Section 1: an overview of the proteomic dataset
Junyan Lu
2020-06-23
Last updated: 2020-11-10
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Knit directory: Proteomics/analysis/
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RNA-protein associations
Process both datasets
protCLL <- protCLL[rowData(protCLL)$uniqueMap,]
colnames(dds) <- dds$PatID
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"]]
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] 0.1718669Influence 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 specified genes
Good correlations
geneList <- c("ZAP70","CD22","CD38","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 =2)
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
#remove genes on sex chromosomes
protCLL.sub <- protCLL[!rowData(protCLL)$chromosome_name %in% c("X","Y"),]
plotMat <- assays(protCLL.sub)[["QRILC"]]
sds <- genefilter::rowSds(plotMat)
plotMat <- as.matrix(plotMat[order(sds,decreasing = TRUE),])
colAnno <- colData(protCLL)[,c("gender","IGHV.status","trisomy12")] %>%
data.frame()
colAnno$trisomy12 <- ifelse(colAnno$trisomy12 %in% 1, "yes","no")
pcOut <- prcomp(t(plotMat), center =TRUE, scale. = TRUE)
pcRes <- pcOut$x
eigs <- pcOut$sdev^2
varExp <- structure(eigs/sum(eigs),names = colnames(pcRes))All proteins are included for PCA analysis
Plot PC1 and PC2
plotTab <- pcRes %>% data.frame() %>% cbind(colAnno[rownames(.),]) %>%
rownames_to_column("patID") %>% as_tibble()
plotPCA12 <- ggplot(plotTab, aes(x=PC1, y=PC2, col = trisomy12, shape = IGHV.status)) + geom_point(size=4) +
xlab(sprintf("PC1 (%1.2f%%)",varExp[["PC1"]]*100)) +
ylab(sprintf("PC2 (%1.2f%%)",varExp[["PC2"]]*100)) +
scale_color_manual(values = colList) +
scale_shape_manual(values = c(M = 16, U =1)) +
xlim(-60,60) + ylim(-60,60) +
theme_full + theme(legend.position = "none")
plotPCA12
Plot PC3 and PC4
plotPCA34 <- ggplot(plotTab, aes(x=PC3, y=PC4, col = trisomy12, shape = IGHV.status)) + geom_point(size=4) +
xlab(sprintf("PC3 (%1.2f%%)",varExp[["PC3"]]*100)) +
ylab(sprintf("PC4 (%1.2f%%)",varExp[["PC4"]]*100)) +
scale_color_manual(values = colList) +
scale_shape_manual(values = c(M = 16, U =1)) +
xlim(-60,60) + ylim(-60,60) +
theme_full
plotPCA34
Correlation test between PCs and IGHV.status
corTab <- lapply(colnames(pcRes), function(pc) {
ighvCor <- t.test(pcRes[,pc] ~ colAnno$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$p.value))
}) %>% bind_rows() %>% mutate(p.adj = p.adjust(p)) %>%
filter(p <= 0.05) %>% arrange(p)
corTab# A tibble: 5 x 4
PC feature p p.adj
<chr> <chr> <dbl> <dbl>
1 PC3 trisomy12 1.09e-10 0.0000000107
2 PC4 IGHV 2.84e- 6 0.000275
3 PC49 IGHV 4.97e- 3 0.477
4 PC2 trisomy12 2.13e- 2 1
5 PC1 IGHV 4.81e- 2 1 Pathway enrichment on PC1 and PC2
PC1
enRes <- list()
gmts = list(H= "../data/gmts/h.all.v6.2.symbols.gmt",
KEGG = "../data/gmts/c2.cp.kegg.v6.2.symbols.gmt",
C6 = "../data/gmts/c6.all.v6.2.symbols.gmt")
proMat <- assays(protCLL.sub)[["QRILC"]]
iPC <- "PC1"
pc <- pcRes[,iPC][colnames(proMat)]
designMat <- model.matrix(~1+pc)
fit <- limma::lmFit(proMat, designMat)
