A multi-use deep learning method for CITE-seq and single-cell RNA-seq data integration with cell surface protein prediction and imputation.

Lakkis, Justin; Schroeder, Amelia; Su, Kenong; Lee, Michelle Y Y; Bashore, Alexander C; Reilly, Muredach P; Li, Mingyao

Lakkis, Justin; Schroeder, Amelia; Su, Kenong; Lee, Michelle Y Y; Bashore, Alexander C; Reilly, Muredach P; Li, Mingyao.

Afiliación

Lakkis J; Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.
Schroeder A; Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.
Su K; Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.
Lee MYY; Graduate Group in Genomics and Computational Biology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.
Bashore AC; Division of Cardiology, Department of Medicine, Columbia University Irving Medical Center, New York, NY 10032, USA.
Reilly MP; Division of Cardiology, Department of Medicine, Columbia University Irving Medical Center, New York, NY 10032, USA.
Li M; Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA.

Nat Mach Intell ; 4(11): 940-952, 2022 Nov.

Article en En | MEDLINE | ID: mdl-36873621

Palabras clave

CITE-seq; deep learning; protein prediction; single-cell RNA-seq; single-cell multi-omics

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Nat Mach Intell Año: 2022 Tipo del documento: Article País de afiliación: Estados Unidos

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