Identify proteins differentially expressed between before and under treatment
Junyan Lu
2021-02-16
Last updated: 2021-02-16
Checks: 5 2
Knit directory: CLLproteomics_batch13/analysis/
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Load packages and datasets
Annotated samples before and after treatment
protSub <- protCLL[,str_detect(colnames(protCLL),"_")]
protSub$treat <- factor(ifelse(str_detect(colnames(protSub),"_1"),"before","after"),
levels = c("before","after"))
protSub$patID <- str_remove(colnames(protSub),"_.")Detect differentially expressed proteins
protMat <- assays(protSub)[["count"]]
design <- model.matrix(~patID + treat, colData(protSub))
fit <- proDA(protMat, design = design)
resTab <- test_diff(fit, contrast = "treatafter") %>%
mutate(symbol = rowData(protCLL[name,])$hgnc_symbol) %>%
arrange(pval)P-value histogram
hist(resTab$pval, breaks = 50)
List of singificant proteins (P <=0.01)
resTab.sig <- filter(resTab, pval <=0.01)
resTab.sig %>% select(symbol, pval, adj_pval, diff, n_obs) %>%
mutate_if(is.numeric, formatC, digits=2) %>%
DT::datatable()Boxplot of candidates
Plot top 9 most down regulated
proTab <- sumToTidy(protSub)
resTab.down <- filter(resTab.sig, diff < 0)
pList <- lapply(unique(resTab.down$symbol), function(nm) {
plotTab <- filter(proTab, hgnc_symbol == nm)
ggplot(plotTab, aes(x=treat, y=count)) +
geom_boxplot() + geom_point(aes(col = treat)) +
geom_line(aes(group = patID), linetype ="dashed",col = "grey50") +
theme_bw() + ggtitle(nm) +
theme(legend.position = "none",
plot.title = element_text(hjust=0.5, face = "bold"))
})
cowplot::plot_grid(plotlist= pList[1:9], ncol=3)
For all DE proteins (as pdf)
jyluMisc::makepdf(pList,"../public/treat_DOWN.pdf", ncol=3,nrow=3, height = 8, width = 12)Plot top 9 most up regulated
proTab <- sumToTidy(protSub)
resTab.up <- filter(resTab.sig, diff > 0)
pList <- lapply(unique(resTab.up$symbol)[1:9], function(nm) {
plotTab <- filter(proTab, hgnc_symbol == nm)
ggplot(plotTab, aes(x=treat, y=count)) +
geom_boxplot() + geom_point(aes(col = treat)) +
geom_line(aes(group = patID), linetype ="dashed",col = "grey50") +
theme_bw() + ggtitle(nm) +
theme(legend.position = "none",
plot.title = element_text(hjust=0.5, face = "bold"))
})
cowplot::plot_grid(plotlist= pList, ncol=3)
For all DE proteins (as pdf)
jyluMisc::makepdf(pList,"../public/treat_UP.pdf", ncol=3,nrow=3, height = 8, width = 12)Pathway enrichment
Enrichment using PAGE
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",
C6 = "../data/gmts/c6.all.v6.2.symbols.gmt")
inputTab <- resTab %>% filter(pval <= 0.05) %>%
distinct(symbol, .keep_all = TRUE) %>%
select(symbol, t_statistic) %>% data.frame() %>% column_to_rownames("symbol")
enRes <- list()
enRes[["HALLMARK"]] <- runGSEA(inputTab, gmts$H, "page")
enRes[["KEGG"]] <- runGSEA(inputTab, gmts$KEGG, "page")
enRes[["C6"]] <- runGSEA(inputTab, gmts$C6, "page")
p1 <- plotEnrichmentBar(enRes[[1]], pCut =0.1, ifFDR= TRUE, setName = "",
title = "HALLMARK gene set", removePrefix = "HALLMARK_", insideLegend=FALSE)
p2 <- plotEnrichmentBar(enRes[[2]], pCut =0.1, ifFDR= TRUE, setName = "",
title = "KEGG gene set", removePrefix = "KEGG_", insideLegend=FALSE)
p3 <- plotEnrichmentBar(enRes[[3]], pCut =0.1, ifFDR= TRUE, setName = "",
title = "C6: Oncogenetic signature", removePrefix = "KEGG_", insideLegend=FALSE)
treatEnrich <- cowplot::plot_grid(p1,p2, ncol=1, align = "hv", axis = "lr")
treatEnrich
It looks like the treatment affected CLL proliferation related pathways, such as OXPHOS and MYC.
