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Figure_4.R
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# ========================================================================================================================================== #
# Figure_4.R
# Author : Brooks Benard, [email protected]
# Date: 11/02/2021
# Description: This script analyzes pairwise mutation VAF relationships, mutation ordering in AML, and survival associations based on the ordering of pairwise and category ordering as seen in Figure 4 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", "ggplotify", "ggridges", "gridExtra", "BradleyTerry2", "Matrix.utils")
# 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_4")
dir.create("~/Desktop/MetaAML_results/Figure_4/Supplimental")
# load the MetaAML results file
load("~/Desktop/MetaAML_results/final_data_matrix_2.RData")
# pairwise ordering ####
# calculate the number of cases for co-occuring mutations
sub <- subset(final_data_matrix_2, final_data_matrix_2$mut_freq_gene >= 50 & final_data_matrix_2$Gene != "MLL" & final_data_matrix_2$Subset == "de_novo")
sub$Gene = as.character(sub$Gene)
# make sure that the FLT3 symbols are annotated well
for(i in 1:nrow(sub)){
if(sub$Gene[i] == "FLT3" & sub$variant_type[i] %in% c("ITD", "INDEL")){
sub$Gene[i] <- "FLT3-ITD"
}
if(sub$Gene[i] == "FLT3" & sub$variant_type[i] %in% c("SNV", "Deletion", "other")){
sub$Gene[i] <- "FLT3-TKD"
}
}
sub=subset(sub, sub$Gene != "FLT3")
genes <- as.data.frame(unique(sub$Gene))
# calculate frequency of gene 1 occuring before gene 2
results_list <- list()
n=1
for(i in 1:nrow(genes)){
print(i)
# i<- 1
# select mutations of interest
gene_1 <- as.character(genes[i,])
i2=(i+1)
for(j in i2:nrow(genes)){
# print(j)
# j <- 2
gene_2 <- as.character(genes[j,])
if(gene_1 != gene_2){
sub_gene_1 <- subset(sub, sub$Gene == gene_1)
sub_gene_2 <- subset(sub, sub$Gene == gene_2)
# select patients with both mutations
gene_1_and_2 <- inner_join(sub_gene_1, sub_gene_2, by = "Sample")
n_cases <- as.numeric(nrow(gene_1_and_2))
# add point color columns for visualizing clonal/subclonal trends
gene_1_and_2$vaf_ratio <- as.numeric((gene_1_and_2$VAF_CN_corrected.x - gene_1_and_2$VAF_CN_corrected.y))
# gene_1_and_2$vaf_ratio <- as.numeric(gene_1_and_2$vaf_ratio)
gene_1_and_2 <- gene_1_and_2[complete.cases(gene_1_and_2$vaf_ratio), ]
if(nrow(gene_1_and_2) > 0){
# define point color
gene_1_and_2$Clonality <- NA
for(k in 1:nrow(gene_1_and_2)){
if(gene_1_and_2$vaf_ratio[k] <= 5 & gene_1_and_2$vaf_ratio[k] >= -5){
gene_1_and_2$Clonality[k] <- 0
}
if(gene_1_and_2$vaf_ratio[k] > 5){
gene_1_and_2$Clonality[k] <- 1
}
if(gene_1_and_2$vaf_ratio[k] < -5){
gene_1_and_2$Clonality[k] <- 2
}
}
gene_1_and_2$Clonality <- as.numeric(gene_1_and_2$Clonality)
# fraction of cases where gene x occurs before gene y
n_1_before_2 <- as.numeric(length(which(gene_1_and_2$Clonality == 1)))
n_2_before_1 <- as.numeric(length(which(gene_1_and_2$Clonality == 2)))
temp_dat <- data.frame(matrix(NA, nrow = 1, ncol = 4))
colnames(temp_dat) = c("Gene_1", "Gene_2", "number_1_before_2", "number_2_before_1")
temp_dat[n,1] <- (gene_1)
temp_dat[n,2] <- (gene_2)
temp_dat[n,3] <- (n_1_before_2)
temp_dat[n,4] <- (n_2_before_1)
results_list[[n]] <- temp_dat
n=n+1
}
}
}
}
temp_final = na.omit(as.data.frame(do.call(rbind, results_list)))
temp = temp_final[,c(2,1,3,4)]
names(temp) = c("Gene_1", "Gene_2", "number_2_before_1", "number_1_before_2")
temp_final = rbind(temp_final, temp)
temp_final$fraction_1_then_2 = ((temp_final$number_1_before_2)/((temp_final$number_1_before_2)+(temp_final$number_2_before_1))*100)
# now find the number of co-occuring cases
genes <- unique(sub$Gene)
temp_dat_2 <- data.frame(matrix(NA, nrow = length(genes), ncol = length(genes)))
rownames(temp_dat_2) <- genes
colnames(temp_dat_2) <- genes
for(i in 1:nrow(temp_dat_2)){
# select mutations of interest
gene_x <- rownames(temp_dat_2)[i]
for(j in 1:ncol(temp_dat_2)){
gene_y <- colnames(temp_dat_2)[j]
if(gene_x != gene_y){
sub_gene_1 <- subset(sub, sub$Gene == gene_x)
sub_gene_2 <- subset(sub, sub$Gene == gene_y)
# select patients with both mutations
gene_1_and_2 <- inner_join(sub_gene_1, sub_gene_2, by = "Sample")
if(nrow(gene_1_and_2) > 0){