fit2 <- eBayes(fit)
corRes <- topTable(fit2, "pc", number = Inf) %>%
data.frame() %>% rownames_to_column("id")
inputTab <- corRes %>% filter(adj.P.Val < 0.1) %>%
mutate(name = rowData(protCLL[id,])$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$H, "page")PC2
proMat <- assays(protCLL.sub)[["QRILC"]]
iPC <- "PC2"
pc <- pcRes[,iPC][colnames(proMat)]
designMat <- model.matrix(~1+pc)
fit <- limma::lmFit(proMat, designMat)
fit2 <- eBayes(fit)
corRes <- topTable(fit2, "pc", number = Inf) %>%
data.frame() %>% rownames_to_column("id")
inputTab <- corRes %>% filter(adj.P.Val < 0.1) %>%
mutate(name = rowData(protCLL[id,])$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$H, "page")cowplot::plot_grid(plotEnrichmentBar(enRes[[1]], ifFDR = TRUE, pCut = 0.05, setName = "",title = "Proteins associated with PC1"),
plotEnrichmentBar(enRes[[2]], ifFDR = TRUE, pCut = 0.05, setName = "", title = "Proteins associated with PC2"),ncol=1)
#using Camera, the results are slightly different
gmts = list(H= "../data/gmts/h.all.v6.2.symbols.gmt",
KEGG = "../data/gmts/c2.cp.kegg.v6.2.symbols.gmt",
C6 = "../data/gmts/c6.all.v6.2.symbols.gmt")
proMat <- assays(protCLL.sub)[["QRILC"]]
iPC <- "PC1"
pc <- pcRes[,iPC][colnames(proMat)]
designMat <- model.matrix(~1+pc)
res1 <- runCamera(proMat, designMat, gmts$H, id = rowData(protCLL[rownames(proMat),])$hgnc_symbol,
plotTitle = "Proteins associated with PC1")$enrichPlot
iPC <- "PC2"
pc <- pcRes[,iPC][colnames(proMat)]
designMat <- model.matrix(~1+pc)
res2 <- runCamera(proMat, designMat, gmts$H, id = rowData(protCLL[rownames(proMat),])$hgnc_symbol,
plotTitle = "Proteins associated with PC2")$enrichPlot
cowplot::plot_grid(res1, res2, nrow=2, align = "hv", rel_heights = c(1,2))Hierarchical clustering
Original dataset
protCLL.sub <- protCLL[!rowData(protCLL)$chromosome_name %in% c("X","Y"),]
plotMat <- assays(protCLL.sub)[["QRILC"]]
colAnno <- colData(protCLL)[,c("IGHV.status","trisomy12")] %>%
data.frame()
colAnno$trisomy12 <- ifelse(colAnno$trisomy12 %in% 1, "yes","no")
plotMat <- mscale(plotMat, center = 5)
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 = "ward.D2",
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)
Data with PC1 and PC2 removed
#Reconstruct data with PC1 and PC2 removed
protCLL.sub <- protCLL[!rowData(protCLL)$chromosome_name %in% c("X","Y"),]
protMat <- assays(protCLL.sub)[["QRILC"]]
mu <- colMeans(protMat)
Xpca <- prcomp(protMat, center = TRUE, scale. = TRUE)
#reconstruct without the first two components
protMat.new <- Xpca$x[,3:ncol(Xpca$x)] %*% t(Xpca$rotation[,3:ncol(Xpca$x)])
protMat.new <- scale(protMat.new, center = -mu, scale = TRUE)
#protMat.new <- t(protMat.new)plotMat <- protMat.new
plotMat <- mscale(plotMat, center = 5)
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 = "ward.D2",
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)
Summarise significant associations
Bar plot of number of significant associations (10% FDR)
load("../output/deResList.RData")plotTab <- resList %>% group_by(Gene) %>%
summarise(nFDR.local = sum(adj.P.Val <= 0.1),
