Last updated: 2020-10-03
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Overview of differentially expressed proteins
A table of associations with 10% FDR
resList <- filter(resList, Gene == "SF3B1") %>%
mutate(adj.P.Val = adj.P.IHW) %>% #use IHW corrected P-value
mutate(Chr = rowData(protCLL[id,])$chromosome_name)
resList %>% filter(adj.P.Val <= 0.1) %>%
select(name, Chr,logFC, P.Value, adj.P.Val) %>%
mutate_if(is.numeric, formatC, digits=2) %>%
DT::datatable()
Heatmap of differentially expressed proteins (10% FDR)
proList <- filter(resList, !is.na(name), adj.P.Val < 0.1) %>% distinct(name, .keep_all = TRUE) %>% pull(id)
plotMat <- assays(protCLL)[["QRILC"]][proList,]
rownames(plotMat) <- rowData(protCLL[proList,])$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 = FALSE, show_colnames = FALSE,
treeheight_row = 0)
Volcano plot
plotTab <- resList
nameList <- c("SUGP1","MSH6")
sf3b1Volcano <- plotVolcano(plotTab, fdrCut =0.1, x_lab="log2FoldChange", posCol = colList[1], negCol = colList[2],
plotTitle = "SF3B1 (Mutants versus WT)", ifLabel = TRUE, labelList = nameList)
Boxplot plot of selected genes
nameList <- c("SUGP1","MSH6")
protTab <- sumToTidy(protCLL, rowID = "uniprotID", colID = "patID")
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)
Compare with RNA sequencing data
Differentially expressed genes related to IGHV
Prepare RNA sequencing data
dds$diag <- patMeta[match(dds$PatID, patMeta$Patient.ID),]$diagnosis
dds$trisomy12 <- patMeta[match(dds$PatID, patMeta$Patient.ID),]$trisomy12
dds$IGHV <- patMeta[match(dds$PatID, patMeta$Patient.ID),]$IGHV.status
dds$SF3B1 <- patMeta[match(dds$PatID, patMeta$Patient.ID),]$SF3B1
ddsCLL <- dds[rownames(dds) %in% rowData(protCLL)$ensembl_gene_id, dds$diag %in% "CLL" & !is.na(dds$IGHV) & !is.na(dds$trisomy12) & !is.na(dds$SF3B1)]
ddsCLL.vst <- varianceStabilizingTransformation(ddsCLL)Differential expression
design(ddsCLL) <- ~ trisomy12 + IGHV + SF3B1
deRes <- DESeq(ddsCLL)
resTab <- results(deRes, contrast = c("SF3B1",1,0), tidy = TRUE) %>%
mutate(name = rowData(ddsCLL[row,])$symbol) %>%
dplyr::rename(P.Value = pvalue)
Boxplot of selected genes
plotTab <- assay(ddsCLL.vst[match(nameList, rowData(ddsCLL.vst)$symbol),]) %>%
data.frame() %>% rownames_to_column("id") %>%
mutate(name = rowData(ddsCLL.vst[id,])$symbol) %>%
gather(key = "patID", value = "count", -id, -name) %>%
mutate(SF3B1 = patMeta[match(patID, patMeta$Patient.ID),]$SF3B1) %>%
mutate(status = ifelse(SF3B1 %in% 1,"Mutated","WT"))%>%
mutate(status = factor(status, levels = c("WT","Mutated")))
pList <- plotBox(plotTab, pValTabel = resTab, y_lab = "RNA expression")
cowplot::plot_grid(plotlist= pList, ncol=2)
Correlations between RNA and protein expression
rnaMat <- assay(ddsCLL.vst)
protMat <- assays(protCLL)[["count"]]
rownames(protMat) <- rowData(protCLL)$ensembl_gene_id
overSample <- intersect(colnames(rnaMat), colnames(protMat))
rnaMat <- rnaMat[,overSample]
protMat <- protMat[,overSample]
plotList <- lapply(nameList, function(n) {
geneId <- rownames(ddsCLL.vst)[match(n, rowData(ddsCLL.vst)$symbol)]
stopifnot(length(geneId) ==1)
plotTab <- tibble(x=rnaMat[geneId,],y=protMat[geneId,])
coef <- cor(plotTab$x, plotTab$y, use="pairwise.complete")
annoPos <- ifelse (coef > 0, "left","right")
plotCorScatter(plotTab, "x","y", showR2 = FALSE, annoPos = annoPos, x_lab = "RNA expression",
y_lab ="Protein expression", title = n,dotCol = colList[4], textCol = colList[1])
})
cowplot::plot_grid(plotlist = plotList, ncol =2)
Differential splicing
