spatialGE Is a User-Friendly Web Application That Facilitates Spatial Transcriptomics Data Analysis

Abstract

Spatial transcriptomics (ST) is a powerful tool for understanding tissue biology and disease mechanisms. However, the advanced data analysis and programming skills required can hinder researchers from realizing the full potential of ST. To address this, we developed spatialGE, a web application that simplifies the analysis of ST data. The application spatialGE provided 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 enabled comparative analysis among samples and supported various ST technologies. The utility of spatialGE was demonstrated through its application in studying the tumor microenvironment of two data sets: 10× Visium samples from a cohort of melanoma metastasis and NanoString CosMx fields of vision from a cohort of Merkel cell carcinoma samples. These results support the ability of spatialGE to identify spatial gene expression patterns that provide valuable insights into the tumor microenvironment and highlight its utility in democratizing ST data analysis for the wider scientific community. Significance: The spatialGE web application enables user-friendly exploratory analysis of spatial transcriptomics data by using a point-and-click interface to guide users from data input to discovery of spatial patterns, facilitating hypothesis generation.

Figure 1.

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 have 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. H&E, hematoxylin and eosin; mIF, multiplex immunofluorescence.

Figure 2.

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.

Figure 3.

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

Figure 4.

Analysis of MCC samples using spatialGE. A, Total number of counts per cell for 10 of the FOVs in the data set. B, Principal component plot resulting from pseudobulk analysis. C, Cell type classifications resulting from InSituType. D, Hallmark gene sets showing evidence of spatially restricted enrichment. The median P value is presented for all FOVs within a tissue sample (STenrich, P < 0.05, red; P ≥ 0.05, blue). All figures (except D) were generated with spatialGE. CR, complete response; PD, progressive disease; PR, partial response.