Overview of cohort composition and drug response pattern
Junyan Lu
4 November 2022
Last updated: 2022-11-04
Checks: 6 1
Knit directory: EMBL2016/analysis/
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Load libraries and dataset
#Libraries
library(Biobase)
library(gridExtra)
library(reshape2)
library(pheatmap)
library(genefilter)
library(cowplot)
library(Rtsne)
library(smallvis)
library(ggrepel)
library(RColorBrewer)
library(colorspace)
library(jyluMisc)
library(ggplot2)
library(ggbeeswarm)
library(gtable)
library(readxl)
library(limma)
library(tidyverse)
knitr::opts_chunk$set(message = FALSE, warning = FALSE, message = FALSE)Load datasets
load("../data/patMeta.RData")
load("../output/screenData.RData")Characterization of patients and drugs
Characterization of drugs
plotTab <- distinct(screenData, Drug, class) %>%
group_by(class) %>% summarise(value = length(Drug)) %>%
arrange(desc(value)) %>%
mutate(text_y = cumsum(value) - value/2) %>%
mutate(class = as.character(class))
df2 <- distinct(screenData, Drug, class) %>%
group_by(class) %>% summarise(value = length(Drug)) %>%
mutate(csum = rev(cumsum(rev(value))),
pos = value/2 + lead(csum, 1),
pos = if_else(is.na(pos), value/2, pos))
ggplot(df2, aes(x = "" , y = value, fill = fct_inorder(class))) +
geom_col(width = 1, color = 1) +
coord_polar(theta = "y") +
geom_label_repel(data = df2,
aes(y = pos, label = class), fill = "grey80",
size = 6, nudge_x = 0.5, show.legend = FALSE) +
theme_void() +
theme(legend.position = "none") 
Target distribution of kinase inhibitors
tarAnno <- read_csv2("~/CLLproject_jlu/data/targetAnno/targetAnnotation_all.csv")
plotTab <- distinct(screenData, Drug, class) %>% mutate(target = tarAnno[match(Drug, tarAnno$nameEMBL2016),]$pathway) %>%
filter(class == "Kinase inhibitor", !is.na(target)) %>%
group_by(target) %>% summarise(n=length(Drug)) %>%
arrange(desc(n)) %>% mutate(target = factor(target, levels = target))
ggplot(plotTab, aes(x=target, y=n)) +
geom_bar(stat="identity", fill = "lightblue") +
ylab("Number of drugs") + xlab("") +
theme_bw() +
theme(axis.text.x = element_text(angle = 90, hjust=1, vjust=0.5)) 
ggsave("targetDist.png", height = 3, width = 5)Summary of diagnosis
Summary of patient background
#Get a patient background table for EBML2016 patients
patBack <- filter(patMeta, Patient.ID %in% screenData$patientID) %>%
mutate(sampleID = screenData[match(Patient.ID, screenData$patientID),]$sampleID) %>%
mutate(pretreat = treatmentTab[match(sampleID, treatmentTab$sampleID),]$pretreat) %>% #add pretreatment status
select(-project, -date.of.diagnosis, -treatment, -date.of.first.treatment, -HIPO.ID) %>%
dplyr::rename(sex = gender)Mutation statistics (for all patients)
# Get a mutation matrix and remove non-important features (CNV, SNV)
mutTab <- select(patBack, -sex, -diagnosis, -Methylation_Cluster, -pretreat) %>%
mutate(IGHV.status = ifelse(is.na(IGHV.status), NA, ifelse(IGHV.status == "M",1,0))) %>%
mutate_at(vars(-Patient.ID), as.character) %>% mutate_at(vars(-Patient.ID), as.integer) %>%
data.frame() %>% remove_rownames() %>% column_to_rownames("Patient.ID")
#only keep the features that have at least 10 records with 5 positive/negative cases
keepCols <- apply(mutTab, 2, function(x) length(table(x)) >=2 & all(table(x) >=5))
mutTab <- mutTab[,keepCols]
#summary mutated, unmutated and NA cases
