Section 1: Overview of CLL proteomic dataset
Junyan Lu
2020-10-09
Last updated: 2021-05-05
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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$set(echo = TRUE, warning = FALSE, message = FALSE, dev = c("png","pdf"))Overview of the patient characteristics in our study cohort
A table shown the clinical characteristics
(all cohorts combined)
labelBatch <- c(batch1 = "batch1", batch2 = "batch3", batch3 = "batch2")
patInfo <- sampleTab %>%
filter(!lowQuality, !duplicatedPat) %>%
select(encID, leukCount, cohort,batch) %>%
mutate(batch = labelBatch[batch]) %>%
arrange(batch, encID) %>%
left_join(select(patMeta, Patient.ID, IGHV.status, trisomy12), by = c(encID = "Patient.ID")) %>%
left_join(select(survT, patID, OS, died, TTT, treatedAfter, TTT, age, sex, pretreat), by = c(encID = "patID")) %>%
mutate(`No.` = seq(nrow(.))) %>%
select(No., encID, age, sex, IGHV.status, trisomy12, leukCount, OS, died, TTT, treatedAfter, pretreat, cohort, batch)
patInfoTab <- patInfo %>% #format
mutate(trisomy12 = ifelse(trisomy12 %in% "1", "yes", "no"),
died = ifelse(is.na(OS),NA, ifelse(died,"yes","no")),
treatedAfter = ifelse(is.na(TTT), NA, ifelse(treatedAfter, "yes","no")),
pretreat = ifelse(pretreat %in% 1, "yes","no"),
age = as.integer(age),
OS = formatC(OS, digits=1),
TTT = formatC(TTT, digits=1)) %>%
mutate_all(replace_na,"NA") %>%
#arrange(cohort, encID) %>%
dplyr::rename(ID = encID,
IGHV = IGHV.status,
`WBC count` = leukCount,
`Survival time (years)` = OS,
Died = died,
`Time to treatment (years)` = TTT,
`Treatment after sampling` = treatedAfter,
`Treatment before sampling` = pretreat)
patInfoTab %>% DT::datatable()Prepare summary matrix for genomics
Get mutations with at least 5 cases
geneMat <- patMeta[match(patInfo$encID, patMeta$Patient.ID),] %>%
select(-Methylation_Cluster) %>%
mutate(IGHV.status = ifelse(!is.na(IGHV.status), ifelse(IGHV.status == "M",1,0),NA)) %>%
mutate(cohort = sampleTab[match(Patient.ID, sampleTab$encID),]$cohort) %>%
mutate(cohort = ifelse(cohort == "exploration",1,0)) %>%
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]#Remove some dubious annotations
geneMat <- geneMat[,!colnames(geneMat) %in% c("del5IgH","gain2p","IgH_break")]
useGeneForComposition <- colnames(geneMat)
useGeneForComposition <- unique(c(useGeneForComposition,"U1","cohort","IGHV.status"))
geneMat <- geneMat[,useGeneForComposition]Plot to summarise genomic background
Separate CNV table and mutation table
cnvCol <- colnames(geneMat)[grepl("del|trisomy|IGHV|cohort",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","IGHV.status","cohort")]
mutMat <- mutMat[,names(sort(colSums(mutMat == 1, na.rm=TRUE)))]
geneMat <- cbind(mutMat,cnvMat)
geneMat[is.na(geneMat)] <- -1sortTab <- 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))
geneMat <- geneMat[,!colnames(geneMat) %in% c("IGHV.status","cohort")]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 ~ "CNA",
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","CNA","gene mutation","NA")))
# get number of mutations
sumMutTab <- group_by(plotTab, var) %>%
summarise(num=sum(value %in% 1))
formatedName <- lapply(levels(plotTab$var), function(n) {
num <- filter(sumMutTab, var == n)$num
if(n %in% cnvCol) {
nameCNV <- c(del17p = "del(17)(p13)", del11q = "del(11)(q22.3)", del13q = "del(13)(q14)")
if (n %in% names(nameCNV)) {
n <- nameCNV[n]
}
sprintf("%s [%s]",n, num)
} else {
bquote(italic(.(n))~"["*.(num)*"]")
}
})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],
"CNA"= 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", hjust = 1),
axis.ticks.length.y = unit(0.05,"npc")) +
ylab("") + xlab("")Column annotations for other characteristics
Cohort
cohortTab <- select(patInfo, encID, cohort) %>%
mutate(patID = encID, status = cohort, type = "cohort") %>%
mutate(status = ifelse(status == "exploration","main","additional")) %>%
mutate(status = factor(status, levels = c("main","additional"))) %>%
filter(patID %in% sortedPat) %>%
mutate(patID = factor(patID, levels = sortedPat)) %>%
select(patID, type, status)
pCohort <- ggplot(cohortTab, aes(x=patID, y = type, fill = status)) +
geom_tile(color = NA) +
theme_void() + xlab("") + ylab("") +
