PUBLICATION
Analysis of multi-condition single-cell data with latent embedding multivariate regression
- Authors
- Ahlmann-Eltze, C., Huber, W.
- ID
- ZDB-PUB-250109-147
- Date
- 2025
- Source
- Nature Genetics : (Journal)
- Registered Authors
- Keywords
- none
- MeSH Terms
-
- Animals
- Neoplasms/genetics
- Multivariate Analysis
- Sequence Analysis, RNA/methods
- Zebrafish*/genetics
- Single-Cell Analysis*/methods
- Regression Analysis
- Gene Expression Profiling/methods
- Alzheimer Disease/genetics
- Humans
- PubMed
- 39753773 Full text @ Nat. Genet.
Citation
Ahlmann-Eltze, C., Huber, W. (2025) Analysis of multi-condition single-cell data with latent embedding multivariate regression. Nature Genetics. :.
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.
Genes / Markers
Expression
Phenotype
Mutations / Transgenics
Human Disease / Model
Sequence Targeting Reagents
Fish
Orthology
Engineered Foreign Genes
Mapping