Metadata-Version: 2.1
Name: pyphenopop
Version: 0.0.1
Summary: Phenotypic deconvolution in heterogeneous cancer cell populations using drug screen data
Home-page: https://github.com/ocbe-uio/pyPhenoPop
Author: Leonard Schmiester, Polina Arsenteva, Even Myklebust, Alvaro Köhn-Luque
Author-email: leonargs@uio.no, apolinaria10@mail.ru, e.m.myklebust@medisin.uio.no, a.k.luque@medisin.uio.no
License: MIT
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Operating System :: OS Independent
Requires-Python: >=3.8.0
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: matplotlib (==3.5.3)
Requires-Dist: scipy (>=1.9.3)
Requires-Dist: numpy (>=1.23.4)
Requires-Dist: pandas (>=1.5.1)
Requires-Dist: tqdm (>=4.64.1)

Mixed Cell Population Identification Package
=======================
[![PyPI](https://badge.fury.io/py/pyphenopop.svg)](https://badge.fury.io/py/pyphenopop)

This package contains methods designed to determine the existance of 
subpopulations with different responses to a given drug from a dataset of 
screenings with that drug on the total cell population. The implementation is
based on the method presented in the article 
["Phenotypic deconvolution in heterogeneous cancer cell populations using drug screening data"](https://doi.org/10.1101/2022.01.17.476604).
The dataset should contatin cell viability data tested on C different drug concentrations over 
N time points and containing R replicates. The package can then estimate the 
number of subpopulations, their mixture proportions and a dose-reponse curve 
for each of the subpopulations. 

## Install
The package can be easily install via `pip install pyphenopop`. You can also install it from the [Github repository](https://github.com/ocbe-uio/pyPhenoPop) using 

`pip install git+https://github.com/ocbe-uio/pyPhenoPop.git` 

or by cloning the repository

`git clone https://github.com/ocbe-uio/pyPhenoPop` 

and installing from the local repository via

`pip install .`

## Usage

A tutorial using data from the original [publication](https://doi.org/10.1101/2022.01.17.476604) is provided in `examples/tutorial.ipynb`. Additional information can be obtained by executing

```
from pyphenopop.mixpopid import mixture_id
help(mixture_id)
