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Ali, M. ; Kuijs, M. ; Hediyeh-Zadeh, S. ; Treis, T. ; Hrovatin, K. ; Palla, G. ; Schaar, A. ; Theis, F.J.

GraphCompass: Spatial metrics for differential analyses of cell organization across conditions.

Bioinformatics 40, i548-i557 (2024)
Verlagsversion DOI PMC
Open Access Hybrid
Creative Commons Lizenzvertrag
SUMMARY: 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. UNLABELLED:  .
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Gene-expression; Breast-cancer; Transition
Sprache englisch
Veröffentlichungsjahr 2024
HGF-Berichtsjahr 2024
e-ISSN 1367-4811
Zeitschrift Bioinformatics
Quellenangaben Band: 40, Heft: Supplement_1, Seiten: i548-i557 Artikelnummer: , Supplement: ,
Verlag Oxford University Press
Verlagsort Oxford
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Computational Biology (ICB)
Institute for Tissue Engineering and Regenerative Medicine (ITERM)
POF Topic(s) 30205 - Bioengineering and Digital Health
Forschungsfeld(er) Enabling and Novel Technologies
PSP-Element(e) G-503800-001
G-503800-004
G-505800-001
Förderungen Munich School for Data Science
Helmholtz Association
Joachim Herz Stiftung via Add-on Fellowships for Interdisciplinary Life Science
Helmholtz Association's Initiative and Networking Fund through CausalCellDynamics
European Union
DOI 10.1093/bioinformatics/btae242
WOS ID 001260513300053
Scopus ID 85197206787
PubMed ID 38940138
Erfassungsdatum 2024-07-23
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