Dear Lisa, limma now has a representation for single colour arrays (ELists), so you can simplify your pre-processing code somewhat: E <- read.maimages(targets,columns=list(G="Median",Gb="MedBackground "),green.only=TRUE) E$genes <- read.delim("Annotation file.txt",stringsAsFactors=FALSE) E <- backgroundCorrect(E, method="subtract") E <- backgroundCorrect(E, method="normexp", offset=50) NormE <- normalizeBetweenArrays(E, method="quantile") I don't understand you question about getting gene names in the toptable, as this should happen automatically. Best wishes Gordon > Date: Fri, 27 Aug 2010 10:59:10 -0700 > From: "Orfe, Lisa" <lorfe at="" vetmed.wsu.edu=""> > To: "'bioconductor at stat.math.ethz.ch'" > <bioconductor at="" stat.math.ethz.ch=""> > Subject: [BioC] Limma script - Opinions requested > Message-ID: > <44F1D6D7EB8CC84F92859EE5C4E6ECB4011374445539 at CVMMBX.vetmed.wsu.edu> > Content-Type: text/plain; charset="us-ascii" > > I am VERY new to R/Bioconductor and over the past two weeks have > managed, I hope, to put together a limma script to process my single > color generic arrays. I was hoping that some of the experts that read > these posts could comment on it - specifically, is it valid? Is there > something I could be doing that is easier/more appropriate? I know it > works as I have been getting the genelists back out at the end, so, my > question is very general...is this how you would process a home-made > single color array? If not, I would LOVE some pointers. > > >> library(limma) >> targets <- readTargets("targets.txt") >> RG <- read.maimages(targets,source="generic" +columns=list(R="Media n",G="Median",Rb="MedBackground",Gb="MedBackground")) >> RG$genes <- read.delim("Annotation file.txt") >> BsubRG <- backgroundCorrect(RG, method="normexp", offset=50) >> NormRG <- normalizeBetweenArrays(BSubRG$G, method="quantile") >> MA <- log2(NormRG) > > ---Now - if I have a single factor I would set up my design as such: >> design <- model.matrix(~0+factor(c(1,2,3,4,1,2,3,4,1,2,3,4))) >> colnames(design) <- c("A", "B", "C", "D") >> corfit <- duplicateCorrelation(MA, design, ndups=4) >> fit <- lmFit(MA, design, ndups=4, correlation=corfit$consensus) >> fit <- eBayes(fit) >> cont.matrix <- makeContrasts(AvsB=A-B, levels=design) >> fit2 <- contrasts.fit(fit, cont.matrix) >> fit2 <- eBayes(fit2) >> GeneList <- topTable(fit2, "AvsB", n=20, adjust="BH", lfc=1) >> write.table(GeneList, file = "file name.txt", quote = FALSE, sep = "\t") > > > ---however, if I have multiple factors, then I set up the design as... >> TS <- paste(targets$columnname, targets$columnname, sep=".") >> TS <- factor(TS, levels=c("xx.xx", "xx.xx", "xx.xx")) >> design <- model.matrix(~0+TS) >> colnames(design) <- levels(TS) > > Everything after is the same as above...in pasting this in I do have a couple of questions. > > 1. Is it recommended/necessary to apply a Bayesian smoothing to both fits? > 2. How do I get back out my gene names *in* the topTable? > > Thanks for your time and I apologize if the answers seem remarkably obvious. > > Lisa > Lisa Orfe > Bustad 405 > 509-335-6320 > "Science is a wonderful thing when one does not have to earn one's living at it." Albert Einstein ______________________________________________________________________ The information in this email is confidential and intend...{{dropped:4}}

