Hi,

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?

Thanks!

Your mice are nested within the groups {res, cpg}

This is addressed in the vignette section. Take another look.