Anti-correlated feature selection prevents false discovery of subpopulations in scRNAseq.
Journal
Nature communications
ISSN: 2041-1723
Titre abrégé: Nat Commun
Pays: England
ID NLM: 101528555
Informations de publication
Date de publication:
24 Jan 2024
24 Jan 2024
Historique:
received:
13
12
2022
accepted:
07
11
2023
medline:
25
1
2024
pubmed:
25
1
2024
entrez:
24
1
2024
Statut:
epublish
Résumé
While sub-clustering cell-populations has become popular in single cell-omics, negative controls for this process are lacking. Popular feature-selection/clustering algorithms fail the null-dataset problem, allowing erroneous subdivisions of homogenous clusters until nearly each cell is called its own cluster. Using real and synthetic datasets, we find that anti-correlated gene selection reduces or eliminates erroneous subdivisions, increases marker-gene selection efficacy, and efficiently scales to millions of cells.
Identifiants
pubmed: 38267438
doi: 10.1038/s41467-023-43406-9
pii: 10.1038/s41467-023-43406-9
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
699Subventions
Organisme : U.S. Department of Health & Human Services | NIH | National Human Genome Research Institute (NHGRI)
ID : K99HG011270
Informations de copyright
© 2024. The Author(s).
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