Methods for the study of the tissue microenvironment using spatial statistics

Welcome to the spatialGE web application, a user friendly, point-and-click implementation of the spatialGE R package. This application contains a collection of methods for visualization and spatial statistics analysis of the tissue microenvironment and heterogeneity using spatial transcriptomics (ST) experiments. For a technical description of the methods, please see our publications at the bottom of this page.

For tutorials and guides on how to use spatialGE, please refer to the “How to get started” section, or click here to log in your account.


If you use spatialGE to generate figures or conduct analysis for your publications, please cite the following papers:

  • 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.
  • Ospina, O. E., Fridley, B. L. 2023. A spatially-informed framework to differential gene expression analysis for spatial transcriptomics experiments. (In Prep)

Some scientific articles using spatialGE methods:

  • Ospina, O., Soupir, A., Fridley, B.L. 2023. A primer on preprocessing, visualization, clustering, and phenotyping of barcode-based spatial transcriptomics data. In: Fridley, B. L., Wang, X. (eds) Statistical Genomics. Methods in Molecular Biology, vol 2629. New York, NY, USA.
  • Alhaddad, H., Ospina, O. E., Khaled, M., Fridley, B. L., Smalley, I. Spatial multi-omics identifies unique tumor-stroma interactions mediating therapy resistance in leptomeningeal melanoma metastasis. (In Prep)