Processing splicing dataset
library(DEXSeq)
load("~/CLLproject_jlu/var/dxdCLL_20190415.RData")
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)]
Differential exon usage test using DEXseq
dxdCLL.sub$sample <- droplevels(dxdCLL.sub$sample)
dxdCLL.sub$batch <- droplevels(dxdCLL.sub$batch)
dxdCLL.sub$condition <- dxdCLL.sub$SF3B1
formulaFullModel <- ~ sample + exon + condition:exon + IGHV:exon + trisomy12:exon + batch:exon
formulaReducedModel <- ~ sample + exon + IGHV:exon + trisomy12:exon + batch:exon
dxdCLL.sub <- estimateDispersions(dxdCLL.sub, formula = formulaFullModel)
dxdCLL.sub <- testForDEU(dxdCLL.sub, reducedModel = formulaReducedModel,
fullModel = formulaFullModel)
#save(dxdCLL.sub, file = "../output/dxdCLL.RData")testID <- c("ENSG00000105705","ENSG00000116062")
dxdCLL.sub2 <- dxdCLL[rowData(dxdCLL)$groupID %in% testID,
!is.na(dxdCLL$SF3B1) & !is.na(dxdCLL$trisomy12) & !is.na(dxdCLL$IGHV)]
dxdCLL.sub2$sample <- droplevels(dxdCLL.sub2$sample)
dxdCLL.sub2$batch <- droplevels(dxdCLL.sub2$batch)
dxdCLL.sub2$condition <- dxdCLL.sub2$SF3B1
formulaFullModel <- ~ sample + exon + condition:exon + IGHV:exon + trisomy12:exon + batch:exon
formulaReducedModel <- ~ sample + exon + IGHV:exon + trisomy12:exon + batch:exon
dxdCLL.sub2 <- estimateDispersions(dxdCLL.sub2, formula = formulaFullModel)
dxdCLL.sub2 <- testForDEU(dxdCLL.sub2, reducedModel = formulaReducedModel,
fullModel = formulaFullModel)
#save(dxdCLL.sub2, file = "../output/dxdCLL2.RData")library(DEXSeq)
#load results
load("../output/dxdCLL.RData")
load("../output/dxdCLL2.RData")resDxd1 <- DEXSeqResults(dxdCLL.sub)
resDxd2 <- DEXSeqResults(dxdCLL.sub2)
resTab <- bind_rows(data.frame(resDxd1), data.frame(resDxd2)) %>%
dplyr::filter(pvalue < 0.05) %>%
mutate(symbol = rowData(dds[groupID,])$symbol)
resTab[,c("symbol", "featureID", "groupID", "pvalue", "padj")] symbol featureID groupID pvalue padj
1 TPP2 E015 ENSG00000134900 2.847909e-02 1.000000e+00
2 PML E024 ENSG00000140464 9.856622e-03 1.000000e+00
3 NT5DC1 E016 ENSG00000178425 7.704909e-03 1.000000e+00
4 SUGP1 E013 ENSG00000105705 1.281459e-14 3.139574e-13
5 SUGP1 E025 ENSG00000105705 9.745709e-25 4.775397e-23
6 MSH6 E001 ENSG00000116062 2.021998e-05 3.302597e-04Two genes pass 10% FDR, SUGP1 and MSH6
Plot exon usage
SUGP1 (ENSG00000105705)
plotDEXSeq(resDxd2, "ENSG00000105705", displayTranscripts = TRUE, legend = FALSE, norCounts = TRUE, expression = FALSE)
MSH6 (ENSG00000116062)
plotDEXSeq(resDxd2, "ENSG00000116062", displayTranscripts = TRUE, legend = FALSE, norCounts = TRUE, expression = FALSE)
Validation on peptide level
load("../output/pepCLL_lumos_enc.RData")stratifier <- "SF3B1"
plotList <- lapply(nameList, function(n) {
mutStatus <- as.character(patMeta[match(colnames(pepCLL), patMeta$Patient.ID),][[stratifier]])
names(mutStatus) <- colnames(pepCLL)
plotPep(pepCLL, n, type = "count", stratifier = stratifier, mutStatus = mutStatus)
})
cowplot::plot_grid(plotlist = plotList, ncol=1)
Validation using timsTOF data
Load timsTOF data
load("../output/proteomic_timsTOF_enc.RData")
load("../output/deResList_timsTOF.RData")resList <- dplyr::filter(resList, Gene == "SF3B1") %>%
mutate(adj.P.Val = adj.P.IHW) %>% #use IHW corrected P-value
mutate(Chr = rowData(protCLL[id,])$chromosome_name)protTab <- sumToTidy(protCLL, rowID = "uniprotID", colID = "patID")
plotTab <- protTab %>% filter(hgnc_symbol %in% nameList) %>%
mutate(SF3B1 = patMeta[match(patID, patMeta$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)
cowplot::plot_grid(plotlist= pList, ncol=2)