mutStat <- rownames_to_column(mutTab, "patID") %>% gather(key = "variant", value = "status", -patID) %>%
mutate(status = ifelse(is.na(status), "unkown", ifelse(status == 0, "wt","mut"))) %>%
mutate(status = factor(status, levels = c("mut","wt","unkown"))) %>%
group_by(variant, status) %>% summarise(count = length(status)) %>% ungroup()
#reorder the variant by number of mutated cases
mutStat <- mutate(mutStat,
variant = factor(variant, levels = filter(mutStat, status == "mut") %>%
arrange(count) %>% pull(variant)))
ggplot(mutStat, aes(x = variant, y = count, fill = status)) +
geom_bar(stat = "identity", position = "dodge") +
scale_fill_manual(values = c(mut = "pink", wt = "lightblue", unkown = "grey60")) +
geom_text(aes(label=count), position=position_dodge(width=0.9), hjust=-0.25) +
theme_classic() +
ylab("number of cases") + xlab("") + coord_flip()
Mutation statistics (only for CLL patients)
# Get a mutation matrix and remove non-important features (CNV, SNV)
mutTab <- filter(patBack, diagnosis %in% "CLL") %>%
select(-sex, -diagnosis, -Methylation_Cluster, -pretreat) %>%
mutate(IGHV.status = ifelse(is.na(IGHV.status), NA, ifelse(IGHV.status == "M",1,0))) %>%
mutate_at(vars(-Patient.ID), as.character) %>% mutate_at(vars(-Patient.ID), as.integer) %>%
data.frame() %>% remove_rownames() %>% column_to_rownames("Patient.ID")
#only keep the features that have at least 10 records with 5 positive/negative cases
keepCols <- apply(mutTab, 2, function(x) length(table(x))>=2 & all(table(x) >=5))
mutTab <- mutTab[,keepCols]
#summary mutated, unmutated and NA cases
mutStat <- rownames_to_column(mutTab, "patID") %>% gather(key = "variant", value = "status", -patID) %>%
mutate(status = ifelse(is.na(status), "unkown", ifelse(status == 0, "wt","mut"))) %>%
mutate(status = factor(status, levels = c("mut","wt","unkown"))) %>%
group_by(variant, status) %>% summarise(count = length(status)) %>% ungroup()
#reorder the variant by number of mutated cases
mutStat <- mutate(mutStat,
variant = factor(variant, levels = filter(mutStat, status == "mut") %>%
arrange(count) %>% pull(variant)))
ggplot(mutStat, aes(x = variant, y = count, fill = status)) +
geom_bar(stat = "identity", position = "dodge") +
geom_text(aes(label=count), position=position_dodge(width=0.9), hjust=-0.25) +
scale_fill_manual(values = c(mut = "pink", wt = "lightblue", unkown = "grey60")) +
theme_classic() + theme(axis.text.x = element_text(angle = 90, hjust =1, vjust =0.5)) +
ylab("number of cases") + xlab("") + coord_flip()
Drug induced overall effect on cell viability
For all samples
Relative cell viabilities for all samples treated with drugs under 9 concentrations
#Color for each concentration
colorCode <- rev(brewer.pal(9,"Blues")[1:9])
names(colorCode) <- levels(viabTab$concIndex)
ggplot(viabTab, aes(x=Drug,y=value, color=concIndex)) +
geom_jitter(size=1, na.rm = TRUE, alpha=0.8, shape =16) +
scale_color_manual(values = colorCode) +
ylab("Viability") +
#ylim(c(0,1.2)) + #no censoring
xlab("") +
guides(color = guide_legend(override.aes = list(size=3,alpha=1),
title = "concentration index")) +
theme_bw() + ggtitle("Drug induced effect on cell viability (all samples)") +
theme(legend.position = "bottom",
axis.text.x = element_text(angle = 90, hjust = 1, vjust=0.5),
legend.key = element_blank(),
plot.title = element_text(hjust=0.5))
For CLL samples only
Prepare data.
