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tissue-ptr.R
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tissue-ptr.R
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options(stringsAsFactors=FALSE)
library(ggplot2)
library(scales)
library(corrplot)
library(RColorBrewer)
library(mvtnorm)
library(fdrtool)
library(xtable)
library(grid)
library(fields)
source("util_functions.R")
## data for the primary analysis comes from Wilhelm
protnm1 <- "Wilhelm"
mrnanm1 <- "illumina"
protnm2 <- "Kim"
mrnanm2 <- "pa"
mrna <- as.matrix(read.csv(sprintf("data/mrna_%s.csv", mrnanm1), row.names=1))
mrna2 <- as.matrix(read.csv(sprintf("data/mrna_%s.csv", mrnanm2), row.names=1))
protein <- as.matrix(read.csv(sprintf("data/protein_%s.csv", protnm1), row.names=1))
protein2 <- as.matrix(read.csv(sprintf("data/protein_%s.csv", protnm2), row.names=1))
## Targeted MS dataset for validating quality of datasets from Edfors et al
protVal <- as.matrix(read.csv("data/validation.csv", row.names=1))
tissue.names <- intersect(colnames(mrna), colnames(protein))
pretty.tissue.names <- sapply(tissue.names, strFormat)
n.tissues <- length(tissue.names)
gene.names <- intersect(rownames(mrna), rownames(protein))
n.genes <- length(gene.names)
mrna <- mrna[gene.names, tissue.names]
protein <- protein[gene.names, tissue.names]
tissue.ratios <- (10^protein) / (10^mrna)
median.ratios <- apply(tissue.ratios, 1, function(x) median(x, na.rm=TRUE))
prediction.ratios <- log10(tissue.ratios * median.ratios)
predicted.raw <- (10^mrna) * median.ratios
keep.indices <- which(log10(predicted.raw) != protein)
cor.raw <- cor(log10(predicted.raw[keep.indices]), as.numeric(protein[keep.indices]))
## number of proteins quantified (intersection and union) by tissue
commonProteins <- intersect(rownames(protein), rownames(protein2))
allProteins <- union(rownames(protein), rownames(protein2))
print(length(commonProteins))
print(length(allProteins))
abundInBoth <- sapply(intersect(colnames(protein), colnames(protein2)), function(tiss) {
sum(!is.na(protein[commonProteins, tiss]) & !is.na(protein2[commonProteins, tiss]))
})
abundInEither <- sapply(intersect(colnames(protein), colnames(protein2)), function(tiss) {
length(union(rownames(protein)[!is.na(protein[, tiss])], rownames(protein2)[!is.na(protein[, tiss])]))
})
print(cbind(abundInBoth, abundInEither))
## Compute mrna / prot correlation
mrnaProtInfo <- getZscores(protein, mrna)
pop.cor <- mrnaProtInfo$pop.cor
print(sprintf("Estimated population correlation: %.3f", pop.cor))
## Q- Value analysis
z.score<- mrnaProtInfo$z.score
fd.out <- fdrtool(z.score,plot=FALSE)
## Have no power to detect differential ptr for individual proteins
head(sort(fd.out$qval), n=20)
print(sprintf("Number of q-values < 0.01: %i",sum(fd.out$qval<0.01)))
print(sprintf("Number of q-values < 0.1: %i",sum(fd.out$qval<0.1)))
#########################################################################
## 1a: fraction of total protein variance explained by scaled mRNA levels
## mRNA is scaled by median across-tissue translation rate, by gene
#########################################################################
pdf("Figs/1a_mrna_v_protein.pdf", width=8, height=7)
print(
ggplot(data=data.frame(x=predicted.raw[keep.indices], y=10^as.numeric(protein[keep.indices])),aes(x=x,y=y))+
geom_point(colour="blue",alpha=0.03)+
scale_x_log10(breaks=10^(3:11),label=scientific_10, limits=c(10^3, 10^11)) +
scale_y_log10(breaks=10^(3:11),labels=scientific_10, limits=c(10^3, 10^11)) +
labs(x="Scaled mRNA",y="Measured Protein",title="")+
annotate("text", x = 10^4, y = 10^9,
label=as.character(as.expression(
substitute(italic(R)[T]^2~"="~r2,
list(r2=formatC(cor.raw^2, format="f", digits=2))))),
parse=TRUE, size=10) +
annotate("text", x = 10^4, y = 10^10,
label=as.character(as.expression(
substitute(italic(R)[T]~"="~r,
list(r=formatC(cor.raw, format="f", digits=2))))),
parse=TRUE, size=10) +
theme(axis.title.x = element_text(face="bold",size=30),
axis.text.x = element_text(size=30),
axis.title.y = element_text(face="bold",size=30),
axis.text.y = element_text(size=30,hjust=0))
)
dev.off()
## Compute between gene mRNA / protein correlation for unscaled mRNA
cor.unscaled <- cor(as.numeric(mrna[keep.indices]), as.numeric(protein[keep.indices]))
pdf("Figs/1a_mrna_v_protein_unscaled.pdf", width=8, height=7)
print(
ggplot(data=data.frame(x=10^as.numeric(mrna[keep.indices]), y=10^as.numeric(protein[keep.indices])),aes(x=x,y=y))+
geom_point(colour="blue", alpha=0.03)+
scale_x_log10(breaks=10^(-2:4),label=scientific_10, limits=c(10^-2, 10^4)) +
scale_y_log10(breaks=10^(3:11),labels=scientific_10, limits=c(10^3, 10^11)) +
labs(x="Measured mRNA",y="Measured Protein",title="")+
annotate("text", x = 10^-1, y = 10^9,
label=as.character(as.expression(
substitute(italic(R)[T]^2~"="~r2,
list(r2=formatC(cor.unscaled^2, format="f", digits=2))))),
