Last updated: 2022-08-13

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Welcome to SpatialPCA!

Overview

SpatialPCA is a spatially aware dimension reduction method that aims to infer a low dimensional representation of the gene expression data in spatial transcriptomics. SpatialPCA builds upon the probabilistic version of PCA, incorporates localization information as additional input, and uses a kernel matrix to explicitly model the spatial correlation structure across tissue locations.

Please read our paper at Nature Communications:

Spatially Aware Dimension Reduction for Spatial Transcriptomics

doi: https://www.nature.com/articles/s41467-022-34879-1