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r2_05_ShiftAnalysis.R
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library(Seurat)
library(GSEABase)
library(ggsignif)
library(ggpubr)
library(Rmisc)
library(cowplot)
library(singscore)
library(SummarizedExperiment)
library(dplyr)
library(ggplot2)
library(cowplot)
# For raincloud plots...
library(scrabble)
library(readr)
library(tidyr)
library(Hmisc)
library(plyr)
library(RColorBrewer)
library(reshape2)
library(PupillometryR)
library(singscore)
library(viridis)
library(future)
# change the current plan to access parallelization
plan(strategy = "sequential")
# plan()
options(future.globals.maxSize = 40000 * 1024^2)
obj.integrated <- readRDS("ali_obj.int1.RDS")
# Define raincloud theme: from https://neuroconscience.wordpress.com/2018/03/15/introducing-raincloud-plots/
raincloud_theme = theme(
text = element_text(size = 10),
axis.title.x = element_text(size = 16),
axis.title.y = element_text(size = 16),
axis.text = element_text(size = 14),
axis.text.x = element_text(angle = 45, vjust = 0.5),
legend.title=element_text(size=16),
legend.text=element_text(size=16),
legend.position = "right",
plot.title = element_text(lineheight=.8, face="bold", size = 16),
panel.border = element_blank(),
panel.grid.minor = element_blank(),
panel.grid.major = element_blank(),
axis.line.x = element_line(colour = 'black', size=0.5, linetype='solid'),
axis.line.y = element_line(colour = 'black', size=0.5, linetype='solid'))
##############################################################################
# obj.integrated <- readRDS(file = "AlisertibAnalysisPipeline/04_output/04_CellTagHu_CytoTRACE.RDS")
#
# Scale RNA assay
DefaultAssay(obj.integrated) <- "RNA"
obj.integrated <- NormalizeData(obj.integrated, assay = "RNA", normalization.method = "LogNormalize", scale.factor = 10000)
obj.integrated <- CellCycleScoring(obj.integrated, s.features = cc.genes.updated.2019$s.genes, g2m.features = cc.genes.updated.2019$g2m.genes)
##----
### setting up vector of genes to keep for scaling
# variableFeatures
variableFeatures <- obj.integrated@[email protected]
# suvaFeatures
suvasigs <- read.csv(file = "SuvaMetaSignatures.csv", header = T, skip = 4)
SuvaNPC1selected <- suvasigs$NPC1
SuvaNPC2selected <- suvasigs$NPC2
SuvaOPCselected <- suvasigs$OPC
SuvaACselected <- suvasigs$AC
SuvaMES1selected <- suvasigs$MES1
SuvaMES2selected <- suvasigs$MES2
suva_genes <- c(SuvaNPC1selected, SuvaNPC2selected, SuvaOPCselected, SuvaACselected, SuvaMES1selected, SuvaMES2selected)
#L1000Features
l1000signatures <- read.delim(file = "matPH3_2_1_0.2_0.3_L1000_Batch2017_Regina_removed.txt", row.names = 1)
L1000Features <- rownames(l1000signatures)
toScale <- unique(c(L1000Features, suva_genes, variableFeatures))
##----
obj.integrated <- ScaleData(obj.integrated,
assay = "RNA",
features = toScale, # only scale features of interest
vars.to.regress = c("G2M.Score", "S.Score"))
saveRDS(obj.integrated,"interestgenes.rds")
dim(obj.integrated@[email protected])
dim(obj.integrated@assays$RNA@data)
# obj.integrated@[email protected]
saveRDS(obj.integrated, file = "alisertib_r2_cellCycleRegressedInt.RDS")
obj.integrated <- readRDS(file = "alisertib_r2_cellCycleRegressedInt.RDS")
# Split into seperate objects
head(obj.integrated$arm)
obj.integrated <- RenameCells(object = obj.integrated, new.names = paste0(obj.integrated$arm, "_", colnames(x = obj.integrated)))
Idents(obj.integrated) <- obj.integrated$arm
Alisertib <- subset(obj.integrated, idents = "alisertib")
metadata <- [email protected]
subtypes_alisertib <- metadata_alisertib$arm
head(metadata)
table(subtypes_alisertib)
#saveRDS(Alisertib,"Alisertib.rds")
DMSO <- subset(obj.integrated, idents = "vehicle")
#saveRDS(DMSO,"dmso.rds)
##############################################################################
##############################################################################
##############################################################################
# singscore
############################
# # Trimmed signatures from Neftel et al...
#
# SuvaNPC1selected <- c("SOX4", "MARCKSL1", "STMN1", "TCF12", "BEX1", "MLLT11", "MEST", "PFN2", "CHD7", "TUBA1A", "PCBP4", "AMOTL2", "DBN1", "SOX11")
# SuvaNPC2selected <- c("RND3", "MLLT11", "SOX11", "NREP", "FNBP1L", "SOX4", "MAP1B", "RBFOX2", "STMN1", "DPYSL3", "ATP1B1", "UCHL1", "DDAH2", "TUBB2A", "LBH", "TCF4", "NFIB",
# "DBN1", "NFIX", "CEP170", "BLCAP")
# SuvaOPCselected <- c("PSAT1", "SIRT2", "THY1", "VCAN", "DBI", "LIMA1", "SCD5", "FGF12", "RNF13", "ALCAM", "PGRMC1", "FABP5", "RAB31", "EPB41L2")
# SuvaACselected <- c("CST3", "PON2", "SPARC", "RAMP1", "DBI", "CLU", "PMP22", "S100A16", "PRCP", "PLTP", "F3", "RAB31", "ANXA5")
# SuvaMES1selected <- c("ANXA2", "ANXA1", "CD44", "VIM", "MT2A", "C1S", "NAMPT", "C1R", "SOD2", "IFITM3", "TIMP1", "S100A11", "MT1X", "S100A10", "FN1", "LGALS1", "S100A16", "CLIC1", "MGST1", "RCAN1", "TAGLN2", "NPC2", "SERPING1", "EMP1", "CTSB", "LGALS3", "MT1E", "EMP3", "ACTN1", "PRDX6", "IGFBP7", "SERPINE1", "PLP2", "CLIC4", "GSN", "NNMT", "TUBA1C", "GJA1", "TNFRSF1A", "WWTR1")
# SuvaMES2selected <- c("HILPDA", "ADM", "DDIT3", "NDRG1", "HERPUD1", "DNAJB9", "AKAP12", "SQSTM1", "MT1X", "ATF3", "NAMPT", "NRN1", "SLC2A1", "BNIP3", "LGALS3", "INSIG2", "PPP1R15A", "VIM", "PLOD2", "GBE1", "SLC2A3", "FTL", "WARS", "XPOT", "HSPA5", "ANXA2", "EPAS1", "LDHA", "P4HA1", "SERTAD1", "PFKP", "PGK1", "EGLN3", "SLC6A6", "BNIP3L", "RPL21", "TRAM1", "UFM1", "ASNS", "GOLT1B", "SLC39A14", "HSPA9")
# SuvaG2_Mselected <- c("CCNB1", "CDC20", "CCNB2", "PLK1", "CCNA2", "CKAP2", "KNSTRN", "RACGAP1", "TROAP", "KIF2C", "AURKA", "CENPF", "KPNA2", "ECT2", "BUB1", "CDCA8", "TACC3", "TTK", "TUBA1C", "NCAPD2", "ARL6IP1", "MZT1", "ANP32E", "KIF11", "TUBB4B", "SMC4", "CDC25B", "REEP4", "FOXM1", "TMPO", "GPSM2", "HMGB3", "RANGAP1", "H2AFZ")
