Explaining STAT2 protein expression using multipmics data
Junyan Lu
2020-03-09
Last updated: 2020-09-14
Checks: 5 2
Knit directory: Proteomics/analysis/
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Feature selection with LASSO
Preprocessing data
Proteomics data
[1] 49 576RNAseq
#subset
ddsSub <- dds[,dds$PatID %in% colnames(protCLL)]
#only keep protein coding genes with symbol
ddsSub <- ddsSub[rowData(ddsSub)$biotype %in% "protein_coding" & !rowData(ddsSub)$symbol %in% c("",NA),]
#remove lowly expressed genes
ddsSub <- ddsSub[rowSums(counts(ddsSub, normalized = TRUE)) > 100,]
#voom transformation
exprMat <- limma::voom(counts(ddsSub), lib.size = ddsSub$sizeFactor)$E
ddsSub.voom <- ddsSub
assay(ddsSub.voom) <- exprMat
rnaMat <- exprMat
rownames(rnaMat) <- rowData(ddsSub.voom)$symbol
# Prefiltering
overSampe <- intersect(names(yVec), colnames(rnaMat))
designMat <- model.matrix(~ yVec[overSampe])
fit <- lmFit(rnaMat[,overSampe], design = designMat)
fit2 <- eBayes(fit)
resTab <- topTable(fit2, number = Inf) %>% data.frame() %>% rownames_to_column("ID")
keepRna <- filter(resTab, adj.P.Val < 0.05)$ID
rnaMat <- t(rnaMat[keepRna, ])
dim(rnaMat)[1] 46 792colnames(rnaMat) <- paste0(colnames(rnaMat),".rna")Genomic data
ighvMap <- c(M = 1, U=0)
methMap <- c(LP= 0, IP=0.5, HP=1 )
#genetics
genData <- filter(patMeta, Patient.ID %in% colnames(protCLL)) %>%
select(-HIPO.ID, -project, -date.of.diagnosis, -treatment, -date.of.first.treatment,
-gender, -diagnosis) %>%
dplyr::rename(IGHV = IGHV.status, MClust= Methylation_Cluster) %>%
mutate_at(vars(-Patient.ID), as.character) %>%
mutate(IGHV = ighvMap[IGHV], MClust = methMap[MClust]) %>%
mutate_at(vars(-Patient.ID), as.numeric) %>%
data.frame() %>% column_to_rownames("Patient.ID")
#remove gene with higher than 40% missing values
genData <- genData[,colSums(is.na(genData))/nrow(genData) <= 0.4]
#remove genes with less than 5 mutated cases
genData <- genData[,colSums(genData, na.rm = TRUE) >= 5]
#fill the missing value with majority
genData <- apply(genData, 2, function(x) {
xVec <- x
avgVal <- mean(x,na.rm= TRUE)
if (avgVal >= 0.5) {
xVec[is.na(xVec)] <- 1
} else xVec[is.na(xVec)] <- 0
xVec
})Drug responses
#choose the first sample
viabMat <- arrange(pheno1000_main, screenDate) %>%
filter(diagnosis == "CLL", patientID %in% colnames(protCLL)) %>%
distinct(patientID, Drug, concIndex, .keep_all = TRUE) %>%
filter(! Drug %in% c("DMSO","PBS")) %>%
mutate(id = paste0(Drug,"_",concIndex)) %>%
select(patientID, id, normVal.adj.sigm) %>%
spread(key = patientID, value = normVal.adj.sigm) %>%
data.frame() %>% column_to_rownames("id") %>%
as.matrix() %>% t()Feature selection with LASSO penalty
#Functions for running glm
runGlm <- function(X, y, method = "ridge", repeats=20, folds = 3, lambda = "lambda.1se") {
modelList <- list()
lambdaList <- c()
varExplain <- c()
coefMat <- matrix(NA, ncol(X), repeats)
rownames(coefMat) <- colnames(X)
if (method == "lasso"){
alpha = 1
} else if (method == "ridge") {
alpha = 0
}
for (i in seq(repeats)) {
if (ncol(X) > 2) {
res <- cv.glmnet(X,y, type.measure = "mse", family="gaussian",
nfolds = folds, alpha = alpha, standardize = FALSE)
lambdaList <- c(lambdaList, res[[lambda]])
modelList[[i]] <- res
coefModel <- coef(res, s = lambda)[-1] #remove intercept row
coefMat[,i] <- coefModel
#calculate variance explained
y.pred <- predict(res, s = lambda, newx = X)
varExp <- cor(as.vector(y),as.vector(y.pred))^2
varExplain[i] <- ifelse(is.na(varExp), 0, varExp)
} else {
fitlm<-lm(y~., data.frame(X))
