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

[Mohamed Lajnef]

Mohamed Lajnef a écrit :
> 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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