{"doi":"10.1038/s42003-021-02146-6","title":"NEBULA is a fast negative binomial mixed model for differential or co-expression analysis of large-scale multi-subject single-cell data","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.","journal":"Communications Biology","year":2021,"id":8600,"datarank":4.107894661934613,"base_score":5.187385805840755,"endowment":5.187385805840755,"self_citation_contribution":0.7781078708761133,"citation_network_contribution":3.3297867910584995,"self_endowment_contribution":0.7781078708761133,"citer_contribution":3.3297867910584995,"corpus_percentile":null,"corpus_rank":null,"citation_count":178,"citer_count":157,"citers_with_citation_signal":119,"citers_with_endowment":119,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.0554,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2021-05-26","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1192,"name":"Jose Davila-Velderrain","orcid":null,"position":1,"is_corresponding":false},{"id":49152,"name":"Tomokazu S. Sumida","orcid":"0000-0002-9806-2642","position":2,"is_corresponding":false},{"id":37332,"name":"David A. Hafler","orcid":"0000-0003-4664-535X","position":3,"is_corresponding":false},{"id":14693,"name":"Sharon L. R. Kardia","orcid":"0000-0002-9853-3379","position":4,"is_corresponding":false},{"id":49162,"name":"Alexander M. Kulminski","orcid":"0000-0002-0205-8228","position":5,"is_corresponding":false},{"id":1204,"name":"José Dávila-Velderrain","orcid":"0000-0003-0271-6267","position":6,"is_corresponding":false},{"id":18245,"name":"Liang He","orcid":"0000-0001-6711-2021","position":0,"is_corresponding":true}],"reference_count":80,"raw_metadata":{"citation_network_status":"fetched"},"created_at":"2026-03-01T18:20:47.508186Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}