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Malte D. Luecken, Maren Büttner, Kridsadakorn Chaichoompu, Anna Danese, Marta Interlandi, M. F. Mueller, Daniel C. Strobl, Luke Zappia, Martin Dugas, Maria Colomé‐Tatché, Fabian Joachim Theis
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.
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Luecken, M. D., Büttner, M., Chaichoompu, K., Danese, A., Interlandi, M., Mueller, M. F., Strobl, D. C., Zappia, L., Dugas, M., Colomé‐Tatché, M., & Theis, F. J. (2021). Benchmarking atlas-level data integration in single-cell genomics. Nature Methods, 19(1), 41–50. https://doi.org/10.1038/s41592-021-01336-8
Luecken, Malte D., et al. "Benchmarking atlas-level data integration in single-cell genomics." Nature Methods, vol. 19, no. 1, 2021, pp. 41–50, https://doi.org/10.1038/s41592-021-01336-8.
Luecken, Malte D., Maren Büttner, Kridsadakorn Chaichoompu, Anna Danese, Marta Interlandi, M. F. Mueller, Daniel C. Strobl, et al. "Benchmarking atlas-level data integration in single-cell genomics." Nature Methods 19, no. 1 (2021): 41–50. https://doi.org/10.1038/s41592-021-01336-8.
Luecken M. D., Büttner M., Chaichoompu K. [и др.] Benchmarking atlas-level data integration in single-cell genomics // Nature Methods. — 2021. — Vol. 19, № 1. — P. 41–50. — URL: https://doi.org/10.1038/s41592-021-01336-8
@article{luecken2021benchmarking,
title = {{Benchmarking atlas-level data integration in single-cell genomics}},
author = {Malte D. Luecken and Maren Büttner and Kridsadakorn Chaichoompu and Anna Danese and Marta Interlandi and M. F. Mueller and Daniel C. Strobl and Luke Zappia and Martin Dugas and Maria Colomé‐Tatché and Fabian Joachim Theis},
journal = {Nature Methods},
year = {2021},
volume = {19},
number = {1},
pages = {41--50},
doi = {10.1038/s41592-021-01336-8},
url = {https://doi.org/10.1038/s41592-021-01336-8}
}TY - JOUR AU - Luecken, Malte D. AU - Büttner, Maren AU - Chaichoompu, Kridsadakorn AU - Danese, Anna AU - Interlandi, Marta AU - Mueller, M. F. AU - Strobl, Daniel C. AU - Zappia, Luke AU - Dugas, Martin AU - Colomé‐Tatché, Maria AU - Theis, Fabian Joachim TI - Benchmarking atlas-level data integration in single-cell genomics JO - Nature Methods VL - 19 IS - 1 SP - 41 EP - 50 PY - 2021 DA - 2021/12/23 DO - 10.1038/s41592-021-01336-8 UR - https://doi.org/10.1038/s41592-021-01336-8 ER -
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