{"doi":"10.1101/2020.11.18.389189","title":"Efficient and precise single-cell reference atlas mapping with Symphony","abstract":"Abstract Recent advances in single-cell technologies and integration algorithms make it possible to construct comprehensive reference atlases encompassing many donors, studies, disease states, and sequencing platforms. Much like mapping sequencing reads to a reference genome, it is essential to be able to map query cells onto complex, multimillion-cell reference atlases to rapidly identify relevant cell states and phenotypes. We present Symphony ( https://github.com/immunogenomics/symphony ), an algorithm for building integrated reference atlases of millions of cells in a convenient, portable format that enables efficient query mapping within seconds. Symphony localizes query cells within a stable low-dimensional reference embedding, facilitating reproducible downstream transfer of reference-defined annotations to the query. We demonstrate the power of Symphony by (1) mapping a multi-donor, multi-species query to predict pancreatic cell types, (2) localizing query cells along a developmental trajectory of human fetal liver hematopoiesis, and (3) inferring surface protein expression with a multimodal CITE-seq atlas of memory T cells.","journal":"bioRxiv (Cold Spring Harbor Laboratory)","year":2020,"id":119022,"datarank":0.519860385419959,"base_score":3.4657359027997265,"endowment":3.4657359027997265,"self_citation_contribution":0.519860385419959,"citation_network_contribution":0.0,"self_endowment_contribution":0.519860385419959,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":31,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.8875,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2020-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":291784,"name":"Aparna Nathan","orcid":"0000-0002-5975-2851","position":1,"is_corresponding":false},{"id":226256,"name":"Fan Zhang","orcid":"0000-0002-6102-2970","position":2,"is_corresponding":false},{"id":76149,"name":"Nghia Millard","orcid":"0000-0002-0518-7674","position":3,"is_corresponding":false},{"id":552254,"name":"Laurie Rumker","orcid":"0000-0001-5522-3402","position":4,"is_corresponding":false},{"id":312635,"name":"D. Branch Moody","orcid":"0000-0003-2306-3058","position":5,"is_corresponding":false},{"id":76148,"name":"Ilya Korsunsky","orcid":"0000-0003-4848-3948","position":6,"is_corresponding":false},{"id":35259,"name":"Soumya Raychaudhuri","orcid":"0000-0002-1901-8265","position":7,"is_corresponding":false},{"id":312324,"name":"Joyce B. Kang","orcid":"0000-0002-1962-1291","position":0,"is_corresponding":true}],"reference_count":62,"raw_metadata":{"citation_network_status":"fetched"},"created_at":"2026-07-18T23:14:03.409507Z","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":[]}