Dear all,

I am having a problem with removing/accounting for the batch effect in my RNAseq experiment using DESeq2.

Initially we did 1 big time-course experiment in one batch of human cells growing with fungus.

The colData is the following:

time
1     0
2     0
3   1.5
4   1.5
5   1.5
6     3
7     3
8     3
9    12
10   12
11   12
12   24
13   24
14   24
15   24
16  24c
17  24c

Then I was comparing all time points against 0 using contrasts.

After getting some initial results, we decided to perform another experiment at 3h with a different fungus (lets call it 3D) to compare the difference between 0vs3 and 3vs3D.

After analyzing the data (not only what is mentioned above, but a similar experimental design with 3 other fungal species) I realized that 3D samples have a strong batch effect (I have seen this by removing the batch effect using limma).

For the case above, when I want to add the batch variable to the design, I get the error the model matrix is not full rank, which of course makes sense.

I am wondering is there is any workaround for this problem? For example, can I somehow use the output of limma removeBatchEffect to construct a new model and make DE analysis? Thank you

It sounds like you have a confounded design. You should have included some 0's in the new batch if you wanted to learn about the 3 vs 0 difference, but as it is, you cannot make much use of these extra samples. You don't know which is closer to the true level of expression, the mean level of expression from 3 or 3D, all you know is that they are different from each other, separated by a batch effect.