Hi BioC, I am relatively new to R and array analysis in general. > sessionInfo() R version 2.5.1 (2007-06-27) i386-pc-mingw32 locale: LC_COLLATE=English_United Kingdom.1252;LC_CTYPE=English_United Kingdom.1252;LC_MONETARY=English_United Kingdom.1252;LC_NUMERIC=C;LC_TIME=English_United Kingdom.1252 attached base packages: [1] "stats" "graphics" "grDevices" "utils" "datasets" "methods" "base" other attached packages: limma "2.10.5" I'm trying to follow the workshops I've found online (Lab 4 - Differential Expression and Linear Modeling using limma) but I'm coming unstuck at the first hurdle. I have 8 files of 2-colour agilent 44k whole human array data. In Limma I'm I use RG <- read.maimages(targets$FileName, source="agilent", quote="") That loads fine. In the workshop the following is then used Now read the CEL file data into an AffyBatch object and normalize using RMA: library(affy) library(hgu95av2cdf) abatch <- ReadAffy(filenames=targets$filename) eset <- rma(abatch) Obviously this will not work on my agilent data. What should I be doing instead? I've ploughed on and got the designing the matrix for my experiment. My arrays fall into 2 groups, pre and post treatment the design matrix looks as follows > f [1] Pre Pre Pre Pre Post Post Post Post Levels: Post Pre > cont.matrix Contrasts Levels PreVPost Post -1 Pre 1 > design Post Pre 1 0 1 2 0 1 3 0 1 4 0 1 5 1 0 6 1 0 7 1 0 8 1 0 attr(,"assign") [1] 1 1 attr(,"contrasts") attr(,"contrasts")$f [1] "contr.treatment" That seems logical but I wanted to check that was in place as well. Right as if that weren't enough I have a second query. I had some agilent 1-colour data as well. I found a post regarding this and tried using http://article.gmane.org/gmane.science.biology.informatics.conductor/1 28 18/match=agilent myFlagFun <- function(x) { > #Weight only strongly positive spots 1, everything else 0 > present <- x$gIsPosAndSignif == 1 > probe <- x$ControlType == 0 > manual <- x$IsManualFlag == 0 > strong <- x$gIsWellAboveBG == 1 > y <- as.numeric(present & probe & manual & strong) > > #Weight weak spots 0.5 > > weak <- strong == FALSE > weak <- (present & probe & manual & weak) > weak <- grep(TRUE,weak) > y[weak] <- 0.5 > > #Weight flagged spots 0.5 > > sat <- x$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] <- 0.5 > y > } > > G <- read.maimages(targets, > columns = list(G = "gMeanSignal", Gb = "gBGUsed", R = > "gProcessedSignal", > Rb = "gBGMedianSignal"), > annotation= c("Row", "Col", "FeatureNum", "ProbeUID", > "ControlType", > "ProbeName", "GeneName", "SystematicName"), > wt.fun=myFlagFun) I keep getting the error Error in readGenericHeader(fullname, columns = columns, sep = sep) : Specified column headings not found in file The only difference I make to this proceedure is changing the g column header to and r as I have red data. I found an article referring to changing the encoding setting of readLines() as a fix but I've had no luck with that. Anyway I hesitate to go hacking with such a little knowledge. Sorry it's such a long post. Any and all help gratefully received. Elliott Harrison This message has been scanned for viruses by BlackSpider Mai...{{dropped}}