#select drug screening data on patient samples
viabTab <- dplyr::select(screenData, sampleID, Drug, concIndex, viab, diagnosis) %>%
rename(value = viab) %>% arrange(concIndex) %>%
filter(! Drug %in% c("DMSO","PBS"), diagnosis %in% "CLL")
#order drug by mean viablitity
drugOrder <- group_by(viabTab, Drug) %>%
summarise(meanViab = mean(value)) %>%
arrange(meanViab)
viabTab$Drug <- factor(viabTab$Drug, levels = drugOrder$Drug)Relative cell viabilities for CLL samples treated with drugs under 9 concentrations
#Color for each concentration
colorCode <- rev(brewer.pal(9,"Blues"))
names(colorCode) <- unique(viabTab$concIndex)
ggplot(viabTab, aes(x=Drug,y=value, color=concIndex)) +
geom_jitter(size=1, na.rm = TRUE, alpha=0.8, shape =16) +
scale_color_manual(values = colorCode) +
ylab("Viability") +
#ylim(c(0,1.2)) + #no censoring
xlab("") +
guides(color = guide_legend(override.aes = list(size=3,alpha=1),
title = "concentration index")) +
theme_bw() + ggtitle("Drug induced effect on cell viability (CLL samples)") +
theme(legend.position = "bottom",
axis.text.x = element_text(angle = 90, hjust = 1, vjust=0.5),
legend.key = element_blank(),
plot.title = element_text(hjust=0.5))
Plot most and least toxic drugs (for each disease type)
viabTab <- group_by(screenData, patientID, Drug, diagnosis, class) %>%
summarise(value = mean(viab.auc, na.rm=TRUE)) %>%
ungroup() %>%
filter(!is.na(value))topN<-25
plotList <- lapply(unique(viabTab$diagnosis), function(n) {
eachTab <- filter(viabTab, diagnosis == n)
drugOrder <- group_by(eachTab, Drug) %>%
summarise(medVal = median(value, na.rm=TRUE)) %>%
filter(!is.na(medVal)) %>%
arrange(medVal)
drugOrder <- drugOrder[c(seq(1,topN),seq(nrow(drugOrder)-topN+1,nrow(drugOrder))),]
eachTab <- filter(eachTab, Drug %in% drugOrder$Drug) %>%
mutate(Drug = factor(Drug, levels = drugOrder$Drug))
p<- ggplot(eachTab, aes(x=Drug,y=value, col = class)) +
geom_jitter(size=2, na.rm = TRUE, alpha=0.8, shape =16, width = 0.2) +
ylab("Viability") +
#ylim(c(0,1.2)) + #no censoring
xlab("") +
theme_bw() + ggtitle(n) +
theme(legend.position = "bottom",
axis.text.x = element_text(angle = 90, hjust = 1, vjust=0.5),
legend.key = element_blank(),
plot.title = element_text(hjust=0.5))
})
jyluMisc::makepdf(plotList, "../docs/drugEffectRank_diagnosis.pdf",
height = 6, width = 12,ncol = 1, nrow = 1)A table of median effect
medTab <- group_by(viabTab, Drug, diagnosis, class) %>%
summarise(medVal = median(value, na.rm=TRUE)) %>%
arrange(diagnosis, medVal) %>% ungroup()
write_csv2(medTab, path = "../docs/allDrug_rank.csv")Plot most and least toxic drugs (for each patient)
viabTab <- group_by(screenData, patientID, Drug,diagnosis, class) %>%
summarise(value = mean(viab.auc, na.rm=TRUE)) %>%
ungroup() %>% arrange(diagnosis) %>%
filter(!is.na(value))topN<-25
plotList <- lapply(unique(viabTab$patientID), function(n) {
eachTab <- filter(viabTab, patientID == n)
drugOrder <- group_by(eachTab, Drug) %>%
