I am now trying to conduct batch effect correction for my data matrix, whose samples (human) have different gender and come from different sequencing platforms. I have tried
limma. However, the outcomes are bad. It seems that
Combat-seq is based on Negative binomial regression models,
Combat is based on Gaussian distribution, and
limma is based on the linear model. I wonder if my data violates their assumption, so I check my data and get this outcome. It seems that my data follows Gamma distribution. I am wondering what I could do in this situation.
The data I am working on is DNAse-seq data, which is a matrix with 16 samples. As the samples come from different platforms, I would like to correct the batch effect. I am also considering gender information as there are male and female samples. To sum up, my questions are:
- Do we need to consider the distribution of the data when we apply the batch correction (limma, combat, and combat-seq)?
- If we need to consider the distribution, which method do I need to correct the data which follows gamma distribution?
- Are there methods designed for read counts? (I know there are many methods designed for surat objects, but do we have others that are more general?)
Thank you very much for all kinds of help.