varExp <- summary(fitlm)$r.squared
varExplain <- c(varExplain, varExp)
}
}
list(modelList = modelList, lambdaList = lambdaList, varExplain = varExplain, coefMat = coefMat)
}#function for scaling predictors
dataScale <- function(x, censor = NULL, robust = FALSE) {
#function to scale different variables
if (length(unique(na.omit(x))) <=3){
#a binary variable, change to -0.5 and 0.5 for 1 and 2
x - 0.5
} else {
if (robust) {
#continuous variable, centered by median and divied by 2*mad
mScore <- (x-median(x,na.rm=TRUE))/mad(x,na.rm=TRUE)
if (!is.null(censor)) {
mScore[mScore > censor] <- censor
mScore[mScore < -censor] <- -censor
}
mScore/2
} else {
mScore <- (x-mean(x,na.rm=TRUE))/(sd(x,na.rm=TRUE))
if (!is.null(censor)) {
mScore[mScore > censor] <- censor
mScore[mScore < -censor] <- -censor
}
mScore/2
}
}
}Clean and integrate multi-omics data
Variance explained for STAT2 expression by multi-omics datasets
Based on this plot, genetics alone alreay explains STAT2 expression quite well. Other datasets do not add much information
Heatmap of selected features
Genetics only
STAT2 protein expression stratified by IGHV and trisomy12
Call:
lm(formula = STAT2 ~ IGHV * trisomy12, data = plotTab)
Residuals:
Min 1Q Median 3Q Max
-0.51109 -0.15429 -0.00532 0.11960 0.74576
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 15.66963 0.07988 196.174 < 2e-16 ***
IGHVU 0.64067 0.11550 5.547 1.46e-06 ***
trisomy12wt -0.40129 0.11077 -3.623 0.000738 ***
IGHVU:trisomy12wt -0.43619 0.15849 -2.752 0.008504 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.2767 on 45 degrees of freedom
Multiple R-squared: 0.6733, Adjusted R-squared: 0.6515
F-statistic: 30.92 on 3 and 45 DF, p-value: 5.271e-11STAT2 protein expression is strongly affected by IGHV and trisomy12 status, U-CLLs with trisomy12 shows significant up-regulation of STAT2
STAT2 RNA expression stratified by IGHV and trisomy12
Samples in the proteomic cohort
Call:
lm(formula = STAT2 ~ IGHV * trisomy12, data = plotTab)
Residuals:
Min 1Q Median 3Q Max
-1.14476 -0.20254 0.05228 0.30685 0.88566
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 33.2115 0.1351 245.761 < 2e-16 ***
IGHVU 0.4575 0.2004 2.283 0.02758 *
trisomy12wt -0.5774 0.1874 -3.081 0.00363 **
IGHVU:trisomy12wt -0.7149 0.2774 -2.577 0.01357 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.4681 on 42 degrees of freedom
Multiple R-squared: 0.5422, Adjusted R-squared: 0.5095
F-statistic: 16.58 on 3 and 42 DF, p-value: 2.951e-07Similar trend can be oberserved in RNAseq data, although not as significant as protein expression
Samples in the whole CLL RNAseq cohort
Call:
lm(formula = STAT2 ~ IGHV * trisomy12, data = plotTab)
Residuals:
Min 1Q Median 3Q Max
-1.12608 -0.26268 0.02179 0.25784 1.09138
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 14.2919 0.1057 135.197 < 2e-16 ***
IGHVU 0.3691 0.1561 2.364 0.019117 *
trisomy12wt -0.6559 0.1126 -5.823 2.47e-08 ***
IGHVU:trisomy12wt -0.5616 0.1671 -3.362 0.000939 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.3811 on 188 degrees of freedom
Multiple R-squared: 0.4182, Adjusted R-squared: 0.4089
F-statistic: 45.05 on 3 and 188 DF, p-value: < 2.2e-16RNA only
Protein only
Drug responses only
Combined
Plot all heatmaps
Pathway enrichment for RNA expressions correlated with STAT2 protein expression
Prepare data
#subset
ddsSub <- dds[,dds$PatID %in% names(yVec)]
#only keep protein coding genes with symbol
ddsSub <- ddsSub[rowData(ddsSub)$biotype %in% "protein_coding" & !rowData(ddsSub)$symbol %in% c("",NA),]
