mr-mathias-f / pydrc

A Python module for analysis and visualization of dose-response data

pydrc: Python Dose-Response Curves

pydrc is a powerful Python module specially designed for the analysis and visualization of dose-response data in fields like toxicology, pharmacology, and environmental sciences.

The package simplifies the process of implementing various dose-response models, offering a uniform interface for a wide range of common models, including but not limited to Hill, Logistic, Gompertz models, and more.

Key Features

• Wide Range of Models: Implementation of a broad selection of dose-response models.
• Robust Estimation: Parameter estimation using state-of-the-art optimization algorithms.
• Model Evaluation: Tools for the evaluation of model performance and selection.
• Data Visualization: Aesthetic and intuitive visualization of dose-response curves using Matplotlib and Seaborn.
• Flexibility: Capability to handle user-defined models.

Built for the scientific community, pydrc bridges the gap between intricate dose-response analyses and Python's ease of use, empowering researchers to concentrate on interpreting results instead of wrestling with the coding of analyses.

Contributions are welcome.

Example

# Include your DataFrame, dose- or concentration variable, and response variable
toxin_mod = LogisticP4Model(data = toxin_df, x = 'Dose', y = 'Response')
# Fit the model to your data
toxin_mod.fit()
# X the the range of desired predicted values of y (response)
X = np.linspace(0.1, 10000, 10000)
toxin_mod.predict(x = X)
# Plot the final model
toxin_mod.plot() 

Parameter	Estimate	Std. Error	t-value	p-value
b	1.467726	0.089677	16.366838	0.000000
c	100.320987	0.817869	122.661497	0.000000
d	6.261767	1.208848	5.179944	0.000009
e	101.744631	4.820496	21.106674	0.000000

Todo

• Implementation of multiple optimization algorithms for existing functions (Current: Levenberg–Marquardt algorithm for unconstrained optimization; Trust Region Reflective for constrained optimization)
• Implement superfunction for data input, variable arguments and specified function to be optimized (built-in functions for now)
• Introduce functions for effective dose estimation and benchmark dosing
• Curve ID argument for summary table and visualization of multiple treatment groups
• Automatic and customizable dose-response curve visualization in Matplotlib and Seaborn with **kwargs
• Integrating and testing each function