Back to Search
Start Over
Discovery and generalization of tissue structures from spatial omics data.
- Source :
-
Cell reports methods [Cell Rep Methods] 2024 Aug 19; Vol. 4 (8), pp. 100838. Date of Electronic Publication: 2024 Aug 09. - Publication Year :
- 2024
-
Abstract
- Tissues are organized into anatomical and functional units at different scales. New technologies for high-dimensional molecular profiling in situ have enabled the characterization of structure-function relationships in increasing molecular detail. However, it remains a challenge to consistently identify key functional units across experiments, tissues, and disease contexts, a task that demands extensive manual annotation. Here, we present spatial cellular graph partitioning (SCGP), a flexible method for the unsupervised annotation of tissue structures. We further present a reference-query extension pipeline, SCGP-Extension, that generalizes reference tissue structure labels to previously unseen samples, performing data integration and tissue structure discovery. Our experiments demonstrate reliable, robust partitioning of spatial data in a wide variety of contexts and best-in-class accuracy in identifying expertly annotated structures. Downstream analysis on SCGP-identified tissue structures reveals disease-relevant insights regarding diabetic kidney disease, skin disorder, and neoplastic diseases, underscoring its potential to drive biological insight and discovery from spatial datasets.<br />Competing Interests: Declaration of interests Z.W., A.K., M.M., E.A.G.B., B.C., M.K.R., A.T.M., and A.E.T. are affiliated with Enable Medicine as employees and/or shareholders. J.Z. is a member of Enable Medicine’s scientific advisory board.<br /> (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)
Details
- Language :
- English
- ISSN :
- 2667-2375
- Volume :
- 4
- Issue :
- 8
- Database :
- MEDLINE
- Journal :
- Cell reports methods
- Publication Type :
- Academic Journal
- Accession number :
- 39127044
- Full Text :
- https://doi.org/10.1016/j.crmeth.2024.100838