Section 1: Overview of CLL proteomic dataset
Junyan Lu
2020-10-09
Last updated: 2021-03-17
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Knit directory: CLLproteomics_batch13/analysis/
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Load packages and datasets
library(limma)
library(DESeq2)
library(cowplot)
library(proDA)
library(pheatmap)
library(SummarizedExperiment)
library(tidyverse)
#load datasets
load("../data/patMeta_enc.RData")
load("../data/ddsrna_enc.RData")
load("../data/proteomic_explore_enc.RData")
source("../code/utils.R")
knitr::opts_chunk$set(echo = TRUE, warning = FALSE, message = FALSE, dev = c("png","pdf"))Overview of the exploration cohort composition
Genomic matrix
Get mutations with at least 5 cases
geneMat <- patMeta[match(colnames(protCLL), patMeta$Patient.ID),] %>%
select(-IGHV.status, -Methylation_Cluster) %>%
mutate_if(is.factor, as.character) %>%
mutate_at(vars(-Patient.ID), as.numeric) %>% #assign a few unknown mutated cases to wildtype
data.frame() %>% column_to_rownames("Patient.ID")
geneMat <- geneMat[,apply(geneMat,2, function(x) sum(x %in% 1, na.rm = TRUE))>=5]Mutations that will be tested
#Remove some dubious annotations
geneMat <- geneMat[,!colnames(geneMat) %in% c("del5IgH","gain2p","IgH_break")]
colnames(geneMat) [1] "del11q" "del13q" "del17p" "trisomy12" "trisomy19" "ATM"
[7] "BRAF" "DDX3X" "EGR2" "MED12" "NOTCH1" "SF3B1"
[13] "TP53" useGeneForComposition <- colnames(geneMat)Dimension
dim(geneMat)[1] 91 13Plot to summarise genomic background
Separate CNV table and mutation table
cnvCol <- colnames(geneMat)[grepl("del|trisomy",colnames(geneMat))]
cnvMat <- geneMat[,cnvCol]
mutMat <- geneMat[,!colnames(geneMat) %in% cnvCol]
cnvMat <- cnvMat[,names(sort(colSums(cnvMat == 1,na.rm=TRUE)))]
#Manually assign CNV feature order for better visualization
cnvMat <- cnvMat[,c("del17p","del11q","del13q","trisomy19","trisomy12")]
mutMat <- mutMat[,names(sort(colSums(mutMat == 1, na.rm=TRUE)))]
geneMat <- cbind(mutMat,cnvMat)
geneMat[is.na(geneMat)] <- -1Sort patient based on CNVs
sortTab <- function(sumTab) {
i <- ncol(sumTab)
#print(i)
if (i == 1) {
return(rownames(sumTab)[order(sumTab[,i])])
}
allLevel <- sort(unique(sumTab[,i]))
orderRow <- lapply(allLevel, function(n) {
sortTab(sumTab[sumTab[,i] %in% n, seq(1,i-1), drop = FALSE])
}) %>% unlist() %>% c()
return(orderRow)
}
sortedPat <- rev(sortTab(geneMat))Prepare table for plot
plotTab <- geneMat %>% as_tibble(rownames="patID") %>% mutate_all(as.character) %>%
pivot_longer(-patID, names_to = "var", values_to = "value") %>%
mutate(status = case_when(
value == -1 ~ "NA",
value == 0 ~ "WT",
value == 1 & var %in% cnvCol ~ "CNV",
value == 1 & !var %in% cnvCol ~ "gene mutation"
)) %>%
mutate(var = factor(var, levels = c(colnames(mutMat),colnames(cnvMat))),
patID = factor(patID, levels = sortedPat),
status = factor(status, levels =c("WT","CNV","gene mutation","NA")))
formatedName <- lapply(levels(plotTab$var), function(n) {
if(n %in% cnvCol) {
n
} else {
bquote(italic(.(n)))
}
})Plot mutation matrix
pMain <- ggplot(plotTab, aes(x=patID, y = var, fill = status)) +
geom_tile(color = "grey80") +
theme_void() +
scale_fill_manual(values = c("gene mutation" = colList[5],
"CNV"= colList[4],
"WT" ="white",
"NA" = "grey80"),
name = "aberrations") +
scale_y_discrete(labels = formatedName) +
theme(axis.text.x = element_blank(),
axis.text.y = element_text(size=11, face = "bold"),
axis.ticks.length.y = unit(0.05,"npc")) +
ylab("") + xlab("")
#pMainAnnotation matrix
IGHV status
ighvTab <- select(patMeta, Patient.ID, IGHV.status) %>%
mutate(patID = Patient.ID, status = IGHV.status, type = "IGHV") %>%
filter(patID %in% sortedPat) %>%
mutate(patID = factor(patID, levels = sortedPat)) %>%
select(patID, type, status)
pIGHV <- ggplot(ighvTab, aes(x=patID, y = type, fill = status)) +
geom_tile(color = NA) +
theme_void() + xlab("") + ylab("") +
coord_cartesian(expand = FALSE) +
scale_fill_manual(values = c(M="black",U="white"), name = "IGHV") +
theme(axis.text.y = element_text(face = "bold", size=11),
axis.ticks.length.y = unit(0.05,"npc"))
#pIGHVSex
sexTab <- select(survT, patID, sex) %>%
mutate(status = as.character(sex), type = "sex") %>%
filter(patID %in% sortedPat) %>%
mutate(patID = factor(patID, levels = sortedPat),
status = case_when(status %in% "m" ~ "male",
status %in% "f" ~ "female")) %>%
select(patID, type, status)
pSex <- ggplot(sexTab, aes(x=patID, y = type, fill = status)) +
geom_tile(color = NA) +
theme_void() + xlab("") + ylab("") +
coord_cartesian(expand = FALSE) +
scale_fill_manual(values = c(male=colList[7],female=colList[5]), name = "sex") +
theme(axis.text.y = element_text(face = "bold",size=11),
axis.ticks.length.y = unit(0.05,"npc"))
#pSexPretreatment
treatTab <- survT %>% filter(patID %in% sortedPat) %>%
select(patID, pretreat) %>%
mutate(treatment = case_when(pretreat %in% 1 ~ "yes",
pretreat %in% 0 ~ "no",
is.na(pretreat) ~ "NA")) %>%
mutate(status = as.character(treatment), type = "treatment") %>%
mutate(patID = factor(patID, levels = sortedPat)) %>%
select(patID, type, status)
pTreat <- ggplot(treatTab, aes(x=patID, y = type, fill = status)) +
geom_tile(color = NA) +
theme_void() + xlab("") + ylab("") +
coord_cartesian(expand = FALSE) +
