Efficient and precise single-cell reference atlas mapping with Symphony Joyce B. Kang1-5, Aparna Nathan1-5, Fan Zhang1-5, Nghia Millard1-5, Laurie Rumker1-5, D. Branch Moody3, Ilya Korsunsky1-5**, Soumya Raychaudhuri1-6** 1 Center for Data Sciences, Brigham and Women’s Hospital, Boston, MA, USA 2 Division of Genetics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA 3 Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA 4 Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA 5 Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA 6 Versus Arthritis Centre for Genetics and Genomics, Centre for Musculoskeletal Research, Manchester Academic Health Science Centre, The University of Manchester, Manchester, UK ** These authors jointly supervised this work 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, 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.