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 |
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| Main Authors: | , , , , , , , , , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
London
Nature Publishing Group UK
01.08.2023
Nature Publishing Group |
| Subjects: | |
| 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.
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. |
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| 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.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. 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. 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. |
| Author | Schiller, Herbert B. Schaar, Anna C. Zappia, Luke Heumos, Lukas Curion, Fabiola Lücken, Malte D. Lance, Christopher Drost, Felix Strobl, Daniel C. Litinetskaya, Anastasia Henao, Juan Theis, Fabian J. |
| Author_xml | – sequence: 1 givenname: Lukas orcidid: 0000-0002-8937-3457 surname: Heumos fullname: Heumos, Lukas organization: Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Institute of Lung Health and Immunity and Comprehensive Pneumology Center, Helmholtz Munich; Member of the German Center for Lung Research (DZL), TUM School of Life Sciences Weihenstephan, Technical University of Munich – sequence: 2 givenname: Anna C. orcidid: 0000-0003-1063-005X surname: Schaar fullname: Schaar, Anna C. organization: Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Department of Mathematics, School of Computation, Information and Technology, Technical University of Munich, Munich Center for Machine Learning, Technical University of Munich – sequence: 3 givenname: Christopher orcidid: 0000-0002-1275-9802 surname: Lance fullname: Lance, Christopher organization: Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Department of Paediatrics, Dr von Hauner Children’s Hospital, University Hospital, Ludwig-Maximilians-Universität München – sequence: 4 givenname: Anastasia surname: Litinetskaya fullname: Litinetskaya, Anastasia organization: Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Department of Mathematics, School of Computation, Information and Technology, Technical University of Munich – sequence: 5 givenname: Felix orcidid: 0000-0002-2600-4048 surname: Drost fullname: Drost, Felix organization: Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, TUM School of Life Sciences Weihenstephan, Technical University of Munich – sequence: 6 givenname: Luke orcidid: 0000-0001-7744-8565 surname: Zappia fullname: Zappia, Luke organization: Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Department of Mathematics, School of Computation, Information and Technology, Technical University of Munich – sequence: 7 givenname: Malte D. orcidid: 0000-0001-7464-7921 surname: Lücken fullname: Lücken, Malte D. organization: Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Institute of Lung Health and Immunity, Helmholtz Munich – sequence: 8 givenname: Daniel C. surname: Strobl fullname: Strobl, Daniel C. organization: Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, TUM School of Life Sciences Weihenstephan, Technical University of Munich, Institute of Clinical Chemistry and Pathobiochemistry, School of Medicine, Technical University of Munich, TranslaTUM, Center for Translational Cancer Research, Technical University of Munich – sequence: 9 givenname: Juan surname: Henao fullname: Henao, Juan organization: Institute of Computational Biology, Department of Computational Health, Helmholtz Munich – sequence: 10 givenname: Fabiola orcidid: 0000-0003-2502-8803 surname: Curion fullname: Curion, Fabiola organization: Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, Department of Mathematics, School of Computation, Information and Technology, Technical University of Munich – sequence: 12 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) – sequence: 13 givenname: Fabian J. orcidid: 0000-0002-2419-1943 surname: Theis fullname: Theis, Fabian J. email: fabian.theis@helmholtz-muenchen.de organization: Institute of Computational Biology, Department of Computational Health, Helmholtz Munich, TUM School of Life Sciences Weihenstephan, Technical University of Munich, Department of Mathematics, School of Computation, Information and Technology, Technical University of Munich, Munich Center for Machine Learning, Technical University of Munich |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/37002403$$D View this record in MEDLINE/PubMed |
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| Contributor | Ansari, Meshal Ostner, Johannes Schubert, Benjamin Büttner, Maren Frishberg, Amit Martens, Laura D Jones, Matthew G Ramírez-Suástegui, Ciro Badia-I-Mompel, Pau Saez-Rodriguez, Julio Sarkar, Hirak Dony, Leander Palla, Giovanni Tanevski, Jovan He, Dongze Aliee, Hananeh Müller, Christian L Weiler, Philipp Nitzan, Mor Patro, Rob Virshup, Isaac Dimitrov, Daniel Lotfollahi, Mohammad Srivastava, Avi Piran, Zoe Dann, Emma Hediyeh-Zadeh, Soroor Sikkema, Lisa Hetzel, Leon Ibarra, Ignacio L |
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| Copyright | Springer Nature Limited 2023 Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. 2023. Springer Nature Limited. Copyright Nature Publishing Group Aug 2023 Springer Nature Limited 2023, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| Copyright_xml | – notice: Springer Nature Limited 2023 Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. – notice: 2023. Springer Nature Limited. – notice: Copyright Nature Publishing Group Aug 2023 – notice: Springer Nature Limited 2023, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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| Snippet | Recent advances in single-cell technologies have enabled high-throughput molecular profiling of cells across modalities and locations. Single-cell... |
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| Title | Best practices for single-cell analysis across modalities |
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