scale_fill_manual(values = c(yes = "black", no = "white","NA" = "grey80"), name = "treatment") +
theme(axis.text.y = element_text(face = "bold",size=11),
axis.ticks.length.y = unit(0.05,"npc"))
#pTreatAge
agePlotTab <- survT %>% filter(patID %in% sortedPat) %>%
select(patID, age) %>%
mutate( status = age, type = "age") %>%
mutate(patID = factor(patID, levels = sortedPat)) %>%
select(patID, type, status)
pAge <- ggplot(agePlotTab, aes(x=patID, y = type, fill = status)) +
geom_tile(color = NA) +
theme_void() + xlab("") + ylab("") +
coord_cartesian(expand = FALSE) +
scale_fill_viridis_b(name = "age") +
theme(axis.text.y = element_text(face = "bold",size=11),
axis.ticks.length.y = unit(0.05,"npc"))
#pAgeCombine all plots
lMain <- get_legend(pMain + geom_tile(color = "black") )
lAge <- get_legend(pAge + geom_tile(color = "black") )
lSex <- get_legend(pSex+ geom_tile(color = "black") )
lIGHV <- get_legend(pIGHV+ geom_tile(color = "black") )
lTreat <- get_legend(pTreat+ geom_tile(color = "black") )
noLegend <- theme(legend.position = "none")
mainPlot <- plot_grid(pAge + noLegend, pSex + noLegend,
pIGHV + noLegend,
pMain + noLegend, ncol=1, align = "v",
rel_heights = c(rep(1,3),20))
legendPlot <- plot_grid(lAge, lSex, lIGHV, lMain,ncol=1, align = "hv")
plot_grid(mainPlot, NULL, plot_grid(legendPlot, ncol=1), ncol=3, rel_widths = c(1,0.05, 0.15))
ggsave("cohortComposition.pdf", height=6, width=12)A table shown the clinical characteristics
(all cohorts combined)
patInfo <- sampleTab %>% select(encID, leukCount, cohort) %>%
left_join(select(patMeta, Patient.ID, IGHV.status, trisomy12), by = c(encID = "Patient.ID")) %>%
left_join(select(survT, patID, OS, died, TTT, treatedAfter, TTT, age, sex, pretreat), by = c(encID = "patID")) %>%
select(encID, age, sex, IGHV.status, trisomy12, leukCount, OS, died, TTT, treatedAfter, pretreat, cohort)
patInfoTab <- patInfo %>% #format
mutate(trisomy12 = ifelse(trisomy12 %in% "1", "yes", "no"),
died = ifelse(is.na(OS),NA, ifelse(died,"yes","no")),
treatedAfter = ifelse(is.na(TTT), NA, ifelse(treatedAfter, "yes","no")),
pretreat = ifelse(pretreat %in% 1, "yes","no"),
age = as.integer(age),
OS = formatC(OS, digits=1),
TTT = formatC(TTT, digits=1)) %>%
mutate_all(replace_na,"NA") %>%
arrange(cohort, encID) %>%
dplyr::rename(ID = encID,
IGHV = IGHV.status,
`WBC count` = leukCount,
`Survival time (years)` = OS,
Died = died,
`Time to treatment (years)` = TTT,
`Treatment after sampling` = treatedAfter,
`Treatment before sampling` = pretreat)Write a csv file
write_csv2(patInfoTab, "./patInfoTab.csv")Save a latex table for supplementary table
library(xtable)
write(print(xtable(patInfoTab,
caption = "Patient characteristics"),
include.rownames=FALSE,
caption.placement = "top"), file = paste0("./patInfoTab.tex"))Summarise key statistics
Numerical variables
sumNumTab <- select(patInfo, age, leukCount, OS, TTT, cohort) %>%
pivot_longer(-cohort) %>%
group_by(cohort,name) %>%
summarise(min = min(value,na.rm = TRUE), max=max(value, na.rm = TRUE), median = median(value, na.rm=TRUE))Exploration cohort
sumNumTab %>% filter(cohort == "exploration")# A tibble: 4 x 5
# Groups: cohort [1]
cohort name min max median
<chr> <chr> <dbl> <dbl> <dbl>
1 exploration age 33.8 89.2 67.0
2 exploration leukCount 20630 413000 78710
3 exploration OS 0.0219 6.60 2.55
4 exploration TTT 0.0110 6.60 0.573Independent cohort
sumNumTab %>% filter(cohort == "independent")# A tibble: 4 x 5
# Groups: cohort [1]
cohort name min max median
<chr> <chr> <dbl> <dbl> <dbl>
1 independent age 37.3 87.5 67.8
2 independent leukCount 11500 265800 71100
3 independent OS 0.0492 10.1 3.36
4 independent TTT 0.00274 6.83 0.168Catagorical variables
sumGroupTab <- select(patInfo, sex, IGHV.status, died, treatedAfter, pretreat, cohort) %>%
mutate_all(as.character) %>%
pivot_longer(-cohort) %>%
filter(!is.na(value)) %>%
group_by(cohort,name,value) %>%
summarise(num = length(value))Exploration cohort
sumGroupTab %>% filter(cohort == "exploration")# A tibble: 10 x 4
# Groups: cohort, name [5]
cohort name value num
<chr> <chr> <chr> <int>
1 exploration died FALSE 80
2 exploration died TRUE 11
3 exploration IGHV.status M 47
4 exploration IGHV.status U 44
5 exploration pretreat 0 82
6 exploration pretreat 1 9
7 exploration sex f 32
8 exploration sex m 59
9 exploration treatedAfter FALSE 44
10 exploration treatedAfter TRUE 46Independent cohort
sumGroupTab %>% filter(cohort == "independent")# A tibble: 10 x 4
# Groups: cohort, name [5]
cohort name value num
<chr> <chr> <chr> <int>
1 independent died FALSE 20
2 independent died TRUE 12
3 independent IGHV.status M 1
4 independent IGHV.status U 30
5 independent pretreat 0 19
6 independent pretreat 1 13
7 independent sex f 10
8 independent sex m 22
9 independent treatedAfter FALSE 8
10 independent treatedAfter TRUE 23RNA-protein associations
Preprocess transcriptomic and proteomic data
#protCLL <- protCLL[rowData(protCLL)$uniqueMap,]
dds <- estimateSizeFactors(dds)
sampleOverlap <- intersect(colnames(protCLL), colnames(dds))
geneOverlap <- intersect(rowData(protCLL)$ensembl_gene_id, rownames(dds))
ddsSub <- dds[geneOverlap, sampleOverlap]
protSub <- protCLL[match(geneOverlap, rowData(protCLL)$ensembl_gene_id), sampleOverlap]
#how many gene don't have RNA expression at all?
