120873883429328 0.071 0.415 1.13288156785177e-111 +"SNX3" 2.2378426357829e-174 0.120803331171376 0.208 0.825 5.48249067340452e-170 +"PPIA" 0.000143216305089191 0.120568544610573 0.888 0.989 1 +"TGFB1" 1.2300931741224e-114 0.12039353317315 0.092 0.454 3.01360526728248e-110 +"UQCR10" 7.63320050911197e-41 0.120362032049904 0.436 0.975 1.87005779272734e-36 +"CD151" 4.52123500383157e-125 0.120336788215471 0.272 0.865 1.1076573635887e-120 +"LRPAP1" 1.86381480604094e-164 0.120263088347004 0.163 0.704 4.5661598933197e-160 +"TBL1XR1" 7.59211482569074e-125 0.120147491753812 0.138 0.572 1.85999221114597e-120 +"F12" 2.01267482999425e-96 0.120023856133605 0.079 0.39 4.93085206600291e-92 +"INO80B" 9.73824130732487e-95 0.120014648679982 0.055 0.336 2.38577173788152e-90 +"C8orf76" 6.11402419077945e-151 0.119962885751189 0.084 0.509 1.49787478649906e-146 +"MARS" 1.22417142603335e-130 0.119724663309956 0.077 0.455 2.99909757663911e-126 +"SMIM4" 1.97078733382126e-66 0.119693764494817 0.046 0.256 4.8282318891287e-62 +"PTRHD1" 8.77558307895916e-184 0.119633052294248 0.147 0.706 2.1499300985142e-179 +"TK1" 9.16967305437998e-56 0.119537480682285 0.162 0.448 2.24647820159255e-51 +"QPRT" 2.05145095214865e-92 0.119526917460208 0.065 0.351 5.02584968766898e-88 +"TMEM70" 4.27342738476369e-171 0.119502630413009 0.158 0.712 1.04694697499326e-166 +"DCTN3" 5.60323422229436e-167 0.119136971286901 0.169 0.719 1.37273635211989e-162 +"G6PD" 4.94511961338909e-134 0.118789441281953 0.131 0.577 1.21150485408419e-129 +"TSTA3" 1.59202820570461e-137 0.11875148935014 0.147 0.619 3.90030990115572e-133 +"MRPL49" 1.64692151158412e-122 0.118697942076987 0.078 0.441 4.03479301122993e-118 +"IGFBP2" 1.39702857421041e-117 0.118644892187976 0.185 0.656 3.42258030395808e-113 +"NANS" 1.08908977693533e-163 0.118569889197837 0.132 0.634 2.66816104451387e-159 +"SERP1" 7.73438458167945e-90 0.11856298993889 0.34 0.948 1.89484687866565e-85 +"COA3" 5.41703253157144e-158 0.118496396029184 0.233 0.847 1.32711879990969e-153 +"CD2BP2" 3.7044528674244e-171 0.118455036844183 0.13 0.644 9.07553907990304e-167 +"SDC3" 1.80438957443461e-86 0.118389019485261 0.046 0.3 4.42057401840734e-82 +"SMS" 1.14857888502376e-138 0.118342882263611 0.252 0.848 2.81390341041972e-134 +"WBP5" 4.46494848296331e-185 0.118311313787211 0.139 0.693 1.09386772884118e-180 +"RBKS" 2.02266947857825e-99 0.118296794825991 0.058 0.352 4.95533795556886e-95 +"NDUFA12" 2.88897504376845e-97 0.118266798835388 0.312 0.885 7.07769995972832e-93 +"HTR3A" 6.25898679424518e-38 0.118202600937394 0.039 0.172 1.53338917472213e-33 +"MDK" 0.307459153571144 0.118154331741827 0.762 0.942 1 +"KIAA1598" 4.23933838587963e-152 0.117915694054053 0.091 0.526 1.03859551115665e-147 +"CARD16" 6.39756578740669e-117 0.117882434899356 0.066 0.404 1.56733964225677e-112 +"NPW" 2.96717919564563e-92 0.117589132731509 0.071 0.365 7.26929231141222e-88 +"MRPL43" 1.66588791569225e-157 0.117578205630867 0.167 0.698 4.08125880465445e-153 +"BCAP31" 1.52294665576844e-134 0.117569689548892 0.264 0.863 3.7310670119671e-130 +"RNF11" 4.64928425861503e-190 0.117523266388013 0.124 0.665 1.1390281505181e-185 +"RGL2" 4.49915305629704e-127 0.117523137455683 0.074 0.442 1.10224750726221e-122 +"CYB5D2" 2.49500576372992e-43 0.117328544042644 0.025 0.157 6.11251462056193e-39 +"HIST1H1C" 2.0776691927038e-61 0.117296432553664 0.071 0.293 5.09008175520504e-57 +"NIPSNAP1" 3.75929972000049e-104 0.117269118190833 0.068 0.384 9.20990838402919e-100 +"OSTF1" 1.58482865743754e-154 0.117196638815582 0.089 0.526 3.88267172785623e-150 +"LARP7" 2.26775127324249e-213 0.11691811943139 0.125 0.703 5.55576384431679e-209 +"PQBP1" 5.01565972306441e-145 0.116874215303499 0.104 0.541 1.22878647555355e-140 +"SRPRB" 7.43890527060505e-151 0.116863461981187 0.097 0.536 1.82245740224553e-146 +"SMIM19" 8.72899884692012e-196 0.116830935234953 0.126 0.675 2.13851742750696e-191 +"GNB2" 3.06702866283924e-102 0.116798600560427 0.314 0.908 7.51391352108985e-98 +"VDAC3" 3.39318066373752e-166 0.116768882630997 0.158 0.7 8.31295330809055e-162 +"HSF1" 2.73650697078709e-139 0.116648423278274 0.141 0.61 6.7041684277313e-135 +"PMF1" 5.75906808079247e-165 0.116388482872116 0.082 0.529 1.41091408911335e-160 +"IGFBP3" 2.05316030717933e-51 0.116149222893709 0.089 0.3 5.03003743655863e-47 +"C1orf50" 2.39353739168122e-104 0.116117214747083 0.061 0.37 5.86392725587983e-100 +"CSNK2B" 6.75807363342624e-101 0.115991779035542 0.299 0.873 1.6556604594531e-96 +"JTB" 1.70014695852752e-84 0.115912668170458 0.347 0.937 4.16519003369657e-80 +"PTOV1" 9.32926034099548e-153 0.115895314752614 0.094 0.532 2.28557549094048e-148 +"DHRS4L2" 1.2584898253173e-154 0.115753585713417 0.115 0.581 3.08317422304485e-150 +"SFR1" 2.79592790917437e-89 0.115730463964122 0.05 0.313 6.8497437846863e-85 +"COX6B1" 0.634437143437779 0.115639060395977 0.641 0.992 1 +"ODF3B" 1.41039287997859e-93 0.115594082059039 0.083 0.393 3.45532151665956e-89 +"TAF10" 6.94054284035161e-84 0.11558469944572 0.333 0.919 1.70036359045774e-79 +"NT5DC2" 2.59707905366133e-121 0.115564651408866 0.115 0.519 6.36258397356489e-117 +"UTP3" 3.84381837293586e-120 0.115522248718741 0.063 0.406 9.41697063185557e-116 +"SUMF2" 3.91603545398183e-158 0.115345373189074 0.115 0.587 9.59389525871008e-154 +"VPS51" 4.40004355006508e-166 0.115235957837451 0.107 0.586 1.07796666933044e-161 +"PHF5A" 1.05503740561379e-148 0.115094046712866 0.087 0.513 2.58473614001322e-144 +"PHGDH" 1.40879333656723e-73 0.115028285120136 0.073 0.325 3.45140279525605e-69 +"VPS28" 7.86808351881812e-113 0.115018307943657 0.274 0.841 1.92760178127525e-108 +"SLC25A10.1" 5.88773271919667e-66 0.114891885189377 0.053 0.268 1.44243563887599e-61 +"PRMT2" 1.13220411313034e-176 0.114863562372575 0.141 0.678 2.77378685675802e-172 +"C6orf1" 2.3684054414685e-120 0.114851762921692 0.083 0.447 5.80235649105367e-116 +"EMC9" 8.81152160325506e-128 0.114807836739543 0.083 0.461 2.15873467758146e-123 +"ARL3" 1.12183173787595e-151 0.114805883975476 0.111 0.567 2.74837557462229e-147 +"MAN2A1" 2.17271040001812e-175 0.114725359180141 0.15 0.695 5.3229232090044e-171 +"ESRP1" 3.45159441856589e-172 0.114486557667329 0.145 0.68 8.45606116604459e-168 +"MRPL23" 5.39922539754451e-101 0.11444348463687 0.299 0.876 1.32275623014443e-96 +"RANGAP1" 2.68086542755061e-113 0.114163819282602 0.073 0.413 6.56785221095623e-109 +"MRPL54" 2.39295226924151e-193 0.114109106709936 0.151 0.732 5.86249376441476e-189 +"APOBEC3G" 5.68536238155526e-71 0.113993655188402 0.043 0.259 1.39285692985722e-66 +"SZRD1" 1.25132414437313e-178 0.113558136040719 0.108 0.611 3.06561902129973e-174 +"DESI1" 7.01945705960907e-166 0.113449521113594 0.164 0.705 1.71969678503363e-161 +"VTI1B" 6.96308160250596e-185 0.113439096474475 0.134 0.676 1.70588536179794e-180 +"MTHFD1L" 1.02720199544966e-98 0.11341300856897 0.06 0.355 2.51654216865211e-94 +"DST" 6.99535100668449e-109 0.113395821616344 0.272 0.818 1.71379104312763e-104 +"REEP6" 5.05783523713713e-66 0.113349553585952 0.048 0.259 1.23911905474623e-61 +"SNX6" 2.60566615946945e-142 0.113338657678915 0.245 0.841 6.3836215240842e-138 +"PPP1R16A" 2.74704900831633e-116 0.113312927020062 0.08 0.434 6.72999536547418e-112 +"CCDC59" 1.57147987934584e-209 0.113220413686466 0.159 0.774 3.84996855640938e-205 +"SS18L2" 2.27778790462379e-171 0.113177978784068 0.119 0.622 5.58035258753782e-167 +"CNP" 1.38391588772472e-138 0.113174428860166 0.074 0.464 3.3904555333368e-134 +"MOB2" 6.09965518556902e-82 0.113132449315904 0.05 0.297 1.49435452391255e-77 +"UQCRC1" 1.95408990636099e-153 0.113036256754097 0.192 0.748 4.7873248615938e-149 +"SFN" 1.5205615282034e-69 0.112909661216383 0.264 0.694 3.72522368794551e-65 +"CALD1" 5.43956696644042e-63 0.112731719124323 0.35 0.843 1.33263951110824e-58 +"TMEM101" 1.94127138273722e-113 0.112695719002329 0.073 0.411 4.75592076056792e-109 +"INPPL1" 2.92999554784047e-97 0.11263789594416 0.052 0.334 7.17819609265437e-93 +"UBE2N" 4.59545807110685e-175 0.112524739786504 0.201 0.809 1.12584127284047e-170 +"EXOSC8" 2.99630252686282e-120 0.112436730120258 0.067 0.413 7.34064156056123e-116 +"CEP290" 5.46003263899469e-158 0.11227857544849 0.099 0.552 1.33765339622731e-153 +"HTRA1" 1.57406490469279e-21 0.111834039952539 0.03 0.111 3.85630161000686e-17 +"LDHA" 0.968614286039896 0.111710473314703 0.749 0.98 1 +"NUBP2" 1.89793342754903e-150 0.111694539447166 0.091 0.523 4.64974710415237e-146 +"RNF220" 2.54863353427929e-165 0.111558400381124 0.093 0.552 6.24389729563083e-161 +"BIN1" 6.06285766768697e-128 0.11153092080925 0.111 0.523 1.48533950000663e-123 +"VAMP5" 4.661759003741e-35 0.11152615869173 0.031 0.149 1.14208433832651e-30 +"FAM45A" 1.13232131273299e-164 0.111460891608926 0.129 0.633 2.77407398406456e-160 +"PRRC2A" 4.94477880906789e-171 0.111387849117811 0.112 0.605 1.21142136043354e-166 +"BATF3" 1.29911541085474e-105 0.111352247431335 0.074 0.398 3.18270284505303e-101 +"HDAC5" 1.22524907912884e-62 0.111271692169256 0.046 0.246 3.00173771895774e-58 +"NUDT2" 3.83591819008727e-100 0.111257738422996 0.069 0.375 9.3976159738948e-96 +"SLC2A3" 3.69377629747547e-102 0.111220948270819 0.078 0.399 9.04938255118515e-98 +"CA2" 2.5660273608973e-45 0.111217006259984 0.036 0.185 6.28651043146229e-41 +"TPD52L1" 1.459131603959e-86 0.111151032562016 0.053 0.315 3.57472651653916e-82 +"STYXL1" 7.85002077263591e-143 0.11107048605206 0.092 0.511 1.92317658908807e-138 +"TNFRSF18" 6.473656485907e-104 0.111069742074764 0.058 0.361 1.58598110248236e-99 +"PIGP" 2.36057790026338e-181 0.11097778351872 0.099 0.591 5.78317979785524e-177 +"ESRRA" 8.52078943871117e-124 0.110976161441324 0.078 0.445 2.08750820458985e-119 +"LAP3" 1.68035994879765e-183 0.110797942433272 0.137 0.681 4.11671383855937e-179 +"GBP2" 2.45566860927525e-100 0.110722022612261 0.087 0.414 6.01614252586342e-96 +"SNRNP27" 6.83392363779612e-174 0.110636073557885 0.137 0.666 1.67424295202367e-169 +"YIF1A" 9.895724320713e-112 0.110631768599726 0.25 0.782 2.42435350133148e-107 +"SDHB" 3.44063184312001e-174 0.110631327701971 0.147 0.685 8.42920395245971e-170 +"UROS" 1.46598388379026e-148 0.110610412052462 0.138 0.618 3.59151391689777e-144 +"POMGNT1" 3.77351073870541e-118 0.110514426702585 0.063 0.4 9.24472395875438e-114 +"UBE2V2" 9.51940520375609e-207 0.110461394639311 0.17 0.797 2.33215908086821e-202 +"MRPS12" 1.39990894433211e-174 0.11045266747628 0.176 0.749 3.42963692271923e-170 +"PSMA7" 0.000106961090233632 0.110439386424037 0.59 0.976 1 +"PGM1" 2.68264307243587e-167 0.110367253399354 0.103 0.58 6.57220726316064e-163 +"RHOF" 1.7285934233723e-105 0.110336982249048 0.07 0.391 4.23488102791979e-101 +"PSMD2" 2.25593669253972e-101 0.110305390828393 0.245 0.736 5.52681930305307e-97 +"UTP18" 1.78381139929763e-163 0.110304377744167 0.09 0.544 4.37015954713927e-159 +"DPCD" 1.19466694563636e-129 0.110273384142394 0.081 0.461 2.92681455011452e-125 +"AATF" 1.87104400378084e-142 0.110219223076598 0.127 0.583 4.58387070486269e-138 +"PTP4A3" 6.30143687942098e-66 0.110126062054682 0.045 0.253 1.54378902108935e-61 +"EIF3J" 1.1754395308403e-198 0.110065453251738 0.155 0.749 2.87970930660566e-194 +"DMKN" 4.59301135682806e-146 0.10985622286541 0.221 0.796 1.12524185230931e-141 +"FAM213A" 2.99626646881159e-157 0.109836407701958 0.175 0.715 7.34055322194151e-153 +"MARVELD1" 1.64511793988271e-79 0.109805860836622 0.054 0.301 4.03037444091866e-75 +"CCNB1IP1" 4.20242297978435e-137 0.109786641448808 0.088 0.49 1.02955160581737e-132 +"CCDC137" 2.06069642340874e-145 0.109777668419418 0.084 0.497 5.04850016770907e-141 +"ATOX1" 5.15940930828926e-108 0.109749542241194 0.312 0.928 1.26400368643778e-103 +"RFC4" 6.7038358714285e-91 0.109658861278441 0.057 0.333 1.64237275014127e-86 +"EIF2A" 4.3934049069837e-171 0.109613976820541 0.175 0.74 1.07634026816194e-166 +"NCBP2-AS2" 4.72476182044972e-114 0.109475483910172 0.07 0.407 1.15751939839198e-109 +"SLC9A3R2" 8.49997554527181e-190 0.109474187965278 0.156 0.733 2.08240900883614e-185 +"COX19" 8.04926200146314e-155 0.109464220983068 0.108 0.568 1.97198869773845e-150 +"TMA7" 3.76986350975122e-07 0.109448989363471 0.566 0.987 0.00923578861253952 +"GTF3A" 1.21986946539176e-170 0.109410270195035 0.19 0.778 2.98855820326327e-166 +"C14orf166" 9.68278409145568e-112 0.109377624904308 0.309 0.919 2.37218527456573e-107 +"CCS" 8.84749482233882e-136 0.109216559166195 0.087 0.487 2.16754775652479e-131 +"MAGED2" 1.23930997831505e-165 0.109124113908449 0.122 0.617 3.03618551587404e-161 +"TBCB" 4.92887129867874e-160 0.109107388273411 0.215 0.808 1.20752417946331e-155 +"UBE2S" 1.7374037645021e-76 0.109011400579498 0.243 0.663 4.25646548265369e-72 +"DMAP1" 2.04923967446499e-130 0.108998110684239 0.078 0.459 5.02043227847178e-126 +"QARS" 3.17142039310055e-167 0.108950207545229 0.095 0.562 7.76966282105703e-163 +"HIST2H2AC" 1.1113743854877e-59 0.108686672981154 0.048 0.243 2.72275610700632e-55 +"TRIQK" 1.47429271096646e-106 0.108683985740631 0.071 0.396 3.61186971259673e-102 +"RPS15A" 4.17491255012438e-07 0.108674089779 0.971 1 0.0102281182565497 +"GNL1" 7.80496675416517e-151 0.108595771590689 0.096 0.534 1.91213880510292e-146 +"CYSTM1" 4.28768234617758e-141 0.108553023357498 0.257 0.872 1.05043929799005e-136 +"MRPL42" 2.01224217247809e-173 0.108551630668953 0.207 0.822 4.92979209835408e-169 +"DDIT3" 3.96577697083222e-99 0.108282487663012 0.108 0.454 9.71575700084187e-95 +"FOXK2" 5.7133891308583e-160 0.108267469013627 0.09 0.538 1.39972320316897e-155 +"AFAP1-AS1" 1.95186692923484e-116 0.108204606355646 0.082 0.436 4.78187878993244e-112 +"NMD3" 2.22818325831773e-147 0.10807699901866 0.092 0.52 5.45882616455262e-143 +"GRINA" 1.99082711679754e-195 0.10807148982282 0.167 0.767 4.8773273534423e-191 +"DALRD3" 8.1989528395505e-141 0.108025556573013 0.09 0.503 2.00866145616148e-136 +"SUCLG1" 3.02208631832401e-182 0.108024974867918 0.139 0.683 7.40380927126199e-178 +"TMSB4X" 5.03343106737814e-06 0.107926852136306 0.945 0.999 0.123314027719697 +"LAMP2" 9.50629907904615e-200 0.107795855879296 0.125 0.683 2.32894821137552e-195 +"LRWD1" 9.83548939019444e-72 0.10777060474739 0.052 0.279 2.40959654570374e-67 +"C1QBP" 4.22379774303119e-129 0.107765473510667 0.279 0.885 1.03478820906521e-124 +"PDCD10" 4.10184670411828e-192 0.107746040640312 0.185 0.801 1.00491142404194e-187 +"BAG6" 5.58830921892304e-167 0.107730459281121 0.125 0.626 1.36907987554396e-162 +"EEF1E1" 8.08473517760613e-175 0.107475875634884 0.171 0.737 1.98067927116172e-170 +"OXLD1" 1.63817626854345e-138 0.107455266590929 0.072 0.458 4.01336804030459e-134 +"ACTR1A" 1.46977120181099e-116 0.107371650944079 0.068 0.409 3.60079246731675e-112 +"FAM192A" 1.76338270913437e-173 0.107352466777073 0.1 0.582 4.32011129910828e-169 +"C20orf27" 9.10367611310745e-117 0.10735024099205 0.079 0.434 2.23030961095019e-112 +"ATP5A1" 2.22776984983075e-160 0.107344206066673 0.217 0.816 5.45781335510036e-156 +"MRPS9" 3.1961838636594e-116 0.107331744371638 0.062 0.396 7.83033084757917e-112 +"CHCHD5" 5.12777365237567e-192 0.107319194270239 0.128 0.675 1.25625326709552e-187 +"ZCRB1" 5.06638337196627e-120 0.107308697753596 0.307 0.932 1.24121326229802e-115 +"SURF2" 2.3884484260189e-142 0.107303842277981 0.107 0.54 5.85145979890371e-138 +"ACAA1" 3.01322044623629e-123 0.107223165989879 0.073 0.433 7.38208877123429e-119 +"EIF4E2" 3.63248044114504e-183 0.107180546403054 0.112 0.626 8.89921383276124e-179 +"IVNS1ABP" 4.19882712453603e-92 0.107151269372758 0.052 0.323 1.02867065724008e-87 +"ACTR10" 3.72797277698592e-159 0.107150992792422 0.086 0.528 9.13316050633781e-155 +"DYNC2LI1" 1.99702570338268e-82 0.106971078253955 0.049 0.297 4.89251327071723e-78 +"C1orf52" 9.12383672206678e-142 0.106690517067969 0.075 0.469 2.23524875853914e-137 +"PPM1G" 1.47965008780994e-89 0.106560253615215 0.307 0.834 3.62499475012558e-85 +"MRPS30" 1.9011342838365e-191 0.106445083025319 0.099 0.612 4.65758888197104e-187 +"HIGD2A" 3.04856593444842e-88 0.106364905207833 0.338 0.935 7.46868168280519e-84 +"PTRH2" 6.36223556981817e-139 0.106196001682082 0.066 0.447 1.55868409224975e-134 +"RPS19BP1" 5.17320385637588e-176 0.106192851292234 0.175 0.757 1.26738321277353e-171 +"UBE2T" 6.38511865064781e-90 0.106128097775558 0.139 0.495 1.56429021822221e-85 +"PDHB" 1.29233283858071e-183 0.106053277313177 0.106 0.61 3.16608622123889e-179 +"RPF2" 1.7570589447967e-130 0.106015129329508 0.079 0.458 4.30461870885743e-126 +"TWF2" 3.20522507473897e-147 0.105983499307853 0.096 0.527 7.85248091060301e-143 +"SCO2" 1.64441357831329e-153 0.105786251904814 0.095 0.536 4.02864882550972e-149 +"GSTK1" 2.61482402651625e-164 0.105677651488484 0.172 0.719 6.40605738256215e-160 +"SHMT2" 5.06008915360234e-141 0.10549450037141 0.131 0.589 1.23967124174104e-136 +"FLOT2" 4.49247157447631e-129 0.105484297590321 0.075 0.448 1.10061061103095e-124 +"PPP1CA" 4.97023439445773e-135 0.105417358132367 0.258 0.857 1.2176577242982e-130 +"ADI1" 1.11563224271825e-163 0.10535992613807 0.222 0.837 2.73318743143545e-159 +"ISOC2" 2.13856523761527e-159 0.105353984303998 0.115 0.59 5.23927097563365e-155 +"GBAS" 1.76753484856468e-182 0.105332460612049 0.156 0.724 4.33028362549862e-178 +"CEBPZ" 2.37565180170729e-173 0.105324064499064 0.158 0.709 5.82010934900268e-169 +"PRKCSH" 1.70588108278226e-188 0.105275171249636 0.101 0.609 4.17923806470825e-184 +"TMEM256" 2.53187670165845e-164 0.105259767239241 0.185 0.753 6.20284473139303e-160 +"TCN1" 5.49789974953437e-44 0.105049149307152 0.058 0.223 1.34693045963843e-39 +"CLNS1A" 2.75421176359741e-164 0.105022575012222 0.224 0.836 6.7475433996373e-160 +"ADH5" 2.81029471245751e-207 0.104958763515148 0.138 0.724 6.88494101604965e-203 +"IFRD2" 2.90711034585375e-168 0.104873682490109 0.132 0.644 7.12212963630711e-164 +"HSPD1" 1.2135762770998e-08 0.104801041177287 0.568 0.94 0.000297314052126681 +"CSRP2" 1.22668301544786e-121 0.104798531534894 0.099 0.482 3.00525071954571e-117 +"MAPK3" 8.64473127683437e-128 0.10476241758852 0.078 0.452 2.11787271551165e-123 +"HLA-DMB" 2.26094961110167e-84 0.104711950198156 0.049 0.302 5.53910045223798e-80 +"BZW2" 1.47597492644875e-104 0.104480827872314 0.203 0.663 3.6159909723068e-100 +"BSG" 5.5290280777957e-166 0.104474102554949 0.231 0.859 1.35455658877917e-161 +"RP11-345J4.5" 2.63382291643177e-188 0.104427523043598 0.109 0.629 6.45260276296619e-184 +"GCAT" 2.88271935436088e-80 0.104420074955936 0.062 0.319 7.06237414624873e-76 +"TCP1" 2.03156524553365e-169 0.104413134594304 0.163 0.709 4.97713169503288e-165 +"LRRC23" 6.77379335590121e-147 0.104388717511107 0.088 0.509 1.65951163426224e-142 +"LSM5" 5.32738572380806e-47 0.104298233827782 0.406 0.913 1.30515622847574e-42 +"CMTM3" 2.15735207423425e-123 0.10428315940273 0.089 0.465 5.28529684666649e-119 +"SQSTM1" 6.62086282742866e-10 0.104090958726643 0.559 0.984 1.62204518409175e-05 +"FBXL15" 2.61486037068591e-109 0.104061123247662 0.063 0.384 6.40614642214341e-105 +"HSP90AA1" 0.0225409766352249 0.104044405985399 0.822 0.995 1 +"WDR13" 1.33427556968045e-153 0.10393338184704 0.095 0.538 3.26884171816014e-149 +"RCC1" 3.99445041858946e-105 0.103636228390105 0.06 0.369 9.78600408050233e-101 +"MRTO4" 1.77700522211379e-165 0.103575170705614 0.095 0.558 4.35348509365658e-161 +"HAX1" 6.23958764356073e-167 0.103408596250259 0.206 0.806 1.52863657679594e-162 +"KRTCAP3" 9.08492111540147e-176 0.103372254050096 0.162 0.719 2.22571482406221e-171 +"CREG1" 1.06034012579513e-126 0.103341074922385 0.078 0.448 2.5977272741855e-122 +"BEX4" 4.76204172619651e-134 0.10317699321944 0.058 0.42 1.16665260250088e-129 +"TCEB1" 4.20398447387107e-48 0.103063358362364 0.414 0.952 1.02993415625367e-43 +"AP2B1" 6.49654413888705e-119 0.102922258166391 0.244 0.778 1.59158834858594e-114 +"ADSSL1" 1.74568993430756e-77 0.102916219238402 0.047 0.282 4.27676577006009e-73 +"AMDHD2" 1.62985156014901e-61 0.102754990480815 0.035 0.221 3.99297333720905e-57 +"GART" 3.22587081218108e-161 0.102520386156385 0.103 0.565 7.90306090276244e-157 +"SFXN4" 1.9179321813892e-133 0.102393279900556 0.094 0.496 4.6987420511854e-129 +"CMAS" 3.90696427819632e-184 0.102288544845701 0.13 0.668 9.57167178515317e-180 +"DRG2" 1.36463074511889e-109 0.102283818436663 0.06 0.376 3.34320886246677e-105 +"VARS" 2.75793960574666e-154 0.102279850197522 0.09 0.526 6.75667624011875e-150 +"APOPT1" 1.58603255656759e-171 0.102242027031307 0.126 0.634 3.88562116033494e-167 +"LRRC42" 1.17792805561777e-180 0.101925260556795 0.103 0.601 2.88580594345797e-176 +"GFPT1" 1.58793362753509e-155 0.101901912445589 0.247 0.872 3.89027859409822e-151 +"MAD1L1" 4.91306751922349e-104 0.101858006985914 0.07 0.387 1.20365241153456e-99 +"EMG1" 3.78029032218541e-157 0.101807526314874 0.165 0.694 9.26133326032205e-153 +"ARL6IP4" 1.43161347241307e-57 0.101652521560408 0.392 0.958 3.50730984606478e-53 +"DYNLL2" 7.29739791438611e-194 0.101516806021322 0.128 0.677 1.78778951504545e-189 +"TMEM14C" 8.04394510588508e-95 0.101459044348952 0.307 0.879 1.97068611149079e-90 +"GMPR2" 2.19496249770704e-163 0.101400434059494 0.103 0.573 5.37743862313248e-159 +"CKS2" 2.99245005689469e-101 0.101341839703443 0.193 0.63 7.3312033943863e-97 +"UBFD1" 2.07755972882332e-189 0.101320200841035 0.109 0.63 5.08981357964425e-185 +"RBP1" 7.49697168112811e-25 0.101209861922112 0.041 0.14 1.83668309215957e-20 +"DDB2" 3.49269860824354e-87 0.10120732392335 0.053 0.316 8.55676232033584e-83 +"MPLKIP" 2.12126860933495e-196 0.101111982132556 0.157 0.749 5.1968959660097e-192 +"RRP7A" 5.0890655958537e-138 0.101084800876412 0.094 0.506 1.2467701803282e-133 +"FAM103A1" 2.52340497969815e-163 0.101076647074335 0.088 0.539 6.1820898597625e-159 +"TIMMDC1" 5.41267599555495e-185 0.101017155216434 0.151 0.716 1.32605149215101e-180 +"PCMT1" 5.76709426868206e-176 0.101011758181733 0.136 0.665 1.41288042488442e-171 +"MALSU1" 2.97900929362029e-127 0.100995741283798 0.247 0.817 7.29827486844034e-123 +"HMGB2" 2.07661903630349e-61 0.100600205865458 0.321 0.765 5.08750897703993e-57 +"COMMD1" 1.46353304253422e-159 0.100594156620441 0.099 0.558 3.58550960090458e-155 +"TYW3" 8.39116614763315e-148 0.100476086076426 0.1 0.536 2.05575179450864e-143 +"RGS19" 4.71258830845658e-80 0.100424457207945 0.048 0.291 1.15453700968878e-75 +"NENF" 2.10422239697615e-140 0.100354657682504 0.269 0.903 5.15513445035188e-136 +"SART1" 2.47563962043449e-110 0.100088261550463 0.06 0.377 6.06506950610246e-106 +"YES1" 1.07760300479994e-279 -0.100070180025725 0.081 0.697 2.64001960145938e-275 +"NCKAP1" 1.20610903079304e-295 -0.100130793208941 0.112 0.787 2.95484651453986e-291 +"FOXA2" 2.5789799079367e-104 -0.100148630945223 0.009 0.25 6.31824287645411e-100 +"SLC25A33" 4.83532925006339e-129 -0.100154363118833 0.018 0.319 1.18460731297303e-124 +"MAP3K1" 3.0973824624985e-179 -0.100162187648795 0.027 0.428 7.58827729487508e-175 +"AMD1" 6.50723675654861e-288 -0.100183963868805 0.121 0.796 1.59420793298684e-283 +"HNRNPC" 3.54508384438805e-92 -0.100223423906477 0.379 0.971 8.68510091036629e-88 +"SIPA1L3" 3.86491426278607e-144 -0.100242248140343 0.024 0.359 9.46865345239959e-140 +"KCTD9" 1.87162075743919e-149 -0.100485172566928 0.024 0.371 4.58528369365028e-145 +"ADNP" 4.87564899095477e-246 -0.100528310957244 0.061 0.609 1.19448524629401e-241 +"ADNP2" 2.50104151178837e-117 -0.100725076427416 0.012 0.284 6.12730159973033e-113 +"TFAP2C" 1.04750681671787e-75 -0.100741622433183 0.006 0.186 2.56628695027711e-71 +"HCG18" 3.14196768687307e-170 -0.100749492209747 0.027 0.412 7.69750663607032e-166 +"ITSN2" 5.90933209668653e-265 -0.100781270929246 0.061 0.632 1.44772727036723e-260 +"THAP2" 3.44877088159648e-150 -0.100843641943966 0.033 0.391 8.4491437828232e-146 +"ATMIN" 1.06219760285062e-147 -0.100905635494129 0.031 0.381 2.60227790722372e-143 +"PAFAH1B2" 5.06408378041426e-293 -0.101026247577304 0.13 0.827 1.24064988536369e-288 +"GSTA4" 1.69817065647735e-80 -0.101064473816411 0.015 0.217 4.16034829130385e-76 +"ZNF710" 1.96715272812556e-146 -0.101159039329439 0.016 0.347 4.81932746863481e-142 +"FBXO21" 5.2570624436566e-252 -0.101180826380459 0.061 0.617 1.28792772807143e-247 +"SMIM5" 8.09041087380045e-66 -0.101190862367161 0.004 0.161 1.98206975997237e-61 +"AHNAK" 4.1988100944991e-108 -0.101297018793026 0.243 0.72 1.02866648505134e-103 +"C18orf63" 2.89408222197731e-50 -0.101471073548836 0.001 0.119 7.09021203562222e-46 +"SRSF1" 2.23386846014827e-270 -0.10148029492657 0.083 0.693 5.47275434051725e-266 +"ZMYM2" 8.22957729244454e-214 -0.101546103952631 0.04 0.511 2.01616414087599e-209 +"MAP3K2" 1.30987001294573e-244 -0.101562946660848 0.069 0.623 3.20905054471575e-240 +"ITGA9" 4.87797853661077e-60 -0.10170207470395 0.001 0.141 1.19505596168427e-55 +"USP10" 2.7551295310464e-216 -0.101705539613738 0.044 0.525 6.74979183811058e-212 +"TUFT1" 2.92697157795148e-86 -0.10171689605979 0.014 0.227 7.17078766882333e-82 +"SLC41A2" 6.31395586818982e-212 -0.101745914054572 0.04 0.507 1.54685604814782e-207 +"ZC3H13" 5.3071921572018e-260 -0.101756500822756 0.043 0.587 1.30020900659287e-255 +"SMURF2" 3.60740248609576e-269 -0.101764648884372 0.07 0.66 8.83777535068601e-265 +"LINC00662" 2.20118167098159e-198 -0.101824999917233 0.038 0.483 5.3926749757378e-194 +"SRSF10.1" 1.58414201451166e-69 -0.101876074554706 0.005 0.171 3.88098952135211e-65 +"AFTPH" 6.99177147245434e-255 -0.102014326334026 0.053 0.6 1.71291409303659e-250 +"TOR1AIP1" 4.80913660473956e-199 -0.102023643400219 0.04 0.488 1.17819037679515e-194 +"FOXA1" 3.41156888664179e-77 -0.102085945833405 0.006 0.19 8.35800261538372e-73 +"FOXJ3" 1.01944602435273e-183 -0.102087241794024 0.033 0.448 2.49754081506174e-179 +"CLK1" 3.72038518676215e-226 -0.102090704391714 0.056 0.566 9.1145716690486e-222 +"TJP1" 2.44725449769876e-284 -0.102202887218866 0.079 0.701 5.99552879391219e-280 +"SLC38A1" 3.34592774831347e-218 -0.102295368163471 