# What if we use full suva sigs...
suvasigs <- read.csv(file = "SuvaMetaSignatures.csv", header = T, skip = 4)
suvasigs
SuvaNPC1selected <- suvasigs$NPC1
SuvaNPC2selected <- suvasigs$NPC2
SuvaOPCselected <- suvasigs$OPC
SuvaACselected <- suvasigs$AC
SuvaMES1selected <- suvasigs$MES1
SuvaMES2selected <- suvasigs$MES2
# Extract data from seurat obj
DefaultAssay(obj.integrated) <- "RNA"
intData <- GetAssayData(obj.integrated, assay = "RNA", slot = "scale.data")
datfram <- as.data.frame(intData)
rankData <- singscore::rankGenes(datfram)
# NPC1
NPC1GS <- GSEABase::GeneSet()
NPC1GS@geneIds <- as.character(SuvaNPC1selected)
ScoredNPC1int <- singscore::simpleScore(rankData = rankData, upSet = NPC1GS)
# for (i in 1:length(ScoredNPC1int$TotalScore)){
# ScoredNPC1int$Arm[i] <- unlist(strsplit(rownames(ScoredNPC1int)[i], split = "_", fixed = TRUE))[1]
# }
# Alisertibscores <- subset(ScoredNPC1int, ScoredNPC1int$Arm == "alisertib")
# DMSOscores <- subset(ScoredNPC1obj.integrated, ScoredNPC1int$Arm == "vehicle")
# singNPC1box <- ggpubr::ggboxplot(ScoredNPC1obj.integrated, x = "Arm", y = "TotalScore",
# color = "Arm", palette = c("aquamarine", "maroon"),
# order = c("vehicle", "alisertib"),
# ylab = "NPC1 enrichment score", xlab = "Treatment")
# plot(singNPC1box + ggpubr::stat_compare_means(method = "t.test"))
# NPC1ttestres <- t.test(TotalScore ~ Arm, data = ScoredNPC1int, paired = FALSE)
# NPC2
NPC2GS <- GeneSet()
NPC2GS@geneIds <- as.character(SuvaNPC2selected)
ScoredNPC2int <- singscore::simpleScore(rankData = rankData, upSet = NPC2GS)
# for (i in 1:length(ScoredNPC2int$TotalScore)){
# ScoredNPC2int$Arm[i] <- unlist(strsplit(rownames(ScoredNPC2int)[i], split = "_", fixed = TRUE))[1]
# }
# Alisertibscores <- subset(ScoredNPC2obj.integrated, ScoredNPC2int$Arm == "Alisertib")
# DMSOscores <- subset(ScoredNPC2obj.integrated, ScoredNPC2int$Arm == "vehicle")
# singNPC2box <- ggpubr::ggboxplot(ScoredNPC2obj.integrated, x = "Arm", y = "TotalScore",
# color = "Arm", palette = c("aquamarine", "maroon"),
# order = c("vehicle", "alisertib"),
# ylab = "NPC2 enrichment score", xlab = "Treatment")
# plot(singNPC2box + ggpubr::stat_compare_means(method = "t.test"))
# NPC2ttestres <- t.test(TotalScore ~ Arm, data = ScoredNPC2obj.integrated, paired = FALSE)
# OPC
OPCGS <- GeneSet()
OPCGS@geneIds <- as.character(SuvaOPCselected)
ScoredOPCint <- singscore::simpleScore(rankData = rankData, upSet = OPCGS)
# for (i in 1:length(ScoredOPCint$TotalScore)){
# ScoredOPCint$Arm[i] <- unlist(strsplit(rownames(ScoredOPCint)[i], split = "_", fixed = TRUE))[1]
# }
# Alisertibscores <- subset(ScoredOPCobj.integrated, ScoredOPCint$Arm == "Alisertib")
# DMSOscores <- subset(ScoredOPCobj.integrated, ScoredOPCint$Arm == "vehicle")
# singOPCbox <- ggpubr::ggboxplot(ScoredOPCobj.integrated, x = "Arm", y = "TotalScore",
# color = "Arm", palette = c("aquamarine", "maroon"),
# order = c("vehicle", "alisertib"),
# ylab = "OPC enrichment score", xlab = "Treatment")
# plot(singOPCbox + ggpubr::stat_compare_means(method = "t.test"))
# OPCttestres <- t.test(TotalScore ~ Arm, data = ScoredOPCobj.integrated, paired = FALSE)
# AC
ACGS <- GeneSet()
ACGS@geneIds <- as.character(SuvaACselected)
ScoredACint <- singscore::simpleScore(rankData = rankData, upSet = ACGS)
# for (i in 1:length(ScoredACint$TotalScore)){
# ScoredACint$Arm[i] <- unlist(strsplit(rownames(ScoredACint)[i], split = "_", fixed = TRUE))[1]
# }
# Alisertibscores <- subset(ScoredACobj.integrated, ScoredACint$Arm == "Alisertib")
# DMSOscores <- subset(ScoredACobj.integrated, ScoredACint$Arm == "vehicle")
# singACbox <- ggpubr::ggboxplot(ScoredACobj.integrated, x = "Arm", y = "TotalScore",
# color = "Arm", palette = c("aquamarine", "maroon"),
# order = c("vehicle", "alisertib"),
# ylab = "AC enrichment score", xlab = "Treatment")
# plot(singACbox + ggpubr::stat_compare_means(method = "t.test"))
# ACttestres <- t.test(TotalScore ~ Arm, data = ScoredACobj.integrated, paired = FALSE)
# MES1
MES1GS <- GeneSet()
MES1GS@geneIds <- as.character(SuvaMES1selected)
ScoredMES1int <- singscore::simpleScore(rankData = rankData, upSet = MES1GS)