coord_cartesian(expand = FALSE) +
scale_fill_manual(values = c(main=colList[3],additional = colList[2]), name = "cohort") +
theme(axis.text.y = element_text(face = "bold", size=11),
axis.ticks.length.y = unit(0.05,"npc"))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)) %>%
mutate(status = factor(status, levels = c("M","U","NA")))
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"))
#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"))
#pSexAge
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"))
#pAgeGenerate combined summarisation plots
Main plot
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") )
lCohort <- get_legend(pCohort + geom_tile(color = "black"))
noLegend <- theme(legend.position = "none")
mainPlot <- plot_grid(pAge + noLegend, pSex + noLegend,
pCohort + noLegend,
pIGHV + noLegend,
pMain + noLegend, ncol=1, align = "v",
rel_heights = c(rep(1,4),18))
legendPlot <- plot_grid(lAge, lSex, lIGHV, lMain,ncol=1, align = "hv")
plot_grid(mainPlot)
Figure legend
legendPlot <- plot_grid(lAge, lSex, lCohort, lIGHV, lMain,nrow=1, align = "hv")
plot_grid(legendPlot, ncol=1, align = "hv")
Number of detected proteins
Proteins identified in at least in 50% of the samples
dim(protCLL)[1] 3314 91RNA-protein associations
Preprocess transcriptomic and proteomic data
Preprocessing RNA sequencing data
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)$ensembl_gene_id), sampleOverlap]
#how many gene don't have RNA expression at all?
noExp <- rowSums(counts(ddsSub)) == 0
#remove those genes in both datasets
ddsSub <- ddsSub[!noExp,]
protSub <- protSub[!noExp,]
protSub <- protSub[!duplicated(rowData(protSub)$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") +
theme(axis.text = element_text(size=18), axis.title = element_text(size=18))
corHistPlot
Median Pearson’s correlation coefficient
median(corTab$coef)[1] 0.1839246Influence of overall protein/RNA abundance on correlation
medProt <- rowMedians(proMat,na.rm = T)
names(medProt) <- rownames(proMat)
medRNA <- rowMedians(rnaMat, na.rm = T)
names(medRNA) <- rownames(rnaMat)
plotTab <- corTab %>% mutate(rnaAbundance = medRNA[id], protAbundance = medProt[id])
plotList <- list()
plotList[["rna"]] <- plotCorScatter(plotTab,"coef","rnaAbundance",
showR2 = FALSE, annoPos = "left",
x_lab ="Correlation coefficient",
y_lab = "Median RNA expression",
title = "", dotCol = colList[5], textCol = colList[1])
plotList[["protein"]] <- plotCorScatter(plotTab,"coef","protAbundance",
showR2 = FALSE, annoPos = "left",
x_lab ="Correlation coefficient",
y_lab = "Median protein expression",
title = "", dotCol = colList[6], textCol = colList[1])
cowplot::plot_grid(plotlist = plotList, ncol =2)
Plot protein-RNA correlation for selected genes
Good correlations
geneList <- c("ZAP70","CD22","CD79A")
plotList <- lapply(geneList, function(n) {
geneId <- rownames(dds)[match(n, rowData(dds)$symbol)]
stopifnot(length(geneId) ==1)
plotTab <- tibble(x=rnaMat[geneId,],y=proMat[geneId,], IGHV=protSub$IGHV.status)
coef <- cor(plotTab$x, plotTab$y, use="pairwise.complete")
annoPos <- ifelse (coef > 0, "left","right")
plotCorScatter(plotTab, "x","y", showR2 = FALSE, annoPos = annoPos, x_lab = "RNA expression", shape = "IGHV",
y_lab ="Protein expression", title = n,dotCol = colList[4], textCol = colList[1], legendPos="none")
})
goodCorPlot <- cowplot::plot_grid(plotlist = plotList, ncol =3)
goodCorPlot
Bad correlations
geneList <- c("DTNBP1","PRPF19")
plotList <- lapply(geneList, function(n) {
geneId <- rownames(dds)[match(n, rowData(dds)$symbol)]
stopifnot(length(geneId) ==1)
plotTab <- tibble(x=rnaMat[geneId,],y=proMat[geneId,], IGHV=protSub$IGHV.status)
coef <- cor(plotTab$x, plotTab$y, use="pairwise.complete")
annoPos <- ifelse (coef > 0, "left","right")
plotCorScatter(plotTab, "x","y", showR2 = FALSE, annoPos = annoPos, x_lab = "RNA expression",
y_lab ="Protein expression", title = n,dotCol = colList[4], textCol = colList[1],
shape = "IGHV", legendPos = "none")
})
badCorPlot <- cowplot::plot_grid(plotlist = plotList, ncol =2)
badCorPlot
Principal component analysis
Calculate PCA
We will perform principal component analysis to identify and annotate the major dimensions in our dataset.
