Section 8: Identifying proteomic biomarkers drug responses and clinical outcomes
Junyan Lu
2020-10-09
Last updated: 2021-03-15
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Load packages and datasets
knitr::opts_chunk$set(echo = TRUE, warning = FALSE, message = FALSE, dev = c("png","pdf"))
library(limma)
library(pheatmap)
library(jyluMisc)
library(survival)
library(survminer)
library(maxstat)
library(glmnet)
library(SummarizedExperiment)
library(cowplot)
library(tidyverse)
load("../data/patMeta_enc.RData")
load("../data/ddsrna_enc.RData")
load("../data/proteomic_explore_enc.RData")
load("../output/deResList.RData") #precalculated differential expression
load("../data/survival_enc.RData")
load("../data/screenData_enc.RData")
#protCLL <- protCLL[rowData(protCLL)$uniqueMap,]
source("../code/utils.R")Correlations between protein abundance and drug response
Preprocess datasets
Drug screening data from 1000CPS: low quality samples after QC are removed; the edge effect corrected viability values are used
viabMat <- screenData %>% filter(!lowQuality, ! Drug %in% c("DMSO","PBS"), patientID %in% colnames(protCLL)) %>%
group_by(patientID, Drug) %>% summarise(viab = mean(normVal.adj.cor_auc)) %>%
spread(key = patientID, value = viab) %>%
data.frame(stringsAsFactors = FALSE) %>% column_to_rownames("Drug") %>%
as.matrix()Proteomics data
proMat <- assays(protCLL)[["count"]]
proMat <- proMat[,colnames(viabMat)]
#Remove proteins without much variance (to lower multi-testing burden)
#sds <- genefilter::rowSds(proMat,na.rm=TRUE)
#proMat <- proMat[sds > genefilter::shorth(sds),]How many samples have both proteomics data and CPS1000 screen data
ncol(proMat)[1] 82Association test
resTab.auc <- lapply(rownames(viabMat),function(drugName) {
viab <- viabMat[drugName, ]
batch <- protCLL[,colnames(viabMat)]$batch
designMat <- model.matrix(~1+viab+batch)
fit <- lmFit(proMat, designMat)
fit2 <- eBayes(fit)
corRes <- topTable(fit2, number ="all", adjust.method = "BH", coef = "viab") %>% rownames_to_column("id") %>%
mutate(symbol = rowData(protCLL[id,])$hgnc_symbol, Drug = drugName)
}) %>% bind_rows() %>% mutate(adj.P.Val = p.adjust(P.Value, method = "BH")) %>% arrange(P.Value)Associations test with blocking for WBC counts
resTab.block <- lapply(rownames(viabMat),function(drugName) {
viab <- viabMat[drugName, ]
batch <- protCLL[,colnames(viabMat)]$batch
leukCount <- log10(sampleTab[match(colnames(viabMat), sampleTab$encID),]$leukCount)
designMat <- model.matrix(~1+viab+batch+leukCount)
fit <- lmFit(proMat, designMat)
fit2 <- eBayes(fit)
corRes <- topTable(fit2, number ="all", adjust.method = "BH", coef = "viab") %>% rownames_to_column("id") %>%
mutate(symbol = rowData(protCLL[id,])$hgnc_symbol, Drug = drugName)
}) %>% bind_rows() %>% mutate(adj.P.Val = p.adjust(P.Value, method = "BH")) %>% arrange(P.Value)Compare results with and without blocking for WBC
tabNoBlock <- resTab.auc %>% mutate(ifSig = adj.P.Val <= 0.05) %>%
select(id, Drug, P.Value, ifSig)
tabBlock <- resTab.block %>% mutate(ifSig.block = adj.P.Val <= 0.05, P.Value.block = P.Value) %>%
select(id, Drug, P.Value.block, ifSig.block)
compareTab <- left_join(tabNoBlock, tabBlock, by = c("id","Drug")) %>%
mutate(group = case_when(
ifSig & ifSig.block ~ "significant in both",
ifSig & !ifSig.block ~ "significant only without blocking",
!ifSig & ifSig.block ~ "significant only with blocking",
TRUE ~ "not significant in both"
))
ggplot(compareTab, aes(x=-log10(P.Value), y=-log10(P.Value.block), col = group)) +
geom_point(alpha=0.5) +
xlab("-log10(P value) without blocking") + ylab("-log10 (P value) with blocking for WBC counts") +