noExp <- rowSums(counts(ddsSub)) == 0
#remove those genes in both datasets
ddsSub <- ddsSub[!noExp,]
protSub <- protSub[!noExp,]
#remove proteins with duplicated identifiers
protSub <- protSub[!duplicated(rowData(protSub)$name)]
geneOverlap <- intersect(rowData(protSub)$ensembl_gene_id, rownames(ddsSub))
ddsSub.vst <- varianceStabilizingTransformation(ddsSub)Calculate correlations between protein abundance and RNA expression
rnaMat <- assay(ddsSub.vst)
proMat <- assays(protSub)[["count_combat"]]
rownames(proMat) <- rowData(protSub)$ensembl_gene_id
corTab <- lapply(geneOverlap, function(n) {
rna <- rnaMat[n,]
pro.raw <- proMat[n,]
res.raw <- cor.test(rna, pro.raw, use = "pairwise.complete.obs")
tibble(id = n,
p = res.raw$p.value,
coef = res.raw$estimate)
}) %>% bind_rows() %>%
arrange(desc(coef)) %>% mutate(p.adj = p.adjust(p, method = "BH"),
symbol = rowData(dds[id,])$symbol,
chr = rowData(dds[id,])$chromosome)Plot the distribution
corHistPlot <- ggplot(corTab, aes(x=coef)) + geom_histogram(position = "identity", col = colList[2], alpha =0.3, bins =50) +
geom_vline(xintercept = 0, col = colList[1], linetype = "dashed") + xlim(-0.7,1) +
xlab("Pearson's correlation coefficient") + theme_half +
ggtitle("Correlation between mRNA and protein expression")
corHistPlot
Median Pearson’s correlation coefficient
median(corTab$coef)[1] 0.1836462Influence of overall protein/RNA abundance on correlation
medProt <- rowMedians(proMat,na.rm = T)
names(medProt) <- rownames(proMat)
medRNA <- rowMedians(rnaMat, na.rm = T)
names(medRNA) <- rownames(rnaMat)
plotTab <- corTab %>% mutate(rnaAbundance = medRNA[id], protAbundance = medProt[id])
plotList <- list()
plotList[["rna"]] <- plotCorScatter(plotTab,"coef","rnaAbundance",
showR2 = FALSE, annoPos = "left",
x_lab ="Correlation coefficient",
y_lab = "Median RNA expression",
title = "", dotCol = colList[5], textCol = colList[1])
plotList[["protein"]] <- plotCorScatter(plotTab,"coef","protAbundance",
showR2 = FALSE, annoPos = "left",
x_lab ="Correlation coefficient",
y_lab = "Median protein expression",
title = "", dotCol = colList[6], textCol = colList[1])
cowplot::plot_grid(plotlist = plotList, ncol =2)
Plot protein RNA correlation for selected genes
Good correlations
geneList <- c("ZAP70","CD22","CD79A")
plotList <- lapply(geneList, function(n) {
print(n)
geneId <- rownames(dds)[match(n, rowData(dds)$symbol)]
stopifnot(length(geneId) ==1)
plotTab <- tibble(x=rnaMat[geneId,],y=proMat[geneId,], IGHV=protSub$IGHV.status)
coef <- cor(plotTab$x, plotTab$y, use="pairwise.complete")
annoPos <- ifelse (coef > 0, "left","right")
plotCorScatter(plotTab, "x","y", showR2 = FALSE, annoPos = annoPos, x_lab = "RNA expression", shape = "IGHV",
y_lab ="Protein expression", title = n,dotCol = colList[4], textCol = colList[1], legendPos="none")
})[1] "ZAP70"
[1] "CD22"
[1] "CD79A"goodCorPlot <- cowplot::plot_grid(plotlist = plotList, ncol =2)
goodCorPlot
CD38 was not detected any more
Bad correlations
geneList <- c("DTNBP1","PRPF19")
plotList <- lapply(geneList, function(n) {
geneId <- rownames(dds)[match(n, rowData(dds)$symbol)]
stopifnot(length(geneId) ==1)
plotTab <- tibble(x=rnaMat[geneId,],y=proMat[geneId,], IGHV=protSub$IGHV.status)
coef <- cor(plotTab$x, plotTab$y, use="pairwise.complete")
annoPos <- ifelse (coef > 0, "left","right")
plotCorScatter(plotTab, "x","y", showR2 = FALSE, annoPos = annoPos, x_lab = "RNA expression",
y_lab ="Protein expression", title = n,dotCol = colList[4], textCol = colList[1],
shape = "IGHV", legendPos = "none")
})
badCorPlot <- cowplot::plot_grid(plotlist = plotList, ncol =2)
badCorPlot
Principal component analysis
Calculate PCA
#remove genes on sex chromosomes
protCLL.sub <- protCLL[!rowData(protCLL)$chromosome_name %in% c("X","Y"),]
plotMat <- assays(protCLL.sub)[["QRILC_combat"]]
sds <- genefilter::rowSds(plotMat)
plotMat <- as.matrix(plotMat[order(sds,decreasing = TRUE),])
colAnno <- colData(protCLL)[,c("gender","IGHV.status","trisomy12")] %>%
data.frame()
colAnno$trisomy12 <- ifelse(colAnno$trisomy12 %in% 1, "yes","no")
pcOut <- prcomp(t(plotMat), center =TRUE, scale. = TRUE)
pcRes <- pcOut$x
eigs <- pcOut$sdev^2
varExp <- structure(eigs/sum(eigs),names = colnames(pcRes))All proteins are included for PCA analysis
Plot PC1 and PC2
plotTab <- pcRes %>% data.frame() %>% cbind(colAnno[rownames(.),]) %>%
rownames_to_column("patID") %>% as_tibble()
plotPCA12 <- ggplot(plotTab, aes(x=PC1, y=PC2, col = trisomy12, shape = IGHV.status)) + geom_point(size=4) +
xlab(sprintf("PC1 (%1.2f%%)",varExp[["PC1"]]*100)) +
ylab(sprintf("PC2 (%1.2f%%)",varExp[["PC2"]]*100)) +
scale_color_manual(values = colList) +
scale_shape_manual(values = c(M = 16, U =1)) +
xlim(-60,60) + ylim(-60,60) +
theme_full + theme(legend.position = "right")
plotPCA12
Plot PC3 and PC4
plotPCA34 <- ggplot(plotTab, aes(x=PC3, y=PC4, col = trisomy12, shape = IGHV.status)) + geom_point(size=4) +
xlab(sprintf("PC3 (%1.2f%%)",varExp[["PC3"]]*100)) +
ylab(sprintf("PC4 (%1.2f%%)",varExp[["PC4"]]*100)) +
scale_color_manual(values = colList) +
scale_shape_manual(values = c(M = 16, U =1)) +
xlim(-60,60) + ylim(-60,60) +
theme_full
plotPCA34
Correlation test between PCs and IGHV.status
corTab <- lapply(colnames(pcRes), function(pc) {
ighvCor <- t.test(pcRes[,pc] ~ colAnno$IGHV.status, var.equal=TRUE)
tri12Cor <- t.test(pcRes[,pc] ~ colAnno$trisomy12, var.equal=TRUE)
tibble(PC = pc,
