Best practices for single-cell analysis across modalities

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 sp...

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Published in:	Nature reviews. Genetics Vol. 24; no. 8; pp. 550 - 572
Main Authors:	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
Format:	Journal Article
Language:	English
Published:	England Nature Publishing Group 01.08.2023
Subjects:	Chromatin Electrons Gene Expression Profiling - methods Proteomics Single-Cell Analysis - methods Transcriptomics
ISSN:	1471-0056, 1471-0064, 1471-0064
Online Access:	Get full text
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Abstract	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.
AbstractList	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.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. 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. 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.Practitioners in the field of single-cell omics are now faced with diverse options for analytical tools to process and integrate data from various molecular modalities. In an Expert Recommendation article, the authors provide guidance on robust single-cell data analysis, including choices of best-performing tools from benchmarking studies.
Author	Strobl, Daniel C Zappia, Luke Heumos, Lukas Curion, Fabiola Theis, Fabian J Lücken, Malte D Schaar, Anna C Lance, Christopher Drost, Felix Schiller, Herbert B Litinetskaya, Anastasia Henao, Juan
Author_xml	– sequence: 1 givenname: Lukas orcidid: 0000-0002-8937-3457 surname: Heumos fullname: Heumos, Lukas organization: TUM School of Life Sciences Weihenstephan, Technical University of Munich, Munich, Germany – sequence: 2 givenname: Anna C orcidid: 0000-0003-1063-005X surname: Schaar fullname: Schaar, Anna C organization: Munich Center for Machine Learning, Technical University of Munich, Garching, Germany – sequence: 3 givenname: Christopher orcidid: 0000-0002-1275-9802 surname: Lance fullname: Lance, Christopher organization: Department of Paediatrics, Dr von Hauner Children's Hospital, University Hospital, Ludwig-Maximilians-Universität München, Munich, Germany – sequence: 4 givenname: Anastasia surname: Litinetskaya fullname: Litinetskaya, Anastasia organization: Department of Mathematics, School of Computation, Information and Technology, Technical University of Munich, Garching, Germany – sequence: 5 givenname: Felix orcidid: 0000-0002-2600-4048 surname: Drost fullname: Drost, Felix organization: TUM School of Life Sciences Weihenstephan, Technical University of Munich, Munich, Germany – sequence: 6 givenname: Luke orcidid: 0000-0001-7744-8565 surname: Zappia fullname: Zappia, Luke organization: Department of Mathematics, School of Computation, Information and Technology, Technical University of Munich, Garching, Germany – sequence: 7 givenname: Malte D orcidid: 0000-0001-7464-7921 surname: Lücken fullname: Lücken, Malte D organization: Institute of Lung Health and Immunity, Helmholtz Munich, Munich, Germany – sequence: 8 givenname: Daniel C surname: Strobl fullname: Strobl, Daniel C organization: TranslaTUM, Center for Translational Cancer Research, Technical University of Munich, Munich, Germany – sequence: 9 givenname: Juan surname: Henao fullname: Henao, Juan organization: Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Munich, Germany – sequence: 10 givenname: Fabiola orcidid: 0000-0003-2502-8803 surname: Curion fullname: Curion, Fabiola organization: Department of Mathematics, School of Computation, Information and Technology, Technical University of Munich, Garching, Germany – sequence: 11 givenname: Herbert B surname: Schiller fullname: Schiller, Herbert B organization: Institute of Lung Health and Immunity and Comprehensive Pneumology Center, Helmholtz Munich; Member of the German Center for Lung Research (DZL), Munich, Germany – sequence: 12 givenname: Fabian J orcidid: 0000-0002-2419-1943 surname: Theis fullname: Theis, Fabian J email: fabian.theis@helmholtz-muenchen.de, fabian.theis@helmholtz-muenchen.de, fabian.theis@helmholtz-muenchen.de, fabian.theis@helmholtz-muenchen.de organization: Munich Center for Machine Learning, Technical University of Munich, Garching, Germany. fabian.theis@helmholtz-muenchen.de
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/37002403$$D View this record in MEDLINE/PubMed
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SubjectTerms	Chromatin Electrons Gene Expression Profiling - methods Proteomics Single-Cell Analysis - methods Transcriptomics
Title	Best practices for single-cell analysis across modalities
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