scale_color_manual(values = c("grey80", colList[2], colList[1], colList[3]), name = "") +
geom_abline(slope = 1, linetype ="dashed") +
ggtitle("Associations with drug responses") +
theme_full +
theme(legend.position = c(0.7,0.2))
Number of associations in each catagory
tt <- table(compareTab$group)
tt
not significant in both significant in both
208175 524
significant only with blocking significant only without blocking
38 45 A percentage of consistent results
(tt[1] + tt[2])/sum(tt)*100not significant in both
99.96025 Bar plot to show the number of associations (10% FDR)
#Select significant associations (10% FDR)
resTab.sig <- filter(resTab.auc, adj.P.Val <= 0.05) %>%
select(Drug, symbol, id,logFC, P.Value, adj.P.Val)
plotTab <- resTab.sig %>% group_by(Drug) %>%
summarise(n = length(id)) %>% ungroup()
ordTab <- group_by(plotTab, Drug) %>% summarise(total = sum(n)) %>%
arrange(desc(total))
plotTab <- mutate(plotTab, Drug = factor(Drug, levels = ordTab$Drug)) %>%
filter(n>0)
drugBar <- ggplot(plotTab, aes(x=Drug, y = n)) + geom_bar(stat="identity",fill=colList[4]) +
geom_text(aes(label = paste0("n=", n)),vjust=-1,col=colList[1]) +
ylim(0,200)+ annotate("text", label = "Number of associations (10% FDR)", x=Inf, y=Inf,hjust=1, vjust=1, size=6)+
theme_half + theme(axis.text.x = element_text(angle = 90, hjust=1, vjust=0.5)) +
ylab("Number") + xlab("")
drugBar
Table of significant associations (10% FDR)
resTab.sig %>% mutate_if(is.numeric, formatC, digits=2, format= "e") %>%
DT::datatable()Correlation plot of selected candidates
plotDrugCorScatter <- function(inputTab, x, y, x_lab = "X", y_lab = "Y", title = "",
col = NULL, showR2 = TRUE, annoPos = "right",
dotCol = colList, textCol="darkred") {
#prepare table for plotting
plotTab <- tibble(x = inputTab[[x]],y=inputTab[[y]])
if (!is.null(col)) plotTab <- mutate(plotTab, status = inputTab[[col]])
plotTab <- filter(plotTab, !is.na(x), !is.na(y))
#prepare annotation values
corRes <- cor.test(plotTab$x, plotTab$y)
pval <- formatNum(corRes$p.value, digits = 1, format = "e")
Rval <- formatNum(corRes$estimate, digits = 1, format = "e")
R2val <- formatNum(corRes$estimate^2, digits = 1, format = "e")
Nval <- nrow(plotTab)
annoP <- bquote(italic("P")~"="~.(pval))
if (showR2) {
annoCoef <- bquote(R^2~"="~.(R2val))
} else {
annoCoef <- bquote(R~"="~.(Rval))
}
annoN <- bquote(N~"="~.(Nval))
corPlot <- ggplot(plotTab, aes(x = x, y = y))
if (!is.null(col)) {
corPlot <- corPlot + geom_point(aes(fill = status), shape =21, size =3) +
scale_fill_manual(values = dotCol)
} else {
corPlot <- corPlot + geom_point(fill = dotCol[1], shape =21, size=3)
}
corPlot <- corPlot + geom_smooth(formula = y~x,method = "lm", se=FALSE, color = "grey50", linetype ="dashed" )
if (annoPos == "right") {
corPlot <- corPlot + annotate("text", x = max(plotTab$x), y = Inf, label = annoN,
hjust=1, vjust =2, size = 5, parse = FALSE, col= textCol) +
annotate("text", x = max(plotTab$x), y = Inf, label = annoP,
hjust=1, vjust =4, size = 5, parse = FALSE, col= textCol) +
annotate("text", x = max(plotTab$x), y = Inf, label = annoCoef,
hjust=1, vjust =6, size = 5, parse = FALSE, col= textCol)
} else if (annoPos== "left") {
corPlot <- corPlot + annotate("text", x = min(plotTab$x), y = Inf, label = annoN,
hjust=0, vjust =2, size = 5, parse = FALSE, col= textCol) +
annotate("text", x = min(plotTab$x), y = Inf, label = annoP,
hjust=0, vjust =4, size = 5, parse = FALSE, col= textCol) +
annotate("text", x = min(plotTab$x), y = Inf, label = annoCoef,
hjust=0, vjust =6, size = 5, parse = FALSE, col= textCol)
}
corPlot <- corPlot + ylab(y_lab) + xlab(x_lab) + ggtitle(title) +
scale_y_continuous(labels = scales::number_format(accuracy = 0.1)) +
