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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...
Autores principales: | , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10066026/ https://www.ncbi.nlm.nih.gov/pubmed/37002403 http://dx.doi.org/10.1038/s41576-023-00586-w |
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author | 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. |
author_facet | 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. |
author_sort | Heumos, Lukas |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-10066026 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-100660262023-04-03 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. Nat Rev Genet Expert Recommendation 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. Nature Publishing Group UK 2023-03-31 /pmc/articles/PMC10066026/ /pubmed/37002403 http://dx.doi.org/10.1038/s41576-023-00586-w Text en © 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. This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | Expert Recommendation 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. Best practices for single-cell analysis across modalities |
title | Best practices for single-cell analysis across modalities |
title_full | Best practices for single-cell analysis across modalities |
title_fullStr | Best practices for single-cell analysis across modalities |
title_full_unstemmed | Best practices for single-cell analysis across modalities |
title_short | Best practices for single-cell analysis across modalities |
title_sort | best practices for single-cell analysis across modalities |
topic | Expert Recommendation |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10066026/ https://www.ncbi.nlm.nih.gov/pubmed/37002403 http://dx.doi.org/10.1038/s41576-023-00586-w |
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