This project involves analyzing immune cell count data from various samples to determine relative frequencies of different cell populations and to explore differences between responders and non-responders to a specific treatment (tr1) in melanoma patients. The analysis is performed using Python with libraries like Pandas, Matplotlib, and Seaborn.

• For detailed insights and analysis check out the following notebook notebooks/treatment_analysis.ipynb.
• For an executive summary check out Insights.md
• For the task related to database design refer file DatabaseSchemaDesign.md

• Docker (optional)
• Docker Compose (optional)
• Git (optional, for cloning the repository)

1. Clone the repository (optional):

   git clone https://github.com/parthasarathydNU/cell-count-analysis.git
   cd cell-count-analysis

2. Install packages and access notebook:

   • Set up virtual environment and activate it
   • Install the packages listed in the requirements.txt file
   • Run the following command to start jupyter lab in the working directory of this application

     jupyter lab

   • Check url printed in the console and follow the link in your browser to access the notebook
   • Navigate to notebooks and double click on the treatment_analysis.ipynb file to open and view the notebook in your browser

3. Running as docker container

   • Run the following command in the working directory of this project
   • To build and run the containers for the first time run the command docker compose up --build
   • To re run the containers after editing any of the scripts run the command docker compose up
   • Logs will be displayed in the terminal. Check out sample_logs.md for sample logs

• scripts/: Contains Python scripts for data analysis.
  • percent_cell_count.py: Script to calculate relative frequencies of cell counts.
  • treatment_analysis.py: Script to analyze differences in cell populations.
• results/plots: Contains outputs from the scripts, including CSV files and plots.
• Dockerfile: Defines the Docker container setup.
• docker-compose.yml: Configures services, networks, and volumes for Docker.
• requirements.txt: Lists Python package dependencies.

Feel free to fork the repository and submit pull requests. You can also open an issue if you find any bugs or have suggestions for further improvements.

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

• Dhruv Parthasarathy

• Thanks to everyone who has contributed to the open-source packages used in this project.