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pathway.R
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library(hgu133a.db)
library(limma)
library(affy)
library(graph)
library(ipred)
library(gdata)
library(penalized)
library(org.Hs.eg.db)
library(penalizedSVM)
library(CoxBoost)
library(ggplot2)
library(BioNet)
options(error=recover)
# The working directory
WDIR="."
test.signature = function(signature, DIR=WDIR, DIROUT=WDIR, signame="", cutoff=0.05,
tests=c("DT", "DO"), map2entrez=NULL){
require(GOstats)
require(gdata)
sig = setdiff(map2entrez[signature], NA)
if(length(sig) > 0)
write.csv(sig, file.path(DIR, paste("Signature_", signame, ".csv", sep="")))
else
write.csv(signature, file.path(DIR, paste("Signature_", signame, ".csv", sep="")),
sep="\t")
print(head(signature))
if(length(signature) == 0)
tests = c()
if("DT" %in% tests){
if(length(signature) == 0)
p.value.dt = 1
else{
if(length(sig) > 0)
signature = sig
allgenes = unique(map2entrez)
drugs = read.csv(file.path(DIR, "DrugTargets.csv"), skip=1, sep="\t")
consensus.drugTargets = table(signature %in%
as.character(setdiff(unique(drugs$Chosen.Gene.IDs.EntrezGene.IDs.), NA)))
others.drugTargets = table(setdiff(allgenes, signature) %in%
as.character(setdiff(unique(drugs$Chosen.Gene.IDs.EntrezGene.IDs.), NA)))
conf.table = rbind(consensus.drugTargets[c(2,1)], others.drugTargets[c(2,1)])
conf.table[is.na(conf.table)] = 0
F = fisher.test(conf.table, alternative="g")
cat("Enrichment of drug targets for signature ", signame, ":\n")
print(F)
if(signame != "")
save(F, file=file.path(DIROUT, paste(signame, "_DrugTargetEnrichment.rda")))
p.value.dt = F$p.value
names(p.value.dt) = "Enrichment of drug targets"
}
}
else
p.value.dt = c()
if("DO" %in% tests){
DOlite = read.xls(file.path(DIR, "do_lite.xls"))
DOterms = c("Cancer", "Advanced cancer", "Breast cancer")
allgenes = unique(as.character(DOlite$Entrez))
if(length(signature) == 0)
pvalues = rep(1, length(DOterms))
else
if(length(sig) > 0)
signature = sig
signature = intersect(signature, allgenes)
pvalues = as.numeric(sapply(DOterms, function(DO){
DO.match = table(signature %in% as.character(DOlite[DOlite$Disease == DO,
"Entrez"]))
others = table(setdiff(allgenes, signature) %in%
as.character(DOlite[DOlite$Disease == DO, "Entrez"]))
conf.table = rbind(DO.match[c(2,1)], others[c(2,1)])
conf.table[is.na(conf.table)] = 0
print(conf.table)
F = fisher.test(conf.table, alternative="g")$p.value
print(F)
F
}))
names(F) = "Enrichment of disease suspectible genes"
names(pvalues) = DOterms
pvalues = p.adjust(pvalues, "bonf")
}
else
pvalues = c()
list(p.value.dt=p.value.dt, p.value.DO=pvalues, p.value.KEGG=p.value.KEGG,
p.value.GO=p.value.GO)
}
fit.survival = function(XX, Y, lambda1, lambda2=0, standardize=FALSE,
coef.penalty=NULL){
cv.loglik. = function(lambdas, ...){
cat("lambdas = ", lambdas, "\n")
if(length(lambdas) > 1){
if(is.null(coef.penalty))
Q = tryCatch(cvl(Y, XX, fold=5, lambda1=2^lambdas[1], lambda2=2^lambdas[2],
standardize=standardize, maxiter=200), error=function(e) list(cvl=-Inf,
model=NULL))
else
Q = tryCatch(cvl(Y, XX, fold=5, lambda1=2^lambdas[1]*coef.penalty,
lambda2=2^lambdas[2]*coef.penalty, standardize=standardize, maxiter=200),
error=function(e) list(cvl=-Inf, model=NULL))
}
else{
if(is.null(coef.penalty))
Q = tryCatch(cvl(Y, XX, fold=5, lambda1=2^lambdas, standardize=standardize,
maxiter=200), error=function(e) list(cvl=-Inf, model=NULL))
else
Q = tryCatch(cvl(Y, XX, fold=5, lambda1=2^lambdas*coef.penalty,
standardize=standardize, maxiter=200), error=function(e) list(cvl=-Inf,
model=NULL))
}
if(Q$cvl == -Inf)
Q$cvl = -1e16
cat("CV likelihood = ", Q$cvl, "\n")
ret <- list(q.val = -Q$cvl, model = Q$fullfit)
class(ret) <- "penalized"
return(ret)
}
# determine optimal hyperparameters via EPSGO algorithm
options(error=dump.frames)
if(length(lambda2) > 1){
bounds = t(data.frame(log2lambda1=lambda1, log2lambda2=lambda2))
colnames(bounds)<-c("lower", "upper")
print(bounds)
ep = try(EPSGO(cv.loglik., bounds=bounds, parms.coding=c("log2", "log2"),
seed=12345), silent=TRUE)
}
else{
bounds = t(data.frame(log2lambda1=lambda1))
colnames(bounds)<-c("lower", "upper")
print(bounds)
ep = try(EPSGO(cv.loglik., bounds=bounds, parms.coding=c("log2"), seed=12345),
silent=TRUE)
}
options(error=recover)
# train penalized object
if(!is.null(ep)){
if(length(lambda2) > 1){
if(is.null(coef.penalty))
fit = penalized(Y, XX, lambda1=2^ep$xmin[1], lambda2=2^ep$xmin[2],
standardize=standardize)
else
fit = penalized(Y, XX, lambda1=2^ep$xmin[1]*coef.penalty,
lambda2=2^ep$xmin[2]*coef.penalty, standardize=standardize)
}
else{
if(is.null(coef.penalty))
fit = penalized(Y, XX, lambda1=2^ep$xmin[1], standardize=standardize)
else
fit = penalized(Y, XX, lambda1=2^ep$xmin[1]*coef.penalty, standardize=standardize)
}
}
else{
stop("No model could be fitted!!!")
