[BioC] Limma to find differentially expressed genes
Sandy [guest]
guest at bioconductor.org
Fri Apr 19 14:03:18 CEST 2013
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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