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Figure_3.R
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# ========================================================================================================================================== #
# Figure_3.R
# Author : Brooks Benard, [email protected]
# Date: 11/02/2021
# Description: This script will perform survival analyses based on VAF thresholds as seen in Figure 3 of the manuscript Benard et al. "Clonal architecture predicts clinical outcomes and drug sensitivity in acute myeloid leukemia".
# ========================================================================================================================================== #
# ================ #
# Load packages ####
# ================ #
# Package names
packages <- c("ggplot2", "dplyr", "cometExactTest", "stringr", "maditr", "reshape2", "data.table", "epitools", "corrplot", "plyr", "muhaz", "reshape", "survival", "survivalAnalysis", "survMisc", "survminer", "ggsci", "vegan", "ggrepel", "ggforce", "rstatix", "effsize", "psych", "maxstat", "RCurl", "ggpubr", "UpSetR", "cowplot", "readxl", "scales", "rlist", "tidyverse", "PupillometryR")
# Install packages not yet installed
installed_packages <- packages %in% rownames(installed.packages())
if (any(installed_packages == FALSE)) {
install.packages(packages[!installed_packages])
}
# Packages loading
invisible(lapply(packages, library, character.only = TRUE))
# create directories
dir.create("~/Desktop/MetaAML_results/Figure_3")
dir.create("~/Desktop/MetaAML_results/Figure_3/Supplimental")
#### CNA correction ####
# Using the cytogenetic data, recalculate the VAF for mutations falling on focal or broad CNAs
# Pappamanuel/Gerstung VAF correction is performed using code provided by the authors
# TCGA VAFs are corrected using a gene-level CNA supplimentary file
# for all other cohorts, manual correction is performed using reported cytogenetic/karyotypic data.
#### Papaemmanuil/Gerstung ####
# download.file("https://github.com/gerstung-lab/AML-multistage/blob/master/data/AMLSG_Clinical_Anon.RData?raw=true", destfile = "~/Desktop/MetaAML_results/raw_data/AMLSG_Clinical_Anon.RData")
load("~/Desktop/MetaAML_results/raw_data/AMLSG_Clinical_Anon.RData")
# 1.3.2.2 Mutation data
# download.file("https://raw.githubusercontent.com/gerstung-lab/AML-multistage/master/data/AMLSG_Genetic.txt", destfile = "~/Desktop/MetaAML_results/raw_data/AMLSG_Genetic.txt")
mutationData = read.table("~/Desktop/MetaAML_results/raw_data/AMLSG_Genetic.txt", sep="\t", header=TRUE, strip.white = TRUE)
mutationData$SAMPLE_NAME <- factor(as.character(mutationData$SAMPLE_NAME), levels = levels(clinicalData$PDID))
## Refactor
mutationTable <- (table(mutationData[mutationData$Result %in% c("ONCOGENIC","POSSIBLE") & mutationData$FINAL_CALL == "OK" ,c("SAMPLE_NAME","GENE")]) > 0)+0
dim(mutationTable)
all(rownames(mutationTable)==clinicalData$PDID)
# 1.3.4 Covariates
dataList <-list(Genetics = data.frame(mutationTable[,colSums(mutationTable)>0]),
Cytogenetics = clinicalData[,grep("^(t_)|(inv)|(abn)|(plus)|(minus)|(mono)|(complex)",colnames(clinicalData))],
Nuisance = data.frame( as.integer(clinicalData$Study),
Date=scale(as.numeric(clinicalData$ERDate), scale=FALSE),
MissingCyto=is.na(clinicalData$t_15_17)+0),
Demographics = clinicalData[,c("AOD","gender")],
Clinical = cbind(clinicalData[, c("Performance_ECOG","BM_Blasts","PB_Blasts","wbc","LDH","HB","platelet",
"Splenomegaly")], as.integer(clinicalData$TypeAML)),
MolRisk = as.integer(clinicalData$M_Risk))
dataList$Genetics$CEBPA <- clinicalData$CEBPA # encoded as 0,1,2
dataList$Genetics$CEBPA_mono <- clinicalData$CEBPA == 1 # encoded as 0,1,2
dataList$Genetics$CEBPA_bi <- clinicalData$CEBPA == 2 # encoded as 0,1,2
dataList$Genetics$CEBPA <- NULL
dataList$Genetics$FLT3 <- NULL
dataList$Genetics$FLT3_ITD <- clinicalData$FLT3_ITD != "0"
dataList$Genetics$FLT3_TKD <- clinicalData$FLT3_TKD != "0"
dataList$Genetics$FLT3_other <- clinicalData$FLT3_other != "0"
dataList$Genetics$IDH2_p172 <- table(mutationData$SAMPLE_NAME[mutationData$GENE=='IDH2' & grepl("172", mutationData$AA_CHANGE)])[]
dataList$Genetics$IDH2_p140 <- table(mutationData$SAMPLE_NAME[mutationData$GENE=='IDH2' & grepl("140", mutationData$AA_CHANGE)])[]
dataList$Genetics$IDH2 <- NULL
dataList$Genetics$NPM1 <- clinicalData$NPM1
dataList$Cytogenetics$MLL_PTD <- NULL
dataList$Genetics = dataList$Genetics + 0
# Condensing to a data.frame
dataRaw <- do.call(cbind,dataList)
names(dataRaw) <- unlist(sapply(dataList, names))
dataFrame = dataRaw
dim(dataFrame)
rownames(dataFrame) <- clinicalData$PDID
# download.file("https://github.com/gerstung-lab/AML-multistage/raw/master/data/AMLSG_Karyotypes.txt", destfile = "~/Desktop/MetaAML_results/raw_data/AMLSG_Karyotypes.txt")
# 1.3.5 Subclonal mutations
copyNumbers = cbind(dataList$Cytogenetics[grep(c("minus|plus|mono"), colnames(dataList$Cytogenetics))], clinicalData$gender)
copyNumbers$minus7 <- (copyNumbers$minus7 | copyNumbers$minus7q) + 0
copyNumbers$minus7q <- NULL
for(i in 1:ncol(copyNumbers)){