feature=c("IGHV", "trisomy12"),
p = c(ighvCor$p.value, tri12Cor$p.value))
}) %>% bind_rows() %>% mutate(p.adj = p.adjust(p)) %>%
filter(p <= 0.05) %>% arrange(p)
corTab# A tibble: 9 x 4
PC feature p p.adj
<chr> <chr> <dbl> <dbl>
1 PC1 trisomy12 0.00000000740 0.00000135
2 PC6 trisomy12 0.0000000919 0.0000166
3 PC3 IGHV 0.0000253 0.00455
4 PC2 trisomy12 0.0000263 0.00470
5 PC5 IGHV 0.000308 0.0549
6 PC1 IGHV 0.000459 0.0813
7 PC90 trisomy12 0.0109 1
8 PC11 IGHV 0.0186 1
9 PC6 IGHV 0.0302 1 Plot PC1 and PC3
plotTab <- pcRes %>% data.frame() %>% cbind(colAnno[rownames(.),]) %>%
rownames_to_column("patID") %>% as_tibble()
plotPCA13 <- ggplot(plotTab, aes(x=PC1, y=PC3, col = trisomy12, shape = IGHV.status)) + geom_point(size=4) +
xlab(sprintf("PC1 (%1.2f%%)",varExp[["PC1"]]*100)) +
ylab(sprintf("PC3 (%1.2f%%)",varExp[["PC3"]]*100)) +
scale_color_manual(values = colList) +
scale_shape_manual(values = c(M = 16, U =1)) +
xlim(-60,60) + ylim(-60,60) +
theme_full
plotPCA13
Pathway enrichment on PC1 and PC2
PC1
enRes <- list()
gmts = list(H= "../data/gmts/h.all.v6.2.symbols.gmt",
KEGG = "../data/gmts/c2.cp.kegg.v6.2.symbols.gmt",
C6 = "../data/gmts/c6.all.v6.2.symbols.gmt")
proMat <- assays(protCLL.sub)[["QRILC_combat"]]
iPC <- "PC1"
pc <- pcRes[,iPC][colnames(proMat)]
designMat <- model.matrix(~1+pc)
fit <- limma::lmFit(proMat, designMat)
fit2 <- eBayes(fit)
corRes <- topTable(fit2, "pc", number = Inf) %>%
data.frame() %>% rownames_to_column("id")
inputTab <- corRes %>% filter(adj.P.Val < 0.05) %>%
mutate(name = rowData(protCLL[id,])$hgnc_symbol) %>% filter(!is.na(name)) %>%
distinct(name, .keep_all = TRUE) %>%
select(name, t) %>% data.frame() %>% column_to_rownames("name")
enRes[["Proteins associated with PC1"]] <- runGSEA(inputTab, gmts$H, "page")PC2
proMat <- assays(protCLL.sub)[["QRILC_combat"]]
iPC <- "PC2"
pc <- pcRes[,iPC][colnames(proMat)]
designMat <- model.matrix(~1+pc)
fit <- limma::lmFit(proMat, designMat)
fit2 <- eBayes(fit)
corRes <- topTable(fit2, "pc", number = Inf) %>%
data.frame() %>% rownames_to_column("id")
inputTab <- corRes %>% filter(adj.P.Val < 0.05) %>%
mutate(name = rowData(protCLL[id,])$hgnc_symbol) %>% filter(!is.na(name)) %>%
distinct(name, .keep_all = TRUE) %>%
select(name, t) %>% data.frame() %>% column_to_rownames("name")
enRes[["Proteins associated with PC2"]] <- runGSEA(inputTab, gmts$H, "page")PC3
proMat <- assays(protCLL.sub)[["QRILC_combat"]]
iPC <- "PC3"
pc <- pcRes[,iPC][colnames(proMat)]
designMat <- model.matrix(~1+pc)
fit <- limma::lmFit(proMat, designMat)
fit2 <- eBayes(fit)
corRes <- topTable(fit2, "pc", number = Inf) %>%
data.frame() %>% rownames_to_column("id")
inputTab <- corRes %>% filter(adj.P.Val < 0.05) %>%
mutate(name = rowData(protCLL[id,])$hgnc_symbol) %>% filter(!is.na(name)) %>%
distinct(name, .keep_all = TRUE) %>%
select(name, t) %>% data.frame() %>% column_to_rownames("name")
enRes[["Proteins associated with PC3"]] <- runGSEA(inputTab, gmts$H, "page")cowplot::plot_grid(plotEnrichmentBar(enRes[[1]], ifFDR = TRUE, pCut = 0.05, setName = "",title = "Proteins associated with PC1", removePrefix = "HALLMARK_"),
plotEnrichmentBar(enRes[[2]], ifFDR = TRUE, pCut = 0.05, setName = "", title = "Proteins associated with PC2", removePrefix = "HALLMARK_"),
plotEnrichmentBar(enRes[[3]], ifFDR = TRUE, pCut = 0.05, setName = "", title = "Proteins associated with PC3", removePrefix = "HALLMARK_"),
ncol=1,
align = "hv",
rel_heights = c(9,3,6))
PCA analysis for batch 1 only
#remove genes on sex chromosomes
protCLL.b1 <- protCLL[!rowData(protCLL)$chromosome_name %in% c("X","Y"), filter(sampleTab, batch == "batch1")$encID]
plotMat <- assays(protCLL.b1)[["QRILC"]]
sds <- genefilter::rowSds(plotMat)
plotMat <- as.matrix(plotMat[order(sds,decreasing = TRUE),])
colAnno <- colData(protCLL)[,c("gender","IGHV.status","trisomy12")] %>%
data.frame()
colAnno$trisomy12 <- ifelse(colAnno$trisomy12 %in% 1, "yes","no")
pcOut <- prcomp(t(plotMat), center =TRUE, scale. = TRUE)
pcRes <- pcOut$x
eigs <- pcOut$sdev^2
varExp <- structure(eigs/sum(eigs),names = colnames(pcRes))Pathway enrichment on PC3 and PC4
PC3
enRes <- list()
proMat <- plotMat
iPC <- "PC3"
pc <- pcRes[,iPC][colnames(proMat)]
designMat <- model.matrix(~1+pc)
fit <- limma::lmFit(proMat, designMat)
fit2 <- eBayes(fit)
corRes <- topTable(fit2, "pc", number = Inf) %>%
data.frame() %>% rownames_to_column("id")
inputTab <- corRes %>% filter(adj.P.Val < 0.05) %>%
mutate(name = rowData(protCLL[id,])$hgnc_symbol) %>% filter(!is.na(name)) %>%
distinct(name, .keep_all = TRUE) %>%
select(name, t) %>% data.frame() %>% column_to_rownames("name")
enRes[["Proteins associated with PC3"]] <- runGSEA(inputTab, gmts$H, "page")PC4
iPC <- "PC4"
pc <- pcRes[,iPC][colnames(proMat)]
designMat <- model.matrix(~1+pc)
fit <- limma::lmFit(proMat, designMat)
fit2 <- eBayes(fit)
corRes <- topTable(fit2, "pc", number = Inf) %>%
data.frame() %>% rownames_to_column("id")
inputTab <- corRes %>% filter(adj.P.Val < 0.05) %>%
mutate(name = rowData(protCLL[id,])$hgnc_symbol) %>% filter(!is.na(name)) %>%
distinct(name, .keep_all = TRUE) %>%
select(name, t) %>% data.frame() %>% column_to_rownames("name")
enRes[["Proteins associated with PC4"]] <- runGSEA(inputTab, gmts$H, "page")cowplot::plot_grid(plotEnrichmentBar(enRes[[1]], ifFDR = TRUE, pCut = 0.05, setName = "",title = "Proteins associated with PC3", removePrefix = "HALLMARK_"),
