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RaceID3_StemID2_class.R
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RaceID3_StemID2_class.R
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## load required packages.
require(tsne)
require(pheatmap)
require(MASS)
require(cluster)
require(mclust)
require(flexmix)
require(lattice)
require(fpc)
require(amap)
require(RColorBrewer)
require(locfit)
require(vegan)
require(Rtsne)
require(scran)
require(DESeq2)
require(randomForest)
## class definition
SCseq <- setClass("SCseq", slots = c(expdata = "data.frame", ndata = "data.frame", fdata = "data.frame", distances = "matrix", tsne = "data.frame", cluster = "list", background = "list", out = "list", cpart = "vector", fcol = "vector", filterpar = "list", clusterpar = "list", outlierpar ="list" ))
setValidity("SCseq",
function(object) {
msg <- NULL
if ( ! is.data.frame(object@expdata) ){
msg <- c(msg, "input data must be data.frame")
}else if ( nrow(object@expdata) < 2 ){
msg <- c(msg, "input data must have more than one row")
}else if ( ncol(object@expdata) < 2 ){
msg <- c(msg, "input data must have more than one column")
}else if (sum( apply( is.na(object@expdata),1,sum ) ) > 0 ){
msg <- c(msg, "NAs are not allowed in input data")
}else if (sum( apply( object@expdata,1,min ) ) < 0 ){
msg <- c(msg, "negative values are not allowed in input data")
}
if (is.null(msg)) TRUE
else msg
}
)
setMethod("initialize",
signature = "SCseq",
definition = function(.Object, expdata ){
.Object@expdata <- expdata
.Object@ndata <- expdata
.Object@fdata <- expdata
validObject(.Object)
return(.Object)
}
)
#changed default
setGeneric("filterdata", function(object, mintotal=3000, minexpr=5, minnumber=1, maxexpr=Inf, downsample=FALSE, sfn=FALSE, hkn=FALSE, dsn=1, rseed=17000, CGenes=NULL, FGenes=NULL, ccor=.4) standardGeneric("filterdata"))
setMethod("filterdata",
signature = "SCseq",
definition = function(object,mintotal,minexpr,minnumber,maxexpr,downsample,sfn,hkn,dsn,rseed,CGenes,FGenes,ccor) {
if ( ! is.numeric(mintotal) ) stop( "mintotal has to be a positive number" ) else if ( mintotal <= 0 ) stop( "mintotal has to be a positive number" )
if ( ! is.numeric(minexpr) ) stop( "minexpr has to be a non-negative number" ) else if ( minexpr < 0 ) stop( "minexpr has to be a non-negative number" )
if ( ! is.numeric(minnumber) ) stop( "minnumber has to be a non-negative integer number" ) else if ( round(minnumber) != minnumber | minnumber < 0 ) stop( "minnumber has to be a non-negative integer number" )
if ( ! ( is.numeric(downsample) | is.logical(downsample) ) ) stop( "downsample has to be logical (TRUE/FALSE)" )
if ( ! ( is.numeric(sfn) | is.logical(sfn) ) ) stop( "sfn has to be logical (TRUE/FALSE)" )
if ( ! ( is.numeric(hkn) | is.logical(hkn) ) ) stop( "hkn has to be logical (TRUE/FALSE)" )
if ( ! is.numeric(dsn) ) stop( "dsn has to be a positive integer number" ) else if ( round(dsn) != dsn | dsn <= 0 ) stop( "dsn has to be a positive integer number" )
if ( ! is.numeric(ccor) ) stop( "ccor has to be a non-negative number between 0 and 1" ) else if ( ccor < 0 | ccor > 1 ) stop( "ccor has to be a non-negative number between 0 and 1 " )
object@filterpar <- list(mintotal=mintotal, minexpr=minexpr, minnumber=minnumber, maxexpr=maxexpr, downsample=downsample, dsn=dsn, sfn=sfn, CGenes=CGenes, FGenes=FGenes)
object@ndata <- object@expdata[,apply(object@expdata,2,sum,na.rm=TRUE) >= mintotal]
if ( downsample ){
set.seed(rseed)
object@ndata <- downsample(object@expdata,n=mintotal,dsn=dsn)
}
if ( sfn ){
d <- computeSumFactors(as.matrix( object@ndata ))
object@ndata <- as.data.frame(t(t(object@ndata)/d)) + .1
}
if ( hkn ){
minE <- 1
minN <- .5 * ncol(object@ndata)
g <- apply(object@expdata[,names(object@ndata)] >= minE,1,sum) >= minN
nf <- apply(object@ndata,2,sum)
x <- as.data.frame(t(t(object@ndata)/nf))*min(nf)
x <- x[g,]
m <- apply(x,1,mean)
v <- apply(x,1,var )
ml <- log2(m)
vl <- log2(v)
f <- ml > -Inf & vl > -Inf
ml <- ml[f]
vl <- vl[f]
mm <- -8
repeat{
fit <- lm(vl ~ ml + I(ml^2))
if( coef(fit)[3] >= 0 | mm >= -1){
break
}
mm <- mm + .5
f <- ml > mm
ml <- ml[f]
vl <- vl[f]
}
lvar <- function(x) 2**(coef(fit)[1] + log2(x)*coef(fit)[2] + coef(fit)[3] * log2(x)**2)
vln <- log2(v) - log2(sapply(m,FUN=lvar))
n <- names(vln)[vln<0]
p <- x/apply(x,1,sum) + 1e-10
E <- -apply(p*log(p),1,sum)
f <- m > quantile(m,.25) & m < quantile(m,.9)
f <- f & names(m) %in% n
f <- f & E > quantile(E[f],.9)
#plot(log2(m),log2(v),col="grey",pch=20)
#lines(log2(m[order(m)]),log2(lvar(m[order(m)])),col="red",lwd=2)
#points(log2(m)[f],log2(v)[f],col="red",pch=20)
nn <- names(m)[f]
nf <- apply(object@expdata[nn,names(object@ndata)],2,mean)
object@ndata <- as.data.frame(t(t(object@expdata[,names(nf)])/nf)*min(nf) + .1)
}
if ( ! ( downsample | sfn | hkn ) ){
x <- object@ndata
object@ndata <- as.data.frame( t(t(x)/apply(x,2,sum))*min(apply(x,2,sum,na.rm=TRUE)) + .1 )
}
x <- object@ndata
object@fdata <- x[apply(x>=minexpr,1,sum,na.rm=TRUE) >= minnumber,]
x <- object@fdata
object@fdata <- x[apply(x,1,max,na.rm=TRUE) < maxexpr,]
if ( downsample ){
x <- object@expdata[,names(object@ndata)]
object@ndata <- as.data.frame( t(t(x)/apply(x,2,sum))*min(apply(x,2,sum,na.rm=TRUE)) + .1 )