if(grepl("plus", colnames(copyNumbers)[i])){
copyNumbers[,i] = copyNumbers[,i]*3
}
}
copyNumbers[copyNumbers==0 | is.na(copyNumbers)] = 2
colnames(copyNumbers) = c(5,7,8,9,12,13,17,18,20,21,22,"Y",11,4,"X")
rownames(copyNumbers) <- clinicalData$PDID
minusY = dataList$Cytogenetics$minusY[is.na(dataList$Cytogenetics$minusY)] <- 0
copyNumbers$Y <- c(1:0)[clinicalData$gender] - minusY
cn = sapply(1:nrow(mutationData), function(i) {
c=copyNumbers[mutationData$SAMPLE_NAME[i],match(mutationData$CHR[i ], colnames(copyNumbers))]; if(length(c)==0) 2 else c
}
)
vaf <- as.numeric(as.character(mutationData$X._MUT_IN_TUM))
depth <- as.numeric(as.character(mutationData$TUM_DEPTH))
CNA = data.frame(cn)
rownames(CNA) = rownames(mutationData)
colnames(CNA) = "CNA"
mutationData$CN_status = as.numeric(CNA$CNA)
mutationData$VAF = as.numeric(mutationData$VAF)
# mutationData$CN_status =mutationData$CN_status %>% replace_na(2)
mutationData$CN_status[is.na(mutationData$CN_status)] <- 2
mutationData$VAF_CN_corrected = mutationData$VAF
for(i in 1:nrow(mutationData)){
if(!is.na(mutationData$VAF_CN_corrected[i])){
if(mutationData$CN_status[i] == 1){
mutationData$VAF_CN_corrected[i] = mutationData$VAF[i]/2
}
if(mutationData$CN_status[i] == 2 & mutationData$VAF[i] > 55){
mutationData$VAF_CN_corrected[i] = mutationData$VAF[i]/2
}
if(mutationData$CN_status[i] == 3 & mutationData$VAF[i] <= 35){
mutationData$VAF_CN_corrected[i] = mutationData$VAF[i]*1.5
}
if(mutationData$CN_status[i] == 3 & mutationData$VAF[i] > 35){
mutationData$VAF_CN_corrected[i] = mutationData$VAF[i]*0.75
}
}
}
for(i in 1:nrow(mutationData)){
if(!is.na(mutationData$VAF_CN_corrected[i])){
if(mutationData$VAF_CN_corrected[i] > 100){
mutationData$VAF_CN_corrected[i] = 100
}
}
}
papaemmanuil_muts = mutationData
# for some reason, this cohort uses SFRS2 instead of SRSF2 like all the other cohorts
for(i in 1:nrow(papaemmanuil_muts)){
if(papaemmanuil_muts$GENE[i] == "SFRS2"){
papaemmanuil_muts$GENE[i] <- "SRSF2"
}
}
papaemmanuil_muts = select(papaemmanuil_muts, SAMPLE_NAME, GENE, VAF, VAF_CN_corrected)
names(papaemmanuil_muts) = c("Sample", "Gene", "VAF", "VAF_CN_corrected")
papaemmanuil_muts$VAF <- round(papaemmanuil_muts$VAF, 2)
load("~/Desktop/MetaAML_results/final_data_matrix.RData")
papaemmanuil_sub = subset(final_data_matrix, final_data_matrix$Cohort == "Papaemmanuil")
papaemmanuil_corrected = left_join(papaemmanuil_sub, papaemmanuil_muts, by=c("Sample","Gene", "VAF"))
#### TCGA ####
load("~/Desktop/MetaAML_results/final_data_matrix.RData")
tcga_dat = subset(final_data_matrix, final_data_matrix$Cohort == "TCGA")
# read in copy number alteration data frame
CNA <- as.data.frame(read.table("~/Desktop/MetaAML_results/raw_data/data_CNA.txt", sep = '\t', header = TRUE, stringsAsFactors = FALSE))
CNA$Entrez_Gene_Id = NULL
# reformat colnames to match those in the mutation dataframe
colnames(CNA) = gsub('\\.', '-', colnames(CNA))
colnames(CNA) = as.character(substr(colnames(CNA), 1, nchar(colnames(CNA))-3))
tcga_dat$cn = NA
for (i in 1:nrow(tcga_dat)) {
current_id <- tcga_dat$Sample[i]
current_symbol <- tcga_dat$Gene[i]
delta <- as.integer(CNA[which(CNA$Hugo_Sym == current_symbol), which(colnames(CNA) == current_id)])
if (length(delta) == 0) {
delta <- 0
}
tcga_dat$cn[i] <- 2 + delta
}
tcga_dat$VAF_CN_corrected = NA
for(i in 1:nrow(tcga_dat)){
if(!is.na(tcga_dat$VAF[i])){
if(tcga_dat$cn[i] == 1){
tcga_dat$VAF_CN_corrected[i] = tcga_dat$VAF[i]/2
}
if(tcga_dat$cn[i] == 2 & tcga_dat$VAF[i] <= 55){
tcga_dat$VAF_CN_corrected[i] = tcga_dat$VAF[i]
}
if(tcga_dat$cn[i] == 2 & tcga_dat$VAF[i] > 55){
tcga_dat$VAF_CN_corrected[i] = tcga_dat$VAF[i]/2
}
if(tcga_dat$cn[i] == 3 & tcga_dat$VAF[i] <= 55){
tcga_dat$VAF_CN_corrected[i] = tcga_dat$VAF[i]*1.5
}
if(tcga_dat$cn[i] == 3 & tcga_dat$VAF[i] > 55){
tcga_dat$VAF_CN_corrected[i] = tcga_dat$VAF[i]*0.75
}
}
}
for(i in 1:nrow(tcga_dat)){
if(is.na(tcga_dat$VAF_CN_corrected[i])) { tcga_dat$VAF_CN_corrected[i] = tcga_dat$VAF[i]}
}
#### Other cohorts ####
# download a file correlating the chromosome loci with gene ID
# http://uswest.ensembl.org/biomart/martview/a3043f537a26692111e9a7a94003ff68
all_genes <- read.table("~/Downloads/mart_export.txt", sep = "\t", header = T, fill = T, stringsAsFactors = FALSE, quote = "")
mut_list = c("TP53", "DNMT3A", "SRSF2", "IDH2", "JAK2", "TET2", "U2AF1", "IDH1", "ETV6", "RUNX1", "BCOR", "ASXL1", "PHF6", "SF3B1", "GATA2", "CBL", "CEBPA", "WT1", "RAD21", "EZH2", "NF1", "KIT", "NPM1","NRAS", "KRAS", "FLT3", "PTPN11")
all_genes = subset(all_genes, all_genes$Gene.name %in% mut_list)
all_genes$Karyotype_arm = gsub("\\..*","",all_genes$Karyotype.band)
all_genes$partial_annotation = paste("(",all_genes$Chromosome.scaffold.name,")","(",all_genes$Karyotype_arm,")", sep = "")
all_genes$full_annotation = paste("(",all_genes$Chromosome.scaffold.name,")","(",all_genes$Karyotype.band,")", sep = "")
# append to mutation file
names(all_genes)[1] = "Gene"
# load and select data from the aggregated cohort
# VAf distribution ####
load("~/Desktop/MetaAML_results/final_data_matrix.RData")
