Hi List,

I am analyzing my RNA-Seq data with edgeR. The next is my experimental
design:


d.GLM
An object of class "DGEList"
$samples
                   group lib.size norm.factors
R4.Hot     HotAdaptedHot 17409289    0.9881635
R5.Hot     HotAdaptedHot 17642552    1.0818144
R9.Hot    ColdAdaptedHot 20010974    0.8621807
R10.Hot   ColdAdaptedHot 14064143    0.8932791
R4.Cold   HotAdaptedCold 11968317    1.0061084
R5.Cold   HotAdaptedCold 11072832    1.0523857
R9.Cold  ColdAdaptedCold 22386103    1.0520949
R10.Cold ColdAdaptedCold 17408532    1.0903311


As you can see, R4 and R5 are replicates of the same biological group (Hot
adapted), and the same is true for R9 and R10 (Cold adapted).

I am interested in measuring for each gene its expression variability
within a biological group (at each temperature) to discern genes that might
be tightly regulated (or under stabilizing selection). The question in
particular is: How can I get tagwise dispersion values for the pairs
(R4.Hot + R5.Hot), (R9.Hot + R10.Hot), (R4.Cold + R5.Cold), (R9.Cold +
R10.Cold). I assume that the square root of each tagwise dispersion value
can be interpreted as the expression variance of the corresponding gene
(i.e., biological variation), as I understood from the edgeR manual. Am I
correct?

I tried to calculate it like this:

R4.R5.HC = edgeR_expressed_genes[,1:2]
#I tell edgeR there is only one factor, two replicates
group = factor(c("HC", "HC"))
Hot.Hot = DGEList(counts = R4.R5.HC, group = group)
Hot.Hot = calcNormFactors(Hot.Hot)
Hot.Hot = estimateCommonDisp(Hot.Hot)
Hot.Hot = estimateTagwiseDisp(Hot.Hot)

(and similarly for (R9.Hot + R10.Hot), (R4.Cold + R5.Cold), (R9.Cold +
R10.Cold)).

What I don't understand is why I just got 20 different dispersion values
for all genes:

dim(table(Hot.Hot$tagwise.dispersion))
[1] 20

However, when I use the d.GLM dataset (i.e., the 8 samples for the 2x2
factor design) I get one different dispersion value for each gene:

> dim(table(d.GLM1$tagwise.dispersion))
[1] 9418


Why is this?

Can I get gene expression variability in a better way to fulfill my aim?


Thank you very much,
Miguel Gallach

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