How to analyze the differential expression of genes in microarray data by limma
This article shows you how to analyze the differential expression of genes in gene chip data by limma. The content is concise and easy to understand, which will definitely brighten your eyes. I hope you can get something through the detailed introduction of this article.
Limma
> suppressPackageStartupMessages (library (CLL))
> data (sCLLex)
> exprSet=exprs (sCLLex) # # sCLLex is an object that depends on CLL as a package
> samples=sampleNames (sCLLex)
> pdata=pData (sCLLex)
> group_list=as.character (pdata [, 2])
> dim (exprSet)
[1] 12625 22
> exprSet [1JV 5pm 1RU 5]
CLL11.CEL CLL12.CEL CLL13.CEL CLL14.CEL CLL15.CEL
1000_at 5.743132 6.219412 5.523328 5.340477 5.229904
1001_at 2.285143 2.291229 2.287986 2.295313 2.662170
1002_f_at 3.309294 3.318466 3.354423 3.327130 3.365113
1003_s_at 1.085264 1.117288 1.084010 1.103217 1.074243
1004_at 7.544884 7.671801 7.474025 7.152482 6.902932
> par (cex = 0.7)
> n.sample=ncol (exprSet)
> if (n.sample > 40) par (cex = 0.5)
> cols boxplot (exprSet, col = cols,main= "expression value", las=2)
> suppressMessages (library (limma))
> design colnames (design) = levels (factor (group_list))
> rownames (design) = colnames (exprSet)
> design
Progres. Stable
CLL11.CEL 1 0
CLL12.CEL 0 1
CLL13.CEL 1 0
CLL14.CEL 1 0
CLL15.CEL 1 0
CLL16.CEL 1 0
CLL17.CEL 0 1
CLL18.CEL 0 1
CLL19.CEL 1 0
CLL20.CEL 0 1
CLL21.CEL 1 0
CLL22.CEL 0 1
CLL23.CEL 1 0
CLL24.CEL 0 1
CLL2.CEL 0 1
CLL3.CEL 1 0
CLL4.CEL 1 0
CLL5.CEL 1 0
CLL6.CEL 1 0
CLL7.CEL 1 0
CLL8.CEL 1 0
CLL9.CEL 0 1
Attr (, "assign")
[1] 1 1
Attr (, "contrasts")
Attr (, "contrasts") $`factor (group_list) `
[1] "contr.treatment"
> contrast.matrix contrast.matrix
Contrasts
Levels progres.-stable
Progres. one
Stable-1
> fit fit2 fit2 tempOutput = topTable (fit2, coef=1, n=Inf)
> nrDEG = na.omit (tempOutput)
> head (nrDEG)
LogFC AveExpr t P.Value adj.P.Val B
39400_at-1.0284628 5.620700-5.835799 8.340576e-06 0.03344118 3.233915
36131_at 0.9888221 9.954273 5.771526 9.667514e-06 0.03344118 3.116707
33791_at 1.8301554 6.950685 5.736161 1.048765e-05 0.03344118 3.051940
1303_at-1.3835699 4.463438-5.731733 1.059523e-05 0.03344118 3.043816
36122_at 0.7801404 7.259612 5.141064 4.205709e-05 0.10619415 1.934581
36939_at 2.5471980 6.915045 5.038301 5.362353e-05 0.11283285 1.736846
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