}
if ( !is.null(FGenes) ){
for ( g in FGenes ){
object@fdata <- object@fdata[! rownames(object@fdata) %in% g,]
}
}
if ( !is.null(CGenes) ){
d <- as.data.frame(t(t(object@expdata)/apply(object@expdata,2,sum)))
d <- d[rownames(object@fdata),names(object@fdata)]
f <- rep(FALSE,nrow(d))
for ( g in CGenes ){
if ( ! g %in% rownames(d) ) next
z <- apply(d,1,function(x,y,cthr) sigcor(x,y,cthr),y=t(d[g,]),cthr=ccor)
f <- f | ( !is.na(z) & ( z > ccor | z < -ccor) )
}
object@fdata <- object@fdata[!f,]
}
return(object)
}
)
sigcor <- function(x,y,cthr=.4){
if ( min(var(x),var(y)) == 0 ) return(NA)
fit <- lm(x ~ y)
pv <- as.data.frame(summary(fit)[4])[2,4]
y <- as.data.frame(summary(fit)[4])[2,1]
if ( is.na(pv) | is.na(y) ) return( NA )
z <- sign(y)*sqrt(summary(fit)$r.square)
if ( is.na(z) ) return(NA)
if ( pv < .01 & abs(z) >= cthr ) return(z) else return(NA)
}
downsample <- function(x,n,dsn){
x <- round( x[,apply(x,2,sum,na.rm=TRUE) >= n], 0)
nn <- min( apply(x,2,sum) )
for ( j in 1:dsn ){
z <- data.frame(GENEID=rownames(x))
rownames(z) <- rownames(x)
initv <- rep(0,nrow(z))
for ( i in 1:dim(x)[2] ){
y <- aggregate(rep(1,nn),list(sample(rep(rownames(x),x[,i]),nn)),sum)
na <- names(x)[i]
names(y) <- c("GENEID",na)
rownames(y) <- y$GENEID
z[,na] <- initv
k <- intersect(rownames(z),y$GENEID)
z[k,na] <- y[k,na]
z[is.na(z[,na]),na] <- 0
}
rownames(z) <- as.vector(z$GENEID)
ds <- if ( j == 1 ) z[,-1] else ds + z[,-1]
}
ds <- ds/dsn + .1
return(ds)
}
dist.gen <- function(x,method="euclidean", ...) if ( method %in% c("spearman","pearson","kendall") ) as.dist( 1 - cor(t(x),method=method,...) ) else if ( method %in% c("eupearson") ) sqrt(2*as.dist( 1 - cor(t(x),method="pearson",...) )) else if ( method %in% c("logpearson") ) as.dist( 1 - cor(log2(t(x)),method="pearson",...) ) else if ( method == "binary" ) dist( 1*(x > .1) ) else if ( method == "relentr") dist(x,FUN=relentr) else if ( method == "cosine" ) dist(x,FUN=coss) else dist(x,method=method,...)
relentr <- function(x,y){ px <- x/sum(x); py <- y/sum(y); H <- sum(px*log2(px/py) + py*log2(py/px)); return(H) }
coss <- function(x,y){ sum(x*y)/sqrt(sum(x*x))/sqrt(sum(y*y)) }
dist.gen.pairs <- function(x,y,...) dist.gen(t(cbind(x,y)),...)
binompval <- function(p,N,n){
pval <- pbinom(n,round(N,0),p,lower.tail=TRUE)
pval[!is.na(pval) & pval > 0.5] <- 1-pval[!is.na(pval) & pval > 0.5]
return(pval)
}
setGeneric("plotgap", function(object) standardGeneric("plotgap"))
setMethod("plotgap",
signature = "SCseq",
definition = function(object){
if ( length(object@cluster$kpart) == 0 ) stop("run clustexp before plotgap")
if ( sum(is.na(object@cluster$gap$Tab[,3])) > 0 ) stop("run clustexp with do.gap = TRUE first")
plot(object@cluster$gap,ylim=c( min(object@cluster$gap$Tab[,3] - object@cluster$gap$Tab[,4]), max(object@cluster$gap$Tab[,3] + object@cluster$gap$Tab[,4])))
}
)
setGeneric("plotjaccard", function(object) standardGeneric("plotjaccard"))
setMethod("plotjaccard",
signature = "SCseq",
definition = function(object){
if ( length(object@cluster$kpart) == 0 ) stop("run clustexp before plotjaccard")
if ( length(unique(object@cluster$kpart)) < 2 ) stop("only a single cluster: no Jaccard's similarity plot")
barplot(object@cluster$jaccard,names.arg=1:length(object@cluster$jaccard),ylab="Jaccard's similarity")
}
)
setGeneric("plotoutlierprobs", function(object) standardGeneric("plotoutlierprobs"))
setMethod("plotoutlierprobs",
signature = "SCseq",
definition = function(object){
if ( length(object@cpart) == 0 ) stop("run findoutliers before plotoutlierprobs")
p <- object@cluster$kpart[ order(object@cluster$kpart,decreasing=FALSE)]
x <- object@out$cprobs[names(p)]
fcol <- object@fcol
for ( i in 1:max(p) ){
y <- -log10(x + 2.2e-16)
y[p != i] <- 0
if ( i == 1 ) b <- barplot(y,ylim=c(0,max(-log10(x + 2.2e-16))*1.1),col=fcol[i],border=fcol[i],names.arg=FALSE,ylab="-log10prob") else barplot(y,add=TRUE,col=fcol[i],border=fcol[i],names.arg=FALSE,axes=FALSE)
}
abline(-log10(object@outlierpar$probthr),0,col="black",lty=2)
d <- b[2,1] - b[1,1]
y <- 0
for ( i in 1:max(p) ) y <- append(y,b[sum(p <=i),1] + d/2)
axis(1,at=(y[1:(length(y)-1)] + y[-1])/2,lab=1:max(p))
box()
}
)
setGeneric("plotbackground", function(object) standardGeneric("plotbackground"))
setMethod("plotbackground",
signature = "SCseq",
definition = function(object){
if ( length(object@cpart) == 0 ) stop("run findoutliers before plotbackground")
m <- apply(object@fdata,1,mean)
v <- apply(object@fdata,1,var)
fit <- locfit(v~lp(m,nn=.7),family="gamma",maxk=500)
plot(log2(m),log2(v),pch=20,xlab="log2mean",ylab="log2var",col="grey")
lines(log2(m[order(m)]),log2(object@background$lvar(m[order(m)],object)),col="red",lwd=2)
lines(log2(m[order(m)]),log2(fitted(fit)[order(m)]),col="orange",lwd=2,lty=2)
legend("topleft",legend=substitute(paste("y = ",a,"*x^2 + ",b,"*x + ",d,sep=""),list(a=round(coef(object@background$vfit)[3],2),b=round(coef(object@background$vfit)[2],2),d=round(coef(object@background$vfit)[1],2))),bty="n")
}
)
setGeneric("plotsensitivity", function(object) standardGeneric("plotsensitivity"))
setMethod("plotsensitivity",
signature = "SCseq",
definition = function(object){