summarise(medVal = median(value, na.rm=TRUE)) %>%
arrange(medVal)
drugOrder <- drugOrder[c(seq(1,topN),seq(nrow(drugOrder)-topN+1,nrow(drugOrder))),]
eachTab <- filter(eachTab, Drug %in% drugOrder$Drug) %>%
mutate(Drug = factor(Drug, levels = drugOrder$Drug))
p<- ggplot(eachTab, aes(x=Drug,y=value, col = class)) +
geom_point(size=2) +
ylab("Viability") +
##ylim(c(0,1.2)) +
xlab("") +
theme_bw() + ggtitle(sprintf("%s (%s)",n, unique(eachTab$diagnosis))) +
theme(legend.position = "bottom",
axis.text.x = element_text(angle = 90, hjust = 1, vjust=0.5),
legend.key = element_blank(),
plot.title = element_text(hjust=0.5))
})
jyluMisc::makepdf(plotList, "../docs/drugEffectRank_patient.pdf",
height = 20, width = 20,ncol = 2, nrow =3)A table of AUC for all drugs
write_csv2(viabTab, path = "../docs/allDrug_AUC.csv")Distribution of AUCs of all drugs (averaged over patients) in difference disease entities
sampleCountTab <- distinct(screenData, diagnosis, patientID) %>%
group_by(diagnosis) %>% summarise(nSample=length(patientID)) %>%
arrange(desc(nSample)) %>% mutate(disease = sprintf("%s(n=%s)", diagnosis, nSample)) %>%
mutate(disease = factor(disease, levels = unique(disease)))
viabTab <- group_by(screenData, Drug, diagnosis) %>%
summarise(value = mean(viab.auc, na.rm=TRUE)) %>%
filter(!is.na(value)) %>%
ungroup() %>% arrange(diagnosis) %>%
left_join(sampleCountTab, by = "diagnosis")All diseases
ggplot(viabTab, aes(x= value, fill = disease, col = disease)) +
geom_density(alpha =0.2) + theme_bw() +
#coord_cartesian(xlim = c(0,1.5)) + #No censoring
geom_vline(xintercept = 1, linetype = "dashed", col = "blue") +
xlab("Mean viability")
Pair-wise comparison
pairList <- combn(sampleCountTab$disease,2)
plotList <- lapply(seq(ncol(pairList)), function(i) {
plotTab <- filter(viabTab, disease %in% pairList[,i])
ggplot(plotTab, aes(x= value, fill = disease, col = disease)) + geom_density(alpha =0.2) + theme_bw() +
theme(legend.position = "top") +
#coord_cartesian(xlim = c(0,1.5)) + #no ceonsoring
geom_vline(xintercept = 1, linetype = "dashed", col = "blue") +
xlab("Mean viability")
})
makepdf(plotList, name = "../docs/disease_desnity_pairwise.pdf", ncol=2, nrow =2, width = 10, height = 6)Distribution of AUCs of each drug in all patients
viabTab <- group_by(screenData, Drug, patientID) %>%
summarise(value = mean(viab.auc, na.rm=TRUE)) %>%
filter(!is.na(value))
plotList <- lapply(unique(viabTab$Drug), function(n) {
plotTab <- filter(viabTab, Drug == n)
ggplot(plotTab, aes(x=value)) +
geom_density(alpha=0.5, fill = "grey50") + theme_bw() +
ggtitle(n) +
# coord_cartesian(xlim = c(0,1.5)) + #no censoring
geom_vline(xintercept = 1, linetype = "dashed", col = "blue") +
xlab("Viability")
})
makepdf(plotList, "../docs/allDrugs_density.pdf", ncol=3, nrow=3, width =10, height = 6)Water fall plots
geneList <- c("IGHV.status","BRAF","del17p","KRAS","U1","trisomy12","NOTCH1","del13q","del14q","TP53",
