Section 7: Proteomic signature of SF3B1
Junyan Lu
2020-10-09
Last updated: 2021-02-10
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Knit directory: CLLproteomics_batch13/analysis/
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Load packages and datasets
library(limma)
library(DESeq2)
library(proDA)
library(cowplot)
library(pheatmap)
library(ggbeeswarm)
library(SummarizedExperiment)
library(tidyverse)
#load datasets
load("../../var/patmeta_200522.RData")
load("~/CLLproject_jlu/var/ddsrna_180717.RData")
load("../data/proteomic_LUMOS_batch13.RData")
load("../output/deResList.RData") #precalculated differential expression
#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 10% FDR
resList <- filter(resList, Gene == "SF3B1") %>%
#mutate(adj.P.Val = adj.P.global) %>% #use IHW corrected P-value
mutate(Chr = rowData(protCLL[id,])$chromosome_name)
resList %>% filter(adj.P.Val <= 0.1) %>%
select(name, Chr,logFC, P.Value, adj.P.Val) %>%
mutate_if(is.numeric, formatC, digits=2) %>%
DT::datatable()Heatmap of differentially expressed proteins (10% FDR)
proList <- filter(resList, !is.na(name), adj.P.Val < 0.1) %>% distinct(name, .keep_all = TRUE) %>% pull(id)
plotMat <- assays(protCLL)[["QRILC_combat"]][proList,]
rownames(plotMat) <- rowData(protCLL[proList,])$hgnc_symbol
colAnno <- filter(patMeta, Patient.ID %in% colnames(protCLL)) %>%
select(Patient.ID, SF3B1, IGHV.status) %>%
arrange(SF3B1) %>%
data.frame() %>% column_to_rownames("Patient.ID")
colAnno$SF3B1 <- ifelse(colAnno$SF3B1 %in% 1, "yes","no")
plotMat <- jyluMisc::mscale(plotMat, censor = 5)
plotMat <- plotMat[,rownames(colAnno)]
annoCol <- list(SF3B1 = c(yes = "black",no = "grey80"),
IGHV.status = c(M = colList[3], U = colList[4]))
pheatmap::pheatmap(plotMat, annotation_col = colAnno, scale = "none", cluster_cols = FALSE,
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)
Volcano plot
plotTab <- resList
nameList <- c("SUGP1","MSH6")
sf3b1Volcano <- plotVolcano(plotTab, fdrCut =0.1, x_lab="log2FoldChange", posCol = colList[1], negCol = colList[2],
plotTitle = "SF3B1 (Mutants versus WT)", ifLabel = TRUE, labelList = nameList)
sf3b1Volcano
Enrichment analysis
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")
inputTab <- resList %>% filter(P.Value < 0.01) %>%
mutate(name = rowData(protCLL[id,])$hgnc_symbol) %>% filter(!is.na(name)) %>%
distinct(name, .keep_all = TRUE) %>%
select(name, t) %>% data.frame() %>% column_to_rownames("name")
enRes <- list()
enRes[["Proteins associated with trisomy12"]] <- runGSEA(inputTab, gmts$H, "page")
p <- plotEnrichmentBar(enRes[[1]], pCut =0.1, ifFDR= TRUE, setName = "HALLMARK gene set",
title = names(enRes)[1], removePrefix = "HALLMARK_", insideLegend=TRUE)