# for (i in 1:length(ScoredMES1int$TotalScore)){
# ScoredMES1int$Arm[i] <- unlist(strsplit(rownames(ScoredMES1int)[i], split = "_", fixed = TRUE))[1]
# }
# Alisertibscores <- subset(ScoredMES1obj.integrated, ScoredMES1int$Arm == "Alisertib")
# DMSOscores <- subset(ScoredMES1obj.integrated, ScoredMES1int$Arm == "vehicle")
# singMES1box <- ggpubr::ggboxplot(ScoredMES1obj.integrated, x = "Arm", y = "TotalScore",
# color = "Arm", palette = c("aquamarine", "maroon"),
# order = c("vehicle", "alisertib"),
# ylab = "MES1 enrichment score", xlab = "Treatment")
# plot(singMES1box + ggpubr::stat_compare_means(method = "t.test"))
# MES1ttestres <- t.test(TotalScore ~ Arm, data = ScoredMES1obj.integrated, paired = FALSE)
# MES2
MES2GS <- GeneSet()
MES2GS@geneIds <- as.character(SuvaMES2selected)
ScoredMES2int <- singscore::simpleScore(rankData = rankData, upSet = MES2GS)
# for (i in 1:length(ScoredMES2int$TotalScore)){
# ScoredMES2int$Arm[i] <- unlist(strsplit(rownames(ScoredMES2int)[i], split = "_", fixed = TRUE))[1]
# }
# Alisertibscores <- subset(ScoredMES2obj.integrated, ScoredMES2int$Arm == "Alisertib")
# DMSOscores <- subset(ScoredMES2obj.integrated, ScoredMES2int$Arm == "vehicle")
# singMES2box <- ggpubr::ggboxplot(ScoredMES2obj.integrated, x = "Arm", y = "TotalScore",
# color = "Arm", palette = c("aquamarine", "maroon"),
# order = c("vehicle", "alisertib"),
# ylab = "MES2 enrichment score", xlab = "Treatment")
# plot(singMES2box + ggpubr::stat_compare_means(method = "t.test"))
# MES2ttestres <- t.test(TotalScore ~ Arm, data = ScoredMES2obj.integrated, paired = FALSE)
# # G2_M
# G2_MGS <- GeneSet()
# G2_MGS@geneIds <- as.character(SuvaG2_Mselected)
# ScoredG2_Mobj.integrated <- singscore::simpleScore(rankData = rankData, upSet = G2_MGS)
# for (i in 1:length(ScoredG2_Mint$TotalScore)){
# ScoredG2_Mint$Arm[i] <- unlist(strsplit(rownames(ScoredG2_Mint)[i], split = "_", fixed = TRUE))[1]
# }
# Alisertibscores <- subset(ScoredG2_Mobj.integrated, ScoredG2_Mint$Arm == "Alisertib")
# DMSOscores <- subset(ScoredG2_Mobj.integrated, ScoredG2_Mint$Arm == "vehicle")
# singG2_Mbox <- ggpubr::ggboxplot(ScoredG2_Mobj.integrated, x = "Arm", y = "TotalScore",
# color = "Arm", palette = c("aquamarine", "maroon"),
# order = c("vehicle", "alisertib"),
# ylab = "G2_M enrichment score", xlab = "Treatment")
# plot(singG2_Mbox + ggpubr::stat_compare_means(method = "t.test"))
##################################################################################################
########## Aggregate data into one dataframe...
ScoredNPC1int$Sig <- "NPC1"
ScoredNPC2int$Sig <- "NPC2"
ScoredOPCint$Sig <- "OPC"
ScoredACint$Sig <- "AC"
ScoredMES1int$Sig <- "MES1"
ScoredMES2int$Sig <- "MES2"
singscoreDat <- rbind(ScoredNPC1int,
ScoredNPC2int,
ScoredOPCint,
ScoredACint,
ScoredMES1int,
ScoredMES2int)
singscoreDat
armDF <- as.data.frame(obj.integrated$arm)
armDF
singscoreDat <- merge(x = singscoreDat, y = armDF, by.x = "row.names", by.y = "row.names")
rownames(singscoreDat) <- singscoreDat$Row.names
singscoreDat$Row.names <- NULL
colnames(singscoreDat) <- c("TotalScore", "TotalDispersion", "Sig", "Arm")
singscoresum <- summarySE(singscoreDat, measurevar = "TotalScore", groupvars = c("Arm", "Sig"))
# ggplot(singscoresum, aes(x = Sig, y=TotalScore, fill=Arm)) +
# geom_bar(position = position_dodge(), stat = "identity") +
# geom_errorbar(aes(ymin=TotalScore-se, ymax=TotalScore+se),
# width = 0.2,
# position = position_dodge(.9))
#Side by side of DMSO vs Alisertib mean single-cell enrichment for Neftel et al signatures
#pdf(file = "AlisertibAnalysisPipeline/05_output/Singscore_NeftelEtAlTranscriptionalStateShiftFig_CI.pdf")
pdf("Neftelsigfor DMSO vs Alisertlib.pdf")
ggplot(singscoresum, aes(x=Sig, y=TotalScore, fill=Arm)) +
geom_bar(aes(fill = Arm), position = position_dodge(), stat = "identity",
colour="black",
size = 0.3) +
geom_errorbar(aes(ymin=TotalScore-ci, ymax = TotalScore+ci),
width = 0.2,
position = position_dodge(.9)) +
scale_fill_manual(values = c("gray80", "chartreuse"),
name = "Treatment",
breaks = c("vehicle", "alisertib"),
labels = c("vehicle", "Alisertib")) +
xlab("Transcriptional State Signature") +
ylab("Mean Signature Enrichment Score") +
theme_bw() +
theme(axis.title.y = element_text(size = rel(1.6), angle = 90),
axis.title.x = element_text(size = rel(1.6), angle = 0))
dev.off()
