Principal components analysis and enrichment analysis on PCs
Junyan Lu
2020-03-13
Last updated: 2020-06-06
Checks: 6 1
Knit directory: Proteomics/analysis/
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Principal component analysis
Calculate PCA
Here, I use 1000 most variant proteins for calculating PCA. Proteins from X and Y chromosomes are excluded.
#remove genes on sex chromosomes
protCLL.sub <- protCLL[!rowData(protCLL)$chromosome_name %in% c("X","Y"),]
plotMat <- assays(protCLL.sub)[["QRILC"]]
sds <- genefilter::rowSds(plotMat)
plotMat <- as.matrix(plotMat[order(sds,decreasing = TRUE)[1:1000],])
colAnno <- colData(protCLL)[,c("gender","IGHV.status","trisomy12")] %>%
data.frame()
pcOut <- prcomp(t(plotMat), center =TRUE, scale. = TRUE)
pcRes <- pcOut$x
eigs <- pcOut$sdev^2
varExp <- structure(eigs/sum(eigs),names = colnames(pcRes))Plot PC1 and PC2
plotTab <- pcRes %>% data.frame() %>% cbind(colAnno[rownames(.),]) %>%
rownames_to_column("patID") %>% as_tibble()
ggplot(plotTab, aes(x=PC1, y=PC2, col = IGHV.status, shape = trisomy12)) + geom_point(size=4) +
xlab(sprintf("PC1 (%1.2f%%)",varExp[["PC1"]]*100)) +
ylab(sprintf("PC2 (%1.2f%%)",varExp[["PC2"]]*100))
Plot PC3 and PC4
plotTab <- pcRes %>% data.frame() %>% cbind(colAnno[rownames(.),]) %>%
rownames_to_column("patID") %>% as_tibble()
ggplot(plotTab, aes(x=PC3, y=PC4, col = IGHV.status, shape = trisomy12)) + geom_point(size=4) +
xlab(sprintf("PC3 (%1.2f%%)",varExp[["PC3"]]*100)) +
ylab(sprintf("PC4 (%1.2f%%)",varExp[["PC4"]]*100))
Plot PC4 and PC5
plotTab <- pcRes %>% data.frame() %>% cbind(colAnno[rownames(.),]) %>%
rownames_to_column("patID") %>% as_tibble()
ggplot(plotTab, aes(x=PC4, y=PC5, col = IGHV.status, shape = trisomy12)) + geom_point(size=4) +
xlab(sprintf("PC4 (%1.2f%%)",varExp[["PC4"]]*100)) +
ylab(sprintf("PC5 (%1.2f%%)",varExp[["PC5"]]*100))
Correlation PCs with trisomy12 and IGHV status
corTab <- lapply(colnames(pcRes), function(pc) {
ighvCor <- t.test(pcRes[,pc] ~ colAnno$IGHV.status)
tri12Cor <- t.test(pcRes[,pc] ~ colAnno$trisomy12)
tibble(PC = pc,
feature=c("IGHV", "trisomy12"),
p = c(ighvCor$p.value, tri12Cor$p.value))
}) %>% bind_rows() %>% mutate(p.adj = p.adjust(p, method = "BH")) %>%
filter(p <= 0.05) %>% arrange(p)
corTab# A tibble: 4 x 4
PC feature p p.adj
<chr> <chr> <dbl> <dbl>
1 PC3 trisomy12 0.00000000812 0.000000795
2 PC4 IGHV 0.000368 0.0180
3 PC5 IGHV 0.00476 0.156
4 PC3 IGHV 0.0345 0.828 PC3 represents trisomy12 and PC4 represents IGHV. From another analysis, we know that PC5 represents CLL-PD.
