I have been working on microarrays using R and Limma for differential gene expression analysis. My current design is fairly simple as I am just using two class "control" and "treatment" and I am only interesting in the DE genes between control and treatment.
design <- model.matrix(~cell_class, data)
But the data also contains different cell lines (around 7), and treatment methods (4), so I have been wondering if it would be better and more precise to use another design like so:
design <- model.matrix(~cell_class + cell_lines + treatment_methods, data)
Both designs lead to very similar output when calling topTable, almost all DE genes are the same but with differences in fold change. I have been searching in limma documentation chap 9, 9.5, 9.7 are quite similar to my question but not exactly the same as they seems to be interested in contrast between sub groups while I am more interested in the global control vs treatment.
ps: I am not posting the design matrix output as it is a little over 300 rows and would not be very usefull