{"doi":"10.1093/bib/bbab039","title":"Knowledge-based classification of fine-grained immune cell types in single-cell RNA-Seq data","abstract":"Single-cell RNA sequencing (scRNA-Seq) is an emerging strategy for characterizing immune cell populations. Compared to flow or mass cytometry, scRNA-Seq could potentially identify cell types and activation states that lack precise cell surface markers. However, scRNA-Seq is currently limited due to the need to manually classify each immune cell from its transcriptional profile. While recently developed algorithms accurately annotate coarse cell types (e.g. T cells versus macrophages), making fine distinctions (e.g. CD8+ effector memory T cells) remains a difficult challenge. To address this, we developed a machine learning classifier called ImmClassifier that leverages a hierarchical ontology of cell type. We demonstrate that its predictions are highly concordant with flow-based markers from CITE-seq and outperforms other tools (+15% recall, +14% precision) in distinguishing fine-grained cell types with comparable performance on coarse ones. Thus, ImmClassifier can be used to explore more deeply the heterogeneity of the immune system in scRNA-Seq experiments.","journal":"Briefings in Bioinformatics","year":2021,"id":187240,"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":18,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9474,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2021-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":11491,"name":"Sara J. C. Gosline","orcid":"0000-0002-6534-4774","position":1,"is_corresponding":false},{"id":393342,"name":"Lance Pflieger","orcid":"0000-0003-2237-5674","position":2,"is_corresponding":false},{"id":566450,"name":"Pierre Wallet","orcid":"0000-0001-6639-9526","position":3,"is_corresponding":false},{"id":580472,"name":"Archana Iyer","orcid":"0000-0001-5848-4675","position":4,"is_corresponding":false},{"id":225104,"name":"Justin Guinney","orcid":"0000-0003-1477-1888","position":5,"is_corresponding":false},{"id":449639,"name":"Andrea H. Bild","orcid":"0000-0001-7906-6675","position":6,"is_corresponding":false},{"id":237237,"name":"Jeffrey T. Chang","orcid":"0000-0002-4578-5636","position":7,"is_corresponding":false},{"id":746288,"name":"Xuan Liu","orcid":"0000-0003-1590-8276","position":0,"is_corresponding":true}],"reference_count":48,"raw_metadata":null,"created_at":"2026-07-18T23:48:51.315888Z","pmid":"33681983","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":[]}