Section 6: Proteomic signature of del(11)(q22.3)
Junyan Lu
2020-10-09
Last updated: 2021-05-06
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Load packages and datasets
library(limma)
library(DESeq2)
library(tidygraph)
library(igraph)
library(ggraph)
library(pheatmap)
library(ggbeeswarm)
library(cowplot)
library(SummarizedExperiment)
library(tidyverse)
#load datasets
load("../data/patMeta_enc.RData")
load("../data/proteomic_explore_enc.RData")
load("../data/ddsrna_enc.RData")
load("../output/deResList.RData") #precalculated differential expression
load("../output/deResListRNA.RData")
#protCLL <- protCLL[rowData(protCLL)$uniqueMap,]
source("../code/utils.R")
knitr::opts_chunk$set(echo = TRUE, warning = FALSE, message = FALSE,dev = c("png","pdf"))Overview of differentially expressed proteins
A table of associations with 5% FDR
resList <- filter(resList, Gene == "del11q") %>%
#mutate(adj.P.Val = adj.P.global) %>% #use IHW corrected P-value
mutate(Chr = rowData(protCLL[id,])$chromosome_name)
resList %>% filter(adj.P.Val <= 0.05) %>%
select(name, Chr,logFC, P.Value, adj.P.Val) %>%
mutate_if(is.numeric, formatC, digits=2) %>%
DT::datatable()How many are on chr11
table(filter(resList,adj.P.Val <= 0.05)$Chr)
11 12 17 22 3 5 6 8 X
11 1 1 1 4 1 2 2 1 Heatmap of differentially expressed proteins (5% FDR)
proList <- filter(resList, !is.na(name), adj.P.Val < 0.05) %>% distinct(name, .keep_all = TRUE) %>% pull(id)
plotMat <- assays(protCLL)[["QRILC_combat"]][proList,]
rownames(plotMat) <- rowData(protCLL[proList,])$hgnc_symbol
colAnno <- filter(patMeta, Patient.ID %in% colnames(protCLL)) %>%
select(Patient.ID, del11q, IGHV.status) %>%
data.frame() %>% column_to_rownames("Patient.ID")
colAnno$del11q <- ifelse(colAnno$del11q %in% 1, "yes","no")
rowAnno <- rowData(protCLL)[proList,c("chromosome_name","hgnc_symbol"),drop=FALSE] %>%
data.frame(stringsAsFactors = FALSE) %>% remove_rownames() %>%
mutate(onChr11 = ifelse(chromosome_name == "11","yes","no")) %>%
select(hgnc_symbol, onChr11) %>% data.frame() %>% column_to_rownames("hgnc_symbol")
plotMat <- jyluMisc::mscale(plotMat, censor = 5)
annoCol <- list(del11q = c(yes = "black",no = "grey80"),
IGHV.status = c(M = colList[3], U = colList[4]),
onChr11 = c(yes = colList[1],no = "white"))
pheatmap::pheatmap(plotMat, annotation_col = colAnno, scale = "none",
annotation_row = rowAnno,
clustering_method = "ward.D2",
color = colorRampPalette(c(colList[2],"white",colList[1]))(100),
breaks = seq(-5,5, length.out = 101), annotation_colors = annoCol,
show_rownames = TRUE, show_colnames = FALSE,
treeheight_row = 0)
Volcano plot
plotTab <- resList %>% mutate(onChr11 = ifelse(Chr %in% "11","yes","no"))
#nameList <- filter(resList, adj.P.Val < 0.1)$name
nameList <- c("ATM","CUL5")
del11qVolcano <- plotVolcano(plotTab, fdrCut =0.05, x_lab="log2FoldChange", posCol = colList[1], negCol = colList[2],