A couple of corrections. Background correct is yet doing everything it should with EList objects, so the background subtraction has to be done manually. And I now see why you're not getting gene names in your toptable. You can use x <- read.maimages(targets,columns=list(G="Median",Gb="MedBackground "),green.only=TRUE) x$genes <- read.delim("Annotation file.txt",stringsAsFactors=FALSE) x$E <- x$E-x$Eb x <- backgroundCorrect(x, method="normexp", offset=50) y <- normalizeBetweenArrays(x, method="quantile") Then lmFit(y,design,) etc and you'll have annotation in your toptable. Gordon On Mon, 30 Aug 2010, Gordon K Smyth wrote: > Dear Lisa, > > limma now has a representation for single colour arrays (ELists), so you can > simplify your pre-processing code somewhat: > > E <- > read.maimages(targets,columns=list(G="Median",Gb="MedBackground"),gr een.only=TRUE) > E$genes <- read.delim("Annotation file.txt",stringsAsFactors=FALSE) > E <- backgroundCorrect(E, method="subtract") > E <- backgroundCorrect(E, method="normexp", offset=50) > NormE <- normalizeBetweenArrays(E, method="quantile") > > I don't understand you question about getting gene names in the toptable, as > this should happen automatically. > > Best wishes > Gordon > > >> Date: Fri, 27 Aug 2010 10:59:10 -0700 >> From: "Orfe, Lisa" <lorfe at="" vetmed.wsu.edu=""> >> To: "'bioconductor at stat.math.ethz.ch'" >> <bioconductor at="" stat.math.ethz.ch=""> >> Subject: [BioC] Limma script - Opinions requested >> Message-ID: >> <44F1D6D7EB8CC84F92859EE5C4E6ECB4011374445539 at CVMMBX.vetmed.wsu.edu> >> Content-Type: text/plain; charset="us-ascii" >> >> I am VERY new to R/Bioconductor and over the past two weeks have managed, I >> hope, to put together a limma script to process my single color generic >> arrays. I was hoping that some of the experts that read these posts could >> comment on it - specifically, is it valid? Is there something I could be >> doing that is easier/more appropriate? I know it works as I have been >> getting the genelists back out at the end, so, my question is very >> general...is this how you would process a home-made single color array? If >> not, I would LOVE some pointers. >> >> >>> library(limma) >>> targets <- readTargets("targets.txt") >>> RG <- read.maimages(targets,source="generic" >>> +columns=list(R="Median",G="Median",Rb="MedBackground",Gb="MedBack ground")) >>> RG$genes <- read.delim("Annotation file.txt") >>> BsubRG <- backgroundCorrect(RG, method="normexp", offset=50) >>> NormRG <- normalizeBetweenArrays(BSubRG$G, method="quantile") >>> MA <- log2(NormRG) >> >> ---Now - if I have a single factor I would set up my design as such: >>> design <- model.matrix(~0+factor(c(1,2,3,4,1,2,3,4,1,2,3,4))) >>> colnames(design) <- c("A", "B", "C", "D") >>> corfit <- duplicateCorrelation(MA, design, ndups=4) >>> fit <- lmFit(MA, design, ndups=4, correlation=corfit$consensus) >>> fit <- eBayes(fit) >>> cont.matrix <- makeContrasts(AvsB=A-B, levels=design) >>> fit2 <- contrasts.fit(fit, cont.matrix) >>> fit2 <- eBayes(fit2) >>> GeneList <- topTable(fit2, "AvsB", n=20, adjust="BH", lfc=1) >>> write.table(GeneList, file = "file name.txt", quote = FALSE, sep = "\t") >> >> >> ---however, if I have multiple factors, then I set up the design as... >>> TS <- paste(targets$columnname, targets$columnname, sep=".") >>> TS <- factor(TS, levels=c("xx.xx", "xx.xx", "xx.xx")) >>> design <- model.matrix(~0+TS) >>> colnames(design) <- levels(TS) >> >> Everything after is the same as above...in pasting this in I do have a >> couple of questions. >> >> 1. Is it recommended/necessary to apply a Bayesian smoothing to both fits? >> 2. How do I get back out my gene names *in* the topTable? >> >> Thanks for your time and I apologize if the answers seem remarkably >> obvious. >> >> Lisa >> Lisa Orfe >> Bustad 405 >> 509-335-6320 >> "Science is a wonderful thing when one does not have to earn one's living >> at it." Albert Einstein > ______________________________________________________________________ The information in this email is confidential and intend...{{dropped:4}}