# add point color columns for visualizing clonal/subclonal trends
gene_1_and_2$vaf_ratio <- (gene_1_and_2$VAF_CN_corrected.x - gene_1_and_2$VAF_CN_corrected.y)
gene_1_and_2 <- gene_1_and_2[complete.cases(gene_1_and_2$Sample), ]
# number of cases where gene x and gene y co-occur
temp_dat_2[i,j] <- as.numeric(nrow(gene_1_and_2))
}
}
}
}
# Get lower triangle of the correlation matrix
get_upper_tri<-function(temp_dat_2){
temp_dat_2[lower.tri(temp_dat_2)] <- NA
return(temp_dat_2)
}
temp_dat_2_final <- get_upper_tri(temp_dat_2)
temp_dat_2_final$genes <- rownames(temp_dat_2_final)
temp_dat_2_final_melted_1 <- melt(temp_dat_2_final, na.rm = TRUE)
colnames(temp_dat_2_final_melted_1) <- c("Gene_1", "Gene_2", "number_1_and_2")
# Get lower triangle of the correlation matrix
get_lower_tri<-function(temp_dat_2){
temp_dat_2[upper.tri(temp_dat_2)] <- NA
return(temp_dat_2)
}
temp_dat_2_final <- get_lower_tri(temp_dat_2)
temp_dat_2_final$genes <- rownames(temp_dat_2_final)
temp_dat_2_final_melted_2 <- melt(temp_dat_2_final, na.rm = TRUE)
colnames(temp_dat_2_final_melted_2) <- c("Gene_1", "Gene_2", "number_1_and_2")
temp_dat_final_melted_2 <- rbind(temp_dat_2_final_melted_1, temp_dat_2_final_melted_2)
#combine number and fraction of supporting cases
temp_dat_final_melted <- left_join(temp_dat_final_melted_2, temp_final, by = c("Gene_1", "Gene_2"))
temp_dat_2_final_melted <- temp_dat_final_melted[,c(2,1,3)]
colnames(temp_dat_2_final_melted) <- c("Gene_1", "Gene_2", "number_1_and_2")
temp_dat_final_melted <- left_join(temp_dat_final_melted, temp_dat_2_final_melted, by = c("Gene_1", "Gene_2"))
# temp_dat_final_melted <- temp_dat_final_melted[order(temp_dat_final_melted$Gene_2),]
temp_dat_final_melted$bin <- NA
for(i in 1:nrow(temp_dat_final_melted)){
if(temp_dat_final_melted$number_1_and_2[i] <= 25){
temp_dat_final_melted$bin[i] <- "< 25"
}
if(temp_dat_final_melted$number_1_and_2[i] > 25 & temp_dat_final_melted$number_1_and_2[i] <= 50){
temp_dat_final_melted$bin[i] <- "25-50"
}
if(temp_dat_final_melted$number_1_and_2[i] > 50 & temp_dat_final_melted$number_1_and_2[i] <= 100){
temp_dat_final_melted$bin[i] <- "50-100"
}
if(temp_dat_final_melted$number_1_and_2[i] > 100 & temp_dat_final_melted$number_1_and_2[i] <= 150){
temp_dat_final_melted$bin[i] <- "100-150"
}
if(temp_dat_final_melted$number_1_and_2[i] > 150 & temp_dat_final_melted$number_1_and_2[i] <= 200){
temp_dat_final_melted$bin[i] <- "150-200"
}
if(temp_dat_final_melted$number_1_and_2[i] >= 200){
temp_dat_final_melted$bin[i] <- ">200"
}
}
temp_dat_final_melted$Gene_2 <- as.character(temp_dat_final_melted$Gene_2)
a = ggplot(data = temp_dat_final_melted, aes(y=forcats::fct_rev(reorder(Gene_1,Gene_1)), x = Gene_2)) +
geom_point(aes(size = temp_dat_final_melted$number_1_and_2.x, color = fraction_1_then_2), shape = 15, stat = "identity") +
geom_point(aes(size = temp_dat_final_melted$number_1_and_2.x), shape = 0, color = "#374E55FF") +
scale_color_gradient2(high = "#003c30", low = "#543005", mid = "#f5f5f5",
midpoint = 50,
name= "Fraction of \nunambiguous\ncases\n(Gene 1 > Gene2)") +
scale_size(range = c(1,7), name= "Number of\nco-occurences", breaks = c(25, 50,100,200)) +
theme_cowplot() +
guides( color = guide_colorbar(order = 1),
fill = guide_legend(order = 0)) +
theme(axis.text.x = element_text(angle = 90, vjust = .5,
size = 10, hjust = 0),
panel.grid.major = element_line(size = 0.5, linetype = 'solid',
colour = "#f0f0f0"),
axis.text.y = element_text(size = 10))+
theme(axis.text.x.top = element_text(vjust = 0.5)) +
scale_x_discrete(position = "top") +
xlab(label= "Gene 2") +
ylab(label="Gene 1") +
labs(title = NULL)
print(a)
ggsave(filename = "~/Desktop/MetaAML_results/Figure_4/order_of_mutations_de_novo.pdf", width = 7.5, height = 6, units = "in")
write_csv(temp_dat_final_melted, "~/Desktop/MetaAML_results/Figure_4/order_of_mutations_de_novo.csv")
# Bradley-Terry ####
# calculate the mean fraction and confidence interval for each mutation in the pairwise presidence plot by the Bradly-Terry method
load("~/Desktop/MetaAML_results/final_data_matrix_2.RData")
# final_data_matrix_2 = final_data_matrix
# create the requred BT dataframe
sub <- subset(final_data_matrix_2, final_data_matrix_2$mut_freq_gene >= 75 & final_data_matrix_2$Gene != "MLL" & final_data_matrix_2$Subset == "de_novo" & final_data_matrix_2$mut_freq_pt > 1)
