Hi Michael,

I am getting the following error 

"Error in DESeqDataSet(se, design = design, ignoreRank) : 

  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"

in the 

dds <- DESeqDataSetFromMatrix(countData = countsMatrix, colData = colData, design = ~ condition + type); 

function

The following is the colData 

                               condition      type
ABCD_GUND_1fb      ABCD         ABCD_UND
ABCD_GUND_2fb      ABCD         ABCD_UND
ABCI_GDIF_1fb         ABCD          ABCD_DIF
ABCD_GDIF_2fb        ABCD         ABCD_DIF
ABCD_GDIF_3fb        ABCD         ABCD_DIF
CA9_UND_1fb            CA9            UND
CA9_UND_2fb            CA9            UND
CA9_UND_3fb            CA9            UND
CA9_DIF10_1fb          CA9            DIF10
CA9_DIF10_2fb          CA9            DIF10
CA9_DIF30_1fb          CA9           DIF30
CA9_DIF10_3fb          CA9           DIF10
CA9_DIF30_2fb          CA9           DIF30
CA9_DIF30_3fb          CA9           DIF30
ABCD_GUND_3fb      ABCD        ABCD_UND

the idea is to compare the two cell lines separately (ABCD and CA9) with respective controls (ABCD_UND, UND) and also CA9 has two time points (DIF10 and DIF30). 

Your help is greatly appreciated.

hi Prasad,

Just use a design ~ type, and do any pairwise comparisons like so:

results(dds, contrast=c("type","ABCD_DIF","ABCD_UND"))

results(dds, contrast=c("type","DIF10","UND"))

etc.

The reason you cannot fit a model with both type and condition is because the condition information can be generated as a linear combination of the type information. So the type is all you need.

Thank you very much Michael.