For microarray gene expression data for
5 species: sp1
, sp2
, sp3
, ...
7 time-points: t1
, t2
, t3
, ...
3 replicates: r1
, r2
, r3
, ...
And the aim is to identify genes differentially expressed genes w.r.t to sp1
(assume sp1
is reference species with whom I want compare expression values).
Using limma I compared created a contrast matrix to compare sp2
-sp1
, sp3
-sp1
, sp3
-sp1
.
Design matrix looks like:
design <- model.matrix(~0+ targets$Organism)
colnames(design) <- c("sp1","sp2","sp3","sp4","sp5","sp6")
head(design)
sp1 sp2 sp3 sp4 sp5 sp6
1 0 1 0 0 0 0
2 0 1 0 0 0 0
3 0 1 0 0 0 0
4 0 1 0 0 0 0
5 0 1 0 0 0 0
6 0 1 0 0 0 0
tail(design)
sp1 sp2 sp3 sp4 sp5 sp6
139 0 0 0 0 0 1
140 0 0 0 0 0 1
141 0 0 0 0 0 1
142 0 0 0 0 0 1
143 0 0 0 0 0 1
144 0 0 0 0 0 1
contrast.matrix <- makeContrasts(sp2-sp1,sp3-sp1,sp4-sp1,sp5-sp1,sp6-sp1,levels=design)
contrast.matrix
Contrasts
Levels sp2 - sp1 sp3 - sp1 sp4 - sp1 sp5 - sp1 sp6 - sp1
sp1 -1 -1 -1 -1 -1
sp2 1 0 0 0 0
sp3 0 1 0 0 0
sp4 0 0 1 0 0
sp5 0 0 0 1 0
sp6 0 0 0 0 1
But for example for sp2-sp1
set, not only genes differentially expressed in sp2 but also differentially expressed in some other species e.g., sp4
is given as output.
So, I am not sure if limma package is suitable for such time-point and species specific data?
Can anyone suggest any other approach or package to identify genes differentially expressed across species?
will limma work for samples without replicates?