{"repo":"tlint101/py50","free":true,"listed":false,"github":"https://github.com/tlint101/py50","clone":"git clone https://github.com/tlint101/py50.git","description":"Generate Dose-Response Curves in Python","language":"Jupyter Notebook","stars":12,"topics":["bioinformatics","cheminformatics","dose-response","python","data-visualization"],"license":"GPL-3.0","category":"analytics","readme_excerpt":"py50: Generate Dose-Response Curves Summary The aim of py50 is to make the generation of dose-response 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 in py50 are built using 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, and X is the concentration. The statistics and annotated plots are wrapped from Pingouin and Statannotations. This may have been done inelegantly and will be updated based on my use or recommendations by others. As things stand, this project meets my needs and the needs of my classmates/coworkers. Hopefully it can meet the needs of others. Installation pacakge can be upgraded specifically using pip with","default_branch":null,"files":null,"tree":[],"storefront":"/r/tlint101","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/tlint101/py50/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}