Benchmarking strategies for cross-species integration of single-cell RNA sequencing data
Benchmarking strategies for cross-species integration of single-cell RNA sequencing data
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单细胞 RNA 测序数据跨物种整合的基准策略
DOI：
10.1101/2022.09.27.509674
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发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Song Y
中科院分区：
文献类型：
--
作者：
Song Y
越来越多的来自不同物种的可用单细胞基因表达数据集为探索跨物种细胞类型之间的进化关系创造了机会。在这种情况下，单细胞RNA测序数据的跨物种整合特别有用。然而，为了有力地做到这一点，必须有严格的基准和适当的指导方针，以确保整合结果真正反映生物学。在这里，我们基准测试28种组合的基因同源性映射方法和数据集成算法在各种生物环境。我们研究了每种策略执行已知同源细胞类型的物种混合的能力，并使用9个已建立的指标来保持生物异质性。我们还开发了一种新的生物保护指标，以解决细胞类型可重复性的维持问题。总的来说，scANVI，scVI和SeuratV4方法实现了物种混合和生物保护之间的平衡。对于进化上遥远的物种，包括旁系同源是有益的。SAMap在整合具有挑战性基因同源性注释的物种之间的全身图谱时表现出色。我们提供免费的跨物种整合和评估管道，以帮助分析新数据和开发新算法。
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.
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影响因子：
9.3
作者：
Baron M;Veres A;Wolock SL;Faust AL;Gaujoux R;Vetere A;Ryu JH;Wagner BK;Shen-Orr SS;Klein AM;Melton DA;Yanai I
通讯作者：
Yanai I
影响因子：
64.5
作者：
Tim Stuart;Andrew Butler;Paul J. Hoffman;Christoph Hafemeister;Efthymia Papalexi;William M. Mauck;
通讯作者：
Tim Stuart;Andrew Butler;Paul J. Hoffman;Christoph Hafemeister;Efthymia Papalexi;William M. Mauck;
影响因子：
48
作者：
Luecken MD;Büttner M;Chaichoompu K;Danese A;Interlandi M;Mueller MF;Strobl DC;Zappia L;Dugas M;Colomé-Tatché M;Theis FJ
通讯作者：
Theis FJ
影响因子：
64.5
作者：
Hao Y;Hao S;Andersen-Nissen E;Mauck WM 3rd;Zheng S;Butler A;Lee MJ;Wilk AJ;Darby C;Zager M;Hoffman P;Stoeckius M;Papalexi E;Mimitou EP;Jain J;Srivastava A;Stuart T;Fleming LM;Yeung B;Rogers AJ;McElrath JM;Blish CA;Gottardo R;Smibert P;Satija R
通讯作者：
Satija R