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

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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.

Data availability

The datasets and computer code produced in this study are available in the following databases:-All scripts related to this manuscript can be consulted here: https://github.com/saezlab/MOFAcell.-The R package implementing multicellular factor analysis can be found in:https://github.com/saezlab/MOFAcellulaR-The python implementation of multicellular factor analysis is available here:https://liana-py.readthedocs.io/en/latest/notebooks/mofacellular.html-A Zenodo entry containing data associated to this manuscript can be accessed here: https://zenodo.org/record/8082895.

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Article and author information

Author details

Ricardo Omar Ramirez Flores

Faculty of Medicine, Heidelberg University, Heidelberg, Germany

For correspondence
roramirezf@uni-heidelberg.de

Competing interests
No competing interests declared.

"This ORCID iD identifies the author of this article:" 0000-0003-0087-371X
Jan David Lanzer

Faculty of Medicine, Heidelberg University, Heidelberg, Germany

Competing interests
No competing interests declared.
Daniel Dimitrov

Faculty of Medicine, Heidelberg University, Heidelberg, Germany

Competing interests
No competing interests declared.
Britta Velten

Centre for Organismal Studies, Heidelberg University, Heidelberg, Germany

Competing interests
No competing interests declared.

"This ORCID iD identifies the author of this article:" 0000-0002-8397-3515
Julio Saez-Rodriguez

Faculty of Medicine, Heidelberg University, Heidelberg, Germany

For correspondence
pub.saez@uni-heidelberg.de

Competing interests
Julio Saez-Rodriguez, reports funding from GSK, Pfizer and Sanofi and fees/honoraria from Travere Therapeutics, Stadapharm, Astex, Pfizer and Grunenthal..

"This ORCID iD identifies the author of this article:" 0000-0002-8552-8976

Funding

DFG CRC 1550 (464424253)

Ricardo Omar Ramirez Flores
Julio Saez-Rodriguez

Informatics for Life

Jan David Lanzer
Julio Saez-Rodriguez

EU ITN Marie Curie StrategyCKD (860329)

Daniel Dimitrov
Julio Saez-Rodriguez

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Copyright

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.