if ( length(object@cpart) == 0 ) stop("run findoutliers before plotsensitivity")
plot(log10(object@out$thr), object@out$stest, type="l",xlab="log10 Probability cutoff", ylab="Number of outliers")
abline(v=log10(object@outlierpar$probthr),col="red",lty=2)
}
)
setGeneric("diffgenes", function(object,cl1,cl2,mincount=1) standardGeneric("diffgenes"))
setMethod("diffgenes",
signature = "SCseq",
definition = function(object,cl1,cl2,mincount){
part <- object@cpart
cl1 <- c(cl1)
cl2 <- c(cl2)
if ( length(part) == 0 ) stop("run findoutliers before diffgenes")
if ( ! is.numeric(mincount) ) stop("mincount has to be a non-negative number") else if ( mincount < 0 ) stop("mincount has to be a non-negative number")
if ( length(intersect(cl1, part)) < length(unique(cl1)) ) stop( paste("cl1 has to be a subset of ",paste(sort(unique(part)),collapse=","),"\n",sep="") )
if ( length(intersect(cl2, part)) < length(unique(cl2)) ) stop( paste("cl2 has to be a subset of ",paste(sort(unique(part)),collapse=","),"\n",sep="") )
f <- apply(object@ndata[,part %in% c(cl1,cl2)],1,max) > mincount
x <- object@ndata[f,part %in% cl1]
y <- object@ndata[f,part %in% cl2]
if ( sum(part %in% cl1) == 1 ) m1 <- x else m1 <- apply(x,1,mean)
if ( sum(part %in% cl2) == 1 ) m2 <- y else m2 <- apply(y,1,mean)
if ( sum(part %in% cl1) == 1 ) s1 <- sqrt(x) else s1 <- sqrt(apply(x,1,var))
if ( sum(part %in% cl2) == 1 ) s2 <- sqrt(y) else s2 <- sqrt(apply(y,1,var))
d <- ( m1 - m2 )/ apply( cbind( s1, s2 ),1,mean )
names(d) <- rownames(object@ndata)[f]
if ( sum(part %in% cl1) == 1 ){
names(x) <- names(d)
x <- x[order(d,decreasing=TRUE)]
}else{
x <- x[order(d,decreasing=TRUE),]
}
if ( sum(part %in% cl2) == 1 ){
names(y) <- names(d)
y <- y[order(d,decreasing=TRUE)]
}else{
y <- y[order(d,decreasing=TRUE),]
}
return(list(z=d[order(d,decreasing=TRUE)],cl1=x,cl2=y,cl1n=cl1,cl2n=cl2))
}
)
plotdiffgenes <- function(z,gene=g){
if ( ! is.list(z) ) stop("first arguments needs to be output of function diffgenes")
if ( length(z) < 5 ) stop("first arguments needs to be output of function diffgenes")
if ( sum(names(z) == c("z","cl1","cl2","cl1n","cl2n")) < 5 ) stop("first arguments needs to be output of function diffgenes")
if ( length(gene) > 1 ) stop("only single value allowed for argument gene")
if ( !is.numeric(gene) & !(gene %in% names(z$z)) ) stop("argument gene needs to be within rownames of first argument or a positive integer number")
if ( is.numeric(gene) ){ if ( gene < 0 | round(gene) != gene ) stop("argument gene needs to be within rownames of first argument or a positive integer number") }
x <- if ( is.null(dim(z$cl1)) ) z$cl1[gene] else t(z$cl1[gene,])
y <- if ( is.null(dim(z$cl2)) ) z$cl2[gene] else t(z$cl2[gene,])
plot(1:length(c(x,y)),c(x,y),ylim=c(0,max(c(x,y))),xlab="",ylab="Expression",main=gene,cex=0,axes=FALSE)
axis(2)
box()
u <- 1:length(x)
rect(u - .5,0,u + .5,x,col="red")
v <- c(min(u) - .5,max(u) + .5)
axis(1,at=mean(v),lab=paste(z$cl1n,collapse=","))
lines(v,rep(mean(x),length(v)))
lines(v,rep(mean(x)-sqrt(var(x)),length(v)),lty=2)
lines(v,rep(mean(x)+sqrt(var(x)),length(v)),lty=2)
u <- ( length(x) + 1 ):length(c(x,y))
v <- c(min(u) - .5,max(u) + .5)
rect(u - .5,0,u + .5,y,col="blue")
axis(1,at=mean(v),lab=paste(z$cl2n,collapse=","))
lines(v,rep(mean(y),length(v)))
lines(v,rep(mean(y)-sqrt(var(y)),length(v)),lty=2)
lines(v,rep(mean(y)+sqrt(var(y)),length(v)),lty=2)
abline(v=length(x) + .5)
}
setGeneric("plottsne", function(object,final=TRUE) standardGeneric("plottsne"))
setMethod("plottsne",
signature = "SCseq",
definition = function(object,final){
if ( length(object@tsne) == 0 ) stop("run comptsne before plottsne")
if ( final & length(object@cpart) == 0 ) stop("run findoutliers before plottsne")
if ( !final & length(object@cluster$kpart) == 0 ) stop("run clustexp before plottsne")
part <- if ( final ) object@cpart else object@cluster$kpart
plot(object@tsne,xlab="Dim 1",ylab="Dim 2",pch=20,cex=1.5,col="lightgrey")
for ( i in 1:max(part) ){
if ( sum(part == i) > 0 ) text(object@tsne[part == i,1],object@tsne[part == i,2],i,col=object@fcol[i],cex=.75,font=4)
}
}
)
setGeneric("plotlabelstsne", function(object,labels=NULL) standardGeneric("plotlabelstsne"))
setMethod("plotlabelstsne",
signature = "SCseq",
definition = function(object,labels){
if ( is.null(labels ) ) labels <- names(object@ndata)
if ( length(object@tsne) == 0 ) stop("run comptsne before plotlabelstsne")
plot(object@tsne,xlab="Dim 1",ylab="Dim 2",pch=20,cex=1.5,col="lightgrey")
text(object@tsne[,1],object@tsne[,2],labels,cex=.5)
}
)
setGeneric("plotsymbolstsne", function(object,types=NULL) standardGeneric("plotsymbolstsne"))
setMethod("plotsymbolstsne",
signature = "SCseq",
definition = function(object,types){
if ( is.null(types) ) types <- names(object@fdata)
if ( length(object@tsne) == 0 ) stop("run comptsne before plotsymbolstsne")
if ( length(types) != ncol(object@fdata) ) stop("types argument has wrong length. Length has to equal to the column number of object@ndata")
coloc <- rainbow(length(unique(types)))
syms <- c()
plot(object@tsne,xlab="Dim 1",ylab="Dim 2",pch=20,col="grey")
for ( i in 1:length(unique(types)) ){
f <- types == sort(unique(types))[i]
