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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DOI：
10.1038/s41467-023-41855-w
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发表时间：
2023-10-14
影响因子：
16.6
通讯作者：
Papatheodorou, Irene
中科院分区：
文献类型：
--
作者：
Song, Yuyao;Miao, Zhichao;Brazma, Alvis;Papatheodorou, Irene
越来越多的来自不同物种的单细胞基因表达数据集为探索不同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尤其具有信息性。然而，为了做到这一点，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，我们在各种生物环境中对28种基因同源定位方法和数据集成算法的组合进行了基准测试。我们研究了每种策略的能力，以执行已知同源细胞类型的物种混合，并使用9个既定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度量来解决细胞类型可区分性的维护。总体而言，scANVI、scVI和SeuratV4方法在物种混合和生物保护之间取得了平衡。对于进化上距离较远的物种，包括近亲是有益的。SAMap在整合具有挑战性的基因同源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。越来越多的来自不同物种的单细胞rna测序数据集为探索不同物种细胞类型之间的进化关系创造了机会。在这里，作者比较了这些数据跨物种整合的不同策略，并提供了有效整合的指导方针。
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. The growing number of available single-cell RNA-sequencing datasets from different species creates opportunities to explore evolutionary relationships between cell types across species. Here, the authors compare different strategies for cross-species integration of these data and offer guidelines for effective integration.
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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：
16.2
作者：
Franjic D;Skarica M;Ma S;Arellano JI;Tebbenkamp ATN;Choi J;Xu C;Li Q;Morozov YM;Andrijevic D;Vrselja Z;Spajic A;Santpere G;Li M;Zhang S;Liu Y;Spurrier J;Zhang L;Gudelj I;Rapan L;Takahashi H;Huttner A;Fan R;Strittmatter SM;Sousa AMM;Rakic P;Sestan N
通讯作者：
Sestan N
跨模态单细胞分析的最佳实践。
DOI：
10.1038/s41576-023-00586-w
发表时间：
2023-08
期刊：
Nature reviews. Genetics
影响因子：
--
作者：
Heumos L;Schaar AC;Lance C;Litinetskaya A;Drost F;Zappia L;Lücken MD;Strobl DC;Henao J;Curion F;Single-cell Best Practices Consortium;Schiller HB;Theis FJ
通讯作者：
Theis FJ
影响因子：
5.5
作者：
Jiang M;Xiao Y;E W;Ma L;Wang J;Chen H;Gao C;Liao Y;Guo Q;Peng J;Han X;Guo G
通讯作者：
Guo G
影响因子：
48
作者：
Korsunsky, Ilya;Millard, Nghia;Raychaudhuri, Soumya
通讯作者：
Raychaudhuri, Soumya