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

Area of Science:

• Genomics
• Bioinformatics
• Computational Biology

Background:

• Spatial transcriptomics (ST) offers powerful insights into tissue biology and disease mechanisms.
• High-level data analysis and programming skills currently limit the widespread adoption of ST.
• A user-friendly solution is needed to make ST data analysis accessible to a broader scientific audience.

Purpose of the Study:

• To introduce spatialGE, a web application designed to simplify and democratize spatial transcriptomics data analysis.
• To provide a user-friendly interface for various ST analysis pipelines, including quality control, normalization, domain detection, and phenotyping.
• To enable comparative analysis across multiple samples and support diverse ST technologies.

Main Methods:

• Development of a web application, spatialGE, featuring an intuitive graphical user interface.
• Integration of comprehensive ST data analysis workflows: quality control, normalization, domain detection, phenotyping, and spatial analyses.
• Implementation of comparative analysis capabilities and support for various ST data formats.

Main Results:

• spatialGE successfully guides users through complex ST data analysis without requiring programming expertise.
• The application facilitates the identification of spatial gene expression patterns and enrichments.
• Demonstrated utility in analyzing the tumor microenvironment in melanoma brain metastasis and Merkel cell carcinoma.

Conclusions:

• spatialGE significantly lowers the barrier to entry for spatial transcriptomics data analysis.
• The tool empowers researchers to gain deeper insights into tissue architecture and disease mechanisms.
• spatialGE democratizes ST analysis, fostering wider scientific community engagement and discovery.