I simulated PM and MM matrix (named pm.raw and mm.raw) (say 100 * 16 *15 --100 genes, 16 probes and 15 chips) Now I try to estimate expression values using RMA. Essentially, RMA has 3 steps: 1) background correction: pm.bgc<- apply(pm.raw, 2, bg.adjust) this is not working. ------------------ >Error in density(x, kernel = "epanechnikov", n = n.pts) : ---------------------------------------- So I use: mypm<-bg.adjust(pm.raw,mm.raw) pm.bgc<-mypm$pm bg.adjust<- function (pm, mm,n.pts = 2^14, ...) { param <- bg.parameters(pm, mm,n.pts) b <- param$sigma pm <- pm - param$mu - param$alpha * b^2 pm + b * ((1/sqrt(2 * pi)) * exp((-1/2) * ((pm/b)^2)))/pnorm(pm/b) list(pm=pm) } ### patch suggested by Laurent Gautier 10/17/2002 #### bg.parameters <- function(pm, mm, n.pts=2^14){ max.density <- function(x,n.pts){ aux <- density(x, kernel="epanechnikov", n=n.pts, na.rm=TRUE) aux$x[order(-aux$y)[1]] } mmbg <- max.density(mm,n.pts) pmbg <- max.density(pm,n.pts) bg.data <- mm[mm < mmbg] bg.data <- bg.data-mmbg bgsd <- sqrt(sum(bg.data^2)/(length(bg.data)-1))*sqrt(2)/.85 sig.data <- pm[pm > pmbg] sig.data <- sig.data-pmbg alpha <- 1/mean(sig.data) mubg <- mmbg list(alpha=alpha,mu=mubg,sigma=bgsd) } Is this correct for background correction???? (2) normalization pm.norm<-normalize.quantiles(pm.bgc) (3) probe specific correction and expression summary It seems that express.summary.stat can do this. However, the input into this function is a ProbeSet object. Now I have pm.norm and mm.raw, how can I create this ProbeSet object???? or how can I get summarized expression values from normalized probel data??? Thanks Pingzhao