HuntOmics Explorer

HuntOmics Explorer is an R Shiny application developed for the comprehensive molecular analysis of Huntington's Disease (HD). It is specifically designed to analyze and visualize data from the study titled "mRNA-Seq Expression profiling of human post-mortem BA9 brain tissue for Huntington’s Disease and neurologically normal individuals" (GSE64810).

Features

The application offers the following functionalities:

1. Sample Information Exploration: Provides an overview of sample metadata, including summary statistics and visualizations.

2. Counts Matrix Exploration: Allows users to explore gene expression counts, visualize scatter plots, clustered heatmaps, and perform Principal Component Analysis (PCA).

3. Differential Expression Analysis: Enables identification of differentially expressed genes between HD and control samples, with visualization options.

4. Gene Set Enrichment Analysis: Assists in identifying enriched pathways and biological processes associated with HD.

Installation

To run HuntOmics Explorer locally, follow these steps:

1. Clone the Repository:

   git clone https://github.com/N3ha-Rao/HuntOmics-Explorer.git

2. Navigate to the Directory:

   cd HuntOmics-Explorer

3. Install Required Packages:

   Ensure you have R and RStudio installed. Then, install the necessary packages.

4. Run the Application:

   library(shiny)
   runApp("N3ha-Rao-App.R")

Usage

Upon launching the application, you will encounter several tabs, each designed for specific analyses:

1. Sample Information Exploration:

   • Input: Upload the Metadata.csv file containing sample information.
   • Features:
     • Displays a summary table of the metadata.
     • Generates histograms for numeric variables.

2. Counts Matrix Exploration:

   • Input: Upload the Counts.csv file containing gene expression counts.
   • Features:
     • Provides a summary of the counts matrix.
     • Offers scatter plots for user-selected genes.
     • Displays clustered heatmaps of filtered counts.
     • Performs PCA and visualizes the results.

3. Differential Expression Analysis:

   • Input: Upload the DE.csv file containing differential expression results.
   • Features:
     • Displays a table of differentially expressed genes.
     • Generates volcano plots to visualize gene expression changes.

4. Gene Set Enrichment Analysis:

   • Input: Uses differential expression results from the Differential Expression Analysis tab.
   • Features:
     • Identifies enriched Gene Ontology (GO) terms and pathways.
     • Displays bar plots and dot plots of enriched terms.

Data Preparation

Ensure that your input files (Metadata.csv, Counts.csv, and DE.csv) are formatted correctly and correspond to the data from the GSE64810 study. The application expects specific column names and data structures as outlined in the N3ha-Rao-App.R script.

Acknowledgements

Special thanks to the authors of the GSE64810 study for providing the data utilized in this application.