scale_x_continuous(labels = scales::number_format(accuracy = 0.1)) +
theme_full
corPlot
}proMat.combat <- assays(protCLL)[["count_combat"]]
proMat.combat <- proMat.combat[,colnames(viabMat)]
pairList <- list(c("Cobimetinib","STAT2"),c("Venetoclax","PGD"),c("Idelalisib", "HEBP2"))
plotList <- lapply(pairList, function(pair) {
textCol <- "darkred"
drugName <- pair[1]
proteinName <- pair[2]
id <- rownames(protCLL)[match(proteinName, rowData(protCLL)$hgnc_symbol)]
plotTab <- tibble(patID = colnames(viabMat),
viab = viabMat[drugName,],
expr = proMat.combat[id,]) %>%
mutate(IGHV = protCLL[,patID]$IGHV.status,
trisomy12 = protCLL[,patID]$trisomy12) %>%
mutate(trisomy12 = ifelse(trisomy12 ==1,"yes","no")) %>%
filter(!is.na(viab),!is.na(expr))
pval <- formatNum(filter(resTab.sig, Drug == drugName, symbol == proteinName)$P.Value, digits = 1, format = "e")
Rval <- sprintf("%1.2f",cor(plotTab$viab, plotTab$expr))
Nval <- nrow(plotTab)
annoP <- bquote(italic("P")~"="~.(pval))
annoN <- bquote(N~"="~.(Nval))
annoCoef <- bquote(R~"="~.(Rval))
corPlot <- ggplot(plotTab, aes(x = viab, y = expr)) +
geom_point(aes(col = trisomy12, shape = IGHV), size=5) +
scale_shape_manual(values = c(M = 19, U = 1)) +
scale_color_manual(values = c(yes = colList[2], no = colList[3])) +
geom_smooth(formula = y~x,method = "lm", se=FALSE, color = "grey50", linetype ="dashed" ) +
ggtitle(sprintf("%s ~ %s", drugName, proteinName)) +
ylab("Protein expression") + xlab("Viability after treatment") +
theme_full +
theme(legend.position = "bottom")
if (Rval < 0) annoPos <- "right" else annoPos <- "left"
if (annoPos == "right") {
corPlot <- corPlot + annotate("text", x = max(plotTab$viab), y = Inf, label = annoN,
hjust=1, vjust =2, size = 5, parse = FALSE, col= textCol) +
annotate("text", x = max(plotTab$viab), y = Inf, label = annoP,
hjust=1, vjust =4, size = 5, parse = FALSE, col= textCol) +
annotate("text", x = max(plotTab$viab), y = Inf, label = annoCoef,
hjust=1, vjust =6, size = 5, parse = FALSE, col= textCol)
} else if (annoPos== "left") {
corPlot <- corPlot + annotate("text", x = min(plotTab$viab), y = Inf, label = annoN,
hjust=0, vjust =2, size = 5, parse = FALSE, col= textCol) +
annotate("text", x = min(plotTab$viab), y = Inf, label = annoP,
hjust=0, vjust =4, size = 5, parse = FALSE, col= textCol) +
annotate("text", x = min(plotTab$viab), y = Inf, label = annoCoef,
hjust=0, vjust =6, size = 5, parse = FALSE, col= textCol)
}
corPlot <- corPlot + ylab("Protein expression") + xlab("Viability after treatment") +
scale_y_continuous(labels = scales::number_format(accuracy = 0.1)) +
scale_x_continuous(labels = scales::number_format(accuracy = 0.1))
corPlot
})
drugCor <- cowplot::plot_grid(plotlist = plotList, ncol =3)
drugCor
Association test with blocking for IGHV and trisomy12
testList <- filter(resTab.auc, adj.P.Val <= 0.05)
resTab.auc.block <- lapply(seq(nrow(testList)),function(i) {
pair <- testList[i,]
expr <- proMat[pair$id,]
viab <- viabMat[pair$Drug, ]
ighv <- protCLL[,colnames(viabMat)]$IGHV.status
tri12 <- protCLL[,colnames(viabMat)]$trisomy12
batch <- protCLL[,colnames(viabMat)]$batch
res <- anova(lm(viab~ighv+tri12+batch+expr))
data.frame(id = pair$id, P.Value = res["expr",]$`Pr(>F)`, symbol = pair$symbol,
Drug = pair$Drug,
P.Value.IGHV = res["ighv",]$`Pr(>F)`,P.Value.trisomy12 = res["tri12",]$`Pr(>F)`,
P.Value.noBlock = pair$P.Value,
stringsAsFactors = FALSE)
}) %>% bind_rows() %>% mutate(adj.P.Val = p.adjust(P.Value, method = "BH")) %>% arrange(P.Value)Assocations that are still significant
resTab.sig %>% mutate_if(is.numeric, formatC, digits=2, format= "e") %>%
DT::datatable()The above mentioned pairs are still significant.
