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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s42003-021-02146-6.pdf
Description
Published version
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3.48 MB
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Checksum (MD5)
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Author(s)
He, Liang
Davila-Velderrain, Jose
Sumida, Tomokazu S
Hafler, David A
Kellis, Manolis
Kulminski, Alexander M
Date Issued
2021
Journal
Communications Biology
Publisher
Springer Science and Business Media LLC
Citation
He, Liang, Davila-Velderrain, Jose, Sumida, Tomokazu S, Hafler, David A, Kellis, Manolis et al. 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).
Version
Final published version
Abstract
AbstractThe 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.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
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Creative Commons Attribution 4.0 International license
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DOI of Published Version
https://doi.org/10.1038/S42003-021-02146-6