syms <- append( syms, ( (i-1) %% 25 ) + 1 )
points(object@tsne[f,1],object@tsne[f,2],col=coloc[i],pch=( (i-1) %% 25 ) + 1,cex=1)
}
legend("topleft", legend=sort(unique(types)), col=coloc, pch=syms)
}
)
setGeneric("plotexptsne", function(object,g,n="",logsc=FALSE) standardGeneric("plotexptsne"))
setMethod("plotexptsne",
signature = "SCseq",
definition = function(object,g,n="",logsc=FALSE){
if ( length(object@tsne) == 0 ) stop("run comptsne before plottsne")
if ( length(intersect(g,rownames(object@ndata))) < length(unique(g)) ) stop("second argument does not correspond to set of rownames slot ndata of SCseq object")
if ( !is.numeric(logsc) & !is.logical(logsc) ) stop("argument logsc has to be logical (TRUE/FALSE)")
if ( n == "" ) n <- g[1]
l <- apply(object@ndata[g,] - .1,2,sum) + .1
if (logsc) {
f <- l == 0
l <- log2(l)
l[f] <- NA
}
mi <- min(l,na.rm=TRUE)
ma <- max(l,na.rm=TRUE)
ColorRamp <- colorRampPalette(rev(brewer.pal(n = 7,name = "RdYlBu")))(100)
ColorLevels <- seq(mi, ma, length=length(ColorRamp))
v <- round((l - mi)/(ma - mi)*99 + 1,0)
layout(matrix(data=c(1,3,2,4), nrow=2, ncol=2), widths=c(5,1,5,1), heights=c(5,1,1,1))
par(mar = c(3,5,2.5,2))
plot(object@tsne,xlab="Dim 1",ylab="Dim 2",main=n,pch=20,cex=0,col="grey")
kk <- order(v,decreasing=F)
points(object@tsne[kk,1],object@tsne[kk,2],col=ColorRamp[v[kk]],pch=20,cex=1.5)
par(mar = c(3,2.5,2.5,2))
image(1, ColorLevels,
matrix(data=ColorLevels, ncol=length(ColorLevels),nrow=1),
col=ColorRamp,
xlab="",ylab="",
xaxt="n")
layout(1)
}
)
plot.err.bars.y <- function(x, y, y.err, col="black", lwd=1, lty=1, h=0.1){
arrows(x,y-y.err,x,y+y.err,code=0, col=col, lwd=lwd, lty=lty)
arrows(x-h,y-y.err,x+h,y-y.err,code=0, col=col, lwd=lwd, lty=lty)
arrows(x-h,y+y.err,x+h,y+y.err,code=0, col=col, lwd=lwd, lty=lty)
}
clusGapExt <-function (x, FUNcluster, K.max, B = 100, verbose = interactive(), method="euclidean",random=TRUE,diss=FALSE,
...)
{
stopifnot(is.function(FUNcluster), length(dim(x)) == 2, K.max >=
2, (n <- nrow(x)) >= 1, (p <- ncol(x)) >= 1)
if (B != (B. <- as.integer(B)) || (B <- B.) <= 0)
stop("'B' has to be a positive integer")
if (is.data.frame(x))
x <- as.matrix(x)
ii <- seq_len(n)
W.k <- function(X, kk) {
clus <- if (kk > 1)
FUNcluster(X, kk, ...)$cluster
else rep.int(1L, nrow(X))
0.5 * sum(vapply(split(ii, clus), function(I) {
if ( diss ){
xs <- X[I,I, drop = FALSE]
sum(xs/nrow(xs))
}else{
xs <- X[I, , drop = FALSE]
sum(dist.gen(xs,method=method)/nrow(xs))
}
}, 0))
}
logW <- E.logW <- SE.sim <- numeric(K.max)
if (verbose)
cat("Clustering k = 1,2,..., K.max (= ", K.max, "): .. ",
sep = "")
for (k in 1:K.max){
cat("k =",k,"\n")
logW[k] <- log(W.k(x, k))
}
if (verbose)
cat("done\n")
if (random){
xs <- scale(x, center = TRUE, scale = FALSE)
m.x <- rep(attr(xs, "scaled:center"), each = n)
V.sx <- svd(xs)$v
rng.x1 <- apply(xs %*% V.sx, 2, range)
logWks <- matrix(0, B, K.max)
if (verbose)
cat("Bootstrapping, b = 1,2,..., B (= ", B, ") [one \".\" per sample]:\n",
sep = "")
for (b in 1:B) {
z1 <- apply(rng.x1, 2, function(M, nn) runif(nn, min = M[1],
max = M[2]), nn = n)
z <- tcrossprod(z1, V.sx) + m.x
##z <- apply(x,2,function(m) runif(length(m),min=min(m),max=max(m)))
##z <- apply(x,2,function(m) sample(m))
for (k in 1:K.max) {
logWks[b, k] <- log(W.k(z, k))
}
if (verbose)
cat(".", if (b%%50 == 0)
paste(b, "\n"))
}
if (verbose && (B%%50 != 0))
cat("", B, "\n")
E.logW <- colMeans(logWks)
SE.sim <- sqrt((1 + 1/B) * apply(logWks, 2, var))
}else{
E.logW <- rep(NA,K.max)
SE.sim <- rep(NA,K.max)
}
structure(class = "clusGap", list(Tab = cbind(logW, E.logW,
gap = E.logW - logW, SE.sim), n = n, B = B, FUNcluster = FUNcluster))
}
clustfun <- function(x,clustnr=20,bootnr=50,metric="pearson",do.gap=FALSE,sat=TRUE,SE.method="Tibs2001SEmax",SE.factor=.25,B.gap=50,cln=0,rseed=17000,FUNcluster="kmedoids",distances=NULL,link="single")
{
if ( clustnr < 2) stop("Choose clustnr > 1")
if ( FUNcluster %in% c("kmedoids","clara","hclust") ){
di <- t(x)
diM <- dist.gen(di,method=metric)
}else{
di <- as.matrix(dist.gen(t(x),method=metric))
}
if ( FUNcluster %in% c("clara") ){
diCL <- as.data.frame( cmdscale(as.matrix(diM),k=nrow(di)-1) )
rownames(diCL) <- names(x)
}
if ( nrow(di) - 1 < clustnr ) clustnr <- nrow(di) - 1
if ( do.gap | sat | cln > 0 ){
gpr <- NULL
f <- if ( cln == 0 ) TRUE else FALSE
if ( do.gap ){
set.seed(rseed)
if ( FUNcluster == "kmeans" ) gpr <- clusGapExt(as.matrix(di), FUN = kmeans, K.max = clustnr, B = B.gap, iter.max=100)
if ( FUNcluster == "kmedoids" ) gpr <- clusGapExt(as.matrix(di), FUN = function(x,k) pam(dist.gen(x,method=metric),k), K.max = clustnr, B = B.gap, method=metric)
if ( FUNcluster == "clara" ) gpr <- clusGapExt(diCL, FUN = function(x,k) clara(x,k), K.max = clustnr, B = B.gap, method="euclidean")
if ( FUNcluster == "hclust" ) gpr <- clusGapExt(as.matrix(di), FUN = function(x,k){ y <- disthclustCBI(dist.gen(x,method=metric),k,link=link,scaling=FALSE,method="ward.D2"); y$cluster <- y$partition; y }, K.max = clustnr, B = B.gap)
if ( f ) cln <- maxSE(gpr$Tab[,3],gpr$Tab[,4],method=SE.method,SE.factor)
}
if ( sat ){
if ( ! do.gap ){
if ( FUNcluster == "kmeans" ) gpr <- clusGapExt(as.matrix(di), FUN = kmeans, K.max = clustnr, B = B.gap, iter.max=100, random=FALSE)