vaf_sub = subset(final_data_matrix, final_data_matrix$Subset == "de_novo")
# vaf_sub = subset(vaf_sub, vaf_sub$Cohort == "Tyner")
vaf_sub = subset(vaf_sub, vaf_sub$Cohort != "TCGA" & vaf_sub$Cohort != "Papaemmanuil")
vaf_sub = subset(vaf_sub, vaf_sub$mut_freq_gene >= 50)
vaf_sub = subset(vaf_sub, vaf_sub$Gene != "MLL")
# append the chromosomal arm/band column to the mutation table
cohort_aggrigate = left_join(vaf_sub, all_genes, by = "Gene")
cohort_aggrigate$Cytogenetics = tolower(cohort_aggrigate$Cytogenetics)
# subset mutations to those where karyotyping data suggests deletions or amplifications
cohort_aggrigate$Cytogenetics = gsub('\\s+', '', cohort_aggrigate$Cytogenetics)
cohort_aggrigate$VAF_CN_corrected = NA
for(i in 1:nrow(cohort_aggrigate)){
if(!is.na(cohort_aggrigate$VAF_male_x[i])){
# find whole chromosome gains or losses
ch_gain = paste("+",cohort_aggrigate$Chromosome.scaffold.name[i], ",", sep = "")
ch_loss = paste("-",cohort_aggrigate$Chromosome.scaffold.name[i], ",", sep = "")
# correct for VAF based on copy number gains for that chromosome
if(grepl(ch_gain, cohort_aggrigate$Cytogenetics[i]) & cohort_aggrigate$VAF_male_x[i] > 35) {cohort_aggrigate$VAF_CN_corrected[i] = cohort_aggrigate$VAF_male_x[i]*0.75}
if(grepl(ch_gain, cohort_aggrigate$Cytogenetics[i]) & cohort_aggrigate$VAF_male_x[i] <= 35) {cohort_aggrigate$VAF_CN_corrected[i] = cohort_aggrigate$VAF_male_x[i]*1.5}
# correct for VAF based on copy number loss for that chromosome
if(grepl(ch_loss, cohort_aggrigate$Cytogenetics[i])){ cohort_aggrigate$VAF_CN_corrected[i] = cohort_aggrigate$VAF_male_x[i]/2}
# find focal chromosome gains or losses
locus_gain = paste("add",cohort_aggrigate$partial_annotation[i],"|","add",cohort_aggrigate$full_annotation[i], sep = "")
locus_loss = paste("del",cohort_aggrigate$partial_annotation[i],"|","del",cohort_aggrigate$full_annotation[i], sep = "")
# correct for VAF based on broad copy number gains at the gene locus
if(grepl(locus_gain, cohort_aggrigate$Cytogenetics[i], fixed = T) & cohort_aggrigate$VAF_male_x[i] > 35){ cohort_aggrigate$VAF_CN_corrected[i] = cohort_aggrigate$VAF_male_x[i]*0.75}
if(grepl(locus_gain, cohort_aggrigate$Cytogenetics[i], fixed = T) & cohort_aggrigate$VAF_male_x[i] <= 35){ cohort_aggrigate$VAF_CN_corrected[i] = cohort_aggrigate$VAF[i]*1.5}
# correct for VAF based on broad copy number loss at the gene locus
if(grepl(locus_loss, cohort_aggrigate$Cytogenetics[i], fixed = T)){ cohort_aggrigate$VAF_CN_corrected[i] = cohort_aggrigate$VAVAF_male_xF[i]/2}
}
}
# TP53 is a unique case. Code manually for focal deletions
tp53_loss1 = "del(17)(p13)"
tp53_loss2 = "del(17)(p11.2p13)"
for(i in 1:nrow(cohort_aggrigate)){
if(grepl(tp53_loss1, cohort_aggrigate$Cytogenetics[i]) & cohort_aggrigate$Gene[i] == "TP53") { cohort_aggrigate$VAF_CN_corrected[i] = cohort_aggrigate$VAF[i]/2}
if(grepl(tp53_loss2, cohort_aggrigate$Cytogenetics[i], fixed = T) & cohort_aggrigate$Gene[i] == "TP53") { cohort_aggrigate$VAF_CN_corrected[i] = cohort_aggrigate$VAF[i]/2}
}
# if no copy number differenes detected, populate the raw VAF
for(i in 1:nrow(cohort_aggrigate)){
if(is.na(cohort_aggrigate$VAF_CN_corrected[i])) { cohort_aggrigate$VAF_CN_corrected[i] = cohort_aggrigate$VAF_male_x[i]}
}
# bind all of the cohorts together into a final data frame
# remove the copy number column from some dataframes to make it compatible with the other data frames
tcga_dat$cn = NULL
cohort_aggrigate[,28:32] = NULL
# bind all cohorts together
final_data_matrix = rbind(papaemmanuil_corrected, tcga_dat, cohort_aggrigate, fill = TRUE)
final_data_matrix_2 = subset(final_data_matrix, final_data_matrix$Subset == "de_novo" & final_data_matrix$VAF_CN_corrected > 0)
save(final_data_matrix_2, file = "~/Desktop/MetaAML_results/final_data_matrix_2.RData")
# delineate FLT3 mutations
for(i in 1:nrow(final_data_matrix_2)){
if(final_data_matrix_2$Gene[i] == "FLT3" & final_data_matrix_2$variant_type[i] %in% c("ITD", "INDEL")){
final_data_matrix_2$Gene[i] <- "FLT3-ITD"
}
if(final_data_matrix_2$Gene[i] == "FLT3" & final_data_matrix_2$variant_type[i] %in% c("SNV", "Deletion", "other")){
final_data_matrix_2$Gene[i] <- "FLT3-TKD"
}
}
# plot the difference in VAF distribution per gene after CNA correction
raw_vaf = select(final_data_matrix_2, Gene, VAF, Cohort)
raw_vaf$group = "VAF"
CNA_vaf = select(final_data_matrix_2, Gene, VAF_CN_corrected, Cohort)
names(CNA_vaf)[2] = "VAF"
CNA_vaf$group = "VAF + \nCytogenetics"
all = rbind(raw_vaf, CNA_vaf)
# select mutations
mut_list = c("TP53", "DNMT3A", "SRSF2", "IDH2", "JAK2", "TET2", "U2AF1", "IDH1", "ETV6", "RUNX1", "BCOR", "ASXL1", "PHF6", "SF3B1", "GATA2", "CBL", "CEBPA", "WT1", "RAD21", "EZH2", "NF1", "KIT", "NPM1", "FLT3-ITD","NRAS", "KRAS", "FLT3-TKD", "PTPN11")
all = subset(all, all$Gene %in% mut_list)
all$group = factor(all$group , levels=c("VAF", "VAF + \nCytogenetics"))
genes = sort(unique(all$Gene))
# Box plot facetted by "gene"
p <- ggpaired(all, x = "group", y = "VAF",
color = "group", palette = "jama",
line.color = "lightgray", line.size = 0.4,
facet.by = "Gene", short.panel.labs = T)
