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Benchmarking alignment methods for spatial transcriptomics data

Nature Computational Science (2026) Cite this article

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Abstract

Reconstructing the three-dimensional molecular architecture of tissues from two-dimensional spatial transcriptomics slices is a central goal in spatial biology. Spatial alignment, the computational registration of multiple tissue slices using their spatial coordinates and gene expression profiles, provides the foundational framework for this integrative perspective. Although numerous alignment methods have emerged, a comprehensive benchmark to guide their application has been notably absent. Here we address this by systematically evaluating a diverse suite of leading methods. Executing 295 distinct alignment tasks across diverse datasets and technologies, our framework quantifies method accuracy, efficiency, usability and robustness, while also assessing the downstream impact of alignment quality. Crucially, our study systematically investigates performance in challenging real-world scenarios, uncovering substantial limitations in current tools. To address these bottlenecks, we propose and validate effective mitigation strategies. Finally, we provide practical guidelines to assist researchers in selecting the optimal alignment method and optimizing their analytical workflows.

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Fig. 1: Overview of benchmarking workflow, methods and datasets.
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Fig. 2: Evaluation in conventional scenarios and impact on downstream task.
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Fig. 3: Robustness evaluation under perturbations.
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Fig. 4: Summary of overall performance and usability.
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Fig. 5: Challenging scenarios: cross-platform, multiple consecutive slices and large-scale data.
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Fig. 6: A practical guideline for alignment.
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Data availability

Data1 to Data4 were downloaded from ref. 53 (https://doi.org/10.1186/s12864-022-08601-w). Data5 to Data9 were obtained from ref. 55 (https://doi.org/10.5061/dryad.8t8s248). Data10 to Data15 were downloaded from ref. 56 (https://doi.org/10.1016/j.cell.2020.08.043). Data16 to Data31 were obtained from https://db.cngb.org/stomics/flysta3d/. Data32 to Data33 were obtained from https://www.10xgenomics.com/cn/datasets/mouse-brain-serial-section-1-sagittal-anterior-1-standard-1-0-0 and https://www.10xgenomics.com/cn/datasets/mouse-brain-serial-section-2-sagittal-anterior-1-standard. Data 34 to Data35 were obtained from https://www.10xgenomics.com/cn/datasets/mouse-brain-serial-section-1-sagittal-posterior-1-standard-1-1-0 and https://www.10xgenomics.com/cn/datasets/mouse-brain-serial-section-2-sagittal-posterior-1-standard. Data36 to Data38 were obtained from ref. 57. Data39 to Data41 were obtained from ref. 57 (https://doi.org/10.1038/s41592-024-02215-8). Data42 to Data44 were obtained from https://info.vizgen.com/mouse-brain-data. Data45 to Data77 were obtained from ref. 58 (https://doi.org/10.1038/s41586-021-03705-x). Data78 to Data79 were obtained via GitHub at https://github.com/JinmiaoChenLab/SEDR_analyses and at https://singlecell.broadinstitute.org/single_cell/study/SCP815, respectively. Data80 to Data98 were obtained from ref. 33 (https://doi.org/10.1016/j.cell.2024.05.055). Data99 to Data227 were obtained from ref. 40 (https://doi.org/10.1038/s41586-023-06808-9). Data228 to Data230 were obtained from ref. 10 (https://doi.org/10.1038/s41467-023-43458-x). Data231 to Data240 were downloaded via ST at https://www.molecularatlas.org/st-js-viewer (21A, ref. 43), via 10x Visium at https://www.10xgenomics.com/datasets/adult-mouse-brain-coronal-section-fresh-frozen-1-standard (ref. 44), via MERFISH at https://info.vizgen.com/mouse-brain-data (S2R3, ref. 45), STARmap PLUS via Zenodo at https://doi.org/10.5281/zenodo.8327576 (well11, ref. 31), via Stereo-seq at https://mouse.digital-brain.cn/spatial-omics (T312, ref. 46), via Slide-seq at https://www.braincelldata.org/cellspatial (46, ref. 41), via Xenium at https://www.10xgenomics.com/datasets/fresh-frozen-mouse-brain-replicates-1-standard (ref. 44), via Xenium 5k at https://www.10xgenomics.com/datasets/xenium-prime-fresh-frozen-mouse-brain (ref. 44), via Visium HD at https://www.10xgenomics.com/datasets/visium-hd-cytassist-gene-expression-mouse-brain-fresh-frozen (ref. 44) and via CosMx at https://nanostring.com/products/cosmx-spatial-molecular-imager/ffpe-dataset/cosmx-smi-mouse-brain-ffpe-dataset/ (ref. 34). Source data are provided with this paper.

