[BioC] Multifactorial analysis with RMA and LIMMA of Affymetrix microarrays

[Jordi Altirriba Gutiérrez]

(Sorry, but I've had some problems with the HTML)
Hello all!
I am a beginner user of R and Bioconductor, sorry if my questions have 
already been discussed previously.
I am studying the effects of a new hypoglycaemic drug for the treatment of 
diabetes and I have done this classical study:
4 different groups:
1.- Healthy untreated
2.- Healthy treated
3.- Diabetic untreated
4.- Diabetic treated
With 3 biological replicates of each group, therefore I have done 12 arrays 
(Affymetrix).
I have treated the raw data with the package RMA of Bioconductor according 
to the article ”Exploration, normalization and summaries of high density 
oligonucleotide array probe level data” (Background=RMA, 
Normalization=quantiles, PM=PMonly, Summarization=medianpolish).
I am currently trying to analyse the object eset with the package LIMMA of 
Bioconductor. I want to know what genes are differentially expressed due to 
diabetes,  to the treatment and to the combination of both (diabetes + 
treatment), being therefore an statistic analysis similar to a two-ways 
ANOVA).
So, my questions are:
1.- I have created a PhenoData in RMA, will the covariates of the PhenoData 
have any influence in the analysis of LIMMA?
2.- Are these commands correct to get these results? (see below) In the 
command TopTable, the output of coef=1 are the genes characteristics of 
diabetes?
3.- If I do not see any effect of the treatment in the healthy untreated 
rats should I design the matrix differently? Something similar to a 
one-way-ANOVA, considering differently the four groups:
( > design<-model.matrix(~ -1+factor(c(1,1,1,2,2,2,3,3,3,4,4,4)))  ).
5.- Any other idea?
Thank you very much for your time and your suggestions.
Yours sincerely,

Jordi Altirriba (PhD student, Hospital Clínic – IDIBAPS, Barcelona, Spain)

>design<-cbind("disease"=c(1,1,1,1,1,1,0,0,0,0,0,0),"treatment"=c(0,0,0,1,1,1,0,0,0,1,1,1))
>fit<-lmFit(eset,design)
>contrast.matrix<-cbind("diabetes"=c(1,0),"drug"=c(0,1),"diabetes-drug"=c(1,1))
>rownames(contrast.matrix)<-colnames(design)
>design
      disease treatment
[1,]        1           0
[2,]        1           0
[3,]        1           0
[4,]        1           1
[5,]        1           1
[6,]        1           1
[7,]        0           0
[8,]        0           0
[9,]        0           0
[10,]        0           1
[11,]        0           1
[12,]        0           1
>contrast.matrix
            diabetes drug diabetes-drug
diabetes      1         0             1
tratamiento   0         1             1
>fit2<-contrasts.fit(fit,contrast.matrix)
>fit2<-eBayes(fit2)
>clas<-classifyTests(fit2)
>vennDiagram(clas)
>topTable(fit2,number=100,genelist=geneNames(eset),coef=1,adjust="fdr")
                     Name        M        t      P.Value        B
11590          1387471_at 19.32575 9.442926 2.743122e-17 29.95299
2500           1369951_at 19.24748 9.404683 2.743122e-17 29.66737
10652          1384778_at 19.22968 9.395981 2.743122e-17 29.60254


>sink("limma-diabetes.txt")
>topTable(fit2,number=1000,genelist=geneNames(eset),coef=1,adjust="fdr")
>sink()

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