Section 2: Identifying proteins associated disease drivers in CLL
Junyan Lu
2020-10-09
Last updated: 2021-05-05
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Load packages and datasets
library(limma)
library(DESeq2)
library(qvalue)
library(proDA)
library(IHW)
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$set(echo = TRUE, warning = FALSE, message = FALSE,dev = c("png","pdf"))
#protCLL <- protCLL[,colnames(protCLL) %in% patMeta$Patient.ID]Preprocessing
Process proteomics data
#protCLL <- protCLL[rowData(protCLL)$uniqueMap,]
protMat <- assays(protCLL)[["count"]] #without imputation
protMatLog <- assays(protCLL)[["log2Norm"]]Prepare genomic background
Get mutations with at least 5 cases
geneMat <- patMeta[match(colnames(protMat), patMeta$Patient.ID),] %>%
select(Patient.ID, IGHV.status, del11q:U1) %>%
mutate_if(is.factor, as.character) %>% mutate(IGHV.status = ifelse(IGHV.status == "M", 1,0)) %>%
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
colnames(geneMat) [1] "IGHV.status" "del11q" "del13q" "del17p" "trisomy12"
[6] "trisomy19" "NOTCH1" "ATM" "BRAF" "DDX3X"
[11] "EGR2" "MED12" "SF3B1" "TP53" Differential protein expression using proDA (LUMOS dataset)
We will use proDA, which is based on a linear model that considers the missing values using a probabilistic drop out model, to identify protein expression changes related to genotypes.
To avoid potential confounding effect of IGHV and trisomy12, which are main drivers in CLL proteomic profile, we will block for IGHV status when we are testing trisomy12 and block trisomy12 when testing for IGHV status. For other genotypes, we will block for both IGHV status and trisomy12.
Test for other variantions (blocking for IGHV and trisomy12)
Fit the probailistic dropout model and test for differentially expressed proteins
otherGenes <- colnames(geneMat)[!colnames(geneMat)%in% c("IGHV.status","trisomy12")]
resList <- lapply(otherGenes, function(n) {
designMat <- geneMat[,c("IGHV.status","trisomy12",n)]
designMat[,"batch"] <- factor(protCLL[,rownames(designMat)]$batch)
designMat <- designMat[!is.na(designMat[[n]]),]
testMat <- protMat[,rownames(designMat)]
fit <- proDA(testMat, design = ~ .,
col_data = designMat)
contra <- n
resTab <- test_diff(fit, contra) %>%
dplyr::rename(id = name, logFC = diff, t=t_statistic,
P.Value = pval, adj.P.Val = adj_pval) %>%
mutate(name = rowData(protCLL[id,])$hgnc_symbol) %>%
select(name, id, logFC, t, P.Value, adj.P.Val, n_obs) %>%
arrange(P.Value) %>% mutate(Gene = n) %>%
as_tibble()
#calculte log2 fold change
lmDesign <- model.matrix(~., designMat)
protMatTest <- protMatLog[,rownames(lmDesign)]
lmFit <- lmFit(protMatTest, design = lmDesign)
fit2 <- eBayes(lmFit)
foldTab <- topTable(fit2, coef = n, number = "all") %>%
as_tibble(rownames = "id") %>% select(id, logFC) %>%
dplyr::rename(log2FC = logFC)
resTab <- left_join(resTab, foldTab, by = "id")
resTab
}) %>% bind_rows()Combine the results
resList <- bind_rows(resList.ighvTri12, resList)
#Adjusting p values
#using BH
resList <- mutate(resList, adj.P.global = p.adjust(P.Value, method = "BH"))
#using IHW
ihwRes <- ihw(P.Value ~ factor(Gene), data= resList, alpha=0.1)
resList <- mutate(resList, adj.P.IHW = adj_pvalues(ihwRes))Save the results for re-using
