Section 2: Identifying proteins associated disease drivers in CLL
Junyan Lu
2021-02-16
Last updated: 2021-02-16
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Knit directory: CLLproteomics_batch13/analysis/
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Load packages and datasets
library(limma)
library(DESeq2)
library(proDA)
library(IHW)
library(SummarizedExperiment)
library(tidyverse)
#load datasets
load("../../var/patmeta_201113.RData")
load("../../var/ddsrna_180717.RData")
load("../../var/proteomic_newLUMOS_2pep_batch2Lib_20210208.RData")
source("../code/utils.R")
knitr::opts_chunk$set(echo = TRUE, warning = FALSE, message = FALSE,dev = c("png","pdf"))Identify differentially expressed proteins related to genomics
Process proteomics data
protCLL <- protCLL[,colnames(protCLL) %in% patMeta$Patient.ID]
protCLL$site <- ifelse(protCLL$aliquotSize =="unknown","BARC","HD")
#protCLL <- protCLL[rowData(protCLL)$uniqueMap,]
protMat <- assays(protCLL)[["count"]] #without imputationNumber of samples and proteins
dim(protMat)[1] 1095 32Prepare genomic background
Update U1 muation status
barcPat <- str_split("P1543,P1544,P1537,P1538,P1539,P1540,P1541,P1545",",")[[1]]
u1Pat <- str_split("P1537,P1538,P1539,P1540,P1541",",")[[1]]
patMeta <- mutate(patMeta, U1 = as.character(U1)) %>%
mutate(U1 = ifelse(!Patient.ID %in% barcPat, U1,
ifelse(Patient.ID %in% u1Pat, "1","0"))) %>%
mutate(U1 = factor(U1))Get mutations with at least 5 cases
geneMat <- patMeta[match(colnames(protMat), patMeta$Patient.ID),] %>%
select(Patient.ID, IGHV.status, del11p: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
geneMat <- geneMat[,! colnames(geneMat) %in% c("del5IgH","IgH_break")]
colnames(geneMat)[1] "del11q" "del13q" "del17p" "NOTCH1" "TP53" "U1" Plot to summarise genomic background
sortTab <- function(sumTab) {
i <- ncol(sumTab)
#print(i)
if (i == 1) {
#print(rownames(sumTab)[order(sumTab[,i])])
return(rownames(sumTab)[order(sumTab[,i])])
}
orderRow <- c(sortTab(sumTab[sumTab[,i]==0, seq(1,i-1), drop = FALSE]), sortTab(sumTab[sumTab[,i]==1, seq(1,i-1), drop = FALSE]))
return(orderRow)
}
geneMat.sort <- geneMat
geneMat.sort[is.na(geneMat.sort)] <- 0
sortedGene <- names(sort(colSums(geneMat.sort)))
geneMat.sort <- geneMat.sort[,sortedGene]
sortedPat <- sortTab(geneMat.sort)plotTab <- geneMat %>% rownames_to_column("patID") %>% mutate_all(as.character) %>%
gather(key = "gene", value = "status", -patID) %>%
mutate(gene = factor(gene, levels = sortedGene), patID = factor(patID, levels = sortedPat)) %>%
mutate(status = ifelse(is.na(status),"NA",status))
ggplot(plotTab, aes(x=patID, y = gene, fill = status)) +
geom_tile(color = "black") +
theme_minimal() +
scale_fill_manual(values = c("1" = colList[4],"0" ="white", "NA" = "grey80")) +
theme(axis.text.x = element_text(angle = 90, hjust =1, vjust=0.5),
panel.grid = element_blank(), legend.position = "none") +
ylab("") + xlab("Patient ID")
Please check if the annotations are correct
Differential protein expression using proDA
otherGenes <- colnames(geneMat)
resList <- lapply(otherGenes, function(n) {
designMat <- geneMat[,n,drop=FALSE]
designMat <- designMat[!is.na(designMat[[n]]),,drop=FALSE]
testMat <- protMat[,rownames(designMat)]
fit <- proDA(testMat, design = ~ .,
col_data = designMat)
contra <- n
resTab <- proDA::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,])$hgnc_symbol) %>%
select(name, id, logFC, t, P.Value, adj.P.Val) %>%
arrange(P.Value) %>% mutate(Gene = n) %>%
as_tibble()
resTab
}) %>% bind_rows()Save the results for re-using
save(resList, file = "../output/deResListBatch2.RData")Load the pre-calculated results (differential expression tests take long time.)
