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ProgR_3.2_Analyses.R
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################################################################
################################################################
# Script: ProgR_3.1_Analyses.R
# Author: Ingrid Lonnstedt
# Date: 15/03/2013
# R version: R 2.15.2
# Details: Statistical Analyses with clinical data
################################################################
################################################################
################################################################
################################################################
#
# File paths, date
#
################################################################
################################################################
setwd(paste(getwd(), '/RESPONSIFY', sep=''))
#setwd('/Users/lonnstedt/Documents/RESPONSIFY')
prefix.norm = paste(getwd(), "/AROMA/output/", sep='')
prefix.raw = paste(getwd(), "/AROMA/rawData/responsify/", sep='')
prefix.ann = paste(getwd(), "/AROMA/annotationData/chipTypes/", sep='')
date = format(Sys.Date())
### Read clinical data
###
dat = read.delim(paste(prefix.raw, 'Clinical_data/spss working-Demos combined Leuven Bordet series-Jan2013.txt',
sep = ''), na.strings = c('', NA), check.names = F, dec = ',')[1:108,1:141]
index = c(94, 95, 97:99, 101, 102, 107)
tmpdate = dat$date_diagnosis[index]
dat$date_diagnosis = as.Date(as.character(dat$date_diagnosis),format='%d/%m/%Y')
dat$date_diagnosis[index] = as.Date(as.character(tmpdate),format='%Y-%m-%d')
index = c(98, 99, 103, 105) #106 set to 1951-07-01 manually
tmpdate = dat$Date_of_birth[index]
dat$Date_of_birth = as.Date(as.character(dat$Date_of_birth),format='%Y-%m-%d')
dat$Date_of_birth[index] = as.Date(as.character(tmpdate),format='%d/%m/%Y')
dat$Date_of_birth[106] = as.Date('1951-07-01',format='%Y-%m-%d')
dat$age = dat$date_diagnosis - dat$Date_of_birth
dat$til = as.numeric(as.character(dat$Stomal_LI.))
#Warning above is OK.
dat$ID = as.character(dat$Frozen_tissue_BO_no)
################################################################
################################################################
#
# Expression data and tils: checks and data preparation
#
################################################################
################################################################
prefix.out = paste(getwd(), "/reports/report1/TILs_on_expression/", sep='')
################################################################
### Organize data
load(paste(prefix.norm, 'Expression/Expression_estimates.RData', sep = ''))
data = data.frame(array = names(expr)[6:ncol(expr)])
data$ID = substr(data$array, 3, 6)
data$index = 1:nrow(data)
data = merge(data, subset(dat, select = c('ID','age','tumor_size','Nodal_status','ER_status', 'til', 'date_diagnosis')),
all.x = T, sort = F)
data$batch = 1
data$array = as.character(data$array)
tmp = nchar(data$array, type = 'c')
data$batch[substr(data$array, tmp, tmp) == '2'] = 2
data$batch[substr(data$array, 7, 9) == 'bis'] = 3
data$batch = as.factor(data$batch)
data$age_diag = as.numeric(data$age/365.25)
data$year_diag = as.numeric(substr(as.character(data$date_diagnosis), 1, 4))
data = data[order(data$index),]
###Load array dates
tmp = read.table(paste(prefix.norm, 'Expression/Array_dates.txt', sep = ''), sep = '\t')
#All arrays are hybridized within 30 minutes, so that is not interesting
Xdata = as.data.frame(t(as.matrix(expr[,6:ncol(expr)])))
names(Xdata) = paste('unit', expr$unit, sep = '')
unitNames = expr$unitName
rm(expr)
#Finally remove samples with missing til:
index = which(!is.na(data$til))
data = data[index,]
Xdata = Xdata[index,]
#Now, data and Xdata could be cbind() and put into linear models.
################################################################
### Clinical data overview
png(paste(prefix.out,'results/Clinical_and_exprbatch_overview.png', sep = ''),
width=400, height=400)
plot(data[,c('tumor_size', 'til', 'age_diag')])
dev.off()
par(mfrow = c(1,2))
boxplot(til~Nodal_status, data = data, xlab = 'Nodal status', ylab = 'Stomal LI (%)', col = 'wheat')
boxplot(til~ER_status, data = data, xlab = 'ER status', ylab = 'Stomal LI (%)', col = 'wheat',
xaxt = 'n')
axis(1, labels = c('ER-','ER+'), at = 1:2)
dev.print(png, file=paste(prefix.out,'results/Til_by_nodal_and_ER.png', sep = ''),
width=400, height=400)
summary(data[,c(5, 8, 11)])
################################################################
### Check unwanted variation through principal components plots
pc = prcomp(Xdata)
## Get the standard deviation and variance per principal component
sd.per.pc <- pc$sdev
var.per.pc <- sd.per.pc^2
## Display the percentage of total variance explained by each
sd.per.pc.percent <- sd.per.pc/sum(sd.per.pc)
var.per.pc.percent <- var.per.pc/sum(var.per.pc)
png(paste(prefix.out,'results/Expression perc variance per pc.png', sep = ''),
width=400, height=400)
barplot(var.per.pc.percent[1:10]*100, main='Gene expression, Percent of variance per component',
xlab='Component', ylab = '% variance')
dev.off()
newids = data$ID
#newids[c(19, 78)] = paste(newids[c(19, 78)], 'bis', sep = '')
#newids[c(25, 26, 46, 51, 80)] = paste(newids[c(25, 26, 46, 51, 80)], '_2', sep = '')
