[BioC] Limma question

[Niccolò Bassani]

Dear users,
I'm having some troubles in figuring out what's going on in limma.
I've got some expression data from Agilent microRNA platform, I've
pre-processed them, and wanted to do some easy differential expression
analysis. Out of 1368 miRNAs (no filtering performed) there are 758 of
them which show EXACTLY the same value on all of the 24 arrays
involved. Arrays are divided in 3 groups, 8 arrays in each group.
Data look like this (in matrix form, first rows and columns):

        LN9      LN10      LN11      LN12      LN13      LN14
1 12.431022 12.186179 13.136163 12.121403 12.643895 12.756163
2  1.137504  1.137504  1.137504  1.137504  1.137504  1.137504
3  1.137504  1.137504  1.137504  1.137504  1.137504  1.137504
4  1.137504  1.137504  1.137504  1.137504  1.137504  1.137504
5  1.137504  1.137504  1.137504  1.137504  1.137504  1.137504
6  1.137504  1.137504  1.137504  1.137504  1.137504  1.137504

I specify the design matrix, and run easy differential expression code:

contrasts = cbind(AvsB = c(-1,1,0),AvsC = c(1,0,-1),AvsB_C =
c(1,-1/2,-1/2),A_BvsC = c(1/2,1/2,-1))
contrasts
     AvsB AvsC AvsB_C A_BvsC
[1,]   -1    1    1.0    0.5
[2,]    1    0   -0.5    0.5
[3,]    0   -1   -0.5   -1.0

fit = lmFit(agilent,design)
fit.contrasts = contrasts.fit(fit,contrasts)
test = eBayes(fit.contrasts)

The strange (or absurd) thing is that invariant microRNAs appear to be
differentially expressed throughout all of the contrasts but the last
one!

test
$p.value
           AvsB       AvsC     AvsB_C    A_BvsC
[1,] 0.53958575 0.42970445 0.41866547 0.5748925
[2,] 0.03471306 0.03471306 0.01644463 1.0000000
[3,] 0.03471306 0.03471306 0.01644463 1.0000000
[4,] 0.03471306 0.03471306 0.01644463 1.0000000
[5,] 1.00000000 0.23359101 0.48667557 0.1713666
1363 more rows ...

I've drilled into the various limma functions code, but it seems that
there's some problem with my data, maybe some kind of
approximation...my point is that the last contrast correctly
identifies no microRNA differentially expressed, whereas the remaining
3 return me t statistic which are non 0 for invariant miRNAs!!

$t
             AvsB       AvsC     AvsB_C     A_BvsC
[1,] 6.236028e-01 -0.8051982 -0.8249186 -0.5697255
[2,] 2.257614e+00 -2.2576137 -2.6068677  0.0000000
[3,] 2.257614e+00 -2.2576137 -2.6068677  0.0000000
[4,] 2.257614e+00 -2.2576137 -2.6068677  0.0000000
[5,] 1.588357e-14 -1.2263878 -0.7080553 -1.4161107
1363 more rows ...

Any suggestions? I've tried to round the dataset to 4 digits but the
problem's still there, only changes the contrast with consistently
non-differentially expressed genes...

Thanx, and merry xmas everybody (know it's early, but who knows what
will be next...)
Niccolò