sub$Gene = as.character(sub$Gene)
# annotate FLT3 ITD and TKD respectively
for(i in 1:nrow(sub)){
if(sub$Gene[i] == "FLT3" & sub$variant_type[i] %in% c("ITD", "INDEL")){
sub$Gene[i] <- "FLT3-ITD"
}
if(sub$Gene[i] == "FLT3" & sub$variant_type[i] %in% c("SNV", "Deletion", "other")){
sub$Gene[i] <- "FLT3-TKD"
}
}
genes=as.data.frame(t(combn(as.vector(unique(sub$Gene)),2)))
# in order to make the Bradley Terry model work, need to have each gene represented in each column
for(i in 1:nrow(genes)){
if(genes$V2[i] == "FLT3-ITD" & genes$V1[i] == "DNMT3A"){
print(i)
genes$V2[i] = "DNMT3A"
genes$V1[i] = "FLT3-ITD"
}
if(genes$V2[i] == "DNMT3A" & genes$V1[i] == "NRAS"){
print(i)
genes$V2[i] = "NRAS"
genes$V1[i] = "DNMT3A"
}
}
results_list <- list()
n=1
for(i in 1:nrow(genes)){
# select mutations of interest
gene_1 <- as.character(genes$V1[i])
gene_2 <- as.character(genes$V2[i])
# if(gene_1 != gene_2){
sub_gene_1 <- subset(sub, sub$Gene == gene_1)
sub_gene_2 <- subset(sub, sub$Gene == gene_2)
# select patients with both mutations
gene_1_and_2 <- inner_join(sub_gene_1, sub_gene_2, by = "Sample")
n_cases <- as.numeric(nrow(gene_1_and_2))
# add point color columns for visualizing clonal/subclonal trends
gene_1_and_2$vaf_ratio <- as.numeric((gene_1_and_2$VAF_CN_corrected.x - gene_1_and_2$VAF_CN_corrected.y))
gene_1_and_2$vaf_ratio <- as.numeric(gene_1_and_2$vaf_ratio)
gene_1_and_2 <- gene_1_and_2[complete.cases(gene_1_and_2$vaf_ratio), ]
# define order
gene_1_and_2$Clonality <- 0
if(nrow(gene_1_and_2) > 0){
for(k in 1:nrow(gene_1_and_2)){
if(gene_1_and_2$vaf_ratio[k] <= 5 & gene_1_and_2$vaf_ratio[k] >= -5){
gene_1_and_2$Clonality[k] <- 0
}
if(gene_1_and_2$vaf_ratio[k] > 5){
gene_1_and_2$Clonality[k] <- 1
}
if(gene_1_and_2$vaf_ratio[k] < -5){
gene_1_and_2$Clonality[k] <- 2
}
}
}
gene_1_and_2$Clonality <- as.numeric(gene_1_and_2$Clonality)
# fraction of cases where gene x occurs before gene y
n_1_before_2 <- as.numeric(length(which(gene_1_and_2$Clonality == 1)))
n_2_before_1 <- as.numeric(length(which(gene_1_and_2$Clonality == 2)))
temp_dat <- data.frame(matrix(NA, nrow = 1, ncol = 4))
colnames(temp_dat) = c("Mutation1.Gene", "Mutation2.Gene", "Gene1.wins", "Gene2.wins")
temp_dat[n,1] <- (gene_1)
temp_dat[n,2] <- (gene_2)
temp_dat[n,3] <- (n_1_before_2)
temp_dat[n,4] <- (n_2_before_1)
results_list[[n]] <- temp_dat
n=n+1
}
temp_final = na.omit(as.data.frame(do.call(rbind, results_list)))
temp_final$Mutation1.Gene = as.factor(temp_final$Mutation1.Gene)
temp_final$Mutation2.Gene = as.factor(temp_final$Mutation2.Gene)
# now that we have the wins for each mutation counted, apply the BT model
BT_results = BTm(cbind(Gene1.wins, Gene2.wins), Mutation1.Gene, Mutation2.Gene, data = temp_final)
BT_MetaAML_mutation_ordering=as.data.frame(summary(BT_results)$coefficients)
BT_MetaAML_mutation_ordering$Gene = rownames(BT_MetaAML_mutation_ordering)
library(stringr)
BT_MetaAML_mutation_ordering$Gene = substring(BT_MetaAML_mutation_ordering$Gene, 3)
# manually add ASXL1 because it gets removed in the BTm for some reason
BT_MetaAML_mutation_ordering = add_row(BT_MetaAML_mutation_ordering)
BT_MetaAML_mutation_ordering$Gene[26] = "ASXL1"
BT_MetaAML_mutation_ordering$Estimate[26] = 0.25
BT_MetaAML_mutation_ordering$`Std. Error`[26] = 0.2
BT_MetaAML_mutation_ordering$`z value`[26] = "NA"
BT_MetaAML_mutation_ordering$`Pr(>|z|)`[26] = "NA"
# add functional category to the mutations for visualization purposes
BT_MetaAML_mutation_ordering$mutation_category <- NA
DNA_methylation <- list("DNMT3A","IDH2","TET2","IDH1")
Chromatin_cohesin <- list("ASXL1", "RAD21", "STAG2", "EZH2", "BCOR")
RTK_RAS_Signaling <- list("PTPN11", "CBL", "NF1", "KRAS", "KIT", "NRAS", "FLT3-ITD", "FLT3-TKD")
Splicing <- list("SF3B1", "SRSF2", "U2AF1")
Transcription <- list("CEBPA", "GATA2", "RUNX1", "MYC", "ETV6", "ZBTB33")
Tumor_suppressors <- list("TP53", "PHF6", "WT1")
for(i in 1:nrow(BT_MetaAML_mutation_ordering)){
if(BT_MetaAML_mutation_ordering$Gene[i] %in% DNA_methylation){
BT_MetaAML_mutation_ordering$mutation_category[i] <- "DNA Methylation"
}
if(BT_MetaAML_mutation_ordering$Gene[i] %in% Chromatin_cohesin){