parse=TRUE, size=10) +
annotate("text", x = 10^-1, y = 10^10,
label=as.character(as.expression(
substitute(italic(R)[T]~"="~r,
list(r=formatC(cor.unscaled, format="f", digits=2))))),
parse=TRUE, size=10) +
theme(axis.title.x = element_text(face="bold",size=30),
axis.text.x = element_text(size=30),
axis.title.y = element_text(face="bold",size=30),
axis.text.y = element_text(size=30,hjust=0))
)
dev.off()
#########################################################################
## 1b: simpson's paradox figure, manually pick genes to make point
#########################################################################
rsquared.vec <- c()
small.slopes <- c()
small.slopes.vals <- c()
for(i in 1:nrow(protein)){
if( sum(!is.na(predicted.raw[i,]) & !is.na(protein[i,])) > 4){
lm.fit <- lm(protein[i,] ~ log10(predicted.raw)[i,])
slope <- lm.fit$coefficients[2]
rsq <- summary(lm.fit)$r.squared
if(rsq < 1 & sum(!is.na(predicted.raw[i, ]*protein[i,])) > 4)
rsquared.vec <- c(rsquared.vec,rsq)
if(slope < 0.2 & slope > -0.2 &
max(log10(predicted.raw[i, ]), na.rm=TRUE) -
min(log10(predicted.raw[i, ]), na.rm=TRUE) < 2) {
small.slopes <- c(small.slopes, i)
small.slopes.vals <- c(small.slopes.vals, slope)
}
}
}
## proteins which are measured in all tissues
full.protein <- names(
which(
apply(protein[small.slopes, ], 1, function(x)
sum(!is.na(x))) ==n.tissues &
apply(predicted.raw[small.slopes, ], 1, function(x)
sum(!is.na(x)))==n.tissues))
focus.prot <- "ENSG00000163631"
mrna.vec <- as.numeric(t(log10(predicted.raw)))
protein.vec <- as.numeric(t(protein))
id <- rep(rownames(mrna), each=n.tissues)
df <- data.frame(x=mrna.vec, y=protein.vec, id=id)
mean.prot <- rowMeans(protein[small.slopes, ], na.rm=TRUE)
qts <- quantile(mean.prot, c(0.05, 0.95))
## manually add in some proteins with high abundances
large.prots <- sample(names(mean.prot[mean.prot > qts[2]]), 10)
small.prots <- sample(names(mean.prot[mean.prot < qts[1]]), 10)
## sample proteins
sub.proteins <- unique(c(large.prots, small.prots,
sample(unique(id)[small.slopes], 80)))
df.sub <- df[df$id %in% sub.proteins,]
cor.segs <- cor(df.sub$x, df.sub$y, use="complete.obs")
pdf("Figs/1b_mrna_v_protein_segs_horiz.pdf", width=8, height=7)
print(
ggplot(data=df.sub, aes(x=x, y=y)) +
geom_smooth(method=lm, se=FALSE, aes(group=id), colour=alpha("black",0.5)) +
scale_x_continuous(breaks=3:11, label=function(x) parse(text=paste("10^", x)), limits=c(3,11)) +
scale_y_continuous(breaks=3:11,labels=function(x) parse(text=paste("10^", x)), limits=c(3,11)) +
labs(x="Scaled mRNA",y="Measured Protein")+
annotate("text", x = 4, y = 9,
label=as.character(as.expression(substitute(
italic(R)[T]^2~"="~r2,
list(r2=formatC(cor.segs^2, format="f", digits=2))))),
parse=TRUE,size=10) +
annotate("text", x = 4, y = 10,
label=as.character(as.expression(substitute(
italic(R)[T]~"="~r,
list(r=formatC(cor.segs, format="f", digits=2))))),
parse=TRUE,size=10)+
theme(axis.title.x = element_text(face="bold",size=30),
axis.text.x = element_text(size=30),
axis.title.y = element_text(face="bold",size=30),
axis.text.y = element_text(size=30,hjust=0)) +
geom_point(data=data.frame(x=log10(predicted.raw[focus.prot,]),y=protein[focus.prot,],Tissue=tissue.names),aes(x=x,y=y,label=length(tissue.names):1,color=Tissue),size=3)+
theme(legend.text=element_text(size=15),legend.title.align=0.5,
legend.background=element_rect(fill=alpha("gray", 0.75)),legend.key=element_blank(),legend.title=element_text(size=14)) +
guides(colour = guide_legend(override.aes = list(size=5)))+
geom_smooth(data=data.frame(x=log10(predicted.raw[focus.prot,]),y=protein[focus.prot,]),method=lm,se=FALSE,colour="black",size=1.5)+theme(legend.justification=c(1,0), legend.position=c(1,0)),
)
dev.off()
within.indices <- which(apply(!is.na(protein * predicted.raw), 1, sum) > 3)
within.cors <- sapply(within.indices,function(i) cor(protein[i,], log(predicted.raw)[i,], use="pairwise.complete.obs"))
between.cors <- sapply(1:ncol(protein),function(i) cor(protein[,i],log10(predicted.raw)[,i],use="pairwise.complete.obs"))
between.cors.raw <- sapply(1:ncol(protein),function(i) cor(protein[,i],mrna[,i],use="pairwise.complete.obs"))
#########################################################################
## Within and between gene correlations
#########################################################################
pdf("Figs/appendix_within_hist.pdf")
ggplot(data.frame(x=within.cors))+geom_histogram(aes(x=x,fill="within"),colour="black",binwidth=0.05,size=0.1)+labs(x=expression(paste("Correlation, ", R[P])),y="",title="Across Tissue, Empirical")+
theme(title = element_text(size=20),
axis.title.x = element_text(face="bold",size=25),
axis.text.x = element_text(size=16),
axis.text.y = element_text(size=16,hjust=0)) +
scale_y_continuous(limits=c(0,300))+
guides(fill=FALSE)
dev.off()
