Section 2: an overview of the associations between genomics and protein expressions
Junyan Lu
2020-06-23
Last updated: 2020-08-06
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Knit directory: Proteomics/analysis/
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Preprocessing
Process proteomics data
protCLL <- protCLL[rowData(protCLL)$uniqueMap,]
protMat <- assays(protCLL)[["count"]] #without imputationPrepare genomic background
Get Mutations with at least 5 cases
geneMat <- patMeta[match(colnames(protMat), patMeta$Patient.ID),] %>%
select(Patient.ID, IGHV.status, del11p:U1) %>%
mutate_if(is.factor, as.character) %>% mutate(IGHV.status = ifelse(IGHV.status == "M", 1,0)) %>%
mutate_at(vars(-Patient.ID), as.numeric) %>% #assign a few unknown mutated cases to wildtype
data.frame() %>% column_to_rownames("Patient.ID")
geneMat <- geneMat[,apply(geneMat,2, function(x) sum(x %in% 1, na.rm = TRUE))>=5]Mutations that will be tested
colnames(geneMat) [1] "IGHV.status" "del11q" "del13q" "trisomy12" "trisomy19"
[6] "DDX3X" "EGR2" "NOTCH1" "SF3B1" "TP53" Plot to summarise genomic background
sortTab <- function(sumTab) {
i <- ncol(sumTab)
#print(i)
if (i == 1) {
#print(rownames(sumTab)[order(sumTab[,i])])
return(rownames(sumTab)[order(sumTab[,i])])
}
orderRow <- c(sortTab(sumTab[sumTab[,i]==0, seq(1,i-1), drop = FALSE]), sortTab(sumTab[sumTab[,i]==1, seq(1,i-1), drop = FALSE]))
return(orderRow)
}
geneMat.sort <- geneMat
geneMat.sort[is.na(geneMat.sort)] <- 0
sortedGene <- names(sort(colSums(geneMat.sort)))
geneMat.sort <- geneMat.sort[,sortedGene]
sortedPat <- sortTab(geneMat.sort)plotTab <- geneMat %>% rownames_to_column("patID") %>% mutate_all(as.character) %>%
gather(key = "gene", value = "status", -patID) %>%
mutate(gene = factor(gene, levels = sortedGene), patID = factor(patID, levels = sortedPat)) %>%
mutate(status = ifelse(is.na(status),"NA",status))
ggplot(plotTab, aes(x=patID, y = gene, fill = status)) +
geom_tile(color = "black") +
theme_minimal() +
scale_fill_manual(values = c("1" = colList[4],"0" ="white", "NA" = "grey80")) +
theme(axis.text.x = element_text(angle = 90, hjust =1, vjust=0.5),
panel.grid = element_blank(), legend.position = "none") +
ylab("") + xlab("Patient ID")
Association test using proDA
For IGHV and trisomy12
Fit the probailistic dropout model
designMat <- geneMat[ ,c("IGHV.status","trisomy12")]
fit <- proDA(protMat, design = ~ .,
col_data = designMat)Test for differentially expressed proteins
resList.ighvTri12 <- lapply(c("IGHV.status","trisomy12"), function(n) {
contra <- n
resTab <- test_diff(fit, contra) %>%
dplyr::rename(id = name, logFC = diff, t=t_statistic,
P.Value = pval, adj.P.Val = adj_pval) %>%
mutate(name = rowData(protCLL[id,])$hgnc_symbol) %>%
select(name, id, logFC, t, P.Value, adj.P.Val) %>%
arrange(P.Value) %>% mutate(Gene = n) %>%
as_tibble()
}) %>% bind_rows()Test for other variantions (blocking for IGHV and trisomy12)
Fit the probailistic dropout model and test for differentially expressed proteins
otherGenes <- colnames(geneMat)[!colnames(geneMat)%in% c("IGHV.status","trisomy12")]
resList <- lapply(otherGenes, function(n) {
designMat <- geneMat[,c("IGHV.status","trisomy12",n)]
designMat <- designMat[!is.na(designMat[[n]]),]
testMat <- protMat[,rownames(designMat)]