Variance explained
Test whether the protein expression can explain additional variance in drug response compared to genetic alone
Prepare genomics
geneMat <- patMeta[match(colnames(proMat), patMeta$Patient.ID),] %>%
select(Patient.ID, IGHV.status, del11q: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
mutate_all(replace_na,0) %>%
data.frame() %>% column_to_rownames("Patient.ID")
geneMat <- geneMat[,apply(geneMat,2, function(x) sum(x %in% 1, na.rm = TRUE))>=5] %>% as.matrix()Genes that will be included in the multivariate model
colnames(geneMat) [1] "IGHV.status" "del11q" "del13q" "del17p" "trisomy12"
[6] "trisomy19" "ATM" "BRAF" "DDX3X" "EGR2"
[11] "NOTCH1" "SF3B1" "TP53" compareR2 <- function(Drug, protName, geneMat) {
viab <- viabMat[Drug,]
protID <- unique(filter(resTab.sig, symbol == protName)$id)
expr <- proMat[protID,]
tabGene <- data.frame(geneMat)
tabGene[["viab"]] <- viab
tabCom <- tabGene
tabCom[[protName]] <- expr
r2Prot <- summary(lm(viab~expr))$r.squared
r2Gene <- summary(lm(viab~., data=tabGene))$r.squared
r2Com <- summary(lm(viab~., data=tabCom))$r.squared
plotTab <- tibble(model = c("genetics",paste0(protName, " expression"),sprintf("genetics + \n%s expression",protName)),
R2 = c(r2Gene, r2Prot, r2Com))
ggplot(plotTab, aes(x=model, y = R2)) + geom_bar(aes(fill = model),stat="identity", width=0.6) + coord_flip() +
ggtitle(Drug) + ylab("Variance expalained (R2)") + xlab("") +
scale_fill_manual(values = colList[4:6]) + theme_full +theme(legend.position = "none")
}plotList <- lapply(pairList, function(p) {
compareR2(p[1],p[2],geneMat)
})
plot_grid(plotlist= plotList, ncol= 1)
Based on the plot of R2 values, including protein expression in multi-variate model could explain additional variance in drug responses compared to genetic alone. For Venetoclax, using a single protein expression value of PGD already explain more variance than genetics.
stat2Bar <- plotList[[1]]
stat2Bar
Protein markers for clincial outcomes
Uni-variate model to identify proteins associated with outcomes
protCLL.sub <- protCLL[!rowData(protCLL)$chromosome_name %in% c("X","Y"),]
protMat <- assays(protCLL.sub)[["count_combat"]]
survTab <- survT %>%
select(patID, OS, died, TTT, treatedAfter, age,sex) %>%
dplyr::rename(patientID = patID) %>%
filter(patientID %in% colnames(protMat))TTT events
table(survTab$treatedAfter)
FALSE TRUE
44 46 OS events
table(survTab$died)
FALSE TRUE
80 11 uniRes.ttt <- lapply(rownames(protMat), function(n) {
testTab <- mutate(survTab, expr = protMat[n, patientID])
com(testTab$expr, testTab$TTT, testTab$treatedAfter, TRUE) %>%
mutate(id = n)
}) %>% bind_rows() %>% mutate(p.adj = p.adjust(p, method = "BH")) %>%
arrange(p) %>% mutate(name = rowData(protCLL[id,])$hgnc_symbol) %>%
mutate(outcome = "TTT")
uniRes.os <- lapply(rownames(protMat), function(n) {
testTab <- mutate(survTab, expr = protMat[n, patientID])
com(testTab$expr, testTab$OS, testTab$died, TRUE) %>%
mutate(id = n)
}) %>% bind_rows() %>% mutate(p.adj = p.adjust(p, method = "BH")) %>%
arrange(p) %>% mutate(name = rowData(protCLL[id,])$hgnc_symbol) %>%
mutate(outcome = "OS")
uniRes <- bind_rows(uniRes.ttt, uniRes.os) %>%
