Hello, I'm using Limma to assess differential expression on double colour microarray data and have a question about the lmFit function. When I fit linear model using lmFit, as I understood, the function uses the weights extracted from the MA object when present and/or specified. Thus, I tried fitting with and without the spot quality weights and I found different results (not very surprising in fact). In fact, when I used weights, zero weighted spots seemed to be removed from the analysis and it's here that I have a problem. For my experiment, I compare two groups (control vs treated) in a classical design experiment "Two Groups: Common Reference" as describe in the Limma documentation. design=modelMatrix(targets,ref="ref") design fit=lmFit(MA,design,weights=MA$weights) /alternative without weights : fit=lmFit(MA,design,weights=NULL)/ cont.matrix=makeContrasts(pollutedVScontrol=polluted- control,polluted,control,levels=design) cont.matrix fit2=contrasts.fit(fit,cont.matrix) fit2=eBayes(fit2) res=toptable(coef=1,number=15744,fit=fit2,genelist=fit2$genes,adjust.m ethod="BH",A=fit2$Amean,eb=fit2,p.value=0.01) The difference between the analysis with and without weights is that when I use weights new genes highly differentially expressed appeared. When I control these genes, in fact they correspond to spots that are flagged (0) on the majority of the arrays (i.e. only one weight at 1 for the control and one weight at 1 for the treated). Thus for these genes the comparison is performed only one "control array" versus one "treated array". So is it possible to specify to lmFit that there must be a minimum of "1" weights or a maximum "0" weights per groups of array ? Thank you for any help you can bring me. Benoit -- Benoit Loup, PhD UMR Biologie du D?veloppement et Reproduction Diff?renciation des Gonades et Perturbations INRA ? Domaine de Vilvert B?timent Jacques Poly 78350 Jouy en Josas France Tel: 33 1 34 65 25 38 Fax: 33 1 34 65 22 41 E-mail: benoit.loup at jouy.inra.fr