##if ( FUNcluster == "kmedoids" ) gpr <- clusGapExt(as.matrix(di), FUN = function(x,k) pam(dist.gen(x,method=metric),k), K.max = clustnr, B = B.gap, random=FALSE, method=metric)
if ( FUNcluster == "kmedoids" ) gpr <- clusGapExt(as.matrix(diM), FUN = function(x,k) pam(as.dist(x),k), K.max = clustnr, B = B.gap, random=FALSE, method=metric,diss=TRUE)
if ( FUNcluster == "clara" ) gpr <- clusGapExt(as.matrix(diCL), FUN = function(x,k) clara(x,k), K.max = clustnr, B = B.gap, random=FALSE, method="euclidean")
if ( FUNcluster == "hclust" ) gpr <- clusGapExt(as.matrix(di), FUN = function(x,k){ y <- disthclustCBI(dist.gen(x,method=metric),k,link=link,scaling=FALSE,method="ward.D2"); y$cluster <- y$partition; y }, K.max = clustnr, B = B.gap, random=FALSE)
}
g <- gpr$Tab[,1]
y <- g[-length(g)] - g[-1]
mm <- numeric(length(y))
nn <- numeric(length(y))
for ( i in 1:length(y)){
mm[i] <- mean(y[i:length(y)])
nn[i] <- sqrt(var(y[i:length(y)]))
}
if ( f ) cln <- max(min(which( y - (mm + nn) < 0 )),1)
}
if ( cln <= 1 ) {
clb <- list(result=list(partition=rep(1,dim(x)[2])),bootmean=1)
names(clb$result$partition) <- names(x)
return(list(x=x,clb=clb,gpr=gpr,di=if ( FUNcluster %in% c("kmedoids","clara","hclust") ) diM else di))
}
if ( FUNcluster == "kmeans" ) clb <- clusterboot(di,B=bootnr,distances=FALSE,bootmethod="boot",clustermethod=kmeansCBI,krange=cln,scaling=FALSE,multipleboot=FALSE,bscompare=TRUE,seed=rseed)
##if ( FUNcluster == "kmedoids" ) clb <- clusterboot(diM,B=bootnr,bootmethod="boot",clustermethod=pamkCBI,k=cln,multipleboot=FALSE,bscompare=TRUE,seed=rseed)
if ( FUNcluster == "kmedoids" ) clb <- clusterboot(diM,B=bootnr,bootmethod="boot",clustermethod=pamkdCBI,scaling=FALSE,diss=TRUE,k=cln,multipleboot=FALSE,bscompare=TRUE,seed=rseed)
if ( FUNcluster == "clara" ) clb <- clusterboot(diCL,B=bootnr,bootmethod="boot",clustermethod=pamkdCBI,usepam=FALSE,scaling=FALSE,krange=cln,multipleboot=FALSE,bscompare=TRUE,seed=rseed)
if ( FUNcluster == "hclust" ) clb <- clusterboot(diM,B=bootnr,distances=TRUE,bootmethod="boot",clustermethod=disthclustCBI,method="ward.D2",k=cln,link=link,scaling=FALSE,multipleboot=FALSE,bscompare=TRUE,seed=rseed,diss=TRUE)
return(list(x=x,clb=clb,gpr=gpr,di=if ( FUNcluster %in% c("kmedoids","clara","hclust") ) diM else di))
}
}
setGeneric("clustexp", function(object,clustnr=20,bootnr=50,metric="pearson",do.gap=FALSE,sat=TRUE,SE.method="Tibs2001SEmax",SE.factor=.25,B.gap=50,cln=0,rseed=17000,FUNcluster="kmedoids",FSelect=TRUE) standardGeneric("clustexp"))
setMethod("clustexp",
signature = "SCseq",
definition = function(object,clustnr,bootnr,metric,do.gap,sat,SE.method,SE.factor,B.gap,cln,rseed,FUNcluster,FSelect) {
if ( ! is.numeric(clustnr) ) stop("clustnr has to be a positive integer") else if ( round(clustnr) != clustnr | clustnr <= 0 ) stop("clustnr has to be a positive integer")
if ( ! is.numeric(bootnr) ) stop("bootnr has to be a positive integer") else if ( round(bootnr) != bootnr | bootnr <= 0 ) stop("bootnr has to be a positive integer")
if ( ! ( metric %in% c( "spearman","pearson","eupearson","logpearson","kendall","euclidean","maximum","manhattan","canberra","binary","minkowski","relentr") ) ) stop("metric has to be one of the following: spearman, pearson, eupearson, logpearson, kendall, euclidean, maximum, manhattan, canberra, binary, minkowski","relentr")
if ( ! ( SE.method %in% c( "firstSEmax","Tibs2001SEmax","globalSEmax","firstmax","globalmax") ) ) stop("SE.method has to be one of the following: firstSEmax, Tibs2001SEmax, globalSEmax, firstmax, globalmax")
if ( ! is.numeric(SE.factor) ) stop("SE.factor has to be a non-negative integer") else if ( SE.factor < 0 ) stop("SE.factor has to be a non-negative integer")
if ( ! ( is.numeric(do.gap) | is.logical(do.gap) ) ) stop( "do.gap has to be logical (TRUE/FALSE)" )
if ( ! ( is.numeric(sat) | is.logical(sat) ) ) stop( "sat has to be logical (TRUE/FALSE)" )
if ( ! is.numeric(B.gap) ) stop("B.gap has to be a positive integer") else if ( round(B.gap) != B.gap | B.gap <= 0 ) stop("B.gap has to be a positive integer")
if ( ! is.numeric(cln) ) stop("cln has to be a non-negative integer") else if ( round(cln) != cln | cln < 0 ) stop("cln has to be a non-negative integer")
if ( ! is.numeric(rseed) ) stop("rseed has to be numeric")
if ( !do.gap & !sat & cln == 0 ) stop("cln has to be a positive integer or either do.gap or sat has to be TRUE")
if ( ! ( FUNcluster %in% c("kmeans","hclust","kmedoids","clara") ) ) stop("FUNcluster has to be one of the following: kmeans, hclust,kmedoids","clara")
if ( ! ( is.numeric(FSelect) | is.logical(FSelect) ) ) stop( "FSelect has to be logical (TRUE/FALSE)" )
object@clusterpar <- list(clustnr=clustnr,bootnr=bootnr,metric=metric,do.gap=do.gap,sat=sat,SE.method=SE.method,SE.factor=SE.factor,B.gap=B.gap,cln=cln,rseed=rseed,FUNcluster=FUNcluster,FSelect=FSelect)
if ( FSelect ){
x <- object@fdata
m <- apply(x,1,mean)
v <- apply(x,1,var )
ml <- log2(m)
vl <- log2(v)
f <- ml > -Inf & vl > -Inf
ml <- ml[f]
vl <- vl[f]
mm <- -8
repeat{
fit <- lm(vl ~ ml + I(ml^2))
if( coef(fit)[3] >= 0 | mm >= -1){
break
}
mm <- mm + .5
f <- ml > mm
ml <- ml[f]
vl <- vl[f]
}
