Shiny for WGCNA

(Under active development)

This Shiny app provides a user-friendly interface for performing Weighted Gene Co-expression Network Analysis (WGCNA) on RNA-seq/Microarray and DNA methylation (Array/Sequencing) data. It allows for data upload, parameter customization, visualization of results, and exporting of analysis outputs.

Input Data

RNA-seq Data: Upload a CSV file with genes as columns and samples as rows. The first column should contain sample names. DNA Methylation Data: Upload a CSV file with the same structure as the RNA-seq data, containing methylation values.

How to run locally

R # laod R >= 4.3.1 library(shiny) shiny::runApp("app.R")

Online webserver

https://shinywgcna.serve.scilifelab.se/app/shinywgcna

PLEASE NOTE Datasets with larger dimensions (e.g., 1000x100) may fail on the server,because it is only running on 1GB RAM allocation. Please use the Docker container in local machine

docker run --rm -p 3838:3838 jd21/shinywgcna:latest

Parameters

WGCNA Parameters

Soft Threshold Power: - Range: 1-20 - Default: 6 - Description: Determines the scale-free topology of the network. Higher values lead to more stringent co-expression relationships.

Minimum Module Size:

• Range: 10-100
• Default: 30
• Description: The minimum number of genes required to form a module.

Merge Cut Height:

Range: 0.1-1 Step: 0.05 Default: 0.25 Description: The height at which the dendrogram tree is cut to merge similar modules. Lower values result in more modules.

Network Type:

Options: "unsigned", "signed", "signed hybrid" Default: "unsigned" Description: Determines how the correlation between genes is treated in the network construction.

Number of Cores:

Range: 1-16 (adjust based on your system's capabilities) Default: 1 Description: Number of CPU cores to use for parallel processing. Higher values can speed up calculations for large datasets.

Functionality

Analysis

Click "Run WGCNA" to start the analysis after setting parameters and uploading data. The app will validate input data, perform WGCNA analysis, and generate visualizations.

Visualizations

• Soft Threshold Plot: Shows the scale-free topology fit and mean connectivity for different soft threshold powers.
• Network Dendrogram: Displays the hierarchical clustering of genes and module colors.
• Module-Trait Correlation: Heatmap showing correlations between module eigengenes and traits (methylation data).
• Gene Dendrogram: Detailed dendrogram of genes with module color assignments.
• Network Heatmap: Visualizes the Topological Overlap Matrix (TOM) of the gene network.
• Scatter Plot: Interactive plot of module eigengene vs. average methylation for selected modules.

Export Options

• Download Module Genes: CSV file containing gene-to-module assignments.
• Download Network Data: RData file containing the Topological Overlap Matrix.
• Download Module Details: CSV file with detailed module membership information.
• Download for Cytoscape: Text file compatible with Cytoscape for external network visualization.

Debug Information

The app provides detailed debug information, including: - Confirmation of WGCNA completion - Number of identified modules - Dimensions of key data structures - Number of common genes between RNA-seq and methylation data - Module sizes - Sample and gene names

Notes

• The app performs data validation to ensure input data meets WGCNA requirements.
• For large datasets, increase the number of cores to improve performance.
• Adjust parameters based on your specific dataset and research questions.
• The app uses parallel processing for improved performance on multi-core systems.

Troubleshooting

• If the app crashes or produces unexpected results, check the console for error messages.
• Ensure your input data is properly formatted and contains no missing values.
• For very large datasets, you may need to increase R's memory limit or use a more powerful computer.

References

Langfelder, P., & Horvath, S. (2008). WGCNA: an R package for weighted correlation network analysis. BMC bioinformatics, 9(1), 559. For more information on WGCNA, visit: https://horvath.genetics.ucla.edu/html/CoexpressionNetwork/Rpackages/WGCNA/

Credits

• Main Author:

  • Jyotirmoy Das (@JD2112)

• Collaborators: ()

Citation

Das, J. (2024). ShinyWGCNA (v1.1). Zenodo. https://doi.org/10.5281/zenodo.14283132

Acknowledgement

We would like to acknowledge the Core Facility, Faculty of Medicine and Health Sciences, Linkping University, Linkping, Sweden and Clinical Genomics Linkping, Science for Life Laboratory, Sweden for their support.