近日，德国亥姆霍兹慕尼黑环境与健康研究中心Fabian J. Theis、M. Colomé-Tatché等研究人员合作完成单细胞基因组学中图谱级数据集成的基准测试。相关论文于2021年12月23日在线发表在《自然—方法学》杂志上。

Author: Luecken, Malte D., Bttner, M., Chaichoompu, K., Danese, A., Interlandi, M., Mueller, M. F., Strobl, D. C., Zappia, L., Dugas, M., Colom-Tatch, M., Theis, Fabian J.

Issue&Volume: 2021-12-23

Abstract: Single-cell atlases often include samples that span locations, laboratories and conditions, leading to complex, nested batch effects in data. Thus, joint analysis of atlas datasets requires reliable data integration. To guide integration method choice, we benchmarked 68 method and preprocessing combinations on 85 batches of gene expression, chromatin accessibility and simulation data from 23 publications, altogether representing >1.2 million cells distributed in 13 atlas-level integration tasks. We evaluated methods according to scalability, usability and their ability to remove batch effects while retaining biological variation using 14 evaluation metrics. We show that highly variable gene selection improves the performance of data integration methods, whereas scaling pushes methods to prioritize batch removal over conservation of biological variation. Overall, scANVI, Scanorama, scVI and scGen perform well, particularly on complex integration tasks, while single-cell ATAC-sequencing integration performance is strongly affected by choice of feature space. Our freely available Python module and benchmarking pipeline can identify optimal data integration methods for new data, benchmark new methods and improve method development. This benchmarking study compares 16 methods for integrating complex single-cell RNA and ATAC datasets and provides a guide to method choice.

DOI: 10.1038/s41592-021-01336-8

Source: https://www.nature.com/articles/s41592-021-01336-8