Nothing
`subtype.cluster.predict` <-
function(sbt.model, data, annot, do.mapping=FALSE, mapping, do.prediction.strength=FALSE, do.BIC=FALSE, plot=FALSE, verbose=FALSE) {
#require(mclust)
if(missing(data) || missing(annot)) { stop("data, and annot parameters must be specified") }
sbtn <- c("ER-/HER2-", "HER2+", "ER+/HER2-")
sbtn2 <- c("ER-/HER2-", "HER2+", "ER+/HER2- High Prolif", "ER+/HER2- Low Prolif")
if(is.list(sbt.model)) {
## retrieve model
subtype.c <- sbt.model[!is.element(names(sbt.model), c("cutoff.AURKA", "mod"))]
model.name <- subtype.c$parameters$variance$modelName
cc <- sbt.model$gaussian.AURKA
mq <- sbt.model$rescale.q
m.mod <- sbt.model$mod
} else {
## read model file
rr <- readLines(con=sbt.model, n=11)[-1]
nn <- unlist(lapply(X=rr, FUN=function(x) { x <- unlist(strsplit(x=unlist(strsplit(x=x, split=":"))[1], split=" ")); x <- x[length(x)]; return(x); }))
m.param <- c(list(rr[[1]]), lapply(X=rr[-1], FUN=function(x) { x <- as.numeric(unlist(strsplit(x=unlist(strsplit(x=x, split=":"))[2], split=" "))); x <- x[!is.na(x)]; return(x)}))
names(m.param) <- nn
cn <- unlist(lapply(strsplit(nn[grep(pattern="mean", x=nn)], split="[.]"), FUN=function(x) { return(x[[2]]) }))
m.mod <- read.m.file(sbt.model, comment.char="#")
#construct a fake mclust object with the parameters of the model
subtype.c <- NULL
tt <- m.param$pro
names(tt) <- cn
subtype.c$parameters$pro <- tt
tt <- sapply(X=m.param[grep(pattern="mean", x=nn)], FUN=function(x) { return(x) })
dimnames(tt) <- list(names(m.mod)[1:2], cn)
subtype.c$parameters$mean <- tt
subtype.c$parameters$variance$modelName <- model.name <- m.param$modelname
subtype.c$parameters$variance$d <- 2
subtype.c$parameters$variance$G <- 3
tt <- matrix(0, ncol=2, nrow=2, dimnames=list(names(m.mod)[1:2], names(m.mod)[1:2]))
diag(tt) <- m.param$sigma
subtype.c$parameters$variance$sigma <- array(tt, dim=c(2,2,3), dimnames=list(names(m.mod)[1:2], names(m.mod)[1:2], cn))
subtype.c$parameters$variance$Sigma <- tt
subtype.c$parameters$variance$scale <- m.param$scale
subtype.c$parameters$variance$shape <- m.param$shape
cc <- c("mean"=m.param$gaussian.AURKA.mean, "sigma"=m.param$gaussian.AURKA.sigma)
mq <- m.param$rescale.q
}
do.scale <- ifelse(is.na(mq), FALSE, TRUE)
sbt <- rep(NA, nrow(data))
names(sbt) <- dimnames(data)[[1]]
sbt.proba <- matrix(NA, nrow(data), ncol=length(sbtn), dimnames=list(dimnames(data)[[1]], sbtn))
sigs.esr1 <- sig.score(x=m.mod$ESR1, data=data, annot=annot, do.mapping=do.mapping, mapping=mapping, verbose=FALSE)
sigs.erbb2 <- sig.score(x=m.mod$ERBB2, data=data, annot=annot, do.mapping=do.mapping, mapping=mapping, verbose=FALSE)
sigs.aurka <- sig.score(x=m.mod$AURKA, data=data, annot=annot, do.mapping=do.mapping, mapping=mapping, verbose=FALSE)
## signature scores
dd <- cbind("ESR1"=sigs.esr1$score, "ERBB2"=sigs.erbb2$score, "AURKA"=sigs.aurka$score)
## mapping
mymap <- list("ESR1"=sigs.esr1$probe, "ERBB2"=sigs.erbb2$probe, "AURLA"=sigs.aurka$probe)
cln <- dimnames(subtype.c$parameters$mean)[[2]] <- as.character(1:ncol(subtype.c$parameters$mean))
