NEBULA is a fast negative binomial mixed model for differential or co-expression analysis of large-scale multi-subject single-cell data
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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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Differential gene expression by NBMM modeled per-gene expression levels per cell type with random effects for within-individual and between-individual effects and fixed effects including APOE4, controlling for amyloid, nft, age_death, and batch.
“Per-gene expression levels per cell type were modeled as a negative binomial model with random effects that model both within-individual and between-individual effects, as implemented in the R package NEBULA (version 1.2.1)40”
From APOE4 impairs myelination via cholesterol dysregulation in oligodendrocytes (2022) · cites this paper as [40]
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- Authors
- He, L., Davila-Velderrain, J., Sumida, T. S., Hafler, D. A., Kellis, M., & Kulminski, A. M.
- Affiliations
- Duke University (US)
- Yale University (US)
- Broad Institute (US)
- Corresponding author
- He, Liang
- Kellis, Manolis
- Kulminski, Alexander M.
- Venue
- Communications Biology
- Published
- 2021-05-26
- Volume
- 4
- Issue
- 1
- Pages
- 629-629
- ISSN
- 2399-3642 (ISSN-L)
- PMC
- PMC8155058
- OpenAlex
- W3164861786
- Cited by
- 181
- References
- 85
- Citations by year
- 2026: 42 · 2025: 40 · 2024: 48 · 2023: 27 · 2022: 19 · 2021: 4 · 2020: 1
- FWCI
- 8.67
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Communications Biology (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc)
- Funders
- National Institutes of Health (5r01ag070488-05, UL1TR001863)
- National Institute on Aging (R01-AG070488, R01AG017917, AG058002, R01AG030146, R01AG058002, R01AG062335, R01AG036836, RF1AG054012, R01AG065477, R01AG048015, U01AG046152, R01AG036042, R01AG015819, P30AG010161, RF1AG062377, U01AG032984, U01AG061356, RC2AG036547, U01AG046161, R01AG061853, RF1AG057473, R01AG067151)
- National Institute of Mental Health (U01MH119509, R01MH109978)
- National Cancer Institute (R01CA227473, P50CA121974)
- National Institute of Allergy and Infectious Diseases (R01AI072222, P01AI073748, U24AI118672, P01AI039671, U19AI089992)
- National Institute of Neurological Disorders and Stroke (R25NS079193, U01NS110453, UG3NS115064)
- National Center for Advancing Translational Sciences
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Keywords
- nebulae, differential expression analysis, co-expression analysis, large sample approximation, computational efficiency, marker gene identification, Alzheimer's disease, apolipoprotein E, microglia, cell type-specific gene expression, isoform expression, genetic risk factors
- MeSH terms
- Alzheimer Disease, Apolipoproteins E, Humans, Models, Statistical, Gene Expression, Binomial Distribution, Microglia, Computational Biology, Gene Expression Profiling, Single-Cell Analysis
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