plotTitle = "del(11)(q22.3)", ifLabel = TRUE, labelList = nameList)
del11qVolcano
Boxplot plot of selected genes
protTab <- sumToTidy(protCLL, rowID = "uniprotID", colID = "patID") %>%
mutate(count = count_combat)
plotTab <- protTab %>% filter(hgnc_symbol %in% nameList) %>%
mutate(del11q = patMeta[match(patID, patMeta$Patient.ID),]$del11q) %>%
mutate(status = ifelse(del11q %in% 1,"del(11)(q22.3)","other"),
name = hgnc_symbol) %>%
mutate(status = factor(status, levels = c("other","del(11)(q22.3)")))
pList <- plotBox(plotTab, pValTabel = resList, y_lab = "Protein expression")
del11qBox <- cowplot::plot_grid(plotlist= pList, ncol=1)
del11qBox
Compare with RNA sequencing data
Differentially expressed genes related to trisomy11q
Prepare RNA sequencing data
ddsCLL <- dds[rownames(dds) %in% rowData(protCLL)$ensembl_gene_id,]
ddsCLL.vst <- varianceStabilizingTransformation(dds)Differential expression
resTab <- filter(resListRNA, Gene == "del11q")Boxplot of selected genes
plotTab <- assay(ddsCLL.vst[match(nameList, rowData(ddsCLL.vst)$symbol),]) %>%
data.frame() %>% rownames_to_column("id") %>%
mutate(name = rowData(ddsCLL.vst[id,])$symbol) %>%
gather(key = "patID", value = "count", -id, -name) %>%
mutate(del11q = patMeta[match(patID, patMeta$Patient.ID),]$del11q) %>%
mutate(status = ifelse(del11q %in% 1,"del11q","other")) %>%
mutate(status = factor(status, levels = c("other","del11q")))
pList <- plotBox(plotTab, pValTabel = resTab, y_lab = "Normalized RNA expression")
cowplot::plot_grid(plotlist= pList, ncol=1)
Correlations between RNA and protein expression
rnaMat <- assay(ddsCLL.vst)
protMat <- assays(protCLL)[["count_combat"]]
rownames(protMat) <- rowData(protCLL)$ensembl_gene_id
overSample <- intersect(colnames(rnaMat), colnames(protMat))
rnaMat <- rnaMat[,overSample]
protMat <- protMat[,overSample]
plotList <- lapply(nameList, function(n) {
geneId <- rownames(ddsCLL.vst)[match(n, rowData(ddsCLL.vst)$symbol)]
stopifnot(length(geneId) ==1)
plotTab <- tibble(x=rnaMat[geneId,],y=protMat[geneId,])
coef <- cor(plotTab$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])
})
cowplot::plot_grid(plotlist = plotList, ncol =2)
Plot RNA and protein expression in the chromosome regions around 11q22.3
load("../data/exprCNV_enc.RData")Normalize protein and RNA expression
normalized <- TRUE
#if perform normalization
if (normalized) {
#for protein
exprMat <- select(allProtTab,patID, id,expr) %>%
distinct(patID, id, .keep_all = TRUE) %>%
spread(key = patID, value =expr) %>% data.frame() %>%
column_to_rownames("id") %>% as.matrix()
qm <- jyluMisc::mscale(exprMat, useMad = F)
normTab <- data.frame(qm) %>% rownames_to_column("id") %>%
gather(key = "patID", value = "expr", -id)
allProtTab <- select(allProtTab, -expr) %>% left_join(normTab, by = c("patID","id"))
#for RNA
exprMat <- select(allRnaTab,patID, id,expr) %>%
distinct(patID, id, .keep_all = TRUE) %>%