}
fit
}
fastGeneRank = function(ex, Aq, d=0.5){
b = (1 - d)*ex
r = solve(Aq, b)
as.numeric(r)
}
compute.geneRanks = function(ex, net.genes, Aq){
net.genes = intersect(net.genes, names(ex))
ex = ex[net.genes]
gr = fastGeneRank(ex, Aq)
names(gr) = net.genes
gr
}
perform.cox = function(x, surv){
p = c()
for(i in 1:ncol(x)){
cat(".")
cox = tryCatch(coxph(surv ~ x[,i]), error=function(e) NULL)
if(is.null(cox))
p = c(p, 1)
else
p = c(p, summary(cox)$logtest["pvalue"])
}
p[is.na(p)] = 1
names(p) = colnames(x)
p
}
# DIR: directory where to find datasets
init = function(KEGG=FALSE, DIR=WDIR){
cat("initializing networks and precomputing QR decomposition for GeneRank
calculation ...")
if(!KEGG){
load(file=file.path(DIR, "PathwayCommons_graph.rda"))
KEGGstring = ""
}
else{
load(file=file.path(DIR, "KEGGgraphTotalUndirected.rda"))
KEGGstring = "_KEGG"
nodes(G) = sub("hsa:", "", nodes(G))
}
require(RBGL)
require(Matrix)
G = ugraph(G)
Gmat = as(G, "matrix")
mapping = data.frame(probesetID=names(map2entrez), graphID=map2entrez)
mapping = mapping[!is.na(mapping$graphID), ]
common.nodes = intersect(mapping$graphID, nodes(G))
W = Gmat[common.nodes, common.nodes]
cs = colSums(W)
W = W[cs > 0, cs > 0]
# precompute several things for speed up reasons
D = diag(1/colSums(W))
A = Matrix(diag(ncol(W))) - 0.5*Matrix(W)%*%Matrix(D)
Aq = qr(A)
save(W, Aq, file=file.path(DIR, paste("GeneRanks", KEGGstring, ".rda", sep="")))
cat("done\n")
}
# x = expression / gene rank matrix (samples x genes)
# times = survival times
# events = 1:event; 0:no event
crossval = function(x, times, events, nfolds=10, repeats=10, method="CoxBoost",
W=NULL, Aq=NULL, map2entrez=NULL, DIR=WDIR){
if(method %in% c("BioNet", "BioNet (KEGG)"))
G = as(W, "graphNEL")
n = nrow(x)
ibrier = matrix(0, ncol=repeats, nrow=nfolds)
genes = list()
orig.genes = list()
fits = list()
kk = 1
coef.penalty = NULL
set.seed(1234567)
if(method %in% c("penGenFilter", "penGenFilter (KEGG)", "penGenFilterDG",
"penGenRank", "penGenRank (KEGG)", "CoxBoost", "CoxBoost (KEGG)", "filter (net.
genes)", "filter (net. genes KEGG)", "CoxBoost (net. genes)", "CoxBoost (net. genes
KEGG)", "penalized (net. genes)", "penalized (net. genes KEGG)", "BioNet", "BioNet
(KEGG)")){
x = x[, !is.na(map2entrez[colnames(x)])]
x = t(avereps(t(x), ID=map2entrez[colnames(x)]))
if(method %in% c("penGenFilter", "penGenFilter (KEGG)", "penGenRank", "penGenRank
(KEGG)", "filter (net. genes)", "filter (net. genes KEGG)", "CoxBoost (net.
genes)", "CoxBoost (net. genes KEGG)","penalized (net. genes)", "penalized (net.