ggplot(data.frame(x=within.cors))+geom_histogram(aes(x=x,fill="within"),colour="black",binwidth=0.05,size=0.1)+labs(x=expression(paste("Correlation, ", R[P])),y="",title="Across Tissue, Empirical")+
theme(title = element_text(size=20),
axis.title.x = element_text(face="bold",size=25),
axis.text.x = element_text(size=16),
axis.text.y = element_text(size=16,hjust=0)) +
scale_y_continuous(limits=c(0,300))+
guides(fill=FALSE)
pdf("Figs/appendix_between_hists.pdf")
ggplot(data.frame(x=between.cors, x2=between.cors.raw, labels=tissue.names)) +
geom_histogram(aes(x=x, fill="Scaled mRNA Levels"), binwidth=0.03, size=0.1) +
geom_histogram(aes(x=x2, fill="Measured mRNA Levels"), binwidth=0.03, size=0.1) +
scale_x_continuous(limits=c(-1, 1)) +
scale_y_continuous(limits=c(0, 5)) +
labs(x=expression(paste("Correlation, ", R[T])), y="",
title="Within Tissue, Empirical") +
theme(title = element_text(size=20),
axis.title.x = element_text(face="bold",size=25),
axis.text.x = element_text(size=16),
axis.text.y = element_text(size=16,hjust=0),
legend.text=element_text(size = 16, face = "bold"),
legend.justification=c(1,1), legend.position=c(1,1),
legend.background = element_rect(fill = "transparent")) +
guides(fill=guide_legend(title=NULL))
dev.off()
#########################################################################
### Histogram of R-squared
#########################################################################
pdf("Figs/histogram-rsq.pdf")
rsq.list <- data.frame(rsq=rsquared.vec)
ggplot(rsq.list,aes(x=rsq))+geom_histogram(binwidth=.05, colour="black", fill="grey")+labs(x="R-Squared",y="Count",title="Across Tissue, Empirical")+
theme(axis.title.x = element_text(face="bold",size=30),
axis.text.x = element_text(size=16),
axis.title.y = element_text(face="bold",size=30),
axis.text.y = element_text(size=16,hjust=0))
dev.off()
## simulate correlations
sim.within.cors <- sapply(1:length(z.score), function(i) {
cor(rmvnorm(n=mrnaProtInfo$n.pairwise[i],
mean=c(0,0),
sigma=matrix(c(1,pop.cor,pop.cor,1),nrow=2)))[1,2]
})
pdf("Figs/appendix_sim_within_hist.pdf")
ggplot(data.frame(x=sim.within.cors))+geom_histogram(aes(x=x,fill="within"),colour="black",binwidth=0.05,size=0.1)+labs(x=expression(paste("Correlation, ", R[P])),y="",title=expression(paste("Across Tissue, Simulated (",rho,"=0.35)",sep="")))+
theme(title = element_text(size=20),
axis.title.x = element_text(face="bold",size=25),
axis.text.x = element_text(size=16),
axis.text.y = element_text(size=16,hjust=0)) +
scale_y_continuous(limits=c(0,300))+
guides(fill=FALSE)
dev.off()
pdf("Figs/sim_and_within_cors.pdf")
pooled.cors <- c(sim.within.cors, within.cors)
label <- factor(rep(c("Simulated", "Empirical"),
levels=c("Simulated", "Empirical"),
each=length(within.cors)), ordered=TRUE)
ggplot(data.frame(cors=pooled.cors, cut=label), aes(x=cors, fill=label)) +
geom_bar(position=position_dodge(width=0.05), binwidth=0.1, size=0,
aes(order=c(rep(c(1,2), each=length(within.cors))))) +
labs(x=expression(paste("Correlation, ", R[P])),
y="", title=expression("Across Tissue")) +
theme(title = element_text(size=20),
axis.title.x = element_text(face="bold",size=25),
axis.text.x = element_text(size=16),
axis.text.y = element_text(size=16,hjust=0),
legend.title=element_blank(),
legend.text = element_text(size = 16, face = "bold"),
legend.justification=c(1,1), legend.position=c(1,1),
legend.background = element_rect(fill = "transparent")) +
scale_y_continuous(limits=c(0,500)) +
geom_vline(xintercept = mrnaProtInfo$pop.cor, linetype=2)
dev.off()
#########################################################################
## Fold error / reliability simulation
#########################################################################
n.prot <- rowSums(!is.na(protein))
sd.prot.err <- uniroot(function(x) pnorm(log10(1.5),mean=0,sd=x)-0.75,lower=0,upper=10)$root
sd.prot.tot <- sqrt(sum((n.prot-1)*apply(protein,1,function(x) var(x,na.rm=TRUE)),na.rm=TRUE)/(sum(n.prot,na.rm=TRUE)-sum(n.prot>1)))
#sd.prot.tot <- median(apply(protein,1,function(x) sd(x,na.rm=TRUE)),na.rm=TRUE)
Rprot <- 1-sd.prot.err^2/sd.prot.tot^2
n.mrna <- rowSums(!is.na(mrna))
sd.mrna.err <- uniroot(function(x) pnorm(log10(1.2),mean=0,sd=x)-0.75,lower=0,upper=10)$root
## pooled estimator
sd.mrna.tot <- sqrt(sum((n.mrna-1)*apply(mrna,1,function(x) var(x,na.rm=TRUE)),na.rm=TRUE)/(sum(n.mrna,na.rm=TRUE)-sum(n.mrna>1)))
Rmrna <- 1-sd.mrna.err^2/sd.mrna.tot^2
##
mar2 <- par()$mar
mar2[2] <- 5.1
par(mar=mar2)
curve(sapply(x,function(fc){ 1-(uniroot(function(s) pnorm(log10(fc),mean=0,sd=s)-0.75,lower=0,upper=1000)$root)^2/sd.prot.tot^2}),from=1.00,to=1.5,
ylab="Reliability",xlab="Median Fold Error (%)",
lwd=3,main="",
cex.lab=2,ylim=c(0,1),col="red",xaxt="n",cex.axis=2,cex.main=2)
grid()
text(1.37,0.6,"mRNA",col="blue",cex=1.5)
text(1.40,0.9,"protein",col="red",cex=1.5)
