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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

The above content is how to analyze the differential expression of genes in gene chip data by limma. Have you learned the knowledge or skills? If you want to learn more skills or enrich your knowledge reserve, you are welcome to follow the industry information channel.

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