dimnames(pc$x)[[1]] = newids
## Plot components PC1 and PC2
plot(pc$x[,1:2],
type='n',
panel.first=grid(col='black'),
main=paste('PCA gene expression ',
nrow(Xdata), 'samples *', ncol(Xdata), 'genes', sep=' '),
xlab='PC1', ylab='PC2')
text(pc$x[,1:2],labels=dimnames(pc$x)[[1]],col=as.numeric(data$batch),pch=0.5)
legend('bottomleft',col=as.numeric(unique(data$batch)),
legend=paste('Batch', as.numeric(unique(data$batch))),pch=1,cex=0.7,bg='white',bty='o')
## Plot components PC1 and PC3
plot(pc$x[,c(1,3)],
type='n',
panel.first=grid(col='black'),
main=paste('PCA gene expression ',
nrow(Xdata), 'samples *', ncol(Xdata), 'genes', sep=' '),
xlab='PC1', ylab='PC3')
text(pc$x[,c(1,3)],labels=dimnames(pc$x)[[1]],col=as.numeric(data$batch),pch=0.5)
legend('bottomleft',col=as.numeric(unique(data$batch)),
legend=paste('Batch', as.numeric(unique(data$batch))),pch=1,cex=0.7,bg='white',bty='o')
## Plot components PC1 and PC4
plot(pc$x[,c(1,4)],
type='n',
panel.first=grid(col='black'),
main=paste('PCA gene expression ',
nrow(Xdata), 'samples *', ncol(Xdata), 'genes', sep=' '),
xlab='PC1', ylab='PC4')
text(pc$x[,c(1,4)],labels=dimnames(pc$x)[[1]],col=as.numeric(data$batch),pch=0.5)
legend('bottomleft',col=as.numeric(unique(data$batch)),
legend=paste('Batch', as.numeric(unique(data$batch))),pch=1,cex=0.7,bg='white',bty='o')
## Plot components PC2 and PC3
plot(pc$x[,c(2,3)],
type='n',
panel.first=grid(col='black'),
main=paste('PCA gene expression ',
nrow(Xdata), 'samples *', ncol(Xdata), 'genes', sep=' '),
xlab='PC2', ylab='PC3')
text(pc$x[,c(2,3)],labels=dimnames(pc$x)[[1]],col=as.numeric(data$batch),pch=0.5)
legend('bottomleft',col=as.numeric(unique(data$batch)),
legend=paste('Batch', as.numeric(unique(data$batch))),pch=1,cex=0.7,bg='white',bty='o')
## Plot components PC2 and PC4
plot(pc$x[,c(2,4)],
type='n',
panel.first=grid(col='black'),
main=paste('PCA gene expression ',
nrow(Xdata), 'samples *', ncol(Xdata), 'genes', sep=' '),
xlab='PC2', ylab='PC4')
text(pc$x[,c(2,4)],labels=dimnames(pc$x)[[1]],col=as.numeric(data$batch),pch=0.5)
legend('bottomleft',col=as.numeric(unique(data$batch)),
legend=paste('Batch', as.numeric(unique(data$batch))),pch=1,cex=0.7,bg='white',bty='o')
## Plot components PC3 and PC4
plot(pc$x[,c(3,4)],
type='n',
panel.first=grid(col='black'),
main=paste('PCA gene expression ',
nrow(Xdata), 'samples *', ncol(Xdata), 'genes', sep=' '),
xlab='PC3', ylab='PC4')
text(pc$x[,c(3,4)],labels=dimnames(pc$x)[[1]],col=as.numeric(data$batch),pch=0.5)
legend('bottomleft',col=as.numeric(unique(data$batch)),
legend=paste('Batch', as.numeric(unique(data$batch))),pch=1,cex=0.7,bg='white',bty='o')
## Plot components PC1 and PC2 coloured by ER status
plot(pc$x[,1:2],
type='n',
panel.first=grid(col='black'),
main=paste('PCA gene expression ',
nrow(Xdata), 'samples *', ncol(Xdata), 'genes', sep=' '),
xlab='PC1', ylab='PC2')
text(pc$x[,1:2],labels=dimnames(pc$x)[[1]],col=as.numeric(data$ER_status)+1,pch=0.5)
legend('bottomleft',col=as.numeric(unique(data$ER_status)+1),
legend=paste('ER', as.numeric(unique(data$ER_status))),pch=1,cex=0.7,bg='white',bty='o')
## Plot components PC1 and PC2 coloured by Nodal status
plot(pc$x[,1:2],
type='n',
panel.first=grid(col='black'),
main=paste('PCA gene expression ',
nrow(Xdata), 'samples *', ncol(Xdata), 'genes', sep=' '),
xlab='PC1', ylab='PC2')
text(pc$x[,1:2],labels=dimnames(pc$x)[[1]],col=as.numeric(data$Nodal_status)+1,pch=0.5)
legend('bottomleft',col=as.numeric(unique(data$Nodal_status)+1),
legend=paste('Nodal status', as.numeric(unique(data$Nodal_status))),pch=1,cex=0.7,bg='white',bty='o')
## Plot components PC1 and PC2 coloured by year of diagnosis
plot(pc$x[,1:2],
type='n',
panel.first=grid(col='black'),
main=paste('PCA gene expression ',
nrow(Xdata), 'samples *', ncol(Xdata), 'genes', sep=' '),
xlab='PC1', ylab='PC2')
text(pc$x[,1:2],labels=dimnames(pc$x)[[1]],col=data$year_diag,pch=0.5)
legend('bottomleft',col=as.numeric(unique(data$year_diag)),
legend=paste('Year of diagnosis', as.numeric(unique(data$year_diag))),pch=1,cex=0.7,bg='white',bty='o')
## Plot components PC1 and PC2 coloured by age_diag
colvec = 1
colvec[data$age_diag>55] = 2
plot(pc$x[,1:2],
type='n',
panel.first=grid(col='black'),
main=paste('PCA gene expression ',
nrow(Xdata), 'samples *', ncol(Xdata), 'genes', sep=' '),
xlab='PC1', ylab='PC2')
text(pc$x[,1:2],labels=dimnames(pc$x)[[1]],col=unique(colvec),pch=0.5)
legend('bottomleft',col=unique(colvec),
legend=paste('Age at diag', unique(colvec)),pch=1,cex=0.7,bg='white',bty='o')
## Plot components PC1 and PC2 coloured by tumor_size
colvec = 1
colvec[data$tumor_size>40] = 2
plot(pc$x[,1:2],
type='n',
panel.first=grid(col='black'),
main=paste('PCA gene expression ',
nrow(Xdata), 'samples *', ncol(Xdata), 'genes', sep=' '),
xlab='PC1', ylab='PC2')
text(pc$x[,1:2],labels=dimnames(pc$x)[[1]],col=unique(colvec),pch=0.5)
legend('bottomleft',col=unique(colvec),
legend=paste('Tumour size', unique(colvec)),pch=1,cex=0.7,bg='white',bty='o')
data$pc1 = pc$x[,1]
plot(data[,5:ncol(data)])
tmp = subset(data, select = c(ID, pc1))
dat = merge(dat, tmp, all.x = T)
par(ask = T)
for (i in 70:(ncol(dat)-1)){ plot(dat$pc1,dat[,i], ylab = names(dat)[i]) }
#We have dependendy between PC1 and some biology variables, but not with array dates or similar.
