ArticleBioinformatics (Oxford, England)2026
A spectral dimension reduction technique that improves pattern detection in multivariate spatial data.
Article in Bioinformatics (Oxford, England), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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10 authors.
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Abstract
motivationWe introduce a statistical approach for pattern recognition in multivariate spatial transcriptomics data.
resultsOur algorithm constructs a projection of the data onto a low-dimensional feature space which is optimal in maximizing Moran's I, a measure of spatial dependency. This projection mitigates non-spatial variation and outperforms principal components analysis for pre-processing. Patterns of spatially variable genes are well represented in this feature space, and their projection can be shown to be a denoising operation. Our framework does not require any parameter tuning, and it furthermore gives rise to a calibrated, powerful test of spatial gene expression. AVAILABILITY AND IMPLEMENTATION: The algorithm is implemented in the open source software R and is available at https://github.com/IMSBCompBio/SpaCo.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.