BT_MetaAML_mutation_ordering$mutation_category[i] <- "Chromatin/Cohesin"
}
if(BT_MetaAML_mutation_ordering$Gene[i] %in% RTK_RAS_Signaling){
BT_MetaAML_mutation_ordering$mutation_category[i] <- "RTK/RAS Signaling"
}
if(BT_MetaAML_mutation_ordering$Gene[i] %in% Splicing){
BT_MetaAML_mutation_ordering$mutation_category[i] <- "Splicing"
}
if(BT_MetaAML_mutation_ordering$Gene[i] %in% Transcription){
BT_MetaAML_mutation_ordering$mutation_category[i] <- "Transcription"
}
if(BT_MetaAML_mutation_ordering$Gene[i] == "NPM1"){
BT_MetaAML_mutation_ordering$mutation_category[i] <- "NPM1"
}
if(BT_MetaAML_mutation_ordering$Gene[i] %in% Tumor_suppressors){
BT_MetaAML_mutation_ordering$mutation_category[i] <- "Tumor suppressors"
}
}
b=ggplot(BT_MetaAML_mutation_ordering,aes(reorder(factor(Gene), Estimate),
y=Estimate,ymin=(Estimate-`Std. Error`),ymax=(Estimate+`Std. Error`))) +
geom_pointrange(size = 0.75,
aes(x = reorder(factor(Gene), Estimate), ymin = (Estimate-`Std. Error`), ymax = (Estimate+`Std. Error`), y = Estimate, color = mutation_category)) +
theme_cowplot() +
scale_color_manual(values = c("DNA Methylation" = "#374E55FF", "Chromatin/Cohesin" = "#DF8F44FF", "RTK/RAS Signaling" = "#00A1D5FF", "Splicing" = "#B24745FF", "Transcription" = "#79AF97FF", "NPM1" = "#80796BFF", "Tumor suppressors" = "#6A6599FF"), name = "Mutation Category") +
scale_y_continuous(position = "right") +
ylab("Point Estimate + 95% CI")+
theme(
axis.title.y=element_blank(),
axis.text.y=element_blank(),
axis.ticks.y=element_blank(),
axis.line.y = element_blank()) +
theme(legend.position = c(0.05, 0.2)) +
coord_flip() +
scale_y_reverse()
print(b)
ggsave(filename = "~/Desktop/MetaAML_results/Figure_4/bradley_terry_order_de_novo.pdf", width = 7.5, height = 6, units = "in")
write.csv(BT_MetaAML_mutation_ordering, "~/Desktop/MetaAML_results/Figure_4/bradley_terry_order_de_novo.csv")
#define the ordering based on the global pairwise analysis
sub <- subset(final_data_matrix_2, final_data_matrix_2$mut_freq_gene >= 75 & final_data_matrix_2$Gene != "MLL" & final_data_matrix_2$Subset == "de_novo" & final_data_matrix_2$mut_freq_pt > 1)
sub$Gene = as.character(sub$Gene)
# annotate FLT3 ITD and TKD respectively
for(i in 1:nrow(sub)){
if(sub$Gene[i] == "FLT3" & sub$variant_type[i] %in% c("ITD", "INDEL")){
sub$Gene[i] <- "FLT3-ITD"
}
if(sub$Gene[i] == "FLT3" & sub$variant_type[i] %in% c("SNV", "Deletion", "other")){
sub$Gene[i] <- "FLT3-TKD"
}
}
sub=subset(sub, sub$Gene != "FLT3")
gene_order = BT_MetaAML_mutation_ordering %>%
select(Gene, Estimate) %>%
arrange(Estimate)
sub <- setDT(sub)[Gene %in% gene_order$Gene]
sub$Gene <- factor(sub$Gene, levels = gene_order$Gene)
# add functional category to the mutations for visualization purposes
sub$mutation_category <- NA
DNA_methylation <- list("DNMT3A","IDH2","TET2","IDH1")
Chromatin_cohesin <- list("ASXL1", "RAD21", "STAG2", "EZH2", "BCOR")
RTK_RAS_Signaling <- list("PTPN11", "CBL", "NF1", "KRAS", "KIT", "NRAS", "FLT3-ITD", "FLT3-TKD")
Splicing <- list("SF3B1", "SRSF2", "U2AF1")
Transcription <- list("CEBPA", "GATA2", "RUNX1", "MYC", "ETV6", "ZBTB33")
Tumor_suppressors <- list("TP53", "PHF6", "WT1")
for(i in 1:nrow(sub)){
if(sub$Gene[i] %in% DNA_methylation){
sub$mutation_category[i] <- "DNA Methylation"
}
if(sub$Gene[i] %in% Chromatin_cohesin){
sub$mutation_category[i] <- "Chromatin/Cohesin"
}
if(sub$Gene[i] %in% RTK_RAS_Signaling){
sub$mutation_category[i] <- "RTK/RAS Signaling"
}
if(sub$Gene[i] %in% Splicing){
sub$mutation_category[i] <- "Splicing"
}
if(sub$Gene[i] %in% Transcription){
sub$mutation_category[i] <- "Transcription"
}
if(sub$Gene[i] == "NPM1"){
sub$mutation_category[i] <- "NPM1"
}
if(sub$Gene[i] %in% Tumor_suppressors){
sub$mutation_category[i] <- "Tumor suppressors"
}
}
c = ggplot(sub, aes(y = Gene, x = VAF_CN_corrected, fill = mutation_category, height = ..density..)) +
geom_density_ridges(
alpha = 1, stat = "density"
) +
theme_cowplot() +
ylab(label = NULL) +
xlab(label = "VAF") +
xlim(0,100) +
theme(legend.title = element_text()) +
scale_fill_manual(name = "", values = c("DNA Methylation" = "#374E55FF", "Chromatin/Cohesin" = "#DF8F44FF", "RTK/RAS Signaling" = "#00A1D5FF", "Splicing" = "#B24745FF", "Transcription" = "#79AF97FF", "NPM1" = "#80796BFF", "Tumor suppressors" = "#6A6599FF")) +