plotEnrichmentBar(enRes[[2]], ifFDR = TRUE, pCut = 0.05, setName = "", title = "Proteins associated with PC4", removePrefix = "HALLMARK_"),
ncol=1,
align = "hv",
rel_heights = c(10,4))
corPureTab <- lapply(colnames(pcRes)[1:20], function(pc) {
testTab <- pcRes[,pc, drop=FALSE] %>% as_tibble(rownames = "patID") %>%
mutate(purity = sampleTab[match(patID, sampleTab$encID),]$purity) %>%
filter(!is.na(purity))
res <- cor.test(testTab[[2]], testTab[[3]])
tibble(PC = pc,
p= res$p.value,
coef = res$estimate)
}) %>% bind_rows() %>% mutate(p.adj = p.adjust(p)) %>%
filter(p <= 0.05) %>% arrange(p)
corPureTab# A tibble: 2 x 4
PC p coef p.adj
<chr> <dbl> <dbl> <dbl>
1 PC9 0.0298 0.682 0.596
2 PC1 0.0300 -0.682 0.596usePC <- "PC1"
plotTab <- pcRes[,usePC, drop=FALSE] %>% as_tibble(rownames = "patID") %>%
mutate(purity = sampleTab[match(patID, sampleTab$encID),]$purity) %>%
filter(!is.na(purity))
ggplot(plotTab, aes_string(x=usePC,y="purity")) + geom_point() +
xlab(sprintf("%s (%1.2f%%)",usePC, varExp[[usePC]]*100)) +
geom_smooth(method ="lm") +
geom_text(x= 10,y=98, label = sprintf("P = %1.2f, Pearson's r = %1.2f", corPureTab[1,]$p, corPureTab[1,]$coef), col="darkred") +
theme_full +
ylab("tumor purity estimated by DNA methylation")
Hierarchical clustering
protCLL.sub <- protCLL[!rowData(protCLL)$chromosome_name %in% c("X","Y"),]
plotMat <- assays(protCLL.sub)[["QRILC_combat"]]
sds <- rowSds(plotMat)
plotMat <- plotMat[order(sds, decreasing = T)[1:1000],]
colAnno <- colData(protCLL)[,c("IGHV.status","trisomy12")] %>%
data.frame()
colAnno$trisomy12 <- ifelse(colAnno$trisomy12 %in% 1, "yes","no")
plotMat <- mscale(plotMat, center = 6)
annoCol <- list(trisomy12 = c(yes = "black",no = "grey80"),
IGHV.status = c(M = colList[3], U = colList[4]))
pheatmap::pheatmap(plotMat, annotation_col = colAnno, scale = "none",
clustering_method = "average", clustering_distance_cols = "correlation",
color = colorRampPalette(c(colList[2],"white",colList[1]))(100),
breaks = seq(-5,5, length.out = 101), annotation_colors = annoCol,
show_rownames = FALSE, show_colnames = FALSE,
treeheight_row = 0)
Correlation matrix
pheatmap(cor(plotMat), annotation_col = colAnno, clustering_method = "ward.D2", annotation_colors = annoCol,
color = colorRampPalette(c(colList[2],"white",colList[1]))(100),border_color = NA,
breaks = seq(-1,1, length.out = 101),show_rownames = FALSE, show_colnames = FALSE)
Summarise significant associations
Bar plot of number of significant associations with proteins (5% FDR)
Load the list of differentially expression proteins generated by Section 2
load("../output/deResList.RData")plotTab <- resList %>% group_by(Gene) %>%
summarise(nFDR.local = sum(adj.P.Val <= 0.05))Individual gene adjusted
plotTab <- arrange(plotTab, desc(nFDR.local)) %>% mutate(Gene = factor(Gene, levels = Gene))
numCorBar <- ggplot(plotTab, aes(x=Gene, y = nFDR.local)) + geom_bar(stat="identity",fill=colList[2]) +
geom_text(aes(label = paste0("n=", nFDR.local)),vjust=-1,col=colList[1]) + ylim(0,1200) +
theme_half + theme(axis.text.x = element_text(angle = 90, hjust=1, vjust=0.5)) +
ylab("Number of associations\n(5% FDR)") + xlab("")
numCorBar
Bar plot of number of significant associations with RNAs (5% FDR)
Load the list of differentially expression proteins generated by Section 2
load("../output/deResListRNA.RData")plotTab <- resListRNA %>% group_by(Gene) %>%
summarise(nFDR.local = sum(adj.P.Val <= 0.05, na.rm=TRUE))Individual gene adjusted
plotTab <- arrange(plotTab, desc(nFDR.local)) %>% mutate(Gene = factor(Gene, levels = Gene))
numCorBarRNA <- ggplot(plotTab, aes(x=Gene, y = nFDR.local)) + geom_bar(stat="identity",fill=colList[2]) +
geom_text(aes(label = paste0("n=", nFDR.local)),vjust=-1,col=colList[1]) + ylim(0,1500) +
theme_half + theme(axis.text.x = element_text(angle = 90, hjust=1, vjust=0.5)) +
ylab("Number of associations\n(5% FDR)") + xlab("")
numCorBarRNA
Compare differentially expressed proteins and RNAs
resList <- resList %>%
mutate(ensembl_id = rowData(protCLL[id,])$ensembl_gene_id)
geneOverlap <- intersect(resListRNA$id, resList$ensembl_id)
rnaResList <- resListRNA %>% filter(id %in% geneOverlap) %>%
select(id, log2FC, adj.P.Val, Gene) %>%
dplyr::rename(rnaFC = log2FC, rnaPadj = adj.P.Val)
protResList <- resList %>% filter(ensembl_id %in% geneOverlap) %>%
mutate(id = ensembl_id) %>%
select(id, log2FC, adj.P.Val, Gene) %>%
dplyr::rename(protFC = log2FC, protPadj = adj.P.Val)
comTab <- left_join(rnaResList, protResList, by =c("id","Gene"))fdrCut <- 0.05
comTab <- comTab %>%
mutate(group = case_when(
rnaPadj < fdrCut & protPadj > fdrCut ~ "rnaOnly",
rnaPadj > fdrCut & protPadj < fdrCut ~ "proteinOnly",
rnaPadj < fdrCut & protPadj < fdrCut & rnaFC*protFC >0 ~ "both",
TRUE ~ "none"
))
plotTab <- group_by(comTab, Gene, group) %>%
summarise(n=length(id)) %>% filter(group != "none") %>%
ungroup() %>%
arrange(desc(n)) %>%
mutate(Gene = factor(Gene, levels= unique(Gene)),
group = factor(group, levels = c("rnaOnly","both","proteinOnly")))
ggplot(plotTab, aes(x=Gene, y=n, fill=group)) +
geom_bar(stat = "identity") +
theme(axis.text.x = element_text(angle = 90, hjust=1, vjust=0.5))
Compare timsTOF and LUMOS platforms
Load both datasets
load("../data/proteomic_explore_enc.RData")
protCLL.lumos <- protCLL[,protCLL$batch %in% "batch1"]
load("../data/proteomic_timsTOF_enc.RData")
protCLL.tims <- protCLLHow many overlapped samples?
