[BioC] commands of affy and moderated t statistics in experiments

[weinong han]

Hi, all
 
I have experiments on 1  normal tissue(the 1 normal tissue pooled from 8 individual  tissues )  and 12 individual tumor tissues using Affymetrix HG-U133A genechips. no replicates. I plan to pre-process the .cel files using RMA and do data analysis with moderated t statistics of Limma package.
 
My steps as follows, pls give me any suggestions and advice.if i want to get boxplot or some graphs, pls tell me how and where to add the related commands.
 
thanks much for your help in advance.
 

> dir()

 [1] "hgu133acdf" "Normal.CEL" "TG_05.CEL"  "TG_10.CEL"  "TG_12.CEL" 

 [6] "TG_15.CEL"  "TG_19.CEL"  "TG_9.CEL"   "TH_04.CEL"  "TH_05.CEL" 

[11] "TH_07.CEL"  "TH_10.CEL"  "TH_11.CEL"  "TH_14.CEL" 

> library(limma)

> library(affy)

Loading required package: Biobase

Loading required package: tools

Welcome to Bioconductor 

         Vignettes contain introductory material.  To view, 

         simply type: openVignette() 

         For details on reading vignettes, see

         the openVignette help page.

Loading required package: reposTools

> Data <- ReadAffy()

> eset <- rma(Data)

Background correcting

Normalizing

Calculating Expression

> pData(eset)

           sample

Normal.CEL      1

TG_05.CEL       2

TG_10.CEL       3

TG_12.CEL       4

TG_15.CEL       5

TG_19.CEL       6

TG_9.CEL        7

TH_04.CEL       8

TH_05.CEL       9

TH_07.CEL      10

TH_10.CEL      11

TH_11.CEL      12

TH_14.CEL      13

> tissue <- c("n","t","t","t","t","t","t","t","t","t","t","t","t")

> design <- model.matrix(~factor(tissue))

> colnames(design) <- c("n","tvsn")

> design

   n tvsn

1  1    0

2  1    1

3  1    1

4  1    1

5  1    1

6  1    1

7  1    1

8  1    1

9  1    1

10 1    1

11 1    1

12 1    1

13 1    1

attr(,"assign")

[1] 0 1

attr(,"contrasts")

attr(,"contrasts")$"factor(tissue)"

[1] "contr.treatment"

 

> fit <- lmFit(eset, design)

> fit <- eBayes(fit)

> options(digits=2)

> topTable(fit, coef=2, n=100, adjust="fdr")

               ID     M   A     t P.Value      B

4556  205029_s_at -6.16 2.9 -37.9 1.4e-10  9.770

4557    205030_at -7.22 3.3 -22.2 7.8e-08  8.874

16787 217422_s_at -1.80 2.9  -8.4 8.4e-03  4.246

568     201040_at -0.97 6.4  -6.7 7.9e-02  2.594

21918    38521_at -1.93 5.5  -6.4 8.7e-02  2.292

5497    205970_at -1.76 4.3  -6.3 8.7e-02  2.238

10456 211010_s_at -1.21 4.4  -6.2 9.6e-02  2.061

18277 218913_s_at -0.98 5.3  -6.0 1.1e-01  1.885

 

in the  topTable,how to select the all significantly differentially expressed genes, and how to discriminate between the up-regulated and down-regulated genes? if i want to get the fold change, where and how to get?

 

Best Regards

 

Weinong Han

 



		
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