bioRxivBioinformaticsPreprint
Dimension Reduction by Spatial Components Analysis Improves Pattern Detection in Multivariate Spatial Data
Niklas KleinenkuhneniD, David KöhleriD, Till BaariD, Chrysa NikopoulouiD, Vangelis KondylisiD, Matthias C. SchmidiD, Peter TessarziD, Achim TreschiD
Abstract
We introduce a multivariate statistical approach for pattern recognition in spatial transcriptomics data. Our algorithm (SPACO) constructs a low-dimensional projection of the data maximising Morans I, which mitigates non-spatial variation and outperforms PCA for pre-processing. Our method also provides a calibrated, powerful test of spatial gene expression that excels in robustness and specificity.
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