################################################################
################################################################
#
# Genes expressions assoxiated with TIL
#
################################################################
################################################################
prefix.out = paste(getwd(), "/reports/report1/TILs_on_expression/", sep='')
#############################################
# RUV model
library(ruv)
rinv<-RUVinv(Y=as.matrix(log2(Xdata)), X=as.matrix(data$til, ncol = 1), ctl = rep(T, ncol(Xdata)), Z = NULL)
#hist(rinv$p,breaks=c(0,0.001,0.01,0.05,0.1,0.2,0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1))
radj<-variance_adjust(rinv)
#hist(radj$p.rsvar,breaks=c(0,0.001,0.01,0.05,0.1,0.2,0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1))
#hist(radj$p.ebayes,breaks=c(0,0.001,0.01,0.05,0.1,0.2,0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1))
hst = hist(radj$p.evar,breaks=c(0,0.001,0.01,0.05,0.1,0.2,0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1),
main = 'Empirical variances expression p-values', xlab = 'p-values')
dev.print(png, file=paste(prefix.out,'results/Pvalue_hist_expression.png', sep = ''),
width=400, height=400)
qqnorm(rinv$betahat)
mtext('Expression ~ TIL coefficients')
dev.print(png, file=paste(prefix.out,'results/TILcoefficients_expression.png', sep = ''),
width=400, height=400)
#Index of the probe sets with p-value<0.01:
indexRUV = which(radj$p.evar<0.01) #Gives 785 probe sets
ann = read.csv(paste(prefix.ann, 'HG-U133_Plus_2/HG-U133_Plus_2.na33.annot.csv',
sep = ''), skip = 25)
#radj has p-values (p.evar)
#indexRUV has probe sets with low p-values
#ann has annotation data for the complete array
#hst has p-value histogram
#############################################
# Rearrangements of RUV model results
#Gene symbol list for GOstat test of enrichment
highann = subset(ann, Probe.Set.ID %in% unitNames[indexRUV])
highsymbols = unique(highann$Gene.Symbol)
highsymbols = highsymbols[highsymbols != '---']
write.table(highsymbols, file = paste(prefix.out, 'Expr_on_TIL_symbols.txt'), col.names=F,
row.names = F, quote = F)
#FDR estimates and toptable
mean.freq = mean((hst$counts/(hst$breaks[-1] - hst$breaks[-length(hst$breaks)]))[3:length(hst$counts)])
toptable = data.frame('Probe.Set.Number' = (1:length(radj$betahat))[indexRUV], 'Probe.Set.ID' = unitNames[indexRUV],
log2exp = radj$betahat[indexRUV], p.value = radj$p.evar[indexRUV])
Nle = numeric(nrow(toptable))
Nle[order(toptable$p.value)] = 1:nrow(toptable)
Ele = toptable$p.value * mean.freq
toptable$FDR = Ele/(Nle)
highann = subset(ann, Probe.Set.ID %in% unitNames[indexRUV])
tmp = subset(highann, select = c('Probe.Set.ID','Gene.Symbol'))
toptable = merge(tmp, toptable)
toptable = toptable[order(toptable$FDR),]
toptable$rank = 1:nrow(toptable)
toptable = toptable[, c(ncol(toptable), 1:(ncol(toptable)-1))]
write.table(toptable, file = paste(prefix.out, 'Expr_by_TIL_toptable.txt'),
row.names = F, quote = F)
#Heatmap
set = paste('unit', c(sample(ncol(Xdata), size = 10), toptable$Probe.Set.Number[c(50:1)]), sep = '')
mat = Xdata[,set]
mat = t(mat)
mat = mat[, order(data$til)]
heatmap(log2(mat), Rowv = NA, Colv = NA, col = heat.colors(256), margin = c(10,7))
abline(h = 11.5)
abline(v = 53.4)
#############################################
# Multiple linear models
out = matrix(NA, ncol = 2, nrow = ncol(Xdata))
colnames(out) = c('Estimate', 'Pr(>|t|)')
for (i in 1:ncol(Xdata)){
data$x = log2(Xdata[,i])
mod = lm(til ~ x + batch + age_diag + tumor_size + Nodal_status + ER_status, data = data)
out[i,] = summary(mod)$coef['x',colnames(out)]
}
hist(out[,2],breaks=c(0,0.001,0.01,0.05,0.1,0.2,0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1),
main = 'Expression linear model P-values', xlab = 'p-values')
out = cbind(out, p.adjust(out[,2], method = 'BH'))
hist(out[,3],breaks=c(0,0.001,0.01,0.05,0.1,0.2,0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1),
main = 'Expression linear model FDR values', xlab = 'FDR')
mtext('Multiple models')
dev.print(png, file=paste(prefix.out,'results/Pvalue2_hist_expression.png', sep = ''),
width=400, height=400)
qqnorm(out[,1])
mtext('Multiple TIL ~ expression model coefficients')
dev.print(png, file=paste(prefix.out,'results/TILcoefficients2_expression.png', sep = ''),
width=400, height=400)
#Index of the probe sets with p-value<0.01:
indexLM = which(out[, 2]<0.001) #Gives 1187 probe sets
#############################################
# Rearrangements of Multiple linear models results
#Gene symbol list for GOstat test of enrichment
highann = subset(ann, Probe.Set.ID %in% unitNames[indexLM])
highsymbols = unique(highann$Gene.Symbol)
highsymbols = highsymbols[highsymbols != '---']
write.table(highsymbols, file = paste(prefix.out, 'TIL_on_expr2_symbols.txt'), col.names=F,
row.names = F, quote = F)
#Toptable
toptable = data.frame('Probe.Set.Number' = (1:nrow(out))[indexLM],
'Probe.Set.ID' = unitNames[indexLM],
log2exp = out[indexLM, 'Estimate'],
p.value = out[indexLM, 'Pr(>|t|)'],
FDR = out[indexLM, 3])
highann = subset(ann, Probe.Set.ID %in% unitNames[indexRUV])
tmp = subset(highann, select = c('Probe.Set.ID','Gene.Symbol'))
toptable = merge(tmp, toptable)
toptable = toptable[order(toptable$FDR),]
toptable$rank = 1:nrow(toptable)
toptable = toptable[, c(ncol(toptable), 1:(ncol(toptable)-1))]
write.table(toptable, file = paste(prefix.out, 'TIL_by_exprLM_toptable.txt'),
row.names = F, quote = F)
#Heatmap
set.seed(567890)
set = paste('unit', c(sample(ncol(Xdata), size = 10), toptable$Probe.Set.Number[c(50:1)]), sep = '')
mat = Xdata[,set]
mat = t(mat)
mat = mat[, order(data$til)]
heatmap(log2(mat), Rowv = NA, Colv = NA, col = heat.colors(256), margin = c(10,7))
save.image(paste(prefix.out, 'TILs_on_expr.RData', sep = ''))
