Valid post-clustering differential analysis for single-cell RNA-Seq

BioRxiv : the Preprint Server for Biology
Jesse M ZhangDavid N Tse

Abstract

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. We observe that because clustering forces separation, reusing the same dataset generates artificially low p-values and hence false discoveries. We introduce a valid post-clustering differential analysis framework which corrects for this problem. We provide software at https://github.com/jessemzhang/tn_test.

Related Concepts

Biological Markers
RNA
Computer Software
Analysis
Cellular Component Organization
Protein Expression

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