Gene Analysis Dashboard

Description

This tool was developed while writing my Bachelor's Thesis in Computer Science. The tool is used for gene analysis using data from the Benito-Kwiecinski and Kutsche datasets. All the visualizations are based on the results of Granger Causality Test.

Setup Instructions

Prerequisites

• Python 3.6 or higher
• Graphviz installed and added to PATH

Installation Instructions

Installing Python

On Windows:

1. Download Python from the official website: Python Downloads.
2. Run the installer and ensure you check the box that says "Add Python to PATH".
3. Follow the installation steps.

On macOS:

1. Download Python from the official website: Python Downloads.
2. Run the installer and follow the installation steps.
3. Alternatively, you can install Python using Homebrew:

   brew install python

On Linux:

1. Use the package manager for your distribution to install Python. For example, on Ubuntu:

   sudo apt update
   sudo apt install python3 python3-venv python3-pip

Installing Graphviz

On Windows:

1. Download Graphviz from the official website: Graphviz Downloads.
2. Run the installer and follow the installation steps.
3. Add Graphviz to your system PATH (usually, the installer does this automatically).

On macOS:

1. Install Graphviz using Homebrew:

   brew install graphviz

On Linux:

1. Use the package manager for your distribution to install Graphviz. For example, on Ubuntu:

   sudo apt update
   sudo apt install graphviz

Installing Git

On Windows:

1. Download Git from the official website: Git for Windows.
2. Run the installer and follow the installation steps.

On macOS:

1. Install Git using Homebrew:

   brew install git

On Linux:

1. Use the package manager for your distribution to install Git. For example, on Ubuntu:

   sudo apt update
   sudo apt install git

Setting Up the Project

1. Clone the Repository:

   git clone https://github.com/LukasMadsenBrandt/gene_analysis_dashboard.git
   cd gene_analysis_dashboard

2. Create and Activate Virtual Environment:

   On Unix-like systems (Linux/macOS):

   python3 -m venv venv
   source venv/bin/activate

   On Windows:

   python -m venv venv
   venv\Scripts\activate

3. Install Dependencies:

   pip install -r requirements.txt

4. Run the Application:

   python app.py

Directory Structure

gene_analysis_dashboard/
├── app.py
├── Data/
│   ├── Benito/
│   │   ├── Benito_Gorilla
│   │   ├── Benito_Human
│   │   ├── gene_id_to_gene_name.txt
│   │   ├── gene_names.txt
│   │   └── map_speciment_to_gene.csv
│   └── Kutsche/
│       ├── genes.txt
│       ├── gene_names.txt
│       ├── Kutsche_Counts.txt
│       └── search_genes.sh
├── gene_analysis_benito/
│   ├── config.py
│   ├── data_filtering.py
│   ├── data_preprocessing.py
│   ├── decorators.py
│   ├── granger_causality.py

├── gene_analysis_kutsche/
│   ├── config.py
│   ├── data_filtering.py
│   ├── data_preprocessing.py
│   ├── decorators.py
│   ├── granger_causality.py

Usage

1. Select the dataset and summarization technique.
2. Press "Send" to generate the graph.
3. Use the community detection options and other controls to customize the graph.

Contributing

Contributions are welcome. Please submit a pull request or open an issue to discuss the changes.

License

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