################################################################
# Naive RUV adjustment model
## Y: expression matrix where the rows are the samples and the columns are the genes.
## cIdx: column index of the negative control genes in Y, for estimation of unwanted variation.
## nuCoeff: regularization parameter for the unwanted variation.
## k: rank of the unwanted variation term.
naiveRandRuv <- function(Y, cIdx, nuCoeff=1e-3, k=m){
## W is the square root of the empirical covariance on the control
## genes.
svdYc <- svd(Y[, cIdx])
W <- svdYc$u[, 1:k] %*% diag(svdYc$d[1:k]) #/ sqrt(length(cIdx)+1)
## Regularization heuristic: nu is a fraction of the largest eigenvalue of WW'
nu <- nuCoeff*svdYc$d[1]^2 #/ (length(cIdx)+1)
## Naive correction: ridge regression of Y against W
nY <- Y - W %*% solve(t(W)%*%W + nu*diag(k), t(W) %*% Y)
return(nY)
}
rnaive10 = naiveRandRuv(Y=as.matrix(log2(Xdata)), cIdx = rep(T, ncol(Xdata)), nuCoeff=1e-3, k=10)
res10 = matrix(NA, ncol = 2, nrow = ncol(Xdata))
colnames(res10) = c('Estimate', 'Pr(>|t|)')
for (i in 1:ncol(Xdata)){
data$x = rnaive10[,i]
mod = lm(til ~ x + age_diag + tumor_size + Nodal_status + ER_status, data = data)
res10[i,] = summary(mod)$coef['x',colnames(res10)]
}
hist(res10[,2],breaks=c(0,0.001,0.01,0.05,0.1,0.2,0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1),
main = 'Naive RUV P-values', xlab = 'p-values')
mtext('k = 10')
rnaive = naiveRandRuv(Y=as.matrix(log2(Xdata)), cIdx = rep(T, ncol(Xdata)), nuCoeff=1e-3, k=20)
res = matrix(NA, ncol = 2, nrow = ncol(Xdata))
colnames(res) = c('Estimate', 'Pr(>|t|)')
for (i in 1:ncol(Xdata)){
data$x = rnaive[,i]
mod = lm(til ~ x + age_diag + tumor_size + Nodal_status + ER_status, data = data)
res[i,] = summary(mod)$coef['x',colnames(res)]
}
hist(res[,2],breaks=c(0,0.001,0.01,0.05,0.1,0.2,0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1),
main = 'Naive RUV P-values', xlab = 'p-values')
mtext('k = 20')
rnaive100 = naiveRandRuv(Y=as.matrix(log2(Xdata)), cIdx = rep(T, ncol(Xdata)), nuCoeff=1e-1, k=20)
res100 = matrix(NA, ncol = 2, nrow = ncol(Xdata))
colnames(res100) = c('Estimate', 'Pr(>|t|)')
for (i in 1:ncol(Xdata)){
data$x = rnaive100[,i]
mod = lm(til ~ x + age_diag + tumor_size + Nodal_status + ER_status, data = data)
res100[i,] = summary(mod)$coef['x',colnames(res100)]
}
hist(res100[,2],breaks=c(0,0.001,0.01,0.05,0.1,0.2,0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1),
main = 'Naive RUV P-values', xlab = 'p-values')
mtext('Times 100')
rnaive.01 = naiveRandRuv(Y=as.matrix(log2(Xdata)), cIdx = rep(T, ncol(Xdata)), nuCoeff=1e-5, k=20)
res.01 = matrix(NA, ncol = 2, nrow = ncol(Xdata))
colnames(res.01) = c('Estimate', 'Pr(>|t|)')
for (i in 1:ncol(Xdata)){
data$x = rnaive.01[,i]
mod = lm(til ~ x + age_diag + tumor_size + Nodal_status + ER_status, data = data)
res.01[i,] = summary(mod)$coef['x',colnames(res.01)]
}
hist(res.01[,2],breaks=c(0,0.001,0.01,0.05,0.1,0.2,0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1),
main = 'Naive RUV P-values', xlab = 'p-values')
mtext('Div 100')
#Index of the probe sets with p-value<0.01:
indexNR10 = which(res10[, 2]<0.001) #Gives 862 probe sets, out of which 859 are shared with indexNR
indexNR = which(res[, 2]<0.001) #Gives 889 probe sets, out of which 835 are shared with indexLM
indexNR100 = which(res100[, 2]<0.001) #Gives 1231 probe sets, out of which 877 are shared with indexNR
#and 1021 are shared with indexLM
indexNR.01 = which(res.01[, 2]<0.001) #Gives 26 probe sets, this histogram looks bad
indexNRx = which(res[, 2]<0.01) #Gives 889 probe sets, out of which 835 are shared with indexNR
#Decided to use the intersect(indexNR, indexLM) as toptable
#Print p-value histogram of the naive RUV with k = 20 and nu = 1e-3 (indexNR model)
hstNR = hist(res[,2],breaks=c(0,0.001,0.01,0.05,0.1,0.2,0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1),
main = 'Expression naive RUV p-values', xlab = 'p-values')
mtext('Multiple models')
dev.print(png, file=paste(prefix.out,'results/Pvalue3_hist_expression.png', sep = ''),
width=400, height=400)
#############################################
# Rearrangements of Naive RUV results, which will be considered primary
(the 835 probes that intersect with the linear models will be primary)
topindex = intersect(indexLM, indexNR)
