[BioC] 2 different factorial analysis codes in LIMMA give different logFC but same values for other components of TOPTABLE

[Garcia Orellana,Miriam]

Dear Dr. Smith and all:
I am sorry to bother you with this matter, my understanting of the microarray anylisis is really basic and I am having hard and long time to finish the analysis of my data. Now, I can't figure out what is happening with the two models I am applying to evaluate my data, because each one of them on the TOPTABLE option when requesting the values for all the filtered genes ( 8026). Both models, (A and B) for each of my 5 contrasts are giving me the top table with the same numerical values for: AveExp, t, Pvalue, adjPvalue and B. However the logFC for contrasts 1, 2 and 3 in model B is exactly half of that in model A, while the logFC for contrasts 4 and 5 in model B is exactly one fourth of that one in model A.
Because this difference in logFC, I am getting different numbers of differentially expresed genes when using a cut off of adjPvalue lower or equal to 0.05 and a rawlogFC greater or equal to 1.5 for example for contrast 3(MR effect) it gives me 47 up- and 84 down-regulated genes with model A, while with model B it gives me only 18 up- and only 2 down-regulated genes under same cut-offs. How that can be possible if all other values are the same? and so what should I follow?

Briefly me data is a factorial design of 3 dam diets (DD: CTL, SFA, EFA) and 2 milk replacers (MR: LLA, HLA), I have three replicates for each of the interaction factors, then a total of 18 arrays. The data was filtered for informative/noninformative probes and plotted for array quality. So from a initial of 24118 bovine probes I endup with 8026 probes. My interest is to compare:
1.       Feeding FAT at prepartum= (SFA +EFA) vs CTL, with CTL as ref
2.       Feeding EFA prepartum = EFA vs SFA, with SFA as ref
3.       Feeding MR to calves= HLA vs LLA, with LLA as reference
4.       Interaction of feeding FAT by MR: (SFA +EFA) vs CTL by MR, with (SFA+EFA) vs CTL by LLA as ref
5.       Interaction of feeding EFA by MR:  EFA vs SFA by MR, with EFA vs SFA by LLA as ref

MODEL A (I created that with the guide of the LIMMA user guide for a factorial design:
TS <- paste(phenoDiet$DD, phenoDiet$MR, sep=".")
TS
TS <- factor(TS, levels=c("Ctl.LLA", "Ctl.HLA","SFA.LLA","SFA.HLA","EFA.LLA", "EFA.HLA"))
design <- model.matrix(~0+TS)
colnames(design) <- levels(TS)
fit <- lmFit(eset2, design, method="robust", maxit=1000)
efit <- eBayes(fit)
#Contrast results
MatContrast=makeContrasts(FAT=(SFA.LLA + SFA.HLA + EFA.LLA + EFA.HLA)/4 - (Ctl.LLA + Ctl.HLA)/2,
                                FA=(EFA.LLA + EFA.HLA)/2 - (SFA.LLA + SFA.HLA)/2,
                                MR=(EFA.HLA+SFA.HLA+Ctl.HLA)/3 - (EFA.LLA+SFA.LLA+Ctl.LLA)/3,
                                FATbyMR=((EFA.HLA+SFA.HLA)/2 - Ctl.HLA) - ((EFA.LLA+SFA.LLA)/2-Ctl.LLA),
                                FAbyMR=( EFA.HLA-SFA.HLA)-(EFA.LLA - SFA.LLA),
                                levels=design)
fitMat<-contrasts.fit(fit,MatContrast)
Contrast.eBayes=eBayes(fitMat)

MODEL B (this model was kindly provided by Dr G. Smith):
DD <-factor(phenoDie$DD, levels = c("Ctl", "SFA", "EFA"))
MR <-factor(phenoDie$MR, levels = c("LLA", "HLA"))
contrasts (DD) <- cbind (SFAEFAvsCtl=c(-2,1,1),EFAvsSFA=c(0,-1,1))
contrasts (MR) <- c(-1,1)
design <-model.matrix (~DD*MR)
design
fit <- lmFit (eset2, design, method="robust",maxit=1000)
efit <- eBayes(fit)

Thanks so much  in advance,
Miriam