axis(1,at=seq(1,1.5,by=0.1),labels=c(0,10,20,30,40,50),cex.axis=2)
segments(1.2,0,1.2,Rmrna,col="dark grey",lwd=3)
segments(0,Rmrna,1.2,Rmrna,col="dark grey",lwd=3)
segments(1.5,0,1.5,Rprot,col="dark grey",lwd=3)
segments(0,Rprot,1.5,Rprot,col="dark grey",lwd=3)
curve(sapply(x,function(fc){ 1-(uniroot(function(s) pnorm(log10(fc),mean=0,sd=s)-0.75,lower=0,upper=1000)$root)^2/sd.mrna.tot^2}),from=1.00,to=1.5,cex.lab=2,lwd=3,add=TRUE,col="blue")
dev.off()
#########################################################################
## R-squared as a function of reliabilities
## assuming true mrna/prot correlation = pop.cor
#########################################################################
mrnaProtInfo <- getZscores(protein, mrna)
pop.cor <- mrnaProtInfo$pop.cor
## Compute mrna reliabilities
mrnaRows <- intersect(rownames(mrna), rownames(mrna2))
mrnaCols <- intersect(colnames(mrna), colnames(mrna2))
mrnaRelInfo <- getZscores(mrna[mrnaRows, mrnaCols], mrna2[mrnaRows, mrnaCols])
mrna.reliabilities <- mrnaRelInfo$within.cors
mrnaRel <- mrnaRelInfo$pop.cor
## Compute protein reliabilities
pcols <- intersect(colnames(protein), colnames(protein2))
prows <- intersect(rownames(protein), rownames(protein2))
cts <- apply(protein[prows, pcols] * protein2[prows, pcols], 1,
function(x) {sum(!is.na(x))})
ppScores <- getZscores(protein[prows, pcols], protein2[prows, pcols])
protein.reliabilities <- ppScores$within.cors
protRel <- ppScores$pop.cor
#########################################################################
## Histogram of across tissue mRNA reliabilities
#########################################################################
pdf("Figs/mrna_reliabilities.pdf")
ggplot(data.frame(x=mrna.reliabilities))+geom_histogram(aes(x=x,fill="mRNA"),colour="black",binwidth=0.05,size=0.1)+labs(x="Reliability",y="",title=expression(paste("mRNA Reliabilities, Median = ", med.mrna, sep="")))+
theme(title = element_text(size=20),
axis.title.x = element_text(face="bold",size=25),
axis.text.x = element_text(size=16),
axis.text.y = element_text(size=16,hjust=0)) +
scale_x_continuous(limits=c(-1,1))+
guides(fill=FALSE)
dev.off()
#########################################################################
## Histogram of across tissue protein reliabilities
#########################################################################
pdf("Figs/protein_reliabilities.pdf")
ggplot(data.frame(x=protein.reliabilities))+geom_histogram(aes(x=x,fill="mRNA"),colour="black",binwidth=0.05,size=0.1)+labs(x="Reliability",y="",title=expression(paste("Protein Reliabilities, Median = ",med.prot,sep="")))+
theme(title = element_text(size=20),
axis.title.x = element_text(face="bold",size=25),
axis.text.x = element_text(size=16),
axis.text.y = element_text(size=16,hjust=0)) +
scale_x_continuous(limits=c(-1,1))+
guides(fill=FALSE)
dev.off()
#########################################################################
## ### Compare Kim and Wilhelm and create consensus dataset
#########################################################################
pcols <- Reduce(intersect,
list(colnames(protein),
colnames(protein2),
colnames(mrna),
colnames(mrna2)))
prows <- Reduce(intersect,
list(rownames(protein),
rownames(protein2),
rownames(mrna),
rownames(mrna2)))
## To facilitate fair comparison, only look at genes
## measured in both data sets.
NAMask <- protein[prows, pcols] * protein2[prows, pcols]
NAMask[!is.na(NAMask)] <- 1
p1m1scores <- getZscores(protein[prows, pcols]*NAMask, mrna[prows ,pcols])
p2m1scores <- getZscores(protein2[prows, pcols]*NAMask, mrna[prows, pcols])
p1m2scores <- getZscores(protein[prows, pcols]*NAMask, mrna2[prows ,pcols])
p2m2scores <- getZscores(protein2[prows, pcols]*NAMask, mrna2[prows, pcols])
p1m1cor <- p1m1scores$pop.cor
p2m1cor <- p2m1scores$pop.cor
p1m2cor <- p1m2scores$pop.cor
p2m2cor <- p2m2scores$pop.cor
mean(!is.na(protein[prows, pcols]*mrna[prows, pcols]))
mean(!is.na(protein[prows, pcols]*mrna2[prows, pcols]))
mean(!is.na(protein2[prows, pcols]*mrna[prows, pcols]))
mean(!is.na(protein2[prows, pcols]*mrna2[prows, pcols]))
protConsensus <- protein2[prows, pcols]
for(i in 1:length(pcols)) {
notNA <- which(!is.na(protein[prows, pcols[i]] * protein2[prows, pcols[i]]))
p1NA <- which(is.na(protein[prows, pcols[i]]) & !is.na(protein2[prows, pcols[i]]))
p2NA <- which(!is.na(protein[prows, pcols[i]]) & is.na(protein2[prows, pcols[i]]))
p1m1 <- cor(protein[prows[notNA], pcols[i]], mrna[prows[notNA], pcols[i]], use="pairwise.complete.obs")
p2m1 <- cor(protein2[prows[notNA], pcols[i]], mrna[prows[notNA], pcols[i]], use="pairwise.complete.obs")
p1p2 <- cor(protein[prows, pcols[i]], protein2[prows, pcols[i]], use="pairwise.complete.obs")
p1rel1 <- p1p2*p1m1/p2m1
p2rel1 <- p1p2*p2m1/p1m1
p1m2 <- cor(protein[prows, pcols[i]], mrna2[prows, pcols[i]], use="pairwise.complete.obs")
