Awesome
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.
These analysis codes can also be accessed through my personal website:
Simulation <br /> DLPFC dataset <br /> Slide-Seq mouse cerebellum dataset <br /> Slide-Seq V2 mouse hippocampus dataset <br /> Human breast cancer ST dataset <br /> Other source data could be downloaded from here.