py50 1.0.4

Generate Dose-Response Curves

py50: Generate Dose-Response Curves

Summary

The aim of py50 is to make the generation of dose-respnose curves and annotated plots with statistics. The project was created primarily for my personal use and for my coworkers/classmates. I found many of my classmates/coworkers were using a program that I find to be unfriendly in generating dose-response curves or with calculating statistics and plots. During my search, I found other helpful repositories that can generate dose-response curves, calculate statistics, or make annotated plots. However, I found that these packages did not meet my requirements:

1. Use Pandas for the Data so that it can be easily plugged into a Jupyter Notebook or Python scripts
2. Adaptable to user needs
3. Easy to use (hopefully!)

The dose-response curves are built on the four parameter logistic regression model: $$Y = \text{Min} + \frac{\text{Max} - \text{Min}}{1 + \left(\frac{X}{\text{IC50}}\right)^{\text{Hill coefficient}}}$$ where min is the minimum response value, max is the maximum response value, Y is the response values of the curves, X is the concentration.

The statistics and annotated plots is a wrapper for Pingouin and Statannotations. This may have been done inelegantly and will be updated based on my use or recommendations by other users. As things stand, this project meets my and the needs of my classmates/coworkers. Hopefully it can meet the needs of others.

NOTE: As of this writing, Statannotations is at v0.6. It is incompatible with Seaborn ≥v0.12 or with Pandas ≥v.2.0. py50 will install Seaborn v0.12. I did this because I wanted to have options to modify errorbars on the barplots. I would love to be able to bcontribute to Statannotations and bump it up to match the Seaborn updates, but it seems to be a daunting challenge and will require time on my part. 🤞Hopefully soon!🤞

Installation

pip install py50

Pacakge can be upgraded specifically using pip with the following:

pip install py50 -U

Tutorial

Documentation can be found here.

A Jupyter Notebook demoing the code can be found here.

A blog post demoing the code can be found at Practice in Code

Web Application

For those who are not versed in python coding, py50 has been converted into a web application using Streamlit!

The web application can be found here: py50-app

The repository for the Streamlit app version can be found here: py50-streamlit

NOTE: Updates to the web application take more time. As of this writing, the py50 Streamlit is running on version 0.3.6. Updates with statistics and plot annotations will be forthcoming.

Future Work

With the release of py50 v1.0.0, I have finished a project that has been on my mind for the past six months. My aim now will be to reformat the code for maintainability and to fix any bugs that I find or others report. I plan on maintaining py50 for the foreseeable future. As such, my current "To-Do" list (in no particular order) are as follows:

• Complete To-Do notes in Python script
• Update Tutorials for clarity
• Update py50 Streamlit to version 1.0.0
• Refactor code for maintainability
• Add error messages!
• (Bonus Points) Provide KNIME workflow?

Citation

If you are interested in citing the repository, I have generated a DOI link using Zenodo here:

Thanks for your interest!