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Benchmarking strategies for cross-species integration of single-cell RNA sequencing data
The growing number of available single-cell gene expression datasets from different species creates opportunities to explore evolutionary relationships between cell types across species. Cross-species integration of single-cell RNA-sequencing data has been particularly informative in this context. H...
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/PMC10576752/ https://www.ncbi.nlm.nih.gov/pubmed/37838716 http://dx.doi.org/10.1038/s41467-023-41855-w |
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author | Song, Yuyao Miao, Zhichao Brazma, Alvis Papatheodorou, Irene |
author_facet | Song, Yuyao Miao, Zhichao Brazma, Alvis Papatheodorou, Irene |
author_sort | Song, Yuyao |
collection | PubMed |
description | The growing number of available single-cell gene expression datasets from different species creates opportunities to explore evolutionary relationships between cell types across species. Cross-species integration of single-cell RNA-sequencing data has been particularly informative in this context. However, in order to do so robustly it is essential to have rigorous benchmarking and appropriate guidelines to ensure that integration results truly reflect biology. Here, we benchmark 28 combinations of gene homology mapping methods and data integration algorithms in a variety of biological settings. We examine the capability of each strategy to perform species-mixing of known homologous cell types and to preserve biological heterogeneity using 9 established metrics. We also develop a new biology conservation metric to address the maintenance of cell type distinguishability. Overall, scANVI, scVI and SeuratV4 methods achieve a balance between species-mixing and biology conservation. For evolutionarily distant species, including in-paralogs is beneficial. SAMap outperforms when integrating whole-body atlases between species with challenging gene homology annotation. We provide our freely available cross-species integration and assessment pipeline to help analyse new data and develop new algorithms. |
format | Online Article Text |
id | pubmed-10576752 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-105767522023-10-16 Benchmarking strategies for cross-species integration of single-cell RNA sequencing data Song, Yuyao Miao, Zhichao Brazma, Alvis Papatheodorou, Irene Nat Commun Article The growing number of available single-cell gene expression datasets from different species creates opportunities to explore evolutionary relationships between cell types across species. Cross-species integration of single-cell RNA-sequencing data has been particularly informative in this context. However, in order to do so robustly it is essential to have rigorous benchmarking and appropriate guidelines to ensure that integration results truly reflect biology. Here, we benchmark 28 combinations of gene homology mapping methods and data integration algorithms in a variety of biological settings. We examine the capability of each strategy to perform species-mixing of known homologous cell types and to preserve biological heterogeneity using 9 established metrics. We also develop a new biology conservation metric to address the maintenance of cell type distinguishability. Overall, scANVI, scVI and SeuratV4 methods achieve a balance between species-mixing and biology conservation. For evolutionarily distant species, including in-paralogs is beneficial. SAMap outperforms when integrating whole-body atlases between species with challenging gene homology annotation. We provide our freely available cross-species integration and assessment pipeline to help analyse new data and develop new algorithms. Nature Publishing Group UK 2023-10-14 /pmc/articles/PMC10576752/ /pubmed/37838716 http://dx.doi.org/10.1038/s41467-023-41855-w Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Song, Yuyao Miao, Zhichao Brazma, Alvis Papatheodorou, Irene Benchmarking strategies for cross-species integration of single-cell RNA sequencing data |
title | Benchmarking strategies for cross-species integration of single-cell RNA sequencing data |
title_full | Benchmarking strategies for cross-species integration of single-cell RNA sequencing data |
title_fullStr | Benchmarking strategies for cross-species integration of single-cell RNA sequencing data |
title_full_unstemmed | Benchmarking strategies for cross-species integration of single-cell RNA sequencing data |
title_short | Benchmarking strategies for cross-species integration of single-cell RNA sequencing data |
title_sort | benchmarking strategies for cross-species integration of single-cell rna sequencing data |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10576752/ https://www.ncbi.nlm.nih.gov/pubmed/37838716 http://dx.doi.org/10.1038/s41467-023-41855-w |
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