lvar <- function(x) 2**(coef(fit)[1] + log2(x)*coef(fit)[2] + coef(fit)[3] * log2(x)**2)
vln <- log2(v) - log2(sapply(m,FUN=lvar))
n <- names(vln)[vln>0]
##n <- names(head(vln[order(vln,decreasing=T)],round(length(vln)/5,0)))
}else{
n <- rownames(object@fdata)
}
y <- clustfun(object@fdata[n,],clustnr,bootnr,metric,do.gap,sat,SE.method,SE.factor,B.gap,cln,rseed,FUNcluster)
object@cluster <- list(kpart=y$clb$result$partition, jaccard=y$clb$bootmean, gap=y$gpr, clb=y$clb, features=n)
object@distances <- as.matrix( y$di )
set.seed(111111)
object@fcol <- sample(rainbow(max(y$clb$result$partition)))
return(object)
}
)
setGeneric("findoutliers", function(object,outminc=5,outlg=2,probthr=1e-3,thr=2**-(1:40),outdistquant=.95) standardGeneric("findoutliers"))
setMethod("findoutliers",
signature = "SCseq",
definition = function(object,outminc,outlg,probthr,thr,outdistquant) {
if ( length(object@cluster$kpart) == 0 ) stop("run clustexp before findoutliers")
if ( ! is.numeric(outminc) ) stop("outminc has to be a non-negative integer") else if ( round(outminc) != outminc | outminc < 0 ) stop("outminc has to be a non-negative integer")
if ( ! is.numeric(outlg) ) stop("outlg has to be a non-negative integer") else if ( round(outlg) != outlg | outlg < 0 ) stop("outlg has to be a non-negative integer")
if ( ! is.numeric(probthr) ) stop("probthr has to be a number between 0 and 1") else if ( probthr < 0 | probthr > 1 ) stop("probthr has to be a number between 0 and 1")
if ( ! is.numeric(thr) ) stop("thr hast to be a vector of numbers between 0 and 1") else if ( min(thr) < 0 | max(thr) > 1 ) stop("thr hast to be a vector of numbers between 0 and 1")
if ( ! is.numeric(outdistquant) ) stop("outdistquant has to be a number between 0 and 1") else if ( outdistquant < 0 | outdistquant > 1 ) stop("outdistquant has to be a number between 0 and 1")
object@outlierpar <- list( outminc=outminc,outlg=outlg,probthr=probthr,thr=thr,outdistquant=outdistquant )
### calibrate background model
m <- log2(apply(object@fdata,1,mean))
v <- log2(apply(object@fdata,1,var))
f <- m > -Inf & v > -Inf
m <- m[f]
v <- v[f]
mm <- -8
repeat{
fit <- lm(v ~ m + I(m^2))
##if( coef(fit)[3] >= 0 | mm >= 3){
if( coef(fit)[3] >= 0 | mm >= -1){
break
}
mm <- mm + .5
f <- m > mm
m <- m[f]
v <- v[f]
}
object@background <- list()
object@background$vfit <- fit
object@background$lvar <- function(x,object) 2**(coef(object@background$vfit)[1] + log2(x)*coef(object@background$vfit)[2] + coef(object@background$vfit)[3] * log2(x)**2)
object@background$lsize <- function(x,object) x**2/(max(x + 1e-6,object@background$lvar(x,object)) - x)
### identify outliers
out <- c()
stest <- rep(0,length(thr))
cprobs <- c()
outgene <- list()
for ( n in 1:max(object@cluster$kpart) ){
if ( sum(object@cluster$kpart == n) == 1 ){ cprobs <- append(cprobs,.5); names(cprobs)[length(cprobs)] <- names(object@cluster$kpart)[object@cluster$kpart == n]; next }
x <- object@fdata[,object@cluster$kpart == n]
x <- x[apply(x,1,max) > outminc,]
z <- t( apply(x,1,function(x){ apply( cbind( pnbinom(round(x,0),mu=mean(x),size=object@background$lsize(mean(x),object)) , 1 - pnbinom(round(x,0),mu=mean(x),size=object@background$lsize(mean(x),object)) ),1, min) } ) )
cp <- apply(z,2,function(x){ y <- p.adjust(x,method="BH"); y <- y[order(y,decreasing=FALSE)]; return(y[outlg]);})
f <- cp < probthr
cprobs <- append(cprobs,cp)
if ( sum(f) > 0 ) out <- append(out,names(x)[f])
for ( j in 1:length(thr) ) stest[j] <- stest[j] + sum( cp < thr[j] )
fg <- apply(z,1,min) < probthr
outgene[[n]] <- if ( sum(fg) > 0 ) z[fg,] else 0
}
object@out <-list(out=out,stest=stest,thr=thr,cprobs=cprobs,outgene=outgene)
### cluster outliers
clp2p.cl <- c()
cols <- names(object@fdata)
cpart <- object@cluster$kpart
di <- as.data.frame(object@distances)
for ( i in 1:max(cpart) ) {
tcol <- cols[cpart == i]
if ( sum(!(tcol %in% out)) > 1 ) clp2p.cl <- append(clp2p.cl,as.vector(t(di[tcol[!(tcol %in% out)],tcol[!(tcol %in% out)]])))
}
clp2p.cl <- clp2p.cl[clp2p.cl>0]
cadd <- list()
if ( length(out) > 0 ){
if (length(out) == 1){
cadd <- list(out)
}else{
n <- out
m <- as.data.frame(di[out,out])
for ( i in 1:length(out) ){
if ( length(n) > 1 ){
o <- order(apply(cbind(m,1:dim(m)[1]),1,function(x) min(x[1:(length(x)-1)][-x[length(x)]])),decreasing=FALSE)
m <- m[o,o]
n <- n[o]
f <- m[,1] < quantile(clp2p.cl,outdistquant) | m[,1] == min(clp2p.cl)
ind <- 1
if ( sum(f) > 1 ) for ( j in 2:sum(f) ) if ( apply(m[f,f][j,c(ind,j)] > quantile(clp2p.cl,outdistquant) ,1,sum) == 0 ) ind <- append(ind,j)
cadd[[i]] <- n[f][ind]
g <- ! n %in% n[f][ind]
n <- n[g]
m <- m[g,g]
if ( sum(g) == 0 ) break
}else if (length(n) == 1){
cadd[[i]] <- n
break
}
}
}
for ( i in 1:length(cadd) ){
cpart[cols %in% cadd[[i]]] <- max(cpart) + 1
}
}
### determine final clusters
for ( i in min(cpart):max(cpart) ){
if ( sum(cpart == i) == 0 ) next
f <- cols[cpart == i]
d <- object@fdata[object@cluster$features,]
if ( length(f) == 1 ){
cent <- d[,f]
md <- f
}else{
if ( object@clusterpar$FUNcluster %in% c("kmedoids","clara","hclust") ){
##x <- apply(as.matrix(dist.gen(t(d[,f]),method=object@clusterpar$metric)),2,mean)
x <- apply(object@distances[f,f],2,mean)
md <- f[which(x == min(x))[1]]
