Dear David, What you do mean by "summarizing". Are you perhaps looking for avereps()? BTW, the read can be done slightly more succintly by RG <- read.maimages(targets,source="agilent.median",green.only=TRUE) Best wishes Gordon > Date: Fri, 2 Mar 2012 15:51:00 +0100 > From: David Westergaard <david at="" harsk.dk=""> > To: bioconductor at r-project.org > Subject: [BioC] Summarizing Single-channel Agilent data > > Hello, > > I am working on normalizing raw data from > http://www.ebi.ac.uk/arrayexpress/experiments/E-GEOD-33005 using the > Limma package. > > Following "standard" procedure, I do background correction, and then normalize: > > # Read target from file > targets <- readTargets("targets") > RG <- read.maimages(targets,source="agilent", columns =list(G = > "gMedianSignal", Gb = "gBGMedianSignal"),green.only=TRUE) > > > # Do backgroundcorrection/normalization > RG <- backgroundCorrect(RG, method="normexp") > RG <- normalizeBetweenArrays(RG, method="quantile") > > Now, what I'm lacking is a summarization method. Googling abit, > "Agi4x44PreProcess" can do the summarization, but it doesn't accept > single-channel data. Furthermore, it expects an RGList as input to > summarize.probe (NormalizeBetweenArrays produces an EList) > > So how would I go about summarizing these data? It would be nice if > there was an existing package doing this. > > > Best Regards, > David Westergaard > Undergraduate student > Technical University of Denmark > ______________________________________________________________________ The information in this email is confidential and intend...{{dropped:4}}

Dear David, Here's a complete limma analysis of the Agilent data on ArrayExpress. It would appear that only the 10 ml/kg treatment is different from the saline control. There are 12000 differential expressed genes for the 10 ml/kg treatment. Best wishes Gordon > SDRF <- read.delim("E-GEOD-33005.sdrf.txt",check.names=FALSE,strings AsFactors=FALSE) > x <- read.maimages(SDRF[,"Array Data File"],source="agilent.median",green.only=TRUE) Read GSM819076_US10283824_252828210181_S01_GE1_107_Sep09_1_4.txt Read GSM819075_US10283824_252828210181_S01_GE1_107_Sep09_1_3.txt Read GSM819074_US10283824_252828210181_S01_GE1_107_Sep09_1_2.txt Read GSM819073_US10283824_252828210180_S01_GE1_107_Sep09_1_4.txt Read GSM819072_US10283824_252828210180_S01_GE1_107_Sep09_1_3.txt Read GSM819071_US10283824_252828210180_S01_GE1_107_Sep09_1_2.txt Read GSM819070_US10283824_252828210180_S01_GE1_107_Sep09_1_1.txt Read GSM819069_US10283824_252828210179_S01_GE1_107_Sep09_1_4.txt Read GSM819068_US10283824_252828210179_S01_GE1_107_Sep09_1_3.txt Read GSM819067_US10283824_252828210179_S01_GE1_107_Sep09_1_2.txt Read GSM819066_US10283824_252828210179_S01_GE1_107_Sep09_1_1.txt Read GSM819065_US10283824_252828210178_S01_GE1_107_Sep09_1_4.txt Read GSM819064_US10283824_252828210178_S01_GE1_107_Sep09_1_3.txt Read GSM819063_US10283824_252828210178_S01_GE1_107_Sep09_1_2.txt Read GSM819062_US10283824_252828210178_S01_GE1_107_Sep09_1_1.txt Read GSM819061_US10283824_252828210177_S01_GE1_107_Sep09_1_4.txt Read GSM819060_US10283824_252828210177_S01_GE1_107_Sep09_1_3.txt Read GSM819059_US10283824_252828210177_S01_GE1_107_Sep09_1_2.txt Read GSM819058_US10283824_252828210177_S01_GE1_107_Sep09_1_1.txt > y <- backgroundCorrect(x,method="normexp") > y <- normalizeBetweenArrays(y,method="quantile") > neg99 <- apply(y$E[y$genes$ControlType==-1,],2,function(x) quantile(x,p=0.99)) > cutoff <- matrix(neg99,nrow(y),ncol(y),byrow=TRUE) > isexpr <- rowSums(y$E > cutoff) >= 4 > table(isexpr) isexpr FALSE TRUE 4321 39933 > y0 <- y[y$genes$ControlType==0 & isexpr,] > Treatment <- SDRF[,"Characteristics[treatment]"] > levels <- c("10 ml/kg saline","2 ml/kg corn oil","5 ml/kg corn oil","10 ml/kg corn oil") > Treatment <- factor(Treatment,levels=levels) > design <- model.matrix(~Treatment) > yave <- avereps(y0,ID=y0$genes[,"SystematicName"]) > fit <- lmFit(yave,design) > fit <- eBayes(fit,trend=TRUE) > summary(decideTests(fit[,-1])) Treatment2 ml/kg corn oil Treatment5 ml/kg corn oil Treatment10 ml/kg corn oil -1 0 0 384 0 24433 24433 23207 1 0 0 842 On Sun, 4 Mar 2012, Gordon K Smyth wrote: > Dear David, > > What you do mean by "summarizing". Are you perhaps looking for avereps()? > > BTW, the read can be done slightly more succintly by > > RG <- read.maimages(targets,source="agilent.median",green.only=TRUE) > > Best wishes > Gordon > >> Date: Fri, 2 Mar 2012 15:51:00 +0100 >> From: David Westergaard <david at="" harsk.dk=""> >> To: bioconductor at r-project.org >> Subject: [BioC] Summarizing Single-channel Agilent data >> >> Hello, >> >> I am working on normalizing raw data from >> http://www.ebi.ac.uk/arrayexpress/experiments/E-GEOD-33005 using the >> Limma package. >> >> Following "standard" procedure, I do background correction, and then >> normalize: >> >> # Read target from file >> targets <- readTargets("targets") >> RG <- read.maimages(targets,source="agilent", columns =list(G = >> "gMedianSignal", Gb = "gBGMedianSignal"),green.only=TRUE) >> >> >> # Do backgroundcorrection/normalization >> RG <- backgroundCorrect(RG, method="normexp") >> RG <- normalizeBetweenArrays(RG, method="quantile") >> >> Now, what I'm lacking is a summarization method. Googling abit, >> "Agi4x44PreProcess" can do the summarization, but it doesn't accept >> single-channel data. Furthermore, it expects an RGList as input to >> summarize.probe (NormalizeBetweenArrays produces an EList) >> >> So how would I go about summarizing these data? It would be nice if >> there was an existing package doing this. >> >> >> Best Regards, >> David Westergaard >> Undergraduate student >> Technical University of Denmark >> > ______________________________________________________________________ The information in this email is confidential and intend...{{dropped:4}}