fit <- proDA(testMat, design = ~ .,
col_data = designMat)
contra <- n
resTab <- test_diff(fit, contra) %>%
dplyr::rename(id = name, logFC = diff, t=t_statistic,
P.Value = pval, adj.P.Val = adj_pval) %>%
mutate(name = rowData(protCLL[id,])$hgnc_symbol) %>%
select(name, id, logFC, t, P.Value, adj.P.Val) %>%
arrange(P.Value) %>% mutate(Gene = n) %>%
as_tibble()
resTab
}) %>% bind_rows()Combine the results
resList <- bind_rows(resList.ighvTri12, resList)
#Adjusting p values
#using BH
resList <- mutate(resList, adj.P.global = p.adjust(P.Value, method = "BH"))
#using IHW
ihwRes <- ihw(P.Value ~ factor(Gene), data= resList, alpha=0.1)
resList <- mutate(resList, adj.P.IHW = adj_pvalues(ihwRes))save(resList, file = "../output/deResList.RData")load("../output/deResList.RData")Summarise significant associations
Bar plot of number of significant associations (10% FDR)
plotTab <- resList %>% group_by(Gene) %>%
summarise(nFDR.local = sum(adj.P.Val <= 0.1),
nFDR.global = sum(adj.P.global <= 0.1),
nFDR.IHW = sum(adj.P.IHW <= 0.1),
nP = sum(P.Value < 0.05))
#use IHW for adjusting p-values
resList <- mutate(resList, adj.P.Val = adj.P.IHW)plotTab <- arrange(plotTab, desc(nFDR.IHW)) %>% mutate(Gene = factor(Gene, levels = Gene))
ggplot(plotTab, aes(x=Gene, y = nFDR.IHW)) + geom_bar(stat="identity",fill=colList[2]) +
geom_text(aes(label = paste0("n=", nFDR.IHW)),vjust=-1,col=colList[1]) + ylim(0,1200) +
theme_half + theme(axis.text.x = element_text(angle = 90, hjust=1, vjust=0.5)) +
ylab("Number of associations\n(10% FDR)") + xlab("")
Bar plot of number of raw P-value < 0.05
plotTab <- arrange(plotTab, desc(nP)) %>% mutate(Gene = factor(Gene, levels = Gene))
ggplot(plotTab, aes(x=Gene, y = nP)) + geom_bar(stat="identity",fill=colList[2]) +
geom_text(aes(label = paste0("n=", nP)),vjust=-1,col=colList[1]) + ylim(0,1500) +
theme_half + theme(axis.text.x = element_text(angle = 90, hjust=1, vjust=0.5)) +
ylab("Number of associations\n(nominal P-value < 0.05)") + xlab("")
Association test in timsTOF data
For IGHV and trisomy12
Load timsTOF data
load("../output/proteomic_timsTOF_enc.RData")
protMat <- assays(protCLL)[["count"]] #without imputationGenetic data
geneMat <- patMeta[match(colnames(protMat), patMeta$Patient.ID),] %>%
select(Patient.ID, IGHV.status, trisomy12, SF3B1, trisomy19, del11q) %>%
mutate_if(is.factor, as.character) %>% mutate(IGHV.status = ifelse(IGHV.status == "M", 1,0)) %>%
mutate_at(vars(-Patient.ID), as.numeric) %>% #assign a few unknown mutated cases to wildtype
data.frame() %>% column_to_rownames("Patient.ID")Fit the probailistic dropout model
designMat <- geneMat[ ,c("IGHV.status","trisomy12")]
fit <- proDA(protMat, design = ~ .,
col_data = designMat)Test for differentially expressed proteins
resList.ighvTri12 <- lapply(c("IGHV.status","trisomy12"), function(n) {
contra <- n
resTab <- test_diff(fit, contra) %>%
dplyr::rename(id = name, logFC = diff, t=t_statistic,
P.Value = pval, adj.P.Val = adj_pval) %>%
mutate(name = rowData(protCLL[id,])$hgnc_symbol) %>%
select(name, id, logFC, t, P.Value, adj.P.Val) %>%
arrange(P.Value) %>% mutate(Gene = n) %>%
as_tibble()
}) %>% bind_rows()Test for other variantions (blocking for IGHV and trisomy12)