p2m2 <- cor(protein2[prows, pcols[i]], mrna2[prows, pcols[i]], use="pairwise.complete.obs")
p1rel2 <- p1p2*p1m2/p2m2
p2rel2 <- p1p2*p2m2/p1m2
## average independent reliability estimates
p1rel <- mean(c(p1rel1, p1rel2))
p2rel <- mean(c(p2rel1, p2rel2))
w1 <- 1 / (1 + 1/(p1rel/p2rel))
w2 <- 1 - w1
print(sprintf("%s weights: %f, %f", pcols[i], w1, w2))
## if measurements observed in both value is weighted average
protConsensus[prows[notNA], pcols[i]] <-
w1 * protein[prows[notNA], pcols[i]] +
w2 * protein2[prows[notNA], pcols[i]]
## if observed in one but not the other
protConsensus[prows[p1NA], pcols[i]] <- protein2[prows[p1NA], pcols[i]]
protConsensus[prows[p2NA], pcols[i]] <- protein[prows[p2NA], pcols[i]]
}
pcm1scores <- getZscores(protConsensus[prows, pcols]*NAMask, mrna[prows, pcols])
pcm1cor <- pcm1scores$pop.cor
pcm2scores <- getZscores(protConsensus[prows, pcols]*NAMask, mrna2[prows, pcols])
pcm2cor <- pcm2scores$pop.cor
## consensus dataset more correlated with mrna datasets
## than either the kim or wilhelm data individually:
cat(sprintf("\nConsensus / %s cor: %f,\n%s / %s cor: %f,\n%s / %s cor: %f\n",
mrnanm1, pcm1cor,
protnm1, mrnanm1, p1m1cor,
protnm2, mrnanm1, p2m1cor))
cat(sprintf("\nConsensus / %s cor: %f,\n%s / %s cor: %f,\n%s / %s cor: %f\n",
mrnanm2, pcm2cor,
protnm1, mrnanm2, p1m2cor,
protnm2, mrnanm2, p2m2cor))
#########################################################################
## ### Compare Kim, Wilhelm and consensus vs empirical prot rel
#########################################################################
prelCuts <- cut(protein.reliabilities,
breaks=quantile(protein.reliabilities, seq(0, 1, length.out=6)))
medianPrel <- aggregate(protein.reliabilities,
list(lev=prelCuts), function(x) median(x, na.rm=TRUE))$x
p1m1relcor <- aggregate(p1m1scores$within.cors[names(protein.reliabilities)],
list(lev=prelCuts), function(x) median(x, na.rm=TRUE))$x
p2m1relcor <- aggregate(p2m1scores$within.cors[names(protein.reliabilities)],
list(lev=prelCuts), function(x) median(x, na.rm=TRUE))$x
pcm1relcor <- aggregate(pcm1scores$within.cors[names(protein.reliabilities)],
list(lev=prelCuts), function(x) median(x, na.rm=TRUE))$x
p1m2relcor <- aggregate(p1m2scores$within.cors[names(protein.reliabilities)],
list(lev=prelCuts), function(x) median(x, na.rm=TRUE))$x
p2m2relcor <- aggregate(p2m2scores$within.cors[names(protein.reliabilities)],
list(lev=prelCuts), function(x) median(x, na.rm=TRUE))$x
pcm2relcor <- aggregate(pcm2scores$within.cors[names(protein.reliabilities)],
list(lev=prelCuts), function(x) median(x, na.rm=TRUE))$x
pdf("Figs/RelVsCor_fagerberg.pdf", width=5, height=5)
plot(x=medianPrel, y=p1m1relcor, type="l", ylim=c(0,0.5), lwd=2,
xlab="Median Protein/Protein Correlation", ylab="Median mRNA/Protein Correlation",
main="Median across-tissue correlations\nversus protein reliability (Fagerberg et al)", cex.axis=1.2, cex.lab=1.3, col="blue")
lines(x=medianPrel, y=p2m1relcor, type="l", ylim=c(0,1), lwd=2, col="black")
lines(x=medianPrel, y=pcm1relcor, type="l", ylim=c(0,1), lwd=2, col="red")
legend("topleft", legend=c("Consensus", "Wilhelm et al.", "Kim et al."), col=c("red", "blue", "black"), lty=1, lwd=2, box.lty=0)
dev.off()
pdf("Figs/RelVsCor_encode.pdf", width=5, height=5)
plot(x=medianPrel, y=p1m2relcor, type="l", ylim=c(0,0.5), lwd=2,
xlab="Median Protein/Protein Correlation", ylab="Median mRNA/Protein Correlation",
main="Median across-tissue correlations\nversus protein reliability (ENCODE)", cex.axis=1.2, cex.lab=1.3, col="blue")
lines(x=medianPrel, y=p2m2relcor, type="l", ylim=c(0,1), lwd=2, col="black")
lines(x=medianPrel, y=pcm2relcor, type="l", ylim=c(0,1), lwd=2, col="red")
legend("topleft", legend=c("Consensus", "Wilhelm et al.", "Kim et al."), col=c("red", "blue", "black"), lty=1, lwd=2, box.lty=0)
dev.off()
#######################################################
## Write complete consensus dataset to file,
## include genes or tissues missing in one datset but not the other
#######################################################
protConsensus <- rbind(protConsensus,
protein[setdiff(rownames(protein), prows), pcols])
protConsensus <- rbind(protConsensus,
protein2[setdiff(rownames(protein2),
rownames(protConsensus)), pcols])
otherCols <- setdiff(union(colnames(protein), colnames(protein2)),
pcols)
## Add tissues that were in one dataset but not the other
## for data set with more coverage
protConsensus <- cbind(protConsensus, matrix(NA,
nrow=nrow(protConsensus),
ncol=length(otherCols),
dimnames=list(
rownames(protConsensus),
otherCols)))
protConsensus[rownames(protein), setdiff(colnames(protein), pcols)] <-
protein[rownames(protein), setdiff(colnames(protein), pcols)]
protConsensus[rownames(protein2), setdiff(colnames(protein2), pcols)] <-
protein2[rownames(protein2), setdiff(colnames(protein2), pcols)]
write.csv(protConsensus, file="data/protein_consensus.csv")
