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

• Published in: Communications Biology Vol. 4; no. 1; pp. 629 - 17
• Main Authors: He, Liang, Davila-Velderrain, Jose, Sumida, Tomokazu S., Hafler, David A., Kellis, Manolis, Kulminski, Alexander M.
• Format: Journal Article
• Language: English
• Published: London Springer Science and Business Media LLC 26.05.2021; Nature Publishing Group UK; Nature Publishing Group; Nature Portfolio
• Subjects: 38/39; 38/91; 631/114/2397; 631/337/2019; 692/617/375/365/1283; Alzheimer Disease; Alzheimer Disease - genetics; Alzheimer's disease; Apolipoprotein E; Apolipoproteins E; Apolipoproteins E - genetics; Approximation; Binomial Distribution; Biology; Biology (General); Biomedical and Life Sciences; Cell culture; Computational Biology; Computational Biology - methods; Datasets; Gene Expression; Gene Expression - genetics; Gene Expression Profiling; Gene Expression Profiling - methods; Humans; Life Sciences; Microglia; Microglia - metabolism; Models, Statistical; Multiple sclerosis; Neurodegenerative diseases; QH301-705.5; Risk factors; Single-Cell Analysis; Single-Cell Analysis - methods; snRNA
• ISSN: 2399-3642; 2399-3642
• DOI: 10.1038/s42003-021-02146-6

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 application of negative binomial mixed models (NBMMs) to single-cell data is computationally demanding. To address this issue, Liang He et al. have developed NEBULA, an efficient algorithm that can analyze differential gene expression or co-expression networks in multi-subject single-cell data sets, and validate it on snRNA-seq and scRNA-seq data sets comprising ~200k cells from cohorts of Alzheimer’s disease and multiple sclerosis patients.