load("../output/deResListBatch2.RData")Summarise significant associations
Bar plot of number of significant associations (10% FDR)
plotTab <- resList %>% group_by(Gene) %>%
summarise(nFDR.local = sum(adj.P.Val <= 0.1),
nP = sum(P.Value < 0.01))#Global adjusted P-values
plotTab <- arrange(plotTab, desc(nFDR.local)) %>% mutate(Gene = factor(Gene, levels = Gene))
ggplot(plotTab, aes(x=Gene, y = nFDR.local)) + geom_bar(stat="identity",fill=colList[2]) +
geom_text(aes(label = paste0("n=", nFDR.local)),vjust=-1,col=colList[1]) + ylim(0,40) +
theme_half + theme(axis.text.x = element_text(angle = 90, hjust=1, vjust=0.5)) +
ylab("Number of associations\n(10% FDR)") + xlab("")
Bar plot of number of raw P-value < 0.01
plotTab <- arrange(plotTab, desc(nP)) %>% mutate(Gene = factor(Gene, levels = Gene))
ggplot(plotTab, aes(x=Gene, y = nP)) + geom_bar(stat="identity",fill=colList[2]) +
geom_text(aes(label = paste0("n=", nP)),vjust=-1,col=colList[1]) + ylim(0,100) +
theme_half + theme(axis.text.x = element_text(angle = 90, hjust=1, vjust=0.5)) +
ylab("Number of associations\n(nominal P-value < 0.01)") + xlab("")
Table of all significant associations (P< 0.01)
resList.sig <- filter(resList, P.Value < 0.01) %>%
select(name, Gene, logFC, P.Value, adj.P.Val,id) %>%
arrange(P.Value)
resList.sig %>%
mutate_if(is.numeric, formatC, digits=2) %>%
DT::datatable()Focused on U1 mutations
Heatmap of associated proteins (10% FDR)
resSub <- filter(resList, Gene == "U1", adj.P.Val < 0.1)
protMat <- assays(protCLL)[["QRILC"]][resSub$id,]
colAnno <- data.frame(row.names = colnames(protMat),
U1 = factor(geneMat[colnames(protMat),]$U1),
site = protCLL$site)
pheatmap::pheatmap(protMat, annotation_col = colAnno, scale = "row",
clustering_method = "ward.D2",
color = colorRampPalette(c(colList[2],"white",colList[1]))(100),
breaks = seq(-5,5, length.out = 101),
show_rownames = TRUE, show_colnames = TRUE,
labels_row = resSub$name,
treeheight_row = 0)
Pathway enrichment
Barplot of enriched pathways
gmts = list(H= "../data/gmts/h.all.v6.2.symbols.gmt",
KEGG = "../data/gmts/c2.cp.kegg.v6.2.symbols.gmt")
protMat <-assays(protCLL)[["QRILC"]] #without imputation
designMat <- model.matrix(~U1, geneMat)
protMat <- protMat[,rownames(designMat) ]
enRes <- runCamera(protMat, designMat, gmts$H,
id = rowData(protCLL)[rownames(protMat),]$hgnc_symbol,
contrast = "U1",
removePrefix = "HALLMARK_", pCut = 0.1, ifFDR = TRUE, plotTitle = "HALLMARK")
p1 <- enRes$enrichPlot
enRes <- runCamera(protMat, designMat, gmts$KEGG,
id = rowData(protCLL)[rownames(protMat),]$hgnc_symbol,
contrast = "U1",
removePrefix = "KEGG_", pCut = 0.1, ifFDR = TRUE, plotTitle = "KEGG")
p2 <- enRes$enrichPlot
cowplot::plot_grid(p1,p2, ncol=1, align = "hv", axis = "lr", rel_heights = c(1,4))
Compare RNA and protein fold changes
Identify RNAs associated with U1 mutations
RNAseq samples are subsetted to the samples with proteomic data
ddsSub <- dds[rownames(dds) %in% rowData(protCLL)$ensembl_gene_id,dds$PatID %in% rownames(geneMat)]
ddsSub$U1 <- geneMat[match(ddsSub$PatID, rownames(geneMat)),]$U1
ddsSub <- ddsSub[,!is.na(ddsSub$U1)]How many RNA seq samples with U1 information?