#######################################################
## Compute "MSE" relative to validation data.
## Use gene / tissue pairs that are observed in all data
#######################################################
pvCols <- Reduce(intersect,
list(colnames(protVal), colnames(protein), colnames(protein2)))
pvRows <- Reduce(intersect,
list(rownames(protVal), rownames(protein), rownames(protein2)))
## Find prot-tissue measurements that are observed in all datasets
comparisonGenes <- which(!is.na(protein[pvRows, pvCols]) & !is.na(protein2[pvRows, pvCols]) & !is.na(protVal[pvRows, pvCols]))
## compute differences after removing mean differences
diffP1 <- scale(
as.vector(protein[pvRows, pvCols])[comparisonGenes] -
as.vector(protVal[pvRows, pvCols])[comparisonGenes],
scale=FALSE)
msep1 <- sum(diffP1^2, na.rm=TRUE) / length(diffP1)
diffP2 <- scale(
as.vector(protein2[pvRows, pvCols])[comparisonGenes] -
as.vector(protVal[pvRows, pvCols])[comparisonGenes],
scale=FALSE)
msep2 <- sum(diffP2^2, na.rm=TRUE) / length(diffP2)
diffPC <- scale(
as.vector(protConsensus[pvRows, pvCols])[comparisonGenes] -
as.vector(protVal[pvRows, pvCols])[comparisonGenes],
scale=FALSE)
mseConsensus <- sum(diffPC^2, na.rm=TRUE) / length(diffPC)
errorMat <- c("All", round(msep1, 2), round(msep2, 2), round(mseConsensus, 2))
## Check if improvement is significant using bootstrap
## Bootstrap interval for MSE improvement over Kim dataset
quantile(sapply(1:1000, function(x) {
idx <- sample(length(diffPC), replace=TRUE)
mean(diffPC[idx]^2) - mean(diffP2[idx]^2)
}), c(0.025, 0.975))
## Bootstrap interval for MAD improvement over Kim dataset
quantile(sapply(1:1000, function(x) {
idx <- sample(length(diffPC), replace=TRUE)
median(abs(diffPC[idx])) - median(abs(diffP2[idx]))
}), c(0.025, 0.975))
for(col in pvCols) {
comparisonGenesTissue <- which(!is.na(protein[pvRows, col]) &
!is.na(protein2[pvRows, col]) &
!is.na(protVal[pvRows, col]))
diffP1 <- scale(
protein[pvRows, col][comparisonGenesTissue] -
protVal[pvRows, col][comparisonGenesTissue],
scale=FALSE)
msep1 <- sum(diffP1^2, na.rm=TRUE) / length(diffP1)
diffP2 <- scale(
protein2[pvRows, col][comparisonGenesTissue] -
protVal[pvRows, col][comparisonGenesTissue],
scale=FALSE)
msep2 <- sum(diffP2^2, na.rm=TRUE) / length(diffP2)
diffPC <- scale(
protConsensus[pvRows, col][comparisonGenesTissue] -
protVal[pvRows, col][comparisonGenesTissue],
scale=FALSE)
mseConsensus <- sum(diffPC^2, na.rm=TRUE) / length(diffPC)
print(c(msep1, msep2, mseConsensus))
errorMat <- rbind(c(pretty.tissue.names[col],
round(msep1, 2), round(msep2, 2), round(mseConsensus, 2)),
errorMat)
}
errorMat <- errorMat[order(errorMat[, 1]), ]
rownames(errorMat) <- errorMat[, 1]
errorMat <- errorMat[, 2:4]
colnames(errorMat) <- c(protnm1, protnm2, "Consensus")
print(xtable(errorMat), hline.after=c(6))
## Validation 1B
rnames <- intersect(rownames(protVal), rownames(mrna))
cnames <- intersect(colnames(protVal), colnames(mrna))
## Edfors
valScores <- getZscores(mrna[rnames, cnames], as.matrix(protVal[rnames, cnames]))
print(valScores$pop.cor)
hist(valScores$within.cors, breaks=10, main="Across tissue mRNA/protein correlation(Edfors et al)", xlab="Correlation")
## Correlation between scaled mRNA and scaled protein
medVal <- apply(protVal[rnames, cnames] - mrna[rnames, cnames], 1, function(x) median(x, na.rm=TRUE))
mRNAVecVal <- as.numeric(mrna[rnames, cnames] + medVal)
protVecVal <- as.numeric(protVal[rnames, cnames])
notEqualIndices <- which(mRNAVecVal != protVecVal)
mRNAVecVal <- mRNAVecVal[notEqualIndices]
protVecVal <- protVecVal[notEqualIndices]
cor(mRNAVecVal, protVecVal)
## Kim on Edfors subset
cnames <- intersect(colnames(protein2), colnames(protVal))
protScores <- getZscores(mrna[rnames, cnames], as.matrix(protein2[rnames, cnames]),
min.pairs=4)
print(protScores$pop.cor)
hist(protScores$within.cors, breaks=10, main="Across tissue mRNA/protein correlation(Edfors et al)", xlab="Correlation")
## Correlation between scaled mRNA and scaled protein
medVal <- apply(protein2[rnames, cnames] - mrna[rnames, cnames], 1, function(x) median(x, na.rm=TRUE))
mRNAVecVal <- as.numeric(mrna[rnames, cnames] + medVal)
protVecVal <- as.numeric(protein2[rnames, cnames])
notEqualIndices <- which(mRNAVecVal != protVecVal)
mRNAVecVal <- mRNAVecVal[notEqualIndices]
protVecVal <- protVecVal[notEqualIndices]
cor(mRNAVecVal, protVecVal)
## Wilhelm on Edfors subset
cnames <- intersect(colnames(protein), colnames(protVal))
protScores <- getZscores(mrna[rnames, cnames], as.matrix(protein[rnames, cnames]),
min.pairs=4)
print(protScores$pop.cor)
hist(protScores$within.cors, breaks=10, main="Across tissue mRNA/protein correlation(Edfors et al)", xlab="Correlation")
## Correlation between scaled mRNA and scaled protein
medVal <- apply(protein[rnames, cnames] - mrna[rnames, cnames], 1, function(x) median(x, na.rm=TRUE))
mRNAVecVal <- as.numeric(mrna[rnames, cnames] + medVal)
protVecVal <- as.numeric(protein[rnames, cnames])
notEqualIndices <- which(mRNAVecVal != protVecVal)
mRNAVecVal <- mRNAVecVal[notEqualIndices]
protVecVal <- protVecVal[notEqualIndices]
cor(mRNAVecVal, protVecVal)
nms <- intersect(names(protScores$within.cors), names(valScores$within.cors))
cor.test(protScores$within.cors[nms], valScores$within.cors[nms])
geneNames <- rep(rnames, length(cnames))[notEqualIndices]
cat <- cut(valScores$within.cors[geneNames], c(-1, quantile(valScores$within.cors[geneNames], c(0.25, 0.75), na.rm=TRUE), 1))
