Hi Paolo,

You just need to put all 4 groups in the design matrix. It's a clear
example in the Limma userguide.:

> f <- factor(targets$Target, levels=c("RNA1","RNA2","RNA3"))
> design <- model.matrix(~0+f)
> colnames(design) <- c("RNA1","RNA2","RNA3")
To make all pair-wise comparisons between the three groups one could proceed
> fit <- lmFit(eset, design)
> contrast.matrix <- makeContrasts(RNA2-RNA1, RNA3-RNA2, RNA3-RNA1,
+ levels=design)
> fit2 <- contrasts.fit(fit, contrast.matrix)
> fit2 <- eBayes(fit2)
A list of top genes for RNA2 versus RNA1 can be obtained from
> topTable(fit2, coef=1, adjust="BH")

Best,

Jack

2012/7/12 Paolo [guest] <guest@bioconductor.org>

>
> 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
>
> --
> Sent via the guest posting facility at bioconductor.org.
>
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