Enrichment analysis on PCs
PC1
gmts = list(H= "../data/gmts/h.all.v6.2.symbols.gmt",
KEGG = "../data/gmts/c2.cp.kegg.v6.2.symbols.gmt",
C6 = "../data/gmts/c6.all.v6.2.symbols.gmt")iPC <- "PC1"
proMat <- assays(protCLL.sub)[["QRILC"]]
pc <- pcRes[,iPC][colnames(proMat)]
designMat <- model.matrix(~1+pc)
res <- runCamera(proMat, designMat, gmts$H, id = rowData(protCLL[rownames(proMat),])$hgnc_symbol)Hallmarks
res <- runCamera(proMat, designMat, gmts$H, id = rowData(protCLL[rownames(proMat),])$hgnc_symbol)
res$enrichPlot
KEGG
res <- runCamera(proMat, designMat, gmts$KEGG, id = rowData(protCLL[rownames(proMat),])$hgnc_symbol)
res$enrichPlot
C6 oncogentic signatures
res <- runCamera(proMat, designMat, gmts$C6, id = rowData(protCLL[rownames(proMat),])$hgnc_symbol)
res$enrichPlot
Proteins associated with PC1
fit <- lmFit(proMat, designMat)
fit2 <- eBayes(fit)
corRes <- topTable(fit2, number ="all", adjust.method = "BH", coef = "pc") %>% rownames_to_column("id") %>%
mutate(symbol = rowData(protCLL[id,])$hgnc_symbol)Table of significant associations (5% FDR)
resTab.sig <- filter(corRes, adj.P.Val < 0.05) %>%
select(symbol, id,logFC, P.Value, adj.P.Val) %>%
arrange(P.Value)
resTab.sig %>% mutate_if(is.numeric, formatC, digits=2, format= "e") %>%
DT::datatable()Heatmap of top100 associated proteins
colAnno <- pcRes[colnames(proMat),iPC, drop= FALSE] %>% data.frame() %>%
rownames_to_column("patID") %>%
mutate(IGHV = protCLL[,patID]$IGHV.status,
trisomy12 = protCLL[,patID]$trisomy12,
TP53 = patMeta[match(patID, patMeta$Patient.ID),]$TP53,
SF3B1 = patMeta[match(patID, patMeta$Patient.ID),]$SF3B1,
NOTCH1 = patMeta[match(patID, patMeta$Patient.ID),]$NOTCH1) %>%
arrange(!!rlang::sym(iPC)) %>% data.frame() %>% column_to_rownames("patID")
plotMat <- proMat[resTab.sig$id[1:100],rownames(colAnno)]
plotMat <- jyluMisc::mscale(plotMat, censor = 6)
pheatmap(plotMat, scale = "none", annotation_col = colAnno, clustering_method = "ward.D2",
cluster_cols = FALSE,
labels_row = resTab.sig$symbol[1:100], color = colorRampPalette(c("navy","white","firebrick"))(100),
breaks = seq(-6,6, length.out = 101))
PC2
iPC <- "PC2"
proMat <- assays(protCLL.sub)[["QRILC"]]
pc <- pcRes[,iPC][colnames(proMat)]
designMat <- model.matrix(~1+pc)
res <- runCamera(proMat, designMat, gmts$H, id = rowData(protCLL[rownames(proMat),])$hgnc_symbol)Hallmarks
res <- runCamera(proMat, designMat, gmts$H, id = rowData(protCLL[rownames(proMat),])$hgnc_symbol)
res$enrichPlot
KEGG
res <- runCamera(proMat, designMat, gmts$KEGG, id = rowData(protCLL[rownames(proMat),])$hgnc_symbol)
res$enrichPlot
C6 oncogentic signatures
res <- runCamera(proMat, designMat, gmts$C6, id = rowData(protCLL[rownames(proMat),])$hgnc_symbol)
res$enrichPlot
Proteins associated with PC2
fit <- lmFit(proMat, designMat)
fit2 <- eBayes(fit)
corRes <- topTable(fit2, number ="all", adjust.method = "BH", coef = "pc") %>% rownames_to_column("id") %>%
mutate(symbol = rowData(protCLL[id,])$hgnc_symbol)Table of significant associations (5% FDR)
resTab.sig <- filter(corRes, adj.P.Val < 0.05) %>%
select(symbol, id,logFC, P.Value, adj.P.Val) %>%
arrange(P.Value)