table(ddsSub$U1)
0 1
16 4 DEseq
design(ddsSub) <- ~U1
deRes <- DESeq(ddsSub)
rnaRes <- results(deRes, tidy = TRUE) %>%
select(row, log2FoldChange, pvalue, padj) %>%
dplyr::rename(enID=row, logFC.rna = log2FoldChange, P.Value.rna = pvalue, adj.P.Val.rna =padj)compareTab <- resList %>% filter(Gene == "U1") %>%
mutate(enID = rowData(protCLL[id,])$ensembl_gene_id) %>%
left_join(rnaRes, by = "enID") %>%
mutate(logFC.rna = ifelse(is.na(logFC.rna),0,logFC.rna),
P.Value.rna = ifelse(is.na(P.Value.rna),1,P.Value.rna)) %>%
mutate(significant = case_when(
adj.P.Val < 0.1 & adj.P.Val.rna < 0.1 ~ "both",
adj.P.Val < 0.1 & adj.P.Val.rna > 0.1 ~ "only protein",
adj.P.Val > 0.1 & adj.P.Val.rna < 0.1 ~ "only RNA",
TRUE ~"none"
)) %>%
filter(significant != "none")Plot
Only gene that show associations with either RNA or proteins at 10% FDR are shown
ggplot(compareTab, aes(x=logFC, y=logFC.rna, col = significant)) +
geom_point() +
theme_bw() +
ggrepel::geom_text_repel(aes(label=name)) +
xlab("logFC (Protein)") + ylab("logFC (RNA)")
Focused on TP53 mutations
Heatmap of associated proteins (P value < 0.01)
Only two proteins passed FDR < 0.1
resSub <- filter(resList, Gene == "TP53", P.Value < 0.01)
protMat <- assays(protCLL)[["QRILC"]][resSub$id,]
colAnno <- data.frame(row.names = colnames(protMat),
TP53 = factor(geneMat[colnames(protMat),]$TP53),
site = protCLL$site)
pheatmap::pheatmap(protMat, annotation_col = colAnno, scale = "row",
clustering_method = "ward.D2",
color = colorRampPalette(c(colList[2],"white",colList[1]))(100),
breaks = seq(-5,5, length.out = 101),
show_rownames = TRUE, show_colnames = TRUE,
labels_row = resSub$name,
treeheight_row = 0)
Pathway enrichment
Barplot of enriched pathways
gmts = list(H= "../data/gmts/h.all.v6.2.symbols.gmt",
KEGG = "../data/gmts/c2.cp.kegg.v6.2.symbols.gmt")
protMat <-assays(protCLL)[["QRILC"]] #without imputation
designMat <- model.matrix(~TP53, geneMat)
protMat <- protMat[,rownames(designMat) ]
enRes <- runCamera(protMat, designMat, gmts$H,
id = rowData(protCLL)[rownames(protMat),]$hgnc_symbol,
contrast = "TP53",
removePrefix = "HALLMARK_", pCut = 0.1, ifFDR = TRUE, plotTitle = "HALLMARK")
p1 <- enRes$enrichPlot
enRes <- runCamera(protMat, designMat, gmts$KEGG,
id = rowData(protCLL)[rownames(protMat),]$hgnc_symbol,
contrast = "TP53",
removePrefix = "KEGG_", pCut = 0.1, ifFDR = TRUE, plotTitle = "KEGG")
p2 <- enRes$enrichPlot
cowplot::plot_grid(p1,p2, ncol=1, align = "hv", axis = "lr", rel_heights = c(1,4))
Compare RNA and protein fold changes
Identify RNAs associated with TP53 mutations
RNAseq samples are subsetted to the samples with proteomic data
ddsSub <- dds[rownames(dds) %in% rowData(protCLL)$ensembl_gene_id,dds$PatID %in% rownames(geneMat)]
ddsSub$TP53 <- geneMat[match(ddsSub$PatID, rownames(geneMat)),]$TP53
ddsSub <- ddsSub[,!is.na(ddsSub$TP53)]How many RNA seq samples with TP53 information?