spread(key = patID, value =expr) %>% data.frame() %>%
column_to_rownames("id") %>% as.matrix()
qm <- jyluMisc::mscale(exprMat, useMad = F)
normTab <- data.frame(qm) %>% rownames_to_column("id") %>%
gather(key = "patID", value = "expr", -id)
allRnaTab <- select(allRnaTab, -expr) %>% left_join(normTab, by = c("patID","id"))
}Function for plotting
plotExprCNV <- function(pat, chr, allBand, allLine, allProtTab, allRnaTab, ifTrend = FALSE, plotTitle = "",
startPos = -Inf, endPos= Inf, showLabel = "none", plotDiff = FALSE, errorBar = FALSE) {
multiPat <- length(unique(pat)) > 1
#table for cyto band
bandTab <- filter(allBand, ChromID == chr)
#table for expression
plotProtTab <- filter(allProtTab, ChromID == chr, patID %in% pat) %>%
mutate(expression = "protein") %>%
mutate_if(is.factor,as.character)
plotRnaTab <- filter(allRnaTab, ChromID == chr, patID %in% pat) %>%
mutate(expression = "rna") %>% mutate_if(is.factor,as.character)
if (!plotDiff) {
plotExprTab <- bind_rows(plotRnaTab, plotProtTab) %>%
filter(start_position > startPos, end_position < endPos)
} else {
plotProtTab <- plotProtTab %>% dplyr::rename(protein = expr)
plotRnaTab <- plotRnaTab %>% select(id, expr) %>%
dplyr::rename(rna = expr)
plotExprTab <- left_join(plotProtTab, plotRnaTab, by = "id") %>%
mutate(expr = protein-rna, expression = "protein-rna") %>%
filter(start_position > startPos, end_position < endPos) %>%
select(-protein,-rna)
}
if (multiPat) {
se <- function(x) sqrt(var(x,na.rm = T)/length(x))
plotExprTab <- group_by(plotExprTab, id, symbol, ChromID, start_position, end_position,mid_position, expression) %>%
summarise(upper = mean(expr,na.rm=T) + 1.96*se(expr), lower = mean(expr,na.rm=T) - 1.96*se(expr),
expr = mean(expr)) %>%
ungroup()
}
#table for copy number
plotLineTab <- filter(allLine, patID %in% pat, ChromID == chr)
#plot range
maxVal <- max(c(max(plotExprTab$expr,na.rm = T),max(plotLineTab$SegmentMean,na.rm = T)),na.rm = T) + 1
minVal <- min(c(min(plotExprTab$expr, na.rm = T),min(plotLineTab$SegmentMean,na.rm = T)),na.rm = T) - 1
#maxVal <- 5
#minVal <- -5
xMax <- max(bandTab$chromEnd, na.rm = T)
#main plot
gg <- ggplot() +
geom_rect(data=bandTab, mapping=aes(xmin=chromStart, xmax=chromEnd, ymin=minVal, ymax=maxVal,
fill=Colour, label = band), alpha=0.1) +
geom_text(data=bandTab, mapping=aes(label=band, x=chromMid), y=maxVal, hjust =1, angle = 90, size=4) +
geom_rect(data=plotLineTab,
mapping=aes(xmin=Start, xmax=End, ymin=SegmentMean,
ymax=SegmentMean+0.5,fill = set),alpha=0.2)
if (multiPat & errorBar) {
gg <- gg + geom_errorbar(data = plotExprTab,
aes(x = mid_position, y = expr + 0.25, ymax = upper + 0.25, ymin=lower + 0.25),
col = "grey60")
}
gg <- gg + geom_rect(data = plotExprTab,
mapping=aes(xmin=start_position,
xmax=end_position, ymin=expr, ymax=expr+0.5,
fill = expression, label = symbol), alpha =0.8) +
