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?