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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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