theme(legend.position = "none")
ggsave(filename = "~/Desktop/MetaAML_results/Figure_4/vaf_distribution_order_by_time.pdf", width = 5, height = 5)
ggarrange(a,
ggarrange(c, b, ncol = 2),
nrow = 2
)
ggsave(filename = "~/Desktop/MetaAML_results/Figure_4/pairwise_ordering_panels.pdf", dpi = 300, width = 9, height = 15, units = "in")
# specifically plot the BT results
ggarrange(c,b,
ncol = 2, nrow = 1, widths = c(0.75, 1))
ggsave(filename = "~/Desktop/MetaAML_results/Figure_4/pairwise_ordering_panels_2.pdf", dpi = 300, width = 7.5, height = 7.5, units = "in")
# pairwise scatterplot function ####
load("~/Desktop/MetaAML_results/final_data_matrix_2.RData")
final_data_matrix_2$VAF_CN_corrected = as.numeric(final_data_matrix_2$VAF_CN_corrected)
# make sure that the FLT3 symbols are annotated properly
for(i in 1:nrow(final_data_matrix_2)){
if(final_data_matrix_2$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$Gene[i] <- "FLT3-TKD"
}
}
vaf_scatterplot_function <- function(pt_subset, gene_1_2, save_plot){
# subset to desired cohorts
if(pt_subset == "All"){
final_data_matrix_sub <- final_data_matrix_2
label1 <- "All"
}
if(pt_subset == "De novo"){
final_data_matrix_sub <- subset(final_data_matrix_2, final_data_matrix_2$Subset == "de_novo")
label1 <- "De novo"
}
if(pt_subset == "Secondary"){
final_data_matrix_sub <- subset(final_data_matrix_2, final_data_matrix_2$Subset == "transformed")
label1 <- "Secondary"
}
if(pt_subset == "Relapse"){
final_data_matrix_sub <- subset(final_data_matrix_2, final_data_matrix_2$Subset == "relapse")
label1 <- "Relapse"
}
if(pt_subset == "Therapy related"){
final_data_matrix_sub <- subset(final_data_matrix_2, final_data_matrix_2$Subset == "therapy")
label1 <- "Therapy related"
}
if(pt_subset == "Other"){
final_data_matrix_sub <- subset(final_data_matrix_2, final_data_matrix_2$Subset == "other")
label1 <- "Other"
}
# select mutations of interest
gene_x <- gene_1_2[1]
gene_y <- gene_1_2[2]
sub_gene_1 <- subset(final_data_matrix_sub, final_data_matrix_sub$Gene == gene_x)
sub_gene_2 <- subset(final_data_matrix_sub, final_data_matrix_sub$Gene == gene_y)
# select patients with both mutations
gene_1_and_2 <- inner_join(sub_gene_1, sub_gene_2, by = "Sample")
gene_1_and_2=as.data.frame(gene_1_and_2) %>% distinct(Sample, VAF_CN_corrected.x, VAF_CN_corrected.y, .keep_all = F)
gene_1_and_2 <- gene_1_and_2[order(gene_1_and_2$Sample, -gene_1_and_2$VAF_CN_corrected.x),]
gene_1_and_2= gene_1_and_2[!duplicated(gene_1_and_2$Sample),]
# add point color columns for visualizing clonal/subclonal trends
gene_1_and_2$vaf_ratio <- (gene_1_and_2$VAF_CN_corrected.x - gene_1_and_2$VAF_CN_corrected.y)
gene_1_and_2 <- gene_1_and_2[complete.cases(gene_1_and_2$vaf_ratio), ]
# define point color
gene_1_and_2$Clonality <- NA
for(i in 1:nrow(gene_1_and_2)){
if(gene_1_and_2$vaf_ratio[i] <= 5 & gene_1_and_2$vaf_ratio[i] >= -5){
gene_1_and_2$Clonality[i] <- "Ambiguous"
}
if(gene_1_and_2$vaf_ratio[i] > 5){
gene_1_and_2$Clonality[i] <- paste(gene_x, "first", sep = " ")
}
if(gene_1_and_2$vaf_ratio[i] < -5){
gene_1_and_2$Clonality[i] <- paste(gene_y, "first", sep = " ")
}
}
if(nrow(gene_1_and_2) > 4){
# make the scatterplot
scatter_plot = ggplot(gene_1_and_2, aes(x = gene_1_and_2$VAF_CN_corrected.y, y = gene_1_and_2$VAF_CN_corrected.x)) +
geom_point(aes(color = Clonality), size = 3, alpha = 0.75) +
xlim(0,(max(gene_1_and_2$VAF_CN_corrected.y) + 5))+
ylim(0,(max(gene_1_and_2$VAF_CN_corrected.x) + 5))+
xlab(paste(gene_y, " VAF", sep ="")) +
ylab(paste(gene_x, " VAF", sep ="")) +
scale_color_manual(values = c("#374E55FF","#8c510a","#01665e")) +
theme_cowplot() +
geom_abline(intercept = 5, slope = (1), color="#969696",
linetype="dashed", size=.5)+
geom_abline(intercept = -5, slope = (1), color="#969696",
linetype="dashed", size=.5)+
geom_point(shape = 1, size = 3,colour = "black") +
theme(plot.title = element_text(hjust = 0.5, paste(gene_y, "vs.", gene_x, sep = "")),
legend.position = "right",
legend.title = element_blank())
print(scatter_plot)
if(save_plot == T){
ggsave(filename =paste("~/Desktop/MetaAML_results/Figure_4/",gene_x, "_",gene_y,"_scatterplot.pdf", sep = ""),width = 4.25, height = 3)
}
}
}
