NeST: nested hierarchical structure identification in spatial transcriptomic data.
Journal
Nature communications
ISSN: 2041-1723
Titre abrégé: Nat Commun
Pays: England
ID NLM: 101528555
Informations de publication
Date de publication:
17 10 2023
17 10 2023
Historique:
received:
13
06
2023
accepted:
06
10
2023
medline:
23
10
2023
pubmed:
18
10
2023
entrez:
17
10
2023
Statut:
epublish
Résumé
Spatial gene expression in tissue is characterized by regions in which particular genes are enriched or depleted. Frequently, these regions contain nested inside them subregions with distinct expression patterns. Segmentation methods in spatial transcriptomic (ST) data extract disjoint regions maximizing similarity over the greatest number of genes, typically on a particular spatial scale, thus lacking the ability to find region-within-region structure. We present NeST, which extracts spatial structure through coexpression hotspots-regions exhibiting localized spatial coexpression of some set of genes. Coexpression hotspots identify structure on any spatial scale, over any possible subset of genes, and are highly explainable. NeST also performs spatial analysis of cell-cell interactions via ligand-receptor, identifying active areas de novo without restriction of cell type or other groupings, in both two and three dimensions. Through application on ST datasets of varying type and resolution, we demonstrate the ability of NeST to reveal a new level of biological structure.
Identifiants
pubmed: 37848426
doi: 10.1038/s41467-023-42343-x
pii: 10.1038/s41467-023-42343-x
pmc: PMC10582109
doi:
Banques de données
Dryad
['10.5061/dryad.8t8s248']
Types de publication
Journal Article
Research Support, U.S. Gov't, Non-P.H.S.
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
6554Informations de copyright
© 2023. Springer Nature Limited.
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