Abstract
Spatial epigenomics (SE) technologies profile epigenomic landscapes within intact tissues, preserving spatial context and enabling the study of gene regulatory mechanisms in situ. However, current SE datasets typically suffer from low signal detection, substantial noise and extremely sparse peak matrices, which pose considerable challenges for downstream analysis. Here we introduce SPEED (spatial epigenomic data denoising), a deep matrix factorization framework that leverages atlas-level single-cell epigenomic data and spatial context to impute and denoise SE data. In comprehensive benchmarks on both simulated data and real SE tissue datasets, SPEED outperformed five state-of-the-art methods across diverse tissues and technologies. Moreover, SPEED’s denoised outputs facilitated downstream analyses such as differential chromatin accessibility analysis, epigenomic spatial domain identification and gene activity inference. Collectively, our results indicate that SPEED is a generalizable tool for improving data quality and biological insights in SE.
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Data availability
All SE, single-cell RNA-seq and scATAC-seq datasets used in this study can be downloaded from public websites or databases: E11.5, E12.5 and E13.5 mouse embryo scATAC-seq data from 16 tissues at https://ngdc.cncb.ac.cn/gsa/browse/CRA003910 (ref. 15). E12.5, E13.5 and E14.5 embryonic mouse cerebellum snATAC-seq data are available in the GEO database under accession GSE178546 (ref. 28). E17.5 embryonic mouse heart scATAC-seq data are available in the GEO database under accession GSE190977 (ref. 27). E12.5, E13.5 and E15.5 mouse embryo snATAC-seq data are available in the GEO database under accession GSE214991 (ref. 12). Three samples of E18 mouse embryo brain snATAC-seq data are available at https://www.10xgenomics.com/datasets (ref. 16). Mouse embryo scRNA-seq data are available in the GEO database under accession GSE119945 (ref. 48). Human brain scATAC-seq data are available in the GEO database under accession GSE147672 (ref. 57). Adult mouse brain scATAC-seq data are available in the GEO database under accession GSE246791 (ref. 22). E13 mouse embryo spatial-ATAC-RNA-seq data are available in the GEO database under accession GSE205055 (ref. 8). E11–E18.5 mouse embryo MISAR-seq data are available at https://www.biosino.org/node/project/detail/OEP003285 (ref. 13). P22 mouse brain Spatial-CUT&Tag-RNA-seq data are available in the GEO database under accession GSE205055 (ref. 8). Human hippocampus spatial-ATAC-RNA-seq data are available in the GEO database under accession GSE205055 (ref. 8). P22 mouse brain spatial-ATAC-RNA-seq data are available in the GEO database under accession GSE205055 (ref. 8). EAE mouse brain spatial-Mux-seq data are available in the GEO database under accession GSE263333 (ref. 11). E13.5 mouse embryonic forebrain, hindbrain, midbrain and limb bulk ATAC-seq data from ENCODE are available at https://www.encodeproject.org (ref. 29). Chromatin state annotations for the E13.5 mouse embryonic forebrain and hindbrain are available at https://genome.ucsc.edu/cgi-bin/hgTrackUi?hgsid=2471038369_O34GqlYujAEy04rHeqMnjX560AHY&g=encode3RenChromHmm (refs. 30,31). Source data are provided with this paper.
Code availability
The open-source package of SPEED is available via GitHub at https://github.com/QuKunLab/SPEED. All codes and scripts used for the analyses and figure plotting in this study are available via Zenodo at https://doi.org/10.5281/zenodo.14948507 (ref. 58).
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Acknowledgements
This work was supported by the National Natural Science Foundation of China grants (grant nos T2125012 and 92574202 to K.Q.; 323B2014 to H.X.), the National Key R&D Program of China (grant no. 2022YFA1303200 to K.Q.), Strategic Priority Research Program of Chinese Academy of Sciences (grant no. XDB0940301 to K.Q.) and USTC Research Funds of the Double First-Class Initiative (grant nos YD9100002026 and YD9100002032 to K.Q.). We thank the USTC supercomputing center and the School of Life Science Bioinformatics Center for providing computing resources for this project.
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K.Q. conceived the project. S.W. and H.X. designed the framework and performed data analysis with help from J.W., Y.X., S.D., J.L., R.C. and X.C. K.Q., S.W. and H.X. wrote the paper with input from all authors. K.Q. supervised the entire project. All authors read and approved the final paper.
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Nature Computational Science thanks Chaoyong Yang and Zexian Zeng for their contribution to the peer review of this work. Peer reviewer reports are available. Primary Handling Editors: Michelle Badri and Ananya Rastogi, in collaboration with the Nature Computational Science team.
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Supplementary information
Supplementary Information
Supplementary Figs. 1–17.
Supplementary Data 1
Detailed information on five single-cell ATAC-seq mouse embryo datasets.
Supplementary Data 2
TSCAS identified from single-cell and bulk E13.5 mouse embryo data.
Supplementary Data 3
Annotation details for spatial transcriptomic and spatial epigenomic datasets.
Supplementary Data 4
Runtime, memory usage, and CPU and GPU requirements of SPEED across datasets.
Supplementary Data 5
Source data for Supplementary Figs. 1, 3 and 6–17.
Source data
Source Data Fig. 2
Statistical source data for Fig. 2.
Source Data Fig. 3
Statistical source data for Fig. 3.
Source Data Fig. 4
Statistical source data for Fig. 4.
Source Data Fig. 5
Statistical source data for Fig. 5.
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Wang, S., Xu, H., Wang, J. et al. Denoising spatial epigenomic data via deep matrix factorization. Nat Comput Sci (2026). https://doi.org/10.1038/s43588-025-00941-3
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DOI: https://doi.org/10.1038/s43588-025-00941-3