nFDR.global = sum(adj.P.global <= 0.1),
nFDR.IHW = sum(adj.P.IHW <= 0.1),
nP = sum(P.Value < 0.05))
#use IHW for adjusting p-values
resList <- mutate(resList, adj.P.Val = adj.P.IHW)plotTab <- arrange(plotTab, desc(nFDR.IHW)) %>% mutate(Gene = factor(Gene, levels = Gene))
numCorBar <- ggplot(plotTab, aes(x=Gene, y = nFDR.IHW)) + geom_bar(stat="identity",fill=colList[2]) +
geom_text(aes(label = paste0("n=", nFDR.IHW)),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(10% FDR)") + xlab("")
numCorBar
Assemble figure
designPlot <- draw_image("../output/Figure1A.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()
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] piano_2.2.0 latex2exp_0.4.0
[3] forcats_0.5.0 stringr_1.4.0
[5] dplyr_1.0.0 purrr_0.3.4
[7] readr_1.3.1 tidyr_1.1.0
[9] tibble_3.0.3 ggplot2_3.3.2
[11] tidyverse_1.3.0 proDA_1.1.2
[13] cowplot_1.0.0 DESeq2_1.26.0
[15] SummarizedExperiment_1.16.1 DelayedArray_0.12.3
[17] BiocParallel_1.20.1 matrixStats_0.56.0
[19] Biobase_2.46.0 GenomicRanges_1.38.0
[21] GenomeInfoDb_1.22.1 IRanges_2.20.2
[23] S4Vectors_0.24.4 BiocGenerics_0.32.0
[25] limma_3.42.2
loaded via a namespace (and not attached):
[1] readxl_1.3.1 backports_1.1.8 Hmisc_4.4-0
[4] fastmatch_1.1-0 workflowr_1.6.2 igraph_1.2.5
[7] shinydashboard_0.7.1 splines_3.6.0 digest_0.6.25
[10] htmltools_0.5.0 magick_2.4.0 gdata_2.18.0
[13] fansi_0.4.1 magrittr_1.5 checkmate_2.0.0
[16] memoise_1.1.0 cluster_2.1.0 annotate_1.64.0
[19] modelr_0.1.8 jpeg_0.1-8.1 colorspace_1.4-1
[22] blob_1.2.1 rvest_0.3.5 haven_2.3.1
[25] xfun_0.15 crayon_1.3.4 RCurl_1.98-1.2
[28] jsonlite_1.7.0 genefilter_1.68.0 survival_3.2-3
[31] glue_1.4.1 gtable_0.3.0 zlibbioc_1.32.0
[34] XVector_0.26.0 scales_1.1.1 pheatmap_1.0.12
[37] DBI_1.1.0 relations_0.6-9 Rcpp_1.0.5
[40] xtable_1.8-4 htmlTable_2.0.1 foreign_0.8-71
[43] bit_4.0.4 Formula_1.2-3 DT_0.14
[46] htmlwidgets_1.5.1 httr_1.4.1 fgsea_1.12.0
[49] gplots_3.0.4 RColorBrewer_1.1-2 acepack_1.4.1
[52] ellipsis_0.3.1 pkgconfig_2.0.3 XML_3.98-1.20
[55] farver_2.0.3 nnet_7.3-14 dbplyr_1.4.4
[58] locfit_1.5-9.4 utf8_1.1.4 tidyselect_1.1.0
[61] labeling_0.3 rlang_0.4.7 later_1.1.0.1
[64] AnnotationDbi_1.48.0 visNetwork_2.0.9 munsell_0.5.0
[67] cellranger_1.1.0 tools_3.6.0 cli_2.0.2
[70] generics_0.0.2 RSQLite_2.2.0 broom_0.7.0
[73] evaluate_0.14 fastmap_1.0.1 yaml_2.2.1
[76] knitr_1.29 bit64_0.9-7 fs_1.4.2
[79] caTools_1.18.0 nlme_3.1-148 mime_0.9
[82] slam_0.1-47 xml2_1.3.2 compiler_3.6.0
[85] rstudioapi_0.11 png_0.1-7 marray_1.64.0
[88] reprex_0.3.0 geneplotter_1.64.0 stringi_1.4.6
[91] lattice_0.20-41 Matrix_1.2-18 shinyjs_1.1
[94] vctrs_0.3.1 pillar_1.4.6 lifecycle_0.2.0
[97] data.table_1.12.8 bitops_1.0-6 httpuv_1.5.4
[100] R6_2.4.1 latticeExtra_0.6-29 promises_1.1.1
[103] KernSmooth_2.23-17 gridExtra_2.3 codetools_0.2-16
[106] gtools_3.8.2 assertthat_0.2.1 rprojroot_1.3-2
[109] withr_2.2.0 GenomeInfoDbData_1.2.2 mgcv_1.8-31
[112] hms_0.5.3 grid_3.6.0 rpart_4.1-15
[115] rmarkdown_2.3 git2r_0.27.1 sets_1.0-18
[118] shiny_1.5.0 lubridate_1.7.9 base64enc_0.1-3