tri12Enrich <- cowplot::plot_grid(p)
tri12Enrich
Boxplot plot of selected genes
nameList <- c("SUGP1","MSH6")
protTab <- sumToTidy(protCLL, rowID = "uniprotID", colID = "patID") %>%
mutate(count = count_combat)
plotTab <- protTab %>% filter(hgnc_symbol %in% nameList) %>%
mutate(SF3B1 = patMeta[match(patID, patMeta$Patient.ID),]$SF3B1) %>%
mutate(status = ifelse(SF3B1 %in% 1,"Mutated","WT"),
name = hgnc_symbol) %>%
mutate(status = factor(status, levels = c("WT","Mutated")))
pList <- plotBox(plotTab, pValTabel = resList, y_lab = "Protein expression")
sf3b1Box <- cowplot::plot_grid(plotlist= pList, ncol=1)
sf3b1Box
Compare with RNA sequencing data
Differentially expressed genes related to IGHV
Prepare RNA sequencing data
dds$diag <- patMeta[match(dds$PatID, patMeta$Patient.ID),]$diagnosis
dds$trisomy12 <- patMeta[match(dds$PatID, patMeta$Patient.ID),]$trisomy12
dds$IGHV <- patMeta[match(dds$PatID, patMeta$Patient.ID),]$IGHV.status
dds$SF3B1 <- patMeta[match(dds$PatID, patMeta$Patient.ID),]$SF3B1
ddsCLL <- dds[rownames(dds) %in% rowData(protCLL)$ensembl_gene_id, dds$diag %in% "CLL" & !is.na(dds$IGHV) & !is.na(dds$trisomy12) & !is.na(dds$SF3B1)]
ddsCLL.vst <- varianceStabilizingTransformation(ddsCLL)Differential expression
design(ddsCLL) <- ~ trisomy12 + IGHV + SF3B1
deRes <- DESeq(ddsCLL)
resTab <- results(deRes, contrast = c("SF3B1",1,0), tidy = TRUE) %>%
mutate(name = rowData(ddsCLL[row,])$symbol) %>%
dplyr::rename(P.Value = pvalue)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(SF3B1 = patMeta[match(patID, patMeta$Patient.ID),]$SF3B1) %>%
mutate(status = ifelse(SF3B1 %in% 1,"Mutated","WT"))%>%
mutate(status = factor(status, levels = c("WT","Mutated")))
pList <- plotBox(plotTab, pValTabel = resTab, y_lab = "RNA expression")
cowplot::plot_grid(plotlist= pList, ncol=2)
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)
Differential splicing
Processing splicing dataset
library(DEXSeq)
dxdCLL <- dxdCLL[,dxdCLL$diag %in% "CLL"]
dxdCLL$SF3B1 <- factor(patMeta[match(dxdCLL$patID, patMeta$Patient.ID),]$SF3B1)
dxdCLL$trisomy12 <- factor(patMeta[match(dxdCLL$patID, patMeta$Patient.ID),]$trisomy12)
dxdCLL$IGHV <- factor(patMeta[match(dxdCLL$patID, patMeta$Patient.ID),]$IGHV.status)
dxdCLL.sub <- dxdCLL[rowData(dxdCLL)$symbol %in% filter(resList, adj.P.Val < 0.1)$name,
!is.na(dxdCLL$SF3B1) & !is.na(dxdCLL$trisomy12) & !is.na(dxdCLL$IGHV)]Assemble Figure
Supplementary Figure
spliceSUGP1 <- ggdraw() + draw_image("../output/SUGP1_splicing.svg")
spliceMSH6 <- ggdraw() + draw_image("../output/MSH6_splicing.svg")
upRow <- plot_grid(sf3b1Volcano, sf3b1Box, rel_widths = c(0.6,0.4),
ncol=2, labels = c("A","B"), label_size = 20)