# Use only p.format as label. Remove method name.
p + stat_compare_means(label = "p.format", paired = TRUE) + xlab(NULL) + ylab("VAF") + theme(legend.title=element_blank())
ggsave(filename = "~/Desktop/MetaAML_results/Figure_3/Supplimental/all_vaf_correction_comparison_facet_gene.pdf", dpi = 300, width = 10, height = 12, units = "in")
# plot total vaf distribution for recurrent mutations
p <- ggboxplot(all, x = "Gene", y = "VAF",
color = "group", palette = "jama",
add = "jitter",
order = genes,
shape = 21)
p + stat_compare_means(aes(group = group), label = "p.signif", hide.ns = TRUE) + xlab(NULL) +
ylim(0,110) +
theme(legend.position="right", axis.text.x = element_text(angle = 45, vjust = 1,hjust = 1)) + theme(legend.title=element_blank())
ggsave(filename = "~/Desktop/MetaAML_results/Figure_3/Supplimental/all_vaf_correction_comparison.pdf", dpi = 300, width = 15, height = 3, units = "in")
# now plot the VAF distribution per gene colored by if the mutation is above or below the median VAF for that gene
genes = unique(final_data_matrix_2$Gene)
final_data_matrix_2$median_threshold = NA
threshold_list = list()
z = 1
for(i in 1:length(genes)){
# select the gene of interest
sub = subset(final_data_matrix_2, Gene == genes[i])
# calculate the median vaf based on the X-corrected VAF
med_vaf = as.numeric(median(sub$VAF_CN_corrected, na.rm = T))
sub$median_threshold = ifelse(sub$VAF >= med_vaf, "High", "Low")
sub$median_threshold_NA = ifelse(sub$VAF_CN_corrected >= med_vaf, "High", "Low")
threshold_list[[i]] = data.frame(sub)
}
threshold_list_all = do.call(rbind, threshold_list)
rm(threshold_list)
threshold_list_all = threshold_list_all[!is.na(threshold_list_all$VAF_CN_corrected),]
# select mutations
mut_list = c("TP53", "DNMT3A", "SRSF2", "IDH2", "JAK2", "TET2", "U2AF1", "IDH1", "ETV6", "RUNX1", "BCOR", "ASXL1", "PHF6", "SF3B1", "GATA2", "CBL", "CEBPA", "WT1", "RAD21", "EZH2", "NF1", "KIT", "NPM1", "FLT3-ITD","NRAS", "KRAS", "FLT3-TKD", "PTPN11")
threshold_list_all = subset(threshold_list_all, threshold_list_all$Gene %in% mut_list)
threshold_list_all$Gene <- with(threshold_list_all, reorder(Gene, -VAF_CN_corrected, median))
counts = as.data.frame(table(threshold_list_all$Gene))
names(counts)[1] = "Gene"
# add n to the plots
# give.n <- function(x){
# return(c(y = 100, label = length(x)))
# # experiment with the multiplier to find the perfect position
# }
# number of patients
n_pts = n_distinct(threshold_list_all$Sample)
p = ggplot(threshold_list_all, aes(x = Gene, y = VAF_CN_corrected)) +
# ggtitle("Main Plot Title") +
ylab("VAF") +
xlab(NULL) +
theme_cowplot(font_size = 10) +
scale_shape_identity() +
scale_colour_brewer(palette = "Set1") +
scale_fill_manual(values = c("#cb181d", "#3690c0")) +
geom_point(aes(fill = median_threshold_NA), alpha = 0.5, color = "black", shape = 21, position=position_jitter(0.15), size = 1) +
geom_flat_violin(position = position_nudge(x = 0.3, y = 0),
color = "grey", fill = "lightgrey",
adjust = 2,
alpha = 0.6,
trim = TRUE,
scale = "width") +
geom_boxplot(position = position_nudge(x = 0.3, y = 0),
notch = FALSE,
width = 0.2,
varwidth = FALSE,
outlier.shape = NA,
alpha = 0.3,
colour = "black",
show.legend = FALSE) +
theme(legend.position="right", axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1))
# stat_summary(fun.data = give.n, geom = "text", fun.y = median)
ggpar(p, legend.title = "VAF")
ggsave(filename = "~/Desktop/MetaAML_results/Figure_3/MetaAML_vaf_distribution_median_corrected_raincloud.pdf", dpi = 300, width = 10, height = 2.5, units = "in")
write.csv(threshold_list_all, "~/Desktop/MetaAML_results/Figure_3/MetaAML_vaf_distribution_median_corrected_raincloud.csv")
# analyze the clinical features that associate with high or low vaf per gene
sub1 = subset(final_data_matrix, mut_freq_gene > 50 & final_data_matrix$Subset == "de_novo" & final_data_matrix$Gene != "MLL")
# make sure that the FLT3 symbols are annotated well
for(i in 1:nrow(sub1)){
if(sub1$Gene[i] == "FLT3" & sub1$variant_type[i] %in% c("ITD", "INDEL")){
sub1$Gene[i] <- "FLT3-ITD"
}
if(sub1$Gene[i] == "FLT3" & sub1$variant_type[i] %in% c("SNV", "Deletion", "other")){
sub1$Gene[i] <- "FLT3-TKD"
}
}
genes = data.frame(unique(sub1$Gene))
# create directories for binary comparisions
# binary
dir.create("~/Desktop/MetaAML_results/Figure_3/Supplimental/VAF_Age")
dir.create("~/Desktop/MetaAML_results/Figure_3/Supplimental/VAF_WBC")
dir.create("~/Desktop/MetaAML_results/Figure_3/Supplimental/VAF_Platelet")
dir.create("~/Desktop/MetaAML_results/Figure_3/Supplimental/VAF_LDH")
dir.create("~/Desktop/MetaAML_results/Figure_3/Supplimental/VAF_Hemoglobin")
dir.create("~/Desktop/MetaAML_results/Figure_3/Supplimental/VAF_PB_blast_percent")
dir.create("~/Desktop/MetaAML_results/Figure_3/Supplimental/VAF_BM_blast_percent")
# summary plots for each variable ####
sub1$Gene <- as.factor(sub1$Gene)
variables = c("Age", "Platelet", "Hemoglobin", "WBC", "LDH", "BM_blast_percent", "PB_blast_percent")
colors = c("#bdbdbd", "#fb6a4a", "#fe9929", "#d4b9da", "#238443", "#e6ab02", "#8dd3c7")
# continuous and discrete correlations ####
variables = data.frame("Age", "Platelet", "Hemoglobin", "WBC", "LDH", "BM_blast_percent", "PB_blast_percent")