#Gene symbol list for GOstat test of enrichment
highann = subset(ann, Probe.Set.ID %in% unitNames[topindex])
highsymbols = unique(highann$Gene.Symbol)
highsymbols = highsymbols[highsymbols != '---']
write.table(highsymbols, file = paste(prefix.out, 'TIL_on_expr_TOP_symbols.txt'), col.names=F,
row.names = F, quote = F, sep = '\t')
#Toptable
toptable = data.frame('Probe.Set.Number' = (1:nrow(res))[topindex],
'Probe.Set.ID' = unitNames[topindex],
log2exp = res[topindex, 'Estimate'],
p.value = res[topindex, 'Pr(>|t|)'],
FDR = p.adjust(res[,2], method = 'fdr')[topindex])
highann = subset(ann, Probe.Set.ID %in% unitNames[topindex])
tmp = subset(highann, select = c('Probe.Set.ID','Gene.Symbol'))
toptable = merge(tmp, toptable)
toptable = toptable[order(toptable$FDR),]
toptable$rank = 1:nrow(toptable)
toptable = toptable[, c(ncol(toptable), 1:(ncol(toptable)-1))]
write.table(toptable, file = paste(prefix.out, 'TIL_by_expression_toptable.txt'),
row.names = F, quote = F, sep = '\t')
#Heatmap
set.seed(12345)
set = paste('unit', c(sample(ncol(Xdata), size = 10), toptable$Probe.Set.Number[c(50:1)]), sep = '')
mat = Xdata[,set]
mat = t(mat)
mat = mat[, order(data$til)]
heatmap(log2(mat), Rowv = NA, Colv = NA, col = heat.colors(256), margin = c(10,7))
library(RColorBrewer)
hmcols<-colorRampPalette(c('blue','white','orange','red', 'black'))(256)
heatmap(log2(mat), Rowv = NA, Colv = NA, col = hmcols, margin = c(5,0), labRow = '', labCol = '',
RowSideColors = c(rep('darkorange', 10), rep('darkblue', 50)))
#axis(1, line = 2, labels = c('', '','','',''), at = seq(0, 800, 200))
title(xlab = 'Samples ordered by TIL (maximum TIL to the right)', line = 2)
#text(x = 1, y = 10, labels = 'Random probe sets')
title(ylab = '10 random probe sets ', line = 0, cex = .8)
title(ylab = ' Top 50 probe sets ordered by p-value', line = 0, cex = .8)
title(ylab = ' (minimum p at the top)', line = -1, cex = .8)
legend("right", fill = c('black','red','orange','white', 'blue'),
legend = rep('', 5), bty = 'n', border = 'black', y.intersp = .5, cex = 2)
text(9.5,.4,'Max expression', cex = .8)
text(9.5,-.4,'Min expression', cex = .8)
#dev.print(png, file=paste(prefix.out,'results/TIL_expression_heatmap.png', sep = ''),
# width=640, height=500)
save.image(paste(prefix.out, 'TILs_on_expr.RData', sep = ''))
#####################################################################
#####################################################################
#
# IDFS relapse associated with gene expression
#
#
#####################################################################
#####################################################################
prefix.out = paste(getwd(), "/AROMA/reports/Relapse_vs_expression/", sep='')
### Read IDFS relapse data and add to the other clinical data in dat
tmp = read.delim(paste(prefix.raw, 'HER2-lev-bordet-Aug2012-responsify.txt',
sep = ''), na.strings = c('', NA), check.names = F, dec = ',')
tmp$ID = as.character(dat$Frozen_tissue_BO_no)
tmp = subset(tmp, select = c('ID','IDFS_Y_N','IDFS_days'))
dat = merge(dat, tmp, sort = F, all.x = T)
################################################################
### Organize data
load(paste(prefix.norm, 'Expression/Expression_estimates.RData', sep = ''))
data = data.frame(array = names(expr)[6:ncol(expr)])
data$ID = substr(data$array, 3, 6)
data$index = 1:nrow(data)
data = merge(data, subset(dat, select = c('ID','age','tumor_size','Nodal_status',
'ER_status', 'til', 'date_diagnosis',
'IDFS_Y_N','IDFS_days')),
all.x = T, sort = F)
data$batch = 1
data$array = as.character(data$array)
tmp = nchar(data$array, type = 'c')
data$batch[substr(data$array, tmp, tmp) == '2'] = 2
data$batch[substr(data$array, 7, 9) == 'bis'] = 3
data$batch = as.factor(data$batch)
data$age_diag = as.numeric(data$age/365.25)
data$year_diag = as.numeric(substr(as.character(data$date_diagnosis), 1, 4))
data = data[order(data$index),]
data$IDFS_days = as.numeric(data$IDFS_days)
Xdata = as.data.frame(t(as.matrix(expr[,6:ncol(expr)])))
names(Xdata) = paste('unit', expr$unit, sep = '')
unitNames = expr$unitName
rm(expr)
gc()
#Finally remove samples with missing IDFS:
index = which(!is.na(data$IDFS_Y_N) & !is.na(data$IDFS_days))
data = data[index,]
Xdata = Xdata[index,]
#82 samples
#Now, data and Xdata could be cbind() and put into Cox models.