0.054 0.549 8.19718839059317e-214 +"EPB41L4B" 9.84733454964424e-128 -0.102349914683331 0.016 0.313 2.41249849131734e-123 +"TMEM63B" 1.61535464476169e-137 -0.102588966011888 0.018 0.335 3.95745734420167e-133 +"SPATA13" 2.24994821322851e-87 -0.102589580823007 0.012 0.226 5.51214812758853e-83 +"BAG3" 7.34945077374268e-273 -0.102645957854037 0.099 0.727 1.80054194505922e-268 +"FUT8" 3.41238772144014e-160 -0.102722114384077 0.026 0.39 8.3600086787562e-156 +"CCAR1" 8.96573490262996e-278 -0.102890327731328 0.088 0.71 2.19651539379531e-273 +"THAP9-AS1" 4.41512788386389e-160 -0.102980600848217 0.026 0.392 1.08166218026781e-155 +"CREBZF" 7.20737579984384e-223 -0.102985189031299 0.044 0.535 1.76573499720374e-218 +"GCC2" 3.54685280805676e-270 -0.103043224667983 0.099 0.723 8.68943469445825e-266 +"ARHGAP12" 7.22413310089948e-262 -0.103099018675473 0.082 0.676 1.76984036838936e-257 +"FAM210B" 1.95597795125565e-163 -0.103221081658601 0.039 0.427 4.79195038278122e-159 +"GFPT2" 1.21259257696095e-70 -0.103262467482904 0.009 0.182 2.97073055429664e-66 +"CBFB" 4.76126738888929e-204 -0.10330040978482 0.05 0.52 1.16646289760399e-199 +"CYB561" 1.11109357697625e-250 -0.103309972410594 0.05 0.589 2.72206815423412e-246 +"IDI1" 3.04708873657051e-168 -0.103346806646921 0.039 0.435 7.46506269572409e-164 +"SEC11C" 6.78812021776901e-188 -0.10366657702795 0.052 0.498 1.66302157215123e-183 +"PER2" 1.61229476487054e-93 -0.103727238937114 0.011 0.234 3.94996094445634e-89 +"MATR3" 3.74390100539857e-211 -0.103838440717945 0.242 0.947 9.17218307312595e-207 +"ZNF146" 5.51400599970118e-275 -0.10385330182887 0.079 0.691 1.35087632986679e-270 +"SRSF10" 1.34516347710198e-218 -0.103880797548979 0.044 0.528 3.29551600255215e-214 +"SPG21" 7.3410328549562e-220 -0.103937297181097 0.048 0.54 1.79847963913572e-215 +"MLTK" 2.44912720341446e-179 -0.104042452848595 0.045 0.468 6.00011673564508e-175 +"SMARCA1" 1.56293951331056e-202 -0.104149812647129 0.042 0.498 3.82904551365953e-198 +"DNAJB6" 7.17004976541487e-212 -0.104171215127808 0.244 0.943 1.75659049202899e-207 +"OXR1" 1.6859280505635e-247 -0.104174322216362 0.092 0.677 4.13035513107552e-243 +"RAD1" 2.97138832398121e-254 -0.104196213408332 0.071 0.637 7.27960425492157e-250 +"DUSP8" 7.48823980377921e-125 -0.104201247541779 0.022 0.319 1.83454386952787e-120 +"SLC38A2" 5.75222025601366e-264 -0.104220525815242 0.132 0.791 1.40923644052079e-259 +"DAPP1" 6.01240494216776e-105 -0.104374518611323 0.021 0.28 1.47297908678168e-100 +"GNB1" 8.12934349632509e-266 -0.104422174741556 0.146 0.817 1.99160786316468e-261 +"RREB1" 3.11045587001945e-235 -0.10444834553151 0.039 0.541 7.62030583596064e-231 +"DCAF5" 1.05379417672435e-100 -0.104525602516134 0.011 0.248 2.58169035355698e-96 +"ABL2" 8.84884998938163e-196 -0.104725532303391 0.034 0.47 2.16787975889861e-191 +"ZNF704" 5.24399567279513e-85 -0.104780162142326 0.008 0.21 1.28472649987808e-80 +"KLF3" 2.3894535876974e-278 -0.104898467373132 0.061 0.654 5.85392234449986e-274 +"DENND5A" 1.33542082649529e-155 -0.105021368323588 0.022 0.375 3.27164748283082e-151 +"UBAP1" 4.05003753458404e-260 -0.105173746722155 0.071 0.648 9.92218695597745e-256 +"ZFX" 1.31432670078344e-211 -0.105280512284593 0.041 0.511 3.21996898424936e-207 +"CALCOCO2" 5.4405267169733e-250 -0.105327174335343 0.061 0.613 1.33287464039129e-245 +"TFPT" 1.53063336295271e-155 -0.105443009444712 0.039 0.415 3.74989867589785e-151 +"FOSL1" 1.55668893997858e-170 -0.105452764108173 0.028 0.416 3.81373223405353e-166 +"USP53" 8.62488635475639e-193 -0.105502040063607 0.034 0.465 2.11301090805177e-188 +"MT-ATP8" 5.8504675006742e-211 -0.105505123509744 0.1 0.64 1.43330603299017e-206 +"KCNQ3" 4.89771428046518e-112 -0.105792964407917 0.02 0.292 1.19989102157116e-107 +"CENPK" 9.28044083175076e-139 -0.105828670277596 0.057 0.423 2.27361519937062e-134 +"XPR1" 1.47125981415286e-244 -0.105859537630389 0.071 0.626 3.60443941869308e-240 +"BDKRB2" 1.41355327490849e-44 -0.105876743558031 0.002 0.11 3.46306416819831e-40 +"EMR2" 6.48853318631371e-53 -0.105953816547424 0.005 0.133 1.58962574531499e-48 +"LBH" 2.13536429889057e-53 -0.106024687464537 0.011 0.149 5.231428995852e-49 +"UVSSA" 6.69639310108553e-105 -0.106101237121302 0.013 0.262 1.64054934583494e-100 +"FBXL5" 1.30429334626058e-228 -0.106135173735221 0.046 0.549 3.1953882690038e-224 +"TREM1" 1.25294552719685e-148 -0.106226663679933 0.058 0.444 3.06959124707957e-144 +"SERPINB8" 1.12665886780685e-74 -0.106229465654436 0.006 0.184 2.76020156024e-70 +"FOPNL" 7.52409295709455e-231 -0.106370176445415 0.06 0.581 1.84332753355859e-226 +"SNX5" 1.23392464682559e-283 -0.10645821805688 0.095 0.732 3.02299199225801e-279 +"IPO9" 1.13153228709337e-240 -0.106566256141078 0.047 0.568 2.77214095015006e-236 +"SET" 1.88119693208981e-69 -0.106689069167691 0.431 0.983 4.60874436392683e-65 +"FAM20A" 1.40862169033558e-99 -0.106702263104798 0.018 0.263 3.45098227915313e-95 +"PUM1" 0 -0.106726549628081 0.105 0.8 0 +"DNAJB14" 3.58437038893306e-260 -0.106817137026335 0.061 0.627 8.7813490158471e-256 +"TGOLN2" 1.05819817103899e-301 -0.106931802902661 0.118 0.812 2.59247969922842e-297 +"ACSL4" 3.50713589092652e-149 -0.107062030708815 0.04 0.404 8.59213221918087e-145 +"ZC3H18" 1.71749473861422e-208 -0.107229872726761 0.027 0.472 4.20769036013098e-204 +"IRF2BPL" 1.10201256632086e-212 -0.107255578264079 0.056 0.545 2.69982058622947e-208 +"ANKRD32" 5.44245685626461e-241 -0.107276723035878 0.048 0.572 1.33334750521627e-236 +"OSBP" 1.42331662989638e-226 -0.107493737853353 0.038 0.526 3.48698341158315e-222 +"RAB10" 2.37143088489868e-285 -0.107886905900326 0.152 0.862 5.80976852491329e-281 +"ATF6" 4.41803664800219e-222 -0.108053579693921 0.035 0.512 1.08237479839406e-217 +"SIRT1" 9.22729563469339e-131 -0.108055561191216 0.023 0.334 2.26059515754353e-126 +"CHD6" 3.85974420930345e-172 -0.108101811649078 0.026 0.412 9.45598733837252e-168 +"PHF3" 5.06865522828306e-285 -0.108109609276404 0.105 0.759 1.24176984437707e-280 +"ERBB2IP" 8.7083697356107e-271 -0.108149636975539 0.147 0.828 2.13346350152727e-266 +"EDN2" 2.7518529734111e-88 -0.108237742952688 0.029 0.266 6.74176459955985e-84 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3.94992635363987e-276 +"EFCAB2" 1.54659592531469e-135 -0.109486064383008 0.025 0.346 3.78900535742846e-131 +"TAOK1" 4.04122674543865e-268 -0.109569752273285 0.073 0.666 9.90060140365015e-264 +"RP11-476D10.1" 2.72377970715805e-54 -0.109581541436264 0.003 0.132 6.67298790456651e-50 +"GLIS3" 3.10308780894324e-259 -0.109625536665372 0.054 0.61 7.60225482313005e-255 +"UBE3A" 2.76110642686915e-251 -0.109793184679703 0.057 0.603 6.76443463518673e-247 +"UBE2G2" 2.42187722071712e-253 -0.109843603421853 0.067 0.633 5.93335700303488e-249 +"ZNF578" 1.06748594493729e-67 -0.109872901622875 0.004 0.165 2.61523381650187e-63 +"ITGAV" 3.14274072297414e-196 -0.109876832237849 0.054 0.515 7.69940049721434e-192 +"ENDOD1" 1.3334266236167e-227 -0.109954895165226 0.065 0.587 3.26676188519855e-223 +"PDZD8" 7.71048949346552e-194 -0.109965218454375 0.04 0.48 1.88899282100412e-189 +"TXNL4A" 7.70192422407636e-241 -0.110010120423415 0.096 0.672 1.88689441565647e-236 +"NCOA2" 6.23279225059445e-191 -0.11024329324971 0.038 0.471 1.52697177347313e-186 +"ATP2B4" 7.9906709275386e-267 -0.110371488852753 0.137 0.801 1.95763447053768e-262 +"RPGR" 1.14424678593758e-72 -0.110579749461314 0.003 0.172 2.80329020086848e-68 +"ANKRD36C" 9.79934459622048e-91 -0.1107157465582 0.018 0.244 2.40074143262805e-86 +"WDR66" 3.62710212257578e-72 -0.110740723653735 0.015 0.2 8.8860374900984e-68 +"MIER3" 7.0839147904025e-142 -0.110909869045867 0.023 0.354 1.73548828450071e-137 +"SPRED1" 1.34397130322202e-132 -0.110917481094895 0.019 0.328 3.29259529576362e-128 +"RALGAPA1" 4.29135574132671e-177 -0.111052071520512 0.023 0.414 1.05133924306763e-172 +"ARID1B" 9.36605261994765e-170 -0.111108517410533 0.027 0.411 2.29458923136097e-165 +"CKAP4" 1.96985835088971e-275 -0.111252480607776 0.108 0.753 4.82595597384469e-271 +"GATAD2B" 1.1940094930405e-162 -0.111337584799687 0.023 0.389 2.92520385699991e-158 +"CNOT6L" 3.958043272031e-201 -0.111456761672112 0.034 0.479 9.69681021214874e-197 +"DIDO1" 1.20360507171068e-178 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9.58620162174486e-85 -0.112771413030216 0.015 0.227 2.34852353531127e-80 +"FBXL17" 1.41214271288246e-172 -0.112808715998664 0.034 0.432 3.45960843229075e-168 +"SQRDL" 1.93498852878525e-153 -0.112866733568192 0.049 0.431 4.74052839667099e-149 +"RC3H1" 3.55669396048005e-189 -0.113148677748223 0.035 0.462 8.71354453378007e-185 +"CYBRD1" 3.54328492525641e-73 -0.113378168378397 0.011 0.192 8.68069373838568e-69 +"MECOM" 7.88645697972795e-122 -0.113395826025255 0.016 0.301 1.93210309546355e-117 +"PCSK6" 1.20278994647212e-72 -0.113402606011666 0.003 0.174 2.94671508986205e-68 +"RHOBTB2" 7.33792403139048e-122 -0.113414459423004 0.015 0.299 1.79771800845035e-117 +"LYSMD3" 7.41629573784067e-213 -0.113587736534188 0.044 0.518 1.81691829281359e-208 +"HIVEP1" 7.27927042601053e-92 -0.113591427088668 0.006 0.22 1.78334846166832e-87 +"EPDR1" 3.48005682324683e-138 -0.113866384707619 0.017 0.333 8.52579121127241e-134 +"TRIP11" 1.42255310389419e-266 -0.113968392849955 0.046 0.603 3.48511284923036e-262 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4.9823137881277e-294 +"RGL1" 4.13925150764176e-79 -0.114881179445749 0.008 0.198 1.01407522685715e-74 +"SNX25" 1.72381446494678e-178 -0.11493271689715 0.031 0.436 4.22317305767313e-174 +"MAN1A2" 8.78947389965033e-221 -0.114979625734031 0.043 0.527 2.15333321067533e-216 +"HOOK2" 1.87500792501754e-195 -0.115016653238335 0.026 0.451 4.59358191550048e-191 +"CEP170" 9.30001992998432e-274 -0.115067124940377 0.075 0.677 2.27841188264686e-269 +"GIGYF2" 4.23540453706875e-262 -0.115186424874975 0.041 0.585 1.03763175753647e-257 +"HOOK1" 2.46010805702443e-241 -0.1154139427821 0.048 0.571 6.02701872890416e-237 +"NUDT4" 2.77424756630485e-244 -0.115493573994309 0.088 0.666 6.79662911269026e-240 +"CMC2" 3.17886345026459e-226 -0.115517943775702 0.066 0.586 7.78789756680322e-222 +"TRIM22" 4.64023797925597e-200 -0.115588726088309 0.056 0.526 1.13681190253792e-195 +"RND1" 9.8550255628307e-68 -0.115598841161973 0.003 0.162 2.41438271263789e-63 +"OAS3" 1.42823237962311e-229 -0.115674132215857 0.07 0.601 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0.82 3.78979503938809e-287 +"PRKDC" 1.14742923488922e-183 -0.164075161325393 0.239 0.866 2.81108688255509e-179 +"SETD5-AS1" 1.00878382606471e-215 -0.164480032882853 0.043 0.518 2.47141949547594e-211 +"JAK1" 0 -0.164620450259698 0.122 0.854 0 +"TANK" 9.63273659032129e-281 -0.164699573244364 0.059 0.649 2.35992413726281e-276 +"ELF1" 2.77385793339137e-296 -0.165136442118025 0.07 0.696 6.79567455101553e-292 +"TGIF1" 1.55962362858875e-279 -0.165313332475091 0.13 0.797 3.82092192767959e-275 +"EPHA4" 4.31205145863807e-113 -0.165728602414236 0.011 0.273 1.05640948685174e-108 +"PPTC7" 4.31399860307406e-288 -0.165866342469467 0.064 0.673 1.05688651776711e-283 +"APP" 1.68059312223512e-303 -0.166105451834859 0.158 0.894 4.11728509016382e-299 +"PVRL3" 1.37947563956231e-109 -0.166535604021914 0.012 0.269 3.3795773693637e-105 +"POGZ" 2.0065307428886e-211 -0.166912632893967 0.029 0.482 4.91579966700279e-207 +"HIST1H4C" 1.43343659405608e-74 -0.166924770073605 0.342 0.802 3.511776311778e-70 +"PURA" 1.09170102246727e-294 -0.167131587217318 0.063 0.678 2.67455833494256e-290 +"TMEM2" 5.72065043188866e-225 -0.167395248946462 0.037 0.521 1.4015021493084e-220 +"EIF4G2" 1.85956069018509e-111 -0.167883331483484 0.384 0.983 4.55573773488444e-107 +"EFR3A" 9.79808495041155e-291 -0.168393035719124 0.125 0.808 2.40043283200132e-286 +"GTF2I" 2.10334749052434e-219 -0.168424132821176 0.251 0.961 5.15299101703559e-215 +"SAR1B" 0 -0.168494879876556 0.132 0.861 0 +"PCP4L1" 5.46042882768985e-88 -0.168665780123998 0.007 0.213 1.33775045849574e-83 +"NCEH1" 3.31579312701132e-173 -0.168714460302466 0.029 0.421 8.12336158186502e-169 +"CSF3R" 1.85247759284501e-96 -0.169203039319772 0.007 0.232 4.538384854711e-92 +"ERBB3" 2.57114773373212e-297 -0.169353049579997 0.094 0.749 6.29905483287033e-293 +"ACO1" 1.02857085053692e-145 -0.169573902987585 0.018 0.347 2.51989572673041e-141 +"ATP10B" 4.70158167905428e-62 -0.169613686415532 0.001 0.144 1.15184049555151e-57 +"IGFBP6" 1.00529898170784e-90 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1.31421896195585e-254 +"ARID4B" 0 -0.173246310482212 0.116 0.842 0 +"KDM3B" 1.3414134723786e-261 -0.17345532557736 0.034 0.567 3.28632886598034e-257 +"TNRC6A" 7.44113420741999e-304 -0.173625608684458 0.071 0.709 1.82300346947582e-299 +"SMCO4" 1.40114854071141e-216 -0.17367907680681 0.223 0.901 3.43267380988888e-212 +"DSTN" 5.79200275678627e-50 -0.173954372861902 0.562 0.996 1.41898275538507e-45 +"CYP4X1" 6.99575184347755e-97 -0.174105054400827 0.006 0.23 1.71388924413356e-92 +"TMEM30A" 5.36069059580374e-294 -0.174154600794262 0.158 0.876 1.31331558906596e-289 +"PLAUR" 4.30682645655371e-230 -0.174463263192888 0.105 0.671 1.05512941359109e-225 +"LHFPL2" 1.88755787881468e-256 -0.174596235474883 0.077 0.656 4.62432804730809e-252 +"SATB1" 1.55417207242643e-93 -0.174729816367376 0.007 0.226 3.8075661602375e-89 +"SLC35E1" 1.1700677592778e-268 -0.17490704454305 0.054 0.621 2.86654900345469e-264 +"CNOT3" 4.85076379902755e-191 -0.174952384711215 0.026 0.442 1.18838862312376e-186 +"CENPF" 4.0168915824593e-69 -0.175047426116136 0.125 0.401 9.84098268786703e-65 +"PDE4D" 1.41477444916699e-148 -0.175059798655172 0.014 0.345 3.46605592301421e-144 +"PRKAR2B" 3.03123281785978e-122 -0.17506755870047 0.017 0.303 7.42621728047468e-118 +"TSC22D2" 6.98116002764343e-301 -0.175384506533239 0.077 0.715 1.71031439517236e-296 +"RASA1" 4.07255678268571e-305 -0.175554476029429 0.073 0.714 9.97735686190173e-301 +"ZNF611" 1.96638208698723e-128 -0.175823976232324 0.007 0.292 4.81743947491001e-124 +"AHCYL1" 3.79378611539801e-298 -0.176054996331596 0.077 0.713 9.29439660411358e-294 +"RGS17" 5.1677541997052e-144 -0.17708507344342 0.024 0.358 1.26604810138578e-139 +"PRPF40A" 6.56010731795104e-266 -0.177452483293576 0.193 0.912 1.60716069182483e-261 +"ZNF320" 4.24120903961205e-147 -0.177590366213392 0.016 0.348 1.03905380261456e-142 +"ZNF148" 0 -0.177626110170156 0.084 0.76 0 +"TRAM1" 2.88213677955975e-253 -0.177691413825194 0.213 0.935 7.06094689624343e-249 +"BAIAP2" 9.45061145323376e-287 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3.54713688691251e-235 -0.179077412898588 0.213 0.91 8.69013065924696e-231 +"PROX1" 2.16052139886835e-70 -0.179409567432175 0.007 0.177 5.29306137508756e-66 +"IL13RA1" 1.12280136489169e-190 -0.179415597273582 0.043 0.48 2.75075106384814e-186 +"PABPC1" 2.04409148020964e-29 -0.179608511605127 0.811 0.998 5.0078197173656e-25 +"ACKR3" 3.23137049106075e-45 -0.179848315991564 0.003 0.114 7.91653456604974e-41 +"TBL1X" 3.87723214256381e-259 -0.179892509483947 0.067 0.639 9.49883102606708e-255 +"LINC00665" 5.80188203767571e-274 -0.179932133261319 0.056 0.629 1.42140308041017e-269 +"TPBG" 1.5662227484013e-259 -0.17997277269689 0.037 0.572 3.83708911130833e-255 +"NME4" 4.61413726601611e-201 -0.180024256342784 0.252 0.934 1.13041748880129e-196 +"BAZ1B" 3.78249473117468e-299 -0.180383472710534 0.107 0.776 9.26673384190486e-295 +"ZSWIM6" 6.59360422095759e-225 -0.180519487749833 0.031 0.508 1.6153670980924e-220 +"DDX5" 2.38345241775598e-66 -0.18054021113758 0.519 0.991 5.83922007826037e-62 +"SGPP2" 0 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-1.00805798190844 0.84 0.998 0 +"ELF3" 0 -1.01732983350088 0.252 0.958 0 +"RNASE1" 6.39766028886436e-256 -1.04367562499079 0.184 0.78 1.56736279416888e-251 +"ACSL1" 0 -1.04432882456894 0.181 0.874 0 +"REL" 0 -1.0763109260203 0.098 0.895 0 +"NCOA7" 0 -1.11395891994241 0.058 0.734 0 +"C8orf4" 0 -1.12015324886978 0.091 0.75 0 +"PLAT" 1.79544702072798e-241 -1.12731424924577 0.037 0.528 4.39866565608148e-237 +"ELL2" 0 -1.13105990269719 0.083 0.774 0 +"PIGR" 5.79337172426637e-242 -1.14874378347595 0.038 0.53 1.41931813872802e-237 +"LRRK2" 8.55283539848091e-163 -1.14880275292261 0.018 0.367 2.09535914427384e-158 +"NEAT1" 0 -1.16284713610746 0.557 0.996 0 +"SOD2" 0 -1.16919049771502 0.187 0.869 0 +"TRIB1" 0 -1.18040640790078 0.057 0.739 0 +"RHOB" 0 -1.18679489512827 0.251 0.964 0 +"CYB5A" 0 -1.18890396191962 0.185 0.883 0 +"SDC4" 0 -1.19396639772756 0.218 0.964 0 +"DUSP6" 0 -1.21234509694374 0.207 0.946 0 +"HPGD" 0 -1.22212234552185 0.221 0.914 0 +"NAMPT" 0 -1.23508577562238 0.185 0.93 0 +"MT-CO3" 0 -1.23608867674672 0.894 0.999 0 +"CXCL1" 6.10387554381085e-174 -1.23756039631618 0.014 0.382 1.49538846947822e-169 +"GPRC5A" 0 -1.24024506253846 0.088 0.926 0 +"ICAM1" 4.11644487286641e-274 -1.24444459591556 0.034 0.566 1.00848782940354e-269 +"MT-CO2" 0 -1.24601470342197 0.92 1 0 +"MT-ND2" 0 -1.32433050945451 0.651 0.998 0 +"HP" 7.18617288329322e-137 -1.33532447193497 0.045 0.373 1.76054049467801e-132 +"DUSP1" 2.48661020649712e-275 -1.38378201093951 0.533 0.987 6.0919463448973e-271 +"MT-CO1" 0 -1.39866033644698 0.887 0.999 0 +"EGR1" 0 -1.40322593238741 0.257 0.918 0 +"C11orf96" 1.73689018695835e-258 -1.40335801776296 0.043 0.559 4.25520726902926e-254 +"MT-ATP6" 0 -1.42674565390891 0.828 0.998 0 +"CEACAM6" 2.28204151460079e-241 -1.43977496926564 0.063 0.557 5.59077350662047e-237 +"MT-CYB" 0 -1.46164262374048 0.763 0.999 0 +"MT-ND4" 0 -1.46296875520481 0.845 0.999 0 +"FOSB" 0 -1.47829724759936 0.083 0.892 0 +"KLF6" 0 -1.47986712179004 0.209 0.97 0 +"MT-ND1" 0 -1.51209154629358 0.747 0.997 0 +"SFTPA2" 2.93126789900603e-170 -1.51808575765598 0.041 0.425 7.18131322577488e-166 +"NFKBIZ" 0 -1.57302931933852 0.11 0.945 0 +"SLC34A2" 7.29456375119282e-183 -1.58304724450594 0.029 0.418 1.78709517340473e-178 +"ZFP36" 0 -1.66272150953507 0.288 0.965 0 +"SFTPA1" 2.27002356730559e-157 -1.74148574181596 0.063 0.435 5.56133073754196e-153 +"CXCL2" 0 -1.77005047994397 0.302 0.927 0 +"CCL20" 2.29196372499577e-256 -1.78703764013556 0.102 0.632 5.61508192986713e-252 +"IL8" 0 -1.83804545757444 0.134 0.778 0 +"SPINK5" 5.97662913490744e-152 -1.87760588174334 0.046 0.397 1.46421437176097e-147 +"SFTPB" 1.49569125426071e-267 -1.94577988501663 0.331 0.881 3.66429400381331e-263 +"ZFP36L1" 0 -1.97918805456509 0.176 0.928 0 +"EREG" 0 -2.07451895764629 0.215 0.948 0 diff --git a/g_ctype_tissue.txt b/g_ctype_tissue.txt new file mode 100644 index 0000000..51466a3 --- /dev/null +++ b/g_ctype_tissue.txt @@ -0,0 +1,4795 @@ +MARC2 KIRC Kidney +MARC2 KIRP Kidney +MARC2 KICH Kidney +MARC2 LIHC Liver +MARCH10 TGCT Testis +SEPT5 GBM Brain +SEPT5 LGG Brain +SEPT12 TGCT Testis +A1BG LIHC Liver +A1CF LIHC Liver +A1CF STAD Stomach +A2M LIHC Liver +A2M LUAD Lung +A2M LUSC Lung +A2ML1 COAD Colon +A2ML1 ESCA  Esophagus +A4GNT STAD Stomach +AADACL2 SKCM Skin +AADACL2 UCEC Uterus +AADACL2 UCS Uterus +AARD TGCT Testis +ABAT LIHC Liver +ABCA10 OV Ovary +ABCA10 TGCT Testis +ABCA13 LAML Bone_Marrow +ABCA13 THCA Thyroid_Gland +ABCA4 KIRC Kidney +ABCA4 KIRP Kidney +ABCA4 KICH Kidney +ABCA4 UVM Eye +ABCB11 LIHC Liver +ABCB4 LIHC Liver +ABCB6 OV Ovary +ABCB6 TGCT Testis +ABCC11 BRCA Breast +ABCC11 TGCT Testis +ABCC2 LIHC Liver +ABCC4 PRAD Prostate +ABCC4 STAD Stomach +ABCC6 KIRC Kidney +ABCC6 KIRP Kidney +ABCC6 KICH Kidney +ABCC6 LIHC Liver +ABCC9 SARC Soft_Tissue +ABHD1 TGCT Testis +ABHD11 STAD Stomach +ABHD12B SKCM Skin +ABHD12B UVM Eye +ABHD2 LIHC Liver +ACADSB KIRC Kidney +ACADSB KIRP Kidney +ACADSB KICH Kidney +ACADSB LIHC Liver +ACD STAD Stomach +ACD TGCT Testis +ACE2 KIRC Kidney +ACE2 KIRP Kidney +ACE2 KICH Kidney +ACE2 TGCT Testis +ACER1 SKCM Skin +ACMSD KIRC Kidney +ACMSD KIRP Kidney +ACMSD KICH Kidney +ACMSD LIHC Liver +ACOT12 LIHC Liver +ACOT4 LIHC Liver +ACOT4 SARC Soft_Tissue +ACPP PRAD Prostate +ACR TGCT Testis +ACRBP TGCT Testis +ACRV1 TGCT Testis +ACSBG2 TGCT Testis +ACSL6 TGCT Testis +ACSM1 BRCA Breast +ACSM1 TGCT Testis +ACSM2B KIRC Kidney +ACSM2B KIRP Kidney +ACSM2B KICH Kidney +ACSM2B LIHC Liver +ACSM5 KIRC Kidney +ACSM5 KIRP Kidney +ACSM5 KICH Kidney +ACSM5 LIHC Liver +ACTG2 COAD Colon +ACTG2 PRAD Prostate +ACTL7A TGCT Testis +ACTL7B TGCT Testis +ACTL8 CESC Cervix +ACTL8 TGCT Testis +ACTL9 TGCT Testis +ACTRT1 TGCT Testis +ACTRT2 TGCT Testis +ACTRT3 TGCT Testis +ACY3 KIRC Kidney +ACY3 KIRP Kidney +ACY3 KICH Kidney +ADAD1 TGCT Testis +ADAD2 TGCT Testis +ADAM15 CESC Cervix +ADAM18 TGCT Testis +ADAM2 TGCT Testis +ADAM20 TGCT Testis +ADAM21 TGCT Testis +ADAM28 OV Ovary +ADAM28 STAD Stomach +ADAM29 TGCT Testis +ADAM30 TGCT Testis +ADAM32 TGCT Testis +ADAMTS13 COAD Colon +ADAMTS13 LIHC Liver +ADAMTS13 TGCT Testis +ADAMTS8 LUAD Lung +ADAMTS8 LUSC Lung +ADCY10 TGCT Testis +ADD2 UVM Eye +ADGRB1 GBM Brain +ADGRB1 LGG Brain +ADGRB3 GBM Brain +ADGRB3 LGG Brain +ADGRE2 LAML Bone_Marrow +ADGRF1 KIRC Kidney +ADGRF1 KIRP Kidney +ADGRF1 KICH Kidney +ADH1A LIHC Liver +ADH1B LIHC Liver +ADH1B SARC Soft_Tissue +ADH1C LIHC Liver +ADH4 LIHC Liver +ADH6 KIRC Kidney +ADH6 KIRP Kidney +ADH6 KICH Kidney +ADH6 LIHC Liver +ADH7 ESCA  Esophagus +ADH7 LUAD Lung +ADH7 LUSC Lung +ADIPOQ BRCA Breast +ADIPOQ SARC Soft_Tissue +ADORA3 TGCT Testis +ADRA1A LIHC Liver +ADSSL1 CESC Cervix +AFM KIRC Kidney +AFM KIRP Kidney +AFM KICH Kidney +AFM LIHC Liver +AFP LIHC Liver +AGBL2 TGCT Testis +AGBL5 CESC Cervix +AGBL5 TGCT Testis +AGER LUAD Lung +AGER LUSC Lung +AGMAT KIRC Kidney +AGMAT KIRP Kidney +AGMAT KICH Kidney +AGR2 STAD Stomach +AGR3 LUAD Lung +AGR3 LUSC Lung +AGR3 STAD Stomach +AGT LIHC Liver +AGTR1 LIHC Liver +AGXT LIHC Liver +AGXT2 KIRC Kidney +AGXT2 KIRP Kidney +AGXT2 KICH Kidney +AGXT2 LIHC Liver +AHCTF1 TGCT Testis +AHCTF1 UCEC Uterus +AHCTF1 UCS Uterus +AHNAK2 SKCM Skin +AHRR TGCT Testis +AHSG LIHC Liver +AHSP LAML Bone_Marrow +AIFM2 BRCA Breast +AIFM2 CESC Cervix +AJUBA CESC Cervix +AJUBA LIHC Liver +AJUBA SKCM Skin +AK4 KIRC Kidney +AK4 KIRP Kidney +AK4 KICH Kidney +AKAP14 TGCT Testis +AKAP3 TGCT Testis +AKAP4 TGCT Testis +AKNAD1 TGCT Testis +AKR1B10 LUAD Lung +AKR1B10 LUSC Lung +AKR1B10 STAD Stomach +AKR1B15 BRCA Breast +AKR1B15 STAD Stomach +AKR1C1 BLCA Bladder +AKR1C1 LIHC Liver +AKR1C2 BLCA Bladder +AKR1C2 LIHC Liver +AKR1C2 LUAD Lung +AKR1C2 LUSC Lung +AKR1C3 BLCA Bladder +AKR1C3 LIHC Liver +AKR1C4 LIHC Liver +AKR1D1 LIHC Liver +AKR7A3 COAD Colon +AKR7A3 PAAD Pancreas +AKR7A3 STAD Stomach +ALAS2 LAML Bone_Marrow +ALAS2 LIHC Liver +ALB LIHC Liver +ALDH1A2 TGCT Testis +ALDH1L1 BLCA Bladder +ALDH1L1 LIHC Liver +ALDH3A1 ESCA  Esophagus +ALDH3A1 STAD Stomach +ALDH3A1 UVM Eye +ALDH3B1 CESC Cervix +ALDH3B1 LUAD Lung +ALDH3B1 LUSC Lung +ALDH3B2 BLCA Bladder +ALDH3B2 SKCM Skin +ALDH6A1 KIRC Kidney +ALDH6A1 KIRP Kidney +ALDH6A1 KICH Kidney +ALDH6A1 LIHC Liver +ALDH8A1 KIRC Kidney +ALDH8A1 KIRP Kidney +ALDH8A1 KICH Kidney +ALDH8A1 LIHC Liver +ALDOB LIHC Liver +ALG3 CESC Cervix +ALKBH7 TGCT Testis +ALLC TGCT Testis +ALOX15B PRAD Prostate +ALOX15B SKCM Skin +ALOXE3 SKCM Skin +ALS2CL CESC Cervix +ALS2CR11 TGCT Testis +ALS2CR12 TGCT Testis +ALX1 KIRC Kidney +ALX1 KIRP Kidney +ALX1 KICH Kidney +ALX1 SKCM Skin +AMACR KIRC Kidney +AMACR KIRP Kidney +AMACR KICH Kidney +AMACR PRAD Prostate +AMBP LIHC Liver +AMDHD1 KIRC Kidney +AMDHD1 KIRP Kidney +AMDHD1 KICH Kidney +AMDHD1 LIHC Liver +AMER2 GBM Brain +AMER2 LGG Brain +AMFR TGCT Testis +AMH TGCT Testis +AMY1C PAAD Pancreas +AMY2A PAAD Pancreas +AMY2B PAAD Pancreas +AMY2B UCEC Uterus +AMY2B UCS Uterus +ANG LIHC Liver +ANGPT1 LAML Bone_Marrow +ANGPT1 LUAD Lung +ANGPT1 LUSC Lung +ANGPTL3 KIRC Kidney +ANGPTL3 KIRP Kidney +ANGPTL3 KICH Kidney +ANGPTL3 LIHC Liver +ANHX TGCT Testis +ANKAR TGCT Testis +ANKRD18A TGCT Testis +ANKRD20A1 TGCT Testis +ANKRD30A BRCA Breast +ANKRD30B TGCT Testis +ANKRD30BL TGCT Testis +ANKRD31 TGCT Testis +ANKRD45 TGCT Testis +ANKRD55 TGCT Testis +ANKRD60 TGCT Testis +ANKRD62 TGCT Testis +ANKRD7 TGCT Testis +ANO7 PRAD Prostate +ANXA1 ESCA  Esophagus +ANXA10 STAD Stomach +ANXA13 COAD Colon +ANXA2 BLCA Bladder +AOC2 TGCT Testis +AOC2 UVM Eye +AOX1 LIHC Liver +APBB1IP LAML Bone_Marrow +APC2 GBM Brain +APC2 LGG Brain +APCS LIHC Liver +APH1B TGCT Testis +APOA1 LIHC Liver +APOA2 LIHC Liver +APOA5 LIHC Liver +APOB LIHC Liver +APOBEC1 COAD Colon +APOBEC4 TGCT Testis +APOC1 LIHC Liver +APOC2 LIHC Liver +APOC3 LIHC Liver +APOC4 LIHC Liver +APOF LIHC Liver +APOH LIHC Liver +APOL1 STAD Stomach +APOL3 UCEC Uterus +APOL3 UCS Uterus +APOM LIHC Liver +AQP12A PAAD Pancreas +AQP12B PAAD Pancreas +AQP2 KIRC Kidney +AQP2 KIRP Kidney +AQP2 KICH Kidney +AQP2 PRAD Prostate +AQP4 GBM Brain +AQP4 LGG Brain +AQP5 STAD Stomach +AQP5 TGCT Testis +AQP6 KIRC Kidney +AQP6 KIRP Kidney +AQP6 KICH Kidney +AQP8 COAD Colon +AQP8 PAAD Pancreas +AR PRAD Prostate +ARAP2 STAD Stomach +ARAP2 THCA Thyroid_Gland +AREG LIHC Liver +AREG STAD Stomach +ARG1 LIHC Liver +ARHGAP15 LAML Bone_Marrow +ARHGAP15 THYM Thymus +ARHGAP24 KIRC Kidney +ARHGAP24 KIRP Kidney +ARHGAP24 KICH Kidney +ARHGAP25 COAD Colon +ARHGAP28 TGCT Testis +ARHGAP6 PRAD Prostate +ARHGDIB BLCA Bladder +ARHGDIB LAML Bone_Marrow +ARID3C LIHC Liver +ARL13A TGCT Testis +ARL14 STAD Stomach +ARL4A TGCT Testis +ARL6 TGCT Testis +ARL9 TGCT Testis +ARMC12 TGCT Testis +ARMC3 TGCT Testis +ARMC4 TGCT Testis +ARMC6 CESC Cervix +ARMC6 LIHC Liver +ARRDC5 TGCT Testis +ARSE KIRC Kidney +ARSE KIRP Kidney +ARSE KICH Kidney +ARSE PAAD Pancreas +ARSF KIRC Kidney +ARSF KIRP Kidney +ARSF KICH Kidney +ARSF SKCM Skin +ART3 TGCT Testis +ARX OV Ovary +ARX PAAD Pancreas +ASB17 TGCT Testis +ASB4 LUAD Lung +ASB4 LUSC Lung +ASB9 PAAD Pancreas +ASB9 TGCT Testis +ASGR1 LIHC Liver +ASGR2 LIHC Liver +ASIC3 TGCT Testis +ASIC4 GBM Brain +ASIC4 LGG Brain +ASIP OV Ovary +ASPRV1 SKCM Skin +ASS1 CESC Cervix +ASS1 LIHC Liver +ASZ1 TGCT Testis +ATCAY GBM Brain +ATCAY LGG Brain +ATF7IP2 