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Spatially aware dimension reduction for spatial transcriptomics.

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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. SpatialPCA is implemented as an open-source R package, freely available at www.xzlab.org/software.html.

drawing

Install the Package

You can install the current version of SpatialPCA from GitHub with:

library(devtools)
install_github("shangll123/SpatialPCA")

Package Tutorial

Please see the SpatialPCA tutorial website.

The tutorial includes main example codes for multiple spatial transcriptomics datasets (e.g. DLPFC, Slide-Seq cerebellum, Slide-Seq V2 hippocampus, Human breast tumor, and Vizgen MERFISH.)

Other analysis codes for this project can be found here.

Operating systems (version 1.3.0 SpatialPCA) tested on:

macOS Catalina 10.15.7

Ubuntu 18.04.5 LTS (Bionic Beaver)

CentOS Linux 7 (Core)

License

SpatialPCA is licensed under the GNU General Public License v3.0.

Citation

Lulu Shang, and Xiang Zhou (2022). Spatially aware dimension reduction for spatial transcriptomics. bioRxiv.

doi: https://doi.org/10.1101/2022.01.19.476966

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