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
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-i557

Subventions

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.

Auteurs

Mayar Ali (M)

Institute of Computational Biology, Helmholtz Munich, Neuherberg, 85764, Germany.
Institute for Tissue Engineering and Regenerative Medicine, Helmholtz Munich, Neuherberg, 85764, Germany.
Graduate School of Systemic Neurosciences, Ludwig Maximilian University of Munich, Planegg-Martinsried, 82152, Germany.

Merel Kuijs (M)

Institute of Computational Biology, Helmholtz Munich, Neuherberg, 85764, Germany.
Department of Mathematics, TUM School of Computation, Information and Technology, Technical University of Munich, Munich, 80333, Germany.

Soroor Hediyeh-Zadeh (S)

Institute of Computational Biology, Helmholtz Munich, Neuherberg, 85764, Germany.
TUM School of Life Sciences, Technical University of Munich, Freising, 85354, Germany.

Tim Treis (T)

Institute of Computational Biology, Helmholtz Munich, Neuherberg, 85764, Germany.
TUM School of Life Sciences, Technical University of Munich, Freising, 85354, Germany.

Karin Hrovatin (K)

Institute of Computational Biology, Helmholtz Munich, Neuherberg, 85764, Germany.
TUM School of Life Sciences, Technical University of Munich, Freising, 85354, Germany.

Giovanni Palla (G)

Institute of Computational Biology, Helmholtz Munich, Neuherberg, 85764, Germany.
TUM School of Life Sciences, Technical University of Munich, Freising, 85354, Germany.

Anna C Schaar (AC)

Institute of Computational Biology, Helmholtz Munich, Neuherberg, 85764, Germany.
Department of Mathematics, TUM School of Computation, Information and Technology, Technical University of Munich, Munich, 80333, Germany.
Munich Center for Machine Learning, Technical University of Munich, Munich, 80333, Germany.

Fabian J Theis (FJ)

Institute of Computational Biology, Helmholtz Munich, Neuherberg, 85764, Germany.
Department of Mathematics, TUM School of Computation, Information and Technology, Technical University of Munich, Munich, 80333, Germany.
TUM School of Life Sciences, Technical University of Munich, Freising, 85354, Germany.

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