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Best practices for single-cell analysis across modalities.
Heumos, Lukas; Schaar, Anna C; Lance, Christopher; Litinetskaya, Anastasia; Drost, Felix; Zappia, Luke; Lücken, Malte D; Strobl, Daniel C; Henao, Juan; Curion, Fabiola; Schiller, Herbert B; Theis, Fabian J.
Afiliação
  • Heumos L; Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany.
  • Schaar AC; Institute of Lung Health and Immunity and Comprehensive Pneumology Center, Helmholtz Munich; Member of the German Center for Lung Research (DZL), Munich, Germany.
  • Lance C; TUM School of Life Sciences Weihenstephan, Technical University of Munich, Munich, Germany.
  • Litinetskaya A; Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany.
  • Drost F; Department of Mathematics, School of Computation, Information and Technology, Technical University of Munich, Garching, Germany.
  • Zappia L; Munich Center for Machine Learning, Technical University of Munich, Garching, Germany.
  • Lücken MD; Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany.
  • Strobl DC; Department of Paediatrics, Dr von Hauner Children's Hospital, University Hospital, Ludwig-Maximilians-Universität München, Munich, Germany.
  • Henao J; Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany.
  • Curion F; Department of Mathematics, School of Computation, Information and Technology, Technical University of Munich, Garching, Germany.
  • Schiller HB; TUM School of Life Sciences Weihenstephan, Technical University of Munich, Munich, Germany.
  • Theis FJ; Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany.
Nat Rev Genet ; 24(8): 550-572, 2023 08.
Article em En | MEDLINE | ID: mdl-37002403
Recent advances in single-cell technologies have enabled high-throughput molecular profiling of cells across modalities and locations. Single-cell transcriptomics data can now be complemented by chromatin accessibility, surface protein expression, adaptive immune receptor repertoire profiling and spatial information. The increasing availability of single-cell data across modalities has motivated the development of novel computational methods to help analysts derive biological insights. As the field grows, it becomes increasingly difficult to navigate the vast landscape of tools and analysis steps. Here, we summarize independent benchmarking studies of unimodal and multimodal single-cell analysis across modalities to suggest comprehensive best-practice workflows for the most common analysis steps. Where independent benchmarks are not available, we review and contrast popular methods. Our article serves as an entry point for novices in the field of single-cell (multi-)omic analysis and guides advanced users to the most recent best practices.
Assuntos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Perfilação da Expressão Gênica / Proteômica Tipo de estudo: Guideline Idioma: En Revista: Nat Rev Genet Assunto da revista: GENETICA Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Alemanha

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Perfilação da Expressão Gênica / Proteômica Tipo de estudo: Guideline Idioma: En Revista: Nat Rev Genet Assunto da revista: GENETICA Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Alemanha