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fig6_d_e_f_g.Rmd
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---
title: "fig6_d_e_f_g.Rmd"
output:
html_document:
df_print: paged
toc: true
toc_depth: 3
pdf_document:
toc: true
toc_depth: 3
fig_caption: yes
includes:
in_header: my_header.tex
number_sections: true
keep_tex: true
word_document: default
date: "2023-07-18"
---
```{r setup, include=FALSE}
suppressPackageStartupMessages(library(gplots))
suppressPackageStartupMessages(library(ggplot2))
suppressPackageStartupMessages(library(ggthemes))
suppressPackageStartupMessages(library(DESeq2))
suppressPackageStartupMessages(library(limma))
suppressPackageStartupMessages(library(edgeR))
suppressPackageStartupMessages(library(ggpubr))
suppressPackageStartupMessages(library(grid))
suppressPackageStartupMessages(library(gridExtra))
suppressPackageStartupMessages(library(dplyr))
suppressPackageStartupMessages(library(UpSetR))
suppressPackageStartupMessages(library(pheatmap))
suppressPackageStartupMessages(library(gplots))
suppressPackageStartupMessages(library(kableExtra))
suppressPackageStartupMessages(library(estimate))
suppressPackageStartupMessages(library(reshape))
suppressPackageStartupMessages(library(sva))
suppressPackageStartupMessages(library(data.table))
suppressPackageStartupMessages(library(ggsci))
suppressPackageStartupMessages(library(parallel))
suppressPackageStartupMessages(library(RColorBrewer))
knitr::opts_chunk$set(echo = FALSE, cache=TRUE, warning=FALSE, message=FALSE)
```
```{r read_in_tcl}
library(openxlsx)
drug_screen <- read.xlsx("TCL_screen_data_reformatted.xlsx")
drug_screen <- drug_screen[!is.na(drug_screen$drug),]
row.names(drug_screen) <- drug_screen$drug
## dropcell line
drug_screen <- drug_screen[,c(-27,-26,-25,-24)]
drug_screen_just_via <- drug_screen[, c(5:23)]
drug_screen_metadata <- read.xlsx("TCL_screen_data_metadata.xlsx")
#remove cell line
drug_screen_metadata <- drug_screen_metadata[c(-20,-21,-22,-23),]
```
```{r pre-process-kegg-db, eval=F}
library(openxlsx)
library(KEGGREST)
library(data.table)
drug_screen <- read.xlsx("TCL_screen_data_reformatted.xlsx")
drug_screen <- drug_screen[!is.na(drug_screen$drug),]
row.names(drug_screen) <- drug_screen$drug
#drug_screen$target
## dropcell line
drug_screen <- drug_screen[,c(-27,-26,-25,-24)]
drug_screen_just_via <- drug_screen[, c(5:23)]
drug_screen <- drug_screen[!duplicated(gsub("\\(.*","",row.names(drug_screen))),]
row.names(drug_screen) <- gsub("\\(.*","",row.names(drug_screen))
drug_screen <- drug_screen[which(row.names(drug_screen) != ""),]
drug_ids = lapply(row.names(drug_screen), function(x) {print(x); keggFind("drug", x)})
drug_ids_df <- lapply(1:length(drug_ids), function(x) {
if(length(drug_ids[[x]]) > 0 ){
df <- data.frame(id =names(drug_ids[[x]])[1], name = drug_ids[[x]][[1]], num=x )
return(df)
}
}) %>% rbindlist()
drug_info <- lapply(1:nrow(drug_ids_df), function(x) {
id = as.character(drug_ids_df[x,]$id)
print(x)
num = drug_ids_df[x,]$num
x = keggGet(id)
target = NA
pathway = NA
class=NA
if(!is.null(x[[1]]$TARGET)){
if( is.atomic(x[[1]]$TARGET) ){
target = x[[1]]$TARGET
} else {
