Dear limma users, In our study we would like to understand which gene differentially expressed in which arabidopsis tissue. We extract the RNA from leaves from various developmental stages in way we cannot completely separate the tissues. However each RNA extraction should include transcripts from different mix of tissues. I think that Linear Models is perfect for such research since we can decide the expression measurement into its tissue components based on a model. The problem is that we are not completely sure as for the existence of one of tissues in one of the RNA extracts. Here is an example of the design LP ML ADL ABL T S B YE1ATH1.CEL 1 1 1 1 1 1 1 YE10ATH1.CEL 1 1 1 1 1 1 1 YE14ATH1.CEL 0 1 1 1 1 0 0 YE15ATH1.CEL 1 0 1 1 1 1 1 YE16ATH1.CEL 0 1 1 1 1 0 0 YE17ATH1.CEL 1 0 1 1 1 1 1 YE2ATH1.CEL 1 0 1 0 1 0 0 YE20ATH1.CEL 1 0 1 1 1 1 1 YE21ATH1.CEL 1 0 1 1 0 1 1 YE22ATH1.CEL 1 0 1 1 1 0 1 YE27ATH1.CEL 1 0 1 1 1 0 1 YE3ATH1.CEL 1 0 0 1 0 1 0 YE5ATH1.CEL 1 0 0 1 0 1 0 YE6ATH1.CEL 1 0 1 0 1 0 0 YE7ATH1.CEL 1 1 1 1 1 1 1 YE9ATH1.CEL 1 0 1 1 1 1 1 where the parameter (column headers) are the tissues. We have replicates here for example YE1ATH1.CEL is duplicate of YE10ATH1.CEL so maybe it was more correct to set the matrix values there into 0.5 and 0.5 for each. The question is if we change one of the tissue design what would be the criteria to design which model is better. Is it something that can be deduced from the coefficients after fitting the models or it is something we should deduce from the expected expression of certain genes? Thanks, Ron