GraphCompass: spatial metrics for differential analyses of cell organization across conditions.
Journal
Bioinformatics (Oxford, England)
ISSN: 1367-4811
Titre abrégé: Bioinformatics
Pays: England
ID NLM: 9808944
Informations de publication
Date de publication:
28 Jun 2024
28 Jun 2024
Historique:
medline:
28
6
2024
pubmed:
28
6
2024
entrez:
28
6
2024
Statut:
ppublish
Résumé
Spatial omics technologies are increasingly leveraged to characterize how disease disrupts tissue organization and cellular niches. While multiple methods to analyze spatial variation within a sample have been published, statistical and computational approaches to compare cell spatial organization across samples or conditions are mostly lacking. We present GraphCompass, a comprehensive set of omics-adapted graph analysis methods to quantitatively evaluate and compare the spatial arrangement of cells in samples representing diverse biological conditions. GraphCompass builds upon the Squidpy spatial omics toolbox and encompasses various statistical approaches to perform cross-condition analyses at the level of individual cell types, niches, and samples. Additionally, GraphCompass provides custom visualization functions that enable effective communication of results. We demonstrate how GraphCompass can be used to address key biological questions, such as how cellular organization and tissue architecture differ across various disease states and which spatial patterns correlate with a given pathological condition. GraphCompass can be applied to various popular omics techniques, including, but not limited to, spatial proteomics (e.g. MIBI-TOF), spot-based transcriptomics (e.g. 10× Genomics Visium), and single-cell resolved transcriptomics (e.g. Stereo-seq). In this work, we showcase the capabilities of GraphCompass through its application to three different studies that may also serve as benchmark datasets for further method development. With its easy-to-use implementation, extensive documentation, and comprehensive tutorials, GraphCompass is accessible to biologists with varying levels of computational expertise. By facilitating comparative analyses of cell spatial organization, GraphCompass promises to be a valuable asset in advancing our understanding of tissue function in health and disease. .
Identifiants
pubmed: 38940138
pii: 7700863
doi: 10.1093/bioinformatics/btae242
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
i548-i557Subventions
Organisme : European Union
Organisme : ERC
ID : DeepCell-101054957
Organisme : Helmholtz Association's Initiative and Networking Fund through CausalCellDynamics
Organisme : Joachim Herz Stiftung via Add-on Fellowships for Interdisciplinary Life Science
Organisme : Helmholtz Association
Organisme : Munich School for Data Science
Informations de copyright
© The Author(s) 2024. Published by Oxford University Press.