if(do.scale) {
## the rescaling needs a large sample size!!!
## necessary if we want to validate the classifier using a different dataset
## the estimation of survival probabilities depends on the scale of the score
dd <- apply(dd, 2, function(x) { return((rescale(x, q=mq, na.rm=TRUE) - 0.5) * 2) })
}
rownames(dd) <- rownames(data)
dd2 <- dd
cc.ix <- complete.cases(dd[ , c("ESR1", "ERBB2"), drop=FALSE])
if(all(!cc.ix)) {
ps.res <- ps.res2 <- BIC.res <- NULL
if(do.prediction.strength) {
tt <- rep(NA, length(sbtn))
names(tt) <- sbtn
tt2 <- rep(NA, length(sbtn2))
names(tt2) <- sbtn2
ps.res <- list("ps"=NA, "ps.cluster"=tt, "ps.individual"=sbt)
ps.res2 <- list("ps"=NA, "ps.cluster"=tt2, "ps.individual"=sbt)
}
if(do.BIC) {
BIC.res <- rep(NA, 10)
names(BIC.res) <- 1:10
}
return(list("subtype"=sbt, "subtype.proba"=sbt.proba, "prediction.strength"=ps.res, "BIC"=BIC.res, "subtype2"=sbt, "prediction.strength2"=ps.res2))
}
dd <- dd[cc.ix, , drop=FALSE]
emclust.ts <- mclust::estep(modelName=model.name, data=dd[ , c("ESR1", "ERBB2"), drop=FALSE], parameters=subtype.c$parameters)
dimnames(emclust.ts$z) <- list(dimnames(dd)[[1]], cln)
class.ts <- mclust::map(emclust.ts$z, warn=FALSE)
names(class.ts) <- dimnames(dd)[[1]]
uclass <- sort(unique(class.ts))
uclass <- uclass[!is.na(uclass)]
ps.res <- ps.res2 <- NULL
if(do.prediction.strength) {
if(nrow(dd) < 10) {
warning("at least 10 observations are required to compute the prediction strength!")
tt <- rep(NA, length(sbtn))
names(tt) <- sbtn
tt2 <- rep(NA, nrow(dd2))
names(tt2) <- dimnames(dd2)[[1]]
ps.res <- list("ps"=0, "ps.cluster"=tt, "ps.individual"=tt2)
tt <- rep(NA, length(sbtn2))
names(tt) <- sbtn2
ps.res2 <- list("ps"=0, "ps.cluster"=tt, "ps.individual"=tt2)
} else {
## computation of the prediction strength of the clustering
rr3 <- mclust::Mclust(data=dd[ , c("ESR1", "ERBB2"), drop=FALSE], modelNames=model.name, G=3)
## redefine classification to be coherent with subtypes
uclass <- sort(unique(rr3$classification))
uclass <- uclass[!is.na(uclass)]
if(length(uclass) != 3) {
warning("less than 3 subtypes are identified!")
