Last updated: 2022-08-13
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Welcome to SpatialPCA!
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