[BioC] Differential expression ( Limma) for illumina microarrays?

[Mohamed Lajnef]

Dear R-Users,

I can not uderstand a result I have ( see  Toptable). I used LIMMA to 
find differentially expressed genes by 3 treatments, my database ( 
illumina files)  includes (48803 probes (rows) and 120 columns ( 40 by 
level)), my program as follows
library(beadarray)
BSData<-readBeadSummaryData(fichier,skip=0,columns = list(exprs = 
"AVG_Signal", se.exprs="BEAD_STDERR",NoBeads = "Avg_NBEADS", 
Detection="Detection.Pval"))
BSData.quantile=normaliseIllumina(BSData, 
method="quantile",transform="log2") #
detection<-Detection(BSData) # Matrix contain detection P value which 
estimates the probability of a probe being detected above the background 
level

# Filtring after normalization
library(genefilter)
 filtre<-function (p = 0.05, A = 100, na.rm = TRUE)

{
    function(x) {
        if (na.rm)
            x <- x[!is.na(x)]
        sum(x <= A)/length(x) >= p
    }
}
ff<-filtre(p=0.80, A=0.01) # i keep rows if pvalues<=0.01, the probe has 
to be over expressed in at least 80% per level ( i have 3 levels)

 i<-genefilter(detection[,1:40],ff)
 j<-genefilter(detection[,41:80],ff)  # I will now  keep 10156 probes 
(after filtring tools)
 k<-genefilter(detection[,81:120],ff)
 
 # Differential expression using Limma after normalization & filtering tools
  library(limma)
  donne<-exprs(BSData.quantile)
  OBSnormfilter<-donne[j,] # keep 10156 probes after normalization
  groups<-as.factor(c(rep("Tem",40),rep("EarlyO",40),rep("LateO",40)))
  design<-model.matrix(~0+groups)
  colnames(design)=levels(groups)
  fit<-lmFit(OBSnormfilter,design)
  cont.matrix<-makeContrasts(Tem-EarlyO,Tem-LateO,EarlyO-LateO, 
levels=design)
  fit2<-contrasts.fit(fit, cont.matrix)
  ebfit<-eBayes(fit2)
  gene1<-topTable(ebfit, coef=1)
  gene2<-topTable(ebfit, coef=2)
  gene3<-topTable(ebfit, coef=3)
 
gene1 ( result of Toptable between the control and  first treatment groups)

          ID              logFC     AveExpr          t            
P.Value           adj.P.Val                B
9300  520255 -0.3209704 6.429487 -3.643323 0.0003963748 0.9998062 -0.6345996
6192 7650097 -0.2677064 6.243968 -3.581163 0.0004921590 0.9998062 -0.7817435
5528   10161  0.2022500 8.002581  3.434507 0.0008116961 0.9998062 -1.1212432
6077 4180725  0.1380486 5.922805  3.423087 0.0008434258 0.9998062 -1.1472217
3569 5080487 -0.1621675 7.717032 -3.308604 0.0012326133 0.9998062 -1.4039040
2265  270332 -0.1996710 6.599011 -3.257771 0.0014545247 0.9998062 -1.5156669
4643 5360301  0.5115730 6.616680  3.188442 0.0018176145 0.9998062 -1.6658702
3885  110523 -0.1489957 6.165416 -3.130799 0.0021819409 0.9998062 -1.7887772
8220 6280053 -0.1379738 6.603755 -3.057891 0.0027397895 0.9998062 -1.9416230
4355 1430626 -0.1867890 6.624203 -3.054026 0.0027727561 0.9998062 -1.9496424

looking at the results, Toptable  show no any signficant genes,  how do 
you explain this?? ( because I have a lot of replication ( 40 by level) ???)

Any help would be appreciated

Regards
ML







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Mohamed Lajnef
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