Section 7: Proteomic signature of SF3B1
Junyan Lu
2020-10-09
Last updated: 2021-05-06
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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("../data/patMeta_enc.RData")
load("../data/ddsrna_enc.RData")
load("../data/proteomic_explore_enc.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 5% FDR
resList <- filter(resList, Gene == "SF3B1") %>%
#mutate(adj.P.Val = adj.P.global) %>% #use IHW corrected P-value
mutate(Chr = rowData(protCLL[id,])$chromosome_name)
resList %>% filter(adj.P.Val <= 0.05) %>%
select(name, Chr,logFC, P.Value, adj.P.Val) %>%
mutate_if(is.numeric, formatC, digits=2) %>%
DT::datatable()Heatmap of differentially expressed proteins (6% FDR)
proList <- filter(resList, !is.na(name), adj.P.Val < 0.05) %>% distinct(name, .keep_all = TRUE) %>% pull(id)
plotMat <- assays(protCLL)[["QRILC_combat"]][proList,]
rownames(plotMat) <- rowData(protCLL[proList,])$hgnc_symbol
colAnno <- filter(patMeta, Patient.ID %in% colnames(protCLL)) %>%
select(Patient.ID, 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 = TRUE, show_colnames = FALSE,
treeheight_row = 0)
Volcano plot
plotTab <- resList
nameList <- c("SUGP1")
sf3b1Volcano <- plotVolcano(plotTab, fdrCut =0.05, x_lab="log2FoldChange", posCol = colList[1], negCol = colList[2],
plotTitle = "SF3B1 (Mutants versus WT)", ifLabel = TRUE, labelList = nameList)
sf3b1Volcano
Boxplot plot of selected proteins
nameList <- c("SUGP1")
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
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)]
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 ggbeeswarm_0.6.0
[11] ggplot2_3.3.3 pheatmap_1.0.12
[13] cowplot_1.1.1 proDA_1.2.0
[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] readxl_1.3.1 backports_1.2.1 fastmatch_1.1-0
[4] drc_3.0-1 jyluMisc_0.1.5 workflowr_1.6.2
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[10] BiocParallel_1.22.0 crosstalk_1.1.1 TH.data_1.0-10
[13] digest_0.6.27 htmltools_0.5.1.1 fansi_0.4.2
[16] magrittr_2.0.1 memoise_2.0.0 cluster_2.1.1
[19] openxlsx_4.2.3 annotate_1.66.0 modelr_0.1.8
[22] sandwich_3.0-0 piano_2.4.0 colorspace_2.0-0
[25] ggrepel_0.9.1 blob_1.2.1 rvest_1.0.0
[28] haven_2.3.1 xfun_0.21 crayon_1.4.1
[31] RCurl_1.98-1.2 jsonlite_1.7.2 genefilter_1.70.0
[34] survival_3.2-7 zoo_1.8-9 glue_1.4.2
[37] survminer_0.4.9 gtable_0.3.0 zlibbioc_1.34.0
[40] XVector_0.28.0 car_3.0-10 abind_1.4-5
[43] scales_1.1.1 mvtnorm_1.1-1 relations_0.6-9
[46] DBI_1.1.1 rstatix_0.7.0 Rcpp_1.0.6
[49] plotrix_3.8-1 xtable_1.8-4 foreign_0.8-81
[52] bit_4.0.4 km.ci_0.5-2 DT_0.17
[55] htmlwidgets_1.5.3 httr_1.4.2 fgsea_1.14.0
[58] gplots_3.1.1 RColorBrewer_1.1-2 ellipsis_0.3.1
[61] farver_2.1.0 pkgconfig_2.0.3 XML_3.99-0.5
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[67] utf8_1.1.4 labeling_0.4.2 tidyselect_1.1.0
[70] rlang_0.4.10 later_1.1.0.1 AnnotationDbi_1.50.3
[73] visNetwork_2.0.9 munsell_0.5.0 cellranger_1.1.0
[76] tools_4.0.2 cachem_1.0.4 cli_2.3.1
[79] generics_0.1.0 RSQLite_2.2.3 broom_0.7.5
[82] evaluate_0.14 fastmap_1.1.0 yaml_2.2.1
[85] knitr_1.31 bit64_4.0.5 fs_1.5.0
[88] zip_2.1.1 survMisc_0.5.5 caTools_1.18.1
[91] mime_0.10 slam_0.1-48 xml2_1.3.2
[94] compiler_4.0.2 rstudioapi_0.13 beeswarm_0.3.1
[97] curl_4.3 ggsignif_0.6.1 marray_1.66.0
[100] reprex_1.0.0 geneplotter_1.66.0 bslib_0.2.4
[103] stringi_1.5.3 highr_0.8 lattice_0.20-41
[106] Matrix_1.3-2 KMsurv_0.1-5 shinyjs_2.0.0
[109] vctrs_0.3.6 pillar_1.5.1 lifecycle_1.0.0
[112] jquerylib_0.1.3 data.table_1.14.0 bitops_1.0-6
[115] httpuv_1.5.5 R6_2.5.0 promises_1.2.0.1
[118] KernSmooth_2.23-18 gridExtra_2.3 rio_0.5.26
[121] vipor_0.4.5 codetools_0.2-18 MASS_7.3-53.1
[124] gtools_3.8.2 exactRankTests_0.8-31 assertthat_0.2.1
[127] rprojroot_2.0.2 withr_2.4.1 multcomp_1.4-16
[130] GenomeInfoDbData_1.2.3 hms_1.0.0 grid_4.0.2
[133] rmarkdown_2.7 carData_3.0-4 ggpubr_0.4.0
[136] git2r_0.28.0 maxstat_0.7-25 sets_1.0-18
[139] shiny_1.6.0 lubridate_1.7.10