I have a set of RNA-seq data where each mouse has a treated side and a control side. My main goal is to compare treatment groups vs their matched controlled side while taking into account the effects of the mouse. I would also like to look at the differences between treated groups as well.
Here is my sample list:
samples2 sample_id condition mouse 1 4362L_RES_T RES_T 4362 2 4362R_RES_C RES_C 4362 3 4363L_RES_T RES_T 4363 4 4363R_RES_C RES_C 4363 5 4364L_RES_C RES_C 4364 6 4364R_RES_T RES_T 4364 7 4365L_RES_C RES_C 4365 8 4365R_RES_T RES_T 4365 9 4366L_CPG_C CPG_C 4366 10 4366R_CPG_T CPG_T 4366 11 4368L_CPG_C CPG_C 4368 12 4368R_CPG_T CPG_T 4368 13 4371L_CPG_C CPG_C 4371 14 4371R_CPG_T CPG_T 4371 15 4372L_CPG_C CPG_C 4372 16 4372R_CPG_T CPG_T 4372
Here is my design:
ddsTxi2 <- DESeqDataSetFromTximport(txi2, colData = samples2, design = ~mouse + condition)
I get the following error:
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've read through the vignette and the section mentioned above but fail to apply it to my example. I am interested in REST vs RESC and also REST vs CPGT (and the other possible permutations of comparisons). what should be my design?