"gain2p","IgH_break","ATM","del11q","FBXW7","del6q","SF3B1")screenDataSub <- filter(screenData, class != "Conventional Chemo") %>%
filter(!is.na(viab.auc))getTopN <- function(x, n=25) {
x <- arrange(x, medAUC)
x <- x[c(seq(1,n), seq(nrow(x)-n+1,nrow(x))),]
return(x)
}
gg_color_hue <- function(n) {
hues = seq(15, 375, length = n + 1)
hcl(h = hues, l = 65, c = 100)[1:n]
}
colorPat <- structure(gg_color_hue(length(unique(screenDataSub$class))),names = unique(screenDataSub$class))
pList <- lapply(geneList, function(eachGene) {
eachTab <- mutate(screenDataSub, geneName = eachGene,
status = patMeta[match(screenDataSub$patientID, patMeta$Patient.ID),][[eachGene]]) %>%
mutate(geneStatus = paste0(geneName, ":", status)) %>%
distinct(patientID, Drug, .keep_all = TRUE) %>%
filter(!status %in% c("NA",NA,""))
geneNum <- distinct(eachTab, patientID, geneStatus) %>%
group_by(geneStatus) %>% summarise(num = length(patientID)) %>%
mutate(geneStatusNum = sprintf("%s (n=%s)",geneStatus, num))
sumTab <- group_by(eachTab, Drug, geneStatus, class) %>%
summarise(medAUC = median(viab.auc, na.rm=TRUE)) %>%
ungroup() %>% left_join(geneNum)
statusClass <- sort(unique(sumTab$geneStatus))
plotTab1 <- filter(sumTab, geneStatus == statusClass[[1]]) %>%
mutate(score = medAUC - median(medAUC))
plotTab1 <- getTopN(plotTab1, 25) %>% arrange(score) %>%
mutate(Drug = factor(Drug, levels = Drug))
plotTab2 <- filter(sumTab, geneStatus == statusClass[[2]])%>%
mutate(score = medAUC - median(medAUC))
plotTab2 <- getTopN(plotTab2, 25) %>% arrange(medAUC) %>%
mutate(Drug = factor(Drug, levels = Drug))
pAll <- ggplot(eachTab, aes(x=Drug, y= viab.auc, col = class)) +
geom_point() + theme(legend.position = "bottom") +
scale_color_manual(values = colorPat)
pL <- get_legend(pAll)
p1 <- ggplot(plotTab1, aes(x=Drug, y = score)) +
geom_bar(aes(fill = class), stat = "identity") +
ggtitle(unique(plotTab1$geneStatusNum)) +
scale_fill_manual(values = colorPat) +
theme(legend.position = "none",
axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5))
p2 <- ggplot(plotTab2, aes(x=Drug, y = score)) +
geom_bar(aes(fill = class), stat = "identity") +
ggtitle(unique(plotTab2$geneStatusNum)) +
scale_fill_manual(values = colorPat)+
theme(legend.position = "none",
axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5))
plot_grid(p1,p2,pL, ncol=1, rel_heights = c(1,1,0.1))
})jyluMisc::makepdf(pList, name = "../docs/waterfall_AUC_genomics.pdf", ncol = 1, nrow=1,width = 12, height = 18)Drug-Drug correlations
For CLL samples
#Prepare viability matrix
viabMat <- filter(screenData, diagnosis %in% "CLL") %>%
group_by(patientID, Drug) %>% summarise(viab = mean(viab.auc, na.rm=TRUE)) %>%
spread(key = patientID, value = "viab") %>% data.frame() %>%
column_to_rownames("Drug") %>% as.matrix()
viabMat <- viabMat[rowSums(!is.na(viabMat))>2,]
#viabMat <- viabMat[complete.cases(viabMat),]Drug-drug correlation matrix (all drugs, except for those that generate AUC data in less than 3 samples)
