R/sim.preProcessed.R

In Sage-Bionetworks/snm: Supervised Normalization of Microarrays

sim.preProcessed <-
		function(seed,perc.bio=0.3,perc.batches=0.3,perc.height=0.1,np=50000) { 
	set.seed(seed)
	na <- 50
	gmeans <- rchisq(np,1,2)
	gmeans[gmeans>15] <- runif(sum(gmeans>15),15,16)
	data <- matrix(gmeans,nr=np,nc=na)
	bio.var <- data.frame(groups=rep(c("A","B"),each=25))
	adj.var <- data.frame(batches=rep(c("A","B","C","D","E"),times=10),
			height=rnorm(50,1,0.5))
	int.var <- NULL
	
	group.effect <- sim.probe.specific(data, bio.var$groups, perc.bio, list(func=rnorm,params=c(mean=1,sd=0.3)))
	batches.effect <- sim.probe.specific(data, adj.var$batches, perc.batches, list(func=rnorm,params=c(mean=0,sd=0.3)))
	height.effect <- sim.probe.specific(data, adj.var$height, perc.height, list(func=rnorm, params=c(mean=1,sd=0.1)))
	
	M <- data + group.effect + batches.effect + height.effect
	
	E <- matrix(rnorm(length(data),0,0.25), nr=nrow(data), nc=ncol(data))
	Y <- M + E
	true.nulls <- which(group.effect[,1] == group.effect[,26])
	
	ret.obj <- 
			list(raw.data=Y, 
					bio.var=model.matrix(~groups,data=bio.var),
					adj.var=model.matrix(~batches+height,data=adj.var),
					int.var=int.var,
					true.nulls=true.nulls)
	ret.obj
}