Hi!

I am performing a differential expression analysis comparing cell lines responders to my treatment against cell lines not responders to the treatment (responders vs not resonders).

Here the sampleTable:

```
Name Cell_line condition
cell_line1_rep1 Cell_line_1 RESPONDERS
cell_line2_rep1 Cell_line_2 NOT_responders
cell_line3_rep1 Cell_line_3 NOT_responders
cell_line4_rep1 Cell_line_4 RESPONDERS
cell_line1_rep2 Cell_line_1 RESPONDERS
cell_line2_rep2 Cell_line_2 NOT_responders
cell_line3_rep2 Cell_line_3 NOT_responders
cell_line4_rep2 Cell_line_4 RESPONDERS
cell_line1_rep3 Cell_line_1 RESPONDERS
cell_line2_rep3 Cell_line_2 NOT_responders
cell_line3_rep3 Cell_line_3 NOT_responders
cell_line4_rep3 Cell_line_4 RESPONDERS
```

In my design I have two cell lines responders and two cell lines not responders. For each cell-line I have three biological replicates.

I construct the dds model considering as covariate both cell_line and condition but I got the error message that the variable are linear.

```
ddsTxi <- DESeqDataSetFromTximport(txi,
colData = samples,
design = ~ Cell_line + condition)
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':
```

I was wondering how I can inform the DESeq function that my replicates come from different cell lines?

Thank you for your help!

Concetta

There is not much that you can do here. You want to compare responders to non-responders, but 'response' (

`condition`

) is confounded by`Cell_line`

. You can make an assumption that there are no cell-line effects in your data, and remove`Cell_line`

from your design, but this assumption is almost certainly incorrect, and, therefore, the statistical inferences would be incorrect.Edit: it seems that you did not show the correct sample table in your question. See below