resTab.sig %>% mutate_if(is.numeric, formatC, digits=2, format= "e") %>%
DT::datatable()Heatmap of top100 associated proteins
colAnno <- pcRes[colnames(proMat),iPC, drop= FALSE] %>% data.frame() %>%
rownames_to_column("patID") %>%
mutate(IGHV = protCLL[,patID]$IGHV.status,
trisomy12 = protCLL[,patID]$trisomy12,
TP53 = patMeta[match(patID, patMeta$Patient.ID),]$TP53,
SF3B1 = patMeta[match(patID, patMeta$Patient.ID),]$SF3B1,
NOTCH1 = patMeta[match(patID, patMeta$Patient.ID),]$NOTCH1) %>%
arrange(!!rlang::sym(iPC)) %>% data.frame() %>% column_to_rownames("patID")
plotMat <- proMat[resTab.sig$id[1:100],rownames(colAnno)]
plotMat <- jyluMisc::mscale(plotMat, censor = 6)
pheatmap(plotMat, scale = "none", annotation_col = colAnno, clustering_method = "ward.D2",
cluster_cols = FALSE,
labels_row = resTab.sig$symbol[1:100], color = colorRampPalette(c("navy","white","firebrick"))(100),
breaks = seq(-6,6, length.out = 101))
PC3
iPC <- "PC3"
proMat <- assays(protCLL.sub)[["QRILC"]]
pc <- pcRes[,iPC][colnames(proMat)]
designMat <- model.matrix(~1+pc)
res <- runCamera(proMat, designMat, gmts$H, id = rowData(protCLL[rownames(proMat),])$hgnc_symbol)Hallmarks
res <- runCamera(proMat, designMat, gmts$H, id = rowData(protCLL[rownames(proMat),])$hgnc_symbol)
res$enrichPlot
KEGG
res <- runCamera(proMat, designMat, gmts$KEGG, id = rowData(protCLL[rownames(proMat),])$hgnc_symbol)
res$enrichPlot
C6 oncogentic signatures
res <- runCamera(proMat, designMat, gmts$C6, id = rowData(protCLL[rownames(proMat),])$hgnc_symbol)
res$enrichPlot
Proteins associated with PC3
fit <- lmFit(proMat, designMat)
fit2 <- eBayes(fit)
corRes <- topTable(fit2, number ="all", adjust.method = "BH", coef = "pc") %>% rownames_to_column("id") %>%
mutate(symbol = rowData(protCLL[id,])$hgnc_symbol)Table of significant associations (5% FDR)
resTab.sig <- filter(corRes, adj.P.Val < 0.05) %>%
select(symbol, id,logFC, P.Value, adj.P.Val) %>%
arrange(P.Value)
resTab.sig %>% mutate_if(is.numeric, formatC, digits=2, format= "e") %>%
DT::datatable()Heatmap of top100 associated proteins
colAnno <- pcRes[colnames(proMat),iPC, drop= FALSE] %>% data.frame() %>%
rownames_to_column("patID") %>%
mutate(IGHV = protCLL[,patID]$IGHV.status,
trisomy12 = protCLL[,patID]$trisomy12,
TP53 = patMeta[match(patID, patMeta$Patient.ID),]$TP53,
SF3B1 = patMeta[match(patID, patMeta$Patient.ID),]$SF3B1,
NOTCH1 = patMeta[match(patID, patMeta$Patient.ID),]$NOTCH1) %>%
arrange(!!rlang::sym(iPC)) %>% data.frame() %>% column_to_rownames("patID")
plotMat <- proMat[resTab.sig$id[1:100],rownames(colAnno)]
plotMat <- jyluMisc::mscale(plotMat, censor = 6)
pheatmap(plotMat, scale = "none", annotation_col = colAnno, clustering_method = "ward.D2",
cluster_cols = FALSE,
labels_row = resTab.sig$symbol[1:100], color = colorRampPalette(c("navy","white","firebrick"))(100),
breaks = seq(-6,6, length.out = 101))
PC4
iPC <- "PC4"
proMat <- assays(protCLL.sub)[["QRILC"]]
pc <- pcRes[,iPC][colnames(proMat)]
designMat <- model.matrix(~1+pc)
res <- runCamera(proMat, designMat, gmts$H, id = rowData(protCLL[rownames(proMat),])$hgnc_symbol)Hallmarks