table(ddsSub$TP53)
0 1
13 9 DEseq
design(ddsSub) <- ~TP53
deRes <- DESeq(ddsSub)
rnaRes <- results(deRes, tidy = TRUE) %>%
select(row, log2FoldChange, pvalue, padj) %>%
dplyr::rename(enID=row, logFC.rna = log2FoldChange, P.Value.rna = pvalue, adj.P.Val.rna =padj)Here I use P.Value < 0.01 as cut-off, otherwise there are only two protein candiates
compareTab <- resList %>% filter(Gene == "TP53") %>%
mutate(enID = rowData(protCLL[id,])$ensembl_gene_id) %>%
left_join(rnaRes, by = "enID") %>%
mutate(logFC.rna = ifelse(is.na(logFC.rna),0,logFC.rna),
P.Value.rna = ifelse(is.na(P.Value.rna),1,P.Value.rna)) %>%
mutate(significant = case_when(
P.Value < 0.01 & P.Value.rna < 0.01 ~ "both",
P.Value < 0.01 & P.Value.rna > 0.01 ~ "only protein",
P.Value > 0.01 & P.Value.rna < 0.01 ~ "only RNA",
TRUE ~"none"
)) %>%
filter(significant != "none")Plot
Only gene that show associations with either RNA or proteins at 10% FDR are shown
ggplot(compareTab, aes(x=logFC, y=logFC.rna, col = significant)) +
geom_point() +
theme_bw() +
ggrepel::geom_text_repel(aes(label=name)) +
xlab("logFC (Protein)") + ylab("logFC (RNA)")
Focused on NOTCH1 mutations
Heatmap of associated proteins (P value < 0.01)
resSub <- filter(resList, Gene == "NOTCH1", P.Value < 0.01)
protMat <- assays(protCLL)[["QRILC"]][resSub$id,]
colAnno <- data.frame(row.names = colnames(protMat),
NOTCH1 = factor(geneMat[colnames(protMat),]$NOTCH1),
site = protCLL$site)
pheatmap::pheatmap(protMat, annotation_col = colAnno, scale = "row",
clustering_method = "ward.D2",
color = colorRampPalette(c(colList[2],"white",colList[1]))(100),
breaks = seq(-5,5, length.out = 101),
show_rownames = TRUE, show_colnames = TRUE,
labels_row = resSub$name,
treeheight_row = 0)
Pathway enrichment
Barplot of enriched pathways
gmts = list(H= "../data/gmts/h.all.v6.2.symbols.gmt",
KEGG = "../data/gmts/c2.cp.kegg.v6.2.symbols.gmt")
protMat <-assays(protCLL)[["QRILC"]] #without imputation
designMat <- model.matrix(~NOTCH1, geneMat)
protMat <- protMat[,rownames(designMat) ]
enRes <- runCamera(protMat, designMat, gmts$H,
id = rowData(protCLL)[rownames(protMat),]$hgnc_symbol,
contrast = "NOTCH1",
removePrefix = "HALLMARK_", pCut = 0.1, ifFDR = TRUE, plotTitle = "HALLMARK")
p1 <- enRes$enrichPlot
enRes <- runCamera(protMat, designMat, gmts$KEGG,
id = rowData(protCLL)[rownames(protMat),]$hgnc_symbol,
contrast = "NOTCH1",
removePrefix = "KEGG_", pCut = 0.1, ifFDR = TRUE, plotTitle = "KEGG")
p2 <- enRes$enrichPlot
cowplot::plot_grid(p1,p2, ncol=1, align = "hv", axis = "lr", rel_heights = c(1,4))
Compare RNA and protein fold changes
Identify RNAs associated with NOTCH1 mutations
RNAseq samples are subsetted to the samples with proteomic data
ddsSub <- dds[rownames(dds) %in% rowData(protCLL)$ensembl_gene_id,dds$PatID %in% rownames(geneMat)]
ddsSub$NOTCH1 <- geneMat[match(ddsSub$PatID, rownames(geneMat)),]$NOTCH1
ddsSub <- ddsSub[,!is.na(ddsSub$NOTCH1)]How many RNA seq samples with NOTCH1 information?
table(ddsSub$NOTCH1)
0 1
14 7 DEseq
design(ddsSub) <- ~NOTCH1
deRes <- DESeq(ddsSub)
rnaRes <- results(deRes, tidy = TRUE) %>%
select(row, log2FoldChange, pvalue, padj) %>%
dplyr::rename(enID=row, logFC.rna = log2FoldChange, P.Value.rna = pvalue, adj.P.Val.rna =padj)Here I use P.Value < 0.01 as cut-off
compareTab <- resList %>% filter(Gene == "NOTCH1") %>%