#remove genes on sex chromosomes
protCLL.sub <- protCLL[!rowData(protCLL)$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
Correlation test between PCs and IGHV.status/trisomy12
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 The first three components are shown to be correlated with trisomy12 and IGHV status.
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) +
geom_hline(yintercept = 0, linetype ="dashed", color = "grey50") +
geom_vline(xintercept = 0, linetype ="dashed", color = "grey50") +
theme_full + theme(legend.position = "bottom", legend.text = element_text(size =15), legend.title = element_text(size=15))
plotPCA12
ggsave("plot_PC1_PC2.pdf", height = 4, width = 5.5)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)) +
geom_hline(yintercept = 0, linetype ="dashed", color = "grey50") +
geom_vline(xintercept = 0, linetype ="dashed", color = "grey50") +
xlim(-60,60) + ylim(-60,60) +
theme_full + theme(legend.position = "bottom", legend.text = element_text(size =15), legend.title = element_text(size=15))
plotPCA34
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) +
geom_hline(yintercept = 0, linetype ="dashed", color = "grey50") +
geom_vline(xintercept = 0, linetype ="dashed", color = "grey50") +
scale_shape_manual(values = c(M = 16, U =1)) +
xlim(-60,60) + ylim(-60,60) +
theme_full + theme(legend.position = "bottom", legend.text = element_text(size =15), legend.title = element_text(size=15))
plotPCA13
Pathway enrichment on PC1 and PC2
We will use gene set enrichment analysis to characterize the principal components (PCs) on pathway level.
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_", setMap = setMap),
plotEnrichmentBar(enRes[[2]], ifFDR = TRUE, pCut = 0.05, setName = "", title = "Proteins associated with PC2", removePrefix = "HALLMARK_", setMap = setMap),
plotEnrichmentBar(enRes[[3]], ifFDR = TRUE, pCut = 0.05, setName = "", title = "Proteins associated with PC3", removePrefix = "HALLMARK_", setMap = setMap),
ncol=1,
align = "hv",
rel_heights = c(9,3,6))
Hierarchical clustering
Heatmap of expression of top 1000 most variant proteins in all samples
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$trisomy12 %in% 1, "yes","no")
plotMat <- mscale(plotMat, center = TRUE, scale = TRUE)
annoCol <- list(trisomy12 = c(yes = "black",no = "grey80"),
IGHV.status = c(M = colList[3], U = colList[4]))
pheatmap::pheatmap(plotMat, annotation_col = colAnno, scale = "none",
clustering_method = "average", clustering_distance_cols = "correlation",
color = colorRampPalette(c(colList[2],"white",colList[1]))(100),
breaks = seq(-5,5, length.out = 101), annotation_colors = annoCol,
show_rownames = FALSE, show_colnames = FALSE,
treeheight_row = 0)
Heatmap of sample-sample correlation matrix based on protein expression
callback = function(hc, mat){
sv = svd(t(mat))$v[,1]
dend = reorder(as.dendrogram(hc), wts = sv)