genes KEGG)", "BioNet", "BioNet (KEGG)")){
net.genes = intersect(rownames(W), colnames(x))
x = x[,net.genes]
}
}
for(r in 1:repeats){
perm = sample(1:n)
for(k in 1:nfolds){
cat("CV fold ", k," (repeat ", r, ")\n\n")
if(nfolds > 1){
tst = perm[seq(k, n, by=nfolds)]
trn = setdiff(1:n, tst)
}
else{
tst = 1:n
trn = 1:n
}
xtrn = x[trn,,drop=F]
xtst = x[tst,,drop=F]
if(method %in% c("CoxBoost", "CoxBoost (KEGG)")){
cidx = match(colnames(W), colnames(x))
optim.res = optimCoxBoostPenalty(times[trn], events[trn], x[trn,], K=5,
maxstepno=1000, pendistmat=W, connected.index=cidx)
cat("optimal penalty =", optim.res$penalty, "\n")
fit <- CoxBoost(times[trn], events[trn], x[trn,], stepno=max(1,
optim.res$cv.res$optimal.step), penalty=optim.res$penalty, pendistmat=W,
connected.index=cidx)
if(NROW(xtst) > 0)
pred = predict(fit, xtst, type="risk", times=times[tst])
genes.idx = which(fit$coefficients[nrow(fit$coefficients),] != 0)
genes[[kk]] = colnames(x)[genes.idx]
orig.genes[[kk]] = colnames(xtrn)
}
else if(method %in% c("CoxBoost (w.o. netw.)", "CoxBoost (net. genes)", "CoxBoost
(net. genes KEGG)")){
optim.res = optimCoxBoostPenalty(times[trn], events[trn], x[trn,], K=5,
maxstepno=1000)
fit <- CoxBoost(times[trn], events[trn], x[trn,], stepno=max(1,
optim.res$cv.res$optimal.step), penalty=optim.res$penalty)
if(NROW(xtst) > 0)
pred = predict(fit, xtst, type="risk", times=times[tst])
genes.idx = which(fit$coefficients[nrow(fit$coefficients),] != 0)
genes[[kk]] = colnames(x)[genes.idx]
orig.genes[[kk]] = colnames(xtrn)
}
else if(method %in% c("penGenFilter", "penGenFilter (KEGG)", "filter", "filter
(net. genes)", "filter (net. genes KEGG)")){ # GeneRank als Filter
p = perform.cox(xtrn, Surv(times[trn], event=events[trn]))
qs = c(0.001, 0.0025, 0.005, 0.0075, 0.01, 0.025)
if(method %in% c("penGenFilter", "penGenFilter (KEGG)")){
raw.ranks = -log(p + 1e-100)
gr = compute.geneRanks(raw.ranks, rownames(W), Aq=Aq)
}
else{
qs = round(qs*ncol(xtrn))
qs = qs[qs < nrow(xtrn) & qs > 0]
}
best = Inf
for(q in qs){
if(method %in% c("filter", "filter (net. genes)", "filter (net. genes KEGG)")){
sel.tmp = names(sort(p)[1:q])
}
else{
sel.tmp = names(sort(gr, decreasing=T)[1:round(min(c(q*length(gr),
NROW(xtrn)-1)))])
}
xtrn.tmp = scale(xtrn[, sel.tmp, drop=F])
cox.total.tmp = try(coxph(Surv(times[trn], event=events[trn]) ~ xtrn.tmp),
silent=T)
if(!is(cox.total.tmp, "try-error")){
bic = -cox.total.tmp$loglik[2] + length(sel.tmp)*NROW(xtrn)/(NROW(xtrn) -
length(sel.tmp) - 1) # AICc criterion
print(bic)
if(bic < best){
best = bic
sel = sel.tmp
mytrn = xtrn.tmp
}
}
}
if(NROW(xtst) > 0){
xtst = scale(xtst[, sel, drop=F], center=attr(mytrn, "scaled:center"),
scale=attr(mytrn, "scaled:scale"))
}
cox.total = coxph(Surv(times[trn], event=events[trn]) ~ mytrn,
control=coxph.control(iter.max=100))
fit = survfit(cox.total, as.data.frame(xtst))
class(fit) = "survfit"
genes[[kk]] = colnames(mytrn)
orig.genes[[kk]] = colnames(xtrn)
print(head(genes[[kk]]))
}
else if(method %in% c("penalized", "penalized (net. genes)", "penalized (net.
genes KEGG)", "penGenRank", "penGenRank (KEGG)", "penPath", "penAvg", "BioNet",
"BioNet (KEGG)", "sigPath", "penCons")){
if(method %in% c("penGenRank", "penGenRank (KEGG)")){ # GeneRank als embedded
Methode
p = perform.cox(xtrn, Surv(times[trn], event=events[trn]))
gr = compute.geneRanks(-log(p + 1e-100), rownames(W),
Aq=Aq)
coef.penalty = abs(1/gr)
xtrn = xtrn[,names(gr)]
xtst = xtst[,names(gr)]
}
else if(method %in% c("BioNet", "BioNet (KEGG)")){
p = perform.cox(xtrn, Surv(times[trn], event=events[trn]))
bum = fitBumModel(p, plot=TRUE)
sc = scoreFunction(bum, fdr=0.1)
if(!all(sc < 0) && !all(sc == Inf)){
G = as(W, "graphNEL")
Gsub = runFastHeinz(G, sc)
print(Gsub)
sel = nodes(Gsub)
xtrn = xtrn[, sel, drop=F]
if(NROW(xtst) > 0)
xtst = xtst[, sel, drop=F]
}
else{ # leeres Modell
xtrn = as.matrix(rep(1, length(trn)))
xtst = as.matrix(rep(1, length(tst)))
}
}
else if(method == "penPath"){ # perform global test to pre-select GENES
mytest = result(gtKEGG(Surv(times[trn], events[trn]), x[trn,],
id=Rkeys(hgu133aPATH), annotation="hgu133a.db", multtest="BH"))
print(mytest)
mypaths = setdiff(rownames(mytest)[mytest$BH < 0.1], NA)
if(length(mypaths) > 0){
sel = unlist(mget(mypaths, revmap(hgu133aPATH),ifnotfound=NA))
xtrn = xtrn[,sel, drop=F]
if(NROW(xtst) > 0)
xtst = xtst[,sel, drop=F]
}
else{
xtrn = as.matrix(rep(1, length(trn)))
xtst = as.matrix(rep(1, length(tst)))
}
}
else if(method %in% c("sigPath")){ # perform global test to pre-select PATHWAYS
mytest = result(gtKEGG(Surv(times[trn], events[trn]), x[trn,],
id=Rkeys(hgu133aPATH), annotation="hgu133a.db", multtest="BH"))
print(mytest)
mypaths = setdiff(rownames(mytest)[mytest$BH < 0.1], NA)
if(length(mypaths) > 0){
sel = mget(mypaths, revmap(hgu133aPATH),ifnotfound=NA)
xtrn = sapply(sel, function(s) rowMeans(xtrn[,s,drop=F]))