length(intersect(colnames(protCLL.lumos), colnames(protCLL.tims)))[1] 49Number of detected proteins
Overlap of all detected proteins (< 50% missing values)
library(Vennerable)
symbolList.all <- list(LUMOS = unique(rowData(protCLL.lumos)$hgnc_symbol),
timsTOF = unique(rowData(protCLL.tims)$hgnc_symbol))
Vpro <- Venn(symbolList.all)
plot(Vpro, doWeights = FALSE)
Expression distribution of common and uniquely detected proteins
commonProtein <- intersect(symbolList.all$LUMOS, symbolList.all$timsTOF)
proteinGroup <- tibble(name = commonProtein, group = "both") %>%
bind_rows(tibble(name = setdiff(symbolList.all$LUMOS, commonProtein), group = "only in LUMOS")) %>%
bind_rows(tibble(name = setdiff(symbolList.all$timsTOF, commonProtein), group = "only in timsTOF"))
exprTab.lumos <- assays(protCLL.lumos)[["log2Norm"]] %>% data.frame() %>%
rownames_to_column("id") %>% mutate(name = rowData(protCLL.lumos[id,])$hgnc_symbol) %>%
gather(key = "patID", value = "expr", -id, -name) %>%
mutate(group = proteinGroup[match(name, proteinGroup$name),]$group,
dataset = "LUMOS")
exprTab.tof<- assays(protCLL.tims)[["log2Norm"]] %>% data.frame() %>%
rownames_to_column("id") %>% mutate(name = rowData(protCLL.tims[id,])$hgnc_symbol) %>%
gather(key = "patID", value = "expr", -id, -name) %>%
mutate(group = proteinGroup[match(name, proteinGroup$name),]$group,
dataset = "timsTOF")
exprTab <- bind_rows(exprTab.lumos, exprTab.tof)
ggplot(exprTab, aes(x = expr, fill = group)) +
geom_histogram(position = "identity", alpha = 0.5, bins=100, col = "grey50") +
facet_wrap(~dataset) +
xlab("log2(protein counts)") +
theme_full
Correlations of commonly detected proteins
Pearson correlation coefficient
sumProtein <- filter(exprTab, group == "both") %>%
filter(!is.na(expr)) %>% group_by(id) %>%
summarise(nLUMOS = sum(dataset == "LUMOS"),nTOF = sum(dataset=="timsTOF")) %>%
filter(nLUMOS >= 10 & nTOF >=10 )
testRes <- filter(exprTab, group == "both", id %in% sumProtein$id) %>%
mutate(expr = log(expr)) %>%
spread(key = dataset, value = expr) %>%
group_by(id) %>% nest() %>%
mutate(m = map(data, ~cor.test(~LUMOS+timsTOF,.))) %>%
mutate(res = map(m, broom::tidy)) %>%
unnest(res)
ggplot(testRes, aes(x=estimate)) + geom_histogram(position = "identity", fill = colList[3], col = "grey50", alpha =0.3, bins =100) +
geom_vline(xintercept = 0, col = "red", linetype = "dashed") +
xlab("Pearson's correlation coefficient") +
theme_full
Correlation of top 10 PCs
library(corrplot)
overSample <- intersect(colnames(protCLL.lumos), colnames(protCLL.tims))
pcResLumos <- prcomp(t(assays(protCLL.lumos[,overSample])[["QRILC"]]), center = TRUE, scale. = TRUE)
pcResTims <- prcomp(t(assays(protCLL.tims[,overSample])[["QRILC"]]), center = TRUE, scale. = TRUE)
pcLumos <- pcResLumos$x[,1:10] %>% as.matrix()
pcTims <- pcResTims$x[,1:10] %>% as.matrix()Variance explained by top10 PCs
eigsLumos <- pcResLumos$sdev^2
eigsTims <- pcResTims$sdev^2
sumVarExpr <- structure(c(sum(eigsLumos[1:10])/sum(eigsLumos), sum(eigsTims[1:10])/sum(eigsLumos)), names = c("LUMOS","timsTOF"))
sumVarExpr LUMOS timsTOF
0.6050520 0.8697672 #function to do correlation test on matrix
cor.mtest <- function(mat1, mat2=NULL, conf.level = 0.99) {
if(is.null (mat2)) mat2 <- mat1
mat1 <- as.matrix(mat1)
mat2 <- as.matrix(mat2)
stopifnot(ncol(mat1) == ncol(mat2))
n1 <- nrow(mat1)
n2 <- nrow(mat2)
p.mat <- cor.rho <- matrix(NA, n1, n2)
for (i in 1:n1) {
for (j in 1:n2) {
tmp <- cor.test(mat1[i, ], mat2[j, ], conf.level = conf.level,
use="pairwise.complete.obs",method="pearson")
p.mat[i, j] <- tmp$p.value
cor.rho[i, j] <- abs(tmp$estimate[[1]])
}
}
colnames(cor.rho) <- colnames(p.mat) <- rownames(mat2)
rownames(cor.rho) <- rownames(p.mat) <- rownames(mat1)
return(list(cor.rho, p.mat))
}
featureCor <- cor.mtest(t(pcLumos), t(pcTims))
#correlation heatmap
corrplot(featureCor[[1]],order="original",p.mat = featureCor[[2]],sig.level = 0.01, cl.lim = c(0,1))
Assemble figure
designPlot <- draw_image("../data/Fig1A.png")
p <- ggdraw() + designPlot
leftCol <- plot_grid(p,
plot_grid(plotPCA12, plotPCA34, ncol=2, rel_widths = c(.45,0.55)),
plot_grid(numCorBar,NULL,rel_widths = c(0.8,0.2),ncol=2), ncol = 1,
labels = c("A","E","F"), label_size = 20, vjust = c(1.5, 0,-0.1),
rel_heights = c(1.2,1,1))
rightCol <- plot_grid(corHistPlot,
goodCorPlot,
badCorPlot, ncol=1, rel_heights = c(0.28,0.48,0.24), labels = c("B","C","D"), label_size = 20)
#pdf("test.pdf", height = 13, width = 18)
