ArticleCommunications biology2021
NEBULA is a fast negative binomial mixed model for differential or co-expression analysis of large-scale multi-subject single-cell data.
Article in Communications biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 130 papers.
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Who cites it
130 citing papers in PubMed.
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- Canon enables causal inference of downstream genes in single-cell CRISPR studies via instrumental variable analysis.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
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- An inflammasome-driven differentiation program in intestinal stem cells protects against Salmonella infection.Nature immunology · 2026Article
- Single-nucleus analysis of menstrual fluid highlights gene expression differences in epithelial cells of endometriosis donors.BMC medicine · 2026Article
- Cell-Type Deconvolution of Equine BALF RNA-Seq: A Critical Comparison with Matched Single-Cell Data.Genes · 2026Article
- Scalable, fast and accurate differential gene expression testing from millions of cells of multiple patients.Nature communications · 2026Article
- Cross-presentation of citrullinated antigens drives cytotoxic CD8bioRxiv : the preprint server for biology · 2026Article
- CytoSignal detects locations and dynamics of ligand-receptor signaling at cellular resolution from spatial transcriptomic data.Nature genetics · 2026Article
- Expansion Revealing of Pathology Resolves Nanostructures Associated with Inflammatory Phenotypes in COVID-19 Decedent Human Brain Tissue.bioRxiv : the preprint server for biology · 2026Article
70 more citing papers are in PubMed but not listed here.
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6 authors.
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Abstract
The increasing availability of single-cell data revolutionizes the understanding of biological mechanisms at cellular resolution. For differential expression analysis in multi-subject single-cell data, negative binomial mixed models account for both subject-level and cell-level overdispersions, but are computationally demanding. Here, we propose an efficient NEgative Binomial mixed model Using a Large-sample Approximation (NEBULA). The speed gain is achieved by analytically solving high-dimensional integrals instead of using the Laplace approximation. We demonstrate that NEBULA is orders of magnitude faster than existing tools and controls false-positive errors in marker gene identification and co-expression analysis. Using NEBULA in Alzheimer's disease cohort data sets, we found that the cell-level expression of APOE correlated with that of other genetic risk factors (including CLU, CST3, TREM2, C1q, and ITM2B) in a cell-type-specific pattern and an isoform-dependent manner in microglia. NEBULA opens up a new avenue for the broad application of mixed models to large-scale multi-subject single-cell data.
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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 Socratic graph.