save(resList, file = "../output/deResList.RData")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 = nFDR.local),vjust=-1,col="black",size=5) + 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
Some examples of protein-gene associations that passed 0.01 p-value but not 5% FDR
protTab <- sumToTidy(protCLL, rowID = "uniprotID", colID = "patID") %>%
mutate(count = count_combat)Del13q
resListSub <- filter(resList, Gene == "del13q")
nameList <- c("CD22","CD72")
plotTab <- protTab %>% filter(hgnc_symbol %in% nameList) %>%
mutate(del13q = patMeta[match(patID, patMeta$Patient.ID),]$del13q) %>%
mutate(status = ifelse(del13q %in% 1,"del(13)(q14)","other"),
name = hgnc_symbol) %>%
mutate(status = factor(status, levels = c("other","del(13)(q14)")))
pList <- plotBox(plotTab, pValTabel = resListSub, y_lab = "Protein expression")
del13qBox <- cowplot::plot_grid(plotlist= pList, ncol=2)
del13qBox
TP53
resListSub <- filter(resList, Gene == "TP53")
nameList <- c("BCAS2")
plotTab <- protTab %>% filter(hgnc_symbol %in% nameList) %>%
mutate(TP53 = patMeta[match(patID, patMeta$Patient.ID),]$TP53) %>%
mutate(status = ifelse(TP53 %in% 1,"Mut","WT"),
name = hgnc_symbol) %>%
mutate(status = factor(status, levels = c("WT","Mut")))
pList <- plotBox(plotTab, pValTabel = resListSub, y_lab = "Protein expression")
tp53Box <- cowplot::plot_grid(plotlist= pList, ncol=1)
tp53Box
Association test in timsTOF data
The same procedure as for the LUMOS dataset will be used.
For IGHV and trisomy12
Load timsTOF data
load("../data/proteomic_timsTOF_enc.RData")
protMat <- assays(protCLL)[["count"]] #without imputationGenetic data
geneMat <- patMeta[match(colnames(protMat), patMeta$Patient.ID),] %>%
select(Patient.ID, IGHV.status, trisomy12, SF3B1, trisomy19, del11q, del13q) %>%
mutate_if(is.factor, as.character) %>% mutate(IGHV.status = ifelse(IGHV.status == "M", 1,0)) %>%
mutate_at(vars(-Patient.ID), as.numeric) %>% #assign a few unknown mutated cases to wildtype
data.frame() %>% column_to_rownames("Patient.ID")Fit the probailistic dropout model
designMat <- geneMat[ ,c("IGHV.status","trisomy12")]
fit <- proDA(protMat, design = ~ .,
col_data = designMat)Test for differentially expressed proteins
resList.ighvTri12 <- lapply(c("IGHV.status","trisomy12"), function(n) {
contra <- n
resTab <- test_diff(fit, contra) %>%
dplyr::rename(id = name, logFC = diff, t=t_statistic,
P.Value = pval, adj.P.Val = adj_pval) %>%
mutate(name = rowData(protCLL[id,])$hgnc_symbol) %>%
select(name, id, logFC, t, P.Value, adj.P.Val) %>%
arrange(P.Value) %>% mutate(Gene = n) %>%
as_tibble()
}) %>% bind_rows()Test for other variantions (blocking for IGHV and trisomy12)
Fit the probailistic dropout model and test for differentially expressed proteins
otherGenes <- colnames(geneMat)[!colnames(geneMat)%in% c("IGHV.status","trisomy12")]
resList <- lapply(otherGenes, function(n) {
designMat <- geneMat[,c("IGHV.status","trisomy12",n)]
designMat <- designMat[!is.na(designMat[[n]]),]
testMat <- protMat[,rownames(designMat)]
fit <- proDA(testMat, design = ~ .,
col_data = designMat)
contra <- n
resTab <- test_diff(fit, contra) %>%
dplyr::rename(id = name, logFC = diff, t=t_statistic,
P.Value = pval, adj.P.Val = adj_pval) %>%
mutate(name = rowData(protCLL[id,])$hgnc_symbol) %>%
