I am new to data science. I have a dataset of single-cell gene expression from multiple cell types in C. Elegans. The dataset is from the paper Comprehensive single-cell transcriptional profiling of a multicellular organism
My main question is, Which approaches should I use for filtering out bad cells in this case when we have multiple cell types in the dataset?
So far I tried to filter out genes that have too high mitochondrial genes content following the Bioconductor “simpleSingleCell” workflow.
However, the tutorial specifically says that the method of filtering out based on mitochondrial genes most probably will not work when the dataset has multiple cell types:
Analyzing all cell types together would unnecessarily inflate the MAD and compromise the removal of low-quality cells, at best; or lead to the entire loss of one cell type, at worst.
Any suggestions would be greatly appreciated.