#scale_x_continuous(expand=c(0,0),limits = c(max(0,startPos),min(xMax,endPos))) +
scale_y_continuous(limits = c(minVal, maxVal), sec.axis = sec_axis(~./1, name = "Copy number")) +
coord_cartesian(xlim = c(max(0,startPos),min(xMax,endPos)), expand = FALSE)+
xlab("Genomic position [Mb]") +
ylab("Expression z-score") +
scale_fill_manual(values = c(even = "white",odd = "grey50",
rna = colList[1], protein = colList[2], `protein-rna` = "salmon",
WES = "darkgreen",WGS = "orange", Methylome = "purple")) +
scale_color_manual(values = c(protein = "blue",rna = "red",`protein-rna` = "salmon")) +
ggtitle(plotTitle) +
theme(plot.title = element_text(face = "bold", size = 18),
axis.text = element_text(size=16),
axis.title = element_text(size=16),
axis.line = element_blank(),
legend.position = "none",
panel.background = element_blank(),
panel.grid.major = element_line(colour="grey90", size=0.1))
if (showLabel != "none") {
gg <- gg +
ggrepel::geom_text_repel(data = filter(plotExprTab,
expression == showLabel),
aes(x=mid_position, y=expr, label = symbol))
}
if (ifTrend) {
gg <- gg + geom_smooth(data =filter(plotExprTab),
mapping = aes(y=expr, x= mid_position,
color = expression),
method = "loess", se=FALSE, span=0.2,
size =0.2)
}
#for legend
## if the patient has CNV data
lgTab <- tibble(x= seq(90),y=seq(90),
Expression = c(rep("protein",30), rep("rna",30),rep("protein-rna",30)),
CNV_data = rep(c("WES","WGS","Methylome"),30))
if (nrow(plotLineTab) >0) {
lgTab <- filter(lgTab, CNV_data %in% unique(plotLineTab$set),
Expression %in% unique(plotExprTab$expression))
lg <- ggplot(lgTab, aes(x=x,y=y)) +
geom_point(aes(fill = Expression), shape =22,size=3) +
geom_line(aes(color = CNV_data),size=5) +
scale_fill_manual(values = c(rna = colList[1], protein = colList[2],`protein-rna` = "salmon")) +
scale_color_manual(values = c(WES = "darkgreen",WGS = "orange", Methylome = "purple"), guide = FALSE) +
theme(legend.position = "bottom",
legend.text = element_text(size=16),
legend.title = element_text(size=16))
} else {
lgTab <- filter(lgTab, Expression %in% unique(plotExprTab$expression))
lg <- ggplot(lgTab, aes(x=x,y=y)) +
geom_point(aes(fill = Expression), shape =22,size=3) +
scale_fill_manual(values = c(rna = colList[1], protein = colList[2],`protein-rna` = "salmon")) +
theme(legend.position = "bottom",
legend.text = element_text(size=16),
legend.title = element_text(size=16))
}
lg <- get_legend(lg)
return(list(main=gg, legend = lg))
}allLine.wes <- filter(allLine, set == "WES")
patList <- intersect(intersect(filter(patMeta, del11q %in% 1)$Patient.ID,allProtTab$patID),allRnaTab$patID)
g <- plotExprCNV(patList,"chr11",allBand, allLine.wes, allProtTab, allRnaTab,
ifTrend = FALSE, startPos = 92.8, endPos = 123, showLabel = "protein",
plotTitle = "chromosome 11: q21 to q24.1")
geneCoordPlot <- plot_grid(g$main, g$legend, ncol = 1, rel_heights = c(1,0.2))
geneCoordPlot
