Alignment of spatial genomics data using deep Gaussian processes
Open accessAbstract
Spatially resolved genomic technologies have allowed us to study the physical organization of cells and tissues, and promise an understanding of local interactions between cells. However, it remains difficult to precisely align spatial observations across slices, samples, scales, individuals and technologies. Here, we propose a probabilistic model that aligns spatially-resolved samples onto a known or unknown common coordinate system (CCS) with respect to phenotypic readouts (for example, gene expression). Our method, Gaussian Process Spatial Alignment (GPSA), consists of a two-layer Gaussian process: the first layer maps observed samples’ spatial locations onto a CCS, and the second layer maps from the CCS to the observed readouts. Our approach enables complex downstream spatially aware analyses that are impossible or inaccurate with unaligned data, including an analysis of variance, creation of a dense three-dimensional (3D) atlas from sparse two-dimensional (2D) slices or association tests across data modalities.
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Loki Align's superiority over PASTE and GPSA likely stems from PASTE's design for linear transformations, which maintains topological integrity but struggles with spatial warping, while GPSA aims to map readouts to a common coordinate system, risking topological fidelity.
“This superiority likely stems from PASTE’s design for linear transformations, which maintains topological integrity but struggles with spatial warping32, while GPSA aims to map readouts to a common coordinate system, risking topological fidelity33.”
From A visual–omics foundation model to bridge histopathology with spatial transcriptomics (2025) · cites this paper as [33]
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Cited by
Sorted by citations
A visual–omics foundation model to bridge histopathology with spatial transcriptomics
Chen, W., Zhang, P., Tran, T. N., Xiao, Y., Li, S., Shah, V. V., Cheng, H., Brannan, K. W., Youker, K., Lai, L., et al. (2025) · Nature Methods · doi:10.1038/s41592-025-02707-1 · Cited by 118 · see citing works
“We measured the distance between Loki-aligned data and the ground truth, and compared Loki Align with PASTE and GPSA, which are designed for ST section alignment 32,33 .”
- Authors
- Jones, A., Townes, F. W., Li, D., & Engelhardt, B. E.
- Affiliations
- Princeton University (US)
- Carnegie Mellon University (US)
- University of North Carolina at Chapel Hill (US)
- Gladstone Institutes (US)
- Corresponding author
- Engelhardt, Barbara E.
- Venue
- Nature Methods
- Published
- 2023-08-17
- Volume
- 20
- Issue
- 9
- Pages
- 1379-1387
- ISSN
- 1548-7091 (ISSN-L)
- 1548-7105
- PMC
- PMC10482692
- OpenAlex
- W4385955242
- Cited by
- 85
- References
- 51
- Citations by year
- 2026: 21 · 2025: 34 · 2024: 23 · 2023: 6 · 2022: 1
- FWCI
- 7.54
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Nature Methods (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- National Science Foundation (AWD1005627)
- U.S. Department of Health & Human Services | NIH | National Cancer Institute (U2CCA233195)
- U.S. Department of Health & Human Services | NIH | National Heart, Lung, and Blood Institute (HL133218)
- 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
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Spatial and Panel Data Analysis
- Keywords
- spatial genomics, spatial alignment, deep Gaussian processes, probabilistic models
- MeSH terms
- Humans, Models, Statistical, Normal Distribution, Genomics
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