Hello,

I have the following experimental design for an experiment in which we conducted RNA sequencing: 2 treatment groups and 2 batches, but one of the batches is exclusively one treatment.

```
sample  treatment  batch
3       control    A
4       control    A
5       control    B
6       control    B
7       control    B
13      control    B
14      control    B
15      BD         A
16      BD         A
17      BD         A
18      BD         A
18      BD         A
```

It is a poor experimental design, but unfortunately it is the data that I currently must work with. To account for my the potential batch effect of the B batch, I am using the following design formula:

```r
dds <- DESeqDataSetFromMatrix(countData = countData,colData = colData,design = ~ batch + treatment)
dds$treatment <- factor(dds$treatment, levels=c("control", "BD"))
dds$batch <- factor(dds$batch, levels=c("A", "B"))
dds <- DESeq(dds, full=design(dds), reduced = ~ batch)
```

The results give me many less DE genes than if I simply ignored the batches and only use `~ treatment`. This makes sense, because according to PCA and clustering, there is a batch effect in my samples. I've read the DESeq2 manual and many posts, but am not a statistician and would love to hear feedback if the design I'm using here makes sense, with the lack of representation of both treatment groups in the batch I am intended to correct for. Thank you!
<p>Your design is correct, it&#39;s not an issue that the batch is just within one treatment since that treatment is itself present in both batch A and B. You only have a big problem when a whole group is present in its own batch.</p>
