[BioC] Visualizing pre- and post-normalization for single-colour arrays

[mrjmorri at ucalgary.ca]

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")
>>
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>