tt <- rep(NA, length(sbtn))
names(tt) <- sbtn
tt2 <- rep(NA, nrow(dd2))
names(tt2) <- dimnames(dd2)[[1]]
ps.res <- list("ps"=0, "ps.cluster"=tt, "ps.individual"=tt2)
tt <- rep(NA, length(sbtn2))
names(tt) <- sbtn2
ps.res2 <- list("ps"=0, "ps.cluster"=tt, "ps.individual"=tt2)
} else {
mm <- NULL
for(i in 1:length(uclass)) {
mm <- c(mm, median(dd[rr3$classification == uclass[i],"ERBB2"], na.rm=TRUE) )
}
nclass <- uclass[order(mm, decreasing=TRUE)[1]]
mm <- NULL
for(i in 1:length(uclass[-nclass])) {
mm <- c(mm, median(dd[rr3$classification == uclass[-nclass][i],"ESR1"], na.rm=TRUE) )
}
nclass <- c(uclass[-nclass][order(mm, decreasing=TRUE)[2]], nclass, uclass[-nclass][order(mm, decreasing=TRUE)[1]])
## nclass contains the new order
ncl <- rr3$classification
for(i in 1:length(uclass)) {
ncl[rr3$classification == nclass[i]] <- i
}
## use the previously computed model to fit a new model in a supervised manner
myclass <- mclust::unmap(ncl)
dimnames(myclass) <- list(dimnames(dd)[[1]], sbtn)
mclust.tr <- mclust::mstep(modelName=model.name, data=dd[ , c("ESR1", "ERBB2"), drop=FALSE], z=myclass)
dimnames(mclust.tr$z) <- dimnames(myclass)
emclust.tr <- mclust::estep(modelName=model.name, data=dd[ , c("ESR1", "ERBB2"), drop=FALSE], parameters=mclust.tr$parameters)
dimnames(emclust.tr$z) <- dimnames(myclass)
class.tr <- mclust::map(emclust.tr$z, warn=FALSE)
names(class.tr) <- dimnames(dd)[[1]]
## prediction strength
ps.res <- ps.cluster(cl.tr=class.ts, cl.ts=class.tr, na.rm=TRUE)
names(ps.res$ps.cluster) <- sbtn
## check for missing values in ps.individual
tt2 <- rep(NA, nrow(dd2))
names(tt2) <- dimnames(dd2)[[1]]
tt2[names(ps.res$ps.individual)] <- ps.res$ps.individual
ps.res$ps.individual <- tt2
## prediction strength with the separation in high and low proliferative tumors
## since proliferation is a continuum we fit a Gaussian using AURKA expression of the ER+/HER2- tumors
## refitted model
tt <- mclust::Mclust(dd[complete.cases(class.tr, dd[ , "AURKA"]) & class.tr == 3, "AURKA"], modelNames="E", G=1)
gauss.prolif <- c("mean"=tt$parameters$mean, "sigma"=tt$parameters$variance$sigmasq)
class.tr2 <- class.tr
class.tr2[class.tr == 3] <- NA
## probability that tumor is highly proliferative
pprolif <- pnorm(q=dd[ , "AURKA"], mean=gauss.prolif["mean"], sd=gauss.prolif["sigma"], lower.tail=TRUE)
## high proliferation
class.tr2[class.tr == 3 & pprolif >= 0.5 & complete.cases(class.tr, pprolif)] <- 3
## low proliferation
class.tr2[class.tr == 3 & pprolif < 0.5 & complete.cases(class.tr, pprolif)] <- 4
## existing model
tt <- mclust::Mclust(dd[complete.cases(class.ts, dd[ , "AURKA"]) & class.ts == 3, "AURKA"], modelNames="E", G=1)
gauss.prolif <- c("mean"=tt$parameters$mean, "sigma"=tt$parameters$variance$sigmasq)
class.ts2 <- class.ts
class.ts2[class.ts == 3] <- NA
## probability that tumor is highly proliferative
pprolif <- pnorm(q=dd[ , "AURKA"], mean=gauss.prolif["mean"], sd=gauss.prolif["sigma"], lower.tail=TRUE)
