Thread: Is there something better than a T-test to use in this situation?

I have some gene expression data I've been given to analyze and I'd like some advice on how to proceed. I have 5 different strains and for each strain we measured the expression of 24 genes over 6 time points. So I have 5 tables, with each table having 24 rows (genes) and 6 columns (times). One of the strains is a parent strain while the other four are modified strains.

What I'd like to find is which genes differentiate between parent and modified strains. So far I used the t-test for each of the modified strains one by one comparing genes from one modified strain against their correpsonding genes in the parent strain. Is it ok to then say that if a gene shows up as being different in all four modified strains it differentiates the group of modified strains as a whole? I didn't think ANOVA would be applicable in this case since it would tell me a gene was different in at least one strain and not differentiate between parent and modified strains.

Is there a better way of accomplishing this? I was thinking of doing some kind of classification analysis but I'm not sure if thats ok with the parent group just having one strain in it while the other group has four.

Any advice is greatly appreciated.

No ideas?