Hi,

I am having trouble understanding the following:

Suppose I have one gene with 26 samples. The first 20 samples are in batch A, the remaining 6 samples are in batch B. In the first 20 samples, there are 12 samples in control group and 8 samples in treated group, all batch B samples are in treated group.  When I use a linear model (no interaction between batch and treatment), I can get some values out of it. However, when I use model with interaction, the variance are all 0, there must be something wrong with the model. I understand this is not a good experimental design, however, I don't understand why this kind of design will yield such kinds of results. Could any one explain to me why? 

Thanks,

James

       
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