variable_vaf_t_list = list()
y = 1
variable_vaf_list = list()
z = 1
for(i in 1:ncol(variables)){
sub = sub1
variable = as.character(variables[1,i])
# var_feature = as.character(variables[1,i])
print(variable)
if(variable == "Platelet"){
y_label <- "Platelet (1e3/μL)"
y_max <- 375
n_label <- 300
point_color = "#fdd49e"
} else if(variable == "Hemoglobin"){
y_label <- "Hemoglobin (g/dL)"
y_max <- 15
n_label <- 12
point_color = "#fe9929"
} else if(variable == "WBC"){
y_label <- "WBC"
y_max <- 300
n_label = 240
point_color = "#d4b9da"
} else if(variable == "LDH"){
y_label <- "LDH"
y_max <- 1750
n_label = 1400
point_color = "#238443"
} else if(variable == "Age"){
y_label <- "Age"
y_text = 90
y_max = 100
n_label = 80
point_color = "#8073ac"
} else if(variable == "BM_blast_percent"){
y_label <- "BM Blast %"
n_label = 80
y_max = 109
point_color = "#e6ab02"
} else if(variable == "PB_blast_percent"){
y_label <- "PB Blast %"
n_label = 80
y_max = 109
point_color = "#8dd3c7"
}
sub$VAF <- as.numeric(sub$VAF)
sub$VAF_CN_corrected <- as.numeric(sub$VAF_CN_corrected)
sub$Age <- as.numeric(sub$Age)
sub$Hemoglobin <- as.numeric(sub$Hemoglobin)
sub$Platelet <- as.numeric(sub$Platelet)
sub$WBC <- as.numeric(sub$WBC)
sub$LDH <- as.numeric(sub$LDH)
sub$BM_blast_percent <- as.numeric(sub$BM_blast_percent)
sub$PB_blast_percent <- as.numeric(sub$PB_blast_percent)
sub$median_vaf = as.factor(ifelse(sub$VAF_CN_corrected > median(sub$VAF_CN_corrected, na.rm = T), "Over", "Under"))
sub = sub[!is.na(sub$median_vaf),]
sub$median_vaf <- factor(sub$median_vaf , levels=c("Under", "Over"))
cnum = which( colnames(sub)==variable)
colnames(sub)[cnum] = "var_ft"
p = ggplot(sub, aes(x=median_vaf, y=var_ft)) +
geom_boxplot(notch=F, outlier.colour = "white", color = "#374E55FF", fill = "lightgrey") +
geom_jitter(aes(fill = median_vaf), color = "black", shape = 21, position=position_jitter(0.2), size = 3) +
scale_fill_manual(values = c("Over"=point_color, "Under" = "lightgrey")) +
stat_compare_means(label.x = 1.1, label.y = y_max) +
theme_cowplot(font_size = 20) +
# labs(title = paste(gene)) +
theme(plot.title = element_text(hjust = 0.5)) +
ylab(label= variable) +
xlab(label = NULL) +
ylim(0,y_max) +
theme(legend.position="right") + labs(fill = "Median VAF") + theme(
axis.text.x = element_blank(),
axis.ticks.x.bottom = element_blank())
#
p + facet_wrap(. ~ Gene, ncol = 6) +
theme(strip.background = element_rect(colour="black", fill="white",
size=1.5, linetype="solid"))
#
ggsave(filename = paste("~/Desktop/MetaAML_results/Figure_3/Supplimental/", "VAF_", variable,"/",variable,"_discrete.png", sep = ""), dpi = 300, width = 15, height = 15, units = "in")
# continuous
# ggscatter(sub, "VAF", y = "Variable",
# color = "black", fill = point_color, size = 5, shape = 21,
# font.label = list(color = "black", size = 9, vjust = 0.5),
# # title = paste(gene),
# xlab = "VAF",
# ylab = y_label) +
# # xlim(0,1) +
# # ylim(0,y_max)+
# geom_smooth(method = "lm", color = "black", alpha = .5) +
# theme(plot.title = element_text(hjust = 0.5, size = 18), axis.title = element_text(size = 18)) +
# stat_cor(
# aes(label = paste(..rr.label..)),
# label.x.npc = 0.65,
# label.y.npc = 1
# ) +
# stat_cor(
# aes(label = paste(..p.label..)),
# label.x.npc = 0.65,
# label.y.npc = 0.9
# ) +
# # geom_text()
# annotate("text", label = paste("n =", n), x = 0.75, y = n_label, size = 4, colour = "black")
# p + facet_wrap(. ~ Gene, ncol = 6) +
# theme(strip.background = element_rect(colour="black", fill="white",
# size=1.5, linetype="solid"))
# ggsave(filename = paste("~/Desktop/MetaAML_results/Data/Figures/", "VAF_", variable,"/",variable,".png", sep = ""), dpi = 300, width = 15, height = 15, units = "in")
for(j in 1:nrow(genes)){
print(j)
variable = as.character(variables[1,i])
gene = as.character(genes$unique.sub1.Gene.[j])
sub_mut = sub
sub_mut <- subset(sub, sub$Gene == gene)
sub_mut$VAF <- as.numeric(sub_mut$VAF)
sub_mut$VAF_CN_corrected <- as.numeric(sub_mut$VAF_CN_corrected)
sub_mut$Age <- as.numeric(sub_mut$Age)
sub_mut$Hemoglobin <- as.numeric(sub_mut$Hemoglobin)
sub_mut$Platelet <- as.numeric(sub_mut$Platelet)
sub_mut$WBC <- as.numeric(sub_mut$WBC)
sub_mut$LDH <- as.numeric(sub_mut$LDH)
sub_mut$BM_blast_percent <- as.numeric(sub_mut$BM_blast_percent)
sub_mut$PB_blast_percent <- as.numeric(sub_mut$PB_blast_percent)
n <- as.numeric(nrow(sub_mut))
print(n)
names(sub_mut)[names(sub_mut) == "var_ft"] <- "Variable"
# t-test on distribution of clinical variable based on VAF threshold
# find if the sample has a VAF greater or less than the median for that mutation
sub_mut$median_vaf = as.factor(ifelse(sub_mut$VAF_CN_corrected > median(sub_mut$VAF_CN_corrected, na.rm = T), "Over", "Under"))
sub_mut = sub_mut[!is.na(sub_mut$median_vaf),]
# calculate a p-value and effect size for the difference in clinical feaures based on VAF differences
p_val = wilcox.test(sub_mut$Variable ~ sub_mut$median_vaf, alternative = "two.sided")$p.value
effect_size = cohens_d(data = sub_mut, Variable ~ median_vaf)$effsize
n_n1 = as.numeric(length(which(sub_mut$median_vaf == "Over")))
n_n2 = as.numeric(length(which(sub_mut$median_vaf == "Under")))
