Multicellular factor analysis of single-cell data for a tissue-centric understanding of disease
- Heidelberg University, Germany
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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Multiplexing droplet-based single cell RNA-sequencing using genetic barcodesGene Expression Omnibus GSE96583.
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Spatial multi-omic map of human myocardial infarctionHuman Cell Atlas Data Portal, e9f36305-d857-44a3-93f0-df4e6007dc97.
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Cells of the Adult Heartad98d3cd-26fb-4ee3-99c9-8a2ab085e737.
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The Reference of the Transcriptional Landscape of Human End-Stage Heart FailureZenodo, doi: 10.5281/zenodo.3797044.
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Pathogenic variants damage cell composition and single cell transcription in cardiomyopathiescellxgene, e75342a8-0f3b-4ec5-8ee1-245a23e0f7cb.
Article and author information
Author details
Ricardo Omar Ramirez Flores
Julio Saez-Rodriguez
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.
Reviewing Editor
- Jihwan Park, Gwangju Institute of Science and Technology, Republic of Korea
Version history
- Preprint posted: February 23, 2023 (view preprint)
- Received: October 5, 2023
- Accepted: November 14, 2023
- Accepted Manuscript published: November 22, 2023 (version 1)
- Version of Record published: December 13, 2023 (version 2)
Copyright
© 2023, Ramirez Flores et al.
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.
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Multicellular factor analysis of single-cell data for a tissue-centric understanding of disease
eLife 12:e93161.
https://doi.org/10.7554/eLife.93161
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