spatialGE: A user-friendly web application to democratize spatial transcriptomics analysis

Abstract

Spatial transcriptomics (ST) is a powerful tool for understanding tissue biology and disease mechanisms. However, its potential is often underutilized due to the advanced data analysis and programming skills required. To address this, we present spatialGE, a web application that simplifies the analysis of ST data. The application spatialGE provides a user-friendly interface that guides users without programming expertise through various analysis pipelines, including quality control, normalization, domain detection, phenotyping, and multiple spatial analyses. It also enables comparative analysis among samples and supports various ST technologies. We demonstrate the utility of spatialGE through its application in studying the tumor microenvironment of melanoma brain metastasis and Merkel cell carcinoma. Our results highlight the ability of spatialGE to identify spatial gene expression patterns and enrichments, providing valuable insights into the tumor microenvironment and its utility in democratizing ST data analysis for the wider scientific community.

Keywords: Spatial biology; bioinformatics; data visualization; spatial transcriptomics; statistical analysis; tumor microenvironment.

Figure 1.

The number of publications per year involving spatial transcriptomics data. Publication records extracted from The Museum of Spatial Transcriptomics (June 2024).

Figure 2.

The functionality of spatialGE. The web application can take ST data directly from the Space Ranger tool for Visium (.h5) or CosMx. The “generic format” is a third input option, accepting .txt or .csv tables with gene expression and spatial coordinates. Tissue images can also be uploaded. Sample-level metadata provided by the user can be easily input in spatialGE, facilitating comparative analysis. Once the ST data has been loaded, spatialGE guides the user through the different steps of an exploratory analysis pipeline, including quality control (gene and spot/cell filtering), count normalization, clustering, spatial analysis, and visualization.

Figure 3.

Analysis of LMM samples using spatialGE. Total number of counts per spot (A); Total number of non-zero genes per spot (B); Tissue domain classification classifications per spot based on STclust followed by differential expression analysis (C); Genes showing significant spatial gradients (STgradient, p-value < 0.05) and Spearman’s r > 0.1 (D). Expression of S100A1 in two samples (E); Expression of CCL18 in two samples (F). All figures (except panel D) were generated with spatialGE.

Figure 4.

Analysis of Merkel cell carcinoma samples using spatialGE. Total number of counts per cell for 10 of the FOVs in the data set (A); A principal components plot resulting from pseudobulk analysis (B); Cell type classifications resulting from Insitutype (C); Hallmark gene sets showing evidence of spatially-restricted enrichment (D). The median p-value is presented for all FOVs within a tissue sample (STenrich, p-value < 0.05 = red, p-value >= 0.05 = blue). All figures (except panel D) were generated with spatialGE.

Figure 5.

A diagram of the architecture of the spatialGE web application. Analysis requests are sent by the users through the web browser to the server, where a Docker container runs either R or Python scripts to generate the results, which are then accessible via the web browser by users.