effect_size_ci = psych::cohen.d.ci(d = effect_size, n = n, n1 = n_n1, n2 = n_n2)
variable_vaf_t <- data.frame(matrix(NA, nrow = 1, ncol = 6))
names(variable_vaf_t) <- c("Variable", "Mutated_Gene", "effect_size", "CI_lower", "CI_upper", "p_value")
variable_vaf_t[1,1] <- paste(variable)
variable_vaf_t[1,2] <- gene
variable_vaf_t[1,3] <- effect_size
variable_vaf_t[1,4] <- effect_size_ci[1]
variable_vaf_t[1,5] <- effect_size_ci[3]
variable_vaf_t[1,6] <- p_val
# Add each list in the loop to a list of lists
variable_vaf_t_list[[y]] = variable_vaf_t
y = y + 1
# put the results of the t-test in a dataframe
sub_mut$median_vaf <- factor(sub_mut$median_vaf , levels=c("Under", "Over"))
ylab_max = max(sub_mut$Variable, na.rm = TRUE)
ggplot(sub_mut, aes(x=median_vaf, y=Variable)) +
geom_boxplot(notch=F, outlier.colour = "white", color = "#374E55FF", fill = "lightgrey") +
geom_jitter(aes(fill = median_vaf), color = "black", shape = 21, position=position_jitter(0.2), size = 3) +
scale_fill_manual(values = c("Over"=point_color, "Under" = "lightgrey")) +
stat_compare_means(label.x = 1.1, label.y = ylab_max) +
theme_cowplot(font_size = 20) +
labs(title = paste(gene)) +
theme(plot.title = element_text(hjust = 0.5)) +
ylab(label= variable) +
xlab(label = NULL) +
ylim(0,ylab_max) +
theme(legend.position="right") + labs(fill = "Median VAF") + theme(
axis.text.x = element_blank(),
axis.ticks.x.bottom = element_blank())
ggsave(filename = paste("~/Desktop/MetaAML_results/Figure_3/Supplimental/", "VAF_", variable,"/",variable, "_vs_", gene,"_VAF.png", sep = ""), dpi = 300, width = 5, height = 5, units = "in")
}
}
# compile dataframes for discrete and continuous analyses
variable_vaf_comparison_discrete <- do.call(rbind, variable_vaf_t_list)
# correct for multiple hypotheses by variable
var2_adj = list()
a = 1
for(i in 1:ncol(variables)){
print(i)
variable = as.character(variables[1,i])
var2= subset(variable_vaf_comparison_discrete, variable_vaf_comparison_discrete$Variable == variable)
var2$q_val = p.adjust(var2$p_value)
var2_adj[[a]] = var2
a = a + 1
}
var2_adj_list <- do.call(rbind, var2_adj)
# variable_vaf_comparison$z_score <- qnorm(variable_vaf_comparison$p_value)
# var2_adj_list$sig = as.factor(ifelse(var2_adj_list$q_val < 0.2, "q < 0.2", "q > 0.2"))
for(i in 1:nrow(var2_adj_list)){
if(var2_adj_list$q_val[i] > 0.2){
var2_adj_list$sig[i] = "q > 0.2"
}
if(var2_adj_list$q_val[i] < 0.2 & var2_adj_list$q_val[i] > 0.1){
var2_adj_list$sig[i] = "q < 0.2"
}
if(var2_adj_list$q_val[i] < 0.1 & var2_adj_list$q_val[i] > 0.05){
var2_adj_list$sig[i] = "q < 0.1"
}
if(var2_adj_list$q_val[i] < 0.05){
var2_adj_list$sig[i] = "q < 0.05"
}
}
var2_adj_list$Variable = gsub("BM_blast_percent", "BM blast %", var2_adj_list$Variable)
var2_adj_list$Variable = gsub("PB_blast_percent", "PB blast %", var2_adj_list$Variable)
# order the factors
var2_adj_list$Variable = factor(var2_adj_list$Variable, levels=c('WBC','Hemoglobin','Platelet','LDH', 'BM blast %', 'PB blast %', 'Age'))
# plot the discrete results
p = ggplot(var2_adj_list, aes(x = Mutated_Gene, y = effect_size, label = p_value)) +
geom_hline(yintercept=0, linetype = "dashed", color = "black") +
theme_cowplot() +
geom_pointrange(size = 0.75, stat = "identity", alpha = 0.75,
aes(x = Mutated_Gene, ymin = CI_lower, ymax = CI_upper, y = effect_size, color = sig)) +
scale_color_manual(values = c("q < 0.2" = "#FDE725FF", "q < 0.1" = "#1F968BFF", "q < 0.05" = "#482677FF", "q > 0.2" = "grey")) +
ylab("Effect Size (high VAF vs. low VAF)")+
xlab("")+
theme(legend.position = "right", legend.title = element_blank(),
axis.title.y=element_blank()) +
coord_flip() +
xlim(rev(levels(factor(var2_adj_list$Mutated_Gene))))
p + facet_grid(. ~ Variable)
p + facet_grid(. ~ Variable) +
theme(strip.background = element_rect(colour="black", fill= "white",
size=1.5, linetype="solid"))
ggsave(filename = "~/Desktop/MetaAML_results/Figure_3/VAF_clinical_features_discrete.pdf", dpi = 300, width = 15, height = 6, units = "in")
write.csv(var2_adj_list, "~/Desktop/MetaAML_results/Figure_3/VAF_discrete_clinical_features.csv")
# optimal vaf cutoff ####
dir.create("~/Desktop/MetaAML_results/Figure_3/Supplimental/optimal_vaf_thresholds")
dir.create("~/Desktop/MetaAML_results/Figure_3/Supplimental/optimal_vaf_thresholds/logrank_plots")
load("~/Desktop/MetaAML_results/final_data_matrix_2.RData")
final_data_matrix_2_sub = subset(final_data_matrix_2, final_data_matrix_2$mut_freq_gene >= 50 & final_data_matrix_2$Gene != "MLL" & final_data_matrix_2$Subset == "de_novo")
final_data_matrix_2_sub$Time_to_OS <- (final_data_matrix_2_sub$Time_to_OS/365)
final_data_matrix_2_sub$Gene = as.character(final_data_matrix_2_sub$Gene)
for(i in 1:nrow(final_data_matrix_2_sub)){
if(final_data_matrix_2_sub$Gene[i] == "FLT3" & final_data_matrix_2_sub$variant_type[i] %in% c("SNV", "Deletion", "other")){
final_data_matrix_2_sub$Gene[i] = "FLT3-TKD"
}
if(final_data_matrix_2_sub$Gene[i] == "FLT3" & final_data_matrix_2_sub$variant_type[i] %in% c("ITD", "INDEL")){
final_data_matrix_2_sub$Gene[i] = "FLT3-ITD"
}
}
genes = data.frame(unique(final_data_matrix_2_sub$Gene))
genes = genes %>%
arrange(genes,unique.final_data_matrix_2_sub.Gene.)
