{"doi":"10.1093/bioinformatics/btac168","title":"EDClust: an EM–MM hybrid method for cell clustering in multiple-subject single-cell RNA sequencing","abstract":"MOTIVATION: Single-cell RNA sequencing (scRNA-seq) has revolutionized biological research by enabling the measurement of transcriptomic profiles at the single-cell level. With the increasing application of scRNA-seq in larger-scale studies, the problem of appropriately clustering cells emerges when the scRNA-seq data are from multiple subjects. One challenge is the subject-specific variation; systematic heterogeneity from multiple subjects may have a significant impact on clustering accuracy. Existing methods seeking to address such effects suffer from several limitations. RESULTS: We develop a novel statistical method, EDClust, for multi-subject scRNA-seq cell clustering. EDClust models the sequence read counts by a mixture of Dirichlet-multinomial distributions and explicitly accounts for cell-type heterogeneity, subject heterogeneity and clustering uncertainty. An EM-MM hybrid algorithm is derived for maximizing the data likelihood and clustering the cells. We perform a series of simulation studies to evaluate the proposed method and demonstrate the outstanding performance of EDClust. Comprehensive benchmarking on four real scRNA-seq datasets with various tissue types and species demonstrates the substantial accuracy improvement of EDClust compared to existing methods. AVAILABILITY AND IMPLEMENTATION: The R package is freely available at https://github.com/weix21/EDClust. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.","journal":"Bioinformatics","year":2022,"id":280680,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":9,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9514,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2022-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":340429,"name":"Ziyi Li","orcid":"0000-0001-8359-0533","position":1,"is_corresponding":false},{"id":233094,"name":"Hongkai Ji","orcid":"0000-0002-6480-0141","position":2,"is_corresponding":false},{"id":21505,"name":"Hao Wu","orcid":"0000-0003-1269-7354","position":3,"is_corresponding":false},{"id":536617,"name":"Xin Wei","orcid":"0000-0002-9720-6771","position":0,"is_corresponding":true}],"reference_count":49,"raw_metadata":null,"created_at":"2026-07-19T00:29:03.247347Z","pmid":"35561178","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":[]}