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A spectral dimension reduction technique that improves pattern detection in multivariate spatial data.

Bioinformatics (Oxford, England)

Authors: David Köhler, Niklas Kleinenkuhnen, Kiarash Rastegar, Till Baar, Chrysa Nikopoulou, Vangelis Kondylis, Vlada Milchevskaya, Matthias Schmid, Peter Tessarz, Achim Tresch

MOTIVATION: We introduce a statistical approach for pattern recognition in multivariate spatial transcriptomics data.

RESULTS: Our algorithm constructs a projection of the data onto a low-dimensional feature space which is optimal in maximising 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.

© The Author(s) 2026. Published by Oxford University Press.

PMID: 41619788

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