1. Computational and Systems Biology

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

  1. Ricardo Omar Ramirez Flores  Is a corresponding author
  2. Jan David Lanzer
  3. Daniel Dimitrov
  4. Britta Velten
  5. Julio Saez-Rodriguez  Is a corresponding author
  1. Heidelberg University, Germany
https://doi.org/10.7554/eLife.93161
Share this article
Cite this article
  1. Ricardo Omar Ramirez Flores
  2. Jan David Lanzer
  3. Daniel Dimitrov
  4. Britta Velten
  5. Julio Saez-Rodriguez
(2023)
Multicellular factor analysis of single-cell data for a tissue-centric understanding of disease
eLife 12:e93161.
https://doi.org/10.7554/eLife.93161
  2. Download BibTeX
  3. Download .RIS

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.

The following previously published data sets were used

Article and author information

Author details

  1. Ricardo Omar Ramirez Flores

    Faculty of Medicine, Heidelberg University, Heidelberg, Germany
    For correspondence
    roramirezf@uni-heidelberg.de
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-0087-371X

  2. Jan David Lanzer

    Faculty of Medicine, Heidelberg University, Heidelberg, Germany
    Competing interests
    No competing interests declared.

  3. Daniel Dimitrov

    Faculty of Medicine, Heidelberg University, Heidelberg, Germany
    Competing interests
    No competing interests declared.

  4. Britta Velten

    Centre for Organismal Studies, Heidelberg University, Heidelberg, Germany
    Competing interests
    No competing interests declared.
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-8397-3515

  5. 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..
    ORCID icon "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

© 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.

Metrics

  • 4,184
    views
  • 530
    downloads
  • 20
    citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  1. Ricardo Omar Ramirez Flores
  2. Jan David Lanzer
  3. Daniel Dimitrov
  4. Britta Velten
  5. Julio Saez-Rodriguez
(2023)
Multicellular factor analysis of single-cell data for a tissue-centric understanding of disease
eLife 12:e93161.
https://doi.org/10.7554/eLife.93161

Share this article

https://doi.org/10.7554/eLife.93161

Categories and tags

Research organism

Further reading

    1. Computational and Systems Biology
    2. Genetics and Genomics

    Risk factors affecting polygenic score performance across diverse cohorts

    Daniel Hui, Scott Dudek ... Marylyn D Ritchie
    Research Article

    Apart from ancestry, personal or environmental covariates may contribute to differences in polygenic score (PGS) performance. We analyzed the effects of covariate stratification and interaction on body mass index (BMI) PGS (PGSBMI) across four cohorts of European (N = 491,111) and African (N = 21,612) ancestry. Stratifying on binary covariates and quintiles for continuous covariates, 18/62 covariates had significant and replicable R2 differences among strata. Covariates with the largest differences included age, sex, blood lipids, physical activity, and alcohol consumption, with R2 being nearly double between best- and worst-performing quintiles for certain covariates. Twenty-eight covariates had significant PGSBMI–covariate interaction effects, modifying PGSBMI effects by nearly 20% per standard deviation change. We observed overlap between covariates that had significant R2 differences among strata and interaction effects – across all covariates, their main effects on BMI were correlated with their maximum R2 differences and interaction effects (0.56 and 0.58, respectively), suggesting high-PGSBMI individuals have highest R2 and increase in PGS effect. Using quantile regression, we show the effect of PGSBMI increases as BMI itself increases, and that these differences in effects are directly related to differences in R2 when stratifying by different covariates. Given significant and replicable evidence for context-specific PGSBMI performance and effects, we investigated ways to increase model performance taking into account nonlinear effects. Machine learning models (neural networks) increased relative model R2 (mean 23%) across datasets. Finally, creating PGSBMI directly from GxAge genome-wide association studies effects increased relative R2 by 7.8%. These results demonstrate that certain covariates, especially those most associated with BMI, significantly affect both PGSBMI performance and effects across diverse cohorts and ancestries, and we provide avenues to improve model performance that consider these effects.

    1. Computational and Systems Biology
    2. Neuroscience

    Multisensory integration operates on correlated input from unimodal transient channels

    Cesare V Parise, Marc O Ernst
    Research Article

    Audiovisual information reaches the brain via both sustained and transient input channels, representing signals’ intensity over time or changes thereof, respectively. To date, it is unclear to what extent transient and sustained input channels contribute to the combined percept obtained through multisensory integration. Based on the results of two novel psychophysical experiments, here we demonstrate the importance of the transient (instead of the sustained) channel for the integration of audiovisual signals. To account for the present results, we developed a biologically inspired, general-purpose model for multisensory integration, the multisensory correlation detectors, which combines correlated input from unimodal transient channels. Besides accounting for the results of our psychophysical experiments, this model could quantitatively replicate several recent findings in multisensory research, as tested against a large collection of published datasets. In particular, the model could simultaneously account for the perceived timing of audiovisual events, multisensory facilitation in detection tasks, causality judgments, and optimal integration. This study demonstrates that several phenomena in multisensory research that were previously considered unrelated, all stem from the integration of correlated input from unimodal transient channels.

    1. Computational and Systems Biology

    CausalXtract, a flexible pipeline to extract causal effects from live-cell time-lapse imaging data

    Franck Simon, Maria Colomba Comes ... Herve Isambert
    Tools and Resources

    Live-cell microscopy routinely provides massive amounts of time-lapse images of complex cellular systems under various physiological or therapeutic conditions. However, this wealth of data remains difficult to interpret in terms of causal effects. Here, we describe CausalXtract, a flexible computational pipeline that discovers causal and possibly time-lagged effects from morphodynamic features and cell–cell interactions in live-cell imaging data. CausalXtract methodology combines network-based and information-based frameworks, which is shown to discover causal effects overlooked by classical Granger and Schreiber causality approaches. We showcase the use of CausalXtract to uncover novel causal effects in a tumor-on-chip cellular ecosystem under therapeutically relevant conditions. In particular, we find that cancer-associated fibroblasts directly inhibit cancer cell apoptosis, independently from anticancer treatment. CausalXtract uncovers also multiple antagonistic effects at different time delays. Hence, CausalXtract provides a unique computational tool to interpret live-cell imaging data for a range of fundamental and translational research applications.