# now define the optimal VAF cutoff for each gene
results_list = list()
n = 1
plot_list = list()
for(i in 1:nrow(genes)){
print(i)
gene <- as.character(genes[i,1])
final_data_matrix_2_sub2 <- subset(final_data_matrix_2_sub, final_data_matrix_2_sub$Gene == gene)
final_data_matrix_2_sub2 = na.omit(distinct(final_data_matrix_2_sub2, Sample, Gene, VAF, VAF_CN_corrected, Time_to_OS, Censor))
final_data_matrix_2_sub2 <- final_data_matrix_2_sub2[order(final_data_matrix_2_sub2$Sample, -final_data_matrix_2_sub2$VAF_CN_corrected),]
final_data_matrix_2_sub2 = final_data_matrix_2_sub2[!duplicated(final_data_matrix_2_sub2$Sample),]
if( nrow(final_data_matrix_2_sub2) >= 15){
# find the cutoff
final_data_matrix_2_sub2$vaf_threshold <- NA
final_data_matrix_2_sub2$Censor = as.numeric(final_data_matrix_2_sub2$Censor)
mstat <- maxstat.test(Surv(Time_to_OS, Censor) ~ VAF_CN_corrected, data=final_data_matrix_2_sub2,
smethod="LogRank", pmethod="exactGauss",
abseps=0.01)
png(filename = paste("~/Desktop/MetaAML_results/Figure_3/Supplimental/optimal_vaf_thresholds/logrank_plots/", gene, "_vaf_logrank_plot.png", sep = ""),res = 300, width = 5, height = 5, units = "in")
plot(mstat)
dev.off()
threshold = round(as.numeric(mstat$estimate), 2)
for(i in 1:nrow(final_data_matrix_2_sub2)){
if(!is.na(final_data_matrix_2_sub2$VAF_CN_corrected[i])){
if(final_data_matrix_2_sub2$VAF_CN_corrected[i] >= threshold){
final_data_matrix_2_sub2$vaf_threshold[i] = "over"
}
if(final_data_matrix_2_sub2$VAF_CN_corrected[i] < threshold){
final_data_matrix_2_sub2$vaf_threshold[i] = "under"
}
}
}
n_over = length(which(final_data_matrix_2_sub2$vaf_threshold == "over"))
n_under = length(which(final_data_matrix_2_sub2$vaf_threshold == "under"))
if(n_distinct(final_data_matrix_2_sub2$vaf_threshold) > 1){
# summarize results from a Cox model
final_data_matrix_2_sub2$vaf_threshold = ifelse(final_data_matrix_2_sub2$vaf_threshold == "over", 1,0)
OS_fit <- survfit(Surv(Time_to_OS, Censor) ~ 1, data=final_data_matrix_2_sub2)
OS_trt_fit <- survfit(Surv(Time_to_OS, Censor) ~ vaf_threshold, data=final_data_matrix_2_sub2, conf.type = "log-log")
# run the Cox model
model <- coxph( Surv(Time_to_OS, Censor) ~ vaf_threshold,
data = final_data_matrix_2_sub2 )
# extract the informative data from the survival model
array_dat = summary(model)$conf.int[1:4]
array_dat[5] = gene
# extract the log-rank p-value for the individual comparisons
array_dat[6] = summary(model)$sctest[3]
array_dat = array_dat[-2]
n_pts = ifelse(array_dat[1] < 1, n_under, n_over)
forest_plot_data <- data.frame("Gene" = array_dat[4], "HR" = array_dat[1], "Lower_CI" = round(as.numeric(array_dat[2]),2), "Upper_CI" = round(as.numeric(array_dat[3]),2), "log_rank_p" = array_dat[5], "VAF_threshold" = threshold, "n_pts" = n_pts)
results_list[[n]] <- forest_plot_data
n=n+1
p = round(summary(model)$sctest[3],3)
if(p <= 0.05){
print(gene)
p = ifelse(p < 0.001, paste0("p < 0.001"), paste("p =", p))
hr = paste("HR = ", round(as.numeric(forest_plot_data$HR), 2), " (", forest_plot_data$Lower_CI, "-", forest_plot_data$Upper_CI, ")", sep = "")
p_hr = paste(p, "\n", hr, sep = "")
cohorts <- c("under", "over")
title <- paste(threshold, "VAF\nthreshold",sep = " ")
# plots the survival
plot_list[[i]] = ggsurvplot(OS_trt_fit,
data = final_data_matrix_2_sub2,
log = (OS_trt_fit),
log.rank.weights = c("survdiff"),
pval = paste0(p_hr),
test.for.trend = F,
pval.method.size = 3,
pval.coord = c(2, 0.95),
conf.int = F,
censor = T,
surv.median.line = "none",
risk.table = T,
risk.table.title = c("Number at risk"),
risk.table.fontsize = 4,
risk.table.height = .3,
risk.table.y.text = T,
break.time.by = 5,
risk.table.pos = c("out"),
palette = c("#80796BFF", "#6A6599FF"),
xlab = "Years",
ylim = c(0, 1.0),
ylab = "Survival Probability",
font.main = c(15, "plain", "#252525"),
ggtheme = theme_cowplot(),
pval.size = 4,
font.x = c(15, "plain", "#252525"),
font.y = c(15, "plain", "#252525"),
font.legend = c(15, "plain"),
font.tickslab = c(15, "plain", "#252525"),
legend.labs = cohorts,
legend.title = paste(title),
legend = "right",
title = gene)
print(plot_list[[i]])
png(filename = paste("~/Desktop/MetaAML_results/Figure_3/Supplimental/optimal_vaf_thresholds/", gene, ".png", sep = ""), res = 300, width = 5, height = 5, units = "in")