cent <- d[,md]
}else{
cent <- apply(d[,f],1,mean)
}
}
if ( i == min(cpart) ) dcent <- data.frame(cent) else dcent <- cbind(dcent,cent)
if ( object@clusterpar$FUNcluster %in% c("kmedoids","clara","hclust") ){
if ( i == min(cpart) ) tmp <- data.frame(object@distances[,md]) else tmp <- cbind(tmp,object@distances[,md])
}else{
if ( i == min(cpart) ) tmp <- data.frame(apply(d,2,dist.gen.pairs,y=cent,method=object@clusterpar$metric)) else tmp <- cbind(tmp,apply(d,2,dist.gen.pairs,y=cent,method=object@clusterpar$metric))
}
}
cpart <- apply(tmp,1,function(x) order(x,decreasing=FALSE)[1])
for ( i in max(cpart):1){if (sum(cpart==i)==0) cpart[cpart>i] <- cpart[cpart>i] - 1 }
object@cpart <- cpart
set.seed(111111)
object@fcol <- sample(rainbow(max(cpart)))
return(object)
}
)
setGeneric("comptsne", function(object,rseed=15555,sammonmap=FALSE,initial_cmd=TRUE,fast=FALSE,perplexity=30,...) standardGeneric("comptsne"))
setMethod("comptsne",
signature = "SCseq",
definition = function(object,rseed,sammonmap,initial_cmd,fast,perplexity,...){
if ( length(object@cluster$kpart) == 0 ) stop("run clustexp before comptsne")
set.seed(rseed)
di <- if ( object@clusterpar$FUNcluster %in% c("kmedoids","clara","hclust")) as.dist(object@distances) else dist.gen(as.matrix(object@distances))
if ( sammonmap ){
object@tsne <- as.data.frame(sammon(di,k=2)$points)
}else{
if ( fast ){
ts <- if ( initial_cmd ) Rtsne(di,dims=2,initial_config=cmdscale(di,k=2),perplexity=perplexity,...)$Y else Rtsne(di,dims=2,perplexity=perplexity,...)$Y
}else{
ts <- if ( initial_cmd ) tsne(di,k=2,initial_config=cmdscale(di,k=2),perplexity=perplexity,...) else tsne(di,k=2,perplexity=perplexity,...)
}
object@tsne <- as.data.frame(ts)
}
return(object)
}
)
setGeneric("clustdiffgenes", function(object,pvalue=.01) standardGeneric("clustdiffgenes"))
setMethod("clustdiffgenes",
signature = "SCseq",
definition = function(object,pvalue){
if ( length(object@cpart) == 0 ) stop("run findoutliers before clustdiffgenes")
if ( ! is.numeric(pvalue) ) stop("pvalue has to be a number between 0 and 1") else if ( pvalue < 0 | pvalue > 1 ) stop("pvalue has to be a number between 0 and 1")
cdiff <- list()
part <- object@cpart
for ( i in 1:max(part) ){
if ( sum(part == i) == 0 ) next
B <- names(part)[part == i]
A <- names(part)[part != i]
de <- diffexpnb(object@fdata, A=A, B=B, method="pooled",norm=FALSE, DESeq=FALSE, vfit=object@background$vfit, locreg=FALSE)
d <- data.frame(mean.ncl=de$res$baseMeanA,mean.cl=de$res$baseMeanB,fc=de$res$foldChange,pv=de$res$pv,padj=de$res$padj)
rownames(d) <- rownames(de$res)
d <- d[order(d$pv,decreasing=FALSE),]
cdiff[[paste("cl",i,sep=".")]] <- d[d$pv < pvalue,]
}
return(cdiff)
}
)
setGeneric("plotsaturation", function(object,disp=FALSE) standardGeneric("plotsaturation"))
setMethod("plotsaturation",
signature = "SCseq",
definition = function(object,disp){
if ( length(object@cluster$gap) == 0 ) stop("run clustexp before plotsaturation")
g <- object@cluster$gap$Tab[,1]
y <- g[-length(g)] - g[-1]
mm <- numeric(length(y))
nn <- numeric(length(y))
for ( i in 1:length(y)){
mm[i] <- mean(y[i:length(y)])
nn[i] <- sqrt(var(y[i:length(y)]))
}
cln <- max(min(which( y - (mm + nn) < 0 )),1)
x <- 1:length(y)
if (disp){
x <- 1:length(g)
plot(x,g,pch=20,col="grey",xlab="k",ylab="log within cluster dispersion")
f <- x == cln
points(x[f],g[f],col="blue")
}else{
plot(x,y,pch=20,col="grey",xlab="k",ylab="Change in log within cluster dispersion")
points(x,mm,col="red",pch=20)
plot.err.bars.y(x,mm,nn,col="red")
points(x,y,col="grey",pch=20)
f <- x == cln
points(x[f],y[f],col="blue")
}
}
)
setGeneric("plotsilhouette", function(object) standardGeneric("plotsilhouette"))
setMethod("plotsilhouette",
signature = "SCseq",
definition = function(object){
if ( length(object@cluster$kpart) == 0 ) stop("run clustexp before plotsilhouette")
if ( length(unique(object@cluster$kpart)) < 2 ) stop("only a single cluster: no silhouette plot")
kpart <- object@cluster$kpart
distances <- if ( object@clusterpar$FUNcluster %in% c("kmedoids","clara","hclust") ) as.dist(object@distances) else dist.gen(object@distances)
si <- silhouette(kpart,distances)
plot(si)
}
)
compmedoids <- function(x,part,metric="pearson"){
m <- c()
for ( i in sort(unique(part)) ){
f <- names(x)[part == i]
if ( length(f) == 1 ){
m <- append(m,f)
}else{
y <- apply(as.matrix(dist.gen(t(x[,f]),method=metric)),2,mean)
m <- append(m,f[which(y == min(y))[1]])
}
}
m
}
setGeneric("clustheatmap", function(object,final=FALSE,hmethod="single") standardGeneric("clustheatmap"))
setMethod("clustheatmap",
signature = "SCseq",
definition = function(object,final,hmethod){
if ( final & length(object@cpart) == 0 ) stop("run findoutliers before clustheatmap")
if ( !final & length(object@cluster$kpart) == 0 ) stop("run clustexp before clustheatmap")
x <- object@fdata
part <- if ( final ) object@cpart else object@cluster$kpart
na <- c()
j <- 0
for ( i in 1:max(part) ){
if ( sum(part == i) == 0 ) next
j <- j + 1
na <- append(na,i)
d <- x[,part == i]
if ( sum(part == i) == 1 ) cent <- d else cent <- apply(d,1,mean)
if ( j == 1 ) tmp <- data.frame(cent) else tmp <- cbind(tmp,cent)
}
names(tmp) <- paste("cl",na,sep=".")