plot_grid(leftCol, rightCol, rel_widths = c(0.6, 0.4))
#dev.off()Supplementary, overview of the indepdent cohort composition
load("../data/proteomic_independent_enc.RData")
protCLL <- protCLL[,colnames(protCLL) %in% patMeta$Patient.ID]Genomic matrix
Get mutations with at least 5 cases
geneMat <- patMeta[match(colnames(protCLL), patMeta$Patient.ID),] %>%
select(-IGHV.status, -Methylation_Cluster) %>%
mutate_if(is.factor, as.character) %>%
mutate_at(vars(-Patient.ID), as.numeric) %>% #assign a few unknown mutated cases to wildtype
data.frame() %>% column_to_rownames("Patient.ID")
#geneMat <- geneMat[,apply(geneMat,2, function(x) sum(x %in% 1, na.rm = TRUE))>=5]
#dim(geneMat)Mutations that will be tested
#Remove some dubious annotations
geneMat <- geneMat[,c(useGeneForComposition,"U1")]
colnames(geneMat) [1] "del11q" "del13q" "del17p" "trisomy12" "trisomy19" "ATM"
[7] "BRAF" "DDX3X" "EGR2" "MED12" "NOTCH1" "SF3B1"
[13] "TP53" "U1" Dimension
dim(geneMat)[1] 26 14Plot to summarise genomic background
Separate CNV table and mutation table
cnvCol <- colnames(geneMat)[grepl("del|trisomy",colnames(geneMat))]
cnvMat <- geneMat[,cnvCol]
mutMat <- geneMat[,!colnames(geneMat) %in% cnvCol]
cnvMat <- cnvMat[,names(sort(colSums(cnvMat == 1,na.rm=TRUE)))]
#
#Manually assign CNV feature order for better visualization
#cnvMat <- cnvMat[,c("del17p","del11q","del13q","trisomy19","trisomy12")]
mutMat <- mutMat[,names(sort(colSums(mutMat == 1, na.rm=TRUE)))]
geneMat <- cbind(mutMat,cnvMat)
geneMat[is.na(geneMat)] <- -1Sort patient based on CNVs
sortTab <- function(sumTab) {
i <- ncol(sumTab)
#print(i)
if (i == 1) {
return(rownames(sumTab)[order(sumTab[,i])])
}
allLevel <- sort(unique(sumTab[,i]))
orderRow <- lapply(allLevel, function(n) {
sortTab(sumTab[sumTab[,i] %in% n, seq(1,i-1), drop = FALSE])
}) %>% unlist() %>% c()
return(orderRow)
}
sortedPat <- rev(sortTab(geneMat))Prepare table for plot
plotTab <- geneMat %>% as_tibble(rownames="patID") %>% mutate_all(as.character) %>%
pivot_longer(-patID, names_to = "var", values_to = "value") %>%
mutate(status = case_when(
value == -1 ~ "NA",
value == 0 ~ "WT",
value == 1 & var %in% cnvCol ~ "CNV",
value == 1 & !var %in% cnvCol ~ "gene mutation"
)) %>%
mutate(var = factor(var, levels = c(colnames(mutMat),colnames(cnvMat))),
patID = factor(patID, levels = sortedPat),
status = factor(status, levels =c("WT","CNV","gene mutation","NA")))
formatedName <- lapply(levels(plotTab$var), function(n) {
if(n %in% cnvCol) {
n
} else {
bquote(italic(.(n)))
}
})Plot mutation matrix
pMain <- ggplot(plotTab, aes(x=patID, y = var, fill = status)) +
geom_tile(color = "grey80") +
theme_void() +
scale_fill_manual(values = c("gene mutation" = colList[5],
"CNV"= colList[4],
"WT" ="white",
"NA" = "grey80"),
name = "aberrations") +
scale_y_discrete(labels = formatedName) +
theme(axis.text.x = element_blank(),
axis.text.y = element_text(size=11, face = "bold"),
axis.ticks.length.y = unit(0.05,"npc")) +
ylab("") + xlab("")
#pMainAnnotation matrix
IGHV status
ighvTab <- select(patMeta, Patient.ID, IGHV.status) %>%
mutate(patID = Patient.ID, status = IGHV.status, type = "IGHV") %>%
filter(patID %in% sortedPat) %>%
mutate(patID = factor(patID, levels = sortedPat)) %>%
select(patID, type, status) %>%
mutate(status = ifelse(is.na(status),"NA",status))
pIGHV <- ggplot(ighvTab, aes(x=patID, y = type, fill = status)) +
geom_tile(color = NA) +
theme_void() + xlab("") + ylab("") +
coord_cartesian(expand = FALSE) +
scale_fill_manual(values = c(M="black",U="white", "NA" = "grey80"), name = "IGHV") +
theme(axis.text.y = element_text(face = "bold", size=11),
axis.ticks.length.y = unit(0.05,"npc"))
table(ighvTab$status)
M NA U
1 1 24 #pIGHVSex
sexTab <- select(survT, patID, sex) %>%
mutate(status = as.character(sex), type = "sex") %>%
filter(patID %in% sortedPat) %>%
mutate(patID = factor(patID, levels = sortedPat),
status = case_when(status %in% "m" ~ "male",
status %in% "f" ~ "female")) %>%
select(patID, type, status)
pSex <- ggplot(sexTab, aes(x=patID, y = type, fill = status)) +
geom_tile(color = NA) +
theme_void() + xlab("") + ylab("") +
coord_cartesian(expand = FALSE) +
scale_fill_manual(values = c(male=colList[7],female=colList[5]), name = "sex") +
theme(axis.text.y = element_text(face = "bold",size=11),
axis.ticks.length.y = unit(0.05,"npc"))
#pSex
table(sexTab$status)
female male
10 16 Pretreatment
treatTab <- survT %>% filter(patID %in% sortedPat) %>%
select(patID, pretreat) %>%
mutate(treatment = case_when(pretreat %in% 1 ~ "yes",
