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

[Wolfgang Huber]

Sep/21/11 4:12 AM, mrjmorri at ucalgary.ca scripsit::
> 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.

Dear Matthew,

there is in fact substantial documentation that has already been helpful 
to others. I suggest you have a look at some of it. Then I am sure your 
questions and their context will become clearer. In particular
- the limma user's guide which comes with the package that you are using
- the book 
http://www.bioconductor.org/help/publications/books/bioconductor-case-studies/

If this seems like a lot of work, don't despair, and type

library("fortunes")
fortune(122)

	Best wishes
	Wolfgang

>
> 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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-- 


Wolfgang Huber
EMBL
http://www.embl.de/research/units/genome_biology/huber