if(NROW(xtst) > 0)
xtst = sapply(sel, function(s) rowMeans(xtst[,s,drop=F]))
}
else{
xtrn = as.matrix(rep(1, length(trn)))
xtst = as.matrix(rep(1, length(tst)))
}
}
else if(method == "penAvg"){ # average PATHWAY expression
sel = mget(Rkeys(hgu133aPATH), revmap(hgu133aPATH),ifnotfound=NA)
xtrn = sapply(sel, function(s) rowMeans(xtrn[,s,drop=F]))
if(NROW(xtst) > 0)
xtst = sapply(sel, function(s) rowMeans(xtst[,s,drop=F]))
}
else if(method == "penCons"){ # penalized consensus signature
consensus = read.csv(file.path(DIR, "Consensus_annotated.csv"))$entrez
sel = setdiff(unlist(mget(as.character(consensus), revmap(hgu133aENTREZID),
ifnotfound=NA)), NA)
xtrn = xtrn[,sel,drop=F]
if(NROW(xtst) > 0)
xtst = xtst[,sel, drop=F]
}
if(!all(xtrn == 1)) # empty model
xtrn = scale(xtrn)
if(NROW(xtst) > 0 & !all(xtst == 1))
xtst = scale(xtst, center=attr(xtrn, "scaled:center"), scale=attr(xtrn,
"scaled:scale"))
if(NCOL(xtrn) > 1)
fit = fit.survival(xtrn, Surv(times[trn], events[trn]), lambda1=c(-10,10),
lambda2=c(-10,0), standardize=FALSE, coef.penalty=coef.penalty)
else
fit = penalized(Surv(times[trn], events[trn]), xtrn, lambda1=1e-100, lambda2=0,
standardize=FALSE)
if(NROW(xtst) > 0){
pred1 = predict(fit, xtst)
pred = sapply(times[tst], function(t) survival(pred1, t))
}
if(method %in% c("penAvg", "sigPath") && nfolds == 1){
genes[[kk]] = unlist(mget(names(coef(fit)), revmap(hgu133aPATH),
ifnotfound=NA))
}
else{
genes[[kk]] = names(coef(fit))
}
orig.genes[[kk]] = colnames(xtrn)
}
if(NROW(xtst) > 0){
if(method %in% c("penGenFilter", "penGenFilter (KEGG)", "filter", "filter (net.
genes)", "filter (net. genes KEGG)")){
ibrier[k,r] = sbrier(Surv(times[tst], event=events[tst]), fit)
}
else
ibrier[k,r] = sbrier(Surv(times[tst], events[tst]), t(pred))
cat("Integrated Brier score (test set) = ", ibrier[k,r], "\n\n")
}
fits[[kk]] = fit
kk = kk + 1
}
}
list(ibrier=ibrier, genes=genes, fit=fits, orig.genes=orig.genes)
}
prediction.analysis = function(dataset, ex, times, events, map2entrez, DIR=WDIR){
print(dataset)
methods = c("penalized", "penalized (net. genes)", "penalized (net. genes KEGG)",
"filter", "filter (net. genes)", "filter (net. genes KEGG)", "sigPath", "penPath",
"penAvg", "penGenRank", "penGenRank (KEGG)", "penGenFilter", "penGenFilter (KEGG)",
"BioNet", "BioNet (KEGG)", "CoxBoost", "CoxBoost (KEGG)", "CoxBoost (w.o. netw.)",
"CoxBoost (net. genes)", "CoxBoost (net. genes KEGG)", "penCons")
results = data.frame()
for(method in methods){
print(method)
if(file.exists(file.path(DIR, paste(dataset, "_", method, "_cvresults.rda",
sep="")))){
load(file.path(DIR, paste(dataset, "_", method, "_cvresults.rda", sep="")))
}
else{
if(length(grep("KEGG", method)) > 0)
load(file=file.path(DIR, "GeneRanks_KEGG.rda"), envir=globalenv())
else
load(file=file.path(DIR, "GeneRanks.rda"), envir=globalenv())
cv.res = crossval(ex, times, events, method=method, W=W, Aq=Aq,
map2entrez=map2entrez)
save(cv.res, file=file.path(DIR, paste(dataset, "_", method, "_cvresults.rda",
sep="")))
}
results = rbind(results, data.frame(integrated.brier=colMeans(cv.res$ibrier),
method=method, dataset=dataset))
}
qplot(method, integrated.brier, data=results, fill=method, position="dodge",
geom="boxplot", xlab="", ylim=c(0,1), ylab="integraded Brier score",
main=paste(dataset, "- Prediction Performance (10 x 10-fold CV)")) + theme_bw() +
coord_flip() + opts(legend.position="none", strip.text.y = theme_text(angle=0))
ggsave(file=file.path(DIR, paste(dataset, "_cvresults.pdf")))
save(results, file=file.path(DIR, paste(dataset, "_cvresultsAll.rda")))
}
prediction.analysis.crossdataset = function(mydataset, ex, times, events,
datasets=setdiff(c("Desmedt et al.", "Ivshina et al.", "Pawitan et al.", "Schmidt et
al.", "Wang et al."), mydataset), DIR=WDIR){
methods = c("penalized", "penAvg", "penGenFilter", "CoxBoost", "penCons")
methods2 = c("ela. net w.o. netw.", "AvgPath", "GeneRankFilter", "CoxBoost",
"ConsSig")
ibrier = matrix(0, ncol=length(datasets), nrow=length(methods))
dimnames(ibrier) = list(methods, datasets)
brier.scores = data.frame()
for(method in methods){
if(file.exists(file.path(DIR, paste(mydataset, "_", method, "_totalfit.rda",
sep="")))){
load(file.path(DIR, paste(mydataset, "_", method, "_totalfit.rda", sep="")))
}
if(method == "penAvg"){
pathways2genes = mget(unlist(fit$orig.genes), revmap(hgu133aPATH), ifnotfound=NA)
xtrn = sapply(pathways2genes, function(s) rowMeans(ex[,s,drop=F]))
}
else
xtrn = ex
xtrn = scale(xtrn)
for(dataset in datasets){
cat("method = ", method, "dataset = ", dataset, "\n")
if(dataset == "Schmidt et al."){
load(file.path(DIR, "Mainz_FARMS.rda"))
xtst = t(exprs(an))
ttimes = pData(an)$t.dmfs
tevents = pData(an)$e.dmfs
}
if(dataset == "Pawitan et al."){
load(file.path(DIR, "PawitanGSE1456_FARMS.rda"))
xtst = t(exprs(an))
ttimes = pData(an)$SURV_RELAPSE..
tevents = pData(an)$RELAPSE..