pretreat %in% 0 ~ "no",
is.na(pretreat) ~ "NA")) %>%
mutate(status = as.character(treatment), type = "treatment") %>%
mutate(patID = factor(patID, levels = sortedPat)) %>%
select(patID, type, status)
pTreat <- ggplot(treatTab, aes(x=patID, y = type, fill = status)) +
geom_tile(color = NA) +
theme_void() + xlab("") + ylab("") +
coord_cartesian(expand = FALSE) +
scale_fill_manual(values = c(yes = "black", no = "white","NA" = "grey80"), name = "treatment") +
theme(axis.text.y = element_text(face = "bold",size=11),
axis.ticks.length.y = unit(0.05,"npc"))
#pTreatAge
agePlotTab <- survT %>% filter(patID %in% sortedPat) %>%
select(patID, age) %>%
mutate( status = age, type = "age") %>%
mutate(patID = factor(patID, levels = sortedPat)) %>%
select(patID, type, status)
pAge <- ggplot(agePlotTab, aes(x=patID, y = type, fill = status)) +
geom_tile(color = NA) +
theme_void() + xlab("") + ylab("") +
coord_cartesian(expand = FALSE) +
scale_fill_viridis_b(name = "age") +
theme(axis.text.y = element_text(face = "bold",size=11),
axis.ticks.length.y = unit(0.05,"npc"))
#pAgeCombine all plots
lMain <- get_legend(pMain + geom_tile(color = "black") )
lAge <- get_legend(pAge + geom_tile(color = "black") )
lSex <- get_legend(pSex+ geom_tile(color = "black") )
lIGHV <- get_legend(pIGHV+ geom_tile(color = "black") )
lTreat <- get_legend(pTreat+ geom_tile(color = "black") )
noLegend <- theme(legend.position = "none")
mainPlot <- plot_grid(pAge + noLegend, pSex + noLegend,
pIGHV + noLegend,
pMain + noLegend, ncol=1, align = "v",
rel_heights = c(rep(1,3),20))
legendPlot <- plot_grid(lAge, lSex, lIGHV, lMain,ncol=1, align = "hv")
plot_grid(mainPlot, NULL, plot_grid(legendPlot, ncol=1), ncol=3, rel_widths = c(1,0.05, 0.15))
ggsave("cohortComposition_batch2.pdf", height=6, width=12)
sessionInfo()R version 4.0.2 (2020-06-22)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS 10.16
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.0/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] parallel stats4 stats graphics grDevices utils datasets
[8] methods base
other attached packages:
[1] corrplot_0.84 Vennerable_3.1.0.9000
[3] piano_2.4.0 xtable_1.8-4
[5] latex2exp_0.4.0 forcats_0.5.1
[7] stringr_1.4.0 dplyr_1.0.5
[9] purrr_0.3.4 readr_1.4.0
[11] tidyr_1.1.3 tibble_3.1.0
[13] ggplot2_3.3.3 tidyverse_1.3.0
[15] pheatmap_1.0.12 proDA_1.2.0
[17] cowplot_1.1.1 DESeq2_1.28.1
[19] SummarizedExperiment_1.18.2 DelayedArray_0.14.1
[21] matrixStats_0.58.0 Biobase_2.48.0
[23] GenomicRanges_1.40.0 GenomeInfoDb_1.24.2
[25] IRanges_2.22.2 S4Vectors_0.26.1
[27] BiocGenerics_0.34.0 limma_3.44.3
loaded via a namespace (and not attached):
[1] readxl_1.3.1 backports_1.2.1 fastmatch_1.1-0
[4] workflowr_1.6.2 plyr_1.8.6 igraph_1.2.6
[7] shinydashboard_0.7.1 splines_4.0.2 BiocParallel_1.22.0
[10] digest_0.6.27 htmltools_0.5.1.1 magick_2.7.0
[13] fansi_0.4.2 magrittr_2.0.1 memoise_2.0.0
[16] cluster_2.1.1 annotate_1.66.0 modelr_0.1.8
[19] colorspace_2.0-0 blob_1.2.1 rvest_1.0.0
[22] haven_2.3.1 xfun_0.21 crayon_1.4.1
[25] RCurl_1.98-1.2 jsonlite_1.7.2 graph_1.66.0
[28] genefilter_1.70.0 survival_3.2-7 glue_1.4.2
[31] gtable_0.3.0 zlibbioc_1.34.0 XVector_0.28.0
[34] scales_1.1.1 DBI_1.1.1 relations_0.6-9
[37] Rcpp_1.0.6 viridisLite_0.3.0 bit_4.0.4
[40] DT_0.17 htmlwidgets_1.5.3 httr_1.4.2
[43] fgsea_1.14.0 gplots_3.1.1 RColorBrewer_1.1-2
[46] ellipsis_0.3.1 pkgconfig_2.0.3 XML_3.99-0.5
[49] farver_2.1.0 sass_0.3.1 dbplyr_2.1.0
[52] locfit_1.5-9.4 utf8_1.1.4 reshape2_1.4.4
[55] tidyselect_1.1.0 labeling_0.4.2 rlang_0.4.10
[58] later_1.1.0.1 AnnotationDbi_1.50.3 munsell_0.5.0
[61] cellranger_1.1.0 tools_4.0.2 visNetwork_2.0.9
[64] cachem_1.0.4 cli_2.3.1 generics_0.1.0
[67] RSQLite_2.2.3 broom_0.7.5 evaluate_0.14
[70] fastmap_1.1.0 yaml_2.2.1 knitr_1.31
[73] bit64_4.0.5 fs_1.5.0 caTools_1.18.1
[76] RBGL_1.64.0 nlme_3.1-152 mime_0.10
[79] slam_0.1-48 xml2_1.3.2 compiler_4.0.2
[82] rstudioapi_0.13 marray_1.66.0 reprex_1.0.0
[85] geneplotter_1.66.0 bslib_0.2.4 stringi_1.5.3
[88] highr_0.8 lattice_0.20-41 Matrix_1.3-2
[91] shinyjs_2.0.0 vctrs_0.3.6 pillar_1.5.1
[94] lifecycle_1.0.0 jquerylib_0.1.3 data.table_1.14.0
[97] bitops_1.0-6 httpuv_1.5.5 R6_2.5.0
[100] promises_1.2.0.1 KernSmooth_2.23-18 gridExtra_2.3
[103] gtools_3.8.2 assertthat_0.2.1 rprojroot_2.0.2
[106] withr_2.4.1 GenomeInfoDbData_1.2.3 mgcv_1.8-34
[109] hms_1.0.0 grid_4.0.2 rmarkdown_2.7
[112] git2r_0.28.0 sets_1.0-18 shiny_1.6.0
[115] lubridate_1.7.10