mutate(p.adj = p.adjust(p, method = "BH"))A table showing significant associations
uniRes %>% filter(p.adj <= 0.05) %>% mutate_if(is.numeric, formatC, digits=2,format="e") %>%
select(name, p, HR, p.adj, outcome) %>% DT::datatable()Blocking for WBC count using multi-variate model
Multi-variate test
uniRes.ttt.block <- lapply(rownames(protMat), function(n) {
riskTab <- select(survTab, patientID) %>%
mutate(leukCount = log10(sampleTab[match(patientID, sampleTab$encID),]$leukCount)) %>%
mutate(protExpr = protMat[n,patientID])
fullModel <- runCox(survTab, riskTab, "TTT","treatedAfter")
res <- summary(fullModel)
tibble(id = n, p = res$coefficients["protExpr",5],)
}) %>% bind_rows() %>% mutate(p.adj = p.adjust(p, method = "BH"))
uniRes.os.block <- lapply(rownames(protMat), function(n) {
riskTab <- select(survTab, patientID) %>%
mutate(leukCount = log10(sampleTab[match(patientID, sampleTab$encID),]$leukCount)) %>%
mutate(protExpr = protMat[n,patientID])
fullModel <- runCox(survTab, riskTab, "OS","died")
res <- summary(fullModel)
tibble(id = n, p = res$coefficients["protExpr",5],)
}) %>% bind_rows() %>% mutate(p.adj = p.adjust(p, method = "BH"))Compare results from blocking and non-blocking
TTT
tabNoBlock <- uniRes.ttt %>% mutate(ifSig = p.adj <= 0.05) %>%
select(id, p, ifSig)
tabBlock <- uniRes.ttt.block %>% mutate(ifSig.block = p.adj <= 0.05, p.block = p) %>%
select(id, p.block, ifSig.block)
compareTab <- left_join(tabNoBlock, tabBlock, by = "id") %>%
mutate(group = case_when(
ifSig & ifSig.block ~ "significant in both",
ifSig & !ifSig.block ~ "significant only without blocking",
!ifSig & ifSig.block ~ "significant only with blocking",
TRUE ~ "not significant in both"
))
ggplot(compareTab, aes(x=-log10(p), y=-log10(p.block), col = group)) +
geom_point(alpha=0.5) +
xlab("-log10(P value) without blocking") + ylab("-log10 (P value) with blocking for WBC counts") +
scale_color_manual(values = c("grey80", colList[2], colList[1], colList[3]), name = "") +
geom_abline(slope = 1, linetype ="dashed") +
ggtitle("Associations with TTT") +
theme_full +
theme(legend.position = c(0.7,0.2))
Number of associations in each catagory
tt <- table(compareTab$group)
tt
not significant in both significant in both
2859 189
significant only with blocking significant only without blocking
104 53 A percentage of consistent results
(tt[1] + tt[2])/sum(tt)*100not significant in both
95.1014 OS
tabNoBlock <- uniRes.os %>% mutate(ifSig = p.adj <= 0.05) %>%
select(id, p, ifSig)
tabBlock <- uniRes.os.block %>% mutate(ifSig.block = p.adj <= 0.05, p.block = p) %>%
select(id, p.block, ifSig.block)
compareTab <- left_join(tabNoBlock, tabBlock, by = "id") %>%
mutate(group = case_when(
ifSig & ifSig.block ~ "significant in both",
ifSig & !ifSig.block ~ "significant only without blocking",
!ifSig & ifSig.block ~ "significant only with blocking",
TRUE ~ "not significant in both"
))
ggplot(compareTab, aes(x=-log10(p), y=-log10(p.block), col = group)) +
geom_point(alpha=0.5) +
xlab("-log10(P value) without blocking") + ylab("-log10 (P value) with blocking for WBC counts") +
scale_color_manual(values = c("grey80", colList[2], colList[1], colList[3]), name = "") +
