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
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?