Multicellular factor analysis of single-cell data for a tissue-centric understanding of disease

• Verfasst von: Ramirez Flores, Ricardo O. [VerfasserIn]
• Verfasst von: Lanzer, Jan David [VerfasserIn]
• Verfasst von: Dimitrov, Daniel [VerfasserIn]
• Verfasst von: Velten, Britta [VerfasserIn]
• Verfasst von: Sáez Rodríguez, Julio [VerfasserIn]
• Titel: Multicellular factor analysis of single-cell data for a tissue-centric understanding of disease
• Verf.angabe: Ricardo Omar Ramirez Flores, Jan David Lanzer, Daniel Dimitrov, Britta Velten, Julio Saez-Rodriguez
• E-Jahr: 2023
• Jahr: 22 November 2023
• Umfang: 27 S.
• Illustrationen: Illustrationen
• Fussnoten: Gesehen am 06.06.2024
• Titel Quelle: Enthalten in: eLife
• Ort Quelle: Cambridge : eLife Sciences Publications, 2012
• Jahr Quelle: 2023
• Band/Heft Quelle: 12(2023), Artikel-ID e93161, Seite 1-27
• ISSN Quelle: 2050-084X
• DOI: doi:10.7554/eLife.93161
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: bulk
• Sach-SW: factor analysis
• Sach-SW: multicellular
• Sach-SW: single-cell atlas
• Sach-SW: spatial
• Sach-SW: tissue
• K10plus-PPN: 1890811661

Abstract

Biomedical single-cell atlases describe disease at the cellular level. However, analysis of this data commonly focuses on cell-type-centric pairwise cross-condition comparisons, disregarding the multicellular nature of disease processes. Here, we propose multicellular factor analysis for the unsupervised analysis of samples from cross-condition single-cell atlases and the identification of multicellular programs associated with disease. Our strategy, which repurposes group factor analysis as implemented in multi-omics factor analysis, incorporates the variation of patient samples across cell-types or other tissue-centric features, such as cell compositions or spatial relationships, and enables the joint analysis of multiple patient cohorts, facilitating the integration of atlases. We applied our framework to a collection of acute and chronic human heart failure atlases and described multicellular processes of cardiac remodeling, independent to cellular compositions and their local organization, that were conserved in independent spatial and bulk transcriptomics datasets. In sum, our framework serves as an exploratory tool for unsupervised analysis of cross-condition single-cell atlases and allows for the integration of the measurements of patient cohorts across distinct data modalities.