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] latex2exp_0.4.0 forcats_0.5.1
[3] stringr_1.4.0 dplyr_1.0.5
[5] purrr_0.3.4 readr_1.4.0
[7] tidyr_1.1.3 tibble_3.1.0
[9] tidyverse_1.3.0 cowplot_1.1.1
[11] ggbeeswarm_0.6.0 pheatmap_1.0.12
[13] ggraph_2.0.5 ggplot2_3.3.3
[15] igraph_1.2.6 tidygraph_1.2.0
[17] DESeq2_1.28.1 SummarizedExperiment_1.18.2
[19] DelayedArray_0.14.1 matrixStats_0.58.0
[21] Biobase_2.48.0 GenomicRanges_1.40.0
[23] GenomeInfoDb_1.24.2 IRanges_2.22.2
[25] S4Vectors_0.26.1 BiocGenerics_0.34.0
[27] limma_3.44.3
loaded via a namespace (and not attached):
[1] utf8_1.1.4 shinydashboard_0.7.1 tidyselect_1.1.0
[4] RSQLite_2.2.3 AnnotationDbi_1.50.3 htmlwidgets_1.5.3
[7] grid_4.0.2 BiocParallel_1.22.0 maxstat_0.7-25
[10] munsell_0.5.0 codetools_0.2-18 DT_0.17
[13] withr_2.4.1 colorspace_2.0-0 highr_0.8
[16] knitr_1.31 rstudioapi_0.13 ggsignif_0.6.1
[19] labeling_0.4.2 git2r_0.28.0 slam_0.1-48
[22] GenomeInfoDbData_1.2.3 KMsurv_0.1-5 polyclip_1.10-0
[25] bit64_4.0.5 farver_2.1.0 rprojroot_2.0.2
[28] vctrs_0.3.6 generics_0.1.0 TH.data_1.0-10
[31] xfun_0.21 sets_1.0-18 R6_2.5.0
[34] graphlayouts_0.7.1 locfit_1.5-9.4 bitops_1.0-6
[37] cachem_1.0.4 fgsea_1.14.0 assertthat_0.2.1
[40] promises_1.2.0.1 scales_1.1.1 multcomp_1.4-16
[43] beeswarm_0.3.1 gtable_0.3.0 sandwich_3.0-0
[46] workflowr_1.6.2 rlang_0.4.10 genefilter_1.70.0
[49] splines_4.0.2 rstatix_0.7.0 broom_0.7.5
[52] yaml_2.2.1 abind_1.4-5 modelr_0.1.8
[55] crosstalk_1.1.1 backports_1.2.1 httpuv_1.5.5
[58] tools_4.0.2 relations_0.6-9 ellipsis_0.3.1
[61] gplots_3.1.1 jquerylib_0.1.3 RColorBrewer_1.1-2
[64] Rcpp_1.0.6 visNetwork_2.0.9 zlibbioc_1.34.0
[67] RCurl_1.98-1.2 ggpubr_0.4.0 viridis_0.5.1
[70] zoo_1.8-9 haven_2.3.1 ggrepel_0.9.1
[73] cluster_2.1.1 exactRankTests_0.8-31 fs_1.5.0
[76] magrittr_2.0.1 data.table_1.14.0 openxlsx_4.2.3
[79] reprex_1.0.0 survminer_0.4.9 mvtnorm_1.1-1
[82] hms_1.0.0 shinyjs_2.0.0 mime_0.10
[85] evaluate_0.14 xtable_1.8-4 XML_3.99-0.5
[88] rio_0.5.26 readxl_1.3.1 gridExtra_2.3
[91] compiler_4.0.2 KernSmooth_2.23-18 crayon_1.4.1
[94] htmltools_0.5.1.1 mgcv_1.8-34 later_1.1.0.1
[97] geneplotter_1.66.0 lubridate_1.7.10 DBI_1.1.1
[100] tweenr_1.0.1 dbplyr_2.1.0 MASS_7.3-53.1
[103] jyluMisc_0.1.5 Matrix_1.3-2 car_3.0-10
[106] cli_2.3.1 marray_1.66.0 km.ci_0.5-2
[109] pkgconfig_2.0.3 foreign_0.8-81 piano_2.4.0
[112] xml2_1.3.2 annotate_1.66.0 vipor_0.4.5
[115] bslib_0.2.4 XVector_0.28.0 drc_3.0-1
[118] rvest_1.0.0 digest_0.6.27 rmarkdown_2.7
[121] cellranger_1.1.0 fastmatch_1.1-0 survMisc_0.5.5
[124] curl_4.3 shiny_1.6.0 gtools_3.8.2
[127] nlme_3.1-152 lifecycle_1.0.0 jsonlite_1.7.2
[130] carData_3.0-4 viridisLite_0.3.0 fansi_0.4.2
[133] pillar_1.5.1 lattice_0.20-41 fastmap_1.1.0
[136] httr_1.4.2 plotrix_3.8-1 survival_3.2-7
[139] glue_1.4.2 zip_2.1.1 bit_4.0.4
[142] ggforce_0.3.3 stringi_1.5.3 sass_0.3.1
[145] blob_1.2.1 caTools_1.18.1 memoise_2.0.0