bmdrc 1.0.0

A package for fitting benchmark dose curves to dichotomous, proportional, and light photomotor response data

bmdrc

Python library for the calculation of BenchMark Dose Response Curves (bmdrc)

See the Sphinx documentation here

We suggest using Python version 3.10 or later.

General Schematic

The bmdrc library was built to calculate benchmark dose (BMD) response curves for dichotomous (morphological) and light photomotor response datasets, where continuous variables are transformed to dichotomous as described in Thomas et al 2019. Potential outputted files include a csv file of all final BMDs and their estimation errors, a csv file of model fits (AIC) for each endpoint, and an html report containing information on how much data was filtered and why, as well as interactive response curve plots. Users may specify their outputs of interest.

1. Input Data Module: Import data into the python library

2. Pre-Processing Module: Combine and remove endpoints as needed

3. Filtering Modules: Apply the EPA recommendations for filtering

4. Model Fitting Modules: Fit EPA-recommended models to data

5. Output Modules: Select tables to output as csvs. View plots in a HTML report.

How to install the package

First, install the package from pip using:

pip install bmdrc

Or from github using:

pip install git+https://github.com/PNNL-CompBio/bmdrc

How to use the package

Vignettes

An example vignette for the dichotomous (binary) data (when plate and well information is available) is located here and an example vignette for when only chemical, endpoint, concentration, and response (ranging from 0-1) information is available is located here. For the light photomotor response data (continuous converted to dichotomous), a vignette is located here.

Example Data

Example data for dichotomous and light photomotor response data can be found here.

Example Report

A sample for the generated report for dichotomous (binary) data can be found here

A sample for the generated report for light photomotor response data can be found here

Data Classes

For datasets with plates and wells, use the BinaryClass object as it contains specific pre-processing and filtering functions that pertain to plates and well. Each value should be 0, 1, or NA, and bmdrc will calculate the response column. If your data does not contain plates and wells, use the SimplifiedClass object which only requires chemical, endpoint, concentration, and response information. The response must be between 0 and 1. If your data is light photomotor response use the LPRClass object. The continuous data will be transformed to dichotomous.