Visualization and Analysis of Spatial Heterogeneity in Spatially-Resolved Gene Expression

Visualization and analysis of spatially resolved transcriptomics data. The 'spatialGE' R package provides methods for visualizing and analyzing spatially resolved transcriptomics data, such as 10X Visium, CosMx, or csv/tsv gene expression matrices. It includes tools for spatial interpolation, autocorrelation analysis, tissue domain detection, gene set enrichment, and differential expression analysis using spatial mixed models.

spatialGE

An R package for the visualization and analysis of spatially-resolved transcriptomics data, such as those generated with 10X Visium. The spatialGE package features a data object (STlist: Spatial Transctiptomics List) to store data and results from multiple tissue sections, as well as associated analytical methods for:

• Visualization: STplot, gene_interpolation, STplot_interpolation to explore gene expression in spatial context.
• Spatial autocorrelation: SThet, compare_SThet to assess the level of spatial uniformity in gene expression by calculating Moran's I and/or Geary's C and qualitatively explore correlations with sample-level metadata (i.e., tissue type, therapy, disease status).
• Tissue domain/niche detection: STclust to perform spatially-informed hierarchical clustering for prediction of tissue domains in samples.
• Gene set spatial enrichment: STenrich to detect gene sets with indications of spatial patterns (i.e., non-spatially uniform gene set expression).
• Gene expression spatial gradients: STgradient to detect genes with evidence of variation in expression with respect to a tissue domain.
• Spatially-informed differential expression: STdiff to test for differentially expressed genes using mixed models with spatial covariance structures to account of spatial dependency among spots/cells. It also supports non-spatial tests (Wilcoxon's and T-test).

The methods in the initial spatialGE release, technical details, and their utility are presented in this publication: https://doi.org/10.1093/bioinformatics/btac145. For details on the recently developed methods STenrich, STgradient, and STdiff please refer to the spatialGE documentation.

Installation

The spatialGE repository is available at GitHub and can be installed via devtools.

options(timeout=9999999) # To avoid R closing connection with GitHub
devtools::install_github("fridleylab/spatialGE")

How to use spatialGE

For tutorials on how to use spatialGE, please go to: https://fridleylab.github.io/spatialGE/

The code for spatialGE can be found here: https://github.com/FridleyLab/spatialGE

spatialGE-Web

A point-and-click web application that allows using spatialGE without coding/scripting is available at https://spatialge.moffitt.org . The web app currently supports Visium outputs and csv/tsv gene expression files paired with csv/tsv coordinate files.

How to cite

When using spatialGE, please cite the following publication:

Ospina, O. E., Wilson C. M., Soupir, A. C., Berglund, A. Smalley, I., Tsai, K. Y., Fridley, B. L. 2022. spatialGE: quantification and visualization of the tumor microenvironment heterogeneity using spatial transcriptomics. Bioinformatics, 38:2645-2647. https://doi.org/10.1093/bioinformatics/btac145