Hi,

I am trying to do some differential expression analysis with deseq2 at the moment and I have samples with four different conditions, two different batches and five different mouse. Here is the colData information.

                condition   precise batch   mouse
ETI1_B13D        B13D   ETI1_B13D     2     1
ETI2_B13T        B13T   ETI2_B13T     2     1
Elsa1_B13T1      B13T Elsa1_B13T1     1     2
Elsa2_B13T3      B13T Elsa2_B13T3     1     3
Elsa3_B13D1      B13D Elsa3_B13D1     1     2
Elsa4_B13D2      B13D Elsa4_B13D2     1     3
Elsa5_BAT1        BAT  Elsa5_BAT1     1     4
Elsa6_BAT2        BAT  Elsa6_BAT2     1     5
Elsa7_BAD1        BAD  Elsa7_BAD1     1     4
Elsa8_BAD2        BAD  Elsa8_BAD2     1     5

I tried the "design ~ batch + condition" to see the batch effect on the samples but still the mouse which sample is derived from also has effect when I checked the clustering. Therefore, I would like to do the analysis with multiple factors. If I put the model as "mouse + batch + condition", it gives the error message as below,

the model matrix is not full rank, so the model cannot be fit as specified. One or more variables or interaction terms in the design formula are linear combinations of the others and must be removed.

Although I went back to vignettes to understand what I need to improve, however, still I did not get further regarding this. If possible, could you give me a piece of advise for analysis?

Thanks in advance!

I’d recommend speaking with a statistician about your experimental design and analysis plan. I don’t have much time to provide statistical consulting beyond the details given in the vignette.