vaf_scatterplot_function(pt_subset = "All", gene_1_2 = c("NRAS", "GATA2"), save_plot = T)
# survival by pairwise ordering ####
load("~/Desktop/MetaAML_results/final_data_matrix_2.RData")
final_data_matrix_sub <- subset(final_data_matrix_2, final_data_matrix_2$Subset == "de_novo" & final_data_matrix_2$Subset != "relapse" & final_data_matrix_2$mut_freq_gene >= 50)
final_data_matrix_sub$Time_to_OS <- (final_data_matrix_sub$Time_to_OS/365)
final_data_matrix_sub = select(final_data_matrix_sub, c("Sample", "Gene", "variant_type", "Censor", "Time_to_OS", "VAF_CN_corrected"))
for(i in 1:nrow(final_data_matrix_sub)){
if(final_data_matrix_sub$Gene[i] == "FLT3" & final_data_matrix_sub$variant_type[i] %in% c("ITD", "INDEL")){
final_data_matrix_sub$Gene[i] <- "FLT3-ITD"
}
if(final_data_matrix_sub$Gene[i] == "FLT3" & final_data_matrix_sub$variant_type[i] %in% c("SNV", "Deletion", "other")){
final_data_matrix_sub$Gene[i] <- "FLT3-TKD"
}
}
# individual gene pairwise survival ####
# select recurrent mutations
genes=as.data.frame(t(combn(as.vector(unique(final_data_matrix_sub$Gene)),2)))
n=1
results_list = list()
for(i in 1:nrow(genes)){
# print(i)
gene_1=as.character(genes[i,1])
gene_2=as.character(genes[i,2])
sub_gene_1 <- subset(final_data_matrix_sub, final_data_matrix_sub$Gene == gene_1)
sub_gene_2 <- subset(final_data_matrix_sub, final_data_matrix_sub$Gene == gene_2)
# select patients with both mutations
gene_1_and_2 <- inner_join(sub_gene_1, sub_gene_2, by = "Sample")
gene_1_and_2 = unique(select(gene_1_and_2, Sample, Censor.x, Time_to_OS.x, VAF_CN_corrected.x, VAF_CN_corrected.y))
gene_1_and_2 <- gene_1_and_2[order(gene_1_and_2$Sample, -gene_1_and_2$VAF_CN_corrected.y),]
gene_1_and_2 = gene_1_and_2[!duplicated(gene_1_and_2$Sample),]
if(nrow(gene_1_and_2) >= 15){
# add point color columns for visualizing clonal/subclonal trends
gene_1_and_2$vaf_difference <- (gene_1_and_2$VAF_CN_corrected.x - gene_1_and_2$VAF_CN_corrected.y)
gene_1_and_2 <- gene_1_and_2[complete.cases(gene_1_and_2$vaf_difference), ]
# define point color
gene_1_and_2$Clonality <- NA
for(j in 1:nrow(gene_1_and_2)){
if(gene_1_and_2$vaf_difference[j] <= 5 & gene_1_and_2$vaf_difference[j] >= -5){
gene_1_and_2$Clonality[j] <- 0
}
if(gene_1_and_2$vaf_difference[j] > 5){
gene_1_and_2$Clonality[j] <- 1
}
if(gene_1_and_2$vaf_difference[j] < -5){
gene_1_and_2$Clonality[j] <- 2
}
}
# minor clone for gene 1 vs clonal
gene_1_and_clonal <- subset(gene_1_and_2, gene_1_and_2$Clonality == 2)
gene_2_and_clonal <- subset(gene_1_and_2, gene_1_and_2$Clonality == 1)
gene_1_and_2 <- subset(gene_1_and_2, gene_1_and_2$Clonality != 0)
# calculate the p value for surival between patients with subclonal mutations in gene 1 compared to those with subclonal mutations in gene 2
if(nrow(gene_2_and_clonal) >= 5 & nrow(gene_1_and_clonal) >= 5 & nrow(gene_1_and_2) >=15){
print(i)
gene_1_and_2$Censor.x = as.numeric(gene_1_and_2$Censor.x)
# hazard ratio compiled table
model <- coxph( Surv(Time_to_OS.x, Censor.x) ~ Clonality,
data = gene_1_and_2 )
# # plots the survival
surv_plot <- ggsurvplot(OS,
data = gene_1_and_2,
log = (OS),
log.rank.weights = c("survdiff"),
pval = 0.002,
test.for.trend = F,
pval.method.size = 3,
pval.coord = c(0, 0),
conf.int = F,
censor = T,
surv.median.line = "none",
risk.table = T,
risk.table.title = "Number at risk",
risk.table.fontsize = 4,
risk.table.height = .3,
risk.table.y.text = F,
break.time.by = 5,
risk.table.pos = c("out"),
# palette = cat_colors,
xlab = "Years",
ylim = c(0, 1.0),
ylab = "Survival Probability",
font.main = c(15, "plain", "#252525"),
pval.size = 4,
font.x = c(12, "plain", "#252525"),
font.y = c(12, "plain", "#252525"),
font.legend = c(12, "plain"),
font.tickslab = c(12, "plain", "#252525"),
legend.labs = comparisons,
legend.title = 'Mutation order',
legend = "none",
linetype = c("solid", "dashed", "solid"),
ggtheme = theme_cowplot())
plot_list[[j]] = surv_plot
#
# print(surv_plot)
# png(filename = paste("~/Desktop/MetaAML_results/Figure_4/survival_by_muation_category_ordering/pngs/",g1,"_",g2, ".png", sep = ""), res = 300, width = 4, height = 2.4, units = "in")
# #
# surv_plot
# print(surv_plot)
# dev.off()
# extract the informative data from the survival model
array_dat = summary(model)$conf.int[1:4]
