Cited by 194Single-cell and spatial transcriptomicsGene expression and cancer classificationGene Regulatory Network Analysis

NEBULA is a fast negative binomial mixed model for differential or co-expression analysis of large-scale multi-subject single-cell data

Open access
He, Liang, Davila-Velderrain, Jose, Sumida, Tomokazu S., Hafler, David A., Kellis, Manolis, Kulminski, Alexander M.·2021·Communications Biology

Published version of a preprint

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.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1038/s42003-021-02146-6

Cites

87 of 87 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • 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”

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 1

  1. [60]Huber, P. J. Robust Statistics. (John Wiley & Sons, 2004).

Cited by

Sorted by citations

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)
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
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

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review