}
if(dataset == "Ivshina et al."){
load(file.path(DIR, "GSE4922_U_FARMS.rda"))
xtst = t(exprs(an))
ttimes = pData(an)$DFS.TIME..yrs.
tevents =
pData(an)$DFS.EVENT..0.censored..1.event.defined.as.any.type.of.recurrence..local..regional.or.distant..or.death.from.breast.cancer
}
if(dataset == "Desmedt et al."){
load(file.path(DIR, "GSE7390_Desmedt_FARMS.rda"))
xtst = t(exprs(an))
ttimes = pData(an)$t.rfs
tevents = pData(an)$e.rfs
}
if(dataset == "Wang et al."){
load(file.path(DIR, "Wang_FARMS.rda"))
xtst = t(exprs(an))
ttimes = pData(an)$time.to.relapse.or.last.follow.up..months.
tevents = pData(an)$relapse..1.True.
}
if(method == "penAvg"){
xtst = sapply(pathways2genes, function(s) rowMeans(xtst[,s,drop=F]))
}
xtst = scale(xtst, center=attr(xtrn, "scaled:center"), scale=attr(xtrn,
"scaled:scale"))
if(method == "penGenFilter"){
ibrier[method,dataset] = sbrier(Surv(ttimes, tevents), fit$fit)
bs = sapply(1:length(ttimes), function(t) sbrier(Surv(ttimes, tevents), fit$fit,
btime=ttimes[t]))
}
else{
if(method %in% c("penCons")){
pred1 = predict(fit$fit, xtst[, unlist(fit$orig.genes), drop=F])
pred = sapply(ttimes, function(t) survival(pred1, t))
}
else if(method %in% c("penalized", "penAvg")){
pred1 = tryCatch(predict(fit$fit, xtst), error=function(e) predict(fit$fit,
xtst[,names(coef(fit$fit)),drop=F]))
pred = sapply(ttimes, function(t) survival(pred1, t))
}
else if(method == "CoxBoost")
pred = predict(fit$fit, xtst, type="risk", times=ttimes)
ibrier[method,dataset] = sbrier(Surv(ttimes, tevents), t(pred))
bs = sapply(1:length(ttimes), function(t) sbrier(Surv(ttimes, tevents),
t(pred)[t,,drop=F], btime=ttimes[t]))
}
bs = bs + rep(runif(1, min=-0.01, max=0.01), length(bs)) # small jitter, just to
see curves separated
cat("Integrated Brier score = ", ibrier[method,dataset], "\n\n")
df = unique(data.frame(method=method, dataset=dataset, time=ttimes, BS=bs))
brier.scores = rbind(brier.scores, df=df[order(df$time),])
}
}
rownames(ibrier) = methods2
levels(brier.scores$method) = methods2
brier.scores$time[brier.scores$dataset == "Schmidt et al."] =
brier.scores$time[brier.scores$dataset == "Schmidt et al."]/365
brier.scores$time[brier.scores$dataset == "Desmedt et al."] =
brier.scores$time[brier.scores$dataset == "Desmedt et al."]/365
brier.scores$time[brier.scores$dataset == "Wang et al."] =
brier.scores$time[brier.scores$dataset == "Wang et al."]/12
qplot(time, BS, data=brier.scores, col=method, facets=~dataset, geom="line",
xlab="time (years)", ylab="Brier score", main=paste("Prediction performance -
models trained on ", mydataset)) + theme_bw() + theme(strip.text.y =
element_text(angle=0)) + scale_color_grey() + geom_point(aes(shape=method))
ggsave(file=file.path(DIR, paste("PerfCrossdataset_TrainedOn", mydataset, ".pdf",
sep="")))
save(ibrier, brier.scores, file=file.path(DIR, paste("PerfCrossdataset_TrainedOn",
mydataset, ".rda", sep="")))
}
perf.comparison = function(resultsDO, resultsDT, datasets=c("Desmedt et al.",
"Ivshina et al.", "Pawitan et al.", "Schmidt et al.", "Wang et al."), DIR=WDIR){
methods = c("penalized", "penalized (net. genes)", "penalized (net. genes KEGG)",
"filter", "filter (net. genes)", "filter (net. genes KEGG)", "CoxBoost (w.o.
