RNAlytics: A Shiny-Based RNA-Seq Analysis Web Application

Version: 1.0.0
Last Updated: April 10, 2025
Author: Jash Trivedi
License: MIT

RNAlytics is a powerful, interactive web application built with R and Shiny, designed for comprehensive RNA-Seq data analysis and visualization. It enables researchers, bioinformaticians, and data scientists to perform differential expression analysis, conduct pathway and functional enrichment, and generate publication-quality visualizations—all within an intuitive interface. With a modular architecture leveraging industry-standard R packages, RNAlytics balances usability and advanced functionality, reflecting decades of software engineering best practices.

Run RNAlytics Online

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Table of Contents

1. Overview
2. Features
   • Differential Expression Analysis
   • PCA Plot
   • Volcano Plot
   • Heatmap
   • Gene Count Plot
   • KEGG Pathway Enrichment
   • Gene Ontology (GO) Analysis
3. Use Cases
4. Installation
   • Prerequisites
   • Local Deployment with Shiny Server
   • Troubleshooting Deployment
5. Usage
6. Support
7. Acknowledgments

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Overview

RNAlytics streamlines RNA-Seq workflows by integrating data processing, statistical analysis, functional enrichment, and visualization into a single platform. Built with R 4.4.3 and Shiny, it leverages packages like DESeq2, ggplot2, pheatmap, EnhancedVolcano and clusterProfiler to deliver high-performance analytics. The app’s modular design—split across data_processing.R, plotting.R, server_logic.R, ui_components.R, and utilities.R—ensures maintainability and extensibility.

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Features

Differential Expression Analysis

• Description: Performs differential expression analysis using DESeq2 or Limma on user-uploaded RNA-Seq count data. Users define sample groups and comparisons for downstream analysis and visualization.
• Inputs: Count matrix (.txt), group definitions, comparison base/contrast.
• Outputs: Table with log2 fold changes and adjusted p-values; downloadable as CSV.

PCA Plot

• Description: Visualizes sample variance via a static PCA plot based on normalized counts. Points are colored by condition, with customizable labels and aesthetics.
• Inputs: Title, axis labels, point/label/font sizes, condition colors.
• Outputs: Scatter plot with variance percentages, downloadable as PDF (16x7 inches, 1200 DPI).

Volcano Plot

• Description: Highlights differentially expressed genes (DEGs) with a volcano plot, based on log2 fold change and adjusted p-value. Top genes are labeled dynamically.
• Inputs: Title, subtitle, axis labels, thresholds, point/label sizes, colors, top gene count.

Heatmap

• Description: Displays a clustered heatmap of the top 50 DEGs, with Z-score scaling and dendrograms. Samples are annotated by condition.
• Inputs: Title, clustering method, row name visibility, font size, color palette.
• Outputs: Static heatmap, downloadable as PDF (15x18 inches, 1200 DPI).

Gene Count Plot

• Description: Plots normalized expression (log2 counts) of a user-specified gene across samples, colored by condition, for detailed gene-level analysis.
• Inputs: Gene name (dropdown), title, point/font sizes, condition colors.
• Outputs: Scatter plot with rotated x-axis labels, downloadable as PDF (12x6 inches, 1200 DPI).

KEGG Pathway Enrichment

• Description: Performs Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis on DEGs, identifying overrepresented biological pathways. Results are stored and visualized as tables or plots.
• Inputs: DEG list from differential analysis, organism type (e.g. human or mouse), p-value cutoff.
• Outputs: Table of enriched pathways with p-values, gene counts, and pathway IDs; downloadable as CSV. Optional bar or dot plot visualization.

Gene Ontology (GO) Analysis

• Description: Conducts Gene Ontology enrichment analysis on DEGs, categorizing genes into Biological Process (BP), Molecular Function (MF), and Cellular Component (CC) terms. Results enhance functional interpretation.
• Inputs: DEG list, ontology type (BP/MF/CC), organism type, p-value cutoff.
• Outputs: Table of enriched GO terms with p-values, gene ratios, and term descriptions; downloadable as CSV. Optional visualization (e.g., bar plot or network).

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Use Cases

1. Exploratory Analysis:

   • Scenario: Assess sample clustering and gene expression variability.
   • Features: PCA Plot, Gene Count Plot.
   • Outcome: Detects outliers and validates experimental design.

2. Differential Expression Studies:

   • Scenario: Compare gene expression across conditions.
   • Features: Differential Expression Analysis, Volcano Plot, Heatmap.
   • Outcome: Identifies and visualizes significant DEGs.

3. Pathway and Functional Insights:

   • Scenario: Investigate biological implications of DEGs in a disease model.
   • Features: KEGG Pathway Enrichment, GO Analysis.
   • Outcome: Reveals enriched pathways (e.g., metabolism) and GO terms (e.g., immune response), aiding hypothesis generation.

4. Candidate Gene Validation:

   • Scenario: Examine expression of a specific gene across samples.
   • Features: Gene Count Plot.
   • Outcome: Confirms gene behavior for targeted studies.

5. Comprehensive Genomic Workflow:

   • Scenario: Analyze RNA-Seq data end-to-end for a publication.
   • Features: All features combined.
   • Outcome: Produces statistical results, visualizations, and functional annotations in one pipeline.

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Support

• Email: jashtrivedi221@gmail.com
• Logs: Share /var/log/shiny-server.log for deployment support.

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Acknowledgments

This web application was developed as part of an effort to streamline RNA-Seq data analysis for researchers and students. I would like to thank the open-source R and Bioconductor communities for their powerful tools, and the developers of packages like DESeq2, clusterProfiler, and EnhancedVolcano for making high-quality bioinformatics accessible. Special thanks to my mentors and peers for their feedback during development and testing.