ArticleNature genetics2025
Analysis of multi-condition single-cell data with latent embedding multivariate regression.
Article in Nature genetics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- The Role of the T Cell Receptor Sequence in Shaping T Cell Functional Fate.Immunological reviews · 2026Review
- Condition-Associated Pattern Extraction and Recovery From Multi-Condition Single-Cell RNA-seq Data With CAPER.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Interpretation, extrapolation and perturbation of single cells.Nature reviews. Genetics · 2026Review
- Representation learning of single-cell RNA-seq data.RNA (New York, N.Y.) · 2026Review
- iAODE for benchmarking and continuum modeling of single-cell chromatin accessibility.Communications biology · 2026Article
- DiCoLo: Integration-free and cluster-free detection of localized differential gene co-expression in single-cell data.bioRxiv : the preprint server for biology · 2026Article
- An expanded role for single-cell chemical genomics profiling in drug discovery.The Biochemical journal · 2026Review
- On feature selection to disentangle cell type and state transcriptional programs.BMC genomics · 2025Article
- Deep-learning-based gene perturbation effect prediction does not yet outperform simple linear baselines.Nature methods · 2025Article
- Comparing phenotypic manifolds with Kompot: Detecting differential abundance and gene expression at single-cell resolution.bioRxiv : the preprint server for biology · 2025Article
- Population-scale skeletal muscle single-nucleus multi-omic profiling reveals extensive context specific genetic regulation.bioRxiv : the preprint server for biology · 2024Article
- A unified model for interpretable latent embedding of multi-sample, multi-condition single-cell data.Nature communications · 2024Article
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Authors and funding
2 authors.
Funding
Abstract
Identifying gene expression differences in heterogeneous tissues across conditions is a fundamental biological task, enabled by multi-condition single-cell RNA sequencing (RNA-seq). Current data analysis approaches divide the constituent cells into clusters meant to represent cell types, but such discrete categorization tends to be an unsatisfactory model of the underlying biology. Here, we introduce latent embedding multivariate regression (LEMUR), a model that operates without, or before, commitment to discrete categorization. LEMUR (1) integrates data from different conditions, (2) predicts each cell's gene expression changes as a function of the conditions and its position in latent space and (3) for each gene, identifies a compact neighborhood of cells with consistent differential expression. We apply LEMUR to cancer, zebrafish development and spatial gradients in Alzheimer's disease, demonstrating its broad applicability.
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Registered trials
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.