# With standard error instead of confidence interval.
#pdf(file = "AlisertibAnalysisPipeline/05_output/Singscore_NeftelEtAlTranscriptionalStateShiftFig_SE.pdf")
pdf("Neftelsigfor DMSO vs Alisertlib.pdf_withstandarderror")
ggplot(singscoresum, aes(x=Sig, y=TotalScore, fill=Arm)) +
geom_bar(aes(fill = Arm), position = position_dodge(), stat = "identity",
colour="black",
size = 0.3) +
geom_errorbar(aes(ymin=TotalScore-se, ymax = TotalScore+se),
width = 0.2,
position = position_dodge(.9)) +
scale_fill_manual(values = c("gray80", "chartreuse"),
name = "Treatment",
breaks = c("vehicle", "alisertib"),
labels = c("vehicle", "UM002")) +
xlab("Transcriptional State Signature") +
ylab("Mean Signature Enrichment Score") +
theme_bw() +
theme(axis.title.y = element_text(size = rel(1.6), angle = 90),
axis.title.x = element_text(size = rel(1.6), angle = 0))
dev.off()
# UMAP plots with neftel state enrichment projected - sorry I need to make this one page...
#pdf(file = "AlisertibAnalysisPipeline/05_output/CellTagHu_NeftelSingScoreFeaturePlots.pdf")
pdf("CellTagHu_NeftelSingScoreFeaturePlots_dim.pdf")
obj.integrated <- AddMetaData(obj.integrated, metadata = ScoredNPC1int[,"TotalScore"], col.name = "NPC1singscore")
FeaturePlot(obj.integrated, features = c("NPC1singscore"), min.cutoff = 0, cols = viridis::viridis(256, option = "B"))
obj.integrated <- AddMetaData(obj.integrated, metadata = ScoredNPC2int[,"TotalScore"], col.name = "NPC2singscore")
FeaturePlot(obj.integrated, features = c("NPC2singscore"), min.cutoff = 0, cols = viridis::viridis(256, option = "B"))
obj.integrated <- AddMetaData(obj.integrated, metadata = ScoredOPCint[,"TotalScore"], col.name = "OPCsingscore")
FeaturePlot(obj.integrated, features = c("OPCsingscore"), min.cutoff = 0, cols = viridis::viridis(256, option = "B"))
obj.integrated <- AddMetaData(obj.integrated, metadata = ScoredACint[,"TotalScore"], col.name = "ACsingscore")
FeaturePlot(obj.integrated, features = c("ACsingscore"), min.cutoff = 0, cols = viridis::viridis(256, option = "B"))
obj.integrated <- AddMetaData(obj.integrated, metadata = ScoredMES1int[,"TotalScore"], col.name = "MES1singscore")
FeaturePlot(obj.integrated, features = c("MES1singscore"), min.cutoff = 0, cols = viridis::viridis(256, option = "B"))
obj.integrated <- AddMetaData(obj.integrated, metadata = ScoredMES2int[,"TotalScore"], col.name = "MES2singscore")
FeaturePlot(obj.integrated, features = c("MES2singscore"), min.cutoff = 0, cols = viridis::viridis(256, option = "B"))
DimPlot(obj.integrated, split.by = "arm")
dev.off()
#### Can we bin cells based on singscores?
singscoredf <- data.frame(row.names = rownames([email protected]),
NPC1 = [email protected]$NPC1singscore,
NPC2 = [email protected]$NPC2singscore,
OPC = [email protected]$OPCsingscore,
AC = [email protected]$ACsingscore,
MES1 = [email protected]$MES1singscore,
MES2 = [email protected]$MES2singscore
)
###
singscoredf$assignment <- "Ambiguous"
for (i in 1:length(rownames(singscoredf))){
if (singscoredf$NPC1[i] > singscoredf$NPC2[i]){
if (singscoredf$NPC1[i] > singscoredf$OPC[i]){
if (singscoredf$NPC1[i] > singscoredf$AC[i]){
if (singscoredf$NPC1[i] > singscoredf$MES1[i]){
if (singscoredf$NPC1[i] > singscoredf$MES2[i]){
singscoredf$assignment[i] <- "NPC1"
}
}
}
}
}
if (singscoredf$NPC2[i] > singscoredf$NPC1[i]){
if (singscoredf$NPC2[i] > singscoredf$OPC[i]){
if (singscoredf$NPC2[i] > singscoredf$AC[i]){
if (singscoredf$NPC2[i] > singscoredf$MES1[i]){
if (singscoredf$NPC2[i] > singscoredf$MES2[i]){
singscoredf$assignment[i] <- "NPC2"
}
}
}
}
}
if (singscoredf$OPC[i] > singscoredf$NPC1[i]){
if (singscoredf$OPC[i] > singscoredf$NPC2[i]){
if (singscoredf$OPC[i] > singscoredf$AC[i]){
if (singscoredf$OPC[i] > singscoredf$MES1[i]){
if (singscoredf$OPC[i] > singscoredf$MES2[i]){
singscoredf$assignment[i] <- "OPC"
}
}
}
}
}
if (singscoredf$AC[i] > singscoredf$NPC1[i]){
if (singscoredf$AC[i] > singscoredf$NPC2[i]){
if (singscoredf$AC[i] > singscoredf$OPC[i]){
if (singscoredf$AC[i] > singscoredf$MES1[i]){
if (singscoredf$AC[i] > singscoredf$MES2[i]){
singscoredf$assignment[i] <- "AC"
}
}
}
}
}
if (singscoredf$MES1[i] > singscoredf$NPC1[i]){
if (singscoredf$MES1[i] > singscoredf$NPC2[i]){
if (singscoredf$MES1[i] > singscoredf$OPC[i]){
if (singscoredf$MES1[i] > singscoredf$AC[i]){
if (singscoredf$MES1[i] > singscoredf$MES2[i]){
singscoredf$assignment[i] <- "MES1"
}
}
}
}
}
if (singscoredf$MES2[i] > singscoredf$NPC1[i]){
if (singscoredf$MES2[i] > singscoredf$NPC2[i]){
if (singscoredf$MES2[i] > singscoredf$OPC[i]){
if (singscoredf$MES2[i] > singscoredf$AC[i]){
if (singscoredf$MES2[i] > singscoredf$MES1[i]){
singscoredf$assignment[i] <- "MES2"
}
}
}
}
}
}
singscoredf$cell <- rownames(singscoredf)