res <- runCamera(proMat, designMat, gmts$H, id = rowData(protCLL[rownames(proMat),])$hgnc_symbol)
res$enrichPlot
KEGG
res <- runCamera(proMat, designMat, gmts$KEGG, id = rowData(protCLL[rownames(proMat),])$hgnc_symbol)
res$enrichPlot
C6 oncogentic signatures
res <- runCamera(proMat, designMat, gmts$C6, id = rowData(protCLL[rownames(proMat),])$hgnc_symbol)
res$enrichPlot
Proteins associated with PC4
fit <- lmFit(proMat, designMat)
fit2 <- eBayes(fit)
corRes <- topTable(fit2, number ="all", adjust.method = "BH", coef = "pc") %>% rownames_to_column("id") %>%
mutate(symbol = rowData(protCLL[id,])$hgnc_symbol)Table of significant associations (5% FDR)
resTab.sig <- filter(corRes, adj.P.Val < 0.05) %>%
select(symbol, id,logFC, P.Value, adj.P.Val) %>%
arrange(P.Value)
resTab.sig %>% mutate_if(is.numeric, formatC, digits=2, format= "e") %>%
DT::datatable()Heatmap of top100 associated proteins
colAnno <- pcRes[colnames(proMat),iPC, drop= FALSE] %>% data.frame() %>%
rownames_to_column("patID") %>%
mutate(IGHV = protCLL[,patID]$IGHV.status,
trisomy12 = protCLL[,patID]$trisomy12,
TP53 = patMeta[match(patID, patMeta$Patient.ID),]$TP53,
SF3B1 = patMeta[match(patID, patMeta$Patient.ID),]$SF3B1,
NOTCH1 = patMeta[match(patID, patMeta$Patient.ID),]$NOTCH1) %>%
arrange(!!rlang::sym(iPC)) %>% data.frame() %>% column_to_rownames("patID")
plotMat <- proMat[resTab.sig$id[1:100],rownames(colAnno)]
plotMat <- jyluMisc::mscale(plotMat, censor = 6)
pheatmap(plotMat, scale = "none", annotation_col = colAnno, clustering_method = "ward.D2",
cluster_cols = FALSE,
labels_row = resTab.sig$symbol[1:100], color = colorRampPalette(c("navy","white","firebrick"))(100),
breaks = seq(-6,6, length.out = 101))
PC5
iPC <- "PC5"
proMat <- assays(protCLL.sub)[["QRILC"]]
pc <- pcRes[,iPC][colnames(proMat)]
designMat <- model.matrix(~1+pc)
res <- runCamera(proMat, designMat, gmts$H, id = rowData(protCLL[rownames(proMat),])$hgnc_symbol)Hallmarks
res <- runCamera(proMat, designMat, gmts$H, id = rowData(protCLL[rownames(proMat),])$hgnc_symbol)
res$enrichPlot
KEGG
res <- runCamera(proMat, designMat, gmts$KEGG, id = rowData(protCLL[rownames(proMat),])$hgnc_symbol)
res$enrichPlot
C6 oncogentic signatures
res <- runCamera(proMat, designMat, gmts$C6, id = rowData(protCLL[rownames(proMat),])$hgnc_symbol)
res$enrichPlot
Proteins associated with PC5
fit <- lmFit(proMat, designMat)
fit2 <- eBayes(fit)
corRes <- topTable(fit2, number ="all", adjust.method = "BH", coef = "pc") %>% rownames_to_column("id") %>%
mutate(symbol = rowData(protCLL[id,])$hgnc_symbol)Table of significant associations (5% FDR)
resTab.sig <- filter(corRes, adj.P.Val < 0.05) %>%
select(symbol, id,logFC, P.Value, adj.P.Val) %>%
arrange(P.Value)
resTab.sig %>% mutate_if(is.numeric, formatC, digits=2, format= "e") %>%
DT::datatable()Heatmap of top100 associated proteins
colAnno <- pcRes[colnames(proMat),iPC, drop= FALSE] %>% data.frame() %>%
rownames_to_column("patID") %>%
mutate(IGHV = protCLL[,patID]$IGHV.status,
trisomy12 = protCLL[,patID]$trisomy12,
TP53 = patMeta[match(patID, patMeta$Patient.ID),]$TP53,
SF3B1 = patMeta[match(patID, patMeta$Patient.ID),]$SF3B1,
