**0**wrote:

I have a data set from the following Design of Experiment(DOE)

```
sample batch condition
FRT19A_se se FRT19A_Control_2
Hinfp_A_G_se se Hinfp_A_G_point_mutant
Hinfp_M2_11_se se Hinfp_M2_11_Deletion_mutant
W1118_se se W1118_Control_1
FRT19A_pe pe FRT19A_Control_2
Hinfp_A_G_pe pe Hinfp_A_G_point_mutant
Hinfp_M2_11_pe pe Hinfp_M2_11_Deletion_mutant
W1118_pe pe W1118_Control_1
```

We want to perform the following comparisons:

```
FRT19A vs Hinfp_A_G
FRT19A vs Hinfp_M2_11
W1118 vs Hinfp_A_G
W1118 vs Hinfp_M2_11
FRT19A vs W1118
```

Note: I have 4 samples, each has a replicate in batch(SE), another replicate in batch(PE)

My question is:

is this doable in DESeq2?

I did setting like

```
ddsFullCountTable<-DESeqDataSetFromMatrix(
countData=dataCount,
colData=DOE,
design= ~batach + condition + batach:condition)
dds <-DESeq(ddsFullCountTable)
```

I got

```
estimating size factors
estimating dispersions
Error in checkForExperimentalReplicates(object, modelMatrix) :
The design matrix has the same number of samples and coefficients to fit,
so estimation of dispersion is not possible. Treating samples
as replicates was deprecated in v1.20 and no longer supported since v1.22.
```

I think this does make sense, because, statistically, For this DOE, parameters are not estimable

Then I tried:

```
ddsFullCountTable<-DESeqDataSetFromMatrix(
countData=rawdata.tli.after.filter.by.cpm,
colData=DOE.2,
design= ~batch+condition)
re.se.pe.DOE.2 <- results(dds.DOE.2)
```

Then DESeq2 did get results !

However, I do not understand How DESeq2 calculate p value for this DOE?

Does anyone can help me on this?

Thank you,

Aimin

**25k**• written 3 months ago by aimin.at.work •

**0**

I had some typo, it should be:

0