
Hi BioC,   I am getting weird results from analyzing a set of microarray datasets to find differentially expressed genes.
Background: basically I have two strains, mutant and wildtype. And I also have a treatment an inducing factor. So in the end, I have 4 conditions: wildtype, mutant, wildtype induced, mutant induced
The way I setup the model is to have 2 factors: STRAIN (wildtype vs mutant) and TREATMENT (induce vs not induce), and I also want to model the interaction between the two. In the end, I want to know which genes show a difference between WT vs MT, induce vs not induce, and respond differently in mutant compared to wild-type cells (i.e. all 3 coefficients are significant).
My code essentially is:> contrasts(STRAIN)<-contr.sum(2);> contrasts(TREATMENT)<-contr.sum(2);> design<-model.matrix(~STRAIN*TREATMENT);> fit<-lmFit(allDatagcRMA,design);> cont.matrix<-cbind(WTvsMT=c(0,4,0,0),YesvsNoinduce=c(0,0,4,0),Interaction=c(0,0,0,4));> fit2<-contrasts.fit(fit,cont.matrix);> fit2<-eBayes(fit2);> results<-decideTests(fit2);
When I viewed my results on vennDiagrams:> vennDiagram(results, include="both");>  vennDiagram(results,include=c("up","down"),counts.col=c("red","green"))I found the sum of the genes up-regulated and down-regulated don't equal to result from the first diagram. For example, the area that overlaps all 3 circles (i.e. the innerest portion) in the first venndiagram don't equal to the sum of the up-regulated and down-regulated genes in that same area.I am wondering is this because of the way I setup my model? Is my model doing what I intend it to do?
Thank you!Sincerely,Casper 		 	   		  
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