############################################################
# Cox models without naive RUV adjustment
library(survival)
out = matrix(NA, ncol = 2, nrow = ncol(Xdata))
colnames(out) = c('exp(coef)', 'Pr(>|z|)')
for (i in 1:ncol(Xdata)){
data$x = log2(Xdata[,i])
mod = coxph(Surv(IDFS_days, IDFS_Y_N) ~ x + age_diag + tumor_size + Nodal_status + ER_status, data = data)
out[i,] = summary(mod)$coef['x',colnames(out)]
}
hist(out[,2],breaks=c(0,0.001,0.01,0.05,0.1,0.2,0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1),
main = 'Cox model P-values', xlab = 'p-values')
mtext('Multiple models')
dev.print(png, file=paste(prefix.out,'Pvalue_hist_relapse_expression.png', sep = ''),
width=400, height=400)
out = cbind(out, p.adjust(out[,2], method = 'BH'))
hist(out[,3],breaks=c(0,0.001,0.01,0.05,0.1,0.2,0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1),
main = 'Cox model FDR values', xlab = 'FDR')
qqnorm(out[,1])
mtext('Multiple Relapse time ~ expression model coefficients')
#Index of the probe sets with p-value<0.01:
indexLM = which(out[, 2]<0.01) #Gives 352 probe sets
length(indexLM)
################################################################
# Naive RUV adjustment (Try 4 different levels of adjustment)
rnaive10 = naiveRandRuv(Y=as.matrix(log2(Xdata)), cIdx = rep(T, ncol(Xdata)), nuCoeff=1e-3, k=10)
res10 = matrix(NA, ncol = 2, nrow = ncol(Xdata))
colnames(res10) = c('exp(coef)', 'Pr(>|z|)')
for (i in 1:ncol(Xdata)){
data$x = rnaive10[,i]
mod = coxph(Surv(IDFS_days, IDFS_Y_N) ~ x + age_diag + tumor_size + Nodal_status + ER_status, data = data)
res10[i,] = summary(mod)$coef['x',colnames(res10)]
}
hist(res10[,2],breaks=c(0,0.001,0.01,0.05,0.1,0.2,0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1),
main = 'Naive RUV P-values', xlab = 'p-values')
mtext('k = 10')
rnaive = naiveRandRuv(Y=as.matrix(log2(Xdata)), cIdx = rep(T, ncol(Xdata)), nuCoeff=1e-3, k=20)
res = matrix(NA, ncol = 2, nrow = ncol(Xdata))
colnames(res) = c('exp(coef)', 'Pr(>|z|)')
for (i in 1:ncol(Xdata)){
data$x = rnaive[,i]
mod = coxph(Surv(IDFS_days, IDFS_Y_N) ~ x + age_diag + tumor_size + Nodal_status + ER_status, data = data)
res[i,] = summary(mod)$coef['x',colnames(res)]
}
hist(res[,2],breaks=c(0,0.001,0.01,0.05,0.1,0.2,0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1),
main = 'Naive RUV P-values', xlab = 'p-values')
mtext('k = 20')
rnaive100 = naiveRandRuv(Y=as.matrix(log2(Xdata)), cIdx = rep(T, ncol(Xdata)), nuCoeff=1e-1, k=20)
res100 = matrix(NA, ncol = 2, nrow = ncol(Xdata))
colnames(res100) = c('exp(coef)', 'Pr(>|z|)')
for (i in 1:ncol(Xdata)){
data$x = rnaive100[,i]
mod = coxph(Surv(IDFS_days, IDFS_Y_N) ~ x + age_diag + tumor_size + Nodal_status + ER_status, data = data)
res100[i,] = summary(mod)$coef['x',colnames(res100)]
}
hist(res100[,2],breaks=c(0,0.001,0.01,0.05,0.1,0.2,0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1),
main = 'Naive RUV P-values', xlab = 'p-values')
mtext('Times 100')
rnaive.01 = naiveRandRuv(Y=as.matrix(log2(Xdata)), cIdx = rep(T, ncol(Xdata)), nuCoeff=1e-5, k=20)
res.01 = matrix(NA, ncol = 2, nrow = ncol(Xdata))
colnames(res.01) = c('exp(coef)', 'Pr(>|z|)')
for (i in 1:ncol(Xdata)){
data$x = rnaive.01[,i]
mod = coxph(Surv(IDFS_days, IDFS_Y_N) ~ x + age_diag + tumor_size + Nodal_status + ER_status, data = data)
res.01[i,] = summary(mod)$coef['x',colnames(res.01)]
}
hist(res.01[,2],breaks=c(0,0.001,0.01,0.05,0.1,0.2,0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1),
main = 'Naive RUV P-values', xlab = 'p-values')
#mtext('Div 100')
mtext('Multiple models, k = 20, nu = 1e-5')
dev.print(png, file=paste(prefix.out,'Pvalue2_hist_relapse_expression.png', sep = ''),
width=400, height=400)
#No significances!!!
