combines cellpypes objects, which is useful for pseudobulking and setting same thresholds for all samples
makes it easier to compute Differential expression
perhaps I could write a deseq <- function(obj, class) function, that returns obj again with DESeq2 results. Then one could start playing with plotting log-foldchanges against each other, e.g. for different marker gene sets or hierarchy levels.
I propose that DE genes in scRNAseq are also something to explore, not a static inference result. While problematic for multiple testing correction (how to correct for interactively playing around until results are judged as good?), I like the idea that DE is also amenable to exploration.
combine() or similar in name
deseq <- function(obj, class)function, that returns obj again with DESeq2 results. Then one could start playing with plotting log-foldchanges against each other, e.g. for different marker gene sets or hierarchy levels. I propose that DE genes in scRNAseq are also something to explore, not a static inference result. While problematic for multiple testing correction (how to correct for interactively playing around until results are judged as good?), I like the idea that DE is also amenable to exploration.