ArticleNature communications2026
Integrative cross-sample alignment and spatially differential gene analysis for spatial transcriptomics.
Article in Nature communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed.
- Integrative cross-sample alignment and spatially differential gene analysis for spatial transcriptomics.Nature communications · 2026Article
- GALA: a unified landmark-free framework for coarse-to-fine spatial alignment across resolutions and modalities in spatial transcriptomics.Briefings in bioinformatics · 2026Article
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Authors and funding
7 authors.
Funding
Abstract
Spatial transcriptomics (ST) technologies offer rich spatial context for gene expression, with varying spatial resolutions and gene coverages. However, aligning and comparing multiple ST slices, whether derived from the same or different platforms, remains challenging due to nonlinear distortions and limited spatial overlap caused by tissue processing. We present CODA, an integrative framework for Cross-sample alignment and spatially Differential gene Analysis. CODA first learns a shared low-dimensional latent feature space across samples. Within the latent space, CODA performs global affine alignment, applies transformer-based feature matching to identify common spatial domains, and utilizes local nonlinear refinements via large deformation diffeomorphic metric mapping, enabling a robust cross-sample comparison and extraction of spatial gene expression patterns. Benchmarking across various ST platforms demonstrates CODA's strong performance in alignment accuracy, computational efficiency, and memory usage. Through dual-color immunofluorescence experiments and enrichment analysis, we show CODA's ability to uncover spatially informative genes associated with normal and disease conditions. These results highlight CODA's broad applicability and effectiveness in ST analysis.
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Registered trials
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