singscoredf
################################################################################
# Conver 6 states to 4 here... will also have to convert in another area?
for (i in 1:length(rownames(singscoredf))){
print(singscoredf$assignment[i])
if (singscoredf$assignment[i] %in% c("NPC1", "NPC2")){
singscoredf$Neftel_State[i] <- "NPC"
} else if (singscoredf$assignment[i] %in% c("MES1", "MES2")){
singscoredf$Neftel_State[i] <- "MES"
} else {
singscoredf$Neftel_State[i] <- as.character(singscoredf$assignment[i])
}
}
singscoredf
################################################################################
singscorebin <- singscoredf[,c("cell", "assignment")]
singscorebin$cell <- NULL
obj.integrated <- AddMetaData(obj.integrated, metadata = singscorebin, col.name = "singscorebin")
singscorebin2 <- singscoredf[,c("cell", "Neftel_State")]
singscorebin2$cell <- NULL
# neftelState <- singscorebin[c("Neftel_State")]
obj.integrated <- AddMetaData(obj.integrated, metadata = singscorebin2)
################################################################################
Idents(obj.integrated) <- obj.integrated$singscorebin
obj.integrated <- subset(obj.integrated, idents = c("NPC1", "NPC2", "OPC", "AC", "MES1", "MES2"))
stateUMAP <- DimPlot(obj.integrated, group.by = "singscorebin")#)cells = WhichCells(obj.integrated, idents = c("NPC1", "NPC2", "OPC", "AC", "MES1", "MES2")
neftelstateUMAP <- DimPlot(obj.integrated, group.by = "Neftel_State", pt.size = 0.5, split.by = "arm", cols = c("forestgreen", "darkred", "navy", "violet")) + theme_void()
pdf("neftelstateumap")
neftelstateUMAP
dev.off()
################################################################################
# test dim redux
################################################################################
# Is there a way to better show the shift via dim redux? run umap on neftel features?
suvafeatures <- c(suvasigs$MES2, suvasigs$MES1, suvasigs$AC, suvasigs$OPC, suvasigs$NPC1, suvasigs$NPC2)
class(suvafeatures)
suvafeatures <- unique(suvafeatures[suvafeatures != ""])
suvafeatures
test <- obj.integrated
test <- RunPCA(test, features = suvafeatures)
ElbowPlot(test)
test <- RunUMAP(test, dims = 1:20, n.neighbors = 5)
DimPlot(test, group.by = "Neftel_State", split.by = "arm")
################################################################################
# Scrabble hierarchy plot...
# need a dataframe, rows are cells, columns are states and scores...
# need to average scores for combined states...
inputdf <- singscoredf[c("NPC1", "NPC2", "OPC", "AC", "MES1", "MES2")]
inputdf$MES <- rowMeans(inputdf[ , c("MES1", "MES2")], na.rm=TRUE)
inputdf$NPC <- rowMeans(inputdf[ , c("NPC1", "NPC2")], na.rm=TRUE)
inputdf <- inputdf[c("AC", "MES", "NPC", "OPC")]
colnames(inputdf) <- c("AC.like", "MES.like", "NPC.like", "OPC.like")
obj.integrated <- AddMetaData(obj.integrated, metadata = inputdf)
hierarchy <- scrabble::hierarchy(m = inputdf, quadrants = NULL, log.scale = T)
scrabble::plot_hierarchy(hierarchy)
# What if we just add the hierarchy output back to the seurat object as a reduction?
hier <- as.matrix(hierarchy)
colnames(hier) <- c("hierarchy_1", "hierarchy_2")
obj.integrated[["hierarchy"]] <- CreateDimReducObject(embeddings = hier, key = "hierarchy_", assay = "RNA")
pdf("hierarchyModuleDimPlot.pdf")
DimPlot(obj.integrated, reduction = "hierarchy", group.by = "Neftel_State") +
xlim(c(-0.4,0.4)) + ylim(c(-0.4,0.4)) +
xlab(label = "Relative Meta-Module Score [log2(| SC1 - SC2 | + 1)]") +
ylab(label = "Relative Meta-Module Score [log2(| SC1 - SC2 | + 1)]") +
theme_minimal()
dev.off()
plist <- FeaturePlot(obj.integrated, reduction = "hierarchy",
features = c("ACsingscore",
"MES1singscore",
"MES2singscore",
"NPC1singscore",
"NPC2singscore",
"OPCsingscore"),
coord.fixed = T,
combine = F, cols = viridis(256, option = "turbo"))
plist <- lapply(X = plist, FUN = function(p) p + xlim(c(-0.4,0.4)) + ylim(c(-0.4,0.4)) +
xlab(label = "Relative Meta-Module Score [log2(| SC1 - SC2 | + 1)]") +
ylab(label = "Relative Meta-Module Score [log2(| SC1 - SC2 | + 1)]") +
theme_minimal())
plist <- CombinePlots(plist)
pdf("hierarchyModuleScorePlots.pdf", width = 21, height = 14)
plist
dev.off()
################################################################################
pdf("CellTagHu_SubtypeBinnedUMAP.pdf")
stateUMAP
dev.off()
singscoreNeftelList <- c(
"ACsingscore",
"MES1singscore",
"MES2singscore",
"NPC1singscore",
"NPC2singscore",
"OPCsingscore"
)
pdf("singscoreCellTagSubtypeDotPlots.pdf",height= 3.5,width = 12)
DotPlot(
obj.integrated,
assay = NULL,
group.by = "singscorebin",
features = singscoreNeftelList,
cols = c("black", "red"),
col.min = -2.5,
col.max = 2.5,
dot.min = 0,
dot.scale = 9,
scale.by = "radius"
)
dev.off()
NeftelList <- c(
"AC.like", "MES.like", "NPC.like", "OPC.like"
)
neftelStateDotPlot <- DotPlot(obj.integrated,
assay = NULL,
group.by = "Neftel_State",
features = NeftelList,
cols = c("white", "red"),
col.min = -2.5,
col.max = 2.5,
dot.min = 0,
dot.scale = 14,
scale.by = "radius",
scale = F) +
coord_flip() +
theme_minimal() + theme(text = element_text(size = 20))
pdf("neftelStateDotPlot.pdf", height = 3.5)
neftelStateDotPlot
dev.off()