levels(cat) <- c("low", "med", "high")
df <- data.frame(mrna=mRNAVecVal, prot=protVecVal, nms=geneNames, cat=cat)
pdf("test.pdf")
ggplot(data=df, aes(x=mrna, y=prot)) +
geom_smooth(method=lm, se=FALSE, aes(group=nms, colour=cat, alpha=cat)) +
scale_colour_manual(values = alpha(c("red","black", "blue"), c(1, 0.2, 1)))
dev.off()
mrna.vec <- as.numeric(t(log10(predicted.raw)))
protein.vec <- as.numeric(t(protein))
id <- rep(rownames(mrna), each=n.tissues)
df <- data.frame(x=mrna.vec, y=protein.vec, id=id)
cor.test(mRNAVecVal, protVecVal)
cor.test(mRNAVecVal[cat=="low"], protVecVal[cat=="low"])
cor.test(mRNAVecVal[cat=="high"], protVecVal[cat=="high"])
cor.test(mRNAVecVal[cat=="med"], protVecVal[cat=="med"])
pdf("Edfors_Conflation.pdf")
plot(mRNAVecVal, protVecVal, pch=19, cex=0)
text(apply(mrna[rnames, cnames] + medVal, 1, function(x) median(x, na.rm=TRUE)),
apply(protVal[rnames, cnames], 1, function(x) median(x, na.rm=TRUE)),
label=round(valScores$within.cors, 2), cex=0.75,
col=ifelse(valScores$within.cors < 0.6, "red", "black"))
text(1.5, 4, label="R^2 = 0.94")
dev.off()
#######################################################
## Figure 2e: R^2 vs reliability for empirical mrna/prot cor= 0.29
#######################################################
pdf("Figs/5e-noise-correction.pdf")
par(xpd=FALSE)
par(mar=c(6.1,5.1,4.1,6.1))
cols <- c("#08306B","#08519C","#2171B5","#4292C6","#9ECAE1")
cols2 <- rep(cols, each=2)
cols <- sapply(1:9, function(i) avg.colors(cols2[i], cols2[i+1]))
cols <- c(cols, "#D8EAF3")
plot(0, xlim=c(0, 1), ylim=c(0, 1), cex=0,
xlab="Reliability of mRNA Measurements",
ylab="Reliability of Protein Measurements",
main=paste0("Fraction of Across-Tissue Protein Variance\n ",
"Explained By Transcript Levels\n"),
cex.lab=2, cex.axis=1.5, cex.main=1.5, xaxs="i", yaxs="i")
out.prev <- curve(p2m1cor^2/x, from=p2m1cor^2, to=1, col="red", lwd=3, n=1000, add=TRUE)
polygon(c(0, 0, out.prev$x,1), c(0, 1, out.prev$y,0), density = c(10, 20), angle = c(45, -45))
count <- 1
for(cur in seq(0.9,0.1,by=-0.1)){
col <- cols[count]
out.cur <- curve(p2m1cor^2/(cur*x), from=p2m1cor^2, to=1,
lwd=1, lty=2,col="black", n=1000, add=TRUE)
polygon(c(out.prev$x, rev(out.cur$x)),
c(out.prev$y, rev(out.cur$y)), col=col, lty=0)
out.prev <- out.cur
count <- count+1
}
polygon(c(out.prev$x, rev(out.prev$x)),
c(out.prev$y, rep(1, length(out.prev$y))),
col="#D8EAF3", lty=0)
null.line <- curve(p2m1cor^2/x, from=p2m1cor^2, to=1,
col="red",lwd=3,n=1000,add=TRUE)
text(mrnaRel, protRel, "X", col="red", cex=2)
text(median(mrna.reliabilities), median(protein.reliabilities), "X", col="red", cex=2)
## colorbar
par(xpd=TRUE)
ybot <- 0.15
xleft <- 1.05
rectHeight <- 0.075
rectWidth <- 0.1
for(i in 1:length(cols)) {
rect(xleft, ybot + rectHeight*(i-1),
xleft + rectWidth, ybot + rectHeight*i, col=rev(cols)[i], lwd=0)
text(xleft + 3/2*rectWidth, ybot + rectHeight*(i-1/2), paste0(i*10, "%"))
}
par(xpd=FALSE)
dev.off()
#######################################################
## Look at variation in expression and abundance across tissues
#######################################################
## noise corrected
sd.mrnas <- apply(mrna, 1, function(x) sd(x,na.rm=TRUE))
sd.prots <- apply(protein, 1, function(x) sd(x,na.rm=TRUE))
## temporary
sd.mrnas.corrected <- sqrt(sd.mrnas^2 * mrnaRel)
sd.prots.corrected <- sqrt(sd.prots^2 * p1.reliability)
sd.diffs.corrected <- sd.prots.corrected - sd.mrnas.corrected
pdf("Figs/physiological_sds.pdf")
ggplot(data.frame(x=sd.mrnas.corrected,x2=sd.prots.corrected),labels=c("mRNA","protein"))+geom_histogram(aes(x=x,fill="mRNA"),binwidth=0.03,colour="black",size=0.1,alpha=1)+geom_histogram(aes(x=x2,fill="protein"),binwidth=0.03,colour="black",size=0.1,alpha=0.5)+scale_x_continuous(limits=c(0,1.5))+labs(x="Standard Deviation",y="Count")+
theme(axis.title.x = element_text(face="bold",size=25),
axis.text.x = element_text(size=16),
axis.title.y = element_text(face="bold",size=25),
axis.text.y = element_text(size=16,hjust=0),
legend.text=element_text(size = 20, face = "bold"),
legend.position=c(0.8,0.9))+
guides(fill=guide_legend(title=NULL))
dev.off()
pdf("Figs/sds-diff.pdf")
ggplot(data.frame(x=(sd.prots.corrected-sd.mrnas.corrected)))+geom_histogram(aes(x=x,fill="protein-mRNA"),binwidth=0.05,colour="black",size=0.1,alpha=1)+
scale_x_continuous(limits=c(-1.5,1.5))+
labs(x="Difference in Standard Deviation",y="Count")+
theme(axis.title.x = element_text(face="bold",size=25),
axis.text.x = element_text(size=16),
axis.title.y = element_text(face="bold",size=25),
axis.text.y = element_text(size=16,hjust=0),
legend.text=element_text(size = 20, face = "bold"),
legend.position=c(0.8,0.9))+
guides(fill=guide_legend(title=NULL))+geom_vline(xintercept=0,size=1.5)
dev.off()
median(sd.prots.corrected/sd.mrnas.corrected,na.rm=TRUE)
################################################
### Fold Change Figs in supplement: Choose 3 tissues
################################################
fold.comparison.tissues <- c("prostate","kidney","liver")
fold.combos <- combn(fold.comparison.tissues, 2)
for(i in 1:ncol(fold.combos)){
t1 <- fold.combos[1,i]
t2 <- fold.combos[2,i]
pdf(sprintf("Figs/2_%s_%s_fold_cor.pdf",t1,t2))
fold.cor <- cor(mrna[, t1] - mrna[, t2],
protein[, t1] - protein[, t2], use="complete.obs")
fold.cor.sq <- fold.cor^2
fold.cor <- round(fold.cor, 2)
fold.cor.sq <- round(fold.cor.sq, 2)