## high proliferation
class.ts2[class.ts == 3 & pprolif >= 0.5 & complete.cases(class.ts, pprolif)] <- 3
## low proliferation
class.ts2[class.ts == 3 & pprolif < 0.5 & complete.cases(class.ts, pprolif)] <- 4
## compute the prediction strength
ps.res2 <- ps.cluster(cl.tr=class.ts2, cl.ts=class.tr2, na.rm=TRUE)
names(ps.res2$ps.cluster) <- sbtn2
## check for missing values in ps.individual
tt2 <- rep(NA, nrow(dd2))
names(tt2) <- dimnames(dd2)[[1]]
tt2[names(ps.res2$ps.individual)] <- ps.res2$ps.individual
ps.res2$ps.individual <- tt2
}
}
}
BIC.res <- NULL
if(do.BIC) {
if(nrow(dd) >= 10) { BIC.res <- mclust::mclustBIC(data=dd[ , c("ESR1", "ERBB2"), drop=FALSE], modelNames=c(model.name), G=1:10)[ ,model.name] } else { warning("at least 10 observations are required to compute the BIC!") }
}
## subtypes
sbt[names(class.ts)] <- sbtn[class.ts]
sbt.proba[dimnames(emclust.ts$z)[[1]], ] <- emclust.ts$z
## discriminate between luminal A and B using AURKA
gauss.prolif <- cc
sbt2 <- sbt
sbt2[sbt == sbtn[3]] <- NA
## probability that tumor is highly proliferative
pprolif <- pnorm(q=dd2[ , "AURKA"], mean=gauss.prolif["mean"], sd=gauss.prolif["sigma"], lower.tail=TRUE)
## high proliferation
sbt2[sbt == sbtn[3] & pprolif >= 0.5 & complete.cases(sbt, pprolif)] <- sbtn2[3]
## low proliferation
sbt2[sbt == sbtn[3] & pprolif < 0.5 & complete.cases(sbt, pprolif)] <- sbtn2[4]
## subtype probabilities for luminal B and A
sbt.proba2 <- matrix(NA, nrow(data), ncol=length(sbtn2), dimnames=list(dimnames(data)[[1]], sbtn2))
tt <- sbt.proba[ , sbtn[3]]
tt2 <- pprolif
tt <- cbind(tt * tt2, tt * (1 - tt2))
colnames(tt) <- sbtn2[3:4]
sbt.proba2[ , sbtn2[1:2]] <- sbt.proba[ , sbtn[1:2]]
sbt.proba2[ , sbtn2[3:4]] <- tt[ , sbtn2[3:4]]
if(plot) {
if(do.scale) {
myxlim <- myylim <- c(-2, 2)
} else {
myxlim <- range(dd[ , "ESR1"])
myylim <- range(dd[ , "ERBB2"])
}
## plot the clusters with proliferation
mycol <- mypch <- rep(NA, length(sbt2))
mycol[sbt2 == sbtn2[1]] <- "darkred"
mycol[sbt2 == sbtn2[2]] <- "darkgreen"
mycol[sbt2 == sbtn2[3]] <- "darkorange"
mycol[sbt2 == sbtn2[4]] <- "darkviolet"
mypch[sbt2 == sbtn2[1]] <- 17
mypch[sbt2 == sbtn2[2]] <- 0
mypch[sbt2 == sbtn2[3] | sbt2 == sbtn2[4]] <- 10
mypch <- as.numeric(mypch)
names(mycol) <- names(mypch) <- names(sbt2)
plot(x=dd[ , "ESR1"], y=dd[ , "ERBB2"], xlim=myxlim, ylim=myylim, xlab="ESR1", ylab="ERBB2", col=mycol[dimnames(dd)[[1]]], pch=mypch[dimnames(dd)[[1]]])
legend(x="topleft", col=c("darkred", "darkgreen", "darkorange", "darkviolet"), legend=sbtn2, pch=c(17, 0, 10, 10), bty="n")
}
return(list("subtype"=sbt, "subtype.proba"=sbt.proba, "prediction.strength"=ps.res, "BIC"=BIC.res, "subtype2"=sbt2, "subtype.proba2"=sbt.proba2, "prediction.strength2"=ps.res2, "module.scores"=dd2, "mapping"=mymap))
}
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