geom_abline(slope = 1, linetype ="dashed") +
ggtitle("Associations with OS") +
theme_full +
theme(legend.position = c(0.7,0.2))
Number of associations in each catagory
tt <- table(compareTab$group)
tt
not significant in both significant only without blocking
3204 1 A percentage of consistent results
(tt[1] + tt[2])/sum(tt)*100not significant in both
100 Selection protein markers independent of known risks using multi-vairate model
Prepare data
#table of known risks
riskTab <- select(survTab, patientID, age, sex) %>%
left_join(patMeta[,c("Patient.ID","IGHV.status","TP53","trisomy12","del17p")], by = c(patientID = "Patient.ID")) %>%
mutate(TP53 = as.numeric(as.character(TP53)),
del17p = as.numeric(as.character(del17p))) %>%
mutate(`TP53.del17p` = as.numeric(TP53 | del17p),
IGHV = factor(ifelse(IGHV.status %in% "U",1,0))) %>%
select(-TP53, -del17p,-IGHV.status) %>%
mutate(age = age/10) Multi-variate test
cTab.ttt <- lapply(filter(uniRes, outcome == "TTT", p.adj <=0.1)$id, function(n) {
risk0 <- riskTab
expr <- protMat[n,]
expr <- (expr - mean(expr,na.rm=TRUE))/sd(expr,na.rm = TRUE)
risk1 <- riskTab %>% mutate(protExpr = expr[patientID])
res0 <- summary(runCox(survTab, risk0, "TTT","treatedAfter"))
fullModel <- runCox(survTab, risk1, "TTT","treatedAfter")
res1 <- summary(fullModel)
tibble(id = n, c0 = res0$concordance[1], c1 = res1$concordance[1],
se0 = res0$concordance[2],se1 = res1$concordance[2],
ci0 = se0*1.96, ci1 = se1*1.96,
p = res1$coefficients["protExpr",5],
fullModel = list(fullModel))
}) %>% bind_rows() %>% mutate(diffC = c1-c0) %>%
arrange(desc(diffC)) %>%
mutate(name=rowData(protCLL[id,])$hgnc_symbol,
outcome = "TTT")
cTab.os <- lapply(filter(uniRes, outcome == "OS", p.adj<=0.1)$id, function(n) {
risk0 <- riskTab
expr <- protMat[n,]
expr <- (expr - mean(expr,na.rm=TRUE))/sd(expr,na.rm = TRUE)
risk1 <- riskTab %>% mutate(protExpr = expr[patientID])
res0 <- summary(runCox(survTab, risk0, "OS","died"))
fullModel <- runCox(survTab, risk1, "OS","died")
res1 <- summary(fullModel)
tibble(id = n, c0 = res0$concordance[1], c1 = res1$concordance[1],
se0 = res0$concordance[2],se1 = res1$concordance[2],
ci0 = se0*1.96, ci1 = se1*1.96,
p = res1$coefficients["protExpr",5],
fullModel = list(fullModel))
}) %>% bind_rows() %>% mutate(diffC = c1-c0) %>%
arrange(desc(diffC)) %>%
mutate(name=rowData(protCLL[id,])$hgnc_symbol,
outcome = "OS")
cTab <- bind_rows(cTab.ttt, cTab.os) %>%
mutate(p.adj = p.adjust(p, method = "BH")) %>%
arrange(p)A table showing idependent protein markers
cTab %>% filter(p.adj <= 0.05) %>% mutate_if(is.numeric, formatC, digits=2,format="e") %>%
select(name, p, p.adj, outcome) %>% DT::datatable()Forest plot of selected markers
plotHazard <- function(survRes, protName, title = "") {
sumTab <- summary(survRes)$coefficients
confTab <- summary(survRes)$conf.int
#correct feature name
nameOri <- rownames(sumTab)
nameMod <- substr(nameOri, 1, nchar(nameOri) -1)
plotTab <- tibble(feature = rownames(sumTab),
nameMod = substr(nameOri, 1, nchar(nameOri) -1),
HR = sumTab[,2],
p = sumTab[,5],
Upper = confTab[,4],
Lower = confTab[,3]) %>%
mutate(feature = ifelse(nameMod %in% names(survRes$xlevels), nameMod, feature)) %>%