array_dat[5] = gene_1
array_dat[6] = gene_2
# extract the log-rank p-value for the individual comparisons
array_dat[7] = summary(model)$sctest[3]
array_dat = array_dat[-2]
forest_plot_data <- data.frame("Gene_1" = array_dat[4], "Gene_2" = array_dat[5], "HR" = array_dat[1], "Lower_CI" = array_dat[2], "Upper_CI" = array_dat[3], "log_rank_p" = array_dat[6], "n_both" = nrow(gene_1_and_2))
results_list[[n]] <- forest_plot_data
n=n+1
}
}
}
temp_final_hr = as.data.frame(do.call(rbind, results_list))
temp_final_hr$q_value <- p.adjust(temp_final_hr$log_rank_p, method = "fdr")
write.csv(temp_final, file = "~/Desktop/MetaAML_results/Figure_4/survival_by_vaf_ordering_co_occuring_mutations.csv", row.names = F)
temp_final_hr = subset(temp_final_hr, temp_final_hr$Upper_CI != "Inf")
temp_final_hr$HR = as.numeric(temp_final_hr$HR)
temp_final_hr$gene1_gene2 = as.character(paste(temp_final_hr$Gene_2, "->", temp_final_hr$Gene_1))
# temp_final_hr$gene1_gene2 <- factor(temp_final_hr$gene1_gene2, levels = temp_final$gene1_gene2[order(temp_final$HR)])
temp_final_hr$gene_1_and_2 = as.factor(temp_final_hr$gene1_gene2)
temp_final_hr$HR = as.numeric(temp_final_hr$HR)
temp_final_hr$sig_color = 0
for(i in 1:nrow(temp_final_hr)){
if(temp_final_hr$log_rank_p[i] < 0.05){
temp_final_hr$sig_color[i] = 1
}
}
temp_final_hr$p_text = NA
temp_final_hr$log_rank_p = as.numeric(temp_final_hr$log_rank_p)
for(i in 1:nrow(temp_final_hr)){
if(temp_final_hr$log_rank_p[i] < 0.05){
temp_final_hr$p_text[i] = temp_final_hr$log_rank_p[i]
}
}
temp_final_hr$p_text = round(temp_final_hr$p_text, 3)
temp_final_hr$q_value = round(temp_final_hr$q_value, 3)
temp_final_hr$p_text = paste("p =", temp_final_hr$p_text, "; ", "q = ", temp_final_hr$q_value, sep = "")
for(i in 1:nrow(temp_final_hr)){
if(temp_final_hr$log_rank_p[i] > 0.05){
temp_final_hr$p_text[i] = ""
}
}
temp_final_hr$sig_color = as.factor(temp_final_hr$sig_color)
temp_final_hr$gene1_gene2 = as.character(temp_final_hr$gene1_gene2)
temp_final_hr$gene1_gene2 = reorder(temp_final_hr$gene1_gene2, temp_final_hr$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)
ggplot(temp_final_hr, aes(x = reorder(gene1_gene2, -HR), y = HR, label = p_text)) +
geom_hline(yintercept=1, linetype="dashed", color = "black") +
geom_text(aes(gene1_gene2, Upper_CI), hjust = 0, nudge_y = 0.5) +
geom_point(aes(size = n_both, color = sig_color), shape = 19, alpha = .9) +
geom_segment(data = temp_final_hr, aes(y = Lower_CI, yend = Upper_CI, x = gene1_gene2, xend = gene1_gene2, col= sig_color), size = 0.5) +
scale_color_manual(values = c("0" = "grey", "1" = "#1b7837", "2" = "#762a83"))+
ylab("Hazard Ratio\n(Mutation 1 before Mutation 2)")+
theme_cowplot() +
scale_size_area(max_size = 5,breaks=c(15,25,50,100, 200)) +
theme(legend.position = c(0.65, .2),
axis.title.y=element_blank()) +
coord_flip(ylim = c(0,10)) +
guides(color = FALSE,
size = guide_legend(override.aes=list(colour="lightgrey"), title = "n. patients with\nnon-ambiguous\nordering"))
ggsave(filename = "~/Desktop/MetaAML_results/Figure_4/gene_order_hr_forest_plot_de_novo_05.pdf", dpi = 300, width = 6, height = 7.5, units = "in")
write_csv(temp_final_hr, "~/Desktop/MetaAML_results/Figure_4/gene_order_hr_forest_plot_de_novo_05.csv")
# mutation categories ####
# because there are still too few cases on a pairwise basis to perform survival analysis, I will now look at pairwise occurence more broadly in terms of mutation categories
dir.create("~/Desktop/MetaAML_results/Figure_4/survival_by_muation_category_ordering")
dir.create("~/Desktop/MetaAML_results/Figure_4/survival_by_muation_category_ordering/pdfs")
load("~/Desktop/MetaAML_results/final_data_matrix_2.RData")
final_data_matrix_sub <- subset(final_data_matrix_2, final_data_matrix_2$Subset == "de_novo")
final_data_matrix_sub$Time_to_OS <- (final_data_matrix_sub$Time_to_OS/365)
# make all combinations of mutation categories
mut_cat=as.data.frame(t(combn(as.vector(unique(final_data_matrix_sub$mutation_category)),2)))
mut_cat=na.omit(mut_cat)
# loop through the different category combinations and subset to patients with both mutaiton types and order timing by VAF
n=1
results_list = list()
plot_list = list()
for(j in 1:nrow(mut_cat)){
print(j)
