Use for pseudobulk differential expression - advantages & logFC values

Hi,

Thank you for your very useful package. I have two questions regarding its use for pseudobulk differential expression analysis.

Firstly, could you outline the reasons why you think your model is better for pseudobulk than alternatives like a manual pseudobulk step and `edgeR`/`DEseq`, given that glmGamPoi's main use seems to be for non-pseudobulk?

Secondly, I have noted strange logFC values when performing pseudobulk differential expression analysis on a Alzheimer's Disease split by 6 cell types. The dataset has approx 50 samples, resulting in >50k cells after quality control. The logFC values can be seen in this volcano plot:


The logFC values for all cell types appears to be split into three groups and does not appear as I would expect in a volcano plot. Have you noted logFC values like this before? I have attached the table of this data for just cell type "A" (to keep the size down).
[DE_analysis_odd_logFC_values.txt](https://github.com/const-ae/glmGamPoi/files/6451782/DE_analysis_odd_logFC_values.txt)