corMat <- cor(t(viabMat), use = "pairwise.complete.obs")
#define color sequences
colorList <- c(colorRampPalette(c("navy", "white"))(20),
colorRampPalette(c("white"))(10),
colorRampPalette(c("white","firebrick3"))(20))
pheatmap(corMat, breaks = seq(-1,1,length.out = 50),
clustering_method = "ward.D2", color = colorList,
treeheight_row = 0, show_colnames = FALSE,
main = "Drug-drug correlation in CLL")
Drug-drug correlation matrix (only drugs that could produce AUC data in all samples)
viabMat <- viabMat[complete.cases(viabMat),]
corMat <- cor(t(viabMat), use = "pairwise.complete.obs")
#define color sequences
colorList <- c(colorRampPalette(c("navy", "white"))(20),
colorRampPalette(c("white"))(10),
colorRampPalette(c("white","firebrick3"))(20))
pheatmap(corMat, breaks = seq(-1,1,length.out = 50),
clustering_method = "ward.D2", color = colorList,
treeheight_row = 0, show_colnames = FALSE,
main = "Drug-drug correlation in CLL")
For M-CLL samples
#Prepare viability matrix
viabMat <- filter(screenData, diagnosis %in% "CLL", patientID %in% filter(patMeta, IGHV.status %in% "M")$Patient.ID) %>%
group_by(patientID, Drug) %>% summarise(viab = mean(viab.auc, na.rm=TRUE)) %>%
spread(key = patientID, value = "viab") %>% data.frame() %>%
column_to_rownames("Drug") %>% as.matrix()
viabMat <- viabMat[rowSums(!is.na(viabMat))>2,]
#viabMat <- viabMat[complete.cases(viabMat),]Drug-drug correlation matrix (all drugs, except for those that generate AUC data in less than 3 samples)
corMat <- cor(t(viabMat), use = "pairwise.complete.obs")
#define color sequences
colorList <- c(colorRampPalette(c("navy", "white"))(20),
colorRampPalette(c("white"))(10),
colorRampPalette(c("white","firebrick3"))(20))
pheatmap(corMat, breaks = seq(-1,1,length.out = 50),
clustering_method = "ward.D2", color = colorList,
treeheight_row = 0, show_colnames = FALSE,
main = "Drug-drug correlation in CLL")
Drug-drug correlation matrix (only drugs that could produce AUC data in all samples)
viabMat <- viabMat[complete.cases(viabMat),]
corMat <- cor(t(viabMat), use = "pairwise.complete.obs")
#define color sequences
colorList <- c(colorRampPalette(c("navy", "white"))(20),
colorRampPalette(c("white"))(10),
colorRampPalette(c("white","firebrick3"))(20))
pheatmap(corMat, breaks = seq(-1,1,length.out = 50),
clustering_method = "ward.D2", color = colorList,
treeheight_row = 0, show_colnames = FALSE,
main = "Drug-drug correlation in CLL")
For U-CLL samples
#Prepare viability matrix
viabMat <- filter(screenData, diagnosis %in% "CLL", patientID %in% filter(patMeta, IGHV.status %in% "U")$Patient.ID) %>%
group_by(patientID, Drug) %>% summarise(viab = mean(viab.auc, na.rm=TRUE)) %>%
spread(key = patientID, value = "viab") %>% data.frame() %>%
column_to_rownames("Drug") %>% as.matrix()
viabMat <- viabMat[rowSums(!is.na(viabMat))>2,]
#viabMat <- viabMat[complete.cases(viabMat),]Drug-drug correlation matrix (all drugs, except for those that generate AUC data in less than 3 samples)
