Dear all,


I am using edgeR to find genes differentially expressed between infected and mock-infected control plants, at 3 time points after infection.
I  have RNAseq data for 3 independent tests, so for every single test I have 6 libraries  (control + infected at 3 time points).
Having three replicates this makes 18 libraries in total.

What I did until now is look at each time point separate and calculate DEgenes at that time point as shown in this script:

> head(x)
    C1  C2  C3  T1  T2  T3
1   0   1   2   0   0   0
2  13   6   4  10   8  12
3  17  16   9  10   8  11
4   2   1   2   2   3   2
5. 1   3   1   2   1   3   0
6  958 457 438 565 429 518

> treatment<-factor(c("C","C","C","T","T","T"))
> test<-factor(c(1,2,3,1,2,3))
> y<-DGEList(counts=x,group=treatment)
Calculating library sizes from column totals.
> cpm.y<-cpm(y)
> y<-y[rowSums(cpm.y>2)>=3,]
> y<-calcNormFactors(y)
> design<-model.matrix(~test+treat)
> y<-estimateGLMCommonDisp(y,design,verbose=TRUE)
Disp = 0.0265 , BCV = 0.1628
> y<-estimateGLMTrendedDisp(y,design)
Loading required package: splines
> y<-estimateGLMTagwiseDisp(y,design)
> fit<-glmFit(y,design)
> lrt<-glmLRT(y,fit)


This works fine but I wonder if I should do the analysis of the different time points all at once? Will this make a difference?
Unfortunately I cannot figure out how to design the matrix.

I hope someone can help me,

Kaat


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