Single-cell computational pipelines involve two critical steps: organizing cells (clustering) and identifying the markers driving this organization (differential expression analysis). State-of-the-art pipelines perform differential analysis after clustering on the same dataset. Stanford University researchers observe that because clustering “forces” separation, reusing the same dataset generates artificially low p values and hence false discoveries. They introduce a valid post-clustering differential analysis framework, which corrects for this problem.
Availability – The developers provide software at https://github.com/jessemzhang/tn_test.