Dear Shan Gao,

You could consider import your experiment to edgeR from a target file(which
contains information of your experiment). It is much easier for you to
track back anything wrong in between if there was one or two.

The Section 3.3 of the latest edgeR User's Guide is worth for you to have a
look.

http://bioconductor.org/packages/2.11/bioc/vignettes/edgeR/inst/doc/edgeRUsersGuide.pdf

You can find the guide how to utilize the makeContrast function.

Hopefully this help.

Best regards,
KJ Lim



On 25 August 2012 07:15, wang peter <wng.peter@gmail.com> wrote:

> I meet such problem, this is the coding
>
> raw.data <- read.table("expression-table.txt",row.names=1)
> lib_size <- read.table("lib_size.txt");
> lib_size <- unlist(lib_size)
> d <- raw.data[, 2:dim(raw.data)[2]]
> length<-raw.data[, 1]
>
> d <- DGEList(counts = d, lib.size = lib_size)
> #normalization
> dge <- calcNormFactors(dge)
> treatment=factor(c(rep('control',6),rep('treated',24),rep('control',5)))
>
> time=factor(c('0h','0h','0h','24h','24h','24h','0h','0h','0h','6h','6h','6h','6h','12h','12h','12h','12h','18h','18h','18h','18h',
>
>  '24h','24h','24h','36h','36h','36h','48h','48h','48h','6h','12h','18h','36h','48h'))
> design <- model.matrix(~treatment*time)
>
> dge <- estimateGLMCommonDisp(dge, design)
> dge <- estimateGLMTagwiseDisp(dge, design)
> glmfit.dge <- glmFit(dge, design, dispersion=dge$tagwise.dispersion)
>
> i want to make contrast between 6h_treated vs 6h_contol, 12h_treated
> vs 12h_contol,
> and so on....
>
> how to use makeContrast or any other function to compare them
>
> thank you very much
>
> --
> shan gao
> Room 231(Dr.Fei lab)
> Boyce Thompson Institute
> Cornell University
> Tower Road, Ithaca, NY 14853-1801
> Office phone: 1-607-254-1267(day)
> Official email:sg839@cornell.edu
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>
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