Alignment and integration of spatial transcriptomics data
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Abstract
Spatial transcriptomics (ST) measures mRNA expression across thousands of spots from a tissue slice while recording the two-dimensional (2D) coordinates of each spot. We introduce probabilistic alignment of ST experiments (PASTE), a method to align and integrate ST data from multiple adjacent tissue slices. PASTE computes pairwise alignments of slices using an optimal transport formulation that models both transcriptional similarity and physical distances between spots. PASTE further combines pairwise alignments to construct a stacked 3D alignment of a tissue. Alternatively, PASTE can integrate multiple ST slices into a single consensus slice. We show that PASTE accurately aligns spots across adjacent slices in both simulated and real ST data, demonstrating the advantages of using both transcriptional similarity and spatial information. We further show that the PASTE integrated slice improves the identification of cell types and differentially expressed genes compared with existing approaches that either analyze single ST slices or ignore spatial information.
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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 [32]
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References 63 · Cited by 298
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- Authors
- Zeira, R., Land, M., Strzalkowski, A., & Raphael, B. J.
- Affiliations
- Princeton University (US)
- Corresponding author
- Raphael, Benjamin J.
- Venue
- Nature Methods
- Published
- 2022-05
- Volume
- 19
- Issue
- 5
- Pages
- 567-575
- ISSN
- 1548-7091 (ISSN-L)
- 1548-7105
- PMC
- PMC9334025
- OpenAlex
- W4280494993
- Semantic Scholar
- 937d97f967eb6e438dd8f941c5ac000e6df3094b
- Cited by
- 298
- References
- 63
- Citations by year
- 2026: 80 · 2025: 86 · 2024: 66 · 2023: 46 · 2022: 16 · 2021: 4
- FWCI
- 17.88
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- Locations
- Nature Methods (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- U.S. Department of Health & Human Services | NIH | National Cancer Institute (U24CA248453, U24CA211000)
- 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 › Molecular Biology › Genomics and Phylogenetic Studies
- Keywords
- spatial transcriptomics, data alignment, data integration, pastes, optimal transport, cell type annotation, differentially expressed genes
- MeSH terms
- Algorithms, Transcriptome
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