Efficient and precise single-cell reference atlas mapping with Symphony

Joyce B. Kang, Aparna Nathan, Kathryn Weinand, Fan Zhang, Nghia Millard, Laurie Rumker, D. Branch Moody, Ilya Korsunsky () and Soumya Raychaudhuri ()
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Joyce B. Kang: Center for Data Sciences, Brigham and Women’s Hospital
Aparna Nathan: Center for Data Sciences, Brigham and Women’s Hospital
Kathryn Weinand: Center for Data Sciences, Brigham and Women’s Hospital
Fan Zhang: Center for Data Sciences, Brigham and Women’s Hospital
Nghia Millard: Center for Data Sciences, Brigham and Women’s Hospital
Laurie Rumker: Center for Data Sciences, Brigham and Women’s Hospital
D. Branch Moody: Brigham and Women’s Hospital and Harvard Medical School
Ilya Korsunsky: Center for Data Sciences, Brigham and Women’s Hospital
Soumya Raychaudhuri: Center for Data Sciences, Brigham and Women’s Hospital

Nature Communications, 2021, vol. 12, issue 1, 1-21

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 large-scale, integrated reference atlases 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 in multiple real-world datasets, including (1) mapping a multi-donor, multi-species query to predict pancreatic cell types, (2) localizing query cells along a developmental trajectory of fetal liver hematopoiesis, and (3) inferring surface protein expression with a multimodal CITE-seq atlas of memory T cells.

Date: 2021
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DOI: 10.1038/s41467-021-25957-x

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