NOTCH1 = patMeta[match(patID, patMeta$Patient.ID),]$NOTCH1) %>%
arrange(!!rlang::sym(iPC)) %>% data.frame() %>% column_to_rownames("patID")
plotMat <- proMat[resTab.sig$id[1:100],rownames(colAnno)]
plotMat <- jyluMisc::mscale(plotMat, censor = 6)
pheatmap(plotMat, scale = "none", annotation_col = colAnno, clustering_method = "ward.D2",
cluster_cols = FALSE,
labels_row = resTab.sig$symbol[1:100], color = colorRampPalette(c("navy","white","firebrick"))(100),
breaks = seq(-6,6, length.out = 101))
sessionInfo()R version 3.6.0 (2019-04-26)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS 10.15.4
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] forcats_0.4.0 stringr_1.4.0
[3] dplyr_0.8.5 purrr_0.3.3
[5] readr_1.3.1 tidyr_1.0.0
[7] tibble_3.0.0 tidyverse_1.3.0
[9] SummarizedExperiment_1.14.0 DelayedArray_0.10.0
[11] BiocParallel_1.18.0 matrixStats_0.54.0
[13] Biobase_2.44.0 GenomicRanges_1.36.0
[15] GenomeInfoDb_1.20.0 IRanges_2.18.1
[17] S4Vectors_0.22.0 BiocGenerics_0.30.0
[19] limma_3.40.2 jyluMisc_0.1.5
[21] pheatmap_1.0.12 piano_2.0.2
[23] cowplot_0.9.4 ggplot2_3.3.0
loaded via a namespace (and not attached):
[1] readxl_1.3.1 backports_1.1.4 fastmatch_1.1-0
[4] drc_3.0-1 workflowr_1.6.0 igraph_1.2.4.1
[7] shinydashboard_0.7.1 splines_3.6.0 crosstalk_1.0.0
[10] TH.data_1.0-10 digest_0.6.19 htmltools_0.4.0
[13] fansi_0.4.0 gdata_2.18.0 memoise_1.1.0
[16] magrittr_1.5 cluster_2.1.0 openxlsx_4.1.0.1
[19] annotate_1.62.0 modelr_0.1.5 sandwich_2.5-1
[22] colorspace_1.4-1 blob_1.1.1 rvest_0.3.5
[25] haven_2.2.0 xfun_0.8 crayon_1.3.4
[28] RCurl_1.95-4.12 jsonlite_1.6 genefilter_1.66.0
[31] survival_2.44-1.1 zoo_1.8-6 glue_1.3.2
[34] survminer_0.4.4 gtable_0.3.0 zlibbioc_1.30.0
[37] XVector_0.24.0 car_3.0-3 abind_1.4-5
[40] scales_1.1.0 mvtnorm_1.0-11 DBI_1.0.0
[43] relations_0.6-8 Rcpp_1.0.1 plotrix_3.7-6
[46] xtable_1.8-4 cmprsk_2.2-8 bit_1.1-14
[49] foreign_0.8-71 km.ci_0.5-2 DT_0.7
[52] htmlwidgets_1.3 httr_1.4.1 fgsea_1.10.0
[55] gplots_3.0.1.1 RColorBrewer_1.1-2 ellipsis_0.2.0
[58] farver_2.0.3 XML_3.98-1.20 pkgconfig_2.0.2
[61] dbplyr_1.4.2 utf8_1.1.4 labeling_0.3
[64] AnnotationDbi_1.46.0 tidyselect_1.0.0 rlang_0.4.5
[67] later_0.8.0 munsell_0.5.0 cellranger_1.1.0
[70] tools_3.6.0 visNetwork_2.0.7 cli_1.1.0
[73] RSQLite_2.1.1 generics_0.0.2 broom_0.5.2
[76] evaluate_0.14 yaml_2.2.0 bit64_0.9-7
[79] knitr_1.23 fs_1.4.0 zip_2.0.2
[82] survMisc_0.5.5 caTools_1.17.1.2 nlme_3.1-140
[85] mime_0.7 slam_0.1-45 xml2_1.2.2
[88] rstudioapi_0.10 compiler_3.6.0 curl_3.3
[91] ggsignif_0.5.0 marray_1.62.0 reprex_0.3.0
[94] stringi_1.4.3 lattice_0.20-38 Matrix_1.2-17
[97] shinyjs_1.0 KMsurv_0.1-5 vctrs_0.2.4
[100] pillar_1.4.3 lifecycle_0.2.0 data.table_1.12.2
[103] bitops_1.0-6 httpuv_1.5.1 R6_2.4.0
[106] promises_1.0.1 KernSmooth_2.23-15 gridExtra_2.3
[109] rio_0.5.16 codetools_0.2-16 MASS_7.3-51.4
[112] gtools_3.8.1 exactRankTests_0.8-30 assertthat_0.2.1
[115] rprojroot_1.3-2 withr_2.1.2 multcomp_1.4-10
[118] GenomeInfoDbData_1.2.1 hms_0.5.2 grid_3.6.0
[121] rmarkdown_1.13 carData_3.0-2 git2r_0.26.1
[124] maxstat_0.7-25 ggpubr_0.2.1 sets_1.0-18
[127] shiny_1.3.2 lubridate_1.7.4