Dimension Reduction by Spatial Components Analysis Improves Pattern Detection in Multivariate Spatial Data

Kleinenkuhnen, N.; Koehler, D.; Baar, T.; Nikopoulou, C.; Kondylis, V.; Schmid, M.; Tessarz, P.; Tresch, A.

doi:10.1101/2023.10.12.562016

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bioRxiv · 10.1101/2023.10.12.562016

Dimension Reduction by Spatial Components Analysis Improves Pattern Detection in Multivariate Spatial Data

Kleinenkuhnen, N.·Koehler, D.·Baar, T.·Nikopoulou, C.·Kondylis, V.·Schmid, M.·Tessarz, P.·Tresch, A.

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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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Kleinenkuhnen, N., Koehler, D., Baar, T., Nikopoulou, C., Kondylis, V., Schmid, M., Tessarz, P., Tresch, A.. 2023-10-16. Dimension Reduction by Spatial Components Analysis Improves Pattern Detection in Multivariate Spatial Data. https://doi.org/10.1101/2023.10.12.562016

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