as.hclust(dend)
}
breaks <- c(seq(-1,-0.39, length.out=20), seq(-0.4,0.4, length.out=40), seq(0.41, 1, length.out =20))
colorList <- c(rep(colList[2],20), colorRampPalette(c(colList[2],"white",colList[1]))(40), rep(colList[1], 20))
#colList <- colorRampPalette(c(colList[2],"white",colList[1]))(100)
corMat <- cor(plotMat)
#corMat <- mscale(corMat, center=FALSE, scale=FALSE, censor = 0.5)
pheatmap(corMat, annotation_col = colAnno, clustering_method = "ward.D2", annotation_colors = annoCol,
color = colorList, border_color = NA,
breaks = breaks,show_rownames = FALSE, show_colnames = FALSE,
treeheight_col = 0, treeheight_row = 20)
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] piano_2.4.0 ggbeeswarm_0.6.0
[3] latex2exp_0.4.0 forcats_0.5.1
[5] stringr_1.4.0 dplyr_1.0.5
[7] purrr_0.3.4 readr_1.4.0
[9] tidyr_1.1.3 tibble_3.1.0
[11] ggplot2_3.3.3 tidyverse_1.3.0
[13] pheatmap_1.0.12 proDA_1.2.0
[15] cowplot_1.1.1 DESeq2_1.28.1
[17] SummarizedExperiment_1.18.2 DelayedArray_0.14.1
[19] matrixStats_0.58.0 Biobase_2.48.0
[21] GenomicRanges_1.40.0 GenomeInfoDb_1.24.2
[23] IRanges_2.22.2 S4Vectors_0.26.1
[25] 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 igraph_1.2.6 shinydashboard_0.7.1
[7] splines_4.0.2 BiocParallel_1.22.0 crosstalk_1.1.1
[10] digest_0.6.27 htmltools_0.5.1.1 fansi_0.4.2
[13] magrittr_2.0.1 memoise_2.0.0 cluster_2.1.1
[16] annotate_1.66.0 modelr_0.1.8 colorspace_2.0-0
[19] blob_1.2.1 rvest_1.0.0 haven_2.3.1
[22] xfun_0.21 crayon_1.4.1 RCurl_1.98-1.2
[25] jsonlite_1.7.2 genefilter_1.70.0 survival_3.2-7
[28] glue_1.4.2 gtable_0.3.0 zlibbioc_1.34.0
[31] XVector_0.28.0 scales_1.1.1 DBI_1.1.1
[34] relations_0.6-9 Rcpp_1.0.6 viridisLite_0.3.0
[37] xtable_1.8-4 bit_4.0.4 DT_0.17
[40] htmlwidgets_1.5.3 httr_1.4.2 fgsea_1.14.0
[43] gplots_3.1.1 RColorBrewer_1.1-2 ellipsis_0.3.1
[46] pkgconfig_2.0.3 XML_3.99-0.5 farver_2.1.0
[49] sass_0.3.1 dbplyr_2.1.0 locfit_1.5-9.4
[52] utf8_1.1.4 tidyselect_1.1.0 labeling_0.4.2
[55] rlang_0.4.10 later_1.1.0.1 AnnotationDbi_1.50.3
[58] visNetwork_2.0.9 munsell_0.5.0 cellranger_1.1.0
[61] tools_4.0.2 cachem_1.0.4 cli_2.3.1
[64] generics_0.1.0 RSQLite_2.2.3 broom_0.7.5
[67] evaluate_0.14 fastmap_1.1.0 yaml_2.2.1
[70] knitr_1.31 bit64_4.0.5 fs_1.5.0
[73] caTools_1.18.1 nlme_3.1-152 mime_0.10
[76] slam_0.1-48 xml2_1.3.2 compiler_4.0.2
[79] rstudioapi_0.13 beeswarm_0.3.1 marray_1.66.0
[82] reprex_1.0.0 geneplotter_1.66.0 bslib_0.2.4
[85] stringi_1.5.3 highr_0.8 lattice_0.20-41
[88] Matrix_1.3-2 shinyjs_2.0.0 vctrs_0.3.6
[91] pillar_1.5.1 lifecycle_1.0.0 jquerylib_0.1.3
[94] data.table_1.14.0 bitops_1.0-6 httpuv_1.5.5
[97] R6_2.5.0 promises_1.2.0.1 KernSmooth_2.23-18
[100] gridExtra_2.3 vipor_0.4.5 gtools_3.8.2
[103] assertthat_0.2.1 rprojroot_2.0.2 withr_2.4.1
[106] GenomeInfoDbData_1.2.3 mgcv_1.8-34 hms_1.0.0
[109] grid_4.0.2 rmarkdown_2.7 git2r_0.28.0
[112] sets_1.0-18 shiny_1.6.0 lubridate_1.7.10