ArticleeLife2023
Multicellular factor analysis of single-cell data for a tissue-centric understanding of disease.
Article in eLife, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.
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Who cites it
36 citing papers in PubMed.
- Article
- Semi-supervised Omics Factor Analysis (SOFA) disentangles known and latent sources of variation in multi-omic data.Nature communications · 2026Article
- Spatial transcriptomics identifies immune-stromal niches associated with cancer in adult dermatomyositis.Nature communications · 2026Article
- Trajectory-guided dimensionality reduction for multi-sample single-cell RNA-seq data reveals biologically relevant sample-level heterogeneity.Bioinformatics (Oxford, England) · 2026Article
- A transcriptional patient map of systemic lupus erythematosus reveals disease-related multicellular immune programs conserved between blood and kidney.bioRxiv : the preprint server for biology · 2026Article
- Interpretation, extrapolation and perturbation of single cells.Nature reviews. Genetics · 2026Review
- Tracing cell communication programs across conditions at single cell resolution with CCC-RISE.bioRxiv : the preprint server for biology · 2026Article
- Lessons from single cell omics: admixed American ancestry and sex confer cardiometabolic disease risk in Mexicans.Genome medicine · 2026Article
- Shared multicellular injury programs of acute and chronic kidney disease enable mechanistic patient stratification.medRxiv : the preprint server for health sciences · 2026Article
- Multi-view deep learning of highly multiplexed imaging data improves association of cell states with clinical outcomes.Bioinformatics advances · 2026Article
- Article
- A cross-study transcriptional patient map of heart failure defines conserved multicellular coordination in cardiac remodeling.Nature communications · 2025Article
- Article
- Coordinated, multicellular patterns of transcriptional variation that stratify patient cohorts are revealed by tensor decomposition.Nature biotechnology · 2025Article
- scACCorDiON: a clustering approach for explainable patient level cell-cell communication graph analysis.Bioinformatics (Oxford, England) · 2025Article
- Integrating Spatially-Resolved Transcriptomics Data Across Tissues and Individuals: Challenges and Opportunities.Small methods · 2025Review
- New Insights and Implications of Cell-Cell Interactions in Developmental Biology.International journal of molecular sciences · 2025Review
- Cell-type-specific roles of FOXP1 in the excitatory neuronal lineage during early neocortical murine development.Cell reports · 2025Article
- Spatial Transcriptomics Identifies Immune-Stromal Niches Associated with Cancer in Adult Dermatomyositis.bioRxiv : the preprint server for biology · 2025Article
- Review
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Authors and funding
5 authors.
Funding
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.
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