mutate(enID = rowData(protCLL[id,])$ensembl_gene_id) %>%
left_join(rnaRes, by = "enID") %>%
mutate(logFC.rna = ifelse(is.na(logFC.rna),0,logFC.rna),
P.Value.rna = ifelse(is.na(P.Value.rna),1,P.Value.rna)) %>%
mutate(significant = case_when(
P.Value < 0.01 & P.Value.rna < 0.01 ~ "both",
P.Value < 0.01 & P.Value.rna > 0.01 ~ "only protein",
P.Value > 0.01 & P.Value.rna < 0.01 ~ "only RNA",
TRUE ~"none"
)) %>%
filter(significant != "none")Plot
Only gene that show associations with either RNA or proteins at 10% FDR are shown
ggplot(compareTab, aes(x=logFC, y=logFC.rna, col = significant)) +
geom_point() +
theme_bw() +
ggrepel::geom_text_repel(aes(label=name)) +
xlab("logFC (Protein)") + ylab("logFC (RNA)")
sessionInfo()R version 3.6.0 (2019-04-26)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS 10.15.7
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.6/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] latex2exp_0.4.0 forcats_0.5.0
[3] stringr_1.4.0 dplyr_1.0.0
[5] purrr_0.3.4 readr_1.3.1
[7] tidyr_1.1.0 tibble_3.0.3
[9] ggplot2_3.3.2 tidyverse_1.3.0
[11] IHW_1.14.0 proDA_1.1.2
[13] DESeq2_1.26.0 SummarizedExperiment_1.16.1
[15] DelayedArray_0.12.3 BiocParallel_1.20.1
[17] matrixStats_0.56.0 Biobase_2.46.0
[19] GenomicRanges_1.38.0 GenomeInfoDb_1.22.1
[21] IRanges_2.20.2 S4Vectors_0.24.4
[23] BiocGenerics_0.32.0 limma_3.42.2
loaded via a namespace (and not attached):
[1] readxl_1.3.1 backports_1.1.8 fastmatch_1.1-0
[4] Hmisc_4.4-0 workflowr_1.6.2 igraph_1.2.5
[7] shinydashboard_0.7.1 splines_3.6.0 crosstalk_1.1.0.1
[10] lpsymphony_1.14.0 digest_0.6.25 htmltools_0.5.0
[13] gdata_2.18.0 fansi_0.4.1 magrittr_1.5
[16] checkmate_2.0.0 memoise_1.1.0 cluster_2.1.0
[19] annotate_1.64.0 modelr_0.1.8 piano_2.2.0
[22] jpeg_0.1-8.1 colorspace_1.4-1 ggrepel_0.8.2
[25] blob_1.2.1 rvest_0.3.5 haven_2.3.1
[28] xfun_0.15 crayon_1.3.4 RCurl_1.98-1.2
[31] jsonlite_1.7.0 genefilter_1.68.0 survival_3.2-3
[34] glue_1.4.1 gtable_0.3.0 zlibbioc_1.32.0
[37] XVector_0.26.0 scales_1.1.1 pheatmap_1.0.12
[40] DBI_1.1.0 relations_0.6-9 Rcpp_1.0.5
[43] xtable_1.8-4 htmlTable_2.0.1 foreign_0.8-71
[46] bit_4.0.4 Formula_1.2-3 DT_0.14
[49] htmlwidgets_1.5.1 httr_1.4.1 fgsea_1.12.0
[52] gplots_3.0.4 RColorBrewer_1.1-2 acepack_1.4.1
[55] ellipsis_0.3.1 pkgconfig_2.0.3 XML_3.98-1.20
[58] farver_2.0.3 nnet_7.3-14 dbplyr_1.4.4
[61] locfit_1.5-9.4 tidyselect_1.1.0 labeling_0.3
[64] rlang_0.4.7 later_1.1.0.1 AnnotationDbi_1.48.0
[67] visNetwork_2.0.9 munsell_0.5.0 cellranger_1.1.0
[70] tools_3.6.0 cli_2.0.2 generics_0.0.2
[73] RSQLite_2.2.0 broom_0.7.0 fdrtool_1.2.15
[76] evaluate_0.14 fastmap_1.0.1 yaml_2.2.1
[79] knitr_1.29 bit64_0.9-7 fs_1.4.2
[82] caTools_1.18.0 mime_0.9 slam_0.1-47
[85] xml2_1.3.2 compiler_3.6.0 rstudioapi_0.11
[88] png_0.1-7 marray_1.64.0 reprex_0.3.0
[91] geneplotter_1.64.0 stringi_1.4.6 lattice_0.20-41
[94] Matrix_1.2-18 shinyjs_1.1 vctrs_0.3.1
[97] pillar_1.4.6 lifecycle_0.2.0 cowplot_1.0.0
[100] data.table_1.12.8 bitops_1.0-6 httpuv_1.5.4
[103] R6_2.4.1 latticeExtra_0.6-29 promises_1.1.1
[106] KernSmooth_2.23-17 gridExtra_2.3 gtools_3.8.2
[109] assertthat_0.2.1 rprojroot_1.3-2 withr_2.2.0
[112] GenomeInfoDbData_1.2.2 hms_0.5.3 grid_3.6.0
[115] rpart_4.1-15 rmarkdown_2.3 git2r_0.27.1
[118] sets_1.0-18 shiny_1.5.0 lubridate_1.7.9
[121] base64enc_0.1-3