target = x[[1]]$TARGET$TARGET
target = gsub("\\[.*","",target)
target = gsub("\\(.*","",target)
target = gsub(" ","",target)
if(!is.null(x[[1]]$TARGET$PATHWAY)){
pathway = x[[1]]$TARGET$PATHWAY
pathway = gsub("\\(.*","",pathway)
}
}
}
if(!is.null( x[[1]]$CLASS[[1]])){
class = x[[1]]$CLASS[[1]]
}
df <- data.frame(id = id , target = paste0(target, collapse = ","), pathway=paste0(pathway, collapse = ","), class=class,num = num)
return(df)
}) %>% rbindlist()
drug_info <- merge(drug_info, drug_ids_df, by="num")
drug_info$name <- as.character(drug_info$name)
#drug_info$name <- gsub("\\(.*","",drug_info$name)
drug_info <- drug_info[!duplicated(drug_info$name),]
drug_info$target <- as.character(drug_info$target)
drug_info$target <- gsub("\\(.*","",drug_info$target)
drug_info$target <- gsub("\\[.*","",drug_info$target)
drug_info$target <- gsub("/",",",drug_info$target)
saveRDS(drug_info, "drug_info.RDS")
drug_screen_just_via_filt <- drug_screen_just_via[drug_info$num,]
row.names(drug_screen_just_via_filt) <- drug_info$name
saveRDS(drug_screen_just_via_filt, "drug_screen_just_via_filt.RDS")
```
```{r load-kegg-drug-db}
drug_info <- readRDS("drug_info.RDS")
drug_screen_just_via_filt <- readRDS("drug_screen_just_via_filt.RDS")
```
```{r load-data}
raw = read.csv("drugscreen.gene.counts.txt", row.names=1, check.names = F)
sub_met = read.csv("drugscreen.metadata.txt", row.names=1, check.names = F)
sub_met$TCL.subtype = factor(sub_met$TCL.subtype)
sub_met$source = factor(sub_met$source)
table(row.names(sub_met) == colnames(raw))
```
```{r pca, fig.align='center', fig.cap="Principal component analysis (PCA) reveals stratification of PDXs and primary tumor samples.", results="asis"}
deseq2.coldata <- data.frame(row.names = colnames(raw), sub_met, stringsAsFactors=F)
deseq2.coldata$source <- factor(deseq2.coldata$source)
deseq2.cds <- DESeq2::DESeqDataSetFromMatrix(countData = raw,colData = deseq2.coldata, design = ~1)
deseq2.cds <- estimateSizeFactors(deseq2.cds)
deseq2.rld <- DESeq2::vst(deseq2.cds, blind=TRUE)
deseq2.rld <- vst(deseq2.cds, blind=TRUE)
ntop = nrow(deseq2.rld)
Pvars <- rowVars(assay(deseq2.rld))
select <- order(Pvars, decreasing = TRUE)[seq_len(min(ntop, length(Pvars)))]
PCA <- prcomp(t(assay(deseq2.rld)[select, ]), scale = F)
percentVar <- round(100*PCA$sdev^2/sum(PCA$sdev^2),1)
dataGG = data.frame(PC1 = PCA$x[,1], PC2 = PCA$x[,2],PC3 = PCA$x[,3], sampleName = row.names(colData(deseq2.rld)),colData(deseq2.rld))
qplot(PC1, PC2, data = dataGG, color =TCL.subtype, size=I(3), main = "Principal component analysis") + labs(x = paste0("PC1: ", round(percentVar[1],4), "% variance"), y = paste0("PC2: ", round(percentVar[2],4), "% variance")) + theme_bw() + theme(legend.position="bottom") + scale_color_jco()
```
# Correlation analysis
To investigate the association between expression and drug sensitivity at gene level, (Pearson) correlation tests were performed to identify genes whose expressions are correlated with the sensitivity to the drugs with respect to the different TCL subtypes. In other words, is the drug response as measured by cell viability related to expression levels of the different TCL subtypes?
