I have two groups of mice - wild type and mutant in which every mice had one brain hemisphere treated/damaged (ipsi) and one hemisphere healthy (contra). I would like to assess if there is a difference in response to the treatment between WT and MUT. Another question is if the healthy hemispheres differ significantly in gene expression levels (mut vs. wt).
> head(metaData) ID genotype hemi 1 WT_1 WT Contra 2 WT_2 WT Contra 3 WT_3 WT Contra 4 WT_1 WT Ipsi 5 WT_2 WT Ipsi 6 WT_3 WT Ipsi
Based on another topic (DESEq2 Paired samples Before and after treatment ) I made my design as:
~ mouseID + hemi* genotype
But I got an error message:
Error in checkFullRank(modelMatrix) : 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.Please read the vignette section 'Model matrix not full rank': vignette('DESeq2')
I read a vignette but I am still not sure how to change the design so it reflects my question (how the genotype affects the response to treatment). Or is there something I am doing wrong with the IDs, should I add a column of "mice.n" similar as in the the topic I am referring to? In that topic I understand the additional column is required because of unequal number of patients in naive/second groups.