Fit the probailistic dropout model and test for differentially expressed proteins
otherGenes <- colnames(geneMat)[!colnames(geneMat)%in% c("IGHV.status","trisomy12")]
resList <- lapply(otherGenes, function(n) {
designMat <- geneMat[,c("IGHV.status","trisomy12",n)]
designMat <- designMat[!is.na(designMat[[n]]),]
testMat <- protMat[,rownames(designMat)]
fit <- proDA(testMat, design = ~ .,
col_data = designMat)
contra <- n
resTab <- test_diff(fit, contra) %>%
dplyr::rename(id = name, logFC = diff, t=t_statistic,
P.Value = pval, adj.P.Val = adj_pval) %>%
mutate(name = rowData(protCLL[id,])$hgnc_symbol) %>%
select(name, id, logFC, t, P.Value, adj.P.Val) %>%
arrange(P.Value) %>% mutate(Gene = n) %>%
as_tibble()
resTab
}) %>% bind_rows()Combine the results
resList <- bind_rows(resList.ighvTri12, resList)
#Adjusting p values
#using BH
resList <- mutate(resList, adj.P.global = p.adjust(P.Value, method = "BH"))
#using IHW
ihwRes <- ihw(P.Value ~ factor(Gene), data= resList, alpha=0.1)
resList <- mutate(resList, adj.P.IHW = adj_pvalues(ihwRes))save(resList, file = "../output/deResList_timsTOF.RData")
sessionInfo()R version 3.6.0 (2019-04-26)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS 10.15.6
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] latex2exp_0.4.0 forcats_0.5.0
[3] stringr_1.4.0 dplyr_1.0.0
[5] purrr_0.3.4 readr_1.3.1
[7] tidyr_1.1.0 tibble_3.0.3
[9] ggplot2_3.3.2 tidyverse_1.3.0
[11] IHW_1.14.0 proDA_1.1.2
[13] DESeq2_1.26.0 SummarizedExperiment_1.16.1
[15] DelayedArray_0.12.3 BiocParallel_1.20.1
[17] matrixStats_0.56.0 Biobase_2.46.0
[19] GenomicRanges_1.38.0 GenomeInfoDb_1.22.1
[21] IRanges_2.20.2 S4Vectors_0.24.4
[23] BiocGenerics_0.32.0 limma_3.42.2
loaded via a namespace (and not attached):
[1] colorspace_1.4-1 ellipsis_0.3.1 rprojroot_1.3-2
[4] htmlTable_2.0.1 XVector_0.26.0 base64enc_0.1-3
[7] fs_1.4.2 rstudioapi_0.11 farver_2.0.3
[10] bit64_0.9-7 fansi_0.4.1 AnnotationDbi_1.48.0
[13] lubridate_1.7.9 xml2_1.3.2 splines_3.6.0
[16] geneplotter_1.64.0 knitr_1.29 Formula_1.2-3
[19] jsonlite_1.7.0 workflowr_1.6.2 broom_0.7.0
[22] annotate_1.64.0 cluster_2.1.0 dbplyr_1.4.4
[25] png_0.1-7 compiler_3.6.0 httr_1.4.1
[28] backports_1.1.8 assertthat_0.2.1 Matrix_1.2-18
[31] cli_2.0.2 later_1.1.0.1 acepack_1.4.1
[34] htmltools_0.5.0 tools_3.6.0 gtable_0.3.0
[37] glue_1.4.1 GenomeInfoDbData_1.2.2 Rcpp_1.0.5
[40] slam_0.1-47 cellranger_1.1.0 vctrs_0.3.1
[43] xfun_0.15 rvest_0.3.5 lifecycle_0.2.0
[46] XML_3.98-1.20 zlibbioc_1.32.0 scales_1.1.1
[49] hms_0.5.3 promises_1.1.1 RColorBrewer_1.1-2
[52] yaml_2.2.1 memoise_1.1.0 gridExtra_2.3
[55] rpart_4.1-15 latticeExtra_0.6-29 stringi_1.4.6
[58] RSQLite_2.2.0 genefilter_1.68.0 checkmate_2.0.0
[61] rlang_0.4.7 pkgconfig_2.0.3 bitops_1.0-6
[64] evaluate_0.14 lattice_0.20-41 lpsymphony_1.14.0
[67] labeling_0.3 htmlwidgets_1.5.1 bit_1.1-15.2
[70] tidyselect_1.1.0 magrittr_1.5 R6_2.4.1
[73] generics_0.0.2 Hmisc_4.4-0 DBI_1.1.0
[76] withr_2.2.0 pillar_1.4.6 haven_2.3.1
[79] foreign_0.8-71 survival_3.2-3 RCurl_1.98-1.2
[82] nnet_7.3-14 modelr_0.1.8 crayon_1.3.4
[85] fdrtool_1.2.15 rmarkdown_2.3 jpeg_0.1-8.1
[88] locfit_1.5-9.4 grid_3.6.0 readxl_1.3.1
[91] data.table_1.12.8 blob_1.2.1 git2r_0.27.1
[94] reprex_0.3.0 digest_0.6.25 xtable_1.8-4
[97] extraDistr_1.8.11 httpuv_1.5.4 munsell_0.5.0