#
plot_list[[i]]
print(plot_list[[i]])
dev.off()
}
}
}
}
temp_final = as.data.frame(do.call(rbind, results_list))
temp_final$q_value = p.adjust(temp_final$log_rank_p, method = "fdr")
write.csv(temp_final, file = "~/Desktop/MetaAML_results/Figure_3/Supplimental/optimal_vaf_thresholds/optimal_vaf_threshold_for_survival_prediction.csv", row.names = F)
# summary plot of all optimal survival curves
plot_list = list.clean(plot_list)
plots = arrange_ggsurvplots(plot_list, print = FALSE,
ncol = 6, nrow = 2)
ggsave("~/Desktop/MetaAML_results/Figure_3/Supplimental/optimal_VAF_survival_grid.pdf", plots, width = 30, height = 10)
# forrest plot of HRs ####
temp_final_hr = as.data.frame(do.call(rbind, results_list))
temp_final_hr$gene <- factor(temp_final_hr$gene, levels = temp_final_hr$gene[order(temp_final_hr$HR)])
temp_final_hr$sig_color = 0
for(i in 1:nrow(temp_final_hr)){
if(temp_final_hr$log_rank_p[i] < 0.05 & temp_final_hr$HR[i] < 1){
temp_final_hr$sig_color[i] = 1
}
if(temp_final_hr$log_rank_p[i] < 0.05 & temp_final_hr$HR[i] > 1){
temp_final_hr$sig_color[i] = 2
}
}
temp_final_hr$sig_color = as.factor(temp_final_hr$sig_color)
temp_final_hr$fdr = p.adjust(temp_final_hr$log_rank_p, method = "fdr")
temp_final_hr$sig_color = as.factor(temp_final_hr$sig_color)
temp_final_hr = subset(temp_final_hr, temp_final_hr$Upper_CI != "Inf")
temp_final_hr$categories <- reorder(temp_final_hr$categories, temp_final_hr$HR)
temp_final_hr$p_text = NA
for(i in 1:nrow(temp_final_hr)){
if(temp_final_hr$log_rank_p[i] < 0.05){
temp_final_hr$p_text[i] = temp_final_hr$log_rank_p[i]
}
}
temp_final_hr$q_text = NA
for(i in 1:nrow(temp_final_hr)){
if(temp_final_hr$fdr[i] < 0.2){
temp_final_hr$q_text[i] = temp_final_hr$fdr[i]
}
}
temp_final_hr$p_text = as.numeric(temp_final_hr$p_text)
temp_final_hr$q_text = as.numeric(temp_final_hr$q_text)
temp_final_hr$p_text = round(temp_final_hr$p_text, 3)
temp_final_hr$q_text = round(temp_final_hr$q_text, 2)
for(i in 1:nrow(temp_final_hr)){
if(temp_final_hr$log_rank_p[i] < 0.01){
temp_final_hr$p_text[i] = "p < 0.01"
}
if(temp_final_hr$log_rank_p[i] >= 0.01 & temp_final_hr$log_rank_p[i] <= 0.05){
temp_final_hr$p_text[i] = paste("p =", paste(temp_final_hr$p_text[i]))
}
}
temp_final_hr$p_q_text = paste(temp_final_hr$p_text, "; q = ", temp_final_hr$q_text, sep = "")
for(i in 1:nrow(temp_final_hr)){
if(temp_final_hr$log_rank_p[i] > 0.05){
temp_final_hr$p_q_text[i] = ""
}
if(temp_final_hr$log_rank_p[i] > 0.05){
temp_final_hr$p_q_text[i] = ""
}
}
temp_final_hr$HR = as.numeric(temp_final_hr$HR)
temp_final_hr$Lower_CI = as.numeric(temp_final_hr$Lower_CI)
temp_final_hr$Upper_CI = as.numeric(temp_final_hr$Upper_CI)
temp_final_hr$Gene <- factor(temp_final_hr$Gene, levels = temp_final_hr$Gene[order(temp_final_hr$HR)])
ggplot(temp_final_hr, aes(x = reorder(Gene, -HR), y = HR, label = p_q_text)) +
geom_hline(yintercept=1, linetype="dashed", color = "black") +
geom_text(aes(Gene, Upper_CI), hjust = 0, nudge_y = 0.5) +
geom_point(aes(size = n_pts, color = sig_color), shape = 19, alpha = .9) +
geom_segment(data = temp_final_hr, aes(y = Lower_CI, yend = Upper_CI, x = Gene, xend = Gene, col= sig_color), size = 0.5) +
scale_color_manual(values = c("0" = "grey", "1" = "#1b7837", "2" = "#762a83"))+
ylab("Hazard Ratio\n(high VAF vs. low VAF)")+
theme_cowplot() +
scale_size_area(max_size = 5,breaks=c(25,50,100,200,300)) +
theme(legend.position = c(0.6, .2),
axis.title.y=element_blank()) +
coord_flip(ylim = c(0,22)) +
guides(color = FALSE,
size = guide_legend(override.aes=list(colour="lightgrey"), title = "n. patients over\nVAF threshold"))
ggsave(filename = "~/Desktop/MetaAML_results/Figure_3/gene_vaf_optimal_vaf_thresholds_hr_forest_plot_de_novo.pdf", dpi = 300, width = 5, height = 6, units = "in")
# HR differences ####
gene_hrs = read.csv("~/Desktop/MetaAML_results/Figure_2/Supplimental/gene_hazard_ratio.csv")
gene_vaf_hrs = read.csv("~/Desktop/MetaAML_results/Figure_3/Supplimental/optimal_vaf_thresholds/optimal_vaf_threshold_for_survival_prediction.csv")
gene_hrs = gene_hrs %>%
select(gene, HR, lower_95, upper_95, q_value)
names(gene_hrs) = c("Gene", "HR", "gene_lower_95", "gene_upper_95", "gene_q_value")