netw.)", "sigPath", "penPath", "penAvg", "penGenRank", "penGenRank (KEGG)",
"penGenFilter", "penGenFilter (KEGG)", "BioNet", "BioNet (KEGG)", "CoxBoost",
"CoxBoost (KEGG)", "CoxBoost (net. genes)", "CoxBoost (net. genes KEGG)",
"penCons")
methods2 = c("ela. net w.o. netw.", "ela. net. net. genes", "ela. net. KEGG genes",
"CoxFilter w.o. netw.", "CoxFilter net. genes", "CoxFilter KEGG genes", "CoxBoost
w.o. netw.", "sigPathAvg", "sigPathGene", "AvgPath", "GeneRankWeight",
"GeneRankWeight (KEGG)", "GeneRankFilter", "GeneRankFilter (KEGG)", "BioNet",
"BioNet (KEGG)", "CoxBoost", "CoxBoost (KEGG)", "CoxBoost (net. genes)", "CoxBoost
(KEGG genes)", "ConsSig")
results = data.frame()
results.stab = data.frame()
results.stab2 = data.frame()
results.ngenes = data.frame()
for(method in methods){
for(dataset in datasets){
cat("method = ", method, "dataset = ", dataset, "\n")
load(file.path(DIR, paste(dataset, "_", method, "_cvresults.rda", sep="")))
mymethod = methods2[which(methods == method)]
results = rbind(results, data.frame(integrated.brier=colMeans(cv.res$ibrier),
method=mymethod, dataset=dataset))
#
ngenes = sapply(cv.res$genes, length)
cv.res$genes=sapply(cv.res$genes, unique)
freq = sort(table(unlist(cv.res$genes)), decreasing=T)[1:10]
H = hist(freq, breaks=seq(0,100, by=10), plot=F)$counts / length(freq) #
convert counts into fractions!
SI = sum(freq, na.rm=T) / (sum(freq != 0, na.rm=T) * 100)
results.stab = rbind(results.stab, data.frame(method=mymethod,
orig.method=method, SI=SI, dataset=dataset))
results.stab2 = rbind(results.stab2, data.frame(method=mymethod,
orig.method=method, appearance.freq=seq(10,100,by=10), fraction=H,
dataset=dataset))
results.ngenes = rbind(results.ngenes, data.frame(method=mymethod,
orig.method=method, ngenes=ngenes, dataset=dataset))
}
}
qplot(method, SI, data=results.stab, fill=method, facets=~dataset, geom="bar",
position="dodge", ylim=c(0, 1), xlab="", ylab="stability index (SI)",
main="Signature stability (top 10 features)") + theme_bw() + coord_flip() +
opts(legend.position="none", strip.text.y = theme_text(angle=0))
ggsave(file=file.path(DIR, "SI.pdf", height=10))
#
qplot(appearance.freq, fraction, data=results.stab2, colour=method,
facets=~dataset, geom="point", xlab="times selected", ylim=c(0,1), ylab="relative
frequency", main="Probeset selection stability (top 10)") + theme_bw() +
geom_line(aes(colour=method))
ggsave(file=file.path(DIR, "GeneSelectionStabilityTotal.pdf"), height=10)
#
results.ngenes2 = results.ngenes[results.ngenes$ngenes != 0, ]
qplot(method, log10(ngenes), data=results.ngenes2, fill=method, facets=~dataset,
geom="boxplot", position="dodge", xlab="", ylab="log10 (no. genes)",
main="Signature size") + theme_bw() + coord_flip() + opts(legend.position="none",
strip.text.y = theme_text(angle=0))
ggsave(file=file.path(DIR, paste("SignatureSizesTotal.pdf", sep="")), height=10)
#
empty.models = aggregate(ngenes~dataset + method, data=results.ngenes,
FUN=function(x) mean(x == 0))
qplot(method, ngenes, data=empty.models, fill=method, facets=~dataset, geom="bar",
position="dodge", xlab="", ylab="fraction of empty models", main="Fraction of empty
models") + theme_bw() + coord_flip() + opts(legend.position="none", strip.text.y =
theme_text(angle=0))
ggsave(file=file.path(DIR, paste("EmptyModelsTotal.pdf", sep="")), height=10)
#
qplot(method, integrated.brier, data=results, fill=method, facets=~dataset,
position="dodge", geom="boxplot", xlab="", ylim=c(0,0.5), ylab="integrated Brier
score", main= "Prediction Performance (10 x 10-fold CV)") + theme_bw() +
coord_flip() + opts(legend.position="none", strip.text.y = theme_text(angle=0))
ggsave(file=file.path(DIR, "CVTotal.pdf"), height=10)
#
resultsDO$method = as.character(resultsDO$method)
for(m in 1:length(methods))
resultsDO$method[resultsDO$method == methods[m]] = methods2[m]
resultsDO$method = factor(resultsDO$method, levels=unique(resultsDO$method))
resultsDO$p.value.DO[resultsDO$p.value.DO == Inf] = 150
qplot(method, p.value.DO, data=resultsDO, geom="bar", facets=~dataset, fill=Term,
position="dodge", xlab="", ylab="-log10(p-value)", main="Enrichment of suspected
disesase genes") + geom_hline(yintercept=-log10(0.05)) + coord_flip() + theme_bw()
+ opts(legend.position="top", strip.text.y = theme_text(angle=0))
ggsave(file=file.path(DIR, "DiseaseGeneEnrichment.pdf"), height=10)
#
resultsDT$method = as.character(resultsDT$method)
for(m in 1:length(methods))
resultsDT$method[resultsDT$method == methods[m]] = methods2[m]
resultsDT$method = factor(resultsDT$method, levels=unique(resultsDT$method))
resultsDT$p.value.DT[resultsDT$p.value.DT == Inf] = 150
qplot(method, p.value.DT, data=resultsDT, geom="bar", facets=~dataset,
position="dodge", xlab="", ylab="-log10(p-value)", main="Enrichment of known drug
targets") + geom_hline(yintercept=-log10(0.05)) + coord_flip() + theme_bw() +
opts(legend.position="none", strip.text.y = theme_text(angle=0))
