Benchmarking single-cell multi-modal data integrations

Shaliu Fu^#^{1

2

3}, Shuguang Wang^#^{1

2

3}, Duanmiao Si¹, Gaoyang Li¹, Yawei Gao¹, Qi Liu^{4

5

6}

Affiliations

¹ State Key Laboratory of Cardiology and Medical Innovation Center, Shanghai East Hospital, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, China.
² Department of Hematology, Tongji Hospital, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, China.
³ National Key Laboratory of Autonomous Intelligent Unmanned Systems, Frontiers Science Center for Intelligent Autonomous Systems, Ministry of Education, Shanghai Research Institute for Intelligent Autonomous Systems, Shanghai, China.
⁴ State Key Laboratory of Cardiology and Medical Innovation Center, Shanghai East Hospital, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, China. qiliu@tongji.edu.cn.
⁵ Department of Hematology, Tongji Hospital, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, China. qiliu@tongji.edu.cn.
⁶ National Key Laboratory of Autonomous Intelligent Unmanned Systems, Frontiers Science Center for Intelligent Autonomous Systems, Ministry of Education, Shanghai Research Institute for Intelligent Autonomous Systems, Shanghai, China. qiliu@tongji.edu.cn.

^# Contributed equally.

PMID: 40640531
DOI: 10.1038/s41592-025-02737-9

Benchmarking single-cell multi-modal data integrations

Shaliu Fu et al. Nat Methods. 2025.

. 2025 Jul 10.

doi: 10.1038/s41592-025-02737-9. Online ahead of print.

Authors

Shaliu Fu^#^{1

2

3}, Shuguang Wang^#^{1

2

3}, Duanmiao Si¹, Gaoyang Li¹, Yawei Gao¹, Qi Liu^{4

5

6}

Affiliations

¹ State Key Laboratory of Cardiology and Medical Innovation Center, Shanghai East Hospital, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, China.
² Department of Hematology, Tongji Hospital, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, China.
³ National Key Laboratory of Autonomous Intelligent Unmanned Systems, Frontiers Science Center for Intelligent Autonomous Systems, Ministry of Education, Shanghai Research Institute for Intelligent Autonomous Systems, Shanghai, China.
⁴ State Key Laboratory of Cardiology and Medical Innovation Center, Shanghai East Hospital, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, China. qiliu@tongji.edu.cn.
⁵ Department of Hematology, Tongji Hospital, Frontier Science Center for Stem Cell Research, Bioinformatics Department, School of Life Sciences and Technology, Tongji University, Shanghai, China. qiliu@tongji.edu.cn.
⁶ National Key Laboratory of Autonomous Intelligent Unmanned Systems, Frontiers Science Center for Intelligent Autonomous Systems, Ministry of Education, Shanghai Research Institute for Intelligent Autonomous Systems, Shanghai, China. qiliu@tongji.edu.cn.

^# Contributed equally.

PMID: 40640531
DOI: 10.1038/s41592-025-02737-9

Abstract

Recent advances have enabled the generation of both unpaired (separate profiling) and paired (simultaneous measurement) single-cell multi-modal datasets, driving rapid development of single-cell multi-modal integration tools. Nevertheless, there is a pressing need for a comprehensive benchmark to assess algorithms under varying integrated dataset types, integrated modalities, dataset sizes and data quality. Here we present a systematic benchmark for 40 single-cell multi-modal integration algorithms involving modalities of DNA, RNA, protein and spatial omics for paired, unpaired and mosaic datasets (a mixture of paired and unpaired datasets). We evaluated usability, accuracy and robustness to assist researchers in selecting suitable integration methods tailored to their datasets and applications. Our benchmark provides valuable guidance in the ever-evolving field of single-cell multi-omics.

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Conflict of interest statement

Competing interests: The authors declare no competing interests.

References

1. Method of the Year 2019: single-cell multimodal omics. Nat.Methods 17, 1 (2020).
1. Baysoy, A., Bai, Z., Satija, R. & Fan, R. The technological landscape and applications of single-cell multi-omics. Nat. Rev. Mol. Cell Biol. 24, 695–713 (2023). - DOI
1. Efremova, M. & Teichmann, S. A. Computational methods for single-cell omics across modalities. Nat. Methods 17, 14–17 (2020). - DOI
1. Zhang, D. et al. Spatial epigenome-transcriptome co-profiling of mammalian tissues. Nature 616, 113–122 (2023). - PMC - DOI
1. Vickovic, S. et al. SM-Omics is an automated platform for high-throughput spatial multi-omics. Nat. Commun. 13, 795 (2022). - PMC - DOI

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Benchmarking single-cell multi-modal data integrations

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Benchmarking single-cell multi-modal data integrations

Authors

Affiliations

Abstract

Conflict of interest statement

References

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