TGCT Testis +ATG4C LAML Bone_Marrow +ATG4D PAAD Pancreas +ATG4D TGCT Testis +ATOH7 GBM Brain +ATOH7 LGG Brain +ATOH7 LIHC Liver +ATP11A LUAD Lung +ATP11A LUSC Lung +ATP4A STAD Stomach +ATP4B STAD Stomach +ATP5A1 UVM Eye +ATP5C1 BLCA Bladder +ATP5C1 LAML Bone_Marrow +ATP5J2 OV Ovary +ATP5L BLCA Bladder +ATP5L LAML Bone_Marrow +ATP6V0A4 KIRC Kidney +ATP6V0A4 KIRP Kidney +ATP6V0A4 KICH Kidney +ATP6V0D2 KIRC Kidney +ATP6V0D2 KIRP Kidney +ATP6V0D2 KICH Kidney +ATP6V1E2 TGCT Testis +ATP6V1G3 KIRC Kidney +ATP6V1G3 KIRP Kidney +ATP6V1G3 KICH Kidney +ATP8B3 TGCT Testis +ATXN3L TGCT Testis +AUNIP TGCT Testis +AURKC TGCT Testis +AVPI1 CESC Cervix +AVPI1 OV Ovary +AXDND1 TGCT Testis +AZGP1 LIHC Liver +AZGP1 PRAD Prostate +AZU1 LAML Bone_Marrow +B3GALNT1 BLCA Bladder +B3GNT3 COAD Colon +B3GNT3 STAD Stomach +B4GALNT3 COAD Colon +B4GALNT3 STAD Stomach +BAALC GBM Brain +BAALC LGG Brain +BAAT LIHC Liver +BAG1 CESC Cervix +BAG1 TGCT Testis +BAG5 TGCT Testis +BANF2 TGCT Testis +BANP TGCT Testis +BARX1 STAD Stomach +BBOX1 KIRC Kidney +BBOX1 KIRP Kidney +BBOX1 KICH Kidney +BCAN GBM Brain +BCAN LGG Brain +BCAS2 BLCA Bladder +BCAS2 LAML Bone_Marrow +BCAT1 PAAD Pancreas +BCAT1 TGCT Testis +BCL2L14 TGCT Testis +BCO2 LIHC Liver +BDH1 LIHC Liver +BEND2 TGCT Testis +BFSP1 TGCT Testis +BFSP1 UVM Eye +BHMT KIRC Kidney +BHMT KIRP Kidney +BHMT KICH Kidney +BHMT LIHC Liver +BHMT2 KIRC Kidney +BHMT2 KIRP Kidney +BHMT2 KICH Kidney +BHMT2 LIHC Liver +BIRC5 TGCT Testis +BIRC5 THYM Thymus +BIRC7 SKCM Skin +BIRC8 TGCT Testis +BIVM LAML Bone_Marrow +BIVM OV Ovary +BMF SARC Soft_Tissue +BMP4 COAD Colon +BMP8A THCA Thyroid_Gland +BMPR1B PRAD Prostate +BMX SKCM Skin +BNC1 TGCT Testis +BOD1L2 TGCT Testis +BOLL TGCT Testis +BPHL KIRC Kidney +BPHL KIRP Kidney +BPHL KICH Kidney +BPHL LIHC Liver +BPI LAML Bone_Marrow +BPIFA1 LUAD Lung +BPIFA1 LUSC Lung +BPIFA3 TGCT Testis +BPIFC SKCM Skin +BRAP TGCT Testis +BRCA2 TGCT Testis +BRDT TGCT Testis +BRPF1 CESC Cervix +BRPF1 TGCT Testis +BRPF3 TGCT Testis +BSND KIRC Kidney +BSND KIRP Kidney +BSND KICH Kidney +BTBD18 TGCT Testis +BTC COAD Colon +BTC KIRC Kidney +BTC KIRP Kidney +BTC KICH Kidney +BTG4 TGCT Testis +BTNL9 BRCA Breast +BTNL9 SARC Soft_Tissue +BUB1 TGCT Testis +BVES LUAD Lung +BVES LUSC Lung +C10orf120 TGCT Testis +C10orf53 TGCT Testis +C10orf62 KIRC Kidney +C10orf62 KIRP Kidney +C10orf62 KICH Kidney +C10orf62 TGCT Testis +C10orf82 TGCT Testis +C10orf99 COAD Colon +C11orf42 TGCT Testis +C11orf54 KIRC Kidney +C11orf54 KIRP Kidney +C11orf54 KICH Kidney +C11orf65 TGCT Testis +C11orf70 TGCT Testis +C11orf71 TGCT Testis +C11orf85 TGCT Testis +C11orf87 GBM Brain +C11orf87 LGG Brain +C11orf94 TGCT Testis +C12orf40 TGCT Testis +C12orf42 TGCT Testis +C12orf50 TGCT Testis +C12orf54 TGCT Testis +C12orf56 TGCT Testis +C14orf105 KIRC Kidney +C14orf105 KIRP Kidney +C14orf105 KICH Kidney +C14orf39 TGCT Testis +C15orf39 LAML Bone_Marrow +C15orf39 TGCT Testis +C15orf43 TGCT Testis +C16orf78 TGCT Testis +C16orf86 SKCM Skin +C16orf86 TGCT Testis +C16orf87 TGCT Testis +C16orf90 TGCT Testis +C16orf92 TGCT Testis +C16orf95 TGCT Testis +C16orf96 TGCT Testis +C17orf105 TGCT Testis +C17orf47 TGCT Testis +C17orf50 TGCT Testis +C17orf64 TGCT Testis +C17orf74 TGCT Testis +C17orf98 TGCT Testis +C18orf54 TGCT Testis +C18orf63 TGCT Testis +C19orf67 TGCT Testis +C19orf81 TGCT Testis +C1D BLCA Bladder +C1D LAML Bone_Marrow +C1orf100 TGCT Testis +C1orf101 TGCT Testis +C1orf105 TGCT Testis +C1orf111 TGCT Testis +C1orf112 CESC Cervix +C1orf112 TGCT Testis +C1orf146 TGCT Testis +C1orf158 TGCT Testis +C1orf159 CESC Cervix +C1orf159 TGCT Testis +C1orf167 TGCT Testis +C1orf185 TGCT Testis +C1orf186 KIRC Kidney +C1orf186 KIRP Kidney +C1orf186 KICH Kidney +C1orf186 UCEC Uterus +C1orf186 UCS Uterus +C1orf234 TGCT Testis +C1orf61 GBM Brain +C1orf61 LGG Brain +C1orf64 BRCA Breast +C1orf64 KIRC Kidney +C1orf64 KIRP Kidney +C1orf64 KICH Kidney +C1orf68 SKCM Skin +C1orf87 TGCT Testis +C1orf94 TGCT Testis +C1QL1 GBM Brain +C1QL1 LGG Brain +C1QL1 PAAD Pancreas +C1QTNF9B TGCT Testis +C1R LIHC Liver +C1R SARC Soft_Tissue +C2 LIHC Liver +C2 SARC Soft_Tissue +C20orf141 TGCT Testis +C20orf144 TGCT Testis +C20orf173 TGCT Testis +C20orf195 TGCT Testis +C20orf85 TGCT Testis +C21orf62 OV Ovary +C21orf62 PAAD Pancreas +C22orf23 TGCT Testis +C22orf31 TGCT Testis +C22orf42 PAAD Pancreas +C22orf42 TGCT Testis +C2CD4A PAAD Pancreas +C2CD4A STAD Stomach +C2CD4B PAAD Pancreas +C2orf16 TGCT Testis +C2orf42 TGCT Testis +C2orf57 TGCT Testis +C2orf61 TGCT Testis +C2orf73 TGCT Testis +C2orf78 TGCT Testis +C3 LIHC Liver +C3orf20 TGCT Testis +C3orf22 TGCT Testis +C3orf30 TGCT Testis +C3orf38 TGCT Testis +C3orf67 TGCT Testis +C3orf84 TGCT Testis +C4A LIHC Liver +C4B LIHC Liver +C4BPA LIHC Liver +C4BPB CESC Cervix +C4BPB LIHC Liver +C4orf17 TGCT Testis +C4orf22 TGCT Testis +C4orf36 TGCT Testis +C4orf45 TGCT Testis +C4orf51 TGCT Testis +C5 LIHC Liver +C5orf47 TGCT Testis +C5orf52 TGCT Testis +C6 LIHC Liver +C6orf10 TGCT Testis +C6orf118 TGCT Testis +C6orf163 TGCT Testis +C6orf201 TGCT Testis +C6orf223 CESC Cervix +C6orf223 COAD Colon +C6orf223 LUAD Lung +C6orf223 LUSC Lung +C7 OV Ovary +C7 SARC Soft_Tissue +C7orf31 TGCT Testis +C7orf34 TGCT Testis +C7orf61 TGCT Testis +C7orf62 TGCT Testis +C7orf72 TGCT Testis +C8A LIHC Liver +C8B LIHC Liver +C8G LIHC Liver +C8G STAD Stomach +C8orf46 GBM Brain +C8orf46 LGG Brain +C8orf48 TGCT Testis +C8orf74 TGCT Testis +C9 LIHC Liver +C9orf116 KIRC Kidney +C9orf116 KIRP Kidney +C9orf116 KICH Kidney +C9orf116 TGCT Testis +C9orf131 TGCT Testis +C9orf135 TGCT Testis +C9orf153 TGCT Testis +C9orf24 TGCT Testis +C9orf43 TGCT Testis +C9orf50 TGCT Testis +C9orf57 TGCT Testis +C9orf84 TGCT Testis +CA1 COAD Colon +CA2 STAD Stomach +CA9 CESC Cervix +CA9 STAD Stomach +CABLES2 TGCT Testis +CABS1 TGCT Testis +CABYR TGCT Testis +CACNA1I GBM Brain +CACNA1I LGG Brain +CACNA1I THCA Thyroid_Gland +CACNA2D2 LUAD Lung +CACNA2D2 LUSC Lung +CACNA2D4 LAML Bone_Marrow +CACNA2D4 TGCT Testis +CACNG2 GBM Brain +CACNG2 LGG Brain +CACNG2 LUAD Lung +CACNG2 LUSC Lung +CACNG3 GBM Brain +CACNG3 LGG Brain +CADPS2 TGCT Testis +CAGE1 TGCT Testis +CALB1 KIRC Kidney +CALB1 KIRP Kidney +CALB1 KICH Kidney +CALML3 SKCM Skin +CALML3 THYM Thymus +CALML5 SKCM Skin +CALR3 TGCT Testis +CAMK2A GBM Brain +CAMK2A LGG Brain +CAMKV GBM Brain +CAMKV LGG Brain +CAPN11 TGCT Testis +CAPN14 ESCA  Esophagus +CAPN3 SKCM Skin +CAPN8 STAD Stomach +CAPN9 STAD Stomach +CAPN9 UVM Eye +CAPNS2 BLCA Bladder +CAPNS2 SKCM Skin +CAPZA3 TGCT Testis +CASC1 TGCT Testis +CASC5 TGCT Testis +CASP10 STAD Stomach +CASP14 SKCM Skin +CASP14 UCEC Uterus +CASP14 UCS Uterus +CASP9 CESC Cervix +CASP9 OV Ovary +CASR KIRC Kidney +CASR KIRP Kidney +CASR KICH Kidney +CAT LAML Bone_Marrow +CAT LIHC Liver +CATSPER1 TGCT Testis +CATSPER3 TGCT Testis +CATSPER4 TGCT Testis +CATSPERD TGCT Testis +CATSPERG TGCT Testis +CBWD1 BLCA Bladder +CBWD1 LAML Bone_Marrow +CBWD2 LAML Bone_Marrow +CBWD2 THCA Thyroid_Gland +CBY3 TGCT Testis +CC2D2B TGCT Testis +CCDC105 TGCT Testis +CCDC110 TGCT Testis +CCDC112 TGCT Testis +CCDC116 TGCT Testis +CCDC129 LUAD Lung +CCDC129 LUSC Lung +CCDC129 TGCT Testis +CCDC13 TGCT Testis +CCDC13 UVM Eye +CCDC136 TGCT Testis +CCDC144NL TGCT Testis +CCDC15 TGCT Testis +CCDC151 TGCT Testis +CCDC151 UVM Eye +CCDC155 TGCT Testis +CCDC158 TGCT Testis +CCDC168 TGCT Testis +CCDC169 TGCT Testis +CCDC170 OV Ovary +CCDC170 TGCT Testis +CCDC171 SKCM Skin +CCDC171 TGCT Testis +CCDC172 TGCT Testis +CCDC173 TGCT Testis +CCDC175 TGCT Testis +CCDC178 TGCT Testis +CCDC179 TGCT Testis +CCDC27 TGCT Testis +CCDC34 TGCT Testis +CCDC36 TGCT Testis +CCDC38 TGCT Testis +CCDC42 TGCT Testis +CCDC54 TGCT Testis +CCDC62 TGCT Testis +CCDC63 GBM Brain +CCDC63 LGG Brain +CCDC63 TGCT Testis +CCDC65 TGCT Testis +CCDC67 TGCT Testis +CCDC7 BLCA Bladder +CCDC7 TGCT Testis +CCDC70 TGCT Testis +CCDC73 TGCT Testis +CCDC74B TGCT Testis +CCDC79 TGCT Testis +CCDC8 OV Ovary +CCDC80 SARC Soft_Tissue +CCDC83 TGCT Testis +CCDC86 CESC Cervix +CCDC86 TGCT Testis +CCDC87 TGCT Testis +CCDC90B BLCA Bladder +CCDC90B TGCT Testis +CCDC91 BLCA Bladder +CCDC91 TGCT Testis +CCER1 TGCT Testis +CCHCR1 CESC Cervix +CCHCR1 TGCT Testis +CCIN TGCT Testis +CCL16 LIHC Liver +CCL18 LUAD Lung +CCL18 LUSC Lung +CCL20 COAD Colon +CCL20 LUAD Lung +CCL20 LUSC Lung +CCL26 OV Ovary +CCL26 UVM Eye +CCL27 SKCM Skin +CCL27 TGCT Testis +CCNA1 LAML Bone_Marrow +CCNA1 TGCT Testis +CCNB3 SARC Soft_Tissue +CCNB3 TGCT Testis +CCSER1 TGCT Testis +CCT6B TGCT Testis +CCT8L2 TGCT Testis +CD163 SARC Soft_Tissue +CD200R1L TGCT Testis +CD207 SKCM Skin +CD247 THYM Thymus +CD300LG BRCA Breast +CD300LG TGCT Testis +CD36 BRCA Breast +CD36 LAML Bone_Marrow +CD36 SARC Soft_Tissue +CD93 LUAD Lung +CD93 LUSC Lung +CD93 SARC Soft_Tissue +CDC25C TGCT Testis +CDCA2 TGCT Testis +CDH16 KIRC Kidney +CDH16 KIRP Kidney +CDH16 KICH Kidney +CDH24 CESC Cervix +CDH24 UCEC Uterus +CDH24 UCS Uterus +CDH4 OV Ovary +CDH4 UVM Eye +CDH6 KIRC Kidney +CDH6 KIRP Kidney +CDH6 KICH Kidney +CDH6 OV Ovary +CDH7 SARC Soft_Tissue +CDHR1 SKCM Skin +CDHR1 UVM Eye +CDHR5 COAD Colon +CDHR5 KIRC Kidney +CDHR5 KIRP Kidney +CDHR5 KICH Kidney +CDK15 OV Ovary +CDK15 TGCT Testis +CDK2 SKCM Skin +CDK5R2 GBM Brain +CDK5R2 LGG Brain +CDK5R2 PAAD Pancreas +CDK7 TGCT Testis +CDKL2 TGCT Testis +CDKL3 TGCT Testis +CDKL4 TGCT Testis +CDKN2A CESC Cervix +CDKN3 TGCT Testis +CDRT1 TGCT Testis +CDRT15L2 TGCT Testis +CDSN SKCM Skin +CDX1 COAD Colon +CDX2 COAD Colon +CDYL2 PRAD Prostate +CDYL2 TGCT Testis +CEACAM1 COAD Colon +CEACAM3 LAML Bone_Marrow +CEACAM5 COAD Colon +CEACAM6 COAD Colon +CEACAM6 LUAD Lung +CEACAM6 LUSC Lung +CEACAM7 COAD Colon +CEACAM8 LAML Bone_Marrow +CEBPA LIHC Liver +CEBPA STAD Stomach +CEL PAAD Pancreas +CELA2A PAAD Pancreas +CELA2B PAAD Pancreas +CELA3A PAAD Pancreas +CELA3B PAAD Pancreas +CEND1 GBM Brain +CEND1 LGG Brain +CENPJ TGCT Testis +CENPJ THCA Thyroid_Gland +CENPK BLCA Bladder +CENPK LAML Bone_Marrow +CENPK TGCT Testis +CEP112 TGCT Testis +CEP170 TGCT Testis +CEP76 TGCT Testis +CEP85L TGCT Testis +CERS3 SKCM Skin +CERS3 TGCT Testis +CES1 LIHC Liver +CETN1 TGCT Testis +CETN3 TGCT Testis +CFB LIHC Liver +CFC1 LUAD Lung +CFC1 LUSC Lung +CFC1 PAAD Pancreas +CFH BLCA Bladder +CFH LIHC Liver +CFHR1 LIHC Liver +CFHR2 LIHC Liver +CFHR3 LIHC Liver +CFHR4 LIHC Liver +CFHR5 LIHC Liver +CFP OV Ovary +CFTR COAD Colon +CFTR PAAD Pancreas +CHD5 GBM Brain +CHD5 LGG Brain +CHEK1 CESC Cervix +CHEK1 TGCT Testis +CHGB PAAD Pancreas +CHGB PCPG Adrenal_Gland +CHGB ACC Adrenal_Gland +CHI3L1 LIHC Liver +CHIT1 LUAD Lung +CHIT1 LUSC Lung +CHMP5 BLCA Bladder +CHMP5 LAML Bone_Marrow +CHODL TGCT Testis +CHRDL2 KIRC Kidney +CHRDL2 KIRP Kidney +CHRDL2 KICH Kidney +CHRDL2 STAD Stomach +CHRDL2 UCEC Uterus +CHRDL2 UCS Uterus +CHRM1 GBM Brain +CHRM1 LGG Brain +CHRM1 PRAD Prostate +CHRNA2 PRAD Prostate +CHRNA4 GBM Brain +CHRNA4 LGG Brain +CHRNA4 LIHC Liver +CHST13 KIRC Kidney +CHST13 KIRP Kidney +CHST13 KICH Kidney +CHST13 LIHC Liver +CHST13 TGCT Testis +CHST7 OV Ovary +CHST7 PAAD Pancreas +CIDEA BRCA Breast +CIDEA SARC Soft_Tissue +CIDEA SKCM Skin +CIDEC BRCA Breast +CIDEC SARC Soft_Tissue +CISH LAML Bone_Marrow +CISH OV Ovary +CISH THCA Thyroid_Gland +CITED1 SKCM Skin +CITED1 TGCT Testis +CLCA4 BLCA Bladder +CLCA4 COAD Colon +CLCA4 ESCA  Esophagus +CLCF1 OV Ovary +CLCF1 STAD Stomach +CLCNKB KIRC Kidney +CLCNKB KIRP Kidney +CLCNKB KICH Kidney +CLDN1 BLCA Bladder +CLDN1 SKCM Skin +CLDN10 KIRC Kidney +CLDN10 KIRP Kidney +CLDN10 KICH Kidney +CLDN10 PAAD Pancreas +CLDN11 TGCT Testis +CLDN16 KIRC Kidney +CLDN16 KIRP Kidney +CLDN16 KICH Kidney +CLDN18 STAD Stomach +CLDN2 COAD Colon +CLDN2 KIRC Kidney +CLDN2 KIRP Kidney +CLDN2 KICH Kidney +CLEC18A KIRC Kidney +CLEC18A KIRP Kidney +CLEC18A KICH Kidney +CLEC1B LIHC Liver +CLEC2A SKCM Skin +CLGN TGCT Testis +CLNK KIRC Kidney +CLNK KIRP Kidney +CLNK KICH Kidney +CLPB TGCT Testis +CLPS PAAD Pancreas +CLRN3 COAD Colon +CLRN3 KIRC Kidney +CLRN3 KIRP Kidney +CLRN3 KICH Kidney +CLRN3 LIHC Liver +CLUL1 UVM Eye +CLYBL KIRC Kidney +CLYBL KIRP Kidney +CLYBL KICH Kidney +CLYBL LIHC Liver +CMBL KIRC Kidney +CMBL KIRP Kidney +CMBL KICH Kidney +CMTM1 TGCT Testis +CMTM2 TGCT Testis +CMTM5 GBM Brain +CMTM5 LGG Brain +CNBD1 TGCT Testis +CNBD2 TGCT Testis +CNGA1 LIHC Liver +CNGA1 UVM Eye +CNGB3 TGCT Testis +CNGB3 UVM Eye +CNIH1 BLCA Bladder +CNIH1 LAML Bone_Marrow +CNIH1 LIHC Liver +CNIH2 GBM Brain +CNIH2 LGG Brain +CNIH2 PAAD Pancreas +CNNM1 TGCT Testis +CNNM3 THYM Thymus +CNTD1 TGCT Testis +CNTN2 GBM Brain +CNTN2 LGG Brain +CNTNAP3 ESCA  Esophagus +CNTNAP4 GBM Brain +CNTNAP4 LGG Brain +COIL TGCT Testis +COL12A1 LAML Bone_Marrow +COL12A1 SARC Soft_Tissue +COL12A1 SKCM Skin +COL13A1 OV Ovary +COL17A1 BLCA Bladder +COL17A1 SKCM Skin +COL1A2 LAML Bone_Marrow +COL1A2 SKCM Skin +COL20A1 GBM Brain +COL20A1 LGG Brain +COL20A1 TGCT Testis +COL23A1 THCA Thyroid_Gland +COL25A1 TGCT Testis +COL4A3 KIRC Kidney +COL4A3 KIRP Kidney +COL4A3 KICH Kidney +COL4A3 UVM Eye +COL4A4 KIRC Kidney +COL4A4 KIRP Kidney +COL4A4 KICH Kidney +COL4A4 LIHC Liver +COL4A6 CESC Cervix +COL4A6 ESCA  Esophagus +COL7A1 BLCA Bladder +COL7A1 PRAD Prostate +COL7A1 SKCM Skin +COL8A1 LAML Bone_Marrow +COL8A1 SARC Soft_Tissue +COL9A1 OV Ovary +COL9A1 PRAD Prostate +COLEC10 LIHC Liver +COLEC11 GBM Brain +COLEC11 LGG Brain +COLEC11 LIHC Liver +COMT SKCM Skin +COX6B2 TGCT Testis +COX7B2 TGCT Testis +COX8C TGCT Testis +CP LIHC Liver +CPA1 PAAD Pancreas +CPA2 PAAD Pancreas +CPA3 LAML Bone_Marrow +CPA3 LUAD Lung +CPA3 LUSC Lung +CPA4 SKCM Skin +CPA5 TGCT Testis +CPAMD8 PRAD Prostate +CPAMD8 UVM Eye +CPB1 PAAD Pancreas +CPB2 LIHC Liver +CPLX1 GBM Brain +CPLX1 LGG Brain +CPLX1 UVM Eye +CPN1 LIHC Liver +CPN1 SKCM Skin +CPN2 LIHC Liver +CPNE4 PRAD Prostate +CPNE9 GBM Brain +CPNE9 LGG Brain +CPNE9 PAAD Pancreas +CPS1 CESC Cervix +CPS1 LIHC Liver +CPXCR1 TGCT Testis +CRABP1 OV Ovary +CRABP1 THCA Thyroid_Gland +CRABP1 UVM Eye +CRABP2 ESCA  Esophagus +CRABP2 UVM Eye +CRB1 TGCT Testis +CRB1 UVM Eye +CRB3 PAAD Pancreas +CREG2 GBM Brain +CREG2 LGG Brain +CRIPT BLCA Bladder +CRIPT LAML Bone_Marrow +CRISP2 TGCT Testis +CRNN ESCA  Esophagus +CRP LIHC Liver +CRYAA KIRC Kidney +CRYAA KIRP Kidney +CRYAA KICH Kidney +CRYAA UVM Eye +CRYBA2 UVM Eye +CRYBA4 TGCT Testis +CRYBA4 UVM Eye +CRYBB2 TGCT Testis +CRYBB2 UVM Eye +CRYGC TGCT Testis +CRYGC UVM Eye +CRYGD OV Ovary +CRYGD UVM Eye +CRYZ KIRC Kidney +CRYZ KIRP Kidney +CRYZ KICH Kidney +CRYZ LIHC Liver +CSF2 LUAD Lung +CSF2 LUSC Lung +CSF3 LUAD Lung +CSF3 LUSC Lung +CSF3 SKCM Skin +CSN1S1 BRCA Breast +CSN2 BRCA Breast +CSN3 BRCA Breast +CSNK1A1L TGCT Testis +CSPG5 GBM Brain +CSPG5 LGG Brain +CST1 BLCA Bladder +CST1 COAD Colon +CST1 PRAD Prostate +CST6 SKCM Skin +CST7 LAML Bone_Marrow +CST8 TGCT Testis +CST9 BRCA Breast +CST9L TGCT Testis +CSTA ESCA  Esophagus +CSTA PRAD Prostate +CSTB ESCA  Esophagus +CSTL1 TGCT Testis +CT45A3 TGCT Testis +CT45A5 TGCT Testis +CT47A11 TGCT Testis +CT47A12 TGCT Testis +CT47B1 TGCT Testis +CT62 TGCT Testis +CTAGE1 TGCT Testis +CTAGE6 TGCT Testis +CTAGE9 TGCT Testis +CTCFL TGCT Testis +CTH LIHC Liver +CTNND2 GBM Brain +CTNND2 LGG Brain +CTRB1 PAAD Pancreas +CTRB2 PAAD Pancreas +CTRC PAAD Pancreas +CTRL PAAD Pancreas +CTSE PAAD Pancreas +CTSE STAD Stomach +CTSG LAML Bone_Marrow +CTSG PRAD Prostate +CTSV TGCT Testis +CTSV THYM Thymus +CTTNBP2 UCEC Uterus +CTTNBP2 UCS Uterus +CTXN3 KIRC Kidney +CTXN3 KIRP Kidney +CTXN3 KICH Kidney +CUBN KIRC Kidney +CUBN KIRP Kidney +CUBN KICH Kidney +CUL4A CESC Cervix +CUX2 LIHC Liver +CUX2 PRAD Prostate +CUZD1 PAAD Pancreas +CXCL12 SARC Soft_Tissue +CXCL17 STAD Stomach +CXCL2 LIHC Liver +CXCL5 LUAD Lung +CXCL5 LUSC Lung +CXCL5 SKCM Skin +CXCL8 LAML Bone_Marrow +CXCL9 LUAD Lung +CXCL9 LUSC Lung +CXCL9 STAD Stomach +CXorf58 TGCT Testis +CXorf65 TGCT Testis +CXorf66 TGCT Testis +CYB5A LIHC Liver +CYCS BLCA Bladder +CYCS LAML Bone_Marrow +CYLC1 TGCT Testis +CYLC2 TGCT Testis +CYP17A1 KIRC Kidney +CYP17A1 KIRP Kidney +CYP17A1 KICH Kidney +CYP17A1 PCPG Adrenal_Gland +CYP17A1 ACC Adrenal_Gland +CYP1A2 LIHC Liver +CYP24A1 KIRC Kidney +CYP24A1 KIRP Kidney +CYP24A1 KICH Kidney +CYP24A1 PRAD Prostate +CYP26A1 LIHC Liver +CYP27B1 KIRC Kidney +CYP27B1 KIRP Kidney +CYP27B1 KICH Kidney +CYP2A6 LIHC Liver +CYP2A7 LIHC Liver +CYP2B6 LIHC Liver +CYP2C18 LIHC Liver +CYP2C8 LIHC Liver +CYP2C9 LIHC Liver +CYP2D6 LIHC Liver +CYP2E1 LIHC Liver +CYP2S1 COAD Colon +CYP2S1 STAD Stomach +CYP3A4 LIHC Liver +CYP3A43 LIHC Liver +CYP3A5 LIHC Liver +CYP3A7 LIHC Liver +CYP46A1 GBM Brain +CYP46A1 LGG Brain +CYP4B1 LUAD Lung +CYP4B1 LUSC Lung +CYP4F2 LIHC Liver +CYP4F3 LIHC Liver +CYP4F8 PRAD Prostate +CYP4F8 SKCM Skin +CYP4V2 KIRC Kidney +CYP4V2 KIRP Kidney +CYP4V2 KICH Kidney +CYP4V2 LIHC Liver +CYP51A1 BLCA Bladder +CYP51A1 TGCT Testis +CYP7A1 LIHC Liver +CYP8B1 LIHC Liver +CYS1 KIRC Kidney +CYS1 KIRP Kidney +CYS1 KICH Kidney +DAGLA GBM Brain +DAGLA LGG Brain +DAGLA PAAD Pancreas +DAO KIRC Kidney +DAO KIRP Kidney +DAO KICH Kidney +DAO LIHC Liver +DAPK2 COAD Colon +DAPK2 THCA Thyroid_Gland +DAW1 TGCT Testis +DAZ4 LUAD Lung +DAZ4 LUSC Lung +DAZL TGCT Testis +DBF4 TGCT Testis +DBH GBM Brain +DBH LGG Brain +DBH PCPG Adrenal_Gland +DBH ACC Adrenal_Gland +DCAF4L1 TGCT Testis +DCAF4L2 TGCT Testis +DCAF8L2 TGCT Testis +DCC TGCT Testis +DCD BRCA Breast +DCD SKCM Skin +DCDC1 TGCT Testis +DCDC1 UVM Eye +DCDC2 KIRC Kidney +DCDC2 KIRP Kidney +DCDC2 KICH Kidney +DCDC2 LIHC Liver +DCDC2C TGCT Testis +DCT SKCM Skin +DCUN1D1 TGCT Testis +DDC KIRC Kidney +DDC KIRP Kidney +DDC KICH Kidney +DDI1 TGCT Testis +DDIT4 CESC Cervix +DDIT4 OV Ovary +DDX20 TGCT Testis +DDX25 TGCT Testis +DDX4 TGCT Testis +DDX43 TGCT Testis +DDX53 TGCT Testis +DECR1 BLCA Bladder +DECR1 LIHC Liver +DECR2 LIHC Liver +DECR2 STAD Stomach +DEFA1 LAML Bone_Marrow +DEFA3 LAML Bone_Marrow +DEFB119 TGCT Testis +DENND5B OV Ovary +DEPDC7 LIHC Liver +DEPDC7 TGCT Testis +DFNB59 TGCT Testis +DGKG ESCA  Esophagus +DHDDS TGCT Testis +DHDH KIRC Kidney +DHDH KIRP Kidney +DHDH KICH Kidney +DHH TGCT Testis +DHRS2 BLCA Bladder +DHRS2 LIHC Liver +DIAPH3 TGCT Testis +DIO1 KIRC Kidney +DIO1 KIRP Kidney +DIO1 KICH Kidney +DIO1 LIHC Liver +DIO1 THCA Thyroid_Gland +DKKL1 TGCT Testis +DLC1 LUAD Lung +DLC1 LUSC Lung +DLC1 SARC Soft_Tissue +DLK2 PRAD Prostate +DMBT1 COAD Colon +DMBT1 STAD Stomach +DMC1 TGCT Testis +DMGDH KIRC Kidney +DMGDH KIRP Kidney +DMGDH KICH Kidney +DMGDH LIHC Liver +DMKN SKCM Skin +DMRT1 TGCT Testis +DMRT2 KIRC Kidney +DMRT2 KIRP Kidney +DMRT2 KICH Kidney +DMRTB1 TGCT Testis +DMRTC2 TGCT Testis +DNAAF1 TGCT Testis +DNAAF3 TGCT Testis +DNAH17 TGCT Testis +DNAH3 TGCT Testis +DNAH5 KIRC Kidney +DNAH5 KIRP Kidney +DNAH5 KICH Kidney +DNAH5 PRAD Prostate +DNAH5 TGCT Testis +DNAH7 TGCT Testis +DNAH9 TGCT Testis +DNAI1 TGCT Testis +DNAI2 TGCT Testis +DNAJB13 TGCT Testis +DNAJB8 TGCT Testis +DNAJC18 TGCT Testis +DNAJC22 COAD Colon +DNAJC22 LIHC Liver +DNAJC5B TGCT Testis +DNASE1L2 SKCM Skin +DNASE1L3 KIRC Kidney +DNASE1L3 KIRP Kidney +DNASE1L3 KICH Kidney +DNASE1L3 LIHC Liver +DNASE2B LUAD Lung +DNASE2B LUSC Lung +DNHD1 TGCT Testis +DNHD1 UCEC Uterus +DNHD1 UCS Uterus +DNMT3A COAD Colon +DNMT3L KIRC Kidney +DNMT3L KIRP Kidney +DNMT3L KICH Kidney +DNMT3L LIHC Liver +DOK5 OV Ovary +DOT1L TGCT Testis +DPCR1 PAAD Pancreas +DPCR1 STAD Stomach +DPEP3 TGCT Testis +DPP10 GBM Brain +DPP10 LGG Brain +DPPA2 TGCT Testis +DPPA4 LAML Bone_Marrow +DPPA4 TGCT Testis +DPT OV Ovary +DPT SARC Soft_Tissue +DPY19L2 TGCT Testis +DPYS KIRC Kidney +DPYS KIRP Kidney +DPYS KICH Kidney +DPYS LIHC Liver +DRC1 TGCT Testis +DRD2 LUAD Lung +DRD2 LUSC Lung +DRD2 PCPG Adrenal_Gland +DRD2 ACC Adrenal_Gland +DRG1 CESC Cervix +DRG1 TGCT Testis +DRP2 GBM Brain +DRP2 LGG Brain +DSC1 SKCM Skin +DSG1 SKCM Skin +DSG1 UVM Eye +DSG3 ESCA  Esophagus +DTL TGCT Testis +DUOX1 BLCA Bladder +DUOX1 THCA Thyroid_Gland +DUOX2 COAD Colon +DUOX2 THCA Thyroid_Gland +DUOXA1 LUAD Lung +DUOXA1 LUSC Lung +DUOXA1 THCA Thyroid_Gland +DUOXA2 COAD Colon +DUOXA2 STAD Stomach +DUSP15 TGCT Testis +DUSP18 TGCT Testis +DUSP21 TGCT Testis +DUSP4 SKCM Skin +DUSP4 STAD Stomach +DUSP9 KIRC Kidney +DUSP9 KIRP Kidney +DUSP9 KICH Kidney +DUSP9 LIHC Liver +DYDC1 TGCT Testis +DYNAP ESCA  Esophagus +DYNLRB2 TGCT Testis +DYRK1B PAAD Pancreas +DYRK1B TGCT Testis +DYRK3 TGCT Testis +DYX1C1 TGCT Testis +DYX1C1 THCA Thyroid_Gland +DZIP1 KIRC Kidney +DZIP1 KIRP Kidney +DZIP1 KICH Kidney +DZIP1 TGCT Testis +E2F1 CESC Cervix +E2F1 TGCT Testis +EAF2 KIRC Kidney +EAF2 KIRP Kidney +EAF2 KICH Kidney +EBLN1 TGCT Testis +ECEL1 OV Ovary +ECEL1 UVM Eye +EDC3 PRAD Prostate +EDC3 TGCT Testis +EDN3 PAAD Pancreas +EEF1A1 BLCA Bladder +EEF1A1 LAML Bone_Marrow +EFCAB11 TGCT Testis +EFCAB2 BLCA Bladder +EFCAB2 THCA Thyroid_Gland +EFCAB3 TGCT Testis +EFCAB5 TGCT Testis +EFCAB6 TGCT Testis +EFCAB8 TGCT Testis +EFCAB9 TGCT Testis +EGF KIRC Kidney +EGF KIRP Kidney +EGF KICH Kidney +EHHADH KIRC Kidney +EHHADH KIRP Kidney +EHHADH KICH Kidney +EHHADH LIHC Liver +EID3 TGCT Testis +EIF1AY LAML Bone_Marrow +EIF2S3 CESC Cervix +EIF2S3 OV Ovary +EIF3E LAML Bone_Marrow +EIF3E OV Ovary +EIF3K LAML Bone_Marrow +EIF3K OV Ovary +EIF3L OV Ovary +EIF3L THYM Thymus +EIF4E BLCA Bladder +EIF4E LAML Bone_Marrow +EIF4E TGCT Testis +EIF5AL1 TGCT Testis +EIF5B TGCT Testis +ELANE LAML Bone_Marrow +ELAVL2 TGCT Testis +ELAVL3 GBM Brain +ELAVL3 LGG Brain +ELF5 STAD Stomach +ELL TGCT Testis +ELOVL3 SKCM Skin +EMB LAML Bone_Marrow +EMP1 ESCA  Esophagus +EMP1 SARC Soft_Tissue +EMX1 KIRC Kidney +EMX1 KIRP Kidney +EMX1 KICH Kidney +EMX2 KIRC Kidney +EMX2 KIRP Kidney +EMX2 KICH Kidney +EMX2 UCEC Uterus +EMX2 UCS Uterus +ENTHD1 SKCM Skin +ENTHD1 TGCT Testis +EPB42 LAML Bone_Marrow +EPB42 LIHC Liver +EPGN ESCA  Esophagus +EPHA10 TGCT Testis +EPHA7 COAD Colon +EPHA7 ESCA  Esophagus +EPHB3 COAD Colon +EPHB3 SKCM Skin +EPHX2 LIHC Liver +EPHX2 PRAD Prostate +EPPIN TGCT Testis +EPS8L3 STAD Stomach +EPSTI1 STAD Stomach +EPX LAML Bone_Marrow +EPX THYM Thymus +EQTN TGCT Testis +EREG LAML Bone_Marrow +EREG SKCM Skin +ERICH2 TGCT Testis +ERICH6 TGCT Testis +ERMN GBM Brain +ERMN LGG Brain +ERN2 COAD Colon +ERN2 STAD Stomach +ESM1 KIRC Kidney +ESM1 KIRP Kidney +ESM1 KICH Kidney +ESM1 LUAD Lung +ESM1 LUSC Lung +ESPN OV Ovary +ESPN TGCT Testis +ESPN UVM Eye +ESRRB KIRC Kidney +ESRRB KIRP Kidney +ESRRB KICH Kidney +ESRRB UVM Eye +ESX1 TGCT Testis +ETNK2 TGCT Testis +ETV3 COAD Colon +ETV3 SKCM Skin +EVI2A GBM Brain +EVI2A LGG Brain +EVI2B LAML Bone_Marrow +EXD1 TGCT Testis +EXOSC8 BLCA Bladder +EXOSC8 TGCT Testis +F11 LIHC Liver +F12 LIHC Liver +F12 STAD Stomach +F13B LIHC Liver +F2 LIHC Liver +F5 LIHC Liver +F7 LIHC Liver +F9 LIHC Liver +FABP1 LIHC Liver +FABP4 BLCA Bladder +FABP4 BRCA Breast +FABP4 SARC Soft_Tissue +FAIM2 GBM Brain +FAIM2 LGG Brain +FAM101A STAD Stomach +FAM104A TGCT Testis +FAM105A CESC Cervix +FAM105A LUAD Lung +FAM105A LUSC Lung +FAM122C OV Ovary +FAM122C TGCT Testis +FAM131B GBM Brain +FAM131B LGG Brain +FAM133A TGCT Testis +FAM135B GBM Brain +FAM135B LGG Brain +FAM135B TGCT Testis +FAM153A TGCT Testis +FAM166A TGCT Testis +FAM170A TGCT Testis +FAM170B TGCT Testis +FAM181A TGCT Testis +FAM181B GBM Brain +FAM181B LGG Brain +FAM186A TGCT Testis +FAM186B TGCT Testis +FAM187B TGCT Testis +FAM19A1 GBM Brain +FAM19A1 LGG Brain +FAM205A TGCT Testis +FAM209A TGCT Testis +FAM209B TGCT Testis +FAM217A TGCT Testis +FAM227B SARC Soft_Tissue +FAM227B TGCT Testis +FAM228A TGCT Testis +FAM229A TGCT Testis +FAM229B TGCT Testis +FAM24A TGCT Testis +FAM3B PAAD Pancreas +FAM3B STAD Stomach +FAM3D COAD Colon +FAM3D STAD Stomach +FAM46B ESCA  Esophagus +FAM46B SKCM Skin +FAM46D TGCT Testis +FAM47A TGCT Testis +FAM47B TGCT Testis +FAM47C TGCT Testis +FAM57A SKCM Skin +FAM57A STAD Stomach +FAM57B TGCT Testis +FAM57B UVM Eye +FAM71A TGCT Testis +FAM71B TGCT Testis +FAM71C TGCT Testis +FAM71D TGCT Testis +FAM71E1 TGCT Testis +FAM71E2 TGCT Testis +FAM71F1 TGCT Testis +FAM71F2 TGCT Testis +FAM81B TGCT Testis +FAM83A ESCA  Esophagus +FAM83A PRAD Prostate +FAM86C1 CESC Cervix +FAM92A1 TGCT Testis +FAM9A TGCT Testis +FAM9B CESC Cervix +FAM9B TGCT Testis +FANCA CESC Cervix +FANCA TGCT Testis +FANCD2 STAD Stomach +FANCD2 TGCT Testis +FANCD2OS TGCT Testis +FANCG CESC Cervix +FANCG TGCT Testis +FAP SKCM Skin +FAP UCEC Uterus +FAP UCS Uterus +FATE1 TGCT Testis +FBLN2 SARC Soft_Tissue +FBN1 SKCM Skin +FBN1 STAD Stomach +FBXL6 STAD Stomach +FBXO15 TGCT Testis +FBXO24 TGCT Testis +FBXO27 PAAD Pancreas +FBXO27 SKCM Skin +FBXO39 TGCT Testis +FBXO43 TGCT Testis +FBXO47 TGCT Testis +FBXW10 TGCT Testis +FCAMR KIRC Kidney +FCAMR KIRP Kidney +FCAMR KICH Kidney +FCAR LAML Bone_Marrow +FCER1A PRAD Prostate +FCER1A SKCM Skin +FCGRT LAML Bone_Marrow +FCGRT OV Ovary +FCN2 LIHC Liver +FDXR CESC Cervix +FDXR PCPG Adrenal_Gland +FDXR ACC Adrenal_Gland +FER1L5 TGCT Testis +FERMT3 LAML Bone_Marrow +FETUB LIHC Liver +FEV PAAD Pancreas +FEV PRAD Prostate +FEZF1 TGCT Testis +FEZF2 GBM Brain +FEZF2 LGG Brain +FGA LIHC Liver +FGB LIHC Liver +FGF14 LUAD Lung +FGF14 LUSC Lung +FGF2 STAD Stomach +FGF2 UCEC Uterus +FGF2 UCS Uterus +FGF21 LIHC Liver +FGFBP1 ESCA  Esophagus +FGFBP1 STAD Stomach +FGFBP3 GBM Brain +FGFBP3 LGG Brain +FGFBP3 OV Ovary +FGFR1OP2 OV Ovary +FGFR3 SKCM Skin +FGG LIHC Liver +FGL1 LIHC Liver +FIBCD1 CESC Cervix +FIBCD1 TGCT Testis +FKBP6 TGCT Testis +FLG SKCM Skin +FLG2 SKCM Skin +FLRT3 KIRC Kidney +FLRT3 KIRP Kidney +FLRT3 KICH Kidney +FLRT3 LUAD Lung +FLRT3 LUSC Lung +FMNL3 SARC Soft_Tissue +FMO1 KIRC Kidney +FMO1 KIRP Kidney +FMO1 KICH Kidney +FMO2 LUAD Lung +FMO2 LUSC Lung +FMO3 LIHC Liver +FMO5 LIHC Liver +FMO5 STAD Stomach +FMR1NB TGCT Testis +FN1 LAML Bone_Marrow +FN1 SARC Soft_Tissue +FN1 SKCM Skin +FNDC7 TGCT Testis +FNDC8 TGCT Testis +FOLH1 PRAD Prostate +FOLR1 CESC Cervix +FOLR1 LUAD Lung +FOLR1 LUSC Lung +FOLR1 OV Ovary +FOSL1 LIHC Liver +FOSL1 SKCM Skin +FOXA1 PRAD Prostate +FOXA2 LUAD Lung +FOXA2 LUSC Lung +FOXA2 STAD Stomach +FOXA3 COAD Colon +FOXA3 LIHC Liver +FOXE1 THCA Thyroid_Gland +FOXF2 CESC Cervix +FOXF2 ESCA  Esophagus +FOXG1 GBM Brain +FOXG1 LGG Brain +FOXL2 OV Ovary +FOXL2 UCEC Uterus +FOXL2 UCS Uterus +FOXN4 TGCT Testis +FOXR1 TGCT Testis +FOXR2 TGCT Testis +FOXRED2 CESC Cervix +FOXRED2 PAAD Pancreas +FPGS CESC Cervix +FPGS OV Ovary +FPR3 LUAD Lung +FPR3 LUSC Lung +FPR3 SARC Soft_Tissue +FREM2 COAD Colon +FREM2 KIRC Kidney +FREM2 KIRP Kidney +FREM2 KICH Kidney +FREM2 THCA Thyroid_Gland +FRMPD1 TGCT Testis +FRMPD1 UVM Eye +FSCB TGCT Testis +FSCN2 THCA Thyroid_Gland +FSCN2 UVM Eye +FSCN3 TGCT Testis +FSIP2 TGCT Testis +FST LIHC Liver +FST OV Ovary +FTCD KIRC Kidney +FTCD KIRP Kidney +FTCD KICH Kidney +FTCD LIHC Liver +FTHL17 TGCT Testis +FTMT TGCT Testis +FTSJ1 CESC Cervix +FUOM LIHC Liver +FUOM PRAD Prostate +FUT3 COAD Colon +FUT3 STAD Stomach +FXYD1 UVM Eye +FXYD2 KIRC Kidney +FXYD2 KIRP Kidney +FXYD2 KICH Kidney +FXYD3 BLCA Bladder +FXYD3 COAD Colon +FXYD4 KIRC Kidney +FXYD4 KIRP Kidney +FXYD4 KICH Kidney +FXYD7 GBM Brain +FXYD7 LGG Brain +FYB LAML Bone_Marrow +FYB THYM Thymus +G2E3 TGCT Testis +G6PC KIRC Kidney +G6PC KIRP Kidney +G6PC KICH Kidney +G6PC LIHC Liver +G6PC2 PAAD Pancreas +GAB2 OV Ovary +GAB4 TGCT Testis +GABBR2 GBM Brain +GABBR2 LGG Brain +GABRA1 GBM Brain +GABRA1 LGG Brain +GABRA4 GBM Brain +GABRA4 LGG Brain +GABRA5 GBM Brain +GABRA5 LGG Brain +GABRD GBM Brain +GABRD LGG Brain +GABRG1 GBM Brain +GABRG1 LGG Brain +GABRR1 LUAD Lung +GABRR1 LUSC Lung +GABRR1 TGCT Testis +GAD2 GBM Brain +GAD2 LGG Brain +GAD2 PAAD Pancreas +GAGE1 TGCT Testis +GAGE12H TGCT Testis +GAGE12J TGCT Testis +GAGE13 TGCT Testis +GAGE2A TGCT Testis +GAGE2C LIHC Liver +GAGE2C TGCT Testis +GAGE4 LIHC Liver +GAGE4 TGCT Testis +GAGE7 LIHC Liver +GAGE7 TGCT Testis +GAGE8 LIHC Liver +GAGE8 TGCT Testis +GAL3ST3 THCA Thyroid_Gland +GAL3ST3 UVM Eye +GALK2 SARC Soft_Tissue +GALNT14 KIRC Kidney +GALNT14 KIRP Kidney +GALNT14 KICH Kidney +GALNT18 LUAD Lung +GALNT18 LUSC Lung +GALNT18 THCA Thyroid_Gland +GALNTL5 TGCT Testis +GALT LAML Bone_Marrow +GALT LIHC Liver +GAMT LIHC Liver +GAN SKCM Skin +GAP43 GBM Brain +GAP43 LGG Brain +GAPDHS SKCM Skin +GAPDHS TGCT Testis +GAST STAD Stomach +GATA3 BLCA Bladder +GATA3 SKCM Skin +GATA4 OV Ovary +GATA6 OV Ovary +GATA6 STAD Stomach +GATM KIRC Kidney +GATM KIRP Kidney +GATM KICH Kidney +GATM LIHC Liver +GATM PAAD Pancreas +GBP4 LUAD Lung +GBP4 LUSC Lung +GBP4 UCEC Uterus +GBP4 UCS Uterus +GBP6 ESCA  Esophagus +GBP7 LIHC Liver +GC LIHC Liver +GCG PAAD Pancreas +GCKR LIHC Liver +GCNT1 STAD Stomach +GCSAML PRAD Prostate +GCSAML TGCT Testis +GDAP1L1 GBM Brain +GDAP1L1 LGG Brain +GDF10 LUAD Lung +GDF10 LUSC Lung +GDF10 TGCT Testis +GDF2 LIHC Liver +GDPD4 TGCT Testis +GFAP GBM Brain +GFAP LGG Brain +GFI1 LAML Bone_Marrow +GFI1B LAML Bone_Marrow +GFI1B LIHC Liver +GGACT KIRC Kidney +GGACT KIRP Kidney +GGACT KICH Kidney +GGN TGCT Testis +GGTLC1 LUAD Lung +GGTLC1 LUSC Lung +GGTLC1 TGCT Testis +GGTLC2 TGCT Testis +GH1 GBM Brain +GH1 LGG Brain +GHRL STAD Stomach +GIF STAD Stomach +GJA8 TGCT Testis +GJB1 LIHC Liver +GJB1 SKCM Skin +GJB3 SKCM Skin +GJB3 STAD Stomach +GJB4 SKCM Skin +GK2 TGCT Testis +GKAP1 TGCT Testis +GKN1 STAD Stomach +GKN2 STAD Stomach +GLB1L TGCT Testis +GLDC KIRC Kidney +GLDC KIRP Kidney +GLDC KICH Kidney +GLDC LIHC Liver +GLIPR1L1 TGCT Testis +GLIPR1L2 TGCT Testis +GLS2 LIHC Liver +GLT6D1 TGCT Testis +GLTSCR1 PRAD Prostate +GLUD2 TGCT Testis +GLYAT KIRC Kidney +GLYAT KIRP Kidney +GLYAT KICH Kidney +GLYAT LIHC Liver +GLYATL1 KIRC Kidney +GLYATL1 KIRP Kidney +GLYATL1 KICH Kidney +GLYATL1 LIHC Liver +GML PCPG Adrenal_Gland +GML ACC Adrenal_Gland +GML TGCT Testis +GNAT1 TGCT Testis +GNAT1 UVM Eye +GNAT2 TGCT Testis +GNAT2 UVM Eye +GNLY STAD Stomach +GNMT LIHC Liver +GNMT PAAD Pancreas +GOLGA6A TGCT Testis +GOLGA6C TGCT Testis +GOLGA6D TGCT Testis +GOLGA6L1 TGCT Testis +GOLGA6L2 TGCT Testis +GOLGA6L6 TGCT Testis +GOLGA8K TGCT Testis +GOLGA8N TGCT Testis +GOLGA8N THCA Thyroid_Gland +GOLGA8O TGCT Testis +GOLGA8O THCA Thyroid_Gland +GOLT1A LIHC Liver +GOLT1A PAAD Pancreas +GOT1L1 TGCT Testis +GP2 PAAD Pancreas +GPA33 COAD Colon +GPC2 GBM Brain +GPC2 LGG Brain +GPC2 TGCT Testis +GPIHBP1 BRCA Breast +GPIHBP1 LUAD Lung +GPIHBP1 LUSC Lung +GPM6A GBM Brain +GPM6A LGG Brain +GPM6B GBM Brain +GPM6B LGG Brain +GPNMB BLCA Bladder +GPR135 TGCT Testis +GPR143 KIRC Kidney +GPR143 KIRP Kidney +GPR143 KICH Kidney +GPR143 SKCM Skin +GPR26 GBM Brain +GPR26 LGG Brain +GPR37L1 GBM Brain +GPR37L1 LGG Brain +GPRIN2 LUAD Lung +GPRIN2 LUSC Lung +GPRIN2 PAAD Pancreas +GPT KIRC Kidney +GPT KIRP Kidney +GPT KICH Kidney +GPT LIHC Liver +GPX2 BLCA Bladder +GPX2 COAD Colon +GPX3 KIRC Kidney +GPX3 KIRP Kidney +GPX3 KICH Kidney +GPX3 UVM Eye +GRB14 KIRC Kidney +GRB14 KIRP Kidney +GRB14 KICH Kidney +GRB14 LIHC Liver +GREM1 LAML Bone_Marrow +GREM1 SKCM Skin +GRHL2 PRAD Prostate +GRIA1 GBM Brain +GRIA1 LGG Brain +GRIA4 GBM Brain +GRIA4 LGG Brain +GRIA4 SARC Soft_Tissue +GRIA4 UVM Eye +GRID2 TGCT Testis +GRIN1 GBM Brain +GRIN1 LGG Brain +GRIN2A GBM Brain +GRIN2A LGG Brain +GRK4 TGCT Testis +GRM2 GBM Brain +GRM2 LGG Brain +GRM2 TGCT Testis +GRM3 GBM Brain +GRM3 LGG Brain +GRM5 GBM Brain +GRM5 LGG Brain +GRM8 COAD Colon +GRM8 TGCT Testis +GRP LUAD Lung +GRP LUSC Lung +GRP STAD Stomach +GRTP1 KIRC Kidney +GRTP1 KIRP Kidney +GRTP1 KICH Kidney +GRTP1 LIHC Liver +GSC2 TGCT Testis +GSDMA SKCM Skin +GSG1 TGCT Testis +GSG1 UVM Eye +GSN SARC Soft_Tissue +GSTA2 LIHC Liver +GSTA2 PAAD Pancreas +GSTA2 TGCT Testis +GSTM3 TGCT Testis +GSTZ1 LIHC Liver +GSTZ1 TGCT Testis +GTF2A1L TGCT Testis +GTF2H3 TGCT Testis +GTSF1 LAML Bone_Marrow +GTSF1 TGCT Testis +GTSF1L TGCT Testis +GUCA1A TGCT Testis +GUCA1A UVM Eye +GUCA1C PAAD Pancreas +GUCA1C UVM Eye +GUCY2D TGCT Testis +GUCY2D UVM Eye +GYPA LAML Bone_Marrow +GYPB LAML Bone_Marrow +GYS2 LIHC Liver +GZF1 TGCT Testis +H1FNT TGCT Testis +H1FOO TGCT Testis +H1FX LAML Bone_Marrow +H1FX LUAD Lung +H1FX LUSC Lung +H2AFB1 TGCT Testis +H2AFV THYM Thymus +H2AFZ LAML Bone_Marrow +H3F3C TGCT Testis +HABP2 KIRC Kidney +HABP2 KIRP Kidney +HABP2 KICH Kidney +HABP2 LIHC Liver +HAL LIHC Liver +HAMP LIHC Liver +HAND2 COAD Colon +HAND2 OV Ovary +HAO1 LIHC Liver +HAO2 KIRC Kidney +HAO2 KIRP Kidney +HAO2 KICH Kidney +HAO2 LIHC Liver +HAPLN2 GBM Brain +HAPLN2 LGG Brain +HAS2 LAML Bone_Marrow +HAS2 SKCM Skin +HAUS8 CESC Cervix +HAUS8 TGCT Testis +HAVCR1 COAD Colon +HAVCR1 KIRC Kidney +HAVCR1 KIRP Kidney +HAVCR1 KICH Kidney +HBB LAML Bone_Marrow +HBD LAML Bone_Marrow +HBM LAML Bone_Marrow +HBM LIHC Liver +HBZ LIHC Liver +HBZ TGCT Testis +HCFC2 TGCT Testis +HCN1 GBM Brain +HCN1 LGG Brain +HCN1 UVM Eye +HDGFL1 TGCT Testis +HEATR4 TGCT Testis +HEBP2 BRCA Breast +HEBP2 LAML Bone_Marrow +HELLS LAML Bone_Marrow +HELLS TGCT Testis +HEMGN LIHC Liver +HEMGN TGCT Testis +HENMT1 TGCT Testis +HEPACAM GBM Brain +HEPACAM LGG Brain +HEPH COAD Colon +HFE2 LIHC Liver +HGD KIRC Kidney +HGD KIRP Kidney +HGD KICH Kidney +HGD LIHC Liver +HGFAC LIHC Liver +HIBCH KIRC Kidney +HIBCH KIRP Kidney +HIBCH KICH Kidney +HIGD1A BLCA Bladder +HIGD1A LAML Bone_Marrow +HIPK4 TGCT Testis +HIRA THCA Thyroid_Gland +HIST1H1T TGCT Testis +HIST1H2AA TGCT Testis +HIST1H2BA TGCT Testis +HIST1H4C LAML Bone_Marrow +HIST1H4H PRAD Prostate +HIST3H3 TGCT Testis +HMG20B PRAD Prostate +HMG20B SKCM Skin +HMGB4 TGCT Testis +HMGCS2 LIHC Liver +HMGN5 LAML Bone_Marrow +HMGN5 TGCT Testis +HMP19 GBM Brain +HMP19 LGG Brain +HMX1 GBM Brain +HMX1 LGG Brain +HMX1 TGCT Testis +HMX2 KIRC Kidney +HMX2 KIRP Kidney +HMX2 KICH Kidney +HNF1B KIRC Kidney +HNF1B KIRP Kidney +HNF1B KICH Kidney +HNF4A COAD Colon +HNF4A KIRC Kidney +HNF4A KIRP Kidney +HNF4A KICH Kidney +HNF4A LIHC Liver +HNMT BLCA Bladder +HNMT KIRC Kidney +HNMT KIRP Kidney +HNMT KICH Kidney +HNRNPA1L2 LAML Bone_Marrow +HOGA1 KIRC Kidney +HOGA1 KIRP Kidney +HOGA1 KICH Kidney +HOGA1 LIHC Liver +HORMAD1 TGCT Testis +HORMAD2 TGCT Testis +HOXA11 UCEC Uterus +HOXA11 UCS Uterus +HOXA13 BLCA Bladder +HOXA13 PRAD Prostate +HOXA3 KIRC Kidney +HOXA3 KIRP Kidney +HOXA3 KICH Kidney +HOXA9 COAD Colon +HOXA9 KIRC Kidney +HOXA9 KIRP Kidney +HOXA9 KICH Kidney +HOXB13 PRAD Prostate +HOXB6 COAD Colon +HOXB6 KIRC Kidney +HOXB6 KIRP Kidney +HOXB6 KICH Kidney +HOXB8 COAD Colon +HOXB8 KIRC Kidney +HOXB8 KIRP Kidney +HOXB8 KICH Kidney +HOXC11 CESC Cervix +HOXC11 SKCM Skin +HOXC13 CESC Cervix +HOXC13 SKCM Skin +HOXC4 KIRC Kidney +HOXC4 KIRP Kidney +HOXC4 KICH Kidney +HOXC6 KIRC Kidney +HOXC6 KIRP Kidney +HOXC6 KICH Kidney +HOXD1 KIRC Kidney +HOXD1 KIRP Kidney +HOXD1 KICH Kidney +HOXD10 KIRC Kidney +HOXD10 KIRP Kidney +HOXD10 KICH Kidney +HOXD10 UCEC Uterus +HOXD10 UCS Uterus +HOXD3 KIRC Kidney +HOXD3 KIRP Kidney +HOXD3 KICH Kidney +HOXD3 UCEC Uterus +HOXD3 UCS Uterus +HOXD8 KIRC Kidney +HOXD8 KIRP Kidney +HOXD8 KICH Kidney +HOXD9 KIRC Kidney +HOXD9 KIRP Kidney +HOXD9 KICH Kidney +HOXD9 PRAD Prostate +HOXD9 UCEC Uterus +HOXD9 UCS Uterus +HP LIHC Liver +HPCAL1 CESC Cervix +HPCAL4 GBM Brain +HPCAL4 LGG Brain +HPD LIHC Liver +HPN LIHC Liver +HPR LIHC Liver +HPX LIHC Liver +HRASLS OV Ovary +HRASLS TGCT Testis +HRASLS5 TGCT Testis +HRG LIHC Liver +HS3ST2 GBM Brain +HS3ST2 LGG Brain +HS3ST2 LUAD Lung +HS3ST2 LUSC Lung +HS3ST6 SKCM Skin +HSD11B1 LIHC Liver +HSD11B1 OV Ovary +HSD17B13 LIHC Liver +HSD17B3 TGCT Testis +HSD17B6 LIHC Liver +HSF2BP TGCT Testis +HSF5 TGCT Testis +HSFY1 TGCT Testis +HSPA1L TGCT Testis +HSPA2 TGCT Testis +HSPA4L TGCT Testis +HSPB9 LUAD Lung +HSPB9 LUSC Lung +HSPB9 PAAD Pancreas +HSPB9 TGCT Testis +HTR2C GBM Brain +HTR2C LGG Brain +HTR2C TGCT Testis +HTR3A LUAD Lung +HTR3A LUSC Lung +HUNK KIRC Kidney +HUNK KIRP Kidney +HUNK KICH Kidney +HUNK UCEC Uterus +HUNK UCS Uterus +HYAL1 CESC Cervix +HYAL1 LIHC Liver +HYLS1 TGCT Testis +IAPP PAAD Pancreas +ICAM5 GBM Brain +ICAM5 LGG Brain +IDO2 LIHC Liver +IGDCC3 TGCT Testis +IGDCC3 UVM Eye +IGF2BP3 LAML Bone_Marrow +IGF2BP3 SKCM Skin +IGF2BP3 TGCT Testis +IGFBPL1 THCA Thyroid_Gland +IGFL1 ESCA  Esophagus +IGFL1 UCEC Uterus +IGFL1 UCS Uterus +IGFL2 SKCM Skin +IGFL3 SKCM Skin +IGLL1 LAML Bone_Marrow +IGLL1 TGCT Testis +IGSF1 TGCT Testis +IGSF11 TGCT Testis +IHH COAD Colon +IL13 TGCT Testis +IL13RA2 TGCT Testis +IL17RE SKCM Skin +IL17RE STAD Stomach +IL19 CESC Cervix +IL19 UCEC Uterus +IL19 UCS Uterus +IL1B LIHC Liver +IL1B STAD Stomach +IL1RAPL1 GBM Brain +IL1RAPL1 LGG Brain +IL1RL2 COAD Colon +IL1RL2 SKCM Skin +IL20RB CESC Cervix +IL20RB SKCM Skin +IL22RA1 COAD Colon +IL22RA1 PAAD Pancreas +IL23A PAAD Pancreas +IL23A TGCT Testis +IL2RG THYM Thymus +IL36G COAD Colon +IL36G SKCM Skin +IL37 SKCM Skin +IL5 TGCT Testis +IL7R LUAD Lung +IL7R LUSC Lung +IL7R THYM Thymus +ILDR2 TGCT Testis +ILDR2 UVM Eye +IMMP1L KIRC Kidney +IMMP1L KIRP Kidney +IMMP1L KICH Kidney +IMMP1L TGCT Testis +IMPA1 BLCA Bladder +IMPA1 LAML Bone_Marrow +IMPA1 TGCT Testis +ING2 PRAD Prostate +ING2 TGCT Testis +ING3 SARC Soft_Tissue +INHA OV Ovary +INHA TGCT Testis +INHBC LIHC Liver +INHBE LIHC Liver +INMT LUAD Lung +INMT LUSC Lung +INMT SARC Soft_Tissue +INS PAAD Pancreas +INSL3 GBM Brain +INSL3 LGG Brain +INSL3 TGCT Testis +INSL6 TGCT Testis +INSM1 LUAD Lung +INSM1 LUSC Lung +INSM1 PAAD Pancreas +INTS6 TGCT Testis +IPCEF1 THCA Thyroid_Gland +IPO11 LAML Bone_Marrow +IPO11 TGCT Testis +IQCA1 KIRC Kidney +IQCA1 KIRP Kidney +IQCA1 KICH Kidney +IQCA1 THCA Thyroid_Gland +IQCF1 TGCT Testis +IQCF2 TGCT Testis +IQCF3 TGCT Testis +IQCF5 TGCT Testis +IQCF6 TGCT Testis +IQCH TGCT Testis +IQGAP3 LAML Bone_Marrow +IQGAP3 OV Ovary +IQGAP3 TGCT Testis +IQUB TGCT Testis +IRAK1BP1 LAML Bone_Marrow +IRAK1BP1 TGCT Testis +IRGC TGCT Testis +IRX5 BRCA Breast +IRX5 LUAD Lung +IRX5 LUSC Lung +ISL1 PRAD Prostate +ISL1 UVM Eye +ISL2 OV Ovary +ISL2 UVM Eye +ISLR OV Ovary +ISLR SARC Soft_Tissue +ISLR2 GBM Brain +ISLR2 LGG Brain +ISLR2 TGCT Testis +ITGAL LAML Bone_Marrow +ITGAM LAML Bone_Marrow +ITGB3 KIRC Kidney +ITGB3 KIRP Kidney +ITGB3 KICH Kidney +ITGB3 THCA Thyroid_Gland +ITGB3BP BLCA Bladder +ITGB3BP TGCT Testis +ITGB6 KIRC Kidney +ITGB6 KIRP Kidney +ITGB6 KICH Kidney +ITGB6 PAAD Pancreas +ITIH1 LIHC Liver +ITIH2 LIHC Liver +ITIH3 LIHC Liver +ITIH4 LIHC Liver +ITM2A LAML Bone_Marrow +ITM2A OV Ovary +ITM2A THYM Thymus +ITM2C THYM Thymus +ITPRIPL1 TGCT Testis +IYD KIRC Kidney +IYD KIRP Kidney +IYD KICH Kidney +IYD THCA Thyroid_Gland +IZUMO1 TGCT Testis +IZUMO2 TGCT Testis +IZUMO3 TGCT Testis +IZUMO4 TGCT Testis +JAKMIP1 GBM Brain +JAKMIP1 LGG Brain +JAKMIP1 UVM Eye +JPH2 TGCT Testis +KATNAL2 TGCT Testis +KATNAL2 THCA Thyroid_Gland +KBTBD3 TGCT Testis +KCNA2 GBM Brain +KCNA2 LGG Brain +KCNA2 UVM Eye +KCNAB2 LAML Bone_Marrow +KCNAB3 UCEC Uterus +KCNAB3 UCS Uterus +KCNAB3 UVM Eye +KCNC2 GBM Brain +KCNC2 LGG Brain +KCNC2 PRAD Prostate +KCNE2 STAD Stomach +KCNF1 GBM Brain +KCNF1 LGG Brain +KCNJ1 KIRC Kidney +KCNJ1 KIRP Kidney +KCNJ1 KICH Kidney +KCNJ15 KIRC Kidney +KCNJ15 KIRP Kidney +KCNJ15 KICH Kidney +KCNJ16 KIRC Kidney +KCNJ16 KIRP Kidney +KCNJ16 KICH Kidney +KCNJ16 THCA Thyroid_Gland +KCNJ4 GBM Brain +KCNJ4 LGG Brain +KCNK12 GBM Brain +KCNK12 LGG Brain +KCNK12 TGCT Testis +KCNK16 PAAD Pancreas +KCNK4 GBM Brain +KCNK4 LGG Brain +KCNK4 TGCT Testis +KCNK7 SKCM Skin +KCNN1 GBM Brain +KCNN1 LGG Brain +KCNN2 SKCM Skin +KCNQ1 COAD Colon +KCNQ2 GBM Brain +KCNQ2 LGG Brain +KCNT2 LIHC Liver +KCNT2 OV Ovary +KCNU1 TGCT Testis +KCNV1 GBM Brain +KCNV1 LGG Brain +KCNV2 TGCT Testis +KCNV2 UVM Eye +KCTD19 TGCT Testis +KDELC1 CESC Cervix +KDELC1 UCEC Uterus +KDELC1 UCS Uterus +KDM1B THCA Thyroid_Gland +KDM4D TGCT Testis +KDM5B TGCT Testis +KDM8 LIHC Liver +KEL TGCT Testis +KHK LIHC Liver +KIAA0101 BLCA Bladder +KIAA0101 LAML Bone_Marrow +KIAA0101 TGCT Testis +KIAA1429 LIHC Liver +KIAA1456 PAAD Pancreas +KIAA1456 THCA Thyroid_Gland +KIAA1683 TGCT Testis +KIF12 KIRC Kidney +KIF12 KIRP Kidney +KIF12 KICH Kidney +KIF15 TGCT Testis +KIF17 TGCT Testis +KIF18A TGCT Testis +KIF20B BLCA Bladder +KIF20B TGCT Testis +KIF27 TGCT Testis +KIF2B TGCT Testis +KIF2C TGCT Testis +KIF4B TGCT Testis +KIF9 TGCT Testis +KIRREL2 LUAD Lung +KIRREL2 LUSC Lung +KIRREL2 PAAD Pancreas +KIT BRCA Breast +KIT OV Ovary +KL KIRC Kidney +KL KIRP Kidney +KL KICH Kidney +KLC3 SKCM Skin +KLHDC7A KIRC Kidney +KLHDC7A KIRP Kidney +KLHDC7A KICH Kidney +KLHDC8A OV Ovary +KLHDC9 TGCT Testis +KLHL10 TGCT Testis +KLHL17 TGCT Testis +KLHL17 UVM Eye +KLK1 KIRC Kidney +KLK1 KIRP Kidney +KLK1 KICH Kidney +KLK1 PAAD Pancreas +KLK10 ESCA  Esophagus +KLK10 OV Ovary +KLK11 OV Ovary +KLK13 ESCA  Esophagus +KLK13 TGCT Testis +KLK14 SKCM Skin +KLK2 PRAD Prostate +KLK3 PRAD Prostate +KLK4 PRAD Prostate +KLK5 OV Ovary +KLK5 SKCM Skin +KLK5 STAD Stomach +KLK7 OV Ovary +KLK7 SKCM Skin +KLKB1 LIHC Liver +KLRC3 CESC Cervix +KNG1 KIRC Kidney +KNG1 KIRP Kidney +KNG1 KICH Kidney +KNG1 LIHC Liver +KNSTRN TGCT Testis +KNSTRN THCA Thyroid_Gland +KPNA5 TGCT Testis +KPRP SKCM Skin +KRBOX1 TGCT Testis +KREMEN2 SKCM Skin +KREMEN2 STAD Stomach +KRT1 SKCM Skin +KRT10 LIHC Liver +KRT10 SKCM Skin +KRT13 ESCA  Esophagus +KRT14 BLCA Bladder +KRT14 SKCM Skin +KRT16 BLCA Bladder +KRT16 SKCM Skin +KRT17 BRCA Breast +KRT17 CESC Cervix +KRT17 SKCM Skin +KRT17 THYM Thymus +KRT2 SKCM Skin +KRT24 ESCA  Esophagus +KRT24 UVM Eye +KRT32 COAD Colon +KRT32 SKCM Skin +KRT34 CESC Cervix +KRT34 SKCM Skin +KRT34 UCEC Uterus +KRT34 UCS Uterus +KRT35 SKCM Skin +KRT37 COAD Colon +KRT37 SKCM Skin +KRT4 ESCA  Esophagus +KRT6B BLCA Bladder +KRT6B ESCA  Esophagus +KRT6B SKCM Skin +KRT6C ESCA  Esophagus +KRT6C SKCM Skin +KRT72 TGCT Testis +KRT77 SKCM Skin +KRT78 ESCA  Esophagus +KRT79 SKCM Skin +KRTAP1-5 CESC Cervix +KRTAP1-5 SKCM Skin +KRTAP1-5 UCEC Uterus +KRTAP1-5 UCS Uterus +KRTAP3-2 ESCA  Esophagus +KRTAP3-2 SKCM Skin +KRTDAP SKCM Skin +KYNU CESC Cervix +KYNU LIHC Liver +L1CAM CESC Cervix +LALBA BRCA Breast +LAMB4 SKCM Skin +LAMC2 BLCA Bladder +LAMP3 LUAD Lung +LAMP3 LUSC Lung +LAMP3 TGCT Testis +LBP LIHC Liver +LCA5L TGCT Testis +LCAT LIHC Liver +LCAT PRAD Prostate +LCE1A SKCM Skin +LCE1B SKCM Skin +LCE1C SKCM Skin +LCE1D SKCM Skin +LCE1E SKCM Skin +LCE1F SKCM Skin +LCE2A SKCM Skin +LCE2B SKCM Skin +LCE2C SKCM Skin +LCE2D SKCM Skin +LCE5A SKCM Skin +LCE6A SKCM Skin +LCN1 ESCA  Esophagus +LCN1 UVM Eye +LCN2 STAD Stomach +LDHAL6A TGCT Testis +LDHAL6B TGCT Testis +LDHC TGCT Testis +LDHD LIHC Liver +LDHD STAD Stomach +LEAP2 LIHC Liver +LECT1 LUAD Lung +LECT1 LUSC Lung +LECT2 LIHC Liver +LEFTY1 COAD Colon +LEFTY1 PAAD Pancreas +LELP1 TGCT Testis +LEMD1 TGCT Testis +LETM2 TGCT Testis +LFNG KIRC Kidney +LFNG KIRP Kidney +LFNG KICH Kidney +LFNG PAAD Pancreas +LGALS4 COAD Colon +LGI1 GBM Brain +LGI1 LGG Brain +LHX1 KIRC Kidney +LHX1 KIRP Kidney +LHX1 KICH Kidney +LHX1 OV Ovary +LHX2 GBM Brain +LHX2 LGG Brain +LHX2 UVM Eye +LHX3 CESC Cervix +LHX3 UVM Eye +LIG3 LAML Bone_Marrow +LIG3 TGCT Testis +LIN28A OV Ovary +LIN28A TGCT Testis +LIPC LIHC Liver +LIPF STAD Stomach +LIPH COAD Colon +LIPH STAD Stomach +LIPJ TGCT Testis +LIPK SKCM Skin +LIPM SKCM Skin +LIPN SKCM Skin +LMAN1L PRAD Prostate +LMAN1L UVM Eye +LMX1B BRCA Breast +LMX1B OV Ovary +LNP1 TGCT Testis +LOR SKCM Skin +LOXHD1 TGCT Testis +LPA LIHC Liver +LPAR3 PRAD Prostate +LPAR3 TGCT Testis +LRCH4 TGCT Testis +LRCH4 THYM Thymus +LRFN4 STAD Stomach +LRFN4 TGCT Testis +LRG1 LIHC Liver +LRIT1 TGCT Testis +LRP2 KIRC Kidney +LRP2 KIRP Kidney +LRP2 KICH Kidney +LRP2 THCA Thyroid_Gland +LRP4 COAD Colon +LRP4 SKCM Skin +LRP8 TGCT Testis +LRP8 THCA Thyroid_Gland +LRR1 PRAD Prostate +LRR1 TGCT Testis +LRRC17 OV Ovary +LRRC17 TGCT Testis +LRRC36 TGCT Testis +LRRC37A TGCT Testis +LRRC37A2 TGCT Testis +LRRC52 TGCT Testis +LRRC70 LAML Bone_Marrow +LRRC72 TGCT Testis +LRRD1 TGCT Testis +LRRIQ3 TGCT Testis +LRRN4 KIRC Kidney +LRRN4 KIRP Kidney +LRRN4 KICH Kidney +LRRN4 LUAD Lung +LRRN4 LUSC Lung +LRRTM2 GBM Brain +LRRTM2 LGG Brain +LRWD1 TGCT Testis +LSR STAD Stomach +LUZP4 TGCT Testis +LY6D ESCA  Esophagus +LY6D PAAD Pancreas +LY6G6C SKCM Skin +LY6H GBM Brain +LY6H LGG Brain +LY6K CESC Cervix +LY6K SKCM Skin +LY6K TGCT Testis +LYAR TGCT Testis +LYPD4 TGCT Testis +LYPD6B OV Ovary +LYPD6B SKCM Skin +LYPLA1 BLCA Bladder +LYPLA1 LAML Bone_Marrow +LYPLA1 TGCT Testis +LYRM5 BLCA Bladder +LYRM5 LAML Bone_Marrow +LYRM5 TGCT Testis +LYZ UVM Eye +LYZL1 TGCT Testis +LYZL2 TGCT Testis +LYZL4 TGCT Testis +LYZL6 TGCT Testis +MAB21L1 BRCA Breast +MAB21L1 UVM Eye +MAD2L1 LAML Bone_Marrow +MAD2L1 TGCT Testis +MAEL TGCT Testis +MAG GBM Brain +MAG LGG Brain +MAGEA1 TGCT Testis +MAGEA10 SKCM Skin +MAGEA10 TGCT Testis +MAGEA12 SKCM Skin +MAGEA12 TGCT Testis +MAGEA3 SKCM Skin +MAGEA3 TGCT Testis +MAGEA4 TGCT Testis +MAGEA6 SKCM Skin +MAGEA6 TGCT Testis +MAGEA8 SKCM Skin +MAGEA8 TGCT Testis +MAGEB1 SKCM Skin +MAGEB1 TGCT Testis +MAGEB10 TGCT Testis +MAGEB16 TGCT Testis +MAGEB17 TGCT Testis +MAGEB2 SKCM Skin +MAGEB2 TGCT Testis +MAGEB6 TGCT Testis +MAGEC1 SKCM Skin +MAGEC1 TGCT Testis +MAGEC2 SKCM Skin +MAGEC2 TGCT Testis +MAK TGCT Testis +MAK UVM Eye +MAL ESCA  Esophagus +MAPK14 BLCA Bladder +MASP2 LIHC Liver +MAT1A LIHC Liver +MATK UVM Eye +MBD3L1 TGCT Testis +MBL2 LIHC Liver +MC1R SKCM Skin +MC1R TGCT Testis +MCCD1 KIRC Kidney +MCCD1 KIRP Kidney +MCCD1 KICH Kidney +MCF2 TGCT Testis +MCHR1 GBM Brain +MCHR1 LGG Brain +MCHR1 UCEC Uterus +MCHR1 UCS Uterus +MCHR2 TGCT Testis +MCRS1 CESC Cervix +MDM1 TGCT Testis +MED26 TGCT Testis +MED4 BLCA Bladder +MED4 LAML Bone_Marrow +MEGF10 GBM Brain +MEGF10 LGG Brain +MEIOB TGCT Testis +MEIS3 LUAD Lung +MEIS3 LUSC Lung +MEIS3 STAD Stomach +MEIS3 UCEC Uterus +MEIS3 UCS Uterus +MEOX1 OV Ovary +METAP1 TGCT Testis +METTL21B KIRC Kidney +METTL21B KIRP Kidney +METTL21B KICH Kidney +MEX3B TGCT Testis +MFAP3L TGCT Testis +MFI2 SKCM Skin +MFSD12 KIRC Kidney +MFSD12 KIRP Kidney +MFSD12 KICH Kidney +MFSD12 SKCM Skin +MFSD7 OV Ovary +MFSD7 TGCT Testis +MGAM KIRC Kidney +MGAM KIRP Kidney +MGAM KICH Kidney +MGARP PCPG Adrenal_Gland +MGARP ACC Adrenal_Gland +MGARP UVM Eye +MGAT4C TGCT Testis +MGAT4C THCA Thyroid_Gland +MGST1 BLCA Bladder +MICALCL TGCT Testis +MIOX KIRC Kidney +MIOX KIRP Kidney +MIOX KICH Kidney +MISP CESC Cervix +MISP COAD Colon +MITF SKCM Skin +MITF UCEC Uterus +MITF UCS Uterus +MKI67 THYM Thymus +MKS1 CESC Cervix +MKS1 TGCT Testis +MKX PRAD Prostate +MKX UCEC Uterus +MKX UCS Uterus +MLANA SKCM Skin +MLC1 GBM Brain +MLC1 LGG Brain +MLF1 BLCA Bladder +MLF1 TGCT Testis +MLXIPL COAD Colon +MLXIPL LIHC Liver +MMADHC BLCA Bladder +MMADHC LAML Bone_Marrow +MME KIRC Kidney +MME KIRP Kidney +MME KICH Kidney +MME SKCM Skin +MMEL1 OV Ovary +MMEL1 TGCT Testis +MMP1 SARC Soft_Tissue +MMP1 SKCM Skin +MMP1 STAD Stomach +MNX1 COAD Colon +MNX1 PAAD Pancreas +MOBP GBM Brain +MOBP LGG Brain +MOG GBM Brain +MOG LGG Brain +MOGAT2 LIHC Liver +MOGAT2 STAD Stomach +MON1A CESC Cervix +MON1A TGCT Testis +MORC1 TGCT Testis +MORN3 TGCT Testis +MORN5 TGCT Testis +MOV10L1 TGCT Testis +MPHOSPH6 BLCA Bladder +MPO LAML Bone_Marrow +MPPED1 GBM Brain +MPPED1 LGG Brain +MPPED1 LIHC Liver +MPZL2 BLCA Bladder +MRAP STAD Stomach +MRC1 LUAD Lung +MRC1 LUSC Lung +MRGBP TGCT Testis +MRGPRG TGCT Testis +MRO KIRC Kidney +MRO KIRP Kidney +MRO KICH Kidney +MRO TGCT Testis +MROH2B TGCT Testis +MROH7 TGCT Testis +MROH8 TGCT Testis +MRPL1 BLCA Bladder +MRPL1 LAML Bone_Marrow +MRPL36 LAML Bone_Marrow +MRPL36 TGCT Testis +MRPL42 LAML Bone_Marrow +MRPL42 TGCT Testis +MRPL52 LAML Bone_Marrow +MS4A13 TGCT Testis +MS4A14 TGCT Testis +MS4A15 LUAD Lung +MS4A15 LUSC Lung +MS4A3 LAML Bone_Marrow +MS4A5 TGCT Testis +MS4A6E TGCT Testis +MSH4 TGCT Testis +MSI1 TGCT Testis +MSI1 UVM Eye +MSL3 CESC Cervix +MSLN CESC Cervix +MSLN LUAD Lung +MSLN LUSC Lung +MSLN OV Ovary +MSMB PRAD Prostate +MSR1 KIRC Kidney +MSR1 KIRP Kidney +MSR1 KICH Kidney +MSR1 LUAD Lung +MSR1 LUSC Lung +MST1 LIHC Liver +MST1R COAD Colon +MST1R PRAD Prostate +MST1R STAD Stomach +MT1F LIHC Liver +MT1F THCA Thyroid_Gland +MT1G LIHC Liver +MT1H LIHC Liver +MT1H UVM Eye +MT3 GBM Brain +MT3 LGG Brain +MT4 SKCM Skin +MTL5 TGCT Testis +MUC1 STAD Stomach +MUC20 KIRC Kidney +MUC20 KIRP Kidney +MUC20 KICH Kidney +MUC21 ESCA  Esophagus +MUC4 COAD Colon +MUC5AC STAD Stomach +MUCL1 BRCA Breast +MUCL1 SKCM Skin +MUM1L1 OV Ovary +MUM1L1 TGCT Testis +MYB BRCA Breast +MYB LAML Bone_Marrow +MYB TGCT Testis +MYBPC1 LIHC Liver +MYCBPAP TGCT Testis +MYCL KIRC Kidney +MYCL KIRP Kidney +MYCL KICH Kidney +MYCL SKCM Skin +MYH11 PRAD Prostate +MYO1A COAD Colon +MYO1A STAD Stomach +MYO1H TGCT Testis +MYO3A TGCT Testis +MZB1 TGCT Testis +MZB1 THYM Thymus +NACA2 TGCT Testis +NANOS2 TGCT Testis +NANOS3 TGCT Testis +NAPSA LUAD Lung +NAPSA LUSC Lung +NAT2 LIHC Liver +NAT8 KIRC Kidney +NAT8 KIRP Kidney +NAT8 KICH Kidney +NBPF3 TGCT Testis +NBPF6 TGCT Testis +NBR1 LIHC Liver +NBR1 TGCT Testis +NCAN GBM Brain +NCAN LGG Brain +NCAPD3 PRAD Prostate +NCAPG CESC Cervix +NCAPG TGCT Testis +NCF4 LAML Bone_Marrow +NCK2 SKCM Skin +NCKAP1L THYM Thymus +NDC80 BLCA Bladder +NDC80 CESC Cervix +NDC80 TGCT Testis +NDP OV Ovary +NDP UCEC Uterus +NDP UCS Uterus +NDUFA7 LAML Bone_Marrow +NDUFA7 TGCT Testis +NDUFAF7 TGCT Testis +NECAB3 CESC Cervix +NECAB3 OV Ovary +NEDD1 BLCA Bladder +NEDD1 TGCT Testis +NEDD4 BLCA Bladder +NEDD4 LIHC Liver +NEFH PRAD Prostate +NEK10 TGCT Testis +NEK11 TGCT Testis +NEK2 BLCA Bladder +NEK2 TGCT Testis +NEU4 GBM Brain +NEU4 LGG Brain +NEU4 LIHC Liver +NEUROD1 PAAD Pancreas +NEUROD1 UVM Eye +NEUROD6 GBM Brain +NEUROD6 LGG Brain +NFASC SARC Soft_Tissue +NFE2 LAML Bone_Marrow +NIFK BLCA Bladder +NIFK LAML Bone_Marrow +NKAIN2 GBM Brain +NKAIN2 LGG Brain +NKAIN4 GBM Brain +NKAIN4 LGG Brain +NKAIN4 KIRC Kidney +NKAIN4 KIRP Kidney +NKAIN4 KICH Kidney +NKAPL TGCT Testis +NKAPL UVM Eye +NKPD1 SKCM Skin +NKX2-1 LUAD Lung +NKX2-1 LUSC Lung +NKX2-1 THCA Thyroid_Gland +NKX2-4 TGCT Testis +NKX3-1 PRAD Prostate +NLRP10 SKCM Skin +NLRP7 TGCT Testis +NMB KIRC Kidney +NMB KIRP Kidney +NMB KICH Kidney +NME5 TGCT Testis +NMU ESCA  Esophagus +NMU OV Ovary +NNMT LIHC Liver +NODAL GBM Brain +NODAL LGG Brain +NODAL TGCT Testis +NOX4 KIRC Kidney +NOX4 KIRP Kidney +NOX4 KICH Kidney +NPAP1 TGCT Testis +NPAS1 SKCM Skin +NPC1L1 LIHC Liver +NPC1L1 PAAD Pancreas +NPHP1 TGCT Testis +NPHS2 KIRC Kidney +NPHS2 KIRP Kidney +NPHS2 KICH Kidney +NPIPB6 TGCT Testis +NPIPB8 TGCT Testis +NPM1 BLCA Bladder +NPM1 LAML Bone_Marrow +NPM1 OV Ovary +NPR3 KIRC Kidney +NPR3 KIRP Kidney +NPR3 KICH Kidney +NPR3 LAML Bone_Marrow +NPTX1 GBM Brain +NPTX1 LGG Brain +NPY PRAD Prostate +NR0B2 LIHC Liver +NR1H3 CESC Cervix +NR1H3 SARC Soft_Tissue +NR1H4 KIRC Kidney +NR1H4 KIRP Kidney +NR1H4 KICH Kidney +NR1H4 LIHC Liver +NR1I2 LIHC Liver +NR1I3 KIRC Kidney +NR1I3 KIRP Kidney +NR1I3 KICH Kidney +NR1I3 LIHC Liver +NR2E3 UVM Eye +NR2F2 OV Ovary +NR2F2 SARC Soft_Tissue +NR4A1 OV Ovary +NR4A1 SARC Soft_Tissue +NR4A3 SKCM Skin +NR5A1 LIHC Liver +NR5A1 TGCT Testis +NR5A2 LIHC Liver +NR5A2 PAAD Pancreas +NR6A1 TGCT Testis +NRGN GBM Brain +NRGN LGG Brain +NRIP3 TGCT Testis +NRL TGCT Testis +NRL UVM Eye +NRP2 OV Ovary +NRSN1 GBM Brain +NRSN1 LGG Brain +NSUN4 TGCT Testis +NT5C1B TGCT Testis +NT5DC4 TGCT Testis +NTNG2 UVM Eye +NTSR1 COAD Colon +NTSR1 PAAD Pancreas +NTSR2 GBM Brain +NTSR2 LGG Brain +NUDT10 TGCT Testis +NUDT11 TGCT Testis +NUF2 TGCT Testis +NUF2 THYM Thymus +NUP155 TGCT Testis +NUP210L TGCT Testis +NUP35 BLCA Bladder +NUP35 TGCT Testis +NUP62CL PAAD Pancreas +NUP62CL TGCT Testis +NUTM1 TGCT Testis +NWD1 PRAD Prostate +NXF2 TGCT Testis +NXF3 OV Ovary +NXF3 STAD Stomach +NXF3 TGCT Testis +OAF LIHC Liver +OASL CESC Cervix +OAZ3 TGCT Testis +OCA2 SARC Soft_Tissue +OCA2 THCA Thyroid_Gland +ODF1 TGCT Testis +ODF2 TGCT Testis +ODF2L BLCA Bladder +ODF3 TGCT Testis +ODF3L1 TGCT Testis +ODF3L2 TGCT Testis +ODF4 TGCT Testis +OGDHL KIRC Kidney +OGDHL KIRP Kidney +OGDHL KICH Kidney +OIP5 TGCT Testis +OIT3 LIHC Liver +OLAH TGCT Testis +OLFM1 GBM Brain +OLFM1 LGG Brain +OLIG1 GBM Brain +OLIG1 LGG Brain +OLIG2 GBM Brain +OLIG2 LGG Brain +ONECUT1 LIHC Liver +ONECUT1 PAAD Pancreas +OOEP TGCT Testis +OOSP2 TGCT Testis +OPALIN GBM Brain +OPALIN LGG Brain +OPCML GBM Brain +OPCML LGG Brain +OPLAH PRAD Prostate +OPLAH TGCT Testis +OPN5 TGCT Testis +OR10J1 TGCT Testis +OR1C1 TGCT Testis +OR3A2 TGCT Testis +OR4M2 TGCT Testis +OR4N4 TGCT Testis +OR51E1 PRAD Prostate +OR51E2 PRAD Prostate +OR52D1 TGCT Testis +OR6F1 TGCT Testis +OR7E24 TGCT Testis +OR8G5 TGCT Testis +ORAI1 OV Ovary +ORAI1 SKCM Skin +ORM1 LIHC Liver +ORM2 LIHC Liver +OSBPL5 CESC Cervix +OSCP1 TGCT Testis +OTOF GBM Brain +OTOF LGG Brain +OTOG TGCT Testis +OTOP2 COAD Colon +OTOP2 TGCT Testis +OTUD6A TGCT Testis +OTX2 UVM Eye +OVOL1 TGCT Testis +OXCT2 TGCT Testis +OXTR BRCA Breast +OXTR UCEC Uterus +OXTR UCS Uterus +P2RX5 CESC Cervix +PABPC3 TGCT Testis +PACRG TGCT Testis +PADI1 ESCA  Esophagus +PADI4 LAML Bone_Marrow +PAEP OV Ovary +PAEP PRAD Prostate +PAEP UCEC Uterus +PAEP UCS Uterus +PAGE1 TGCT Testis +PAGE2 TGCT Testis +PAGE2B TGCT Testis +PAGE3 TGCT Testis +PAGE4 PRAD Prostate +PAGE5 SKCM Skin +PAGE5 TGCT Testis +PAH KIRC Kidney +PAH KIRP Kidney +PAH KICH Kidney +PAH LIHC Liver +PAK7 GBM Brain +PAK7 LGG Brain +PANK1 KIRC Kidney +PANK1 KIRP Kidney +PANK1 KICH Kidney +PANK1 LIHC Liver +PAPOLB TGCT Testis +PAQR6 GBM Brain +PAQR6 LGG Brain +PARD6G SKCM Skin +PARD6G STAD Stomach +PASD1 TGCT Testis +PATE1 PRAD Prostate +PAX2 KIRC Kidney +PAX2 KIRP Kidney +PAX2 KICH Kidney +PAX3 SKCM Skin +PAX6 PAAD Pancreas +PAX6 UVM Eye +PAX8 KIRC Kidney +PAX8 KIRP Kidney +PAX8 KICH Kidney +PAX8 OV Ovary +PAX8 THCA Thyroid_Gland +PBK TGCT Testis +PBLD KIRC Kidney +PBLD KIRP Kidney +PBLD KICH Kidney +PBLD LIHC Liver +PBX4 LUAD Lung +PBX4 LUSC Lung +PBX4 THCA Thyroid_Gland +PCBD1 LIHC Liver +PCBD1 PAAD Pancreas +PCBP4 UVM Eye +PCDH8 GBM Brain +PCDH8 LGG Brain +PCK1 KIRC Kidney +PCK1 KIRP Kidney +PCK1 KICH Kidney +PCK1 LIHC Liver +PCP2 TGCT Testis +PCP4 PRAD Prostate +PCP4 UVM Eye +PCSK2 PAAD Pancreas +PCSK2 THCA Thyroid_Gland +PCSK4 TGCT Testis +PCSK6 LIHC Liver +PCSK9 CESC Cervix +PCSK9 LIHC Liver +PDCD10 BLCA Bladder +PDCD10 LAML Bone_Marrow +PDCD4 PAAD Pancreas +PDCD4 THYM Thymus +PDCL2 TGCT Testis +PDE5A SARC Soft_Tissue +PDE6A TGCT Testis +PDE6A UVM Eye +PDE8B THCA Thyroid_Gland +PDE9A PRAD Prostate +PDGFD TGCT Testis +PDHA2 TGCT Testis +PDIA2 PAAD Pancreas +PDIA4 LIHC Liver +PDIA4 THCA Thyroid_Gland +PDILT STAD Stomach +PDILT TGCT Testis +PDX1 PAAD Pancreas +PDYN GBM Brain +PDYN LGG Brain +PDZD4 GBM Brain +PDZD4 LGG Brain +PDZD9 TGCT Testis +PDZK1 KIRC Kidney +PDZK1 KIRP Kidney +PDZK1 KICH Kidney +PDZK1IP1 KIRC Kidney +PDZK1IP1 KIRP Kidney +PDZK1IP1 KICH Kidney +PFN4 TGCT Testis +PGA3 STAD Stomach +PGA4 STAD Stomach +PGA5 STAD Stomach +PGC STAD Stomach +PGK2 TGCT Testis +PGLYRP2 LIHC Liver +PHF7 TGCT Testis +PHOX2A GBM Brain +PHOX2A LGG Brain +PHOX2A PCPG Adrenal_Gland +PHOX2A ACC Adrenal_Gland +PHOX2B COAD Colon +PHOX2B PCPG Adrenal_Gland +PHOX2B ACC Adrenal_Gland +PIAS2 TGCT Testis +PIH1D2 TGCT Testis +PIK3CG LAML Bone_Marrow +PIK3CG TGCT Testis +PIK3R3 BLCA Bladder +PIK3R3 TGCT Testis +PIP PRAD Prostate +PIP UVM Eye +PIPOX KIRC Kidney +PIPOX KIRP Kidney +PIPOX KICH Kidney +PIPOX LIHC Liver +PITX1 CESC Cervix +PITX1 ESCA  Esophagus +PITX2 UVM Eye +PIWIL1 TGCT Testis +PIWIL2 TGCT Testis +PKD2L2 TGCT Testis +PKDREJ TGCT Testis +PKHD1 KIRC Kidney +PKHD1 KIRP Kidney +PKHD1 KICH Kidney +PKHD1L1 THCA Thyroid_Gland +PKHD1L1 UCEC Uterus +PKHD1L1 UCS Uterus +PKLR KIRC Kidney +PKLR KIRP Kidney +PKLR KICH Kidney +PKLR LIHC Liver +PKP2 COAD Colon +PLA1A LUAD Lung +PLA1A LUSC Lung +PLA1A SKCM Skin +PLA2G12B KIRC Kidney +PLA2G12B KIRP Kidney +PLA2G12B KICH Kidney +PLA2G12B LIHC Liver +PLA2G1B PAAD Pancreas +PLA2G4E SKCM Skin +PLA2G4F PRAD Prostate +PLA2G4F SKCM Skin +PLA2G5 OV Ovary +PLA2G5 UVM Eye +PLA2G6 THCA Thyroid_Gland +PLAC8L1 TGCT Testis +PLAT SKCM Skin +PLCB2 LAML Bone_Marrow +PLCXD3 OV Ovary +PLCXD3 PAAD Pancreas +PLCZ1 TGCT Testis +PLD6 TGCT Testis +PLG KIRC Kidney +PLG KIRP Kidney +PLG KICH Kidney +PLG LIHC Liver +PLGLB1 LIHC Liver +PLGLB2 LIHC Liver +PLP1 GBM Brain +PLP1 LGG Brain +PLPP2 COAD Colon +PLPPR4 GBM Brain +PLPPR4 LGG Brain +PLSCR2 TGCT Testis +PM20D1 PAAD Pancreas +PM20D1 SKCM Skin +PMEL SKCM Skin +PMP2 GBM Brain +PMP2 LGG Brain +PNLDC1 TGCT Testis +PNLIP PAAD Pancreas +PNLIPRP1 LIHC Liver +PNLIPRP1 PAAD Pancreas +PNLIPRP2 PAAD Pancreas +PNMA5 TGCT Testis +PNMT PCPG Adrenal_Gland +PNMT ACC Adrenal_Gland +PNMT UVM Eye +PNPLA1 SKCM Skin +PNPLA3 COAD Colon +PNPLA3 LIHC Liver +PNPLA3 SKCM Skin +POC5 PRAD Prostate +POC5 TGCT Testis +POF1B COAD Colon +POF1B SKCM Skin +POF1B STAD Stomach +POLR2A TGCT Testis +POLR2D TGCT Testis +POM121L12 TGCT Testis +POM121L2 TGCT Testis +PON1 LIHC Liver +PON3 LIHC Liver +POTEC TGCT Testis +POTED TGCT Testis +POTEG PRAD Prostate +POTEH PRAD Prostate +POTEH TGCT Testis +POU2F3 COAD Colon +POU2F3 SKCM Skin +POU3F1 SKCM Skin +POU3F2 GBM Brain +POU3F2 LGG Brain +POU3F3 KIRC Kidney +POU3F3 KIRP Kidney +POU3F3 KICH Kidney +POU4F1 TGCT Testis +POU4F2 TGCT Testis +PP2D1 TGCT Testis +PPARGC1A KIRC Kidney +PPARGC1A KIRP Kidney +PPARGC1A KICH Kidney +PPARGC1A LIHC Liver +PPEF1 TGCT Testis +PPIL2 STAD Stomach +PPIL2 THCA Thyroid_Gland +PPIL6 TGCT Testis +PPM1E TGCT Testis +PPM1J TGCT Testis +PPP1R14D COAD Colon +PPP1R32 TGCT Testis +PPP1R36 PAAD Pancreas +PPP1R36 TGCT Testis +PPP2R2C GBM Brain +PPP2R2C LGG Brain +PPP3R2 TGCT Testis +PPY PAAD Pancreas +PRAC1 PRAD Prostate +PRAME SKCM Skin +PRAME TGCT Testis +PRDM7 TGCT Testis +PRDM8 PRAD Prostate +PRDM8 UVM Eye +PRDX1 BLCA Bladder +PRDX1 THCA Thyroid_Gland +PRDX3 BLCA Bladder +PRDX3 LAML Bone_Marrow +PRG4 LIHC Liver +PRG4 SARC Soft_Tissue +PRH2 ESCA  Esophagus +PRH2 TGCT Testis +PRKACG TGCT Testis +PRKCG GBM Brain +PRKCG LGG Brain +PRM1 TGCT Testis +PRM2 TGCT Testis +PRM3 TGCT Testis +PRND TGCT Testis +PROC LIHC Liver +PROCA1 TGCT Testis +PRODH2 KIRC Kidney +PRODH2 KIRP Kidney +PRODH2 KICH Kidney +PRODH2 LIHC Liver +PROZ KIRC Kidney +PROZ KIRP Kidney +PROZ KICH Kidney +PROZ LIHC Liver +PRPH2 TGCT Testis +PRPH2 UVM Eye +PRPS1L1 TGCT Testis +PRPS2 OV Ovary +PRPS2 TGCT Testis +PRR19 TGCT Testis +PRR23B TGCT Testis +PRR23C TGCT Testis +PRR30 TGCT Testis +PRR9 SKCM Skin +PRR9 TGCT Testis +PRSS1 PAAD Pancreas +PRSS21 CESC Cervix +PRSS21 TGCT Testis +PRSS27 ESCA  Esophagus +PRSS3 PAAD Pancreas +PRSS37 TGCT Testis +PRSS38 TGCT Testis +PRSS50 TGCT Testis +PRSS54 TGCT Testis +PRSS55 TGCT Testis +PRSS58 TGCT Testis +PSAT1 CESC Cervix +PSCA PRAD Prostate +PSCA STAD Stomach +PSD2 GBM Brain +PSD2 LGG Brain +PSEN1 COAD Colon +PSEN2 PRAD Prostate +PSMA4 BLCA Bladder +PSMA4 LAML Bone_Marrow +PSMA4 TGCT Testis +PSMA8 TGCT Testis +PSMC3IP SKCM Skin +PSMC3IP TGCT Testis +PSMD6 PAAD Pancreas +PSMD6 TGCT Testis +PSMG2 BLCA Bladder +PSMG2 TGCT Testis +PSORS1C1 TGCT Testis +PSORS1C2 SKCM Skin +PTCH2 TGCT Testis +PTCHD3 TGCT Testis +PTF1A PAAD Pancreas +PTGDR2 PAAD Pancreas +PTGDR2 STAD Stomach +PTGDR2 THYM Thymus +PTGDS TGCT Testis +PTGDS UVM Eye +PTGER1 KIRC Kidney +PTGER1 KIRP Kidney +PTGER1 KICH Kidney +PTGER3 KIRC Kidney +PTGER3 KIRP Kidney +PTGER3 KICH Kidney +PTGER3 UCEC Uterus +PTGER3 UCS Uterus +PTGS2 LUAD Lung +PTGS2 LUSC Lung +PTGS2 PRAD Prostate +PTK2B STAD Stomach +PTK6 CESC Cervix +PTK6 STAD Stomach +PTPN5 GBM Brain +PTPN5 LGG Brain +PTPN6 LAML Bone_Marrow +PTPRB KIRC Kidney +PTPRB KIRP Kidney +PTPRB KICH Kidney +PTPRB LUAD Lung +PTPRB LUSC Lung +PTPRR CESC Cervix +PTPRZ1 GBM Brain +PTPRZ1 LGG Brain +PTX3 KIRC Kidney +PTX3 KIRP Kidney +PTX3 KICH Kidney +PTX3 SKCM Skin +PXT1 TGCT Testis +PYCR1 PAAD Pancreas +PYCR1 STAD Stomach +PYDC1 SKCM Skin +PYHIN1 LAML Bone_Marrow +PZP LIHC Liver +QRICH2 TGCT Testis +QSOX1 LUAD Lung +QSOX1 LUSC Lung +RAB23 UCEC Uterus +RAB23 UCS Uterus +RAB37 LAML Bone_Marrow +RAB38 SKCM Skin +RAD21L1 TGCT Testis +RAD51AP1 BLCA Bladder +RAD51AP1 LAML Bone_Marrow +RAD51AP1 TGCT Testis +RAD51AP2 TGCT Testis +RAD9B TGCT Testis +RAE1 TGCT Testis +RAET1L ESCA  Esophagus +RAG1 TGCT Testis +RAG1 THYM Thymus +RAG2 TGCT Testis +RAG2 THCA Thyroid_Gland +RAG2 THYM Thymus +RASA1 LAML Bone_Marrow +RASGEF1B LUAD Lung +RASGEF1B LUSC Lung +RASGRP2 LAML Bone_Marrow +RASGRP4 LAML Bone_Marrow +RASL10A GBM Brain +RASL10A LGG Brain +RBM41 BLCA Bladder +RBM41 TGCT Testis +RBM44 TGCT Testis +RBM46 TGCT Testis +RBMXL2 TGCT Testis +RBMXL3 TGCT Testis +RBP4 LIHC Liver +RBP5 KIRC Kidney +RBP5 KIRP Kidney +RBP5 KICH Kidney +RBP7 BRCA Breast +RBP7 PAAD Pancreas +RBPJL PAAD Pancreas +RCL1 LIHC Liver +RDH11 PRAD Prostate +RDH12 SKCM Skin +RDH12 UVM Eye +RDH16 LIHC Liver +RDH16 STAD Stomach +RDH8 TGCT Testis +RDH8 UVM Eye +RDM1 TGCT Testis +REG1A PAAD Pancreas +REG1B PAAD Pancreas +REG3A PAAD Pancreas +REG3A STAD Stomach +REG3G PAAD Pancreas +REG3G TGCT Testis +REG4 COAD Colon +REG4 STAD Stomach +REN COAD Colon +REN KIRC Kidney +REN KIRP Kidney +REN KICH Kidney +REXO4 CESC Cervix +REXO4 TGCT Testis +RFC4 BLCA Bladder +RFC4 LAML Bone_Marrow +RFC4 TGCT Testis +RFPL1 GBM Brain +RFPL1 LGG Brain +RFPL2 PRAD Prostate +RFPL3 TGCT Testis +RFPL4B TGCT Testis +RFX2 LAML Bone_Marrow +RFX2 TGCT Testis +RFX2 THYM Thymus +RFX4 TGCT Testis +RFX6 PAAD Pancreas +RFX6 STAD Stomach +RGAG1 TGCT Testis +RGN KIRC Kidney +RGN KIRP Kidney +RGN KICH Kidney +RGPD3 TGCT Testis +RGPD4 TGCT Testis +RGPD8 TGCT Testis +RGR GBM Brain +RGR LGG Brain +RGR UVM Eye +RGS16 THCA Thyroid_Gland +RGS22 TGCT Testis +RGS8 GBM Brain +RGS8 LGG Brain +RGS8 TGCT Testis +RGSL1 TGCT Testis +RHAG LAML Bone_Marrow +RHAG LIHC Liver +RHBG KIRC Kidney +RHBG KIRP Kidney +RHBG KICH Kidney +RHBG OV Ovary +RHCE BLCA Bladder +RHCE LAML Bone_Marrow +RHCG ESCA  Esophagus +RHD LAML Bone_Marrow +RHOXF1 TGCT Testis +RHOXF2 TGCT Testis +RHOXF2B TGCT Testis +RHPN1 STAD Stomach +RHPN1 THCA Thyroid_Gland +RILP CESC Cervix +RILP THCA Thyroid_Gland +RIMBP3 TGCT Testis +RIMBP3B TGCT Testis +RIN3 LAML Bone_Marrow +RIT2 GBM Brain +RIT2 LGG Brain +RLBP1 GBM Brain +RLBP1 LGG Brain +RLBP1 UVM Eye +RLN1 PRAD Prostate +RMI1 LIHC Liver +RMI1 TGCT Testis +RNASE11 TGCT Testis +RNASE2 LAML Bone_Marrow +RNASE3 LAML Bone_Marrow +RNASE4 LIHC Liver +RNF113B TGCT Testis +RNF133 TGCT Testis +RNF138 TGCT Testis +RNF148 TGCT Testis +RNF151 TGCT Testis +RNF152 KIRC Kidney +RNF152 KIRP Kidney +RNF152 KICH Kidney +RNF17 TGCT Testis +RNF183 TGCT Testis +RNF183 UCEC Uterus +RNF183 UCS Uterus +RNF186 COAD Colon +RNF186 KIRC Kidney +RNF186 KIRP Kidney +RNF186 KICH Kidney +RNF186 PAAD Pancreas +RNF32 STAD Stomach +RNF32 TGCT Testis +RNFT1 TGCT Testis +ROPN1 TGCT Testis +ROPN1B TGCT Testis +ROPN1L TGCT Testis +RPA4 TGCT Testis +RPGRIP1 TGCT Testis +RPH3A GBM Brain +RPH3A LGG Brain +RPL10L TGCT Testis +RPL14 OV Ovary +RPL19 OV Ovary +RPL19 UVM Eye +RPL36AL LAML Bone_Marrow +RPL36AL OV Ovary +RPL37 OV Ovary +RPL38 LAML Bone_Marrow +RPL38 OV Ovary +RPL39L TGCT Testis +RPL4 CESC Cervix +RPL4 OV Ovary +RPS12 OV Ovary +RPS12 THYM Thymus +RPS18 OV Ovary +RPS27 LAML Bone_Marrow +RPS27 OV Ovary +RPS27 THYM Thymus +RPS29 LAML Bone_Marrow +RPS29 OV Ovary +RPS6KA4 CESC Cervix +RPS9 LAML Bone_Marrow +RPS9 OV Ovary +RSL24D1 BLCA Bladder +RSL24D1 LAML Bone_Marrow +RSPH1 PAAD Pancreas +RSPH1 TGCT Testis +RSPH6A TGCT Testis +RSPH9 TGCT Testis +RSPH9 UVM Eye +RTEL1 STAD Stomach +RTKN2 LUAD Lung +RTKN2 LUSC Lung +RTN1 GBM Brain +RTN1 LGG Brain +RTN4R CESC Cervix +RTN4R OV Ovary +RTP3 LIHC Liver +RUNDC3A GBM Brain +RUNDC3A LGG Brain +S100A12 LAML Bone_Marrow +S100A14 ESCA  Esophagus +S100A14 STAD Stomach +S100A2 BLCA Bladder +S100A2 ESCA  Esophagus +S100A8 LAML Bone_Marrow +S100A9 LAML Bone_Marrow +S100P STAD Stomach +SAA1 LIHC Liver +SAA2 LIHC Liver +SAA4 LIHC Liver +SAG TGCT Testis +SAG UVM Eye +SAGE1 TGCT Testis +SALL1 KIRC Kidney +SALL1 KIRP Kidney +SALL1 KICH Kidney +SALL1 THCA Thyroid_Gland +SAMD10 CESC Cervix +SAMD10 STAD Stomach +SAMD13 TGCT Testis +SASH3 LAML Bone_Marrow +SASH3 TGCT Testis +SATB2 COAD Colon +SAXO1 TGCT Testis +SAYSD1 TGCT Testis +SCGB1A1 LUAD Lung +SCGB1A1 LUSC Lung +SCGB1A1 PRAD Prostate +SCGB1D2 BRCA Breast +SCGB1D2 SKCM Skin +SCGB2A2 BRCA Breast +SCGB3A1 LUAD Lung +SCGB3A1 LUSC Lung +SCGB3A1 PRAD Prostate +SCGB3A2 LUAD Lung +SCGB3A2 LUSC Lung +SCML2 TGCT Testis +SCN2B GBM Brain +SCN2B LGG Brain +SCNN1G KIRC Kidney +SCNN1G KIRP Kidney +SCNN1G KICH Kidney +SCP2 BLCA Bladder +SCP2 LAML Bone_Marrow +SCP2 LIHC Liver +SCP2D1 TGCT Testis +SCRG1 TGCT Testis +SCTR LUAD Lung +SCTR LUSC Lung +SCTR PAAD Pancreas +SCUBE3 THCA Thyroid_Gland +SDCCAG3 CESC Cervix +SDCCAG3 TGCT Testis +SDHD BLCA Bladder +SDHD LAML Bone_Marrow +SDS LIHC Liver +SEC16B LIHC Liver +SEL1L2 TGCT Testis +SELT BLCA Bladder +SELT LAML Bone_Marrow +SEMA3D OV Ovary +SEMA3D THCA Thyroid_Gland +SEMA3G BRCA Breast +SEMA3G SARC Soft_Tissue +SEMG1 PRAD Prostate +SEMG2 PRAD Prostate +SENP1 TGCT Testis +SENP8 KIRC Kidney +SENP8 KIRP Kidney +SENP8 KICH Kidney +SENP8 TGCT Testis +SEPP1 LIHC Liver +SEPP1 SARC Soft_Tissue +SERPINA1 LIHC Liver +SERPINA10 LIHC Liver +SERPINA11 LIHC Liver +SERPINA12 SKCM Skin +SERPINA4 LIHC Liver +SERPINA4 PAAD Pancreas +SERPINA5 LIHC Liver +SERPINA5 TGCT Testis +SERPINA6 LIHC Liver +SERPINA7 LIHC Liver +SERPINB1 LAML Bone_Marrow +SERPINB4 LAML Bone_Marrow +SERPINB4 UCEC Uterus +SERPINB4 UCS Uterus +SERPINC1 LIHC Liver +SERPIND1 LIHC Liver +SERPINF2 KIRC Kidney +SERPINF2 KIRP Kidney +SERPINF2 KICH Kidney +SERPINF2 LIHC Liver +SERPINI2 PAAD Pancreas +SERTAD3 OV Ovary +SETD9 TGCT Testis +SFRP5 PAAD Pancreas +SFRP5 UVM Eye +SFTA2 LUAD Lung +SFTA2 LUSC Lung +SFTPA1 LUAD Lung +SFTPA1 LUSC Lung +SFTPA2 LUAD Lung +SFTPA2 LUSC Lung +SFTPB LUAD Lung +SFTPB LUSC Lung +SFTPC LUAD Lung +SFTPC LUSC Lung +SFTPD LUAD Lung +SFTPD LUSC Lung +SGK2 COAD Colon +SGK2 KIRC Kidney +SGK2 KIRP Kidney +SGK2 KICH Kidney +SGK2 LIHC Liver +SGOL1 LAML Bone_Marrow +SGOL2 TGCT Testis +SH2D1A LUAD Lung +SH2D1A LUSC Lung +SH2D1A THYM Thymus +SH3GL3 TGCT Testis +SH3TC2 LAML Bone_Marrow +SH3TC2 TGCT Testis +SHCBP1 SARC Soft_Tissue +SHCBP1 TGCT Testis +SHCBP1L TGCT Testis +SHISA2 KIRC Kidney +SHISA2 KIRP Kidney +SHISA2 KICH Kidney +SHISA2 THCA Thyroid_Gland +SIGLEC11 OV Ovary +SIGLEC5 TGCT Testis +SIGLECL1 TGCT Testis +SIM1 KIRC Kidney +SIM1 KIRP Kidney +SIM1 KICH Kidney +SIM1 PAAD Pancreas +SIM2 COAD Colon +SIM2 KIRC Kidney +SIM2 KIRP Kidney +SIM2 KICH Kidney +SIM2 OV Ovary +SIRPB1 LAML Bone_Marrow +SIRPD TGCT Testis +SLC10A1 LIHC Liver +SLC12A1 KIRC Kidney +SLC12A1 KIRP Kidney +SLC12A1 KICH Kidney +SLC12A3 CESC Cervix +SLC12A3 KIRC Kidney +SLC12A3 KIRP Kidney +SLC12A3 KICH Kidney +SLC12A5 GBM Brain +SLC12A5 LGG Brain +SLC12A5 UVM Eye +SLC13A1 KIRC Kidney +SLC13A1 KIRP Kidney +SLC13A1 KICH Kidney +SLC13A3 KIRC Kidney +SLC13A3 KIRP Kidney +SLC13A3 KICH Kidney +SLC13A5 LIHC Liver +SLC14A1 PRAD Prostate +SLC16A12 KIRC Kidney +SLC16A12 KIRP Kidney +SLC16A12 KICH Kidney +SLC16A12 PAAD Pancreas +SLC16A6 CESC Cervix +SLC16A6 SKCM Skin +SLC16A7 KIRC Kidney +SLC16A7 KIRP Kidney +SLC16A7 KICH Kidney +SLC16A7 TGCT Testis +SLC17A1 KIRC Kidney +SLC17A1 KIRP Kidney +SLC17A1 KICH Kidney +SLC17A2 LIHC Liver +SLC17A3 KIRC Kidney +SLC17A3 KIRP Kidney +SLC17A3 KICH Kidney +SLC17A4 COAD Colon +SLC17A4 LIHC Liver +SLC17A7 GBM Brain +SLC17A7 LGG Brain +SLC1A2 GBM Brain +SLC1A2 LGG Brain +SLC22A1 LIHC Liver +SLC22A10 LIHC Liver +SLC22A12 KIRC Kidney +SLC22A12 KIRP Kidney +SLC22A12 KICH Kidney +SLC22A13 KIRC Kidney +SLC22A13 KIRP Kidney +SLC22A13 KICH Kidney +SLC22A14 TGCT Testis +SLC22A16 TGCT Testis +SLC22A18 COAD Colon +SLC22A18AS COAD Colon +SLC22A2 KIRC Kidney +SLC22A2 KIRP Kidney +SLC22A2 KICH Kidney +SLC22A25 LIHC Liver +SLC22A31 LUAD Lung +SLC22A31 LUSC Lung +SLC22A6 KIRC Kidney +SLC22A6 KIRP Kidney +SLC22A6 KICH Kidney +SLC22A7 KIRC Kidney +SLC22A7 KIRP Kidney +SLC22A7 KICH Kidney +SLC22A7 LIHC Liver +SLC22A8 KIRC Kidney +SLC22A8 KIRP Kidney +SLC22A8 KICH Kidney +SLC22A9 LIHC Liver +SLC23A1 KIRC Kidney +SLC23A1 KIRP Kidney +SLC23A1 KICH Kidney +SLC24A2 GBM Brain +SLC24A2 LGG Brain +SLC25A18 LIHC Liver +SLC25A2 TGCT Testis +SLC25A31 TGCT Testis +SLC25A47 LIHC Liver +SLC25A52 TGCT Testis +SLC26A3 COAD Colon +SLC26A4 THCA Thyroid_Gland +SLC26A7 KIRC Kidney +SLC26A7 KIRP Kidney +SLC26A7 KICH Kidney +SLC26A7 PRAD Prostate +SLC26A7 THCA Thyroid_Gland +SLC26A8 TGCT Testis +SLC26A9 PAAD Pancreas +SLC26A9 STAD Stomach +SLC27A2 KIRC Kidney +SLC27A2 KIRP Kidney +SLC27A2 KICH Kidney +SLC27A2 LIHC Liver +SLC27A5 LIHC Liver +SLC28A1 KIRC Kidney +SLC28A1 KIRP Kidney +SLC28A1 KICH Kidney +SLC28A1 LIHC Liver +SLC28A1 STAD Stomach +SLC2A12 PRAD Prostate +SLC2A12 STAD Stomach +SLC2A14 TGCT Testis +SLC2A2 KIRC Kidney +SLC2A2 KIRP Kidney +SLC2A2 KICH Kidney +SLC2A2 LIHC Liver +SLC2A3 TGCT Testis +SLC2A5 TGCT Testis +SLC2A9 KIRC Kidney +SLC2A9 KIRP Kidney +SLC2A9 KICH Kidney +SLC30A2 KIRC Kidney +SLC30A2 KIRP Kidney +SLC30A2 KICH Kidney +SLC30A2 PAAD Pancreas +SLC30A3 TGCT Testis +SLC30A4 PRAD Prostate +SLC30A8 PAAD Pancreas +SLC34A1 KIRC Kidney +SLC34A1 KIRP Kidney +SLC34A1 KICH Kidney +SLC34A2 LUAD Lung +SLC34A2 LUSC Lung +SLC34A2 OV Ovary +SLC35E2 COAD Colon +SLC35E2 THYM Thymus +SLC35G3 TGCT Testis +SLC35G6 TGCT Testis +SLC36A2 KIRC Kidney +SLC36A2 KIRP Kidney +SLC36A2 KICH Kidney +SLC36A3 TGCT Testis +SLC38A3 LIHC Liver +SLC38A3 UVM Eye +SLC38A4 LIHC Liver +SLC39A12 GBM Brain +SLC39A12 LGG Brain +SLC39A2 PRAD Prostate +SLC39A2 SKCM Skin +SLC39A4 OV Ovary +SLC3A1 KIRC Kidney +SLC3A1 KIRP Kidney +SLC3A1 KICH Kidney +SLC44A3 COAD Colon +SLC44A3 STAD Stomach +SLC45A2 SKCM Skin +SLC45A3 PRAD Prostate +SLC47A2 KIRC Kidney +SLC47A2 KIRP Kidney +SLC47A2 KICH Kidney +SLC4A1 LAML Bone_Marrow +SLC4A1 LIHC Liver +SLC4A11 OV Ovary +SLC4A11 THCA Thyroid_Gland +SLC4A4 KIRC Kidney +SLC4A4 KIRP Kidney +SLC4A4 KICH Kidney +SLC4A4 PAAD Pancreas +SLC4A9 KIRC Kidney +SLC4A9 KIRP Kidney +SLC4A9 KICH Kidney +SLC51A LIHC Liver +SLC51A TGCT Testis +SLC51B COAD Colon +SLC51B KIRC Kidney +SLC51B KIRP Kidney +SLC51B KICH Kidney +SLC5A10 CESC Cervix +SLC5A10 KIRC Kidney +SLC5A10 KIRP Kidney +SLC5A10 KICH Kidney +SLC5A12 KIRC Kidney +SLC5A12 KIRP Kidney +SLC5A12 KICH Kidney +SLC5A2 KIRC Kidney +SLC5A2 KIRP Kidney +SLC5A2 KICH Kidney +SLC5A2 TGCT Testis +SLC5A6 LIHC Liver +SLC5A6 STAD Stomach +SLC5A8 KIRC Kidney +SLC5A8 KIRP Kidney +SLC5A8 KICH Kidney +SLC5A8 THCA Thyroid_Gland +SLC6A13 KIRC Kidney +SLC6A13 KIRP Kidney +SLC6A13 KICH Kidney +SLC6A16 TGCT Testis +SLC6A18 KIRC Kidney +SLC6A18 KIRP Kidney +SLC6A18 KICH Kidney +SLC6A20 COAD Colon +SLC7A1 ESCA  Esophagus +SLC7A1 SKCM Skin +SLC7A13 KIRC Kidney +SLC7A13 KIRP Kidney +SLC7A13 KICH Kidney +SLC7A5 CESC Cervix +SLC7A5 TGCT Testis +SLC8A2 GBM Brain +SLC8A2 LGG Brain +SLC8A2 PAAD Pancreas +SLC9A2 COAD Colon +SLC9A2 STAD Stomach +SLC9A4 STAD Stomach +SLC9B1 TGCT Testis +SLC9C2 TGCT Testis +SLCO1B1 LIHC Liver +SLCO1B3 LIHC Liver +SLCO1C1 GBM Brain +SLCO1C1 LGG Brain +SLCO4C1 KIRC Kidney +SLCO4C1 KIRP Kidney +SLCO4C1 KICH Kidney +SLCO6A1 TGCT Testis +SLFNL1 TGCT Testis +SLIT1 GBM Brain +SLIT1 LGG Brain +SLITRK1 GBM Brain +SLITRK1 LGG Brain +SLPI CESC Cervix +SLPI UCEC Uterus +SLPI UCS Uterus +SMAD2 BLCA Bladder +SMC1B TGCT Testis +SMC3 TGCT Testis +SMC4 BLCA Bladder +SMC4 TGCT Testis +SMCP TGCT Testis +SMIM2 TGCT Testis +SMIM21 TGCT Testis +SMKR1 TGCT Testis +SMOX CESC Cervix +SMPD2 STAD Stomach +SMPD2 TGCT Testis +SNAP25 GBM Brain +SNAP25 LGG Brain +SNCB GBM Brain +SNCB LGG Brain +SNCB UVM Eye +SNTG2 TGCT Testis +SNX16 TGCT Testis +SNX20 LAML Bone_Marrow +SNX6 OV Ovary +SOCS3 OV Ovary +SOCS3 SARC Soft_Tissue +SOHLH2 TGCT Testis +SORBS1 SARC Soft_Tissue +SOX3 GBM Brain +SOX3 LGG Brain +SOX3 TGCT Testis +SOX30 TGCT Testis +SOX4 LAML Bone_Marrow +SOX4 OV Ovary +SOX4 THYM Thymus +SOX8 GBM Brain +SOX8 LGG Brain +SP110 LAML Bone_Marrow +SP8 GBM Brain +SP8 LGG Brain +SP8 PRAD Prostate +SPA17 TGCT Testis +SPACA1 TGCT Testis +SPACA3 TGCT Testis +SPACA4 TGCT Testis +SPACA7 TGCT Testis +SPAG17 LUAD Lung +SPAG17 LUSC Lung +SPAG17 TGCT Testis +SPAG6 TGCT Testis +SPAG8 TGCT Testis +SPAM1 TGCT Testis +SPANXA1 LIHC Liver +SPANXA1 TGCT Testis +SPANXA2 LIHC Liver +SPANXA2 TGCT Testis +SPANXC TGCT Testis +SPANXD TGCT Testis +SPANXN2 TGCT Testis +SPANXN3 TGCT Testis +SPANXN4 TGCT Testis +SPANXN5 TGCT Testis +SPATA12 TGCT Testis +SPATA16 TGCT Testis +SPATA17 TGCT Testis +SPATA18 TGCT Testis +SPATA19 TGCT Testis +SPATA21 TGCT Testis +SPATA22 TGCT Testis +SPATA24 TGCT Testis +SPATA3 TGCT Testis +SPATA31A6 TGCT Testis +SPATA31D1 TGCT Testis +SPATA31E1 TGCT Testis +SPATA32 TGCT Testis +SPATA4 TGCT Testis +SPATA8 TGCT Testis +SPATA9 TGCT Testis +SPATC1 TGCT Testis +SPDYA TGCT Testis +SPDYC LIHC Liver +SPDYC TGCT Testis +SPDYE1 TGCT Testis +SPDYE3 TGCT Testis +SPDYE4 TGCT Testis +SPEM1 TGCT Testis +SPERT TGCT Testis +SPESP1 TGCT Testis +SPHK1 CESC Cervix +SPHK1 SKCM Skin +SPINK1 PAAD Pancreas +SPINK1 STAD Stomach +SPINK2 TGCT Testis +SPO11 TGCT Testis +SPON1 LUAD Lung +SPON1 LUSC Lung +SPON1 OV Ovary +SPP1 KIRC Kidney +SPP1 KIRP Kidney +SPP1 KICH Kidney +SPP2 LIHC Liver +SPPL2C TGCT Testis +SPRR2A ESCA  Esophagus +SPRR2D ESCA  Esophagus +SPRR2E ESCA  Esophagus +SPRR2E SKCM Skin +SPRR2F ESCA  Esophagus +SPRR2F OV Ovary +SPRR2G SKCM Skin +SPRR3 ESCA  Esophagus +SPRR4 PRAD Prostate +SPRR4 SKCM Skin +SPTA1 LAML Bone_Marrow +SPTA1 LIHC Liver +SPZ1 TGCT Testis +SRD5A2 PRAD Prostate +SRP9 BLCA Bladder +SRP9 LAML Bone_Marrow +SSBP4 STAD Stomach +SSMEM1 TGCT Testis +SST PAAD Pancreas +SST STAD Stomach +SSTR1 CESC Cervix +SSTR1 STAD Stomach +SSX1 LAML Bone_Marrow +SSX1 SKCM Skin +SSX1 TGCT Testis +SSX2 SKCM Skin +SSX2 TGCT Testis +SSX3 TGCT Testis +ST6GAL2 TGCT Testis +ST6GAL2 THCA Thyroid_Gland +ST6GALNAC1 CESC Cervix +ST6GALNAC1 COAD Colon +ST7L TGCT Testis +ST8SIA6 BRCA Breast +ST8SIA6 LUAD Lung +ST8SIA6 LUSC Lung +STAC2 BRCA Breast +STAC2 UVM Eye +STARD6 TGCT Testis +STC1 KIRC Kidney +STC1 KIRP Kidney +STC1 KICH Kidney +STC1 THCA Thyroid_Gland +STEAP1 PRAD Prostate +STEAP2 COAD Colon +STEAP2 PRAD Prostate +STK31 TGCT Testis +STK32B KIRC Kidney +STK32B KIRP Kidney +STK32B KICH Kidney +STK32B TGCT Testis +STK32C CESC Cervix +STK32C TGCT Testis +STK33 TGCT Testis +STK35 TGCT Testis +STK35 THYM Thymus +STK40 STAD Stomach +STK40 TGCT Testis +STMN2 GBM Brain +STMN2 LGG Brain +STMN4 GBM Brain +STMN4 LGG Brain +STPG1 TGCT Testis +STX1A GBM Brain +STX1A LGG Brain +STX1A OV Ovary +STX1B GBM Brain +STX1B LGG Brain +STYXL1 OV Ovary +STYXL1 TGCT Testis +SULT1C2 KIRC Kidney +SULT1C2 KIRP Kidney +SULT1C2 KICH Kidney +SULT1C2 STAD Stomach +SULT2A1 LIHC Liver +SULT2B1 PRAD Prostate +SULT4A1 GBM Brain +SULT4A1 LGG Brain +SUMO2 BLCA Bladder +SUN3 TGCT Testis +SUN5 TGCT Testis +SUPV3L1 TGCT Testis +SUPV3L1 UCEC Uterus +SUPV3L1 UCS Uterus +SUV39H2 TGCT Testis +SUZ12 LAML Bone_Marrow +SUZ12 TGCT Testis +SVOP GBM Brain +SVOP LGG Brain +SWT1 TGCT Testis +SYCE1 TGCT Testis +SYCE1L TGCT Testis +SYCE2 TGCT Testis +SYCE3 TGCT Testis +SYCN PAAD Pancreas +SYCP1 TGCT Testis +SYCP2 STAD Stomach +SYCP2 TGCT Testis +SYCP3 TGCT Testis +SYN1 GBM Brain +SYN1 LGG Brain +SYN2 GBM Brain +SYN2 LGG Brain +SYNGR4 TGCT Testis +SYNPR GBM Brain +SYNPR LGG Brain +SYP GBM Brain +SYP LGG Brain +SYP UVM Eye +SYPL1 BLCA Bladder +SYT16 GBM Brain +SYT16 LGG Brain +SYT16 PAAD Pancreas +SYT16 TGCT Testis +SYT5 GBM Brain +SYT5 LGG Brain +SYT5 PAAD Pancreas +SYTL4 OV Ovary +TACO1 LIHC Liver +TACO1 THYM Thymus +TAF1L TGCT Testis +TAF7L TGCT Testis +TARP PRAD Prostate +TAS1R1 TGCT Testis +TAT LIHC Liver +TATDN1 BLCA Bladder +TATDN1 LAML Bone_Marrow +TBATA TGCT Testis +TBATA THYM Thymus +TBC1D21 TGCT Testis +TBC1D26 TGCT Testis +TBC1D28 TGCT Testis +TBC1D29 TGCT Testis +TBC1D3F TGCT Testis +TBC1D3G TGCT Testis +TBL2 TGCT Testis +TBL3 STAD Stomach +TBL3 TGCT Testis +TBPL1 LAML Bone_Marrow +TBPL1 TGCT Testis +TBR1 GBM Brain +TBR1 LGG Brain +TBX1 LUAD Lung +TBX1 LUSC Lung +TBX1 TGCT Testis +TBX5 LUAD Lung +TBX5 LUSC Lung +TCEB3B TGCT Testis +TCERG1L THCA Thyroid_Gland +TCF7 THYM Thymus +TCIRG1 LAML Bone_Marrow +TCP10 TGCT Testis +TCP10L TGCT Testis +TCP11 TGCT Testis +TCTE3 TGCT Testis +TDO2 LIHC Liver +TDRD1 TGCT Testis +TDRD10 TGCT Testis +TDRD10 UCEC Uterus +TDRD10 UCS Uterus +TDRD12 TGCT Testis +TDRD15 TGCT Testis +TDRD5 LUAD Lung +TDRD5 LUSC Lung +TDRD5 TGCT Testis +TDRD6 PRAD Prostate +TDRD6 TGCT Testis +TDRD9 PAAD Pancreas +TDRD9 TGCT Testis +TEKT1 TGCT Testis +TEKT3 TGCT Testis +TEKT5 TGCT Testis +TEPP PRAD Prostate +TEPP TGCT Testis +TERF2 TGCT Testis +TEX101 TGCT Testis +TEX12 TGCT Testis +TEX13A TGCT Testis +TEX14 TGCT Testis +TEX19 TGCT Testis +TEX26 TGCT Testis +TEX29 TGCT Testis +TEX30 BLCA Bladder +TEX30 TGCT Testis +TEX33 TGCT Testis +TEX35 TGCT Testis +TEX36 TGCT Testis +TEX37 TGCT Testis +TEX38 TGCT Testis +TEX40 TGCT Testis +TF LIHC Liver +TF UVM Eye +TFAP2B BRCA Breast +TFAP2B KIRC Kidney +TFAP2B KIRP Kidney +TFAP2B KICH Kidney +TFAP2B UVM Eye +TFDP3 TGCT Testis +TFEC KIRC Kidney +TFEC KIRP Kidney +TFEC KICH Kidney +TFEC LUAD Lung +TFEC LUSC Lung +TFF1 STAD Stomach +TFF2 STAD Stomach +TFF3 COAD Colon +TFF3 THCA Thyroid_Gland +TFR2 LIHC Liver +TG THCA Thyroid_Gland +TGFBI KIRC Kidney +TGFBI KIRP Kidney +TGFBI KICH Kidney +TGFBI SKCM Skin +TGIF2LX TGCT Testis +TGIF2LY TGCT Testis +TGM1 ESCA  Esophagus +TGM3 ESCA  Esophagus +TGM4 PRAD Prostate +THEG TGCT Testis +THEGL TGCT Testis +THEM5 SKCM Skin +THRSP LIHC Liver +THRSP SARC Soft_Tissue +THRSP SKCM Skin +TIMD4 TGCT Testis +TIMM17A BLCA Bladder +TIMM17A LAML Bone_Marrow +TIMM50 PAAD Pancreas +TIMM50 TGCT Testis +TIMM8A BLCA Bladder +TIMM8A LAML Bone_Marrow +TIMM8A LIHC Liver +TINAG KIRC Kidney +TINAG KIRP Kidney +TINAG KICH Kidney +TIPIN BLCA Bladder +TIPIN TGCT Testis +TKTL1 TGCT Testis +TKTL2 TGCT Testis +TLE6 STAD Stomach +TLE6 THCA Thyroid_Gland +TLX2 TGCT Testis +TLX2 UVM Eye +TM4SF4 LIHC Liver +TM4SF4 PAAD Pancreas +TM4SF5 KIRC Kidney +TM4SF5 KIRP Kidney +TM4SF5 KICH Kidney +TM4SF5 LIHC Liver +TMC5 COAD Colon +TMC5 STAD Stomach +TMCO2 TGCT Testis +TMCO5A TGCT Testis +TMED6 PAAD Pancreas +TMEFF1 GBM Brain +TMEFF1 LGG Brain +TMEFF1 TGCT Testis +TMEFF2 PRAD Prostate +TMEM100 LUAD Lung +TMEM100 LUSC Lung +TMEM100 OV Ovary +TMEM116 TGCT Testis +TMEM139 KIRC Kidney +TMEM139 KIRP Kidney +TMEM139 KICH Kidney +TMEM174 KIRC Kidney +TMEM174 KIRP Kidney +TMEM174 KICH Kidney +TMEM196 PAAD Pancreas +TMEM196 UVM Eye +TMEM202 TGCT Testis +TMEM207 KIRC Kidney +TMEM207 KIRP Kidney +TMEM207 KICH Kidney +TMEM210 TGCT Testis +TMEM217 TGCT Testis +TMEM220 LIHC Liver +TMEM220 PRAD Prostate +TMEM225 TGCT Testis +TMEM247 TGCT Testis +TMEM257 GBM Brain +TMEM257 LGG Brain +TMEM27 KIRC Kidney +TMEM27 KIRP Kidney +TMEM27 KICH Kidney +TMEM31 TGCT Testis +TMEM45A SKCM Skin +TMEM45B CESC Cervix +TMEM45B COAD Colon +TMEM52B KIRC Kidney +TMEM52B KIRP Kidney +TMEM52B KICH Kidney +TMEM54 COAD Colon +TMEM56 CESC Cervix +TMEM56 LIHC Liver +TMEM59L GBM Brain +TMEM59L LGG Brain +TMEM61 KIRC Kidney +TMEM61 KIRP Kidney +TMEM61 KICH Kidney +TMEM61 PAAD Pancreas +TMEM67 TGCT Testis +TMEM72 KIRC Kidney +TMEM72 KIRP Kidney +TMEM72 KICH Kidney +TMEM89 TGCT Testis +TMEM92 COAD Colon +TMEM92 STAD Stomach +TMEM95 TGCT Testis +TMEM99 TGCT Testis +TMIGD3 TGCT Testis +TMPRSS12 TGCT Testis +TMPRSS2 PRAD Prostate +TMPRSS3 COAD Colon +TMPRSS6 LIHC Liver +TNFRSF10B COAD Colon +TNKS2 BLCA Bladder +TNKS2 LAML Bone_Marrow +TNP1 TGCT Testis +TNP2 TGCT Testis +TNR GBM Brain +TNR LGG Brain +TOE1 CESC Cervix +TOE1 TGCT Testis +TOMM20L TGCT Testis +TOPAZ1 TGCT Testis +TP53TG5 TGCT Testis +TP63 BLCA Bladder +TP63 SKCM Skin +TPD52L3 TGCT Testis +TPMT LAML Bone_Marrow +TPMT THCA Thyroid_Gland +TPO THCA Thyroid_Gland +TPP2 TGCT Testis +TPPP2 TGCT Testis +TPRX1 TGCT Testis +TPSG1 COAD Colon +TPTE TGCT Testis +TPTE2 TGCT Testis +TRAF2 CESC Cervix +TRAF2 TGCT Testis +TRAPPC12 LAML Bone_Marrow +TRAPPC12 TGCT Testis +TREX2 SKCM Skin +TRIB3 CESC Cervix +TRIB3 LIHC Liver +TRIM10 LIHC Liver +TRIM29 BLCA Bladder +TRIM29 CESC Cervix +TRIM29 SKCM Skin +TRIM36 TGCT Testis +TRIM42 TGCT Testis +TRIM55 KIRC Kidney +TRIM55 KIRP Kidney +TRIM55 KICH Kidney +TRIM6 KIRC Kidney +TRIM6 KIRP Kidney +TRIM6 KICH Kidney +TRIM61 TGCT Testis +TRIM69 TGCT Testis +TRIM7 COAD Colon +TRIM71 TGCT Testis +TRIM74 STAD Stomach +TRIM74 TGCT Testis +TRIML1 TGCT Testis +TRIP13 TGCT Testis +TRMT6 CESC Cervix +TRMT6 TGCT Testis +TRPM1 SKCM Skin +TRPM1 TGCT Testis +TRPM3 SARC Soft_Tissue +TRPM3 UVM Eye +TRPM6 COAD Colon +TRPM6 KIRC Kidney +TRPM6 KIRP Kidney +TRPM6 KICH Kidney +TRPM8 PRAD Prostate +TRPV6 PAAD Pancreas +TRPV6 PRAD Prostate +TSACC TGCT Testis +TSC22D1 LUAD Lung +TSC22D1 LUSC Lung +TSGA10 TGCT Testis +TSGA10IP TGCT Testis +TSGA13 OV Ovary +TSGA13 TGCT Testis +TSHR OV Ovary +TSHR THCA Thyroid_Gland +TSKS TGCT Testis +TSNAXIP1 TGCT Testis +TSPAN1 COAD Colon +TSPAN1 KIRC Kidney +TSPAN1 KIRP Kidney +TSPAN1 KICH Kidney +TSPAN16 TGCT Testis +TSPY1 TGCT Testis +TSPY10 TGCT Testis +TSPY2 TGCT Testis +TSPY8 TGCT Testis +TSPYL2 OV Ovary +TSPYL6 TGCT Testis +TSSK1B TGCT Testis +TSSK2 TGCT Testis +TSSK4 TGCT Testis +TSSK6 TGCT Testis +TTC16 OV Ovary +TTC16 TGCT Testis +TTC21A TGCT Testis +TTC23L TGCT Testis +TTC26 OV Ovary +TTC26 TGCT Testis +TTC29 TGCT Testis +TTC30A LAML Bone_Marrow +TTC30A TGCT Testis +TTC8 OV Ovary +TTC8 PRAD Prostate +TTC9B GBM Brain +TTC9B LGG Brain +TTK TGCT Testis +TTLL10 TGCT Testis +TTLL2 TGCT Testis +TTLL5 TGCT Testis +TTLL6 TGCT Testis +TTLL8 TGCT Testis +TTLL9 TGCT Testis +TTPA LIHC Liver +TTR LIHC Liver +TTR PAAD Pancreas +TTYH1 GBM Brain +TTYH1 LGG Brain +TUB KIRC Kidney +TUB KIRP Kidney +TUB KICH Kidney +TUBA3C TGCT Testis +TUBA3D TGCT Testis +TUBA3E TGCT Testis +TUBB2B GBM Brain +TUBB2B LGG Brain +TUBG1 TGCT Testis +TULP2 TGCT Testis +TUSC5 BRCA Breast +TUSC5 SARC Soft_Tissue +TWIST1 BRCA Breast +TWIST1 SKCM Skin +TXNDC2 TGCT Testis +TXNDC8 TGCT Testis +TYR SKCM Skin +TYRP1 SKCM Skin +TYRP1 UVM Eye +TYSND1 LAML Bone_Marrow +TYSND1 OV Ovary +TYSND1 TGCT Testis +UBE2D3 THYM Thymus +UBE2U TGCT Testis +UBE2V2 BLCA Bladder +UBE2V2 LAML Bone_Marrow +UBL4B TGCT Testis +UBQLN3 TGCT Testis +UBQLNL TGCT Testis +UCN2 SKCM Skin +UGT1A3 LIHC Liver +UGT1A3 TGCT Testis +UGT1A4 LIHC Liver +UGT1A4 TGCT Testis +UGT1A6 TGCT Testis +UGT1A7 ESCA  Esophagus +UGT1A7 TGCT Testis +UGT2B10 LIHC Liver +UGT2B11 BRCA Breast +UGT2B11 LIHC Liver +UGT2B4 LIHC Liver +UGT2B7 KIRC Kidney +UGT2B7 KIRP Kidney +UGT2B7 KICH Kidney +UGT2B7 LIHC Liver +UGT3A1 KIRC Kidney +UGT3A1 KIRP Kidney +UGT3A1 KICH Kidney +UGT3A1 TGCT Testis +UGT3A2 SKCM Skin +UGT3A2 TGCT Testis +UMOD KIRC Kidney +UMOD KIRP Kidney +UMOD KICH Kidney +UNC5CL KIRC Kidney +UNC5CL KIRP Kidney +UNC5CL KICH Kidney +UNC93A SKCM Skin +UNC93A STAD Stomach +UPB1 KIRC Kidney +UPB1 KIRP Kidney +UPB1 KICH Kidney +UPB1 LIHC Liver +UPK3B UCEC Uterus +UPK3B UCS Uterus +UPP2 KIRC Kidney +UPP2 KIRP Kidney +UPP2 KICH Kidney +UROC1 LIHC Liver +USH1C CESC Cervix +USH1C COAD Colon +USH1G ESCA  Esophagus +USH1G TGCT Testis +USH2A UVM Eye +USP1 BLCA Bladder +USP1 TGCT Testis +USP2 TGCT Testis +USP29 TGCT Testis +USP44 TGCT Testis +USP50 TGCT Testis +USP6 TGCT Testis +UTF1 TGCT Testis +VCAM1 KIRC Kidney +VCAM1 KIRP Kidney +VCAM1 KICH Kidney +VCX TGCT Testis +VCX2 TGCT Testis +VCX3A TGCT Testis +VCX3B TGCT Testis +VCY TGCT Testis +VCY1B TGCT Testis +VGF GBM Brain +VGF LGG Brain +VGF PAAD Pancreas +VGF SKCM Skin +VIL1 COAD Colon +VIL1 LIHC Liver +VIT OV Ovary +VIT UVM Eye +VPS29 BLCA Bladder +VRK1 BLCA Bladder +VRK1 LAML Bone_Marrow +VRK1 TGCT Testis +VSIG1 STAD Stomach +VSIG1 TGCT Testis +VSIG2 STAD Stomach +VSTM1 LAML Bone_Marrow +VTCN1 BRCA Breast +VTCN1 OV Ovary +VTN LIHC Liver +VWA1 PAAD Pancreas +VWA3B TGCT Testis +VWCE LIHC Liver +VWCE OV Ovary +WBP2NL TGCT Testis +WBP5 BLCA Bladder +WBSCR28 TGCT Testis +WDR53 TGCT Testis +WDR62 TGCT Testis +WDR63 TGCT Testis +WDR64 TGCT Testis +WDR72 KIRC Kidney +WDR72 KIRP Kidney +WDR72 KICH Kidney +WDR72 THCA Thyroid_Gland +WDR87 TGCT Testis +WDR88 COAD Colon +WDR88 TGCT Testis +WFDC12 SKCM Skin +WFDC2 CESC Cervix +WFDC2 OV Ovary +WFDC2 UCEC Uterus +WFDC2 UCS Uterus +WFDC3 TGCT Testis +WFDC5 SKCM Skin +WFDC8 TGCT Testis +WFIKKN2 OV Ovary +WIF1 LUAD Lung +WIF1 LUSC Lung +WIF1 UVM Eye +WISP3 BLCA Bladder +WISP3 TGCT Testis +WNK3 TGCT Testis +WNK4 COAD Colon +WNK4 KIRC Kidney +WNK4 KIRP Kidney +WNK4 KICH Kidney +WNT3 SKCM Skin +WNT3 STAD Stomach +WNT5B PRAD Prostate +WT1 OV Ovary +WT1 UCEC Uterus +WT1 UCS Uterus +XAGE5 TGCT Testis +XKR3 TGCT Testis +XKR7 GBM Brain +XKR7 LGG Brain +XKR7 LUAD Lung +XKR7 LUSC Lung +XKR9 COAD Colon +XKR9 TGCT Testis +XRCC4 LAML Bone_Marrow +XRCC4 TGCT Testis +YBX2 TGCT Testis +ZBBX TGCT Testis +ZBTB20 TGCT Testis +ZBTB32 TGCT Testis +ZC2HC1B TGCT Testis +ZC2HC1C TGCT Testis +ZCCHC13 TGCT Testis +ZCWPW1 TGCT Testis +ZDHHC19 TGCT Testis +ZDHHC22 GBM Brain +ZDHHC22 LGG Brain +ZFAND4 TGCT Testis +ZFP64 SARC Soft_Tissue +ZFPM1 LUAD Lung +ZFPM1 LUSC Lung +ZFPM1 TGCT Testis +ZFY KIRC Kidney +ZFY KIRP Kidney +ZFY KICH Kidney +ZFY TGCT Testis +ZG16 COAD Colon +ZMAT4 THCA Thyroid_Gland +ZMAT4 UVM Eye +ZMYND10 TGCT Testis +ZMYND12 TGCT Testis +ZMYND15 TGCT Testis +ZNF100 TGCT Testis +ZNF165 TGCT Testis +ZNF185 ESCA  Esophagus +ZNF185 LAML Bone_Marrow +ZNF2 CESC Cervix +ZNF2 TGCT Testis +ZNF280B SKCM Skin +ZNF280B TGCT Testis +ZNF282 CESC Cervix +ZNF282 PRAD Prostate +ZNF286A LAML Bone_Marrow +ZNF286A OV Ovary +ZNF35 PRAD Prostate +ZNF35 TGCT Testis +ZNF365 GBM Brain +ZNF365 LGG Brain +ZNF385A LAML Bone_Marrow +ZNF385A SKCM Skin +ZNF385A UVM Eye +ZNF467 LUAD Lung +ZNF467 LUSC Lung +ZNF467 THYM Thymus +ZNF473 TGCT Testis +ZNF479 TGCT Testis +ZNF541 TGCT Testis +ZNF560 TGCT Testis +ZNF563 TGCT Testis +ZNF564 COAD Colon +ZNF564 LUAD Lung +ZNF564 LUSC Lung +ZNF565 PAAD Pancreas +ZNF565 TGCT Testis +ZNF57 TGCT Testis +ZNF571 TGCT Testis +ZNF571 THCA Thyroid_Gland +ZNF578 TGCT Testis +ZNF610 TGCT Testis +ZNF610 UVM Eye +ZNF613 PRAD Prostate +ZNF628 SKCM Skin +ZNF628 TGCT Testis +ZNF645 TGCT Testis +ZNF679 TGCT Testis +ZNF683 TGCT Testis +ZNF709 LUAD Lung +ZNF709 LUSC Lung +ZNF709 TGCT Testis +ZNF728 TGCT Testis +ZNF729 TGCT Testis +ZNF761 PRAD Prostate +ZNF789 STAD Stomach +ZNF789 TGCT Testis +ZNF804B THCA Thyroid_Gland +ZNF827 PRAD Prostate +ZNF829 TGCT Testis +ZNF85 TGCT Testis +ZNF92 OV Ovary +ZNRF4 TGCT Testis +ZPBP TGCT Testis +ZPBP2 TGCT Testis +ZSCAN5B TGCT Testis +ZSWIM2 TGCT Testis +ZWILCH TGCT Testis diff --git a/metabolic_tumor.r b/metabolic_tumor.r new file mode 100644 index 0000000..fca7315 --- /dev/null +++ b/metabolic_tumor.r @@ -0,0 +1,238 @@ +#!/usr/bin/Rscript +################################### Metabolic######################### +library(Seurat) +library(scater) +library(stringr) +library("Rtsne") +library(pheatmap) +library(RColorBrewer) +library(reshape2) +library(scales) +library(ggplot2) +library(dplyr) +library(ggrepel) +options(stringsAsFactors=FALSE) +library(gtools) +library(scran) + +source('/public/workspace/lily/software/SingleCellMetabolic/utils.R') +source('/public/workspace/lily/software/SingleCellMetabolic/runGSEA_preRank.R') + + +# read data +# all cell is tumor +tmp_data=readRDS("/public/workspace/lily/Lung2Brain/Version5/Data/Tumor_pur/pur_LCBM_clust.RDS") +all_data=as.matrix(tmp_data[['RNA']]@data) # make sure your data is correct size which can use as.matrix +cell_type=as.vector(tmp_data$seurat_clusters) # cell type which means cluster or cell type +tumor=unname(tmp_data$orig.ident) # this do not need to change ,this means all cells need to analysis + + + +#======================================================================================================================= +col_data <- data.frame(tumor=tumor,cellType=as.character(cell_type),row.names=names(cell_type)) +pathways <- gmtPathways("/public/workspace/lily/software/SingleCellMetabolic/Data/KEGG_metabolism.gmt") +metabolics <- unique(as.vector(unname(unlist(pathways)))) +row_data <- data.frame(metabolic=rep(FALSE,nrow(all_data)),row.names = rownames(all_data)) +row_data[rownames(row_data)%in%metabolics,"metabolic"]=TRUE +#set SingleCellExperiment object which include expr matrix ,cluster info ,and gene info +sce <- SingleCellExperiment( + assays = all_data, + colData = col_data, + rowData = row_data +) + +selected_tumor_sce <- sce #the example code use "selected_tumor_sce" as the name +selected_tumor_metabolic_sce <- sce[rowData(sce)$metabolic,] # dims :1506 37637; 1506 metabolic genes + +######pheatmap show metabolic sce object +# ###### can not alloate size ? +# dat <- assay(selected_tumor_metabolic_sce) +# dist_dat <- cor(dat,method="spearman") +# hc <- hclust(as.dist(1-dist_dat),method="ward.D2") +# mycolor = colorRampPalette(c("white","red"))(10) +# mycolor=colorRampPalette(rev(brewer.pal(n = 7, name ="RdYlBu")))(8) +# col_row <- as.data.frame(colData(selected_tumor_metabolic_sce)[,"tumor",drop=F]) +# col_row$tumor <- factor(col_row$tumor) +# pdf("/public/workspace/lily/res_cor_inte/Metabolic/malignant_metabolic_correlationMatrix.pdf",width=5,height=4,onefile=T) +# pheatmap(dist_dat,show_rownames = F,show_colnames = F,cluster_rows = hc,cluster_cols =hc,annotation_row = col_row,color=mycolor, annotation_legend = T) +# dev.off() + +# metabolic landscape +# set.seed(12345) +# tsne_out <- Rtsne(t(assay(selected_tumor_metabolic_sce)),initial_dims=20,theta=0.0,perplexity = 30) +# tmp <- data.frame(x=tsne_out$Y[,1],y=tsne_out$Y[,2],group = colData(selected_tumor_metabolic_sce)$tumor) +# tmp$cluster=factor(gsub('[0-9]*','',tmp$group)) # delete the number of sample names +# g <- ggplot(tmp) + geom_point(aes(x, y, colour = cluster), size = 1) + +# labs(x = "tSNE1",y = "tSNE2") +theme_bw() + ggtitle("Rtsne") +# ggsave("/public/workspace/lily/res_cor_inte/Metabolic/tumor_metabolic_tsne_PM_PwoM_LM.png",g,width=4,height=3) + + + + +###################################### scRNA_pathway_activity ########################################## +pathway_file <- "/public/workspace/lily/software/SingleCellMetabolic/Data/KEGG_metabolism.gmt" +pathways <- gmtPathways(pathway_file) +pathway_names <- names(pathways) +all_cell_types <- as.vector(selected_tumor_metabolic_sce$cellType) +cell_types <- unique(all_cell_types) + +gene_pathway_number <- num_of_pathways(pathway_file,rownames(selected_tumor_metabolic_sce)[rowData(selected_tumor_metabolic_sce)$metabolic]) +set.seed(123) +normalization_method <- "Deconvolution" + +##Calculate the pathway activities +#mean ratio of genes in each pathway for each cell type +mean_expression_shuffle <- matrix(NA,nrow=length(pathway_names),ncol=length(cell_types),dimnames = list(pathway_names,cell_types)) +mean_expression_noshuffle <- matrix(NA,nrow=length(pathway_names),ncol=length(cell_types),dimnames = list(pathway_names,cell_types)) +###calculate the pvalues using shuffle method +pvalues_mat <- matrix(NA,nrow=length(pathway_names),ncol=length(cell_types),dimnames = (list(pathway_names, cell_types))) + +norm_tpm <- all_data + + +for(p in pathway_names){ + genes <- pathways[[p]] + genes_comm <- intersect(genes, rownames(norm_tpm)) + if(length(genes_comm) < 5) next + + pathway_metabolic_tpm <- norm_tpm[genes_comm, ] + pathway_metabolic_tpm <- pathway_metabolic_tpm[rowSums(pathway_metabolic_tpm)>0,] + + mean_exp_eachCellType <- apply(pathway_metabolic_tpm, 1, function(x)by(x, all_cell_types, mean)) + + #remove genes which are zeros in any celltype to avoid extreme ratio value + keep <- colnames(mean_exp_eachCellType)[colAlls(mean_exp_eachCellType>0.001)] + + if(length(keep)<3) next + + #using the loweset value to replace zeros for avoiding extreme ratio value + pathway_metabolic_tpm <- pathway_metabolic_tpm[keep,] + pathway_metabolic_tpm <- t( apply(pathway_metabolic_tpm,1,function(x) {x[x<=0] <- min(x[x>0]);x} )) + + + pathway_number_weight = 1 / gene_pathway_number[keep,] + # + mean_exp_eachCellType <- apply(pathway_metabolic_tpm, 1, function(x)by(x, all_cell_types, mean)) + ratio_exp_eachCellType <- t(mean_exp_eachCellType) / colMeans(mean_exp_eachCellType) + #exclude the extreme ratios + col_quantile <- apply(ratio_exp_eachCellType,2,function(x) quantile(x,na.rm=T)) + col_q1 <- col_quantile["25%",] + col_q3 <- col_quantile["75%",] + col_upper <- col_q3 * 3 + col_lower <- col_q1 / 3 + outliers <- apply(ratio_exp_eachCellType,1,function(x) {any( (x>col_upper)|(x1){ + pval <- sum(shuffle_results[,c] > mean_expression_shuffle[p,c]) / 5000 + }else if(mean_expression_shuffle[p,c]<1){ + pval <- sum(shuffle_results[,c] < mean_expression_shuffle[p,c]) / 5000 + } + if(pval>0.01) mean_expression_shuffle[p, c] <- NA ### NA is blank in heatmap + pvalues_mat[p,c] <- pval + } +} +all_NA <- rowAlls(is.na(mean_expression_shuffle)) +mean_expression_shuffle <- mean_expression_shuffle[!all_NA,] +#heatmap +dat <- mean_expression_shuffle + +sort_row <- c() +sort_column <- c() + +for(i in colnames(dat)){ + select_row <- which(rowMaxs(dat,na.rm = T) == dat[,i]) + tmp <- rownames(dat)[select_row][order(dat[select_row,i],decreasing = T)] + sort_row <- c(sort_row,tmp) +} +sort_column <- apply(dat[sort_row,],2,function(x) order(x)[nrow(dat)]) +sort_column <- names(sort_column) +dat[is.na(dat)] <- 1 +# 2020-12-28 lly annoatote +# lly think do not need to plot heatmap +# pdf("/public/workspace/lily/res_cor_inte/Metabolic/KEGGpathway_activity_heatmap.pdf",onefile=T,width=6,height=9) +# mybreaks <- c( +# seq(0.8, 0.85, length.out=23), +# seq(0.86, 1.05, length.out=43), +# seq(1.06, max(dat),length.out=34) +# ) +# color <- colorRampPalette(c("blue","white","red"))(100) +# pheatmap(dat[sort_row,sort_column],cluster_cols = F,cluster_rows = F, +# color=color,breaks=mybreaks,fontsize_row=6,fontsize_col=6,cellwidth=9,cellheight=6) + +# # default parmater +# mybreaks <- c( +# seq(0, 0.5, length.out=33), +# seq(0.51, 1.5, length.out=33), +# seq(1.51, max(dat),length.out=34) +# ) +# pheatmap(dat[sort_row,sort_column],cluster_cols = F,cluster_rows = F, +# color=color,breaks=mybreaks,fontsize_row=6,fontsize_col=6,cellwidth=9,cellheight=6) +# dev.off() + +write.table(mean_expression_noshuffle,file="/public/workspace/lily/Lung2Brain/Version5/Metabolism/KEGGpathway_activity_noshuffle.txt",row.names=T,col.names=T,quote=F,sep="\t") +write.table(mean_expression_shuffle,file="/public/workspace/lily/Lung2Brain/Version5/Metabolism/KEGGpathway_activity_shuffle.txt",row.names=T,col.names=T,quote=F,sep="\t") +write.table(pvalues_mat,file="/public/workspace/lily/Lung2Brain/Version5/Metabolism/KEGGpathway_activity_shuffle_pvalue.txt",row.names=T,col.names=T,quote=F,sep="\t") + +#boxplot show the distribution of pathway activity +# scRNA_dat <- as.data.frame(mean_expression_noshuffle) +# scRNA_dat$X <- NULL + +# scRNA_df <- melt(scRNA_dat) +# scRNA_df <- scRNA_df[!is.na(scRNA_df$value),] +# g <- ggplot(scRNA_df,aes(x=variable,y=value,fill=variable)) + +# scale_y_continuous(limits=c(0,3),breaks=0:3,labels=0:3)+ +# geom_violin(trim=F,size=0.2,show.legend = F,width=1.0) + labs(y=NULL,x=NULL) + +# stat_summary(fun.y = median,geom="point",size=1,color="blue")+ +# scale_fill_brewer(palette="Set2") + +# theme_classic() + +# theme(legend.position="none", +# axis.text.x=element_text(colour="black", size = 6,angle=45,hjust=1,vjust=1), +# axis.text.y=element_text(colour="black", size = 6), +# axis.line=element_line(size=0.2,color="black"), +# axis.ticks = element_line(colour = "black",size=0.2), +# panel.border = element_blank(), panel.background = element_blank(), +# axis.ticks.length= unit(.5, "mm")) + +# ggsave("/public/workspace/lily/res_cor_inte/Metabolic/pathway_activity_violinplot.pdf",g,width = 2.5,height=1.5,units="in",device="pdf",useDingbats=FALSE) + + + + + + + + + diff --git a/pySCENIC.sh b/pySCENIC.sh new file mode 100644 index 0000000..383b27f --- /dev/null +++ b/pySCENIC.sh @@ -0,0 +1,35 @@ +#!/usr/bin/sh + + + +# Run pySCENIC by pipeline +/.bio-apps/pipelines/pySCENIC_bf7ab7f7-ed4a-4157-8a56-f53c41ed8c59/run.sh -j inteS16_tumor \ +-a /public/workspace/lily/REF/scenic_ref/human/hs_hgnc_curated_tfs.txt \ +-b /public/workspace/lily/REF/scenic_ref/human/hg38.recommend.feather.list.txt \ +-c /public/workspace/lily/REF/scenic_ref/human/motifs-v9-nr.hgnc-m0.001-o0.0.tbl \ +-i /public/workspace/lily/Lung2Brain/Version6/Data/Tumor.RDS \ +-o /public/workspace/lily/Lung2Brain/Version6/PySCENIC/ -t 10 + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/sig_find_record.r b/sig_find_record.r new file mode 100644 index 0000000..936543a --- /dev/null +++ b/sig_find_record.r @@ -0,0 +1,389 @@ + +# 2021-4-29 +# calculate BMS signature +##################################################################################################################### +# code recording + +# 0. get tumor cell from Inte7_ann.RDS +##################################################################################################################### +library(Seurat) +dat <- readRDS("/public/workspace/lily/Lung2Brain/inte7/Data/inte7_ann.RDS") +tumor <- subset(dat,cells=which(dat$type=="maliganant"&dat$type_group=="LCBM")) +tumor$sample <- tumor$orig.ident +# re clustering +inte.list <- list() +samplelist <- unique(tumor$sample) +for(i in 1:length(samplelist)){ + tmp <- subset(tumor,cells=which(tumor$sample==samplelist[i])) + DefaultAssay(tmp) <- "RNA" + inte.list[[i]] <- tmp +} +integration.anchors <- FindIntegrationAnchors(object.list = inte.list) +inte <- IntegrateData(anchorset = integration.anchors) +#FindVariableFeatures +inte <- FindVariableFeatures(inte) +##Scaling the integrateda +all.genes <- rownames(inte) +inte <- ScaleData(inte, features = all.genes) +#PCA +inte <- RunPCA(inte) +#cluster +inte <- FindNeighbors(inte) +inte <- FindClusters(inte,resolution=0.5) +#TSNE +# if Umap can not use +inte <- RunTSNE(inte) +saveRDS(inte,file="/public/workspace/lily/Lung2Brain/inte7/Data/LCBM_tumor.RDS") +#==================================================================================================================== +library(Seurat) +dat <- readRDS("/public/workspace/lily/Lung2Brain/inte7/Data/inte7_ann.RDS") +tumor <- subset(dat,cells=which(dat$type=="maliganant"&dat$type_group=="LC")) +tumor$sample <- tumor$orig.ident +# re clustering +inte.list <- list() +samplelist <- unique(tumor$sample) +for(i in 1:length(samplelist)){ + tmp <- subset(tumor,cells=which(tumor$sample==samplelist[i])) + DefaultAssay(tmp) <- "RNA" + inte.list[[i]] <- tmp +} +integration.anchors <- FindIntegrationAnchors(object.list = inte.list,k.score= 100,k.filter= 100) +inte <- IntegrateData(anchorset = integration.anchors) +#FindVariableFeatures +inte <- FindVariableFeatures(inte) +##Scaling the integrateda +all.genes <- rownames(inte) +inte <- ScaleData(inte, features = all.genes) +#PCA +inte <- RunPCA(inte) +#cluster +inte <- FindNeighbors(inte) +inte <- FindClusters(inte,resolution=0.5) +#TSNE +# if Umap can not use +inte <- RunTSNE(inte) + +saveRDS(inte,file="/public/workspace/lily/Lung2Brain/inte7/Data/LC_tumor.RDS") + + + + + + + + + + + + +# 1. purify tumor cells with PTPRC +##################################################################################################################### +library(Seurat) +dat <- readRDS("/public/workspace/lily/Lung2Brain/inte7/Data/LCBM_tumor.RDS") +tmp.data <- dat[['RNA']]@data +sub.dat <- subset(dat,cells=which(tmp.data["PTPRC",]==0)) +saveRDS(sub.dat,file="/public/workspace/lily/Lung2Brain/version_4_27/pur_LCBM_tumor.RDS") +rm(list=ls()) +dat <- readRDS("/public/workspace/lily/Lung2Brain/inte7/Data/LC_tumor.RDS") +tmp.data <- dat[['RNA']]@data +sub.dat <- subset(dat,cells=which(tmp.data["PTPRC",]==0)) +saveRDS(sub.dat,file="/public/workspace/lily/Lung2Brain/version_4_27/pur_LC_tumor.RDS") + + + + + + + + + +# 2. re-integrtion and re-clustering tumor cells +##################################################################################################################### +# re-clustering +library(Seurat) +lcbm <- readRDS("/public/workspace/lily/Lung2Brain/version_4_27/pur_LCBM_tumor.RDS") +inte.list <- list() +samplelist <- unique(lcbm$sample) +for(i in 1:length(samplelist)){ + tmp <- subset(lcbm,cells=which(lcbm$sample==samplelist[i])) + DefaultAssay(tmp) <- "RNA" + inte.list[[i]] <- tmp +} +integration.anchors <- FindIntegrationAnchors(object.list = inte.list) +inte <- IntegrateData(anchorset = integration.anchors) +#FindVariableFeatures +inte <- FindVariableFeatures(inte) +##Scaling the integrateda +all.genes <- rownames(inte) +inte <- ScaleData(inte, features = all.genes) +#PCA +inte <- RunPCA(inte) +#cluster +inte <- FindNeighbors(inte) +inte <- FindClusters(inte,resolution=0.5) +#TSNE +# if Umap can not use +inte <- RunTSNE(inte) +saveRDS(inte,file="/public/workspace/lily/Lung2Brain/version_4_27/diedai/pur_LCBM_clust.RDS") +#===================================================================================================================== +luad <- readRDS("/public/workspace/lily/Lung2Brain/version_4_27/pur_LC_tumor.RDS") +inte.list <- list() +samplelist <- unique(luad$sample) +for(i in 1:length(samplelist)){ + tmp <- subset(luad,cells=which(luad$sample==samplelist[i])) + DefaultAssay(tmp) <- "RNA" + inte.list[[i]] <- tmp +} +integration.anchors <- FindIntegrationAnchors(object.list = inte.list,k.score= 50,k.filter= 50) +inte <- IntegrateData(anchorset = integration.anchors) +#FindVariableFeatures +inte <- FindVariableFeatures(inte) +##Scaling the integrateda +all.genes <- rownames(inte) +inte <- ScaleData(inte, features = all.genes) +#PCA +inte <- RunPCA(inte) +#cluster +inte <- FindNeighbors(inte) +inte <- FindClusters(inte,resolution=0.5) +#TSNE +# if Umap can not use +inte <- RunTSNE(inte) +saveRDS(inte,file="/public/workspace/lily/Lung2Brain/version_4_27/diedai/pur_LUAD_clust.RDS") + + + + + + + + + + + + + + + + +# 3. 0 calculate pathway to re-define +#################################################################################################################### +dat <- readRDS("/public/workspace/lily/Lung2Brain/version_4_27/diedai/pur_LCBM_clust.RDS") + +pathway<-c("Apoptosis","Autophagy","Chemokine_signaling_pathway","Circadian_rhythm","Cytosolic_DNA-sensing_pathway","Endocytosis", + "HALLMARK_ANGIOGENESIS","HALLMARK_EPITHELIAL_MESENCHYMAL_TRANSITION","HALLMARK_HYPOXIA","HALLMARK_IL6_JAK_STAT3_SIGNALING", + "HALLMARK_INFLAMMATORY_RESPONSE","KEGG_Focal_adhesion","KEGG_JAK-STAT_signaling_pathway","KEGG_MAPK_signaling_pathway", + "KEGG_NF-kappa_B_signaling_pathway","KEGG_Notch_signaling_pathway","KEGG_p53_signaling_pathway","KEGG_PI3K-Akt_signaling_pathway", + "KEGG_TGF-beta_signaling_pathway","KEGG_Wnt_signaling_pathway","Toll-like_receptor_signaling_pathway") + +source('~/software/ssGSEA/ssgseaMOD.r') +mod <- mod.analyze2(as.matrix(dat[['RNA']]@data),pathway,'/public/workspace/zhangtt/Project/PDAC/Mod/',permN=0) +mod <- as.data.frame(mod) +mod.f <- mod[,22:42] +mod.f$Cluster <- paste0("C",dat$seurat_clusters) +tmp.res <- aggregate(.