ggsave(file=file.path(DIR, "DTEnrichment.pdf"), height=10)
sig = lapply(levels(results$dataset), function(d){
pwt = matrix(NA, ncol=nlevels(results$method), nrow=nlevels(results$method))
dimnames(pwt) = list(levels(results$method), levels(results$method))
for(m1 in levels(results$method)){
for(m2 in levels(results$method)){
if(m1 != m2){
pwt[m1, m2] = wilcox.test(results$integrated.brier[results$dataset ==d &
results$method == m1], results$integrated.brier[results$dataset ==d &
results$method == m2], paired=T, alternative="l")$p.value
}
}
}
pwt = matrix(p.adjust(pwt, method="BH"), ncol=ncol(pwt))
dimnames(pwt) = list(levels(results$method), levels(results$method))
write.csv(pwt, file=file.path(DIR, paste("PerformanceComparison", d, "csv",
sep="")))
pwt
})
require(MADAM)
pwt2 = matrix(NA, ncol=ncol(sig[[1]]), nrow=nrow(sig[[1]]))
dimnames(pwt2) = dimnames(sig[[1]])
for(i in 1:ncol(sig[[1]])){
for(j in 1:nrow(sig[[1]])){
if(i != j){
pwt2[i, j] = fisher.method(t(as.matrix(sapply(sig, function(x) x[i,j]))),
p.corr="none")$p.adj
}
}
}
pwt2 = matrix(p.adjust(pwt2, method="BH"), ncol=ncol(pwt2))
dimnames(pwt2) = dimnames(sig[[1]])
write.csv(pwt2, file=file.path(DIR, "PerformanceComparisonAverage.csv"))
pdf(file="ANOVAVerificationSI.pdf")
plot(lm(SI~method + dataset, data=results.stab))
dev.off()
model2 = aov(SI~method + dataset, data=results.stab)
print(summary(model2))
tuk = TukeyHSD(model2, which="method", ordered=TRUE)
tuk$method = tuk$method[order(tuk$method[,"diff"]),]
tuk = data.frame(tuk$method, comparison=rownames(tuk$method))
tuk$comparison = factor(tuk$comparison, levels=tuk$comparison)
ggplot (tuk, aes (x=comparison, y=diff, ymin=lwr, ymax=upr)) + geom_pointrange () +
ylab ("difference in mean SI") + theme_bw() + coord_flip ()
ggsave(file=file.path(DIR, "TukeyMethodStability.pdf"), height=10)
write.csv(tuk, file=file.path(DIR, "TukeyMethodComparisonStability.csv"))
}
interpretability.analysis = function(dataset, ex, times, events, map2entrez,
DIR=WDIR){
resultsDO = data.frame()
resultsDT = data.frame()
methods = c("penalized", "penalized (net. genes)", "penalized (net. genes KEGG)",
"filter", "filter (net. genes)", "filter (net. genes KEGG)", "CoxBoost (w.o.
netw.)", "sigPath", "penPath", "penAvg", "penGenRank", "penGenRank (KEGG)",
"penGenFilter", "penGenFilter (KEGG)", "BioNet", "BioNet (KEGG)", "CoxBoost",
"CoxBoost (KEGG)", "CoxBoost (net. genes)", "CoxBoost (net. genes KEGG)",
"penCons")
for(method in methods){
print(method)
if(file.exists(file.path(file.path(DIR, paste(dataset, "_", method,
"_totalfit.rda", sep=""))))){
load(file.path(DIR, paste(dataset, "_", method, "_totalfit.rda", sep="")))
}
else{
if(length(grep("KEGG", method)) > 0)
load(file=file.path(DIR, "GeneRanks_KEGG.rda"))
else
load(file=file.path(DIR, "GeneRanks.rda"))
drugs = read.csv(file.path(DIR, "DrugTargets.csv"), skip=1, sep="\t")
DOlite = read.xls(file.path(DIR, "do_lite.xls"))
dgenes =
union(as.character(setdiff(unique(drugs$Chosen.Gene.IDs.EntrezGene.IDs.), NA)),
DOlite$Entrez[DOlite$Disease %in% c("Cancer", "Advanced Cancer", "Breast
Cancer")])
fit = crossval(ex, times, events, method=method, nfolds=1, repeats=1, W=W, Aq=Aq,
map2entrez=map2entrez, L=L, dgenes=dgenes) # fit model to WHOLE dataset
print(length(unlist(fit$genes)))
mytest = test.signature(unlist(fit$genes), signame=paste(dataset, "_", method,
sep=""), map2entrez=map2entrez) # enrichment analysis
save(fit, mytest, file=file.path(DIR, paste(dataset, "_", method,
"_totalfit.rda", sep="")))
print(fit)
}
print(length(unlist(fit$genes)))
mytest = test.signature(unlist(fit$genes), signame=paste(dataset, "_", method,
sep=""), map2entrez=map2entrez) # enrichment
if(length(unlist(fit$genes)) > 0){
if(length(mytest$p.value.DO) > 0)
resultsDO = rbind(resultsDO, data.frame(p.value.DO=-log10(mytest$p.value.DO),
method=method, Term=names(mytest$p.value.DO), dataset=dataset))
if(length(mytest$p.value.dt) > 0)
resultsDT = rbind(resultsDT, data.frame(p.value.DT=-log10(mytest$p.value.dt),
method=method, dataset=dataset))
}
}
if(NROW(resultsDO) > 0){
qplot(Term, p.value.DO, facets=method~., data=resultsDO, geom="bar",
position="dodge", xlab="", ylab="-log10(p-value)", main=paste("DO Enrichment (",
dataset, ")", sep="")) + geom_hline(yintercept=-log10(0.05)) + coord_flip() +
theme_bw() + opts(legend.position="none", strip.text.y = theme_text(angle=0))
ggsave(file=file.path(DIR, paste(dataset, "DOEnrichment.pdf", sep="")))
}
if(NROW(resultsDT) > 0){
qplot(method, p.value.DT, data=resultsDT, geom="bar", position="dodge", xlab="",
ylab="-log10(p-value)", main=paste("Drug Target Enrichment (", dataset, ")",
sep="")) + geom_hline(yintercept=-log10(0.05)) + coord_flip()+ theme_bw() +
opts(legend.position="none")
ggsave(file=file.path(DIR, paste(dataset, "DTEnrichment.pdf", sep="")))
}
list(resultsDO=resultsDO, resultsDT=resultsDT)
}
#init(TRUE) # initialization for KEGG pathways => DONE (file GeneRanks_KEGG.rda)!