# Idents(int) <- int$singscorebin
# StateMarkers <- FindAllMarkers(obj.integrated, test.use = "MAST", only.pos = TRUE)
# library(dplyr)
#
# StateMarkers
# topStateMarkers <- StateMarkers %>% group_by(cluster) %>% top_n(5, avg_log2FC)
# topStateMarkers
# stateHeatmap <- DoHeatmap(obj.integrated, features = topStateMarkers$gene, label = FALSE, assay = "RNA")
# pdf(file = "AlisertibAnalysisPipeline/05_output/alisertib_DMSO_stateHeatmap.pdf")
# stateHeatmap
# dev.off()
Idents(obj.integrated) <- obj.integrated$arm
bincounts <- table(obj.integrated$singscorebin, obj.integrated$arm)
DMSOsum <- sum(bincounts[,"vehicle"])
Alisertibsum <- sum(bincounts[,"alisertib"])
bincounts2 <- as.data.frame(bincounts)
colnames(bincounts2) <- c("State", "Treatment", "Freq")
for (i in 1:length(bincounts2$Freq)){
if (bincounts2$Treatment[i] == "vehicle"){
bincounts2$Percent[i] <- bincounts2$Freq[i]/DMSOsum*100
}
if (bincounts2$Treatment[i] == "alisertib"){
bincounts2$Percent[i] <- bincounts2$Freq[i]/Alisertibsum*100
}
}
library(reshape2)
bincounts3 <- recast(bincounts2, formula = State ~ Treatment, measure.var = "Percent")
rownames(bincounts3) <- bincounts3$State
bincounts3$State <- NULL
bincounts4 <- as.matrix(bincounts3)
#' Plots a series of barplots and connects them
#' Modified from https://stackoverflow.com/questions/22560850/barplot-with-connected-series
#'
#' @param dat NxM matrix with N rows as features and M columns as samples
#' @param color Vector of N colors
#' @param space Space between barplots
#' @param alpha Alpha for area connecting barplots
#'
#' @examples
#' dat <- matrix(rnorm(100),10,10)
#' dat <- abs(matrix(rnorm(100),10,10))
#' connectedBarplot(dat, color=rainbow(nrow(dat)))
#'
connectedBarplot <- function(dat, color=rainbow(nrow(dat)), space=1, alpha=0.5, ...) {
b <- barplot(dat, col=color, space = space, ...)
for (i in seq_len(ncol(dat) - 1)) {
lines(c(b[i]+0.5, b[i+1]-0.5), c(0, 0)) ## bottom line
for (j in seq_len(nrow(dat))) {
if (j == 1) {
lines(c(b[i]+0.5, b[i+1]-0.5), c(dat[j,i], dat[j,i+1]))
polygon(c(b[i]+0.5, b[i]+0.5, b[i+1]-0.5, b[i+1]-0.5),
c(0, dat[j,i], dat[j,i+1], 0),
col=adjustcolor(color[j], alpha.f=alpha))
}
if (j == 2) {
lines(c(b[i]+0.5, b[i+1]-0.5), c(colSums(dat[1:j,])[i], colSums(dat[1:j,])[i+1]))
polygon(c(b[i]+0.5, b[i]+0.5, b[i+1]-0.5, b[i+1]-0.5),
c(dat[1,i], colSums(dat[1:j,])[i], colSums(dat[1:j,])[i+1], dat[1,i+1]),
col=adjustcolor(color[j], alpha.f=alpha))
}
if (j > 2) {
lines(c(b[i]+0.5, b[i+1]-0.5), c(colSums(dat[1:j,])[i], colSums(dat[1:j,])[i+1]))
polygon(c(b[i]+0.5, b[i]+0.5, b[i+1]-0.5, b[i+1]-0.5),
c(colSums(dat[1:(j-1),])[i], colSums(dat[1:j,])[i], colSums(dat[1:j,])[i+1], colSums(dat[1:(j-1),])[i+1]),
col=adjustcolor(color[j], alpha.f=alpha))
}
}
}
}
pdf("NeftelEtAlStateShiftBarplot_singscore_total.pdf")
connectedBarplot(dat = bincounts4)
dev.off()
connectedBarplot(dat = bincounts4)
#######################################################################################
#######################################################################################
#######################################################################################
### Cell Cyucle Shift Analysis
## Part I - Seurat method
## Part II - Singscore enrichment
#######################################################################################
#######################################################################################
#######################################################################################
# Part I - Seurat module scoring
DefaultAssay(obj.integrated) <- "RNA"
#obj.integrated <- CellCycleScoring(obj.integrated, s.features = cc.genes.updated.2019$s.genes, g2m.features = cc.genes.updated.2019$g2m.genes, set.ident = TRUE)
obj.integrated$CellCycleIdents <- Idents(obj.integrated)
# RidgePlot(obj.integrated, features = c("G2M.Score", "S.Score"))
DimPlot(obj.integrated, group.by = "CellCycleIdents")
VlnPlot(obj.integrated, features = c("G2M.Score", "S.Score"), group.by = "arm", pt.size = 0)
CellCycleCounts <- table(obj.integrated$CellCycleIdents, obj.integrated$arm)
DMSOsum <- sum(CellCycleCounts[,"vehicle"])
Alisertibsum <- sum(CellCycleCounts[,"alisertib"])
CellCycleCounts2 <- as.data.frame(CellCycleCounts)
CellCycleCounts2
colnames(CellCycleCounts2) <- c("Phase", "Treatment", "Freq")
for (i in 1:length(CellCycleCounts2$Freq)){
if (CellCycleCounts2$Treatment[i] == "vehicle"){
CellCycleCounts2$Percent[i] <- CellCycleCounts2$Freq[i]/DMSOsum*100
}
if (CellCycleCounts2$Treatment[i] == "alisertib"){
CellCycleCounts2$Percent[i] <- CellCycleCounts2$Freq[i]/Alisertibsum*100
}
}
library(reshape2)
CellCycleCounts3 <- recast(CellCycleCounts2, formula = Phase ~ Treatment, measure.var = "Percent")
rownames(CellCycleCounts3) <- CellCycleCounts3$Phase
CellCycleCounts3$Phase <- NULL
CellCycleCounts4 <- as.matrix(CellCycleCounts3)
#' Plots a series of barplots and connects them
#' Modified from https://stackoverflow.com/questions/22560850/barplot-with-connected-series
#'
#' @param dat NxM matrix with N rows as features and M columns as samples
#' @param color Vector of N colors
#' @param space Space between barplots
#' @param alpha Alpha for area connecting barplots
#'
#' @examples
#' dat <- matrix(rnorm(100),10,10)
#' dat <- abs(matrix(rnorm(100),10,10))
#' connectedBarplot(dat, color=rainbow(nrow(dat)))
#'
connectedBarplot <- function(dat, color=rainbow(nrow(dat)), space=1, alpha=0.5, ...) {
b <- barplot(dat, col=color, space = space, ...)