In order to answer to perform correlation, we need two matrices of equal sizes. The cell viability assay contains replicates for most PDX samples; thus, we will take the average of each replicate to reduce the measurement of any each PDX sample to one.
There is not a 1:1 mapping of passage time points in the cell viability data to the expression data. So, we will take the closest RNA-seq passage. Below is a table of the mapping we used:
```{r mapping, echo=FALSE}
## let's merge reps from viability
il69.p3 <- rowMeans(drug_screen_just_via_filt[,c(1,2)])
il69.p11 <- rowMeans(drug_screen_just_via_filt[,c(3,4)])
DN03.p18 <- rowMeans(drug_screen_just_via_filt[,c(5,6)])
il79.p7 <- drug_screen_just_via_filt[,c(7)]
il79.p8 <- rowMeans(drug_screen_just_via_filt[,c(8,9)])
il2.p5 <- rowMeans(drug_screen_just_via_filt[,c(10,11)])
il2.p7 <- rowMeans(drug_screen_just_via_filt[,c(12,13)])
mt05.p6 <-drug_screen_just_via_filt[,c(14)]
mt05.p8 <-rowMeans(drug_screen_just_via_filt[,c(15,16)])
mt05.p9 <- drug_screen_just_via_filt[,c(17)]
il26a.p4 <- rowMeans(drug_screen_just_via_filt[,c(18,19)])
drugs_via_merge <- data.frame(il69.p3=il69.p3,
il69.p11=il69.p11,
DN03.p18=DN03.p18,
il79.p7=il79.p7,
il79.p8=il79.p8,
il2.p5=il2.p5,
il2.p7=il2.p7,
mt05.p6=mt05.p6,
mt05.p8=mt05.p8,
mt05.p9=mt05.p9,
il26a.p4=il26a.p4)
df = data.frame("Drug-assay sample"= colnames(drugs_via_merge), "RNA-seq sample" = colData(deseq2.rld)$sampleName, TCL.subtype=colData(deseq2.rld)$TCL.subtype)
kable(df, "latex", booktabs = T, row.names=FALSE, caption = 'Summary of samples for correlation analysis. The cell viability for each drug test was correlated with the expression levels each gene across the different TCL subtypes .')
```
```{r corr-setup, fig.align='center', fig.width=12, fig.height=12, results="asis", echo=FALSE}
table(row.names(sub_met) == colnames(raw))
deseq2.coldata <- data.frame(row.names = colnames(raw), sub_met, stringsAsFactors=F)
deseq2.coldata$source <- factor(deseq2.coldata$source)
deseq2.cds <- DESeq2::DESeqDataSetFromMatrix(countData = raw,colData = deseq2.coldata, design = ~1)
deseq2.cds <- estimateSizeFactors(deseq2.cds)
deseq2.rld <- DESeq2::vst(deseq2.cds, blind=TRUE)
deseq2.rld <- vst(deseq2.cds, blind=TRUE)
deseq2.rld.unfilt <- vst(deseq2.cds, blind=TRUE)
keep <- apply(assay(deseq2.rld), 1, function(x) any(x >= 9))
deseq2.rld <- deseq2.rld[keep,]
## let's merge reps from viability
il69.p3 <- rowMeans(drug_screen_just_via_filt[,c(1,2)])
il69.p11 <- rowMeans(drug_screen_just_via_filt[,c(3,4)])
DN03.p18 <- rowMeans(drug_screen_just_via_filt[,c(5,6)])
il79.p7 <- drug_screen_just_via_filt[,c(7)]
il79.p8 <- rowMeans(drug_screen_just_via_filt[,c(8,9)])
il2.p5 <- rowMeans(drug_screen_just_via_filt[,c(10,11)])
il2.p7 <- rowMeans(drug_screen_just_via_filt[,c(12,13)])
mt05.p6 <-drug_screen_just_via_filt[,c(14)]