Code availability

The code and tutorials are available via GitHub at https://github.com/Yunzhi-Yan/SABench and via Zenodo at https://doi.org/10.5281/zenodo.18605715 (ref. 65).

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Acknowledgements

This work is supported by the Shanghai Municipal Science and Technology Major Project (grant number 2023SHZDZX02), the National Key R&D Project of China (grant numbers 2023YFC3402501 and 2023YFC3402500) and the AI for Science Foundation of Fudan University (grant number FudanX24A1031) to B.Z.Q. Z.Y. acknowledges the support by National Nature Science Foundation of China (grant numbers 62303119 (Z.Y.) and 32470706 (Z.Y.)), Shanghai Science and Technology Development Funds (grant number 23YF1403000 (Z.Y.)), Fund of Fudan University and Cao’ejiang Basic Research (grant number 24FCA10 (Z.Y.)), the Computational Biology Program (number 25JS2850200 (Z.Y.)) of Science and Technology Commission of Shanghai Municipality (STCSM). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

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Authors and Affiliations

  1. Fudan University Shanghai Cancer Center, Shanghai Medical College, Center for Integrative Spatial-Omics Research, The Human Phenome Institute, Zhangjiang-Fudan International Innovation Center, Fudan University, Shanghai, China

    Yunzhi Yan, Tianyi Gu, Chengcheng Sun, Yinghao Zhang, Yihan Zhao, Zhihong Lin & Bin-Zhi Qian

  2. Center for Medical Research and Innovation, Shanghai Pudong Hospital, Fudan University Pudong Medical Center, Fudan University, Shanghai, China

    Yan Cui, Senlin Lin, Qi Zou, Yixuan Du, Chuangyi Han, Kairan Kang, Sijie Li & Zhiyuan Yuan

  3. Institute of Science and Technology for Brain-Inspired Intelligence, MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, MOE Frontiers Center for Brain Science, Center for Integrative Spatial-Omics Research, Fudan University, Shanghai, China

    Yan Cui, Senlin Lin, Qi Zou, Yixuan Du, Chuangyi Han, Kairan Kang, Sijie Li & Zhiyuan Yuan

  4. Center of Intelligent Medicine, School of Control Science and Engineering, Shandong University, Jinan, China

    Qi Zou

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  1. Yunzhi Yan
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Contributions

Author contributions are described using CRediT roles (https://credit.niso.org/). Y.Y.: conceptualization, data curation, formal analysis, investigation, methodology, software, visualization, writing—original draft and writing—review and editing. T.G.: investigation, methodology, software and writing—review and editing. C.S.: investigation, methodology, software and writing—review and editing. Y. Zhang: investigation, methodology, software and writing—review and editing. Y.C.: investigation, methodology and software. S. Lin: software. Q.Z.: software. Y.D.: investigation. C.H.: investigation. K.K.: investigation. S. Li: investigation. Y. Zhao: investigation. Z.L.: investigation. Z.Y.: conceptualization, investigation, methodology, resources,. supervision and writing—review and editing. B.-Z.Q.: conceptualization, investigation, methodology, resources, supervision and writing—review and editing.

Corresponding authors

Correspondence to Zhiyuan Yuan or Bin-Zhi Qian.

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Nature Computational Science thanks Xi Jiang, Chen-Rui Xia and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available. Primary Handling Editors: Michelle Badri and Kaitlin McCardle, in collaboration with the Nature Computational Science team.

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Yan, Y., Gu, T., Sun, C. et al. Benchmarking alignment methods for spatial transcriptomics data. Nat Comput Sci (2026). https://doi.org/10.1038/s43588-026-00977-z

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