Dimension reduction, cell clustering, and cell-cell communication inference for single-cell transcriptomics with DcjComm.
Cell clustering
Cell–cell communication
Joint learning
Non-negative matrix factorization
Single-cell
Journal
Genome biology
ISSN: 1474-760X
Titre abrégé: Genome Biol
Pays: England
ID NLM: 100960660
Informations de publication
Date de publication:
09 Sep 2024
09 Sep 2024
Historique:
received:
09
01
2024
accepted:
30
08
2024
medline:
10
9
2024
pubmed:
10
9
2024
entrez:
9
9
2024
Statut:
epublish
Résumé
Advances in single-cell transcriptomics provide an unprecedented opportunity to explore complex biological processes. However, computational methods for analyzing single-cell transcriptomics still have room for improvement especially in dimension reduction, cell clustering, and cell-cell communication inference. Herein, we propose a versatile method, named DcjComm, for comprehensive analysis of single-cell transcriptomics. DcjComm detects functional modules to explore expression patterns and performs dimension reduction and clustering to discover cellular identities by the non-negative matrix factorization-based joint learning model. DcjComm then infers cell-cell communication by integrating ligand-receptor pairs, transcription factors, and target genes. DcjComm demonstrates superior performance compared to state-of-the-art methods.
Identifiants
pubmed: 39252099
doi: 10.1186/s13059-024-03385-6
pii: 10.1186/s13059-024-03385-6
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
241Informations de copyright
© 2024. The Author(s).
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