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Valid Post-clustering Differential Analysis for Single-Cell RNA-Seq
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DOI：10.1016/j.cels.2019.07.012.png)
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Abstract
En 中文
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.
Keywords:
GENE-EXPRESSION
QUALITY-CONTROL
REVEALS
HETEROGENEITY
EMBRYOS
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