mt05.p8 <-rowMeans(drug_screen_just_via_filt[,c(15,16)])
mt05.p9 <- drug_screen_just_via_filt[,c(17)]
il26a.p4 <- rowMeans(drug_screen_just_via_filt[,c(18,19)])
drugs_via_merge <- data.frame(il69.p3=il69.p3,
il69.p11=il69.p11,
DN03.p18=DN03.p18,
il79.p7=il79.p7,
il79.p8=il79.p8,
il2.p5=il2.p5,
il2.p7=il2.p7,
mt05.p6=mt05.p6,
mt05.p8=mt05.p8,
mt05.p9=mt05.p9,
il26a.p4=il26a.p4)
df = data.frame("Drug-assay sample"= colnames(drugs_via_merge), "RNA-seq sample" = colData(deseq2.rld)$sampleName, TCL.subtype=colData(deseq2.rld)$TCL.subtype)
```
```{r corr, fig.align='center', fig.width=12, fig.height=12, results="asis", echo=FALSE, eval=T}
drug_info <- as.data.frame(drug_info)
drug_info$pathway <- as.character(drug_info$pathway)
drug_info = drug_info[grep("Ruxolitinib", drug_info$name),]
all = lapply( drug_info$num , function(y) {
num = as.numeric(y)
df <- drug_info[ drug_info[["num"]] == num , ]
df <- df[,c(2,7,5,3,4)]
colnames(df)[1] <- "id"
t_df <- t(df)
doi <- as.numeric(drugs_via_merge[as.numeric(colnames(t_df)),] )
env<-new.env()
env$plots1Names<-c()
plots1 <- lapply( strsplit(as.character(drug_info[ drug_info[["num"]] == num , ]$target), ",")[[1]] , function(x) {
# print(x)
gene = x
if(gene != "NA"){
if(length(grep(gene, row.names(deseq2.rld.unfilt))) > 0){
lapply( grep(gene, row.names(deseq2.rld.unfilt), value=T), function(x) {
# cat("\n\n##", x, "\n\n")
df = data.frame(doi=doi, rna=as.numeric(assay(deseq2.rld.unfilt[x,])), subtype = colData(deseq2.rld.unfilt)$TCL.subtype, patient = colData(deseq2.rld.unfilt)$patient, passage = colData(deseq2.rld.unfilt)$passage, name = colData(deseq2.rld.unfilt)$sampleName)
row.names(df) <- colnames(deseq2.rld.unfilt)
df$subtype <- as.character(df$subtype)
p1 <- ggbarplot(df, x="name", y="rna", fill="subtype", ylab="Expression level", xlab="", sort.by.groups=F, sort.val="none", color = "white", x.text.angle = 90) + scale_fill_jco() + coord_flip() + facet_grid(subtype~., scales="free")
p2 <- ggscatter(df, x="doi", y="rna", color="subtype", xlab="Cell viability", ylab="Expression level", cor.coef=TRUE, size=5, label="patient", legend = "bottom", title=paste0("Target", " : ", x)) + stat_smooth(method=lm, fill = "lightgray") + scale_color_jco()
#grid.arrange(p1, p2, ncol=2)
#return(grid.arrange(p1, p2, ncol=2))
env$plots1Names <- c(env$plots1Names, gene)
return(arrangeGrob(p1, p2, ncol=2))
})
}}
})
names(plots1) <- env$plots1Names
tmp = do.call(rbind, mclapply(1:nrow(deseq2.rld), function(i) {
res = cor.test(doi, assay(deseq2.rld[i,])[1,], method = "pearson")
data.frame(coef=unname(res$estimate), p=res$p.value)
}))
corResult <- tibble(ID = rownames(deseq2.rld),
symbol = rownames(deseq2.rld),
coef = tmp$coef,
p = tmp$p)
corResult <- arrange(corResult, p) %>% mutate(p.adj = p.adjust(p, method="BH"))
pCut = 0.05
corResult.sig <- filter(corResult, p <= pCut)
