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Welcome to GD2Viz

Welcome to GD2Viz, a powerful and user-friendly visualization tool designed to help researchers and clinicians explore, analyze, and interpret GD2 Scores across various RNA-Seq datasets. Utilizing the advanced methodology outlined by Ustjanzew et al. (2024), GD2Viz offers an in-depth examination of precomputed GD2 Scores from publicly available RNA-Seq datasets such as TCGA, GTEx, and TARGET. Users also have the flexibility to upload and analyze their own datasets within the context of GD2 Scores.

GD2Viz provides a range of interactive visualizations and precomputed RNA-seq datasets. Additionally, the GD2Viz R package includes several functions for computing Reaction Activity Scores of glycosphingolipid metabolism and predicting GD2 Scores directly within the R environment. For comprehensive guidance, refer to the articles at GD2Viz webpage.

⚠️ Note: The local version does not include access to precomputed public datasets (e.g., TCGA, GTEx, TARGET). To explore GD2 Scores across these datasets, please use the online version of the app, available at: http://shiny.imbei.uni-mainz.de:3838/GD2Viz

🔑 Key Features

GD2Viz provides a comprehensive framework for the analysis, visualization, and interpretation of GD2 expression potential across bulk RNA-Seq datasets, with a focus on glycosphingolipid metabolism. The package includes both an interactive Shiny application and a set of R functions for programmatic use.

Predictive GD2 Scoring

  • Implements a trained Support Vector Machine (SVM) model to infer GD2 Scores from transcriptomic data.
  • Integrates pathway informed features from glycosphingolipid metabolism via Reaction Activity Scores (RAS).

Visualization & Exploration

  • Interactive Shiny App: Explore GD2 Scores across large public datasets online (TCGA, GTEx, TARGET, St. Jude Cloud, CBTTC).
  • Visualize GD2 Scores using scatter, box, and violin plots grouped by clinical or molecular subtypes.
  • Heatmaps, scatter plots, and pathway diagrams to interpret RAS distributions.

Custom Dataset Analysis

  • Upload your own RNA-Seq data and compute RAS and GD2 Scores locally.
  • Supports input as raw count matrices with metadata (as .tsv files) or DESeqDataSet objects.

Differential Expression & Group Comparison

  • Perform GD2-based sample stratification (e.g., high vs. low) and run DEA directly in the app or programmatically.
  • Visualize DEA results with volcano plots, MA plots, p-value and log2 fold-change histograms.
  • Searchable gene tables with built-in annotation and gene-specific expression profiles.

Pathway-Specific Insights

  • Focused analysis of the ganglioside branch of glycosphingolipid metabolism.
  • Log2 fold-change plots of RAS values between experimental groups.
  • Network-style visualizations of ganglioside pathway activity changes.

Programmatic Interface (R Package)

  • Compute RAS and predict GD2 Scores in batch mode for custom analyses.
  • Seamless integration with Bioconductor workflows and standard transcriptomics formats.

Documentation

⚠️ For detailed instructions and examples on how to use the app, please refer to the vignette GD2Viz app - Explore public datasets and analyze your data.

⚠️ If you want to use the core functions in your R environment, please refer to the vignette “Computing GD2 Scores Programmatically Using GD2Viz”.

Installation

You can install the GD2Viz package from GitHub using the following commands in R:

# Install the devtools package if you haven't already
install.packages("devtools")

# Use devtools to install GD2Viz from GitHub
devtools::install_github("arsenij-ust/GD2Viz")

or using the remotes package:

install.packages("remotes")
remotes::install_github("arsenij-ust/GD2Viz")

Usage

After installation, you can load the package and launch the app:

library(GD2Viz)

# Launch the Shiny application
GD2Viz()

Development Team

GD2Viz was developed at the Institute for Medical Biostatistics, Epidemiology, and Informatics (IMBEI) of the University Medical Center of the Johannes Gutenberg University Mainz. The development team includes:

  • Arsenij Ustjanzew: Developer
  • Federico Marini: Developer
  • Claudia Paret: Methodological and Clinical Support

Cite us:

Ustjanzew et al. Predicting GD2 expression across cancer types by the integration of pathway topology and transcriptome data. 2025

For more information, visit our website or consult the GD2Viz Vignette. If you have any questions or need assistance, please don’t hesitate to reach out to our support team.

License

This project is licensed under the MIT License - see the LICENSE file for details.