Valid Post-clustering Differential Analysis for Single-Cell RNA-Seq
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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.
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Wilcoxon rank-sum tests performed best for identifying marker genes.
“For this purpose, Wilcoxon rank-sum tests performed best, but owing to the nature of clustering, P values can be inflated and might lead to false discoveries, as the same data are used to define the labels that we test for differences between60,61.”
From Best practices for single-cell analysis across modalities (2023) · cites this paper as [61]
P values from marker gene tests can be inflated and lead to false discoveries because the same data are used to define labels and test differences.
“For this purpose, Wilcoxon rank-sum tests performed best, but owing to the nature of clustering, P values can be inflated and might lead to false discoveries, as the same data are used to define the labels that we test for differences between60,61.”
From Best practices for single-cell analysis across modalities (2023) · cites this paper as [61]
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- Authors
- Zhang, J. M., Kamath, G. M., & Tse, D. N.
- Affiliations
- Stanford University (US)
- Corresponding author
- Tse, David N.
- Venue
- Cell Systems
- Publisher
- Elsevier BV
- Published
- 2019-10
- Volume
- 9
- Issue
- 4
- Pages
- 383-392.e6
- ISSN
- 2405-4712 (ISSN-L)
- PMC
- PMC7202736
- OpenAlex
- W2973156019
- Cited by
- 72
- References
- 41
- Citations by year
- 2026: 5 · 2025: 12 · 2024: 13 · 2023: 9 · 2022: 8 · 2021: 14 · 2020: 7 · 2019: 3 · 2015: 1
- FWCI
- 2.65
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Cell Systems (publishedVersion)
- SSRN Electronic Journal (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Human Genome Research Institute (R01HG008164)
- Guangxi Experiment Center of Information Science
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Keywords
- single-cell RNA sequencing, cell clustering, false discovery, p-value adjustment, marker detection, differential expression analysis
- MeSH terms
- Humans, Software, Selection Bias, Cluster Analysis, Sequence Analysis, RNA, Computational Biology, Gene Expression Profiling, Single-Cell Analysis, Datasets as Topic
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