QuEStVar Manuscript Analysis

Status	License	Language	Release	Zenodo	Citation

This repository contains the code and data supporting the manuscript "Statistical Testing for Protein Equivalence Identifies Core Functional Modules Conserved Across 360 Cancer Cell Lines and Presents a General Approach to Investigating Biological Systems". Here, you'll find the scripts and Jupyter Notebooks used for:

• Spike-in Data Analysis: Preprocessing and applying QuEStVar to a benchmark dataset, demonstrating the method's capabilities.
• Simulation Studies: Evaluating QuEStVar's sample equivalence index metric's performance compared to correlation under various simulated scenarios.
• Cancer Cell Line Analysis: Using QuEStVar to explore quantitative protein stability and variability to identify conserved functional modules across a large collection of cancer cell lines.

Repository Structure

• 2022_Frohlich:
  • Notebooks for spike-in dataset analysis (data prep, QuEStVar application, simulation comparison).
  • Subfolders for data (raw, processed, results, supplementary) and figures.
• 2022_Goncalves:
  • Notebooks for cancer cell line analysis (data prep, statistical testing, stability analysis).
  • Subfolders for data (raw, processed, results, supplementary) and figures.
• Misc:
  • Notebook describing libraries, functions, and software versions used.
• questvar:
  • Source code for the QuEStVar statistical testing framework.
• supp_notebooks:
  • HTML versions of notebooks (generated using nb_to_html.sh script).
• requirement.txt, LICENSE, README.md, .gitignore, nb_to_html.sh

Note: The data and figures folders are ignored by git to avoid storing large files. The raw data to be placed in the data/raw folders can be obtained from the zenodo link provided above.

How to Use

1. Clone the repository.
2. Install dependencies
   • Create a virtual environment: It's highly recommended to work in an isolated virtual environment to avoid conflicts. Here's how to create one:
     • conda:

       conda create --name my_env python=3.9  # Replace 'my_env' with your desired name
       conda activate my_env

     • pip:

       python3 -m venv my_env  # Replace 'my_env' with your desired name
       source my_env/bin/activate  # Linux/macOS
       my_env\Scripts\activate    # Windows

   • Install packages:
     • Using requirements.txt (if provided):

       pip install -r requirements.txt 

     • Manually (if requirements.txt is not provided):

       conda install <package_name>  # Use conda for each package
       # OR
       pip install <package_name>    # Use pip for each package

3. Explore the Jupyter Notebooks in the 2022_Frohlich and 2022_Goncalves folders to follow the analyses.
4. Refer to the questvar folder for the core implementation of the QuEStVar method.