Integrating spatial transcriptomics data across different conditions, technologies and developmental stages
Open accessPublished version of a preprint
Abstract
With the rapid generation of spatial transcriptomics (ST) data, integrative analysis of multiple ST datasets from different conditions, technologies and developmental stages is becoming increasingly important. Here we present a graph attention neural network called STAligner for integrating and aligning ST datasets, enabling spatially aware data integration, simultaneous spatial domain identification and downstream comparative analysis. We apply STAligner to ST datasets of the human cortex slices from different samples, the mouse olfactory bulb slices generated by two profiling technologies, the mouse hippocampus tissue slices under normal and Alzheimer's disease conditions, and the spatiotemporal atlases of mouse organogenesis. STAligner efficiently captures the shared tissue structures across different slices, the disease-related substructures and the dynamical changes during mouse embryonic development. In addition, the shared spatial domain and nearest-neighbor pairs identified by STAligner can be further considered as corresponding pairs to guide the three-dimensional reconstruction of consecutive slices, achieving more accurate local structure-guided registration than the existing method.
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Zhou et al. developed STAligner to integrate spatial transcriptomic data and scRNA-seq data and map scRNA-seq data to specific spatial locations based on location information provided by spatial transcriptomic data.
“Zhou et al. [128] developed the graph attention neural network STAligner to integrate spatial transcriptomic data and scRNA-seq data and map scRNA-seq data to specific spatial locations based on the location information provided by spatial transcriptomic data.”
From Challenges in AI-driven Biomedical Multimodal Data Fusion and Analysis (2025) · cites this paper as [128]
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Challenges in AI-driven Biomedical Multimodal Data Fusion and Analysis
Liu, J., Cen, X., Yi, C., Wang, F.-A., Ding, J., Cheng, J., Wu, Q., Gai, B., Zhou, Y., He, R., et al. (2025) · Genomics, Proteomics & Bioinformatics · doi:10.1093/gpbjnl/qzaf011 · Cited by 75 · see citing works
- Authors
- Zhou, X., Dong, K., & Zhang, S.
- Affiliations
- Chinese Academy of Sciences (CN)
- Corresponding author
- Zhang, Shihua
- Venue
- Nature Computational Science
- Published
- 2023-10-12
- Volume
- 3
- Issue
- 10
- Pages
- 894-906
- ISSN
- 2662-8457 (ISSN-L)
- OpenAlex
- W4387580076
- Cited by
- 160
- References
- 10
- Citations by year
- 2026: 49 · 2025: 64 · 2024: 41 · 2023: 6
- FWCI
- 14.35
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- Locations
- Nature Computational Science (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
- PubMed (publishedVersion)
- Funders
- National Natural Science Foundation of China (2021YFA1302500, 61621003, 12126605, 61621003, 12126605, 2019YFA0709501, XDPB17, 2020B1111190001, XDA16021400)
- Chinese Academy of Sciences (XDA16021400, XDPB17, YSBR-034)
- National Key Research and Development Program of China (XDA16021400, 2019YFA0709501, XDPB17, 2021YFA1302500)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Biophysics › Cell Image Analysis Techniques
- Keywords
- spatial transcriptomics, multi-omics integration, spatial domain identification, mouse hippocampus, Alzheimer's disease, embryonic development
- MeSH terms
- Alzheimer Disease, Animals, Awareness, Cerebral Cortex, Female, Humans, Pregnancy, Gene Expression Profiling, Embryonic Development, Mice
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