~Cluster,data=mod.f,FUN=median) +rownames(tmp.res) <- tmp.res$Cluster +tmp.res$Cluster <- NULL +res.f <- t(tmp.res) +pdf("/public/workspace/lily/Lung2Brain/version_4_27/diedai/LCBM_metastasis_pathwway.pdf") +pheatmap::pheatmap(res.f,scale="row") +dev.off() +saveRDS(mod,file="/public/workspace/lily/Lung2Brain/version_4_27/diedai/LCBM_metastasis_pathway.RDS") + +# hallmark +#================================================================================================================== +rm(list=ls()) +dat <- readRDS("/public/workspace/lily/Lung2Brain/version_4_27/diedai/pur_LCBM_clust.RDS") +source('~/software/ssGSEA/ssgseaMOD.r') +modlist <- gsub("\\.mod","",dir("/public/workspace/lily/MOD_file/HALLMARK/")) +mod <- mod.analyze2(as.matrix(dat[['RNA']]@data),modlist,'/public/workspace/lily/MOD_file/HALLMARK/',permN=0) +mod <- data.frame(mod) +mod.f <- mod[,51:100] +mod.f$Cluster <- paste0("C",dat$seurat_clusters) +tmp.res <- aggregate(.~Cluster,data=mod.f,FUN=median) +rownames(tmp.res) <- tmp.res$Cluster +tmp.res$Cluster <- NULL +res.f <- t(tmp.res) +pdf("/public/workspace/lily/Lung2Brain/version_4_27/diedai/LCBM_Hallmark.pdf") +pheatmap::pheatmap(res.f,scale="row") +dev.off() +saveRDS(mod,file="/public/workspace/lily/Lung2Brain/version_4_27/diedai/LCBM_hallmark.RDS") + + + + + + +# 3.1 choose clusters +################################################################################################################## +# now calculate clusters +# +library(Seurat) +BM <- readRDS("/public/workspace/lily/Lung2Brain/version_4_27/diedai/pur_LCBM_clust.RDS") +LC <- readRDS("/public/workspace/lily/Lung2Brain/version_4_27/diedai/pur_LUAD_clust.RDS") + +data1 <- BM[['RNA']]@data +data2 <- LC[['RNA']]@data +data <-as.matrix(cbind(data1,data2)) + +res <- t(apply(data,1,function(x){ + tmp.BM <- x[1:7496] + tmp.LC <- x[7497:9403] + c( length(which(x[]>0))/length(x), + length(which(tmp.BM>0))/length(tmp.BM), # pct.BM + length(which(tmp.LC>0))/length(tmp.LC) # pct.LC + + ) + })) +res <- data.frame(res) +colnames(res) <- c("pct.all","pct.BM","pct.LC") +res$pct <- res$pct.BM -res$pct.LC +res.f <- res[which(res$pct.all > 0.05),] # pct.all means this gene need to expression in 5% cells +gene.up <- rownames(res.f)[which(res.f$pct>0)] +gene.down <- rownames(res.f)[which(res.f$pct<0)] + +################################################################################################################ +tmp1 <- data1[c(gene.up,gene.down),] +res_BM <- t(apply(tmp1,2,function(x){ + c( + length(which(names(x)%in%gene.up&x[]>0))/length(gene.up), + length(which(names(x)%in%gene.down&x[]>0))/length(gene.down) + ) +})) + +res_BM <- data.frame(res_BM) +colnames(res_BM) <- c("pct.up","pct.down") +res_BM$Cluster <- BM$seurat_clusters +res_BM$pct1 <- res_BM$pct.up-res_BM$pct.down +res_BM$pct2 <- res_BM$pct.up/res_BM$pct.down + +aggregate(.~Cluster,data=res_BM,FUN=median) -> tmp1 +tmp1[order(tmp1$pct1),] +# so choose cluster 4 and 7 +#=================================================================================================================== + +tmp2 <- data2[c(gene.up,gene.down),] +res_LC <- t(apply(tmp2,2,function(x){ + c( + length(which(names(x)%in%gene.up&x[]>0))/length(gene.up), + length(which(names(x)%in%gene.down&x[]>0))/length(gene.down) + ) + })) + +res_LC <- data.frame(res_LC) +colnames(res_LC) <- c("pct.up","pct.down") +res_LC$Cluster <- LC$seurat_clusters +res_LC$pct1 <- res_LC$pct.up-res_LC$pct.down +res_LC$pct2 <- res_LC$pct.up/res_LC$pct.down + +aggregate(.~Cluster,data=res_LC,FUN=median) -> tmp2 +tmp2[order(tmp2$pct1),] +# so choose 1 and 8 + + + + +# 4. calculate gene +####################################################################################################################### +# library(Seurat) +# BM <- readRDS("/public/workspace/lily/Lung2Brain/version_4_27/diedai/pur_LCBM_clust.RDS") +# LC <- readRDS("/public/workspace/lily/Lung2Brain/version_4_27/diedai/pur_LUAD_clust.RDS") +# subset1 <- subset(BM,cells=which(BM$seurat_clusters%in%c(4,7))) +# subset2 <- subset(LC,cells=which(LC$seurat_clusters%in%c(1,8))) +# tmp <- merge(subset1,subset2) +# # tmp$type_group <- "LC" +# # tmp$type_group[which(tmp$orig.ident%in%c("A20190305","A20190312","T-Bsc1"))] <- "LCBM" +# tmp@active.ident <- as.factor(tmp$type_group) +# geneset_tmp <- FindMarkers(tmp,ident.1="LCBM",ident.2="LC",assay="RNA",logfc.threshold = 0.1,min.pct = 0.1) +# geneset_tmp$pct <- geneset_tmp$pct.1-geneset_tmp$pct.2 +# geneset_tmp$pct1 <- geneset_tmp$pct.1/geneset_tmp$pct.2 + + + +# calculate by myself +BM <- readRDS("/public/workspace/lily/Lung2Brain/version_4_27/diedai/pur_LCBM_clust.RDS") +LC <- readRDS("/public/workspace/lily/Lung2Brain/version_4_27/diedai/pur_LUAD_clust.RDS") +subset1 <- subset(BM,cells=which(BM$seurat_clusters%in%c(4,7))) +subset2 <- subset(LC,cells=which(LC$seurat_clusters%in%c(1,8))) +tmp <- merge(subset1,subset2) +tmp.data <- as.matrix(tmp[['RNA']]@data) +idx.bm <- unname(which(tmp$type_group=="LCBM")) +idx.lc <- unname(which(tmp$type_group=="LC")) + +tmp.res <- t(apply(tmp.data,1,function(x){ + bm.exp <- mean(expm1(x[idx.bm])) + lc.exp <- mean(expm1(x[idx.lc])) + logfc <- log2(bm.exp/lc.exp) + pct.bm <- length(which(x[idx.bm]>0))/length(idx.bm) + pct.lc <- length(which(x[idx.lc]>0))/length(idx.lc) + p <- wilcox.test(expm1(x[idx.bm]),expm1(x[idx.lc]))$p.value + + c(bm.exp,lc.exp,logfc,pct.bm,pct.lc,p) +})) +tmp.res <- data.frame(tmp.res) +colnames(tmp.res) <- c("BM.exp","LC.exp","log2FC","pct.BM","pct.LC","pvalue") +tmp.res$p.adj <- p.adjust(tmp.res$pvalue) + + +res.f <- tmp.res[which(tmp.res$p.adj<0.01&tmp.res$pct.BM>0.01&tmp.res$pct.LC>0.01),] +res.f$pct1 <- res.f$pct.BM - res.f$pct.LC +res.f$pct2 <- res.f$pct.BM / res.f$pct.LC + + + +rownames(res.f[which(res.f$BM.exp>1&res.f$log2FC>2&res.f$pct1>0.25&res.f$pct2>5),]) -> gene + + +# > gene +# [1] "F3" "IER5" "ASPM" "G0S2" "TP53I3" "BCYRN1" +# [7] "SFTPB" "GLS" "ALCAM" "MGLL" "NMU" "HOPX" +# [13] "IL8" "CXCL2" "EREG" "FAM13A" "IRX2" "FCHO2" +# [19] "ELL2" "TGFBI" "HMMR" "MGAT4B" "SKAP2" "ANLN" +# [25] "STARD3NL" "UPP1" "TFPI2" "BRI3" "CAV1" "MET" +# [31] "CREB3L2" "RARRES2" "SH3KBP1" "PLS3" "CEBPD" "CHCHD7" +# [37] "LACTB2" "FAM83A" "PLEC" "LCN2" "PLAU" "SNCG" +# [43] "MYOF" "RGS10" "TAF10" "SAA1" "VEGFB" "BIRC3" +# [49] "SCNN1A" "GPRC5A" "LMO3" "KRT6A" "DUSP6" "RGCC" +# [55] "KLF5" "RNASE1" "MPRIP" "ITGA3" "COL1A1" "MXRA7" +# [61] "TK1" "BIRC5" "BAIAP2" "ALYREF" "TPX2" "TGM2" +# [67] "DMKN" "C19orf33" "CEACAM6" "NAPSA" "COL6A1" "COL6A2" +# > length(gene) +# [1] 72 + + +gene <- c('F3','IER5','ASPM','G0S2','TP53I3','BCYRN1','SFTPB','GLS','ALCAM','MGLL','NMU','HOPX','IL8','CXCL2','EREG','FAM13A','IRX2','FCHO2','ELL2','TGFBI','HMMR','MGAT4B','SKAP2','ANLN','STARD3NL','UPP1','TFPI2','BRI3','CAV1','MET','CREB3L2','RARRES2','SH3KBP1','PLS3','CEBPD','CHCHD7','LACTB2','FAM83A','PLEC','LCN2','PLAU','SNCG','MYOF','RGS10','TAF10','SAA1','VEGFB','BIRC3','SCNN1A','GPRC5A','LMO3','KRT6A','DUSP6','RGCC','KLF5','RNASE1','MPRIP','ITGA3','COL1A1','MXRA7','TK1','BIRC5','BAIAP2','ALYREF','TPX2','TGM2','DMKN','C19orf33','CEACAM6','NAPSA','COL6A1','COL6A2' +) +saveRDS(gene,file="/public/workspace/lily/Lung2Brain/Version5/Signature/BMS_update_gene.RDS") + + +# generate a mouse gene +tmp <- read.table("~/REF/Mouse_Human_gene.txt",sep="\t",header=T) +unique(as.vector(tmp[which(tmp$Human.gene.name%in%gene),1])) -> gene.m +source('/public/workspace/lily/software/ssGSEA/ssgseaMOD.r') +mod.generate(gene.m,"BMS_update.m",out=paste0("/public/workspace/lily/Lung2Brain/Version5/Signature/BMS_update.m.mod")) # make a mod file +mod.generate(gene.m,"BMS_update.m",out=paste0("/public/workspace/lily/MOD_file/BMS_update.m.mod")) # make a mod file + +# make a mod file +#================================================================================================= +source('/public/workspace/lily/software/ssGSEA/ssgseaMOD.r') +mod.generate(gene,"BMS_update",out=paste0("/public/workspace/lily/Lung2Brain/Version5/Signature/BMS_update.mod")) # make a mod file + +# source("/public/workspace/lily/Lung2Brain/version_4_27/sig_test.r") +# sigtest(gene,name="BMS") + + + + + + + + + + + + + + + + + diff --git a/sig_test.r b/sig_test.r new file mode 100644 index 0000000..d40eb7b --- /dev/null +++ b/sig_test.r @@ -0,0 +1,439 @@ +#!/usr/bin/Rscript + +# 2021-4-27 +# BMS test program +#======================================================================================================================================== +# 1. GSE126548 test +# 2. single cell test +# 3. TCGA survival +# 4. cell line test +# 5. TCGA satge test +# 6. KEGG pathway analysis +######################################################################################################################################## +sigtest <- function(gene,name="gene_test"){ + +# dir.create(paste0("/public/workspace/lily/Lung2Brain/version_4_27/sig_test/",name)) # 2021-4-30 change this code +respath <- paste0("/public/workspace/lily/Lung2Brain/Version5/Fig2/",name,"/") # 2021-4-30 change this code + +library(Seurat) +library(clusterProfiler) +library(org.Hs.eg.db) +require(DOSE) +require(doseplot) +library(igraph) +library(ggplot2) +library(survminer) +library(survival) +library(ggpubr) +library(dplyr) + +# 0. make a mod +source('/public/workspace/lily/software/ssGSEA/ssgseaMOD.r') +mod.generate(gene,name,out=paste0(respath,name,".mod")) # make a mod file + +# 1. GSE126548 +#============================================================================================================= +load('/public/workspace/lily/metastasis/data/verify/GSE126548/GSE126548_rpkm.RData') +gse126548_mod <- mod.analyze2(as.matrix(rpkm),name,respath,permN=0) +saveRDS(gse126548_mod,file=paste0(respath,'GSE126548_sig_mod.RDS')) + +# plot result +pdf(paste0(respath,'GSE126548_boxplot','.pdf'),useDingbats=F) # boxplot for GSE126548 (3 vs. 3) +boxplot(gse126548_mod[c(1:3),2],gse126548_mod[c(4:6),2],names=c('BM-','BM+'),main=paste0(name)) +legend('topright',legend=paste('p =',round(wilcox.test(gse126548_mod[c(1:3),2],gse126548_mod[c(4:6),2])$p.value,5)),bty='n') +dev.off() + + + +# 2. GSE123902 Single cell +#============================================================================================================== +GSE123902 <- readRDS("/public/workspace/lily/Lung2Brain/inte7/Pyscenic/GSE123902/primary_LUAD.RDS") +GSE123902$group <- "Early" +GSE123902$group[which(GSE123902$sample=="LX675")] <- "Advanced" +gse123902_mod <- mod.analyze2(as.matrix(GSE123902[['RNA']]@data),name,respath,permN=0) # calculate mod +gse123902_mod <- data.frame(gse123902_mod) +saveRDS(gse123902_mod,file=paste0(respath,'GSE123902_sig_mod.RDS')) + +# plot result +gse123902_mod$group <- GSE123902$group +pdf(paste0(respath,'GSE123902_boxplot','.pdf'),useDingbats=F) # boxplot for GSE123902 (Single cell ) +boxplot(gse123902_mod[,2]~group,data=gse123902_mod,FUN=median) +legend('topright',legend=paste('p =',round(wilcox.test(gse123902_mod[,2]~group,data=gse123902_mod,FUN=median)$p.value,5)),bty='n') +dev.off() + + + + + + +# 3. TCGA survival +#=============================================================================================================== +# use no normalized expression matrix (log2 trans) +# tmp <- read.table("/public/workspace/lily/metastasis/data/verify/TCGA_LUAD/LUAD_RNAseq_Exp.txt",header=T) +# rownames(tmp) <- tmp$sample +# tmp$sample <- NULL +# saveRDS(tmp,file="/public/workspace/lily/metastasis/data/verify/TCGA_LUAD/TCGA_LUAD_exp.RDS") +LUAD <- readRDS("/public/workspace/lily/metastasis/data/verify/TCGA_LUAD/TCGA_LUAD_exp.RDS") +LUAD_mod <- mod.analyze2(as.matrix(LUAD),name,respath,permN=0) # calculate mod +LUAD_mod <- data.frame(LUAD_mod) +saveRDS(LUAD_mod,file=paste0(respath,'TCGA_LUAD_sig_mod.RDS')) + +# clinical information +# load('/public/workspace/lily/metastasis/data/verify/TCGA_LUAD/TCGA_LUAD_anno.RData') +# saveRDS(luad_ann,file="/public/workspace/lily/metastasis/data/verify/TCGA_LUAD/TCGA_LUAD_anno.RDS") +# rm(ann) +luad_ann <- readRDS("/public/workspace/lily/metastasis/data/verify/TCGA_LUAD/TCGA_LUAD_anno.RDS") + +rownames(luad_ann) <- gsub("-",".",rownames(luad_ann)) +luad_ann <- luad_ann[colnames(LUAD),] + + +luad_ann$type_five <- rep("M",nrow(luad_ann)) +luad_ann[which(LUAD_mod[,2]>unname(quantile(LUAD_mod[,2],0.8))),"type_five"] <- "H" +luad_ann[which(LUAD_mod[,2]unname(quantile(LUAD_mod[,2],0.5))),"type_two"] <- "H" +# LUAD three quantile +luad_ann$type_three <- rep("M",nrow(luad_ann)) +luad_ann[which(LUAD_mod[,2]>unname(quantile(LUAD_mod[,2],0.67))),"type_three"] <- "H" +luad_ann[which(LUAD_mod[,2]unname(quantile(res.ff$BMS_test_norm,0.67)))] <- "High" +res.ff$OS_status <- 0 #change type info +res.ff$OS_status[which(res.ff$Status=="Dead")] <- 1 + +table(res.ff$type) +res.ff$mths_to_last_clinical_assessment <- as.numeric(as.vector(res.ff$mths_to_last_clinical_assessment)) +surv <- Surv(res.ff$mths_to_last_clinical_assessment,res.ff$OS_status) +km <- survfit(surv~type,data=res.ff) +pdf("./GSE68465.pdf") +ggsurvplot(km,pval=T,surv.median.line='hv')$plot+scale_colour_manual(values=c(rgb(251,176,59,maxColorValue = 255),rgb(140,198,63,maxColorValue = 255),'grey')) +dev.off() + + + + + + + + + + + + + + + + + + + +} + + + + + + + + + + + +# annotation +# record how GSE123902 primary data come from +################################################################################################################################### +filelist <- grep("csv",dir("~/metastasis/data/verify/GSE123904/GSE123902"),value=T) + +inte.list <- list() +for(i in 1:length(filelist)){ + tmp <- as.data.frame(data.table::fread(paste0("~/metastasis/data/verify/GSE123904/GSE123902/",filelist[i]))) + tmp$V1 <- paste0("Cell_",tmp$V1) + rownames(tmp) <- tmp$V1 + tmp$V1 <- NULL + tmp.f <- t(tmp) + + # create a seurat object + tmp.obj <- CreateSeuratObject(counts=tmp.f,project = strsplit(filelist[i],"_")[[1]][4]) + tmp.obj$sample <- as.vector(strsplit(filelist[i],"_")[[1]][3]) + tmp.obj$group <- as.vector(strsplit(filelist[i],"_")[[1]][4]) + tmp.obj = NormalizeData(object = tmp.obj) + tmp.obj <- FindVariableFeatures(object = tmp.obj) + + inte.list[i] <- tmp.obj +} + +integration.anchors <- FindIntegrationAnchors(object.list = inte.list) +inte <- IntegrateData(anchorset = integration.anchors) +#FindVariableFeatures +inte <- FindVariableFeatures(inte) +##Scaling the integrateda +all.genes <- rownames(inte) +inte <- ScaleData(inte, features = all.genes) +#PCA +inte <- RunPCA(inte) +#cluster +inte <- FindNeighbors(inte) +inte <- FindClusters(inte) +#TSNE +# if Umap can not use +inte <- RunTSNE(inte) +saveRDS(inte,file="/public/workspace/lily/metastasis/data/verify/GSE123904/GSE123902.RDS") + +# FeaturePlot(inte,features=c("EPCAM","EGFR"),label=T) +# show cluster 6 maybe tumor cell beacuse of EPCAM expression +# https://www.nature.com/articles/s41591-019-0750-6/figures/7 this show primary sample +# and this data do not have therapy data, do not have other metastasis and 2021-6-23 lly copy it to Version5/MSK_scRNA/ +#======================================================================================================================================= +dat <- readRDS("/public/workspace/lily/metastasis/data/verify/GSE123904/GSE123902.RDS") +sub.dat <- subset(dat,cells=which(dat$seurat_clusters==6&dat$group=="PRIMARY")) + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/signature_find.r b/signature_find.r new file mode 100644 index 0000000..de04487 --- /dev/null +++ b/signature_find.r @@ -0,0 +1,553 @@ + +# 2021-4-27 +# this program is used to find gene signature +######################################################################################################################################### +# use hallmark and other result has show C5 maybe the subgroup + +# 0. how LCBM tumor RDS come from . +library(Seurat) +dat <- readRDS("/public/workspace/lily/Lung2Brain/inte7/Data/inte7_ann.RDS") +tumor <- subset(dat,cells=which(dat$type=="maliganant"&dat$type_group=="LCBM")) +tumor$sample <- tumor$orig.ident +# re clustering +inte.list <- list() +samplelist <- unique(tumor$sample) +for(i in 1:length(samplelist)){ + tmp <- subset(tumor,cells=which(tumor$sample==samplelist[i])) + DefaultAssay(tmp) <- "RNA" + inte.list[[i]] <- tmp +} +integration.anchors <- FindIntegrationAnchors(object.list = inte.list) +inte <- IntegrateData(anchorset = integration.anchors) +#FindVariableFeatures +inte <- FindVariableFeatures(inte) +##Scaling the integrateda +all.genes <- rownames(inte) +inte <- ScaleData(inte, features = all.genes) +#PCA +inte <- RunPCA(inte) +#cluster +inte <- FindNeighbors(inte) +inte <- FindClusters(inte,resolution=0.5) +#TSNE +# if Umap can not use +inte <- RunTSNE(inte) + +saveRDS(inte,file="/public/workspace/lily/Lung2Brain/inte7/Data/LCBM_tumor.RDS") +#======================================================================================================= +library(Seurat) +dat <- readRDS("/public/workspace/lily/Lung2Brain/inte7/Data/inte7_ann.RDS") +tumor <- subset(dat,cells=which(dat$type=="maliganant"&dat$type_group=="LC")) +tumor$sample <- tumor$orig.ident +# re clustering +inte.list <- list() +samplelist <- unique(tumor$sample) +for(i in 1:length(samplelist)){ + tmp <- subset(tumor,cells=which(tumor$sample==samplelist[i])) + DefaultAssay(tmp) <- "RNA" + inte.list[[i]] <- tmp +} +integration.anchors <- FindIntegrationAnchors(object.list = inte.list,k.score= 100,k.filter= 100) +inte <- IntegrateData(anchorset = integration.anchors) +#FindVariableFeatures +inte <- FindVariableFeatures(inte) +##Scaling the integrateda +all.genes <- rownames(inte) +inte <- ScaleData(inte, features = all.genes) +#PCA +inte <- RunPCA(inte) +#cluster +inte <- FindNeighbors(inte) +inte <- FindClusters(inte,resolution=0.5) +#TSNE +# if Umap can not use +inte <- RunTSNE(inte) + +saveRDS(inte,file="/public/workspace/lily/Lung2Brain/inte7/Data/LC_tumor.RDS") + +library(Seurat) +dat <- readRDS("/public/workspace/lily/Lung2Brain/inte7/Data/LCBM_tumor.RDS") +tmp.data <- dat[['RNA']]@data +sub.dat <- subset(dat,cells=which(tmp.data["PTPRC",]==0)) +saveRDS(sub.dat,file="/public/workspace/lily/Lung2Brain/version_4_27/pur_LCBM_tumor.RDS") +rm(list=ls()) +dat <- readRDS("/public/workspace/lily/Lung2Brain/inte7/Data/LC_tumor.RDS") +tmp.data <- dat[['RNA']]@data +sub.dat <- subset(dat,cells=which(tmp.data["PTPRC",]==0)) +saveRDS(sub.dat,file="/public/workspace/lily/Lung2Brain/version_4_27/pur_LC_tumor.RDS") +#=========================================================================================================================================== +# try to find gene expression percentage +# purify tumor cell +lcbm <- readRDS("/public/workspace/lily/Lung2Brain/version_4_27/pur_LCBM_tumor.RDS") +luad <- readRDS("/public/workspace/lily/Lung2Brain/version_4_27/pur_LC_tumor.RDS") +index1 <- which(lcbm$seurat_clusters==5) # C5 cells +# index2 <- which(lcbm$seurat_clusters==2) # C2 cells +data1 <- as.matrix(lcbm[['RNA']]@data) +data2 <- as.matrix(luad[["RNA"]]@data) + + +res <- t(apply(data1,1,function(x){ + tmp.C5 <- x[index1] + tmp.other <- x[-index1] + c( length(which(x[]>0))/length(x), + length(which(tmp.C5>0))/length(tmp.C5), #pct.C5 + length(which(tmp.other>0))/length(tmp.other) #pct.other + + ) + })) +res <- data.frame(res) +colnames(res) <- c("pct.all","pct.C5","pct.other") +res$pct <- res$pct.C5 -res$pct.other +res.bm <- res[order(res$pct,decreasing=T),] +# head(res[which(res$pct.all>0.5&res$pct>0.1),]) + + + +# use Lung cancer to filter +data <-as.matrix(cbind(data1,data2)) + +res.filter <- t(apply(data,1,function(x){ + tmp.C5 <- x[index1] + tmp.BM <- x[1:7496] + tmp.LC <- x[7497:9403] + c( length(which(x[]>0))/length(x), + length(which(tmp.C5>0))/length(tmp.C5), + length(which(tmp.BM>0))/length(tmp.BM), #pct.BM + length(which(tmp.LC>0))/length(tmp.LC) #pct.LC + + ) + })) +res.filter <- data.frame(res.filter) +colnames(res.filter) <- c("pct.all","pct.C5","pct.BM","pct.LC") +res.filter$pct1 <- res.filter$pct.C5 -res.filter$pct.LC +res.filter$pct2 <- res.filter$pct.BM -res.filter$pct.LC + + +# another way to filter +#============================================================================ +# res <- t(apply(data2,1,function(x){ +# tmp.C2 <- x[index2] +# tmp.other <- x[-index2] +# c( length(which(x[]>0))/length(x), +# length(which(tmp.C2>0))/length(tmp.C2), #pct.C2 +# length(which(tmp.other>0))/length(tmp.other) #pct.other + +# ) +# })) +# res <- data.frame(res) +# colnames(res) <- c("pct.all","pct.C2","pct.other") +# res$pct <- res$pct.C2 -res$pct.other +# res.lc <- res[order(res$pct,decreasing=T),] +# head(res[which(res$pct.all>0.5&res$pct>0.1),]) + + + +# for(i in seq(from=0.55,to=0.9,by=0.05)){ +# for(j in seq(from=0.5,to=0.9,by=0.05)){ +# res.f <- res[which(res$pct.all>0.5&res$pct.C5>i&res$pct>0.10),] +# res.filter.f <- res.filter[which(res.filter$pct1>j&res.filter$pct2>0.5),] +# gene <- intersect(rownames(res.filter.f),rownames(res.f)) + +# if(length(grep("^MT-",gene))>0){ +# gene <- gene[-grep("^MT-",gene)] +# } +# if(length(gene)<=1){ +# next +# } +# source("/public/workspace/lily/Lung2Brain/version_4_27/sig_test.r") +# sigtest(gene,name=paste0("Type_0.5_",i,"_",j,"_gene")) + +# } +# } + + + +res.f <- res[which(res$pct.all>0.5&res$pct.C5>0.85&res$pct>0.10),] +res.filter.f <- res.filter[which(res.filter$pct1>0.7&res.filter$pct2>0.5),] +gene <- intersect(rownames(res.filter.f),rownames(res.f)) +gene <- gene[-grep("^MT-",gene)] + +write.table(gene,file="~/tmp.txt",sep="\t",row.names=F,quote=F,col.names=F) + + + + + +# test +source("/public/workspace/lily/Lung2Brain/version_4_27/sig_test.r") +sigtest(gene,name=paste0("Type_0.5_",i,"_",j,"_gene")) + + + + + + + + + +#===================================================================================================== +dat <- readRDS("/public/workspace/lily/Lung2Brain/inte7/Data/LC_tumor.RDS") +# Calculata Lung cancer hallmark + +pathway<-c("Apoptosis","Autophagy","Chemokine_signaling_pathway","Circadian_rhythm","Cytosolic_DNA-sensing_pathway","Endocytosis", + "HALLMARK_ANGIOGENESIS","HALLMARK_EPITHELIAL_MESENCHYMAL_TRANSITION","HALLMARK_HYPOXIA","HALLMARK_IL6_JAK_STAT3_SIGNALING", + "HALLMARK_INFLAMMATORY_RESPONSE","KEGG_Focal_adhesion","KEGG_JAK-STAT_signaling_pathway","KEGG_MAPK_signaling_pathway", + "KEGG_NF-kappa_B_signaling_pathway","KEGG_Notch_signaling_pathway","KEGG_p53_signaling_pathway","KEGG_PI3K-Akt_signaling_pathway", + "KEGG_TGF-beta_signaling_pathway","KEGG_Wnt_signaling_pathway","Toll-like_receptor_signaling_pathway") + +dat <- readRDS("/public/workspace/lily/Lung2Brain/inte7/Data/LC_tumor.RDS") +source('~/software/ssGSEA/ssgseaMOD.r') +mod <- mod.analyze2(as.matrix(dat[['RNA']]@data),pathway,'/public/workspace/zhangtt/Project/PDAC/Mod/',permN=0) +mod <- as.data.frame(mod) +mod.f <- mod[,22:42] +mod.f$Cluster <- paste0("C",dat$seurat_clusters) +tmp.res <- aggregate(.~Cluster,data=mod.f,FUN=median) +rownames(tmp.res) <- tmp.res$Cluster +tmp.res$Cluster <- NULL +res.f <- t(tmp.res) +pdf("/public/workspace/lily/Lung2Brain/version_4_27/LUAD_metastasis_pathwway.pdf") +pheatmap::pheatmap(res.f,scale="row") +dev.off() + +saveRDS(mod,file="/public/workspace/lily/Lung2Brain/inte7/res_Data/LC_metastasis_pathway.RDS") + + + + + +dat <- readRDS("/public/workspace/lily/Lung2Brain/inte7/Data/LC_tumor.RDS") +source('~/software/ssGSEA/ssgseaMOD.r') +modlist <- gsub("\\.mod","",dir("/public/workspace/lily/MOD_file/HALLMARK/")) +mod <- mod.analyze2(as.matrix(dat[['RNA']]@data),modlist,'/public/workspace/lily/MOD_file/HALLMARK/',permN=0) +mod <- data.frame(mod) +mod.f <- mod[,51:100] +mod.f$Cluster <- paste0("C",dat$seurat_clusters) +tmp.res <- aggregate(.~Cluster,data=mod.f,FUN=median) +rownames(tmp.res) <- tmp.res$Cluster +tmp.res$Cluster <- NULL +res.f <- t(tmp.res) +pdf("/public/workspace/lily/Lung2Brain/version_4_27/LC_Hallmark.pdf") +pheatmap::pheatmap(res.f,scale="row") +dev.off() +saveRDS(mod,file="/public/workspace/lily/Lung2Brain/inte7/res_Data/LC_hallmark.RDS") + + + + + + + + + + + + + + + + + + + + + + + + + + + +#============================================================================================================================================ +# diedai re-clustering +# 2021-4-28 +#============================================================================================================================================= +# re-clustering +library(Seurat) +lcbm <- readRDS("/public/workspace/lily/Lung2Brain/version_4_27/pur_LCBM_tumor.RDS") +inte.list <- list() +samplelist <- unique(lcbm$sample) +for(i in 1:length(samplelist)){ + tmp <- subset(lcbm,cells=which(lcbm$sample==samplelist[i])) + DefaultAssay(tmp) <- "RNA" + inte.list[[i]] <- tmp +} +integration.anchors <- FindIntegrationAnchors(object.list = inte.list) +inte <- IntegrateData(anchorset = integration.anchors) +#FindVariableFeatures +inte <- FindVariableFeatures(inte) +##Scaling the integrateda +all.genes <- rownames(inte) +inte <- ScaleData(inte, features = all.genes) +#PCA +inte <- RunPCA(inte) +#cluster +inte <- FindNeighbors(inte) +inte <- FindClusters(inte,resolution=0.5) +#TSNE +# if Umap can not use +inte <- RunTSNE(inte) +saveRDS(inte,file="/public/workspace/lily/Lung2Brain/version_4_27/diedai/pur_LCBM_clust.RDS") + +################################################################################################################## +luad <- readRDS("/public/workspace/lily/Lung2Brain/version_4_27/pur_LC_tumor.RDS") +inte.list <- list() +samplelist <- unique(luad$sample) +for(i in 1:length(samplelist)){ + tmp <- subset(luad,cells=which(luad$sample==samplelist[i])) + DefaultAssay(tmp) <- "RNA" + inte.list[[i]] <- tmp +} +integration.anchors <- FindIntegrationAnchors(object.list = inte.list,k.score= 50,k.filter= 50) +inte <- IntegrateData(anchorset = integration.anchors) +#FindVariableFeatures +inte <- FindVariableFeatures(inte) +##Scaling the integrateda +all.genes <- rownames(inte) +inte <- ScaleData(inte, features = all.genes) +#PCA +inte <- RunPCA(inte) +#cluster +inte <- FindNeighbors(inte) +inte <- FindClusters(inte,resolution=0.5) +#TSNE +# if Umap can not use +inte <- RunTSNE(inte) +saveRDS(inte,file="/public/workspace/lily/Lung2Brain/version_4_27/diedai/pur_LUAD_clust.RDS") + + + +#===================================================================================================================== +dat <- readRDS("/public/workspace/lily/Lung2Brain/version_4_27/diedai/pur_LCBM_clust.RDS") + +pathway<-c("Apoptosis","Autophagy","Chemokine_signaling_pathway","Circadian_rhythm","Cytosolic_DNA-sensing_pathway","Endocytosis", + "HALLMARK_ANGIOGENESIS","HALLMARK_EPITHELIAL_MESENCHYMAL_TRANSITION","HALLMARK_HYPOXIA","HALLMARK_IL6_JAK_STAT3_SIGNALING", + "HALLMARK_INFLAMMATORY_RESPONSE","KEGG_Focal_adhesion","KEGG_JAK-STAT_signaling_pathway","KEGG_MAPK_signaling_pathway", + "KEGG_NF-kappa_B_signaling_pathway","KEGG_Notch_signaling_pathway","KEGG_p53_signaling_pathway","KEGG_PI3K-Akt_signaling_pathway", + "KEGG_TGF-beta_signaling_pathway","KEGG_Wnt_signaling_pathway","Toll-like_receptor_signaling_pathway") + +source('~/software/ssGSEA/ssgseaMOD.r') +mod <- mod.analyze2(as.matrix(dat[['RNA']]@data),pathway,'/public/workspace/zhangtt/Project/PDAC/Mod/',permN=0) +mod <- as.data.frame(mod) +mod.f <- mod[,22:42] +mod.f$Cluster <- paste0("C",dat$seurat_clusters) +tmp.res <- aggregate(.