save.image(paste(prefix.out, 'IDFSs_on_expr.RData', sep = ''))
#List of genes with p-value <0.01 in both RUV (res.01) and non-adjusted Cox-models:
ixRUV = which(res.01[,2]<0.01)
ixCox = which(out[,2]<0.01)
topindex = intersect(ixRUV, ixCox)
#Gene symbol list for GOstat test of enrichment
highann = subset(ann, Probe.Set.ID %in% unitNames[topindex])
highsymbols = unique(highann$Gene.Symbol)
highsymbols = highsymbols[highsymbols != '---']
write.table(highsymbols, file = paste(prefix.out, 'Relapse_on_expr_TOP_symbols.txt'), col.names=F,
row.names = F, quote = F, sep = '\t')
#Toptable
toptable = data.frame('Probe.Set.Number' = (1:nrow(res))[topindex],
'Probe.Set.ID' = unitNames[topindex],
exp.coef = res.01[topindex, 'exp(coef)'],
p.value = res.01[topindex, 'Pr(>|z|)'],
FDR = p.adjust(res.01[,2], method = 'fdr')[topindex])
highann = subset(ann, Probe.Set.ID %in% unitNames[topindex])
tmp = subset(highann, select = c('Probe.Set.ID','Gene.Symbol'))
toptable = merge(tmp, toptable)
toptable = toptable[order(toptable$FDR),]
toptable$rank = 1:nrow(toptable)
toptable = toptable[, c(ncol(toptable), 1:(ncol(toptable)-1))]
write.table(toptable, file = paste(prefix.out, 'Relapse_by_expression_toptable.txt'),
row.names = F, quote = F, sep = '\t')
#Heatmap
set.seed(12345)
set = paste('unit', c(sample(ncol(Xdata), size = 10), toptable$Probe.Set.Number[c(50:1)]), sep = '')
mat = Xdata[,set]
mat = t(mat)
mat = mat[, order(data$IDFS_Y_N, -data$IDFS_days)]
heatmap(log2(mat), Rowv = NA, Colv = NA, col = heat.colors(256), margin = c(10,7))
library(RColorBrewer)
hmcols<-colorRampPalette(c('blue','white','orange','red', 'black'))(256)
heatmap(log2(mat), Rowv = NA, Colv = NA, col = hmcols, margins = c(5,0), labRow = '', labCol = '',
RowSideColors = c(rep('darkorange', 10), rep('darkblue', 50)),
ColSideColors = c(rep('darkgreen', table(data$IDFS_Y_N)[1]), rep('red', table(data$IDFS_Y_N)[2])))
#axis(1, line = 2, labels = c('', '','','',''), at = seq(0, 800, 200))
#text(x = 1, y = 10, labels = 'Random probe sets')
title(ylab = '10 random probe sets ', line = -2, cex = .8)
title(ylab = ' Top 50 probe sets ordered by p-value', line = -2, cex = .8)
title(ylab = ' (minimum p at the top)', line = -3, cex = .8)
legend("right", fill = c('black','red','orange','white', 'blue'),
legend = rep('', 5), bty = 'n', border = 'black', y.intersp = .5, cex = 2)
text(.97,.6,'Max', cex = .8)
text(.97,.4,'Min', cex = .8)
dev.print(png, file=paste(prefix.out,'Relapse_expression_heatmap.png', sep = ''),
width=750, height=500)
################################################################
################################################################
#
# CN segments and tils: data preparation
#
################################################################
################################################################
prefix.out = paste(getwd(), "/reports/report1/TILs_on_CN_segments/", sep='')
###Organise CN data in significant segments
#gis = read.delim(file.path('GISTIC', 'Output','WithXp0.9.all_lesions.conf_90.txt'))
#gis = read.delim(file.path('GISTIC', 'Output','WithX.all_lesions.conf_75.txt'))
gis = read.delim(file.path('GISTIC', 'Output','WithXq0.1.all_lesions.conf_75.txt'))
nc = nchar(as.character(gis[,1]))
ind = which(substr(as.character(gis[,1]), nc-5, nc) == 'values')
gis.I = gis[setdiff(1:nrow(gis), ind),]
gis = gis[ind,]
Xdata = t(gis[,10:63])
nc = nchar(as.character(gis[,1]))
colnames(Xdata) = substr(as.character(gis[,1]), 1, nc-12)
###Prepare clinical variables
data = subset(dat, select = c('ID','age','tumor_size','Nodal_status','ER_status', 'til', 'date_diagnosis'))
data$batch = 1
data$batch[data$ID %in% c('3860','4541','4868')] = 2
gis.id = unlist(lapply(strsplit(rownames(Xdata), split = '.', fixed = T),function(x) x[2]))
data = subset(data, ID %in% gis.id) #Limit the arrays to those for which we have CNs
data$index = 1:nrow(data)
data$age_diag = as.numeric(data$age/365.25)
data$year_diag = as.numeric(substr(as.character(data$date_diagnosis), 1, 4))
data = data[order(data$index),]
#Finally remove samples with missing til:
index = which(!is.na(data$til))
data = data[index,]
Xdata = Xdata[index,]
#Now, data and Xdata could be cbind() and put into linear models.
#There are 53 patients with CNs and TILs (possibly add 2 nexus later)
################################################################
### Clinical data overview
plot(data[,c('tumor_size', 'til', 'age_diag')]) #No essential difference from the plots of those
#samples that have gene expression values, although the patient with the highest TIL in expression plot is
#not in this dataset.
png(paste(prefix.out,'Til_by_nodal_and_ER_CN.png', sep = ''), width = 400, height = 400)
par(mfrow = c(1,2))
boxplot(til~Nodal_status, data = data, xlab = 'Nodal status', ylab = 'Stomal LI (%)', col = 'wheat')
boxplot(til~ER_status, data = data, xlab = 'ER status', ylab = 'Stomal LI (%)', col = 'wheat',
xaxt = 'n')
axis(1, labels = c('ER-','ER+'), at = 1:2)
dev.off()
#and the ones we have show a higher median TIL in ER+ than ER-.
