Enabling single-cell trajectory network enrichment.
Journal
Nature computational science
ISSN: 2662-8457
Titre abrégé: Nat Comput Sci
Pays: United States
ID NLM: 101775476
Informations de publication
Date de publication:
Feb 2021
Feb 2021
Historique:
received:
31
05
2020
accepted:
15
01
2021
medline:
1
2
2021
pubmed:
1
2
2021
entrez:
13
1
2024
Statut:
ppublish
Résumé
Single-cell sequencing (scRNA-seq) technologies allow the investigation of cellular differentiation processes with unprecedented resolution. Although powerful software packages for scRNA-seq data analysis exist, systems biology-based tools for trajectory analysis are rare and typically difficult to handle. This hampers biological exploration and prevents researchers from gaining deeper insights into the molecular control of developmental processes. Here, to address this, we have developed Scellnetor; a network-constraint time-series clustering algorithm. It allows extraction of temporal differential gene expression network patterns (modules) that explain the difference in regulation of two developmental trajectories. Using well-characterized experimental model systems, we demonstrate the capacity of Scellnetor as a hypothesis generator to identify putative mechanisms driving haematopoiesis or mechanistically interpretable subnetworks driving dysfunctional CD8 T-cell development in chronic infections. Altogether, Scellnetor allows for single-cell trajectory network enrichment, which effectively lifts scRNA-seq data analysis to a systems biology level.
Identifiants
pubmed: 38217228
doi: 10.1038/s43588-021-00025-y
pii: 10.1038/s43588-021-00025-y
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
153-163Subventions
Organisme : Villum Fonden (Villum Foundation)
ID : 13154
Informations de copyright
© 2021. The Author(s), under exclusive licence to Springer Nature America, Inc.
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