Kernel-based testing for single-cell differential analysis.
Differential analysis
Kernel methods
Single cell epigenomics
Single cell transcriptomics
Journal
Genome biology
ISSN: 1474-760X
Titre abrégé: Genome Biol
Pays: England
ID NLM: 100960660
Informations de publication
Date de publication:
03 May 2024
03 May 2024
Historique:
received:
25
07
2023
accepted:
22
04
2024
medline:
4
5
2024
pubmed:
4
5
2024
entrez:
3
5
2024
Statut:
epublish
Résumé
Single-cell technologies offer insights into molecular feature distributions, but comparing them poses challenges. We propose a kernel-testing framework for non-linear cell-wise distribution comparison, analyzing gene expression and epigenomic modifications. Our method allows feature-wise and global transcriptome/epigenome comparisons, revealing cell population heterogeneities. Using a classifier based on embedding variability, we identify transitions in cell states, overcoming limitations of traditional single-cell analysis. Applied to single-cell ChIP-Seq data, our approach identifies untreated breast cancer cells with an epigenomic profile resembling persister cells. This demonstrates the effectiveness of kernel testing in uncovering subtle population variations that might be missed by other methods.
Identifiants
pubmed: 38702740
doi: 10.1186/s13059-024-03255-1
pii: 10.1186/s13059-024-03255-1
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
114Subventions
Organisme : Agence Nationale de la Recherche
ID : ANR-18-CE45-0023
Organisme : Agence Nationale de la Recherche
ID : ANR-22-PESN-0002
Organisme : Institut National Du Cancer
ID : INCA-DGOS-INSERM-12558
Informations de copyright
© 2024. The Author(s).
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