for (i in seq_len(ncol(dat) - 1)) {
lines(c(b[i]+0.5, b[i+1]-0.5), c(0, 0)) ## bottom line
for (j in seq_len(nrow(dat))) {
if (j == 1) {
lines(c(b[i]+0.5, b[i+1]-0.5), c(dat[j,i], dat[j,i+1]))
polygon(c(b[i]+0.5, b[i]+0.5, b[i+1]-0.5, b[i+1]-0.5),
c(0, dat[j,i], dat[j,i+1], 0),
col=adjustcolor(color[j], alpha.f=alpha))
}
if (j == 2) {
lines(c(b[i]+0.5, b[i+1]-0.5), c(colSums(dat[1:j,])[i], colSums(dat[1:j,])[i+1]))
polygon(c(b[i]+0.5, b[i]+0.5, b[i+1]-0.5, b[i+1]-0.5),
c(dat[1,i], colSums(dat[1:j,])[i], colSums(dat[1:j,])[i+1], dat[1,i+1]),
col=adjustcolor(color[j], alpha.f=alpha))
}
if (j > 2) {
lines(c(b[i]+0.5, b[i+1]-0.5), c(colSums(dat[1:j,])[i], colSums(dat[1:j,])[i+1]))
polygon(c(b[i]+0.5, b[i]+0.5, b[i+1]-0.5, b[i+1]-0.5),
c(colSums(dat[1:(j-1),])[i], colSums(dat[1:j,])[i], colSums(dat[1:j,])[i+1], colSums(dat[1:(j-1),])[i+1]),
col=adjustcolor(color[j], alpha.f=alpha))
}
}
}
}
pdf("Alisertib_DMSO_CellCycleShift_connectedBarplot.pdf")
connectedBarplot(dat = CellCycleCounts4)
dev.off()
pdf("CellTag_Hu_CellCycleIdents.pdf")
DimPlot(obj.integrated, group.by = "CellCycleIdents")
dev.off()
# Now, is this significant? growth of G1, loss of G2M and S?
CellCycleScoreDF <- data.frame(G2M.Score = obj.integrated$G2M.Score, S.Score = obj.integrated$S.Score, Treatment = obj.integrated$arm)
mlt <- melt(CellCycleScoreDF)
colnames(mlt) <- c("Treatment", "Phase", "Score")
library(Rmisc)
CellCyclesum <- summarySE(mlt, measurevar = "Score", groupvars = c("Treatment", "Phase"))
CellCyclesum
pdf("CellTTagHu_UM002_CellCyclePhaseShiftPairedBarplot.pdf")
# 95% CI instead of SE...
ggplot(CellCyclesum, aes(x=Phase, y=Score, fill=Treatment)) +
geom_bar(aes(fill = Treatment), position = position_dodge(), stat = "identity",
colour="black",
size = 0.3) +
geom_errorbar(aes(ymin=Score-ci, ymax = Score+ci),
width = 0.2,
position = position_dodge(.9)) +
# geom_signif(y_position = c(0.33), xmin=c(1.23, 1.77, 2.77, 3.77, 4.77, 5.77),
# xmax = c(.77, 2.23, 3.23, 4.23, 5.23, 6.23),
# annotations = c("NS", "NS", "NS", "NS", "*", "*"),
# tip_length = c(0.55, 0.55, 0.24, 0.3, 1.1, 1.15, 1.05, 1.05, 0.85, 0.73, 0.85, 1.1),
# textsize = 5) +
scale_fill_manual(values = c("gray80", "chartreuse"),
name = "Treatment",
breaks = c("vehicle", "alisertib"),
labels = c("vehicle", "alisertib")) +
xlab("Cell Cycle Phase Signature") +
ylab("Module Score") +
theme_bw() +
theme(axis.title.y = element_text(size = rel(1.6), angle = 90),
axis.title.x = element_text(size = rel(1.6), angle = 0))
dev.off()
### How to make normalized plot showing growth of cells in S phase...?
g2mmlt <- subset(mlt, mlt$Phase == "G2M.Score")
smlt <- subset(mlt, mlt$Phase == "S.Score")
g2mres <- t.test(Score ~ Treatment, data = g2mmlt, paired = FALSE)
g2mres
sres <- t.test(Score ~ Treatment, data = smlt, paired = FALSE)
sres
############################################################################
#obj.integrated <- readRDS("targetfeatures.rds")
# Differential expression between DMSO and Alisertib treated cells.
Idents(obj.integrated) <- obj.integrated$arm
AlisertibMarkers <- FindAllMarkers(obj.integrated, test.use = "t", only.pos = TRUE)
AlisertibMarkers2 <- subset(AlisertibMarkers, AlisertibMarkers$avg_log2FC != "Inf")
AlisertibMarkers2 <- subset(AlisertibMarkers2, AlisertibMarkers$avg_log2FC != "-Inf")
topnmarkers <- AlisertibMarkers2 %>% group_by(cluster) %>% top_n(25, wt = avg_log2FC)
# topnmarkers <- su
topnmarkers$gene
top25heatmap <- DoHeatmap(obj.integrated, features = c(topnmarkers$gene), slot = "scale.data")
#top25heatmap
pdf("top25heatmap.pdf")
top25heatmap
dev.off()
### Make normalized state shift barplot...
singscoreDat <- rbind(ScoredNPC1obj.integrated,
ScoredNPC2obj.integrated,
ScoredOPCobj.integrated,
ScoredACobj.integrated,
ScoredMES1obj.integrated,
ScoredMES2obj.integrated)
