Dear list members, My question relates to processing of Agilent single-color arrays. With data generated using Agilent's feature extraction software. My script so-for is mentioned below. The flag information contained in raw data files is transformed to weights. I have used 1 for 'good' and 0 for 'bad' in myFlagFun. If this weight information is loaded: -where does LIMMA use this information? Does the lmFit,contrastsFit need additional statements? -what happens if a gene is flagged 'bad' on one array, but not on the other? -is there a way to verify that only 'good' genes have been used? Second question, My E.avg (MAlist) contains E.avg$genes with gene information, But after applying fit->this gene information is lost? No gene annotation my topTable. Am I missing an argument? Thank you for providing insight ! Patrick setwd('D:/VITO/R') library('Biobase') library('convert') library('limma') AgilentFiles <- list.files(pattern="US") myFlagFun <- function(x) { #Weight only strongly positive spots 1, everything else 0 present <- x$gIsPosAndSignif == 1 probe <- x$ControlType == 0 manual <- x$IsManualFlag == 0 strong <- x$gIsWellAboveBG == 1 y <- as.numeric(present & probe & manual & strong) #Weight weak spots 0 weak <- strong == FALSE #with values not well above background weak <- (present & probe & manual & weak) weak <- grep(TRUE,weak) y[weak] <- 0 #Weight flagged spots 0 sat <- x$gIsSaturated == 0 xdr <- x$gIsLowPMTScaledUp == 0 featureOL1 <- x$gIsFeatNonUnifOL == 0 featureOL2 <- x$gIsFeatPopnOL == 0 flagged <- (sat & xdr & featureOL1 & featureOL2) flagged <- grep(FALSE, flagged) good <- grep(TRUE, y==1) flagged <- intersect(flagged, good) y[flagged] <- 0 y } targets <- readTargets("targets.txt") rawObj<-read.maimages(AgilentFiles, columns = list(G = "gMeanSignal", Gb = "gBGUsed", R ="gProcessedSignal", Rb = "gBGMedianSignal"), annotation= c("Row", "Col", "FeatureNum", "ProbeUID", "ControlType", "ProbeName", "GeneName", "SystematicName"), wt.fun=myFlagFun) Obj.corrected <- backgroundCorrect(rawObj, method="normexp", offset=1) Obj<-Obj.corrected Obj$R <- normalizeBetweenArrays(Obj.corrected$R, method="quantile") Obj$R <- log2(Obj$R) E <- new("MAList", list(targets=Obj$targets, genes=Obj$genes, weights=Obj$weights, source=Obj$source, M=Obj$R, A=Obj$G)) E.avg <- avereps(E, ID=E$genes$ProbeName) design<- as.matrix(read.table("targets.txt", row.names="FileName",header=T)) fit<-lmFit(E.avg$M,design, weights=E.avg$weights) cont.matrix <- makeContrasts(group1vsgroup2=Group1-Group2, levels=design) fit2 <- contrasts.fit(fit, cont.matrix) fit2 <- eBayes(fit2) topTable(fit2, genelist=fit2$genes, adjust="BH") Patrick De Boever, PhD, MSc Flemish Institute for Technological Research (VITO) Unit Environmental Risk and Health, Toxicology group Industriezone Vlasmeer 7, 2400 Mol, Belgium Tel. + 32 14 33 51 45 Fax. + 32 14 58 05 23 patrick.deboever@vito.be<mailto:patrick.deboever@vito.be> Visit our website: www.vito.be/english<http: www.vito.be="" english=""> --- This e-mail, any attachments and the information it contains are confidential and meant only for the use of the addressee(s) only. Access to this e-mail by anyone other than the addressee(s) is unauthorized. If you are not the intended addressee (or responsible for delivery of the message to such person), you may not use, copy, distribute or deliver to anyone this message (or any part of its contents) or take any action in reliance on it. In such case, you should destroy this message and notify the sender immediately. If you have received this e-mail in error, please notify us immediately by e-mail or telephone and delete the e-mail from any computer. All reasonable precautions have been taken to ensure no viruses are present in this e-mail and its attachments. As our company cannot accept responsibility for any loss or damage arising from the use of this e-mail or attachments we recommend that you subject these to your virus checking procedures prior to use. [[alternative HTML version deleted]]