IonVisualise

Welcome to the IonVisualise! This app is designed to help you visualize and analyze data from various sources, including PCA plots, scatter plots, volcano plots, and time series. The app provides a simple interface for uploading data files and generating intuitive and interactive visualizations.

Features

• PCA (Principal Component Analysis) Plot: Visualize data using PCA, helping you understand variance and relationships in high-dimensional datasets.
• Scatter Plot with Smooth Lines: Generate scatter plots with LOWESS smoothing lines to capture trends in your data.
• Volcano Plot: Perform differential expression analysis with volcano plots, marking significant data points.
• Time Series Plot: Track changes in data over time with interactive time series plots.
• Team Introduction Page: Meet the team behind the app on the "Meet the Team" page.

Project Directory Structure

./
├── helper_functions
│   ├── __init__.py              # Helper functions initializer
│   ├── file_operations.py       # File operation functions (e.g., removing old files)
│   ├── pca.py                   # PCA plot generation functions
│   ├── scatterplot.py           # Scatter plot generation functions
│   ├── timeseries.py            # Time series plot generation functions
│   └── volcano_plot.py          # Volcano plot generation functions
├── pages
│   └── Meet_The_Team.py         # Introduction of the team
├── environment.yaml             # Conda environment configuration
└── Home.py                      # Main script to run the app

Installation Instructions

This app runs in a Conda environment. Follow the steps below to set it up on your local machine.

1. Install Anaconda (if you don't have it already)

Download and install Anaconda from https://www.anaconda.com/products/individual.

2. Clone the Repository

git clone [email protected]:IonVisualisation/IonVisualise.git
cd IonVisualise

3. Create the Conda Environment

To set up the environment, use the environment.yaml file provided. This will create a Conda environment with all the required dependencies for the app.

conda env create -f environment.yaml

This will create an environment named data_visualisation with Python 3.12 and all the necessary packages.

4. Activate the Environment

Once the environment is created, activate it using:

conda activate data_visualisation

5. Run the App

With the environment activated, you can now run the Streamlit app using the following command:

streamlit run Home.py

The app should open in your web browser. If it doesn't, navigate to http://localhost:8501 in your browser.

6. Usage

• Upload Data: Upload .csv, .txt, or .xls files to visualize.
• Navigate Pages: Use the navigation buttons at the top of the app to explore different visualizations (PCA, Volcano Plot, Scatter Plot, Time Series).
• Meet the Team: Visit the "Meet The Team" page to learn more about the app creators.

Troubleshooting

• If you encounter an issue with missing packages, make sure the Conda environment is activated (conda activate data_visualisation).
• For issues with specific visualizations, ensure the uploaded data files are formatted correctly (e.g., proper column headers for Volcano and PCA plots).

Contributing

If you'd like to contribute to the project, feel free to submit pull requests or open issues. We welcome all contributions that improve the functionality, design, or performance of the app.

Please ensure all commits follow the conventional commits standard and that all pull requests to main pass all tests in the .github/workflows/test.yml folder.

Contacts

For any issues with the repository please contact:

• Eschal Najmi Github Email

For any issues with the web application itself please contact:

• Eschal Najmi Github Email
• Yew Mun Yip Github Email

For any issues with data visualisation within the web app please contact:

• Yew Mun Yip Github Email
• Georgia Whitton Github Email
• Sara Patti Github Email
• Spencer Duvwiama Github Linkedin

License

This project is licensed under the MIT License.

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Thank you for using the IonVisualise! We hope this tool helps streamline your data analysis workflows.