singscoreDat
# Make column of arm from barcodes...
# for (i in 1:length(rownames(singscoreDat))){
# singscoreDat$Arm[i] <- unlist(strsplit(as.character(rownames(singscoreDat)[i]), split = "_", fixed = TRUE))[1]
# }
singscoreAli <- subset(singscoreDat, singscoreDat$Arm == "alisertib")
singscoreAli
singscoresumDMSO <- subset(singscoresum, singscoresum$Arm == "vehicle")
rownames(singscoresumDMSO) <- singscoresumDMSO$Sig
singscoresumDMSO["AC","TotalScore"]
for (i in 1:length(rownames(singscoreAli))){
j <- singscoreAli$Sig[i]
print(j)
k <- singscoreAli$TotalScore[i]
print(k)
singscoreAli$deltaMean[i] <- k - singscoresumDMSO[j, "TotalScore"]
}
singscoreAli
singscoreAlisum <- summarySE(singscoreAli, measurevar = "deltaMean", groupvars = c("Arm", "Sig"))
singscoreAlisum
################################################################################
# Normalized Module Score Plot...
normAliShiftPlot1 <- ggplot(singscoreAlisum, aes(x=Sig, y=deltaMean, fill=Sig)) +
geom_bar(aes(fill = Sig), position = position_dodge(), stat = "identity",
colour="black",
size = 0.3) +
geom_errorbar(aes(ymin=deltaMean-ci, ymax = deltaMean+ci),
width = 0.2,
position = position_dodge(.9)) +
xlab("Transcriptional State Signature") +
ylab("Delta Mean Signature Enrichment Score") +
theme_bw() +
theme(axis.title.y = element_text(size = rel(1.6), angle = 90),
axis.title.x = element_text(size = rel(1.6), angle = 0))
normAliShiftPlot1
################################################################################
# 4 States
nefteldf <- data.frame(cell_barcode = rownames([email protected]),
AC.like = obj.integrated$AC.like,
MES.like = obj.integrated$MES.like,
NPC.like = obj.integrated$NPC.like,
OPC.like = obj.integrated$OPC.like,
Arm = obj.integrated$arm)
nefteldf <- melt(nefteldf, id.vars = c("cell_barcode", "Arm"))
aliNeftelDF <- subset(nefteldf, nefteldf$Arm == "alisertib")
colnames(aliNeftelDF)
neftelstatesum <- summarySE(nefteldf, measurevar = "value",
groupvars = c("Arm", "variable"))
neftelstatesumDMSO <- subset(neftelstatesum, neftelstatesum$Arm == "alisertib")
rownames(neftelstatesumDMSO) <- neftelstatesumDMSO$variable
neftelstatesumDMSO["AC","value"]
for (i in 1:length(rownames(aliNeftelDF))){
j <- aliNeftelDF$variable[i]
print(j)
k <- aliNeftelDF$value[i]
print(k)
aliNeftelDF$deltaMean[i] <- k - neftelstatesumDMSO[j, "value"]
}
aliNeftelDF
neftelAlisum <- summarySE(aliNeftelDF, measurevar = "deltaMean", groupvars = c("Arm", "variable"))
neftelAlisum
write.csv(neftelAlisum, file = "inVivoNeftelShiftSummary2.csv")
normvehShiftPlot_fourStates <- ggplot(neftelAlisum, aes(x=variable, y=deltaMean, fill=variable)) +
geom_bar(aes(fill = variable), position = position_dodge(), stat = "identity",
colour="black",
size = 0.3) +
geom_errorbar(aes(ymin=deltaMean-ci, ymax = deltaMean+ci),
width = 0.2,
position = position_dodge(.9)) +
xlab("Transcriptional State Signature") +
ylab("Normalized Enrichment Score") +
theme_minimal() + NoLegend() +
theme(axis.title.y = element_text(size = rel(1.0), angle = 90),
axis.title.x = element_text(size = rel(1.0), angle = 0),
text = element_text(size = 30))
pdf("normvehShiftPlot_4States1.pdf",width=10)
normvehShiftPlot_fourStates
dev.off()
################################################################################
# Turn this into a raincloud plot...
# plotdat <- [email protected]
# plotdat1 <- melt(plotdat, id.vars = c("Arm"),
# measure.vars = c("oxphog_Signature"),
# variable.name = "oxphog_Signature",
# value.name = "enrichmentScore")
# Calculate Summary Statistics
lb <- function(x) mean(x) - sd(x)
ub <- function(x) mean(x) + sd(x)
singscoreAli$logDeltaMean <- log1p(singscoreAli$deltaMean)
colnames(singscoreAli)
singscoreAli
sumId <- ddply(singscoreAli, ~logDeltaMean, summarise,
mean = mean(logDeltaMean), median = median(logDeltaMean),
lower = lb(logDeltaMean), upper = ub(logDeltaMean))
g <- ggplot(data = singscoreAli, aes(y = logDeltaMean, x = Sig, fill = Sig)) +
geom_flat_violin(position = position_nudge(x = .2, y = 0), alpha = .8) +
geom_point(aes(y = logDeltaMean, color = Sig), position = position_jitter(width = .15), size = .5, alpha = 0.2) +
geom_boxplot(width = .3, guides = FALSE, outlier.shape = NA, alpha = 0.5) +
expand_limits(x = 5.25) +
guides(fill = FALSE) +
guides(color = FALSE) +
scale_color_discrete() +
scale_fill_discrete() +
# coord_flip() +
theme_bw() +
raincloud_theme
pdf("StateBin_Raincloud.pdf")
g
dev.off()
################################################################################
# Test for normality of normalized values...
################################################################################
ggdensity(singscoreAli$deltaMean,
main = "Alisertib vs. DMSO",
xlab = "Normalized Enrichment")
sample <- sample_n(singscoreAli, 30)
shapiro.test(sample$deltaMean)
ggqqplot(singscoreAli, x = "deltaMean")
ggqqplot(sample, x = "deltaMean")
pdf("stateShift.pdf",width = 14)
plot_grid(plotlist = list(stateUMAP, normAliShiftPlot), ncol = 2, labels = "AUTO")
dev.off()
## 20210606 - Characterize the mesenchymal, alisertib resistant population.