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predictAttractor <- function(tpost, perturbations, wildtype=TRUE, verbose=FALSE){
fg <- perturbations$perturbed.genes
fs <- perturbations$forced.states+2
n.post <- dim(tableObjs(tpost))[3]
n.gene <- dim(perturbationObj(tpost))[1]
n.sample <- dim(perturbationObj(tpost))[2]
attractor.list <- matrix(nrow=n.post, ncol=n.gene)
for (i in c(1:n.post)) {
tn.model <- list(table=tableObjs(tpost)[,,i], graph=graphObjs(tpost)[,,i], degree=degreeObjs(tpost)[i,])
vec0 <- rep(2,length(tn.model$degree))
vec0[fg] <- fs
junk <- attractor.distance.summary(vec0, tn.model, wildtype, fg, fs)
nc <- dim(junk$attractor)[2]
attractor.list[i,] <- summarize.attractor.2(junk$attractor[,2:nc])
if(verbose) if(i %% 1000 == 0) cat(paste(i,"/",n.post,"\n"))
}
tmp <- condense.attractor(attractor.list)
return(list(post.prob=tmp$p.attr, attractor.summary=tmp$uni.attr))
}
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