[BioC] Limma to find differentially expressed genes

[Sandy [guest]]

I have my matrix designed in the following way which I name as mat1

                 Probes  sample1  sample1 sample2 sample2 sample3 sample3 sample4 sample4  
                         rep1      rep2    rep1   rep2    rep1    rep2    rep1    rep2
                 ------------------------------------------------------------------------
                   gene1   5.098   5.076   5.072  4.677  7.450   7.456   8.564   8.555
                   gene2   8.906   8.903   6.700  6.653  6.749   6.754   7.546   7.540
                   gene3   7.409   7.398   5.392  5.432  6.715   6.724   5.345   5.330
                   gene4   4.876   4.869   5.864  5.981  4.280   4.290   4.267   4.255
                   gene4   3.567   3.560   3.554  3.425  8.500   8.564   6.345   6.330 
                   gene5   2.569   2.560   8.600  8.645  5.225   5.234   7.345   7.333

I use the limma package to find the DEG's 

      Group <- factor(c("p1", "p1", "p2", "p2","p3", "p4","p4")
      design <- model.matrix(~0 + Group)
      colnames(design) <- gsub("Group","", colnames(design))
      fit <- lmFit(mat1[,1:4],design)
      contrast.matrix<-makeContrasts(p1-p2,levels=design)
      fit2<-contrasts.fit(fit,contrast.matrix)
      fit2<-eBayes(fit2)
      sel.diif<-p.adjust(fit2$F.p.value,method="fdr")<0.05
      deg<-mat1[,1:4][sel.diif,]

So will "deg" just give me those genes which are significant in sample one versus two. I am interested in those genes which are significant only in first sample but not in the second sample and am not sure if this is the right approach. 


 -- output of sessionInfo(): 

R version 2.15.2 (2012-10-26)
Platform: i686-redhat-linux-gnu (32-bit)

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C               LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8    LC_PAPER=C                 LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C             LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] limma_3.14.4         csSAM_1.2.1          GOstats_2.24.0       RSQLite_0.10.0       DBI_0.2-5           
 [6] graph_1.36.2         Category_2.22.0      AnnotationDbi_1.20.5 affy_1.36.1          Biobase_2.16.0      
[11] BiocGenerics_0.4.0   R.utils_1.23.2       R.oo_1.13.0          R.methodsS3_1.4.2   

loaded via a namespace (and not attached):
 [1] affyio_1.22.0         annotate_1.36.0       AnnotationForge_1.0.3 BiocInstaller_1.8.3   genefilter_1.40.0    
 [6] GO.db_2.8.0           GSEABase_1.18.0       IRanges_1.16.6        parallel_2.15.2       preprocessCore_1.18.0
[11] RBGL_1.34.0           splines_2.15.2        stats4_2.15.2         survival_2.36-14      tools_2.15.2         
[16] XML_3.9-4             xtable_1.6-0          zlibbioc_1.4.0       

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