corMat <- cor(t(viabMat), use = "pairwise.complete.obs")
#define color sequences
colorList <- c(colorRampPalette(c("navy", "white"))(20),
colorRampPalette(c("white"))(10),
colorRampPalette(c("white","firebrick3"))(20))
pheatmap(corMat, breaks = seq(-1,1,length.out = 50),
clustering_method = "ward.D2", color = colorList,
treeheight_row = 0, show_colnames = FALSE,
main = "Drug-drug correlation in U-CLL")
Drug-drug correlation matrix (only drugs that could produce AUC data in all samples)
viabMat <- viabMat[complete.cases(viabMat),]
corMat <- cor(t(viabMat), use = "pairwise.complete.obs")
#define color sequences
colorList <- c(colorRampPalette(c("navy", "white"))(20),
colorRampPalette(c("white"))(10),
colorRampPalette(c("white","firebrick3"))(20))
pheatmap(corMat, breaks = seq(-1,1,length.out = 50),
clustering_method = "ward.D2", color = colorList,
treeheight_row = 0, show_colnames = FALSE,
main = "Drug-drug correlation in CLL")
For MCL samples
Drug-drug correlation matrix (all drugs, except for those that generate AUC data in less than 3 samples)
corMat <- cor(t(viabMat), use = "pairwise.complete.obs")
pheatmap(corMat, color = colorList,
breaks = seq(-1,1,length.out = 50), clustering_method = "ward.D2",
treeheight_row = 0, show_colnames = FALSE,
main = "Drug-drug correlation in MCL")
Drug-drug correlation matrix (only drugs that could produce AUC data in all samples)
viabMat <- viabMat[complete.cases(viabMat),]
corMat <- cor(t(viabMat))
pheatmap(corMat, color = colorList,
breaks = seq(-1,1,length.out = 50), clustering_method = "ward.D2",
treeheight_row = 0, show_colnames = FALSE,
main = "Drug-drug correlation in MCL")
For T-PLL samples
#Prepare viability matrix
viabMat <- filter(screenData, ! Drug %in% c("DMSO","PBS"), diagnosis %in% "T-PLL") %>%
group_by(patientID, Drug) %>% summarise(viab = mean(viab.auc, na.rm=TRUE)) %>%
spread(key = patientID, value = "viab") %>% data.frame() %>%
column_to_rownames("Drug") %>% as.matrix()
#viabMat <- viabMat[complete.cases(viabMat),]
viabMat <- viabMat[rowSums(!is.na(viabMat))>2,]Drug-drug correlation matrix (all drugs, except for those that generate AUC data in less than 3 samples)
corMat <- cor(t(viabMat),use="pairwise.complete.obs")
pheatmap(corMat, color = colorList,
breaks = seq(-1,1,length.out = 50), clustering_method = "ward.D2",
treeheight_row = 0, show_colnames = FALSE,
main = "Drug-drug correlation in T-PLL")
viabMat <- viabMat[complete.cases(viabMat),]
corMat <- cor(t(viabMat),use="pairwise.complete.obs")
pheatmap(corMat, color = colorList,
breaks = seq(-1,1,length.out = 50), clustering_method = "ward.D2",
treeheight_row = 0, show_colnames = FALSE,
main = "Drug-drug correlation in T-PLL")
sessionInfo()R version 4.2.0 (2022-04-22)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Big Sur/Monterey 10.16
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRlapack.dylib
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] forcats_0.5.1 stringr_1.4.0 dplyr_1.0.9
