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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.

2023-10-16 bioinformatics
10.1101/2023.10.12.562016 bioRxiv
Show 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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"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.