ArticleNature methods2025
Deep generative modeling of sample-level heterogeneity in single-cell genomics.
Article in Nature methods, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.
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Who cites it
23 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Towards a consensus atlas of human and mouse adipose tissue at single-cell resolution.Nature metabolism · 2025Guideline
- CytoVI: deep generative modeling of antibody-based single cell data.Nature methods · 2026Article
- Review
- Data-centric feedback loops for next-generation immunotherapy development.Nature biomedical engineering · 2026Review
- Article
- ARCADIA reveals spatially dependent transcriptional programs through integration of scRNA-seq and spatial proteomics.Bioinformatics (Oxford, England) · 2026Article
- Scalable genotyping in fixed transcriptomes resolves clonal heterogeneity via single-cell sequencing.bioRxiv : the preprint server for biology · 2026Article
- Article
- Multiplexed single-cell transcriptomics reveals diverse phenotypic outcomes for pathogenic SHP2 variants.Science advances · 2026Article
- A transcriptomics-native foundation model for universal cell representation and virtual cell synthesis.bioRxiv : the preprint server for biology · 2026Article
- Single-cell and spatial profiling in cancer biology and clinical oncology.Nature cancer · 2026Review
- Patches: A Representation Learning Framework for Decoding Shared and Condition-Specific Transcriptional Programs in Wound Healing.bioRxiv : the preprint server for biology · 2026Article
- Mapping kinase-dependent tumor immune adaptation with multiplexed single-cell CRISPR screens.bioRxiv : the preprint server for biology · 2026Article
- An expanded role for single-cell chemical genomics profiling in drug discovery.The Biochemical journal · 2026Review
- VIST: variational inference for single cell time series.Genome biology · 2026Article
- ARCADIA Reveals Spatially Dependent Transcriptional Programs through Integration of scRNA-seq and Spatial Proteomics.bioRxiv : the preprint server for biology · 2025Article
- Variational inference of single cell time series.bioRxiv : the preprint server for biology · 2025Article
- A variational deep-learning approach to modeling memory T cell dynamics.PLoS computational biology · 2025Article
- Comparing phenotypic manifolds with Kompot: Detecting differential abundance and gene expression at single-cell resolution.bioRxiv : the preprint server for biology · 2025Article
- A heterogeneous pharmaco-transcriptomic landscape induced by targeting a single oncogenic kinase.bioRxiv : the preprint server for biology · 2025Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
Single-cell genomic studies were recently conducted on hundred of samples exhibiting complex designs. These data have tremendous potential for discovering how sample- or tissue-level phenotypes relate to cellular and molecular composition. However, current analyses are often based on simplified representations of these data by averaging information across cells. We present multi-resolution variational inference (MrVI), a deep generative model designed to realize the potential of cohort studies at the single-cell level. MrVI tackles two fundamental, intertwined problems: stratifying samples into groups and evaluating the cellular and molecular differences between groups, without requiring predefined cell states. Leveraging its single-cell perspective, MrVI detects clinically relevant stratifications of cohorts of people with COVID-19 or inflammatory bowel disease that are manifested in only certain cellular subsets, enabling new discoveries that would otherwise be overlooked. MrVI can de novo identify groups of small molecules with similar biochemical properties and evaluate their effects on cellular composition and gene expression in large-scale perturbation studies. MrVI is an open-source tool at scvi-tools.org .
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.