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Rigorous integration of single-cell ATAC-seq data using regularized barycentric mapping

Nature Machine Intelligence volume 7, pages 1461–1477 (2025)Cite this article

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Abstract

Single-cell assay for transposase-accessible chromatin using sequencing (scATAC-seq) deciphers genome-wide chromatin accessibility, providing profound insights into gene regulation mechanisms. With the rapid advance of sequencing technologies, scATAC-seq data typically encompass numerous samples from various conditions, resulting in complex batch effects, thus necessitating reliable integration tools. While numerous batch integration tools exist for single-cell RNA sequencing data, inherent data characteristic differences limit their effectiveness on scATAC-seq data. Existing integration methods for scATAC-seq data suffer from several fundamental limitations, such as disrupting the biological heterogeneity and focusing solely on low-dimensional correction, which may distort data and hinder downstream analysis. Here we propose Fountain, a deep learning framework for scATAC-seq data integration via rigorous barycentric mapping. Barycentric mapping transforms one data distribution to another in a principled and effective manner through optimal transport. By regularizing barycentric mapping with geometric data information, Fountain achieves accurate batch alignment while preserving biological heterogeneity. Comprehensive experiments across diverse real-world datasets demonstrate the advantages of Fountain over existing methods in batch correction and biological conservation. In addition, the trained Fountain model can integrate data from new batches alongside already integrated data without retraining, enabling continuous online data integration. Moreover, Fountain’s reconstruction strategy generates batch-corrected ATAC profiles, improving the capture of cellular heterogeneity and revealing cell-type-specific implications such as expression enrichment analysis and partitioned heritability analysis.

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Fig. 1: Overview of Fountain.
Fig. 2: Fountain removes batch effects and preserves biological heterogeneity in scATAC-seq data.
Fig. 3: Fountain integrates datasets with various types and degrees of imbalance.
Fig. 4: Comparison between the raw data and Fountain-enhanced data.
Fig. 5: Fountain effectively captures cell-type-specific biological signals.

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Data availability

The following publicly available datasets were used in this study: the MB dataset from Gene Expression Omnibus (GEO) under accession number GSE126724 and 10x Genomic (https://support.10xgenomics.com/single-cell-atac/datasets/1.1.0/atac_v1_adult_brain_fresh_5k?; free registration required), the BMMC dataset from GSE123581, the HI dataset from GSE149683, the HL dataset from GSE161383 and the mouse cortex and marmoset cortex (MM) dataset from GSE204851. The methods for unifying cell type labels across batches, and the sources of those labels in these datasets, are described in Supplementary Note 1. The download methods of the two human peripheral blood mononuclear cell datasets (PBMCA and PBMCB) are provided in Supplementary Note 1. More detailed descriptions and source of datasets are provided in Supplementary Note 1 and Supplementary Table 1.

Code availability

Fountain is implemented in Python using the PyTorch framework. The MIT-licensed Fountain software with detailed documentation and tutorials is available via GitHub at https://github.com/BioX-NKU/Fountain. The source code is also available via Zenodo at https://doi.org/10.5281/zenodo.14924285 (ref. 64).

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Acknowledgements

We thank H. Yuan of Calico Life Sciences LLC, S. Li of Nankai University and R. Li of Xi’an Jiaotong University for their input on this project. This work was supported by the National Natural Science Foundation of China (grant nos. 62473212 and 62203236 to S.C.) and the Young Elite Scientists Sponsorship Program by CAST (grant no. 2023QNRC001 to S.C.).

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  1. These authors contributed equally: Shuchen Zhu, Heyang Hua.

Authors and Affiliations

  1. School of Mathematical Sciences and LPMC, Nankai University, Tianjin, China

    Shuchen Zhu, Heyang Hua & Shengquan Chen

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  1. Shuchen Zhu
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Contributions

S.C. conceived the study and supervised the project; S.Z. designed and implemented Fountain; S.Z., H.H. and S.C. performed research; S.Z. and H.H. analysed the results; and S.Z., H.H. and S.C. wrote the paper.

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Correspondence to Shengquan Chen.

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Nature Machine Intelligence thanks Zhana Duren and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Supplementary Notes 1–15, Figs. 1–20 and Tables 1–8.

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Zhu, S., Hua, H. & Chen, S. Rigorous integration of single-cell ATAC-seq data using regularized barycentric mapping. Nat Mach Intell 7, 1461–1477 (2025). https://doi.org/10.1038/s42256-025-01099-3

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