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Updated: Feb 2, 2026

Cross-Modal Multivariate Pattern Analysis
Published on: November 9, 2011
A spectral dimension reduction technique that improves pattern detection in multivariate spatial data
David Köhler1,2, Niklas Kleinenkuhnen2,3, Kiarash Rastegar2
1University of Bonn, University Hospital Bonn, Institute for Medical Biometry, Informatics, and Epidemiology, Bonn 53127, Germany.
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 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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