mutate(feature = str_replace(feature, "[.]","/")) %>%
mutate(feature = str_replace(feature, "[_]","-")) %>%
mutate(candidate = ifelse(feature == "protExpr", "yes","no")) %>%
mutate(feature = ifelse(feature == "protExpr", protName, feature)) %>%
arrange(desc(abs(p))) %>% mutate(feature = factor(feature, levels = feature)) #%>%
#mutate(type = ifelse(HR >1 ,"up","down")) %>%
# mutate(Upper = ifelse(Upper > 10, 10, Upper))
p <- ggplot(plotTab, aes(x=feature, y = HR, color = candidate)) +
geom_hline(yintercept = 1, linetype = "dotted") +
geom_point(position = position_dodge(width=0.8), size=3) +
geom_errorbar(aes(ymin = Lower, ymax = Upper), width = 0.3, size=1) +
geom_text(position = position_nudge(x = 0.3),
aes(y = HR, label = sprintf("italic(P)~'='~'%s'",
formatNum(p, digits = 1))),
color = "black", size =5, parse = TRUE) +
#expand_limits(y=c(-0.5,0))+
scale_color_manual(values = c(yes = "darkred", no = "black")) +
ggtitle(title) + scale_y_log10() +
ylab("Hazard ratio") +
coord_flip() +
theme_full +
theme(legend.position = "none", axis.title.y = element_blank())
return(p)
}NFKB2
protName <- "NFKB2"
outcomeName <- "TTT"
survRes <- filter(cTab, outcome == outcomeName , name == protName)$fullModel[[1]]
hr.prmt5 <- plotHazard(survRes, protName, outcomeName)
hr.prmt5
PURA
protName <- "PURA"
outcomeName <- "TTT"
survRes <- filter(cTab, outcome == outcomeName , name == protName)$fullModel[[1]]
hr.prmt5 <- plotHazard(survRes, protName, outcomeName)
hr.prmt5
PRMT5
protName <- "PRMT5"
outcomeName <- "TTT"
survRes <- filter(cTab, outcome == outcomeName , name == protName)$fullModel[[1]]
hr.prmt5 <- plotHazard(survRes, protName, outcomeName)
hr.prmt5
PYGB
protName <- "PYGB"
outcomeName <- "TTT"
survRes <- filter(cTab, outcome == outcomeName , name == protName)$fullModel[[1]]
hr.pygb <- plotHazard(survRes, protName, outcomeName)
hr.pygb
PES1
protName <- "PES1"
outcomeName <- "TTT"
survRes <- filter(cTab, outcome == outcomeName , name == protName)$fullModel[[1]]
hr.pes1 <- plotHazard(survRes, protName, outcomeName)
hr.pes1
RRAS2
protName <- "RRAS2"
outcomeName <- "OS"
survRes <- filter(cTab, outcome == outcomeName , name == protName)$fullModel[[1]]
hr.rras <- plotHazard(survRes, protName, outcomeName)
hr.rras
Bar plots comparing C-index
TTT
nameList <- c("PRMT5","PYGB","PES1")
cTab.sub <- filter(cTab, outcome == "TTT", name %in% nameList)
plotList <- lapply(seq(nrow(cTab.sub)), function(i) {
plotTab <- tibble(model = c("knownRisks","plusProtein"),
cindex = c(cTab.sub[i,]$c0, cTab.sub[i,]$c1),
ci = c(cTab.sub[i,]$ci0,cTab.sub[i,]$ci1))
ggplot(plotTab, aes(x=model, y=cindex, fill = model)) +
geom_bar(stat="identity",width=0.6) +
geom_errorbar(aes(ymin=cindex + ci, ymax = cindex-ci), width=0.5) +
theme_full+ scale_fill_manual(values = colList[6:7]) +
ggtitle(cTab.ttt[i,]$name) + theme(legend.position = "none") +
ylab("Harrell's C-index")
})
cTTT <- plot_grid(plotlist = plotList, ncol =3)
cTTT
TTT (other candiates)
nameList <- c("PURA","NFKB2","CLTB")
cTab.sub <- filter(cTab, outcome == "TTT", name %in% nameList)
plotList <- lapply(seq(nrow(cTab.sub)), function(i) {
plotTab <- tibble(model = c("knownRisks","plusProtein"),