select(name, id, logFC, t, P.Value, adj.P.Val) %>%
arrange(P.Value) %>% mutate(Gene = n) %>%
as_tibble()
resTab
}) %>% bind_rows()Combine the results
resList <- bind_rows(resList.ighvTri12, resList)
#Adjusting p values
#using BH
resList <- mutate(resList, adj.P.global = p.adjust(P.Value, method = "BH"))
#using IHW
ihwRes <- ihw(P.Value ~ factor(Gene), data= resList, alpha=0.1)
resList <- mutate(resList, adj.P.IHW = adj_pvalues(ihwRes))save(resList, file = "../output/deResList_timsTOF.RData")Bar plot of number of significant associations with proteins (5% FDR)
Load the list of differentially expression proteins generated by Section 2
load("../output/deResList_timsTOF.RData")plotTab <- resList %>% group_by(Gene) %>%
summarise(nFDR.local = sum(adj.P.Val <= 0.05))plotTab <- arrange(plotTab, desc(nFDR.local)) %>% mutate(Gene = factor(Gene, levels = Gene))
numCorBar <- ggplot(plotTab, aes(x=Gene, y = nFDR.local)) + geom_bar(stat="identity",fill=colList[2]) +
geom_text(aes(label = nFDR.local),vjust=-1,col="black",size=5) + ylim(0,1000) +
theme_half + theme(axis.text.x = element_text(angle = 90, hjust=1, vjust=0.5)) +
ylab("Number of associations\n(5% FDR)") + xlab("")
numCorBar
Identify assocations between RNA expression and genotypes
DEseq2 will be used to identify RNA expression changes related to genotypes. The same blocking strategy as used for the proteomic data will also be used for RNAseq data.
Prepare RNA seq data
Subset for samples with proteomics
ddsSub <- dds[,dds$PatID %in% colnames(protCLL)]
#how many samples?
ddsSub <- ddsSub[rownames(ddsSub) %in% rowData(protCLL)$ensembl_gene_id,]
#how many genes without any RNA expression detected?
#table(rowSums(counts(ddsSub)) > 0)
ddsSub <- ddsSub[rowSums(counts(ddsSub)) > 0, ]
colData(ddsSub) <- cbind(colData(ddsSub), geneMat[colnames(ddsSub),])Test For IGHV and trisomy12
design(ddsSub) <- ~ IGHV.status + trisomy12
deRes <- DESeq(ddsSub)Test for differentially expressed proteins
resList.ighvTri12 <- lapply(c("IGHV.status","trisomy12"), function(n) {
resTab <- results(deRes, name = n, tidy = TRUE) %>%
dplyr::rename(id = row, log2FC = log2FoldChange, t=stat,
P.Value = pvalue, adj.P.Val = padj) %>%
mutate(name = rowData(ddsSub[id,])$symbol) %>%
select(name, id, log2FC, t, P.Value, adj.P.Val) %>%
arrange(P.Value) %>% mutate(Gene = n) %>%
as_tibble()
}) %>% bind_rows()Test for other variantions (blocking for IGHV and trisomy12)
otherGenes <- colnames(geneMat)[!colnames(geneMat)%in% c("IGHV.status","trisomy12")]
resList <- lapply(otherGenes, function(n) {
ddsTest <- ddsSub[,!is.na(ddsSub[[n]])]
design(ddsTest) <- as.formula(paste0("~ IGHV.status + trisomy12 + ",n))
deRes <- DESeq(ddsTest, betaPrior = FALSE)
resTab <- results(deRes, name = n, tidy = TRUE) %>%
dplyr::rename(id = row, log2FC = log2FoldChange, t=stat,
P.Value = pvalue, adj.P.Val = padj) %>%
mutate(name = rowData(ddsSub[id,])$symbol) %>%
select(name, id, log2FC, t, P.Value, adj.P.Val) %>%
arrange(P.Value) %>% mutate(Gene = n) %>%
as_tibble()
resTab
}) %>% bind_rows()Combine the results
resListRNA <- bind_rows(resList.ighvTri12, resList)Save the results for re-using
save(resListRNA, file = "../output/deResListRNA.RData")Load the pre-calculated results (differential expression tests take long time.)