ld <- if ( object@clusterpar$FUNcluster %in% c("kmedoids","clara","hclust") ) dist.gen(t(tmp),method=object@clusterpar$metric) else dist.gen(as.matrix(dist.gen(t(tmp),method=object@clusterpar$metric)))
if ( max(part) > 1 ) cclmo <- hclust(ld,method=hmethod)$order else cclmo <- 1
q <- part
for ( i in 1:max(part) ){
q[part == na[cclmo[i]]] <- i
}
part <- q
di <- if ( object@clusterpar$FUNcluster %in% c("kmedoids","clara","hclust") ) object@distances else as.data.frame( as.matrix( dist.gen(t(object@distances)) ) )
pto <- part[order(part,decreasing=FALSE)]
ptn <- c()
for ( i in 1:max(pto) ){ pt <- names(pto)[pto == i]; z <- if ( length(pt) == 1 ) pt else pt[hclust(as.dist(t(di[pt,pt])),method=hmethod)$order]; ptn <- append(ptn,z) }
col <- object@fcol
mi <- min(di,na.rm=TRUE)
ma <- max(di,na.rm=TRUE)
layout(matrix(data=c(1,3,2,4), nrow=2, ncol=2), widths=c(5,1,5,1), heights=c(5,1,1,1))
ColorRamp <- colorRampPalette(brewer.pal(n = 7,name = "RdYlBu"))(100)
ColorLevels <- seq(mi, ma, length=length(ColorRamp))
if ( mi == ma ){
ColorLevels <- seq(0.99*mi, 1.01*ma, length=length(ColorRamp))
}
par(mar = c(3,5,2.5,2))
image(as.matrix(di[ptn,ptn]),col=ColorRamp,axes=FALSE)
abline(0,1)
box()
tmp <- c()
for ( u in 1:max(part) ){
ol <- (0:(length(part) - 1)/(length(part) - 1))[ptn %in% names(x)[part == u]]
points(rep(0,length(ol)),ol,col=col[cclmo[u]],pch=15,cex=.75)
points(ol,rep(0,length(ol)),col=col[cclmo[u]],pch=15,cex=.75)
tmp <- append(tmp,mean(ol))
}
axis(1,at=tmp,lab=cclmo)
axis(2,at=tmp,lab=cclmo)
par(mar = c(3,2.5,2.5,2))
image(1, ColorLevels,
matrix(data=ColorLevels, ncol=length(ColorLevels),nrow=1),
col=ColorRamp,
xlab="",ylab="",
xaxt="n")
layout(1)
return(cclmo)
}
)
plot3dtsne <- function(object,perplexity=30,fast=FALSE,x=NULL,g=NULL,logsc=FALSE,final=TRUE,ret=FALSE,tp=1){
require(rgl)
if ( is.null(x) ){
di <- dist.gen(as.matrix(object@distances))
if (fast){
tt <- Rtsne(di,dims=3,initial_config=cmdscale(di,k=3),perplexity=perplexity)$Y
}else{
tt <- tsne(di,k=3,initial_config=cmdscale(di,k=3),perplexity=perplexity)
}
}else{
tt <- x
}
if ( final ) part <- object@cpart else part <- object@cluster$kpart
plot3d(tt[,1], tt[,2], tt[,3], xlab = "", ylab = "", zlab = "", alpha = tp, col = "grey", pch="16", type="p", size = 8, point_antialias = TRUE)
if ( is.null(g) ){
for ( i in sort(unique(part)) ){ f <- part == i; text3d(tt[f,1], tt[f,2], tt[f,3], rep(i,sum(f)), font=10, alpha=tp, size=9, depth_test = "always", color=object@fcol[i])}
}
if ( !is.null(g) ){
l <- apply(object@ndata[g,] - .1,2,sum) + .1
if (logsc) {
f <- l == 0
l <- log2(l)
l[f] <- NA
}
mi <- min(l,na.rm=TRUE)
ma <- max(l,na.rm=TRUE)
ColorRamp <- colorRampPalette(rev(brewer.pal(n = 7,name = "RdYlBu")))(100)
ColorLevels <- seq(mi, ma, length=length(ColorRamp))
v <- round((l - mi)/(ma - mi)*99 + 1,0)
kk <- order(v,decreasing=F)
apply(cbind(tt[kk,],ColorRamp[v[kk]]),1,function(x) points3d(x[1],x[2],x[3],col=x[4],pch="16",size=8))
image(1, ColorLevels,
matrix(data=ColorLevels, ncol=length(ColorLevels),nrow=1),
col=ColorRamp,
xlab="",ylab="",
xaxt="n")
}
if (ret) return(tt)
}
name2id <- function(x,id) {
n <- c()
for ( j in x ){ n <- append(n,id[grep(j,id)])
}
n
}
varRegression <- function(x,vars){
z <- apply(log2(x),1,
function(x,vars){
d <- cbind(vars, x); names(d) <- c(names(vars),"gene");
f <- as.formula(paste("gene ", " ~ ", paste(names(vars),collapse="+"),sep=""));
m <- lm( f, data=d)
residuals(m) + coef(m)[1]
},vars=vars)
z <- as.data.frame(t(2**z))
names(z) <- names(x)
rownames(z) <- rownames(x)
z
}
CCcorrect <- function(x,vset=NULL,CGenes=NULL,ccor=.4,nComp=NULL,pvalue=.01,quant=.01,mode="pca"){
x <- log2(x)
if ( is.null(nComp) ) nComp <- min(ncol(x),nrow(x))
## x <- object@fdata
## mode pca or ica