#init(FALSE) # initialization for PPI network => DONE (file GeneRanks.rda)!
#datasets = c("Desmedt et al.", "Ivshina et al.", "Pawitan et al.", "Schmidt et
al.", "Wang et al.")
datasets = "Schmidt et al." # just one example dataset
resultsDO = data.frame()
resultsDT = data.frame()
for(dataset in datasets){
print(dataset)
if(dataset == "Schmidt et al."){
load(file.path(WDIR, "Mainz_FARMS.rda"))
map2entrez = unlist(mget(featureNames(an), hgu133aENTREZID, ifnotfound=NA))
prediction.analysis("Schmidt et al.", t(exprs(an)), pData(an)$t.dmfs,
pData(an)$e.dmfs, map2entrez=map2entrez)
res = interpretability.analysis("Schmidt et al.", t(exprs(an)), pData(an)$t.dmfs,
pData(an)$e.dmfs, map2entrez=map2entrez)
}
if(dataset == "Pawitan et al."){
load(file.path(WDIR, "PawitanGSE1456_FARMS.rda"))
map2entrez = unlist(mget(featureNames(an), hgu133aENTREZID, ifnotfound=NA))
prediction.analysis("Pawitan et al.", t(exprs(an)), pData(an)$SURV_RELAPSE..,
pData(an)$RELAPSE.., map2entrez=map2entrez)
res = interpretability.analysis("Pawitan et al.", t(exprs(an)),
pData(an)$SURV_RELAPSE.., pData(an)$RELAPSE.., map2entrez=map2entrez)
}
if(dataset == "Ivshina et al."){
load(file.path(WDIR, "GSE4922_U_FARMS.rda"))
map2entrez = unlist(mget(featureNames(an), hgu133aENTREZID, ifnotfound=NA))
prediction.analysis("Ivshina et al.", t(exprs(an)), pData(an)$DFS.TIME..yrs.,
pData(an)$DFS.EVENT..0.censored..1.event.defined.as.any.type.of.recurrence..local..regional.or.distant..or.death.from.breast.cancer,
map2entrez=map2entrez)
res = interpretability.analysis("Ivshina et al.", t(exprs(an)),
pData(an)$DFS.TIME..yrs.,
pData(an)$DFS.EVENT..0.censored..1.event.defined.as.any.type.of.recurrence..local..regional.or.distant..or.death.from.breast.cancer,
map2entrez=map2entrez)
}
if(dataset == "Desmedt et al."){
load(file.path(WDIR, "GSE7390_Desmedt_FARMS.rda"))
map2entrez = unlist(mget(featureNames(an), hgu133aENTREZID, ifnotfound=NA))
prediction.analysis("Desmedt et al.", t(exprs(an)), pData(an)$t.rfs,
pData(an)$e.rfs, map2entrez=map2entrez)
res = interpretability.analysis("Desmedt et al.", t(exprs(an)), pData(an)$t.rfs,
pData(an)$e.rfs, map2entrez=map2entrez)
}
if(dataset == "Wang et al."){
load(file.path(WDIR, "Wang_FARMS.rda"))
map2entrez = unlist(mget(featureNames(an), hgu133aENTREZID, ifnotfound=NA))
prediction.analysis("Wang et al.", t(exprs(an)),
pData(an)$time.to.relapse.or.last.follow.up..months., pData(an)$relapse..1.True.,
map2entrez=map2entrez)
res = interpretability.analysis("Wang et al.", t(exprs(an)),
pData(an)$time.to.relapse.or.last.follow.up..months., pData(an)$relapse..1.True.,
map2entrez=map2entrez)
}
resultsDO = rbind(resultsDO, res$resultsDO)
resultsDT = rbind(resultsDT, res$resultsDT)
list(resultsDO=resultsDO, resultsDT=resultsDT)
}
perf.comparison(resultsDO, resultsDT, datasets=datasets)
for(dataset in datasets){
print(dataset)
if(dataset == "Schmidt et al."){
load(file.path(WDIR, "Mainz_FARMS.rda"))
prediction.analysis.crossdataset("Schmidt et al.", t(exprs(an)), pData(an)$t.dmfs,
pData(an)$e.dmfs)
}
if(dataset == "Pawitan et al."){
load(file.path(WDIR, "PawitanGSE1456_FARMS.rda"))
prediction.analysis.crossdataset("Pawitan et al.", t(exprs(an)),
pData(an)$SURV_RELAPSE.., pData(an)$RELAPSE..)
}
if(dataset == "Ivshina et al."){
load(file.path(WDIR, "GSE4922_U_FARMS.rda"))
prediction.analysis.crossdataset("Ivshina et al.", t(exprs(an)),
pData(an)$DFS.TIME..yrs.,
pData(an)$DFS.EVENT..0.censored..1.event.defined.as.any.type.of.recurrence..local..regional.or.distant..or.death.from.breast.cancer)
}
if(dataset == "Desmedt et al."){
load(file.path(WDIR, "GSE7390_Desmedt_FARMS.rda"))
prediction.analysis.crossdataset("Desmedt et al.", t(exprs(an)), pData(an)$t.rfs,
pData(an)$e.rfs)
}
if(dataset == "Wang et al."){
load(file.path(WDIR, "Wang_FARMS.rda"))
prediction.analysis.crossdataset("Wang et al.", t(exprs(an)),
pData(an)$time.to.relapse.or.last.follow.up..months.,
pData(an)$relapse..1.True.)
}
}