write.xlsx(corResult.sig,paste0(gsub(" ", "", drug_info[ drug_info[["num"]] == num , ]$name), ".pearsonCorResultsSig0.05.xlsx"))
p1 <- gghistogram(corResult, x="coef", color = "#00AFBB", fill="#00AFBB", rug=T, xlab="Correlation coefficient", main = "Histogram of coefficients")
p2 <- gghistogram(corResult, x="p", color = "#E7B800", fill="#E7B800", rug=T, xlab = "P value", main = "Histogram of P values") + geom_vline(xintercept=0.05)
p3 <- gghistogram(corResult.sig, x="coef", color = "#00AFBB", fill="#00AFBB", rug=T, xlab="Correlation coefficient", main = "Histogram of coefficients (P < 0.05)")
p4 <- gghistogram(corResult.sig, x="p", color = "#E7B800", fill="#E7B800", rug=T, xlab = "P value", main = "Histogram of P values (P < 0.05)") + geom_vline(xintercept=0.05)
# grid.arrange(p1, p2, p3, p4, ncol=2)
stats_df = data.frame("genes tested for correlation" = nrow(deseq2.rld), "number correlated (P < 0.05)" = nrow(corResult.sig), check.names = F)
gmt.kegg.list <- msigdbr::msigdbr(species = "Homo sapiens", category = "C2", subcategory = "CP:KEGG") %>% dplyr::select(gs_name, gene_symbol) %>% split(x = .$gene_symbol, f = .$gs_name)
gmt.kegg <- msigdbr::msigdbr(species = "Homo sapiens", category = "C2", subcategory = "CP:KEGG") %>% dplyr::select(gs_id, gs_name, gene_symbol)
kegg.gs <- EnrichmentBrowser::getGenesets(org="hsa", db="kegg", cache=FALSE)
kegg.gs.m = reshape2::melt(kegg.gs)
kegg.gs.m$value <- as.character(kegg.gs.m$value )
eg <- clusterProfiler::bitr(kegg.gs.m$value, fromType="ENTREZID", toType="SYMBOL", OrgDb="org.Hs.eg.db") %>% as.data.table
kegg.gs.df = merge(kegg.gs.m, eg, by.x="value", by.y="ENTREZID")
colnames(kegg.gs.df) <- c("entrezid", "pathway", "symbol")
kegg.gs.df$pathwayid <- gsub("_.*","",kegg.gs.df$pathway)
kegg.gs.df$pathwayname <- sub('.*?_',"",kegg.gs.df$pathway) %>% gsub("_", " ", .)
kegg.gs.list <- kegg.gs.df %>% split(x = .$symbol, f = .$pathwayid)
library(gridExtra)
env<-new.env()
env$stats<-data.frame()
env$plots2Names<-c()
if(drug_info[ drug_info[["num"]] == num , ]$pathway != "NA"){
plots2 = lapply( strsplit(as.character(drug_info[ drug_info[["num"]] == num , ]$pathway), ",")[[1]] , function(x) {
set = x
if(length(corResult.sig$symbol[corResult.sig$symbol %in% kegg.gs.list[[set]]]) >=2){
# cat('\\pagebreak')
# cat("\n\n##", x, "\n\n")
env$plots2Names <- c(env$plots2Names, x)
stats.sub = data.frame(set= length(corResult.sig$symbol[corResult.sig$symbol %in% kegg.gs.list[[set]]]))
colnames(stats.sub) <- paste0("num sig (P < 0.05) in ", set)
if (nrow(env$stats) > 0) {
env$stats <- cbind(env$stats, stats.sub)
} else {
env$stats <- stats.sub
}
title =paste0(unique(subset(kegg.gs.df, pathwayid == set)$pathwayid), " : ", unique(subset(kegg.gs.df, pathwayid == set)$pathwayname))
annotation_col = data.frame(subtype = colData(deseq2.rld)$TCL.subtype, viability = doi)
row.names(annotation_col) <- colnames(assay(deseq2.rld))
