**270**wrote:

Hello, I have 3 tumor samples and 3 control samples (paired)

i read that I should use the following design formula for DESeq2:

```
design(dds) <- ~ patient + condition
```

patient: colnames(cts) , and condition: levels normal, tumor (where normal is the base level). Then results(dds) will build a result table for tumor vs normal, controlling for the patient effect.

This is my coldata:

```
sampleCondition patient
IMLQK13_S1 C IIMLQ3_S1
IMLQ20_S2 C IMLQ20_S2
IMLQ21_S10 C IMLQ21_S10
IMLQ22_S4 T IMLQ22_S4
IMLQ24_S6 T IMLQ24_S6
IIMLQ26_S9 T IMLQ26_S9
```

But when I do :

```
dds_2 <- DESeqDataSetFromMatrix(countData = cts,colData = coldata,design = ~ patient + sampleCondition)
```

I get:

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')

So how do you suggest I do a paired analysis with deseq2? Not adding the patient attribute to the formula?

Thank you

**30**• written 23 months ago by Pin.Bioinf •

**270**