print(ggplot(data=data.frame(mrna=10^(mrna[,t1]-mrna[,t2]),
protein=10^(protein[,t1]-protein[,t2]))) +
geom_point(aes(x=mrna,y=protein),colour="blue",alpha=0.3) +
scale_x_log10(breaks=10^(-2:2), label=scientific_10,
limits=c(10^-2,10^2)) +
scale_y_log10(breaks=10^(-3:3), labels=scientific_10,
limits=c(10^-3,10^3)) +
labs(x="mRNA Fold Change",y="Protein Fold Change")+
annotate("text", x = 10^-1.2, y = 10^2.5,label=as.character(as.expression(substitute(italic(R)~"="~r,list(r=round(fold.cor, digits=2))))),parse=TRUE,size=10)+
annotate("text", x = 10^-1.2, y = 10^2,label=as.character(as.expression(substitute(italic(R)^2~"="~r2,list(r2=round(fold.cor.sq, digits=2))))),parse=TRUE,size=10)+
theme(axis.title.x = element_text(face="bold",size=30),
axis.text.x = element_text(size=30),
axis.title.y = element_text(face="bold",size=30),
axis.text.y = element_text(size=30,hjust=0)))
dev.off()
pdf(sprintf("Figs/2_%s_scaledby_%s.pdf", t1, t2))
pred <- mrna[, t1] + (protein[, t2] - mrna[, t2])
raw <- protein[, t1]
cor.raw <- cor(pred, raw, use="complete.obs")
cor.raw.sq <- cor.raw^2
cor.raw <- round(cor.raw, 2)
cor.raw.sq <- round(cor.raw.sq, 2)
print(
ggplot(data=data.frame(mrna=pred, protein=raw)) +
geom_point(aes(x=mrna, y=protein), colour="blue",alpha=0.3) +
theme(plot.margin=unit(c(5.5, 12.5, 5.5, 5.5), "points")) +
scale_x_continuous(breaks=1:10,labels=function(x) parse(text=paste("10^", x)),limits=c(3,10))+
scale_y_continuous(breaks=1:10,labels=function(x) parse(text=paste("10^", x)),limits=c(3,10))+
labs(x=sprintf("%s mRNA scaled by %s PTR", strFormat(t1), strFormat(t2)),
y=sprintf("Measured Protein in %s",strFormat(t1)))+
annotate("text", x = 4, y = 9.5,label=as.character(as.expression(substitute(italic(R)~"="~r,list(r=format(cor.raw, digits=2))))),parse=TRUE,size=10)+
annotate("text", x = 4, y = 9,label=as.character(as.expression(substitute(italic(R)^2~"="~r2,list(r2=format(cor.raw.sq, digits=2))))),parse=TRUE,size=10)+
theme(axis.title.x = element_text(face="bold",size=22),
axis.text.x = element_text(size=22),
axis.title.y = element_text(face="bold",size=30),
axis.text.y = element_text(size=30,hjust=0))
)
dev.off()
}
########################################
## Raw Correlations
########################################
for(tis in fold.comparison.tissues){
pdf(sprintf("Figs/2_%s_median_scaled_cor.pdf", tis))
pred <- log10(predicted.raw[, tis])
raw <- protein[,tis]
keep <- which(pred!=raw)
pred <- pred[keep]
raw <- raw[keep]
cor.raw <- cor(pred, raw, use="complete.obs")
cor.raw.sq <- cor.raw^2
cor.raw <- round(cor.raw, 2)
cor.raw.sq <- round(cor.raw.sq, 2)
print(
ggplot(data=data.frame(mrna=pred,protein=raw))+geom_point(aes(x=mrna,y=protein),colour="blue",alpha=0.3) +
theme(plot.margin=unit(c(5.5, 12.5, 5.5, 5.5), "points")) +
scale_x_continuous(breaks=1:10,labels=function(x) parse(text=paste("10^", x)),limits=c(3,10))+
scale_y_continuous(breaks=1:10,labels=function(x) parse(text=paste("10^", x)),limits=c(3,10))+
labs(x=sprintf("Scaled mRNA in %s",strFormat(tis)),
y=sprintf("Measured Protein in %s",strFormat(tis)))+
annotate("text", x = 4, y = 9.5,label=as.character(as.expression(substitute(italic(R)~"="~r,list(r=format(cor.raw,digits=2))))),parse=TRUE, size=10)+
annotate("text", x = 4, y = 9,label=as.character(as.expression(substitute(italic(R)^2~"="~r2,list(r2=format(cor.raw.sq, digits=2))))),parse=TRUE, size=10)+
theme(axis.title.x = element_text(face="bold",size=30),
axis.text.x = element_text(size=30),
axis.title.y = element_text(face="bold",size=30),
axis.text.y = element_text(size=30, hjust=0))
)
dev.off()
}
#############################################################################
############ Raw Correlations unscaled for tissues #########################
#############################################################################
for(t in fold.comparison.tissues){
pdf(sprintf("Figs/2_unscaled_raw_%s_cor.pdf", t), width=8)
pred <- mrna[, t]
raw <- protein[, t]
keep <- which(pred!=raw)
pred <- pred[keep]
raw <- raw[keep]
cor.raw <- cor(pred, raw, use="complete.obs")
cor.raw <- round(cor.raw, 2)
cor.raw.sq <- round(cor.raw^2, 2)
print(
ggplot(data=data.frame(mrna=pred, protein=raw)) +
geom_point(aes(x=mrna, y=protein), colour="blue", alpha=0.3)+
scale_x_continuous(breaks=-2:5, labels=function(x) parse(text=paste("10^", x)), limits=c(-2,5)) +
scale_y_continuous(breaks=1:10, labels=function(x) parse(text=paste("10^", x)), limits=c(3,10)) +
labs(x=sprintf("Measured mRNA"),
y=sprintf("Measured protein")) +
annotate("text", x = -1, y = 9.5,label=as.character(as.expression(substitute(italic(R)~"="~r,list(r=format(cor.raw, digits=2))))),parse=TRUE,size=10)+
annotate("text", x = -1, y = 8.8,label=as.character(as.expression(substitute(italic(R)^2~"="~r2,list(r2=format(cor.raw.sq, digits=2))))),parse=TRUE,size=10)+
theme(axis.title.x = element_text(face="bold", size=40),
axis.text.x = element_text(size=30),
axis.title.y = element_text(face="bold", size=40),
axis.text.y = element_text(size=30, hjust=0))
)
dev.off()
}
#############################################################################
############ Significant GO terms box plot ##################################
#############################################################################
## Compute mrna / prot correlation
mrnaProtInfo <- getZscores(protein, mrna)
pop.cor <- mrnaProtInfo$pop.cor
mrnaProtInfo <- getZscores(protein, protein2)