Hello Paz, I changed my targets file as specified in the file you sent me (my headings are now SampleNumber FileName Treatment GErep Population Tank Array Sex However, when I follow the instructions in the manual, I put in the code library("Agi4x44PreProcess") targets=read.targets(infile="targets2.txt") dd=read.AgilentFE(targets, makePLOT=FALSE) At this point I get an error message: Error in readGenericHeader(fullname, columns = columns, sep = sep) : Specified column headings not found in file Is this because my Feature Extraction files are from a scanner that can read 8x60 K arrays? Thanks, Matthew > > Do you have algun example of target file? > >> Date: Tue, 20 Sep 2011 20:12:19 -0600 >> From: mrjmorri at ucalgary.ca >> To: bioconductor at r-project.org >> Subject: [BioC] Visualizing pre- and post-normalization for >> single-colour arrays >> >> Hello, >> >> My name is Matthew Morris, I'm a second year Master's student at the >> University of Calgary, and I am fairly new to the world of >> microarrays! >> My project involves single-colour arrays, for which helpful documents >> seem >> to be rather limited. >> >> As such, I have some questions. I'll ask the questions first, and then >> show you the code. If you can only answer one of the three, some info >> is >> better than none! >> >> 1. How do I visualize my data pre- and post-normalization? What will I be >> looking for? >> >> 2. I am using code from someone else to flag nonuniform and feature >> population outliers. It certainly alters my results, but I'm not sure >> how >> to check if it is working correctly. >> >> 3. How can I incorporate things like sex and length into my model? (to >> clarify, I am looking at four populations of fish called Cran, Hog, OL >> and >> LCM respectively, raised at 7 or 23 degrees, and I would like to >> eliminate >> the effects of sex, tank and length) >> >> >> Thank you very much, >> Matthew Morris >> >> Code is as follows: >> >> library(limma) >> >> #read Targets file (make sure to set directory first through File) >> targets<-readTargets("targets.txt") >> >> #checks that file was read correctly >> targets$FileName >> >> #weight OL >> >> wtAgilent.mRGOLFilter <- function(qta) >> {mapply(min,1-qta[,"gIsFeatNonUnifOL"],1-qta[,"gIsFeatNonUnifOL"],1 -qta[,"gIsFeatPopnOL"],1-qta[,"gIsFeatPopnOL"])} >> >> #read data from array ouput files into E >> E<-read.maimages(targets$FileName, >> source="agilent.median",path="actualall", wt.fun=wtAgilent.mRGOLFilter, >> columns=list(E="gProcessedSignal"), >> other.columns=list(saturated="gIsSaturated", >> nonuniform="gIsFeatNonUnifOL", popnoutlier="gIsFeatPopnOL", >> flag="IsManualFlag", wellabovebg="gIsWellAboveBG")) >> >> #to see that everything looks fine >> E >> >> #normalizing between arrays: >> normalize<-normalizeBetweenArrays(E, method="quantile") >> >> >> #remove control spots >> dat1<-normalize[normalize$genes$ControlType==0,] >> >> #average values for identical probes within an array >> Eavg<-avereps(dat1, ID=dat1$genes$ProbeName) >> >> >> #analyze factorial design: first identify the factors in template >> TS <- paste(targets$Population, targets$Temperature, sep=".") >> >> #check to see it worked >> TS >> >> #set up design >> TS <- factor(TS, levels=c("Hog.7","Cran.7","LCM.7","OL.7", "Hog.23", >> "Cran.23", "OL.23", "LCM.23", "Hog.15")) >> design <- model.matrix(~0+TS) >> colnames(design) <- levels(TS) >> fit <- lmFit(Eavg, design) >> cont.matrix <- makeContrasts(Hog.7vs23=Hog.23-Hog.7, >> Cran.7vs23=Cran.23-Cran.7, LCM.7vs23=LCM.23-LCM.7, OL.7vs23=OL.23-OL.7, >> levels=design) >> fit2 <- contrasts.fit(fit, cont.matrix) >> fit2 <- eBayes(fit2) >> >> #check results >> >> topTable(fit2, coef=1, adjust="BH") >> results <- decideTests(fit2) >> vennCounts(results) >> vennDiagram(results) >> >> write.table (fit2, file="fit2.txt") >> >> _______________________________________________ >> Bioconductor mailing list >> Bioconductor at r-project.org >> https://stat.ethz.ch/mailman/listinfo/bioconductor >> Search the archives: >> http://news.gmane.org/gmane.science.biology.informatics.conductor >