cindex = c(cTab.sub[i,]$c0, cTab.sub[i,]$c1),
ci = c(cTab.sub[i,]$ci0,cTab.sub[i,]$ci1))
ggplot(plotTab, aes(x=model, y=cindex, fill = model)) +
geom_bar(stat="identity",width=0.6) +
geom_errorbar(aes(ymin=cindex + ci, ymax = cindex-ci), width=0.5) +
theme_full+ scale_fill_manual(values = colList[6:7]) +
ggtitle(cTab.ttt[i,]$name) + theme(legend.position = "none") +
ylab("Harrell's C-index")
})
cTTT <- plot_grid(plotlist = plotList, ncol =3)
cTTT
Save for comparison
save(uniRes, cTab, file ="../output/resOutcome_batch13.RData")
sessionInfo()R version 4.0.2 (2020-06-22)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS 10.16
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] BiocParallel_1.22.0 latex2exp_0.4.0
[3] forcats_0.5.1 stringr_1.4.0
[5] dplyr_1.0.5 purrr_0.3.4
[7] readr_1.4.0 tidyr_1.1.3
[9] tibble_3.1.0 tidyverse_1.3.0
[11] cowplot_1.1.1 SummarizedExperiment_1.18.2
[13] DelayedArray_0.14.1 matrixStats_0.58.0
[15] Biobase_2.48.0 GenomicRanges_1.40.0
[17] GenomeInfoDb_1.24.2 IRanges_2.22.2
[19] S4Vectors_0.26.1 BiocGenerics_0.34.0
[21] glmnet_4.1-1 Matrix_1.3-2
[23] maxstat_0.7-25 survminer_0.4.9
[25] ggpubr_0.4.0 ggplot2_3.3.3
[27] survival_3.2-7 jyluMisc_0.1.5
[29] pheatmap_1.0.12 limma_3.44.3
loaded via a namespace (and not attached):
[1] readxl_1.3.1 backports_1.2.1 fastmatch_1.1-0
[4] drc_3.0-1 workflowr_1.6.2 igraph_1.2.6
[7] shinydashboard_0.7.1 splines_4.0.2 crosstalk_1.1.1
[10] TH.data_1.0-10 digest_0.6.27 foreach_1.5.1
[13] htmltools_0.5.1.1 fansi_0.4.2 magrittr_2.0.1
[16] cluster_2.1.1 openxlsx_4.2.3 modelr_0.1.8
[19] sandwich_3.0-0 piano_2.4.0 colorspace_2.0-0
[22] rvest_1.0.0 haven_2.3.1 xfun_0.21
[25] crayon_1.4.1 RCurl_1.98-1.2 jsonlite_1.7.2
[28] zoo_1.8-9 iterators_1.0.13 glue_1.4.2
[31] gtable_0.3.0 zlibbioc_1.34.0 XVector_0.28.0
[34] car_3.0-10 shape_1.4.5 abind_1.4-5
[37] scales_1.1.1 mvtnorm_1.1-1 DBI_1.1.1
[40] relations_0.6-9 rstatix_0.7.0 Rcpp_1.0.6
[43] plotrix_3.8-1 xtable_1.8-4 foreign_0.8-81
[46] km.ci_0.5-2 DT_0.17 htmlwidgets_1.5.3
[49] httr_1.4.2 fgsea_1.14.0 gplots_3.1.1
[52] RColorBrewer_1.1-2 ellipsis_0.3.1 farver_2.1.0
[55] pkgconfig_2.0.3 sass_0.3.1 dbplyr_2.1.0
[58] utf8_1.1.4 labeling_0.4.2 tidyselect_1.1.0
[61] rlang_0.4.10 later_1.1.0.1 munsell_0.5.0
[64] cellranger_1.1.0 tools_4.0.2 visNetwork_2.0.9
[67] cli_2.3.1 generics_0.1.0 broom_0.7.5
[70] evaluate_0.14 fastmap_1.1.0 yaml_2.2.1
[73] knitr_1.31 fs_1.5.0 zip_2.1.1
[76] survMisc_0.5.5 caTools_1.18.1 nlme_3.1-152
[79] mime_0.10 slam_0.1-48 xml2_1.3.2
[82] rstudioapi_0.13 compiler_4.0.2 curl_4.3
[85] ggsignif_0.6.1 marray_1.66.0 reprex_1.0.0
[88] bslib_0.2.4 stringi_1.5.3 highr_0.8
[91] lattice_0.20-41 shinyjs_2.0.0 KMsurv_0.1-5
[94] vctrs_0.3.6 pillar_1.5.1 lifecycle_1.0.0
[97] jquerylib_0.1.3 data.table_1.14.0 bitops_1.0-6
[100] httpuv_1.5.5 R6_2.5.0 promises_1.2.0.1
[103] KernSmooth_2.23-18 gridExtra_2.3 rio_0.5.26
[106] codetools_0.2-18 MASS_7.3-53.1 gtools_3.8.2
[109] exactRankTests_0.8-31 assertthat_0.2.1 rprojroot_2.0.2
[112] withr_2.4.1 multcomp_1.4-16 GenomeInfoDbData_1.2.3
[115] mgcv_1.8-34 hms_1.0.0 grid_4.0.2
[118] rmarkdown_2.7 carData_3.0-4 git2r_0.28.0
[121] sets_1.0-18 shiny_1.6.0 lubridate_1.7.10