Enrichment using CAMERA
protMat <- assays(protSub)[["QRILC"]]
enRes <- runCamera(protMat, design, gmts$H,
id = rowData(protCLL)[rownames(protMat),]$hgnc_symbol,
contrast = "treatafter",
removePrefix = "HALLMARK_", pCut = 0.1, ifFDR = TRUE, plotTitle = "HALLMARK")
p1 <- enRes$enrichPlot
enRes <- runCamera(protMat, design, gmts$KEGG,
id = rowData(protCLL)[rownames(protMat),]$hgnc_symbol,
contrast = "treatafter",
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(3,7))
Similar results can be observed.
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 gridExtra_2.3
[3] latex2exp_0.4.0 forcats_0.5.0
[5] stringr_1.4.0 dplyr_1.0.0
[7] purrr_0.3.4 readr_1.3.1
[9] tidyr_1.1.0 tibble_3.0.2
[11] ggplot2_3.3.2 tidyverse_1.3.0
[13] SummarizedExperiment_1.18.1 DelayedArray_0.14.0
[15] matrixStats_0.56.0 Biobase_2.48.0
[17] GenomicRanges_1.40.0 GenomeInfoDb_1.24.2
[19] IRanges_2.22.2 S4Vectors_0.26.1
[21] BiocGenerics_0.34.0 proDA_1.2.0
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 gdata_2.18.0
[16] fansi_0.4.1 magrittr_1.5 cluster_2.1.0
[19] openxlsx_4.1.5 limma_3.44.3 modelr_0.1.8
[22] sandwich_2.5-1 colorspace_1.4-1 blob_1.2.1
[25] rvest_0.3.5 haven_2.3.1 xfun_0.15
[28] crayon_1.3.4 RCurl_1.98-1.2 jsonlite_1.7.0
[31] survival_3.2-3 zoo_1.8-8 glue_1.4.1
[34] survminer_0.4.7 gtable_0.3.0 zlibbioc_1.34.0
[37] XVector_0.28.0 car_3.0-8 abind_1.4-5
[40] scales_1.1.1 mvtnorm_1.1-1 DBI_1.1.0
[43] relations_0.6-9 rstatix_0.6.0 Rcpp_1.0.5
[46] plotrix_3.7-8 xtable_1.8-4 foreign_0.8-80
[49] km.ci_0.5-2 DT_0.14 htmlwidgets_1.5.1
[52] httr_1.4.1 fgsea_1.14.0 gplots_3.0.4
[55] ellipsis_0.3.1 pkgconfig_2.0.3 farver_2.0.3
[58] dbplyr_1.4.4 tidyselect_1.1.0 labeling_0.3
[61] rlang_0.4.6 later_1.1.0.1 visNetwork_2.0.9
[64] munsell_0.5.0 cellranger_1.1.0 tools_4.0.2
[67] cli_2.0.2 generics_0.0.2 broom_0.5.6
[70] evaluate_0.14 fastmap_1.0.1 yaml_2.2.1
[73] knitr_1.29 fs_1.4.2 zip_2.0.4
[76] survMisc_0.5.5 caTools_1.18.0 nlme_3.1-148
[79] mime_0.9 slam_0.1-47 xml2_1.3.2
[82] compiler_4.0.2 rstudioapi_0.11 curl_4.3
[85] ggsignif_0.6.0 marray_1.66.0 reprex_0.3.0
[88] stringi_1.4.6 lattice_0.20-41 Matrix_1.2-18
[91] KMsurv_0.1-5 shinyjs_2.0.0 vctrs_0.3.1
[94] pillar_1.4.4 lifecycle_0.2.0 data.table_1.12.8
[97] cowplot_1.0.0 bitops_1.0-6 httpuv_1.5.4
[100] R6_2.4.1 promises_1.1.1 KernSmooth_2.23-17
[103] rio_0.5.16 codetools_0.2-16 MASS_7.3-51.6
[106] gtools_3.8.2 exactRankTests_0.8-31 assertthat_0.2.1
[109] rprojroot_2.0.2 withr_2.2.0 multcomp_1.4-13
[112] GenomeInfoDbData_1.2.3 hms_0.5.3 grid_4.0.2
[115] rmarkdown_2.3 carData_3.0-4 ggpubr_0.4.0
[118] git2r_0.27.1 maxstat_0.7-25 sets_1.0-18
[121] shiny_1.5.0 lubridate_1.7.9