cat_1 = subset(final_data_matrix_sub, final_data_matrix_sub$mutation_category == mut_cat[j,1])
cat_2 = subset(final_data_matrix_sub, final_data_matrix_sub$mutation_category == mut_cat[j,2])
# find patients in both groups and rbind
cat_1_sub = setDT(cat_1)[Sample %chin% cat_2$Sample]
cat_2_sub = setDT(cat_2)[Sample %chin% cat_1$Sample]
cat_1_cat_2=rbind(cat_1_sub, cat_2_sub)
cat_1_cat_2$order=NA
pts=as.data.frame(unique(cat_1_cat_2$Sample))
g1=as.character(mut_cat[j,1])
g2=as.character(mut_cat[j,2])
for(i in 1:nrow(pts)){
# print(i)
sample=as.character(pts[i,1])
# print(sample)
sub=subset(cat_1_cat_2, cat_1_cat_2$Sample == sample)
sub = sub[order(sub$Gene, -sub$VAF_CN_corrected),]
sub = sub[!duplicated(sub$Gene),]
sub = sub[!duplicated(sub$mutation_category),]
vaf_cat_1=sub$VAF_CN_corrected[1]
vaf_cat_2=sub$VAF_CN_corrected[2]
if(!is.na(vaf_cat_1) & !is.na(vaf_cat_2)){
diff=as.numeric(vaf_cat_1-vaf_cat_2)
if(diff > 5){
o=3
}
if(diff < -5){
o=1
}
if(diff >= -5 & diff <= 5){
o=2
}
for(k in 1:nrow(cat_1_cat_2)){
if(cat_1_cat_2$Sample[k] == sample){
cat_1_cat_2$order[k]=o
}
}
}
}
# create the survival data object for plotting
final=as.data.frame(cat_1_cat_2) %>% distinct(Sample, Censor, Time_to_OS, order, .keep_all = F)
final=na.omit(final)
final$Censor=as.numeric(final$Censor)
final$Time_to_OS=as.numeric(final$Time_to_OS)
# survival object
final$OS <- with(final, Surv(Time_to_OS, Censor == 1))
OS <- survfit(OS ~ order, data = final, conf.type = "log-log")
# now run the model on only the non-ambiguous ordering patients to get a p-value for the difference in survival
final_sub = subset(final, final$order == 1 | final$order == 3)
n1 = nrow(subset(final_sub, order == 1))
n3 = nrow(subset(final_sub, order == 3))
if(n1 >= 10 & n3 >= 10){
n_both = n1+n3
model <- coxph( Surv(Time_to_OS, Censor) ~ order,
data = final_sub)
# extract the informative data from the survival model
array_dat = summary(model)$conf.int[1:4]
array_dat[5] = g1
array_dat[6] = g2
# extract the log-rank p-value for the individual comparisons
array_dat[7] = summary(model)$sctest[3]
array_dat = array_dat[-2]
forest_plot_data <- data.frame("Category_1" = array_dat[4], "Category_2" = array_dat[5], "HR" = array_dat[1], "Lower_CI" = array_dat[2], "Upper_CI" = array_dat[3], "log_rank_p" = array_dat[6], "n_both" = n_both)
results_list[[n]] <- forest_plot_data
n=n+1
# replace space with underscore in the mutation categories in order to save the plot
g1 <- gsub(" ", "_", g1)
g1 <- gsub("/", "_", g1)
g2 <- gsub(" ", "_", g2)
g2 <- gsub("/", "_", g2)
# specify colors to match those in other plots
cat_colors = c("DNA Methylation first" = "#374E55FF", "Chromatin/Cohesin first" = "#DF8F44FF", "RTK/RAS Signaling first" = "#00A1D5FF", "Splicing first" = "#B24745FF", "Transcription first" = "#79AF97FF", "NPM1 first" = "#80796BFF", "Tumor suppressors first" = "#6A6599FF", "Ambiguous" = "lightgrey")
# define the comparison labels for plotting
comparisons = c(paste(mut_cat[j,2], "first"), "Ambiguous", paste(mut_cat[j,1], "first"))
# comparisons = c(paste(mut_cat[j,2], "first"), paste(mut_cat[j,1], "first"))
# find the p-value for the comparison of order
p_val = round(summary(model)$sctest[3], 3)
# # # plots the survival
surv_plot <- ggsurvplot(OS,
data = final,
log = (OS),
log.rank.weights = c("survdiff"),
pval = p_val,
test.for.trend = F,
pval.method.size = 3,
pval.coord = c(5, 1),
conf.int = F,
censor = T,
surv.median.line = "none",
risk.table = T,
risk.table.title = "Number at risk",
risk.table.fontsize = 4,
risk.table.height = .1,
risk.table.y.text = F,
break.time.by = 5,
risk.table.pos = c("in"),
palette = cat_colors,
xlab = "Years",
ylim = c(0, 1.0),
ylab = "Survival Probability",
font.main = c(15, "plain", "#252525"),
pval.size = 4,
font.x = c(12, "plain", "#252525"),
font.y = c(12, "plain", "#252525"),
font.legend = c(12, "plain"),
font.tickslab = c(12, "plain", "#252525"),
legend.labs = comparisons,
legend.title = 'Mutation order',
legend = "right",
linetype = c("solid", "dashed", "solid"),
ggtheme = theme_cowplot())
plot_list[[j]] = surv_plot
pdf(paste("~/Desktop/MetaAML_results/Figure_4/survival_by_muation_category_ordering/pdfs/",g1,"_",g2, "2.pdf", sep = ""), width = 6, height = 3.5)
print(surv_plot, newpage = FALSE)
dev.off()