https://github.com/hannahgsimon/halmodeling2024graphs

Created code to develop and analyze statistical graphs for the spatial radiotherapy model, which can be found at https://github.com/hannahgsimon/HALModeling2024. This project was in association with the Cleveland Clinic Lerner Research Institute, Jacob Scott Lab.
https://github.com/hannahgsimon/halmodeling2024graphs

agent-based-model bifurcation-analysis cancer-models computational-biology data-visualization hybrid-automata immune-response mathematical-modelling ordinary-differential-equations radiation-therapy spatial-model statistics systems-biology

Last synced: 8 months ago
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Created code to develop and analyze statistical graphs for the spatial radiotherapy model, which can be found at https://github.com/hannahgsimon/HALModeling2024. This project was in association with the Cleveland Clinic Lerner Research Institute, Jacob Scott Lab.

• Host: GitHub
• URL: https://github.com/hannahgsimon/halmodeling2024graphs
• Owner: hannahgsimon
• License: mit
• Created: 2024-08-01T22:27:02.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2025-01-10T20:40:45.000Z (9 months ago)
• Last Synced: 2025-01-10T21:41:57.821Z (9 months ago)
• Topics: agent-based-model, bifurcation-analysis, cancer-models, computational-biology, data-visualization, hybrid-automata, immune-response, mathematical-modelling, ordinary-differential-equations, radiation-therapy, spatial-model, statistics, systems-biology
• Language: Python
• Homepage: https://www.lerner.ccf.org/translational-hematology-oncology/scott/
• Size: 40 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
# HALModeling2024Graphs

## Overview

The HALModeling2024Graphs repository contains Python scripts to generate and analyze graphs for the HALModeling2024 project, found at https://github.com/hannahgsimon/HALModeling2024. This code graphs cell population changes in response to radiotherapy, as well as the immune response.

## Prerequisites

- **Python**: Version 3.6 or higher.

- **Required Python packages**: You can install them using `pip`:

   ```bash

   pip install pandas matplotlib

## Installation

1. Clone the repository:

    - git clone https://github.com/hannahgsimon/HALModeling2024Graphs.git

2. Navigate to the project directory with the cd command.

3. Import the project into your preferred Python IDE.

4. Build the project.

## Features

- **Plot Trial Cell Populations:** Plots cell counts at each timestep for the last trial.

- **Plot Trial Immune Response:** Plots the total immune response, primary immune response, and secondary immune response at each timestep.

- **Plot Scenario Cell Populations with Confidence Intervals:** For the indicated scenario, plots the average cell counts with confidence intervals (±1 SD) at each timestep across all trials. For scenarios C, D, and E, will only plot trials with initial escape & radiation.

- **Plot Tumor Cells at Equilibrium:** For trials in which bifurcation to escape occurred, plots a box and whisker plot of the number of tumor cells at equilibrium across each scenario.

- **Plot Timesteps to Equilibrium:** For trials in which bifurcation to escape occurred, plots a box and whisker plot of the number of timesteps to equilibrium across each scenario.

## Usage

Before running the code, you will need to update the file paths.  

In `HALModeling2024Graphs.py`:  

  ```python

  file_path = r'C:\Users\Hannah\Documents\HALModeling2024Outs\TrialRunCounts.csv'

  folder_path = fr'C:\Users\Hannah\Documents\HALModeling2024Outs\Scenario{scenario}\*.csv'

  file_path = fr'C:\Users\Hannah\Documents\HALModeling2024Outs\Scenario{scenario}\*.csv'

  save_path = fr'C:\Users\Hannah\Documents\HALModeling2024Outs\TrialRunGraphScenario{scenario}.png'

  save_path = r'C:\Users\Hannah\Documents\HALModeling2024Outs\TrialRunGraphImmuneResponse.png'

   ```

In `HALModeling2024Plots.py`:  

  ```python

  file_path = r'C:\Users\Hannah\Documents\HALModeling2024Outs\AvgTimestepstoEscape.csv'

  timesteps_save_path = fr'C:\Users\Hannah\Documents\HALModeling2024Outs\BoxplotTimesteps.png'

  tumorcells_save_path = fr'C:\Users\Hannah\Documents\HALModeling2024Outs\BoxplotTumorCells.png'

  ```

##### Usage in `HALModeling2024Graphs.py`:

- Input 1: `TrialRunCounts.csv` — Contains data from the final trial (non-scenario-specific), generated when `printCounts = true` and `scenarioActive = false` in `OnLattice2DGrid.java`.

- Input 2: A folder containing trials for a specific scenario, generated when `printCounts = true` and `scenarioActive = true` in `OnLattice2DGrid.java`.

The simulation starts with the below initial conditions (modifiable in the code). You can update these parameters in the indicated lines of code to fit your specific simulation requirements.

- **Plot with Confidence Intervals:** Disabled. For the indicated scenario, plots the average cell counts with confidence intervals (±1 SD) at each timestep across all trials. For scenarios C, D, and E, will only plot trials with initial escape & radiation.

     ```python

    plot_with_CIs = False

     ```

- **Scenario Analysis:** Disabled. For the last trial of the indicated scenario, plots the cell counts at each timestep.

     ```python

     scenarioAnalysis = False

     ```

- **Scenario:** A. The indicated scenario that will be graphed for `plot_with_CIs = True` or `scenarioAnalysis = True`.

     ```python

    scenario = 'A'

     ```

- **Timesteps 500:** Enabled. For `plot_with_CIs = True`, only the first 500 timesteps are plotted.  In the original `OnLattice2DGrid.java` code, this corresponds to the time by which the triggering cell population typically reaches 0. This occurs because there are initially 500 triggering cells, with 1 removed per timestep; however, the population may reach 0 sooner if cells are killed by radiation.

     ```python

    timesteps500 = True

     ```

- **Plot Immune Response:** Enabled. Plots the total immune response, primary immune response, and secondary immune response at each timestep. If `scenarioAnalysis = True`, plots the data for the last trial of the indicated scenario. If `scenarioAnalysis = False`, plots the data for the last trial (titled `TrialRunCounts.csv`, not a specific scenario).

     ```python

    graph_immune = True

     ```

- Note: When both `plot_with_CIs = False` and `scenarioAnalysis = False`, plots cell counts at each timestep for the last trial (`TrialRunCounts.csv`, not tied to a specific scenario).

#### Usage in `HALModeling2024Plots.py`:

- Input: A CSV file with 5 columns: "Scenario", "Trial" number, "Bifurcation to Escape" (Y/N), "Timesteps to Equilibrium", and "Tumor Cells at Equilibrium". The last 2 columns are blank for non-escape trials.

- Output 1: For trials in which bifurcation to escape occurred, plots a box and whisker plot of the number of tumor cells at equilibrium across each scenario.

- Output 2: For trials in which bifurcation to escape occurred, plots a box and whisker plot of the number of timesteps to equilibrium across each scenario.

## Contributing

Contributions are welcome! To contribute:

1. Fork the repository.

2. Create a new branch for your feature or bug fix:

    - git checkout -b feature-name

3. Commit your changes and push the branch:

    - git commit -m "Add new feature"

    - git push origin feature-name

4. Open a pull request and describe your changes in detail.

## License

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

## Contact

For questions or feedback, please contact Hannah G. Simon at hgsimon2@gmail.com.