This repository showcases logistic regression model fitting for two-dimensional cell growth with limited growing space (e.g., tissue culture areas).
The primary objective of this project is to:
- Identify if a treatment affects cell growth post-confluence.
- Analyze growth dynamics using a logistic regression model to fit cell growth data over time.
Key focus:
Increased fitted maximum cell growth (asymptote) may indicate a loss of contact inhibition, which is a hallmark of oncogenesis.
- Cell growth in limited space: Cells in a tissue culture grow until they reach confluence (the maximum capacity of the culture area).
- Contact inhibition: Normal cells stop dividing upon confluence. Loss of contact inhibition is associated with tumorigenic behavior.
- Logistic regression fitting: The logistic growth model is used to assess changes in growth patterns and to determine whether treatments alter the asymptote (maximum growth capacity).
- Data Preparation:
- Time-series data of cell growth in treated and control groups.
- Normalization and preprocessing to ensure model accuracy.
- Model Fitting:
- Logistic regression is applied to fit growth curves for each condition.
- Extract parameters such as the growth rate, maximum growth (asymptote), and inflection point.
- Interpretation:
- Compare parameters between treated and control groups.
- Assess whether the treatment leads to increased asymptote values, indicating loss of contact inhibition.
- Scripts: R scripts for data preprocessing, model fitting, and visualization.
- Data: Sample or representative datasets used for analysis.
- Results: Plots and statistical summaries of fitted logistic regression models.
- Clone the repository:
git clone https://github.com/yourusername/longitudinal-logistic-regression.git