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

Publication ,  Journal Article
He, L; Davila-Velderrain, J; Sumida, TS; Hafler, DA; Kellis, M; Kulminski, AM
Published in: Communications biology
May 2021

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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Published In

Communications biology

DOI

EISSN

2399-3642

ISSN

2399-3642

Publication Date

May 2021

Volume

4

Issue

1

Start / End Page

629

Related Subject Headings

  • Single-Cell Analysis
  • Models, Statistical
  • Microglia
  • Humans
  • Gene Expression Profiling
  • Gene Expression
  • Computational Biology
  • Binomial Distribution
  • Apolipoproteins E
  • Alzheimer Disease
 

Citation

APA
Chicago
ICMJE
MLA
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He, L., Davila-Velderrain, J., Sumida, T. S., Hafler, D. A., Kellis, M., & Kulminski, A. M. (2021). NEBULA is a fast negative binomial mixed model for differential or co-expression analysis of large-scale multi-subject single-cell data. Communications Biology, 4(1), 629. https://doi.org/10.1038/s42003-021-02146-6
He, Liang, Jose Davila-Velderrain, Tomokazu S. Sumida, David A. Hafler, Manolis Kellis, and Alexander M. Kulminski. “NEBULA is a fast negative binomial mixed model for differential or co-expression analysis of large-scale multi-subject single-cell data.Communications Biology 4, no. 1 (May 2021): 629. https://doi.org/10.1038/s42003-021-02146-6.
He L, Davila-Velderrain J, Sumida TS, Hafler DA, Kellis M, Kulminski AM. NEBULA is a fast negative binomial mixed model for differential or co-expression analysis of large-scale multi-subject single-cell data. Communications biology. 2021 May;4(1):629.
He, Liang, et al. “NEBULA is a fast negative binomial mixed model for differential or co-expression analysis of large-scale multi-subject single-cell data.Communications Biology, vol. 4, no. 1, May 2021, p. 629. Epmc, doi:10.1038/s42003-021-02146-6.
He L, Davila-Velderrain J, Sumida TS, Hafler DA, Kellis M, Kulminski AM. NEBULA is a fast negative binomial mixed model for differential or co-expression analysis of large-scale multi-subject single-cell data. Communications biology. 2021 May;4(1):629.

Published In

Communications biology

DOI

EISSN

2399-3642

ISSN

2399-3642

Publication Date

May 2021

Volume

4

Issue

1

Start / End Page

629

Related Subject Headings

  • Single-Cell Analysis
  • Models, Statistical
  • Microglia
  • Humans
  • Gene Expression Profiling
  • Gene Expression
  • Computational Biology
  • Binomial Distribution
  • Apolipoproteins E
  • Alzheimer Disease