[Bioc-sig-seq] interaction factor in edgeR

[Biase, Fernando]

Dear list users,

I am not a statistician, so pardon my ignorance.

When using edgeR package to analyse RNA-seq data the number of differential expressed genes vary depending on whether I use an interaction factor in the design. Can anyone suggest why does it happen?

Example:

if I use:
design <- model.matrix(~ a + b  , data=targets)

I have:
 summary(decideTests_eset_b_tmm)
   [,1] 
-1  2855
0  12346
1   4928

if I use:
design <- model.matrix(~ a + b + a:b , data=targets)

then:
summary(decideTests_eset_b_tmm)
   [,1]
-1 3343
0  9490
1  4191


When having more than one factor, is it more appropriate to have the interaction factor in the design?


Thanks a lot
Best,

Fernando