downRow <- plot_grid(spliceSUGP1, spliceMSH6, ncol=2, labels = c("C","D"), label_size = 20)
plot_grid(upRow, downRow, ncol=1, rel_heights = c(0.5,0.5))
sessionInfo()R version 4.0.2 (2020-06-22)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Catalina 10.15.7
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 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_3.0.2 tidyverse_1.3.0
[11] ggbeeswarm_0.6.0 ggplot2_3.3.2
[13] pheatmap_1.0.12 cowplot_1.0.0
[15] proDA_1.2.0 DESeq2_1.28.1
[17] SummarizedExperiment_1.18.1 DelayedArray_0.14.0
[19] matrixStats_0.56.0 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_1.1.8 fastmatch_1.1-0
[4] drc_3.0-1 jyluMisc_0.1.5 workflowr_1.6.2
[7] igraph_1.2.5 shinydashboard_0.7.1 splines_4.0.2
[10] BiocParallel_1.22.0 crosstalk_1.1.0.1 TH.data_1.0-10
[13] digest_0.6.25 htmltools_0.5.0 magick_2.4.0
[16] gdata_2.18.0 fansi_0.4.1 magrittr_1.5
[19] memoise_1.1.0 cluster_2.1.0 openxlsx_4.1.5
[22] annotate_1.66.0 modelr_0.1.8 sandwich_2.5-1
[25] colorspace_1.4-1 ggrepel_0.8.2 blob_1.2.1
[28] rvest_0.3.5 haven_2.3.1 xfun_0.15
[31] crayon_1.3.4 RCurl_1.98-1.2 jsonlite_1.7.0
[34] genefilter_1.70.0 survival_3.2-3 zoo_1.8-8
[37] glue_1.4.1 survminer_0.4.7 gtable_0.3.0
[40] zlibbioc_1.34.0 XVector_0.28.0 car_3.0-8
[43] abind_1.4-5 scales_1.1.1 mvtnorm_1.1-1
[46] relations_0.6-9 DBI_1.1.0 rstatix_0.6.0
[49] Rcpp_1.0.5 plotrix_3.7-8 xtable_1.8-4
[52] foreign_0.8-80 bit_1.1-15.2 km.ci_0.5-2
[55] DT_0.14 htmlwidgets_1.5.1 httr_1.4.1
[58] fgsea_1.14.0 gplots_3.0.4 RColorBrewer_1.1-2
[61] ellipsis_0.3.1 farver_2.0.3 pkgconfig_2.0.3
[64] XML_3.99-0.4 dbplyr_1.4.4 locfit_1.5-9.4
[67] labeling_0.3 tidyselect_1.1.0 rlang_0.4.6
[70] later_1.1.0.1 AnnotationDbi_1.50.1 visNetwork_2.0.9
[73] munsell_0.5.0 cellranger_1.1.0 tools_4.0.2
[76] cli_2.0.2 generics_0.0.2 RSQLite_2.2.0
[79] broom_0.5.6 evaluate_0.14 fastmap_1.0.1
[82] yaml_2.2.1 knitr_1.29 bit64_0.9-7
[85] fs_1.4.2 zip_2.0.4 survMisc_0.5.5
[88] caTools_1.18.0 nlme_3.1-148 mime_0.9
[91] slam_0.1-47 xml2_1.3.2 compiler_4.0.2
[94] rstudioapi_0.11 beeswarm_0.2.3 curl_4.3
[97] ggsignif_0.6.0 marray_1.66.0 reprex_0.3.0
[100] geneplotter_1.66.0 stringi_1.4.6 lattice_0.20-41
[103] Matrix_1.2-18 KMsurv_0.1-5 shinyjs_2.0.0
[106] vctrs_0.3.1 pillar_1.4.4 lifecycle_0.2.0
[109] data.table_1.12.8 bitops_1.0-6 httpuv_1.5.4
[112] R6_2.4.1 promises_1.1.1 KernSmooth_2.23-17
[115] gridExtra_2.3 rio_0.5.16 vipor_0.4.5
[118] codetools_0.2-16 MASS_7.3-51.6 gtools_3.8.2
[121] exactRankTests_0.8-31 assertthat_0.2.1 rprojroot_2.0.2
[124] withr_2.2.0 multcomp_1.4-13 GenomeInfoDbData_1.2.3
[127] mgcv_1.8-31 hms_0.5.3 grid_4.0.2
[130] rmarkdown_2.3 carData_3.0-4 ggpubr_0.4.0
[133] git2r_0.27.1 maxstat_0.7-25 sets_1.0-18
[136] shiny_1.5.0 lubridate_1.7.9