On Sat, Aug 25, 2012 at 12:15 AM, 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
>
>
I'd suggest making a single factor, call it fac, derived by combining the
time and treatment variables so that your factor looks like:  control_0h,
control_0h, ..., treated_0h, ..., treated_48h, etc.

design = model.matrix( ~ fac)

contmat = makeContrasts(treated_6h-control_6h,treated_12h-control_12h)

I didn't test this, but this is a simple approach to the problem that often
works nicely for two-factor designs.

Sean

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