I have RNAseq data from a relatively complicated experimental design with variables = genotype, treatment, time, and batch. I have 2 biological replicates for each genotype/condition, however unfortunately in the first iteration of the experiment one sample had poor RNA quality and is unusable. To get around this lack of a replicate, we repeated the experiment for the two biological replicates in the condition of the lost sample, and did a new library prep/sequencing from this repeated experiment. The repeated experiment obviously lead to a batch effect, and although it isn't the main driver of variance, it is a significant factor. I would like to model or remove the batch effect from the experiment using DESeq2 if possible, but after reading this similar post: can I model technical replicates in DESeq2? I am concerned about inflating the power of the test for those conditions that were replicated.
Here is a diagram of the experimental design to make it more easy to understand:
My "colData" for the DESeq2 object looks like this:
My previous thought was to simply collapse the genotype+time+treatment variables into a single combined factor "condition" and then model the batch effect using a design of "~batch + condition". However this doesn't really address the concern of inflating the power of the tests due to technical replication.
Should I Just collapse the counts of these replicated conditions even though the batch affect will affect the combined count values? Or should I use a different tool than DESeq2 ? Or is there a way for me to remove the batch effect prior to collapsing the counts for replicates?
What would you guys recommend?