~Cluster,data=mod.f,FUN=median) +rownames(tmp.res) <- tmp.res$Cluster +tmp.res$Cluster <- NULL +res.f <- t(tmp.res) +pdf("/public/workspace/lily/Lung2Brain/version_4_27/diedai/LCBM_metastasis_pathwway.pdf") +pheatmap::pheatmap(res.f,scale="row") +dev.off() + + + +# hallmark ======================================================================================================== +rm(list=ls()) +dat <- readRDS("/public/workspace/lily/Lung2Brain/version_4_27/diedai/pur_LCBM_clust.RDS") +source('~/software/ssGSEA/ssgseaMOD.r') +modlist <- gsub("\\.mod","",dir("/public/workspace/lily/MOD_file/HALLMARK/")) +mod <- mod.analyze2(as.matrix(dat[['RNA']]@data),modlist,'/public/workspace/lily/MOD_file/HALLMARK/',permN=0) +mod <- data.frame(mod) +mod.f <- mod[,51:100] +mod.f$Cluster <- paste0("C",dat$seurat_clusters) +tmp.res <- aggregate(.~Cluster,data=mod.f,FUN=median) +rownames(tmp.res) <- tmp.res$Cluster +tmp.res$Cluster <- NULL +res.f <- t(tmp.res) +pdf("/public/workspace/lily/Lung2Brain/version_4_27/diedai/LCBM_Hallmark.pdf") +pheatmap::pheatmap(res.f,scale="row") +dev.off() +saveRDS(mod,file="/public/workspace/lily/Lung2Brain/inte7/res_Data/LCBM_hallmark.RDS") + + + + + + + + + + + + + + + + + + + + + + + +# now calculate clusters +#===================================================================================================================== +library(Seurat) +BM <- readRDS("/public/workspace/lily/Lung2Brain/version_4_27/diedai/pur_LCBM_clust.RDS") +LC <- readRDS("/public/workspace/lily/Lung2Brain/version_4_27/diedai/pur_LUAD_clust.RDS") + +data1 <- BM[['RNA']]@data +data2 <- LC[['RNA']]@data +data <-as.matrix(cbind(data1,data2)) + +res <- t(apply(data,1,function(x){ + tmp.BM <- x[1:7496] + tmp.LC <- x[7497:9403] + c( length(which(x[]>0))/length(x), + length(which(tmp.BM>0))/length(tmp.BM), #pct.BM + length(which(tmp.LC>0))/length(tmp.LC) #pct.LC + + ) + })) +res <- data.frame(res) +colnames(res) <- c("pct.all","pct.BM","pct.LC") +res$pct <- res$pct.BM -res$pct.LC +res.f <- res[which(res$pct.all > 0.05),] +gene.up <- rownames(res.f)[which(res.f$pct>0)] +gene.down <- rownames(res.f)[which(res.f$pct<0)] + + +tmp1 <- data1[c(gene.up,gene.down),] +res_BM <- t(apply(tmp1,2,function(x){ + c( + length(which(names(x)%in%gene.up&x[]>0))/length(gene.up), + length(which(names(x)%in%gene.down&x[]>0))/length(gene.down) + ) + })) + +res_BM <- data.frame(res_BM) +colnames(res_BM) <- c("pct.up","pct.down") +res_BM$Cluster <- BM$seurat_clusters +res_BM$pct1 <- res_BM$pct.up-res_BM$pct.down +res_BM$pct2 <- res_BM$pct.up/res_BM$pct.down + +aggregate(.~Cluster,data=res_BM,FUN=median) -> tmp1 +tmp1[order(tmp1$pct1),] +# so choose cluster 4 and 7 +#=================================================================================================================== + +tmp2 <- data2[c(gene.up,gene.down),] +res_LC <- t(apply(tmp2,2,function(x){ + c( + length(which(names(x)%in%gene.up&x[]>0))/length(gene.up), + length(which(names(x)%in%gene.down&x[]>0))/length(gene.down) + ) + })) + +res_LC <- data.frame(res_LC) +colnames(res_LC) <- c("pct.up","pct.down") +res_LC$Cluster <- LC$seurat_clusters +res_LC$pct1 <- res_LC$pct.up-res_LC$pct.down +res_LC$pct2 <- res_LC$pct.up/res_LC$pct.down + +aggregate(.~Cluster,data=res_LC,FUN=median) -> tmp2 +tmp2[order(tmp2$pct1),] +# so choose 1 and 8 + + +###################################################################################################################### +library(Seurat) +BM <- readRDS("/public/workspace/lily/Lung2Brain/version_4_27/diedai/pur_LCBM_clust.RDS") +LC <- readRDS("/public/workspace/lily/Lung2Brain/version_4_27/diedai/pur_LUAD_clust.RDS") +subset1 <- subset(BM,cells=which(BM$seurat_clusters%in%c(4,7))) +subset2 <- subset(LC,cells=which(LC$seurat_clusters%in%c(1,8))) +tmp <- merge(subset1,subset2) +# tmp$type_group <- "LC" +# tmp$type_group[which(tmp$orig.ident%in%c("A20190305","A20190312","T-Bsc1"))] <- "LCBM" +tmp@active.ident <- as.factor(tmp$type_group) +geneset_tmp <- FindMarkers(tmp,ident.1="LCBM",ident.2="LC",assay="RNA",logfc.threshold = 0.1,min.pct = 0.1) +geneset_tmp$pct <- geneset_tmp$pct.1-geneset_tmp$pct.2 +geneset_tmp$pct1 <- geneset_tmp$pct.1/geneset_tmp$pct.2 + + + +# calculate by myself +BM <- readRDS("/public/workspace/lily/Lung2Brain/version_4_27/diedai/pur_LCBM_clust.RDS") +LC <- readRDS("/public/workspace/lily/Lung2Brain/version_4_27/diedai/pur_LUAD_clust.RDS") +subset1 <- subset(BM,cells=which(BM$seurat_clusters%in%c(4,7))) +subset2 <- subset(LC,cells=which(LC$seurat_clusters%in%c(1,8))) +tmp <- merge(subset1,subset2) +tmp.data <- as.matrix(tmp[['RNA']]@data) +idx.bm <- unname(which(tmp$type_group=="LCBM")) +idx.lc <- unname(which(tmp$type_group=="LC")) + +tmp.res <- t(apply(tmp.data,1,function(x){ + bm.exp <- mean(expm1(x[idx.bm])) + lc.exp <- mean(expm1(x[idx.lc])) + logfc <- log2(bm.exp/lc.exp) + pct.bm <- length(which(x[idx.bm]>0))/length(idx.bm) + pct.lc <- length(which(x[idx.lc]>0))/length(idx.lc) + p <- wilcox.test(expm1(x[idx.bm]),expm1(x[idx.lc]))$p.value + + c(bm.exp,lc.exp,logfc,pct.bm,pct.lc,p) +})) +tmp.res <- data.frame(tmp.res) +colnames(tmp.res) <- c("BM.exp","LC.exp","log2FC","pct.BM","pct.LC","pvalue") +tmp.res$p.adj <- p.adjust(tmp.res$pvalue) + + +res.f <- tmp.res[which(tmp.res$p.adj<0.01&tmp.res$pct.BM>0.01&tmp.res$pct.LC>0.01),] +res.f$pct1 <- res.f$pct.BM - res.f$pct.LC +res.f$pct2 <- res.f$pct.BM / res.f$pct.LC + + +gene <- rownames(geneset_tmp[which(geneset_tmp$avg_logFC>1&geneset_tmp$p_val_adj<0.01&geneset_tmp$pct>0.5&geneset_tmp$pct1>1),]) + +gene <- rownames(geneset_tmp[which(geneset_tmp$avg_logFC>1&geneset_tmp$p_val_adj<0.01&geneset_tmp$pct1>4),]) +if(length(grep("^MT-",gene))>0){ + gene <- gene[-grep("^MT-",gene)] +}else{ + gene <- gene +} + +write.table(gene,file="~/tmp.txt",sep="\t",row.names=F,quote=F,col.names=F) + +# test +source("/public/workspace/lily/Lung2Brain/version_4_27/sig_test.r") +sigtest(gene,name="BMS") + + + + +# test -5 +# rownames(res.f[which(res.f$BM.exp>1&res.f$log2FC>2&res.f$pct1>0.25&res.f$pct2>5),]) -> gene + + + + + + + +# test for gene_version5 +BM <- readRDS("/public/workspace/lily/Lung2Brain/version_4_27/diedai/pur_LCBM_clust.RDS") +LC <- readRDS("/public/workspace/lily/Lung2Brain/version_4_27/diedai/pur_LUAD_clust.RDS") + +data.bm <- BM[['RNA']]@data + +source('/public/workspace/lily/software/ssGSEA/ssgseaMOD.r') +mod <- mod.analyze2(as.matrix(data.bm),c("test_5"),"/public/workspace/lily/Lung2Brain/version_4_27/sig_test/test_5/",permN=0) +mod <- as.data.frame(mod) + + + + +data.lc <- LC[["RNA"]]@data +source('/public/workspace/lily/software/ssGSEA/ssgseaMOD.r') +mod <- mod.analyze2(as.matrix(data.lc),c("test_5"),"/public/workspace/lily/Lung2Brain/version_4_27/sig_test/test_5/",permN=0) +mod <- as.data.frame(mod) + + + +dat <- readRDS("/public/workspace/lily/Lung2Brain/inte7/Data/LCBM_tumor.RDS") +source('/public/workspace/lily/software/ssGSEA/ssgseaMOD.r') +mod <- mod.analyze2(as.matrix(dat[["RNA"]]@data),c("test_5"),"/public/workspace/lily/Lung2Brain/version_4_27/sig_test/test_5/",permN=0) +mod <- as.data.frame(mod) + + + + + + + + + diff --git a/specInt.R b/specInt.R new file mode 100644 index 0000000..4c419b1 --- /dev/null +++ b/specInt.R @@ -0,0 +1,34 @@ +# cell interaction +celldb = read.table('/public/workspace/lily/Lung2Brain/Version5/PairsLigRec.txt',header=T,sep='\t') + +# e.g., specInt(inte123.expr,inte123$celltype2,celldb[,c('Ligand.ApprovedSymbol','Receptor.ApprovedSymbol')],'3','myeloid') +specInt <- function(mat,label,intdb,L,R){ + rs = as.data.frame(t(apply(intdb,1,function(x){ + if(length(which(rownames(mat) %in% x)) == 2){ + tmp1 = tapply(unlist(mat[x[1],]),label,mean); + tmp2 = tapply(unlist(mat[x[2],]),label,mean); + idx1 = which(names(tmp1) %in% L); + idx2 = which(names(tmp2) %in% R); + sec1max = max(tmp1[-idx1],na.rm=T); + sec2max = max(tmp2[-idx2],na.rm=T); + sig1 = wilcox.test(unlist(mat[x[1],which(label %in% L)]),unlist(mat[x[1],-which(label %in% L)]),alternative='greater')$p.value + sig2 = wilcox.test(unlist(mat[x[2],which(label %in% R)]),unlist(mat[x[2],-which(label %in% R)]),alternative='greater')$p.value + c(mean(tmp1[idx1])/sec1max,mean(tmp2[idx2])/sec2max,mean(tmp1[idx1]),mean(tmp2[idx2]),mean(tmp1[-idx1],na.rm=T),mean(tmp2[-idx2],na.rm=T),sig1,sig2) + }else{ + rep(NA,8) + } + }))) + colnames(rs) = c('L.Ratio','R.Ratio','L.mean','R.mean','nL.mean','nR.mean','L.pval','R.pval') + rs[order(rs$L.Ratio*rs$R.Ratio,decreasing=T),] +} +# +# +# rs = specInt(inte123.expr[,which(inte123$celltype!='unknown')],inte123$seurat_clusters[which(inte123$celltype!='unknown')],celldb[,c('Ligand.ApprovedSymbol','Receptor.ApprovedSymbol')],c('4','9'),c('1','10','12','15','17')) +# rs.f = rs[which(rs$L.mean>0.1 & rs$R.mean>0.1),] +# rs.f$L.fc = rs.f$L.mean/rs.f$nL.mean +# rs.f$R.fc = rs.f$R.mean/rs.f$nR.mean +# head(rs.f[order(apply(rs.f[,c('L.fc','R.fc')],1,min),decreasing=T),],10) + + + + diff --git a/trajectory.r b/trajectory.r new file mode 100644 index 0000000..b11f8a5 --- /dev/null +++ b/trajectory.r @@ -0,0 +1,305 @@ + +# this program is used to do tumor trajecoty 2021-5-13 +# 1. do PATHE +bytlib load phate +library(Seurat) + +#.libPaths(c('/public/workspace/liangyuan/pipelines/bitapps_phate/ly_5873c054-b79d-478e-9ae5-bc6da1635829_RV3.6.0/','/bioapps/Rlibs/3.6.0/',"/public/workspace/lily/R/x86_64-pc-linux-gnu-library/3.6.0")) +#install.packages("phateR") + +library(Seurat) +library(phateR) + +dat <- readRDS("/public/workspace/lily/Lung2Brain/Version5/Data/Tumor_pur/pur_LCBM_clust.RDS") +# filter some genes +mat<-as.matrix(dat@assays$RNA@counts) +keep_rows <- rowSums(mat>0) > 10 +mat2<-mat[keep_rows,] +mat2 <- library.size.normalize(mat2) +mat2 <- sqrt(mat2) +pe<-phate(t(mat2),ndim=2,knn=20,decay=1000,gamma=1,t="auto") +dat[["phate"]] <- CreateDimReducObject(embeddings = pe$embedding, key = "phate_", assay = DefaultAssay(dat)) +saveRDS(dat,file="/public/workspace/lily/Lung2Brain/Version5/Trajectory/PHATE/LCBM_tumor_phate.RDS") +# tmp <- readRDS("/public/workspace/lily/Lung2Brain/Version5/Trajectory/monocle/LCBM_tumor_monocle.RDS") + + + + +# 2. do DPT + +library(destiny) +library(Seurat) +library(Biobase) + +knn=NULL +n_eigs=NULL +density_norm=NULL +distance_method=NULL +tips_method=NULL +seed_num=NULL + +dat <- readRDS("/public/workspace/lily/Lung2Brain/Version5/Data/Tumor_pur/pur_LCBM_clust.RDS") +as.matrix(dat@assays$RNA@data)->exp +anno=dat@meta.data +es <- Biobase::ExpressionSet(exp, phenoData=Biobase::AnnotatedDataFrame(anno)) + +dmap <- DiffusionMap(es,sigma = "local",verbose=T,n_eigs=20, + density_norm = TRUE,distance = "euclidean") + +# add a meta info +dat$type.tumor <- "tumor.l" +dat$type.tumor[which(dat$seurat_clusters%in%c(4,7))] <- "tumor.h" +dpt <- DPT(dmap) + + + + + +# 3. TSCAN +# use R-4.0.2 +library(TSCAN) +library(Seurat) +dat <- readRDS("/public/workspace/lily/Lung2Brain/Version5/Data/Tumor_pur/pur_LCBM_clust.RDS") +seuratdf<-as.matrix(dat@assays$RNA@counts) +procdata <- preprocess(seuratdf,cvcutoff = 0) +lpsmclust <- exprmclust(procdata) +mclustobj <- lpsmclust +saveRDS(mclustobj,file="/public/workspace/lily/Lung2Brain/Version5/Trajectory/TSCAN/LCBM_tumor_TSCAN.RDS") +# make a dataframe to plot result +# lib_info_with_pseudo <- data.frame(State = mclustobj$clusterid,sample_name = names(mclustobj$clusterid)) +# S_matrix <- mclustobj$pcareduceres +# pca_space_df <- data.frame(S_matrix[, c(1, 2)]) +# colnames(pca_space_df) <- c("pca_dim_1", "pca_dim_2") +# pca_space_df$sample_name <- row.names(pca_space_df) +# edge_df <- merge(pca_space_df, lib_info_with_pseudo, by.x = "sample_name", by.y = "sample_name") # plot data +# rownames(edge_df) <- edge_df$sample_name +# edge_df <- edge_df[colnames(dat),] +# all(rownames(edge_df)==colnames(dat)) + +# 2021-5-13 maybe just use pseudotime to plot + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +# 3.1 use double lung cacner brain metastasis sample +library(Seurat) +library(monocle) +dat.d <- readRDS("/public/workspace/lily/Lung2Brain/Multiple_LungBrain/tumor.D.RDS") + +source('/public/workspace/lily/software/ssGSEA/ssgseaMOD.r') +mod <- mod.analyze2(as.matrix(dat.d[['RNA']]@data),c("BMS_update","HPSC_C5","Brain_gene","Lung_gene"),"/public/workspace/lily/MOD_file/",permN=0) +mod <- as.data.frame(mod) +dat.d$BMS_update <- mod[,5] +dat.d$HPSC <- mod[,6] +dat.d$Brain_gene <- mod[,7] +dat.d$Lung_gene <- mod[,8] + +DefaultAssay(dat.d) <- "RNA" +tmp.dat <- Seurat::as.CellDataSet(dat.d) +tmp.dat <- estimateSizeFactors(tmp.dat) +tmp.dat <- detectGenes(tmp.dat, min_expr = 1) +# fData(dat)$use_for_ordering <-fData(dat)$num_cells_expressed > 0.05 * ncol(dat) +expressed_genes <- row.names(subset(fData(tmp.dat),num_cells_expressed >= 10)) +dat.d <- FindVariableFeatures(dat.d) +genes <- VariableFeatures(dat.d)[1:500] +ordering_genes <- genes +tmp.dat <- setOrderingFilter(tmp.dat, ordering_genes = ordering_genes) +tmp.dat <- reduceDimension(tmp.dat, method = 'DDRTree') +tmp.dat <- orderCells(tmp.dat) +# plot_cell_trajectory(tmp.dat,color_by="State") + +pdf("tmp_d.pdf") +plot_cell_trajectory(tmp.dat,color_by="BMS_update") +plot_cell_trajectory(tmp.dat,color_by="HPSC") +plot_cell_trajectory(tmp.dat,color_by="Lung_gene") +plot_cell_trajectory(tmp.dat,color_by="Brain_gene") +plot_cell_trajectory(tmp.dat,color_by="State") +dev.off() + +aggregate(Lung_gene~State,data=pData(tmp.dat),FUN=median) + + +# another sample +library(Seurat) +library(monocle) +dat.e <- readRDS("/public/workspace/lily/Lung2Brain/Multiple_LungBrain/tumor.E.RDS") + +source('/public/workspace/lily/software/ssGSEA/ssgseaMOD.r') +mod <- mod.analyze2(as.matrix(dat.e[['RNA']]@data),c("BMS_update","HPSC_C5","Brain_gene","Lung_gene"),"/public/workspace/lily/MOD_file/",permN=0) +mod <- as.data.frame(mod) +dat.e$BMS_update <- mod[,5] +dat.e$HPSC <- mod[,6] +dat.e$Brain_gene <- mod[,7] +dat.e$Lung_gene <- mod[,8] + +DefaultAssay(dat.e) <- "RNA" +tmp.dat <- Seurat::as.CellDataSet(dat.e) +tmp.dat <- estimateSizeFactors(tmp.dat) +tmp.dat <- detectGenes(tmp.dat, min_expr = 1) +# fData(dat)$use_for_ordering <-fData(dat)$num_cells_expressed > 0.05 * ncol(dat) +expressed_genes <- row.names(subset(fData(tmp.dat),num_cells_expressed >= 10)) +dat.d <- FindVariableFeatures(dat.e) +genes <- VariableFeatures(dat.e)[1:1500] +ordering_genes <- genes +tmp.dat <- setOrderingFilter(tmp.dat, ordering_genes = ordering_genes) +tmp.dat <- reduceDimension(tmp.dat, method = 'DDRTree') +tmp.dat <- orderCells(tmp.dat) +# plot_cell_trajectory(tmp.dat,color_by="State") + +pdf("tmp_e.pdf") +plot_cell_trajectory(tmp.dat,color_by="BMS_update") +plot_cell_trajectory(tmp.dat,color_by="HPSC") +plot_cell_trajectory(tmp.dat,color_by="Lung_gene") +plot_cell_trajectory(tmp.dat,color_by="Brain_gene") +plot_cell_trajectory(tmp.dat,color_by="State") +dev.off() + +aggregate(Lung_gene~State,data=pData(tmp.dat),FUN=median) + + +# use two sample integration result +library(Seurat) +library(monocle) +dat.e <- readRDS("/public/workspace/lily/Lung2Brain/Multiple_LungBrain/tumor.E.RDS") +dat.d <- readRDS("/public/workspace/lily/Lung2Brain/Multiple_LungBrain/tumor.D.RDS") + +integration.anchors <- FindIntegrationAnchors(object.list = c(dat.e,dat.d)) +inte <- IntegrateData(anchorset = integration.anchors) +#FindVariableFeatures +inte <- FindVariableFeatures(inte) +##Scaling the integrateda +all.genes <- rownames(inte) +inte <- ScaleData(inte, features = all.genes) +#PCA +inte <- RunPCA(inte) +#cluster +inte <- FindNeighbors(inte) +inte <- FindClusters(inte) +#TSNE +# if Umap can not use +inte <- RunTSNE(inte) +saveRDS(inte,file="/public/workspace/lily/Lung2Brain/Multiple_LungBrain/tumor.DE.RDS") + +# run trajectory +dat <- readRDS("/public/workspace/lily/Lung2Brain/Multiple_LungBrain/tumor.DE.RDS") +source('/public/workspace/lily/software/ssGSEA/ssgseaMOD.r') +mod <- mod.analyze2(as.matrix(dat[['RNA']]@data),c("BMS_update","HPSC_C5","Brain_gene","Lung_gene"),"/public/workspace/lily/MOD_file/",permN=0) +mod <- as.data.frame(mod) +dat$BMS_update <- mod[,5] +dat$HPSC <- mod[,6] +dat$Brain_gene <- mod[,7] +dat$Lung_gene <- mod[,8] + +DefaultAssay(dat) <- "RNA" +tmp.dat <- Seurat::as.CellDataSet(dat) +tmp.dat <- estimateSizeFactors(tmp.dat) +tmp.dat <- detectGenes(tmp.dat, min_expr = 1) +# fData(dat)$use_for_ordering <-fData(dat)$num_cells_expressed > 0.05 * ncol(dat) +expressed_genes <- row.names(subset(fData(tmp.dat),num_cells_expressed >= 10)) +DefaultAssay(dat) <- "integrated" +genes <- VariableFeatures(dat)[1:1600] +ordering_genes <- genes +tmp.dat <- setOrderingFilter(tmp.dat, ordering_genes = ordering_genes) +tmp.dat <- reduceDimension(tmp.dat, method = 'DDRTree') +tmp.dat <- orderCells(tmp.dat) + + +pdf("tmp_DE.pdf") +plot_cell_trajectory(tmp.dat,color_by="BMS_update") +plot_cell_trajectory(tmp.dat,color_by="HPSC") +plot_cell_trajectory(tmp.dat,color_by="Lung_gene") +plot_cell_trajectory(tmp.dat,color_by="Brain_gene") +plot_cell_trajectory(tmp.dat,color_by="State") +dev.off() + +aggregate(Lung_gene~State,data=pData(tmp.dat),FUN=median) + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/velocity.sh b/velocity.sh new file mode 100644 index 0000000..bd555bb --- /dev/null +++ b/velocity.sh @@ -0,0 +1,203 @@ + +# Final lly use 202.195.187.3 conda to trun this result +# however result is not good +# this program is used to run volocity +conda create -n velocyto python=3.6 + +conda activate velocyto +conda install numpy scipy cython numba matplotlib scikit-learn h5py click +pip install pysam +pip install velocyto + +# run bam to +module load samtools-1.9 + +# run in 202.195.187.3 +conda activate velocity +velocyto run10x -m /public/workspace/lily/REF/hg19_rmsk.gtf \ + -@ 10 \ + /public/workspace/lily/Lung2Brain/before_6_5/Rawdata/A20190312/ \ + ~/ref/refdata-cellranger-GRCh38-3.0.0/genes/genes.gtf + + + + + + + +# run python +# 2021-5-12 +#==================================================================================================================================== +import scvelo as scv +scv.logging.print_version() +scv.settings.verbosity = 3 # show errors(0), warnings(1), info(2), hints(3) +scv.settings.presenter_view = True # set max width size for presenter view +scv.settings.set_figure_params('scvelo') # for beautified visualization + + + +## for A20190305 +adata_obj = scv.read("/public/workspace/lily/Lung2Brain/velocity/LCBM_tumor_veolcyto/A05.loom", cache=True) +adata = scv.read('/public/workspace/lily/Lung2Brain/velocity/LCBM_veolcyto/A20190305.loom', cache=True) +adata_merged_a05 = scv.utils.merge(adata_obj, adata) + +# run velocity for A20190305 +scv.utils.show_proportions(adata_merged_a05) # show splice and unsplice +adata_merged_a05 +scv.pp.filter_and_normalize(adata_merged_a05, min_shared_counts=20, n_top_genes=2000) +scv.pp.moments(adata_merged_a05, n_pcs=30, n_neighbors=30) +scv.tl.velocity(adata_merged_a05) +scv.tl.velocity_graph(adata_merged_a05) +scv.tl.tsne(adata_merged_a05) +scv.tl.umap(adata_merged_a05) +scv.pl.velocity_embedding_stream(adata_merged_a05, basis='umap') +scv.pl.velocity_embedding_stream(adata_merged_a05, basis='umap', color=['seurat_clusters']) +scv.pl.velocity_embedding(adata_merged_a05, basis='umap', arrow_length=2, arrow_size=1.5, dpi=150,color=['seurat_clusters']) + + + +# run velocity for A20190305 +adata_obj = scv.read("/public/workspace/lily/Lung2Brain/velocity/LCBM_tumor_veolcyto/A12.loom", cache=True) +adata = scv.read('/public/workspace/lily/Lung2Brain/velocity/LCBM_veolcyto/A20190312.loom', cache=True) +adata_merged_a05 = scv.utils.merge(adata_obj, adata) + +# run velocity for A20190305 +scv.utils.show_proportions(adata_merged_a05) # show splice and unsplice +adata_merged_a05 +scv.pp.filter_and_normalize(adata_merged_a05, min_shared_counts=20, n_top_genes=2000) +scv.pp.moments(adata_merged_a05, n_pcs=30, n_neighbors=30) +scv.tl.velocity(adata_merged_a05) +scv.tl.velocity_graph(adata_merged_a05) +scv.tl.tsne(adata_merged_a05) +scv.tl.umap(adata_merged_a05) +scv.pl.velocity_embedding_stream(adata_merged_a05, basis='umap') +scv.pl.velocity_embedding_stream(adata_merged_a05, basis='umap', color=['seurat_clusters']) +# scv.pl.velocity_embedding(adata_merged_a05, basis='umap', arrow_length=2, arrow_size=1.5, dpi=150,color=['seurat_clusters']) + + + + + + +# run velocity for A20190305 +adata_obj = scv.read("/public/workspace/lily/Lung2Brain/velocity/LCBM_tumor_veolcyto/TB.loom", cache=True) +adata = scv.read('/public/workspace/lily/Lung2Brain/velocity/LCBM_veolcyto/T_Bsc1.loom', cache=True) +adata_merged_a05 = scv.utils.merge(adata_obj, adata) + +# run velocity for A20190305 +scv.utils.show_proportions(adata_merged_a05) # show splice and unsplice +adata_merged_a05 +scv.pp.filter_and_normalize(adata_merged_a05, min_shared_counts=20, n_top_genes=2000) +scv.pp.moments(adata_merged_a05, n_pcs=30, n_neighbors=30) +scv.tl.velocity(adata_merged_a05) +scv.tl.velocity_graph(adata_merged_a05) +scv.tl.tsne(adata_merged_a05) +scv.tl.umap(adata_merged_a05) +scv.pl.velocity_embedding_stream(adata_merged_a05, basis='umap', color='seurat_clusters') +# scv.pl.velocity_embedding_stream(adata_merged_a05, basis='umap') +# scv.pl.velocity_embedding_stream(adata_merged_a05, basis='umap', color=['seurat_clusters']) + + + + + + + + + + + + + + +################################################################################################################# +# 2021-5-25 +# run in 202.195.187.3 +# run for CRC + +conda activate velocity + +python +import scvelo as scv +scv.logging.print_version() +scv.settings.verbosity = 3 # show errors(0), warnings(1), info(2), hints(3) +scv.settings.presenter_view = True # set max width size for presenter view +scv.settings.set_figure_params('scvelo') # for beautified visualization + + + +## for A20190305 +adata_obj = scv.read("/public/workspace/zhumy/CRC_liver/result/RD001T.loom") +adata = scv.read('/public/workspace/zhumy/CRC_liver/result/cellranger_result1/RD-20180913-001-SR18282/velocyto/RD-20180913-001-SR18282.loom') +adata_merged_a05 = scv.utils.merge(adata_obj, adata) + +# run velocity for A20190305 +scv.utils.show_proportions(adata_merged_a05) # show splice and unsplice +adata_merged_a05 +scv.pp.filter_and_normalize(adata_merged_a05, min_shared_counts=20, n_top_genes=2000) +scv.pp.moments(adata_merged_a05, n_pcs=30, n_neighbors=30) +scv.tl.velocity(adata_merged_a05) +scv.tl.velocity_graph(adata_merged_a05) +scv.tl.tsne(adata_merged_a05) +scv.tl.umap(adata_merged_a05) +scv.pl.velocity_embedding_stream(adata_merged_a05, basis='umap') +scv.pl.velocity_embedding_stream(adata_merged_a05, basis='umap', color=['seurat_clusters']) +scv.pl.velocity_embedding_stream(adata_merged_a05, basis='umap', color=['seurat_clusters'], show=True, save="RD001T_dynamic_seurat_clusters.pdf", dpi=300) +scv.pl.velocity_embedding(adata_merged_a05, basis='umap', arrow_length=2, arrow_size=1.5, dpi=150,color=['seurat_clusters']) + + + + + + +## for RD002 +import scvelo as scv +scv.logging.print_version() +scv.settings.verbosity = 3 # show errors(0), warnings(1), info(2), hints(3) +scv.settings.presenter_view = True # set max width size for presenter view +scv.settings.set_figure_params('scvelo') # for beautified visualization + +adata_obj = scv.read("/public/workspace/zhumy/CRC_liver/result/cellranger_result1/RD-20180913-002-SR18282/velocyto/RD-20180913-002-SR18282.loom") +adata = scv.read('/public/workspace/zhumy/CRC_liver/result/RD002T.loom') +adata_merged_a05 = scv.utils.merge(adata_obj, adata) + +# run velocity for A20190305 +scv.utils.show_proportions(adata_merged_a05) # show splice and unsplice +adata_merged_a05 +scv.pp.filter_and_normalize(adata_merged_a05, min_shared_counts=20, n_top_genes=2000) +scv.pp.moments(adata_merged_a05, n_pcs=30, n_neighbors=30) +scv.tl.velocity(adata_merged_a05) +scv.tl.velocity_graph(adata_merged_a05) +scv.tl.tsne(adata_merged_a05) +scv.tl.umap(adata_merged_a05) +scv.pl.velocity_embedding_stream(adata_merged_a05, basis='umap') +scv.pl.velocity_embedding_stream(adata_merged_a05, basis='umap', color=['seurat_clusters'], show=True, save="RD002T_dynamic_seurat_clusters.pdf", dpi=300) +scv.pl.velocity_embedding_stream(adata_merged_a05, basis='umap', color=['seurat_clusters']) +scv.pl.velocity_embedding(adata_merged_a05, basis='umap', arrow_length=2, arrow_size=1.5, dpi=150,color=['seurat_clusters']) + + + + + + + + + + + + + + + + + + + + + + + + + + +