[4] purrr_0.3.4 readr_2.1.2 tidyr_1.2.0
[7] tibble_3.1.8 tidyverse_1.3.2 limma_3.52.2
[10] readxl_1.4.0 gtable_0.3.0 ggbeeswarm_0.6.0
[13] jyluMisc_0.1.5 colorspace_2.0-3 RColorBrewer_1.1-3
[16] ggrepel_0.9.1 ggplot2_3.3.6 smallvis_0.0.0.9000
[19] Rtsne_0.16 cowplot_1.1.1 genefilter_1.78.0
[22] pheatmap_1.0.12 reshape2_1.4.4 gridExtra_2.3
[25] Biobase_2.56.0 BiocGenerics_0.42.0
loaded via a namespace (and not attached):
[1] utf8_1.2.2 shinydashboard_0.7.2
[3] tidyselect_1.1.2 RSQLite_2.2.15
[5] AnnotationDbi_1.58.0 htmlwidgets_1.5.4
[7] grid_4.2.0 BiocParallel_1.30.3
[9] maxstat_0.7-25 munsell_0.5.0
[11] ragg_1.2.2 codetools_0.2-18
[13] DT_0.23 withr_2.5.0
[15] highr_0.9 knitr_1.39
[17] rstudioapi_0.13 stats4_4.2.0
[19] ggsignif_0.6.3 MatrixGenerics_1.8.1
[21] labeling_0.4.2 git2r_0.30.1
[23] slam_0.1-50 GenomeInfoDbData_1.2.8
[25] KMsurv_0.1-5 bit64_4.0.5
[27] farver_2.1.1 rprojroot_2.0.3
[29] vctrs_0.4.1 generics_0.1.3
[31] TH.data_1.1-1 xfun_0.31
[33] sets_1.0-21 R6_2.5.1
[35] GenomeInfoDb_1.32.2 bitops_1.0-7
[37] cachem_1.0.6 fgsea_1.22.0
[39] DelayedArray_0.22.0 assertthat_0.2.1
[41] vroom_1.5.7 promises_1.2.0.1
[43] scales_1.2.0 multcomp_1.4-19
[45] googlesheets4_1.0.0 beeswarm_0.4.0
[47] sandwich_3.0-2 workflowr_1.7.0
[49] rlang_1.0.4 systemfonts_1.0.4
[51] splines_4.2.0 rstatix_0.7.0
[53] gargle_1.2.0 broom_1.0.0
[55] BiocManager_1.30.18 yaml_2.3.5
[57] abind_1.4-5 modelr_0.1.8
[59] backports_1.4.1 httpuv_1.6.5
[61] tools_4.2.0 relations_0.6-12
[63] ellipsis_0.3.2 gplots_3.1.3
[65] jquerylib_0.1.4 Rcpp_1.0.9
[67] plyr_1.8.7 visNetwork_2.1.0
[69] zlibbioc_1.42.0 RCurl_1.98-1.7
[71] ggpubr_0.4.0 S4Vectors_0.34.0
[73] zoo_1.8-10 SummarizedExperiment_1.26.1
[75] haven_2.5.0 cluster_2.1.3
[77] exactRankTests_0.8-35 fs_1.5.2
[79] magrittr_2.0.3 data.table_1.14.2
[81] reprex_2.0.1 survminer_0.4.9
[83] googledrive_2.0.0 mvtnorm_1.1-3
[85] matrixStats_0.62.0 hms_1.1.1
[87] shinyjs_2.1.0 mime_0.12
[89] evaluate_0.15 xtable_1.8-4
[91] XML_3.99-0.10 IRanges_2.30.0
[93] compiler_4.2.0 KernSmooth_2.23-20
[95] crayon_1.5.1 htmltools_0.5.3
[97] later_1.3.0 tzdb_0.3.0
[99] lubridate_1.8.0 DBI_1.1.3
[101] dbplyr_2.2.1 MASS_7.3-58
[103] BiocStyle_2.24.0 Matrix_1.4-1
[105] car_3.1-0 cli_3.3.0
[107] marray_1.74.0 parallel_4.2.0
[109] igraph_1.3.4 GenomicRanges_1.48.0
[111] pkgconfig_2.0.3 km.ci_0.5-6
[113] piano_2.12.0 xml2_1.3.3
[115] annotate_1.74.0 vipor_0.4.5
[117] bslib_0.4.0 XVector_0.36.0
[119] drc_3.0-1 rvest_1.0.2
[121] digest_0.6.29 Biostrings_2.64.0
[123] rmarkdown_2.14 cellranger_1.1.0
[125] fastmatch_1.1-3 survMisc_0.5.6
[127] shiny_1.7.2 gtools_3.9.3
[129] lifecycle_1.0.1 jsonlite_1.8.0
[131] carData_3.0-5 fansi_1.0.3
[133] pillar_1.8.0 lattice_0.20-45
[135] KEGGREST_1.36.3 fastmap_1.1.0
[137] httr_1.4.3 plotrix_3.8-2
[139] survival_3.4-0 glue_1.6.2
[141] png_0.1-7 bit_4.0.4
[143] stringi_1.7.8 sass_0.4.2
[145] blob_1.2.3 textshaping_0.3.6
[147] caTools_1.18.2 memoise_2.0.1