#remove lowly expressed genes
ddsSub <- ddsSub[rowSums(counts(ddsSub, normalized = TRUE)) > 100,]
#voom transformation
exprMat <- limma::voom(counts(ddsSub), lib.size = ddsSub$sizeFactor)$E
ddsSub.voom <- ddsSub
assay(ddsSub.voom) <- exprMat
rnaMat <- exprMat
rownames(rnaMat) <- rowData(ddsSub.voom)$symbol
overSampe <- intersect(names(yVec), colnames(rnaMat))
rnaMat <- rnaMat[,overSampe]
yVec <- yVec[overSampe]Test
no blocking for IGHV or trisomy12
blocking for IGHV and trisomy12
The pathways are similar as for no blocking
Pathway enrichment for protein expressions correlated with STAT2 protein level
Test
no blocking
blocking for IGHV and trisomy12
Not many significant results when blocking for IGHV, suggesting most associations could be coufounded by IGHV and trisomy12
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] DESeq2_1.26.0 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_3.0.3 ggplot2_3.3.2
[11] tidyverse_1.3.0 SummarizedExperiment_1.16.1
[13] DelayedArray_0.12.3 BiocParallel_1.20.1
[15] matrixStats_0.56.0 Biobase_2.46.0
[17] GenomicRanges_1.38.0 GenomeInfoDb_1.22.1
[19] IRanges_2.20.2 S4Vectors_0.24.4
[21] BiocGenerics_0.32.0 glmnet_4.0-2
[23] Matrix_1.2-18 gtable_0.3.0
[25] limma_3.42.2 jyluMisc_0.1.5
[27] pheatmap_1.0.12 piano_2.2.0
[29] cowplot_1.0.0
loaded via a namespace (and not attached):
[1] shinydashboard_0.7.1 tidyselect_1.1.0 RSQLite_2.2.0
[4] AnnotationDbi_1.48.0 htmlwidgets_1.5.1 grid_3.6.0
[7] maxstat_0.7-25 munsell_0.5.0 codetools_0.2-16
[10] DT_0.14 withr_2.2.0 colorspace_1.4-1
[13] knitr_1.29 rstudioapi_0.11 ggsignif_0.6.0
[16] labeling_0.3 git2r_0.27.1 slam_0.1-47
[19] GenomeInfoDbData_1.2.2 KMsurv_0.1-5 farver_2.0.3
[22] bit64_0.9-7 rprojroot_1.3-2 vctrs_0.3.1
[25] generics_0.0.2 TH.data_1.0-10 xfun_0.15
[28] sets_1.0-18 R6_2.4.1 locfit_1.5-9.4
[31] bitops_1.0-6 fgsea_1.12.0 assertthat_0.2.1
[34] promises_1.1.1 scales_1.1.1 multcomp_1.4-13
[37] nnet_7.3-14 sandwich_2.5-1 workflowr_1.6.2
[40] rlang_0.4.7 genefilter_1.68.0 splines_3.6.0
[43] rstatix_0.6.0 acepack_1.4.1 broom_0.7.0
[46] checkmate_2.0.0 yaml_2.2.1 abind_1.4-5
[49] modelr_0.1.8 backports_1.1.8 httpuv_1.5.4
[52] Hmisc_4.4-0 tools_3.6.0 relations_0.6-9
[55] ellipsis_0.3.1 gplots_3.0.4 RColorBrewer_1.1-2
[58] Rcpp_1.0.5 base64enc_0.1-3 visNetwork_2.0.9
[61] zlibbioc_1.32.0 RCurl_1.98-1.2 ggpubr_0.4.0
[64] rpart_4.1-15 zoo_1.8-8 haven_2.3.1
[67] cluster_2.1.0 exactRankTests_0.8-31 fs_1.4.2
[70] magrittr_1.5 data.table_1.12.8 openxlsx_4.1.5
[73] reprex_0.3.0 survminer_0.4.7 mvtnorm_1.1-1
[76] hms_0.5.3 shinyjs_1.1 mime_0.9
[79] evaluate_0.14 xtable_1.8-4 XML_3.98-1.20
[82] rio_0.5.16 jpeg_0.1-8.1 readxl_1.3.1
[85] gridExtra_2.3 shape_1.4.4 compiler_3.6.0
[88] KernSmooth_2.23-17 crayon_1.3.4 htmltools_0.5.0
[91] later_1.1.0.1 Formula_1.2-3 geneplotter_1.64.0
[94] lubridate_1.7.9 DBI_1.1.0 dbplyr_1.4.4
[97] MASS_7.3-51.6 car_3.0-8 cli_2.0.2
[100] marray_1.64.0 gdata_2.18.0 igraph_1.2.5
[103] pkgconfig_2.0.3 km.ci_0.5-2 foreign_0.8-71
[106] xml2_1.3.2 foreach_1.5.0 annotate_1.64.0
[109] XVector_0.26.0 drc_3.0-1 rvest_0.3.5
[112] digest_0.6.25 rmarkdown_2.3 cellranger_1.1.0
[115] fastmatch_1.1-0 survMisc_0.5.5 htmlTable_2.0.1
[118] curl_4.3 shiny_1.5.0 gtools_3.8.2
[121] lifecycle_0.2.0 jsonlite_1.7.0 carData_3.0-4
[124] fansi_0.4.1 pillar_1.4.6 lattice_0.20-41
[127] fastmap_1.0.1 httr_1.4.1 plotrix_3.7-8
[130] survival_3.2-3 glue_1.4.1 zip_2.0.4
[133] png_0.1-7 iterators_1.0.12 bit_1.1-15.2
[136] stringi_1.4.6 blob_1.2.1 latticeExtra_0.6-29
[139] caTools_1.18.0 memoise_1.1.0