summary(data[,c(5, 8, 11)])
################################################################
### CN segment overview
wp = as.character(gis.I$Wide.Peak.Limits)
tmp = unlist(lapply(strsplit(wp, split = ':', fixed = T), function(x) x[2]))
tmp = unlist(lapply(strsplit(tmp, split = '(', fixed = T), function(x) x[1]))
reglen = unlist(lapply(strsplit(tmp, split = '-', fixed = T), function(x) as.numeric(x[2])-as.numeric(x[1])))
Is = gis.I[,10:63]
#Region overview
par(mfrow = c(1,3))
plot(reglen/1000, apply(Is, 1, sum), log = 'x', xlab = 'Region length', ylab = 'Sample scores')
plot(reglen/1000, apply(Is == 1, 1, sum), log = 'x', xlab = 'Region length',
ylab = 'Samples with minor CNA')
plot(reglen/1000, apply(Is == 2, 1, sum), log = 'x', xlab = 'Region length',
ylab = 'Samples with major CNA')
#############################################
# Check scatter plots
par(mfrow = c(5, 5), ask = T, mar = c(.1, .1, .1, .1))
for (i in 1:ncol(Xdata)){
data$x = Xdata[,i]
boxplot(til ~ x, data = data)
}
#############################################
# Multiple linear models
out = matrix(NA, ncol = 2, nrow = ncol(Xdata))
colnames(out) = c('Estimate', 'Pr(>|t|)')
for (i in 1:ncol(Xdata)){
data$x = Xdata[,i]
mod = lm(til ~ x + batch + age_diag + tumor_size + Nodal_status + ER_status, data = data)
out[i,] = summary(mod)$coef['x',colnames(out)]
}
par(mfrow=c(1,2))
hist(out[,2],breaks=c(0,0.001,0.01,0.05,0.1,0.2,0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1),
xlab = 'p', main = '')
out = cbind(out, p.adjust(out[,2], method = 'BH'))
hist(out[,3],breaks=c(0,0.001,0.01,0.05,0.1,0.2,0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1),
xlab = 'FDR', main = '')
dev.print(png, file=paste(prefix.out,'Pvalue_CN.png', sep = ''),
width=400, height=300)
qqnorm(out[,1])
#Index of the probe sets with p-value<0.01:
indexLM = which(out[, 2]<0.01)
length(indexLM) #[1] 231
#############################################
# Rearrangements of Naive RUV 100 results, which will be considered primary
#(the 155 segments that intersect with the linear models will be preliminary primary)
topindex = intersect(indexLM, indexNR100)
topsegs = seginfo[topindex,]
topsegs$space = as.character(topsegs$space)
table(topsegs$space)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
23 43 0 2 0 6 2 3 2 0 3 5 0 30 0 13 12 0 0 5 0 0 6
##### Read Refseq gene info
#Like to tie these segments to gene symbol, so we can find corresponding expression probes
plac = read.delim(paste(prefix.ann, "../refGene_hg19.txt",sep=''), header=F)
plac = subset(plac, select=c('V3', 'V5','V6','V13'))
names(plac) = c('chrom','start','end','Gene.Symbol')
plac$chrom = as.character(plac$chrom)
plac$chrom[plac$chrom == 'chrX'] = 'chr23'
plac$chrom[plac$chrom == 'chrY'] = 'chr24'
plac$chrom[plac$chrom == 'chrM'] = 'chr25'
plac = subset(plac, !is.na(start) & !is.na(end) & !is.na(chrom))
plac$chrom = substr(plac$chrom, 4, nchar(plac$chrom, type = 'c'))
plac$width = plac$end - plac$start +1
oldind = mclapply(1:length(unique(plac$Gene.Symbol)), function(i){
index = which(plac$Gene.Symbol == unique(plac$Gene.Symbol)[i])
newindex = (index[order(-plac$width[index])])[1]
setdiff(index, newindex)
})
oldind = unlist(oldind)
plac0 = plac[setdiff(which(!is.na(plac$chrom)), oldind),] #Null set for GO Fisher
ann = read.csv(paste(prefix.ann, 'HG-U133_Plus_2/HG-U133_Plus_2.na33.annot.csv',
sep = ''), skip = 25)
tmp = subset(ann, Gene.Symbol != '---', select = c('Gene.Symbol','Probe.Set.ID'))
plac = merge(plac0, tmp, all.x = T)
plac = RangedData(IRanges(start = plac$start, end = plac$end),
space = plac$chrom, universe = 'hg19',
Gene.Symbol = plac$Gene.Symbol, Probe.Set.ID= plac$Probe.Set.ID)
topsegs = RangedData(IRanges(start = topsegs$start, end = topsegs$end),
space = topsegs$space, universe = 'hg19')
hits = findOverlaps(query = plac, subject = topsegs)
mymax = 0
for (i in 1:length(names(hits))){
nhits = countSubjectHits(hits[[names(hits)[i]]])
m = max(nhits)
if (m>mymax) mymax = m
} #Warnings are OK
mymax
[1] 31
load(paste(prefix.norm, 'Expression/Expression_estimates.RData', sep = ''))
GEdata = as.data.frame(t(as.matrix(expr[,6:ncol(expr)])))
index = which(!is.na(data$til))
GEdata = GEdata[index,] #The 51 CN samples' expression data only used
names(GEdata) = expr$unitName
rm(expr)
gc()
#############################################
#Toptable
toptable = data.frame('Segment.Number' = (1:nrow(res100))[topindex],
'Chromosome' = seginfo$space[topindex],
'Start' = seginfo$start[topindex],
'End' = seginfo$end[topindex],
'Width' = seginfo$width[topindex],
'CN.Coef' = res100[topindex, 'Estimate'],
p.value = res100[topindex, 'Pr(>|t|)'],
FDR = p.adjust(res100[,2], method = 'fdr')[topindex])
ge = matrix(NA, ncol = 4*mymax, nrow = nrow(toptable))
colnames(ge) = rep(c('Probe.Set.ID','Gene.Symbol','Mean.log2.expr','SD.log2.expr'), mymax)
ge = as.data.frame(ge)
for (i in 1:nrow(toptable)){
chrom = toptable$Chromosome[i]
x = hits[[as.character(chrom)]]
myhits = queryHits(x)[subjectHits(x) == i]
if (length(myhits) > 0){
tmp = as.data.frame(plac[space(plac) == as.character(chrom),])[myhits,]
tmp$Gene.Symbol = as.character(tmp$Gene.Symbol)
tmp$Probe.Set.ID = as.character(tmp$Probe.Set.ID)
ge[i, seq(1, 4*nrow(tmp), 4)] = tmp$Probe.Set.ID
ge[i, seq(2, 4*nrow(tmp), 4)] = tmp$Gene.Symbol
notna.ind = which(!is.na(tmp$Probe.Set.ID))
ex = log2(GEdata[,tmp$Probe.Set.ID[notna.ind]])
if (is.null(dim(ex))) ex = as.matrix(ex, ncol = 1)
ge[i, seq(3, 4*nrow(tmp), 4)[notna.ind]] = apply(ex, 2, mean)
ge[i, seq(4, 4*nrow(tmp), 4)[notna.ind]] = apply(ex, 2, sd)
}
}
plac1 = na.omit(unique(unlist(ge[,seq(2, ncol(ge), 4)]))) #Hit genes for GO analysis
toptable = cbind(toptable, ge)
toptable = toptable[order(toptable$FDR),]
toptable$rank = 1:nrow(toptable)
toptable = toptable[,c(ncol(toptable), 1:(ncol(toptable)-1))]
write.table(toptable, file = paste(prefix.out, 'TIL_by_CN_toptable.txt'),
row.names = F, quote = F, sep = '\t', na = '')