load("../output/deResListRNA.RData")Bar plot of number of significant associations (5% FDR)
fdrCut = 0.05
plotTab <- resListRNA %>% group_by(Gene) %>%
summarise(nFDR.local = sum(adj.P.Val <= fdrCut, na.rm=TRUE),
nP = sum(P.Value < 0.05))P values adjusted for each variant
#local adjusted P-values
plotTab <- arrange(plotTab, desc(nFDR.local)) %>% mutate(Gene = factor(Gene, levels = Gene))
ggplot(plotTab, aes(x=Gene, y = nFDR.local)) + geom_bar(stat="identity",fill=colList[2]) +
geom_text(aes(label = paste0(nFDR.local)),vjust=-1,col="black") +
theme_half + theme(axis.text.x = element_text(angle = 90, hjust=1, vjust=0.5)) +
ylab("Number of associations\n(5% FDR)") + xlab("")
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] ggbeeswarm_0.6.0 latex2exp_0.4.0
[3] forcats_0.5.1 stringr_1.4.0
[5] dplyr_1.0.5 purrr_0.3.4
[7] readr_1.4.0 tidyr_1.1.3
[9] tibble_3.1.0 ggplot2_3.3.3
[11] tidyverse_1.3.0 IHW_1.16.0
[13] proDA_1.2.0 qvalue_2.20.0
[15] DESeq2_1.28.1 SummarizedExperiment_1.18.2
[17] DelayedArray_0.14.1 matrixStats_0.58.0
[19] Biobase_2.48.0 GenomicRanges_1.40.0
[21] GenomeInfoDb_1.24.2 IRanges_2.22.2
[23] S4Vectors_0.26.1 BiocGenerics_0.34.0
[25] limma_3.44.3
loaded via a namespace (and not attached):
[1] colorspace_2.0-0 ellipsis_0.3.1 rprojroot_2.0.2
[4] XVector_0.28.0 fs_1.5.0 rstudioapi_0.13
[7] farver_2.1.0 bit64_4.0.5 AnnotationDbi_1.50.3
[10] fansi_0.4.2 lubridate_1.7.10 xml2_1.3.2
[13] codetools_0.2-18 splines_4.0.2 cachem_1.0.4
[16] geneplotter_1.66.0 knitr_1.31 jsonlite_1.7.2
[19] workflowr_1.6.2 broom_0.7.5 annotate_1.66.0
[22] dbplyr_2.1.0 compiler_4.0.2 httr_1.4.2
[25] backports_1.2.1 assertthat_0.2.1 Matrix_1.3-2
[28] fastmap_1.1.0 cli_2.3.1 later_1.1.0.1
[31] htmltools_0.5.1.1 tools_4.0.2 gtable_0.3.0
[34] glue_1.4.2 GenomeInfoDbData_1.2.3 reshape2_1.4.4
[37] Rcpp_1.0.6 slam_0.1-48 cellranger_1.1.0
[40] jquerylib_0.1.3 vctrs_0.3.6 xfun_0.21
[43] rvest_1.0.0 lifecycle_1.0.0 XML_3.99-0.5
[46] zlibbioc_1.34.0 scales_1.1.1 hms_1.0.0
[49] promises_1.2.0.1 RColorBrewer_1.1-2 yaml_2.2.1
[52] memoise_2.0.0 sass_0.3.1 stringi_1.5.3
[55] RSQLite_2.2.3 highr_0.8 genefilter_1.70.0
[58] BiocParallel_1.22.0 rlang_0.4.10 pkgconfig_2.0.3
[61] bitops_1.0-6 evaluate_0.14 lattice_0.20-41
[64] lpsymphony_1.16.0 labeling_0.4.2 cowplot_1.1.1
[67] bit_4.0.4 tidyselect_1.1.0 plyr_1.8.6
[70] magrittr_2.0.1 R6_2.5.0 generics_0.1.0
[73] DBI_1.1.1 pillar_1.5.1 haven_2.3.1
[76] withr_2.4.1 survival_3.2-7 RCurl_1.98-1.2
[79] modelr_0.1.8 crayon_1.4.1 fdrtool_1.2.16
[82] utf8_1.1.4 rmarkdown_2.7 locfit_1.5-9.4
[85] grid_4.0.2 readxl_1.3.1 blob_1.2.1
[88] git2r_0.28.0 reprex_1.0.0 digest_0.6.27
[91] xtable_1.8-4 extraDistr_1.9.1 httpuv_1.5.5
[94] munsell_0.5.0 beeswarm_0.3.1 vipor_0.4.5
[97] bslib_0.2.4