[BioC] #Identify differentially expressed genes

[Paolo [guest]]

Hi all,
I have an illumina dataset, VST transformed and normalized. 4 samples each in triplicates,
this is the sampleType=

sampleType <- c('A','A','A','B','B','B','C','D','D','C','C','D')
Now I would like to do perform each pairwise comparison, A vs B, A vs C, A vs D...etc.)

but I am confused how to set the colnames(design)

here i what i did

if (require(limma)) {
sampleType <- c('TG_TAC','TG_TAC','TG_TAC','WT_TAC','WT_TAC','WT_TAC','WT_SHAM','TG_SHAM','TG_SHAM','WT_SHAM','WT_SHAM','TG_SHAM')
## compare 'A' and 'B'
design <- model.matrix(~ factor(sampleType))
colnames(design) <-c('A','B' )
fit <- lmFit(selDataMatrix, design)
fit <- eBayes(fit)
## Add gene symbols to gene properties
	if (require(lumiMouseAll.db) & require(annotate)) {
               geneSymbol <- getSYMBOL(probeList, 'lumiMouseAll.db')
               geneName <- sapply(lookUp(probeList, 'lumiMouseAll.db', 'GENENAME'), function(x) x[1])
               fit$genes <- data.frame(ID= probeList, geneSymbol=geneSymbol, geneName=geneName, stringsAsFactors=FALSE)
          }
# print the top 50 genes
	print(topTable(fit, adjust='fdr', number=5))

## get significant gene list with FDR adjusted p.values less than 0.01
	p.adj <- p.adjust(fit$p.value[,2])		
	sigGene.adj <- probeList[ p.adj < 0.01]
	## without FDR adjustment
	sigGene <- probeList[ fit$p.value[,2] < 0.01]
} 



how do I properly set up each pairwise comparison?

thanks
paolo






 -- output of sessionInfo(): 

R version 2.15.0 (2012-03-30)
Platform: i386-pc-mingw32/i386 (32-bit)

locale:
[1] LC_COLLATE=Italian_Italy.1252  LC_CTYPE=Italian_Italy.1252    LC_MONETARY=Italian_Italy.1252 LC_NUMERIC=C                  
[5] LC_TIME=Italian_Italy.1252    

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

other attached packages:
 [1] annotate_1.34.1           lumiMouseAll.db_1.18.0    org.Mm.eg.db_2.7.1        limma_3.12.1             
 [5] lumiMouseIDMapping_1.10.0 RSQLite_0.11.1            DBI_0.2-5                 AnnotationDbi_1.18.1     
 [9] lumi_2.8.0                nleqslv_1.9.3             methylumi_2.2.0           ggplot2_0.9.1            
[13] reshape2_1.2.1            scales_0.2.1              Biobase_2.16.0            BiocGenerics_0.2.0       

loaded via a namespace (and not attached):
 [1] affy_1.34.0           affyio_1.24.0         bigmemory_4.2.11      BiocInstaller_1.4.7   Biostrings_2.24.1    
 [6] bitops_1.0-4.1        BSgenome_1.24.0       colorspace_1.1-1      dichromat_1.2-4       digest_0.5.2         
[11] DNAcopy_1.30.0        GenomicRanges_1.8.7   genoset_1.6.0         grid_2.15.0           hdrcde_2.16          
[16] IRanges_1.14.4        KernSmooth_2.23-8     labeling_0.1          lattice_0.20-6        MASS_7.3-19          
[21] Matrix_1.0-7          memoise_0.1           mgcv_1.7-18           munsell_0.3           nlme_3.1-104         
[26] plyr_1.7.1            preprocessCore_1.18.0 proto_0.3-9.2         RColorBrewer_1.0-5    RCurl_1.91-1.1       
[31] Rsamtools_1.8.5       rtracklayer_1.16.2    stats4_2.15.0         stringr_0.6           tools_2.15.0         
[36] XML_3.9-4.1           xtable_1.7-0          zlibbioc_1.2.0     

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