I'm running DESeq with the design=~Cell_Treat + MGMT. I'm working with 6 samples from two different patients, so 12 samples in total. There are two factors, Cell line (HF2303 or HF 2927) and Treatment (untrt or trt). I'm trying to include a continuous covariate into the design (MGMT) to control for the effect of the factors based off of MGMT presence in the cell.
> colData(se) DataFrame with 12 rows and 5 columns sample Cell Treat MGMT Cell_Treat <factor> <factor> <integer> <factor> <factor> 1 accepted_hits.CSC.Contc.HF2303 A 1 pos A-1 2 accepted_hits.CSC.TMZa.HF2303 A 2 pos A-2 3 accepted_hits.SDC.Contb.HF2303 A 1 pos A-1 4 accepted_hits.SDC.Contc.HF2303 A 1 pos A-1 5 accepted_hits.SDC.TMZb.HF2303 A 2 pos A-2 ... ... ... ... ... ... 8 accepted_hits.CSC.TMZb.HF2927 B 2 neg B-2 9 accepted_hits.CSC.TMZc.HF2927 B 2 neg B-2 10 accepted_hits.SDC.Contc.HF2927 B 1 neg B-1 11 accepted_hits.SDC.TMZa.HF2927 B 2 neg B-2 12 accepted_hits.SDC.TMZc.HF2927 B 2 neg B-2
When I try to set up the DESeqDataSet with design=~Cell_Treat + MGMT it gives me an error that the model matrix isn't in full rank. I thought this might be due to that MGMT factors correlate the same as the Cell factor, but I'm not sure if I should get rid of the column because id like to base the results off of whether or not the MGMT variable is present of not and the cell column would be needed to separate HF2303 from HF2927?
Am I doing something wrong in the way the design is set up or columns?
This is the full error message:
> dds<-DESeqDataSet(se, design=~Cell_Treat + MGMT) 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. See the section 'Model matrix not full rank' in vignette('DESeq2')