annotation_row = data.frame(coef=corResult.sig$coef[corResult.sig$symbol %in% kegg.gs.list[[set]]], p=corResult.sig$p[corResult.sig$symbol %in% kegg.gs.list[[set]]])
row.names(annotation_row) <- corResult.sig$symbol[corResult.sig$symbol %in% kegg.gs.list[[set]]]
hm = pheatmap(assay(deseq2.rld)[corResult.sig$symbol[corResult.sig$symbol %in% kegg.gs.list[[set]]],], scale="row", cluster_cols = T, annotation_col = annotation_col, annotation_row=annotation_row, color = viridis::inferno(100),cellwidth =25, cellheight = 400/nrow(annotation_row), main = title, silent=T)
hm2 = pheatmap(assay(deseq2.rld)[corResult.sig$symbol[corResult.sig$symbol %in% kegg.gs.list[[set]]],], scale="row", cluster_cols = F, annotation_col = annotation_col, annotation_row=annotation_row, color = viridis::inferno(100),cellwidth =25, cellheight = 400/nrow(annotation_row), main = title, silent=T)
return(arrangeGrob(grobs = list(hm[[4]]), ncol=1))
} else if (length(corResult.sig$symbol[corResult.sig$symbol %in% kegg.gs.list[[set]]]) > 0){
env$plots2Names <- c(env$plots2Names, x)
stats.sub = data.frame(set= length(corResult.sig$symbol[corResult.sig$symbol %in% kegg.gs.list[[set]]]))
colnames(stats.sub) <- paste0("num sig (P < 0.05) in ", set)
if (nrow(env$stats) > 0) {
env$stats <- cbind(env$stats, stats.sub)
} else {
env$stats <- stats.sub
}
goi = corResult.sig$symbol[corResult.sig$symbol %in% kegg.gs.list[[set]]]
cpm_df <- assay(deseq2.rld[goi,]) %>% reshape2::melt(., id.vars = rownames)
names(cpm_df) <- c("gene","sample","exprs")
cpm_df <- merge(cpm_df, colData(deseq2.rld), by.x = "sample", by.y="sampleName")
p <- ggdotplot(as.data.frame(cpm_df), x="sample", y="exprs", fill="TCL.subtype", color="TCL.subtype", facet.by=c("gene", "TCL.subtype"), scales="free", ylab = "Expression level")
}
}) %>% invisible()
}
names(plots2) <- env$plots2Names
cat('\\pagebreak')
cat("\n\n#", drug_info[ drug_info[["num"]] == num , ]$name, "\n\n")
if(nrow(env$stats) > 0 ){
print(kable(rbind(t_df, t(stats_df), (t(env$stats))), "latex", booktabs = T, row.names=T, col.names="") %>% kable_styling(latex_options = c("striped")))
} else {
print(kable(rbind(t_df, t(stats_df), t(data.frame("num sig in pathways" = 0, check.names = F))), "latex", booktabs = T, row.names=T, col.names="") %>% kable_styling(latex_options = c("striped")))
}
print(grid.arrange(p1, p2, p3, p4, ncol=2))
lapply(names(plots1), function(x) {
if(!is.null(plots1[[x]])){
x = x
cat("\n\n##", x, "\n\n")
grid.arrange((arrangeGrob(grobs = list(plots1[[x]][[1]]))))
cat('\\pagebreak')
}
})
lapply(names(plots2), function(x) {
if(!is.null(plots2[[x]])){
cat('\\pagebreak')
grid.arrange((arrangeGrob(grobs = list(plots2[[x]]))))
cat("\n\n##", x, "\n\n")
}
})
})
```
# Session Info
```{r session, message=FALSE, warning=FALSE, cache=FALSE,echo=FALSE, fig.width=10, fig.height=5.5, context="data"}
sessionInfo()
```