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". 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. A Matlab package of the method is available at https://github.com/ocbe-uio/PhenoPop.
The package can be easily install via pip install pyphenopop. You can also install it from the Github repository 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 .
A tutorial using data from the original publication is provided in examples/tutorial.ipynb. Additional information can be obtained by executing
from pyphenopop.mixpopid import mixture_id
help(mixture_id)
When using pyPhenoPop in your project, please cite
- Köhn-Luque, A., Myklebust, E. M., Tadele, D. S., Giliberto, M., Schmiester, L., Noory, J., ... & Foo, J. (2023). Phenotypic deconvolution in heterogeneous cancer cell populations using drug-screening data. Cell Reports Methods, 3(3).
@article{kohn2023phenotypic,
title={Phenotypic deconvolution in heterogeneous cancer cell populations using drug-screening data},
author = {Alvaro Köhn-Luque and Even Moa Myklebust and Dagim Shiferaw Tadele and Mariaserena Giliberto and Leonard Schmiester and Jasmine Noory and Elise Harivel and Polina Arsenteva and Shannon M. Mumenthaler and Fredrik Schjesvold and Kjetil Taskén and Jorrit M. Enserink and Kevin Leder and Arnoldo Frigessi and Jasmine Foo},
journal={Cell Reports Methods},
volume={3},
number={3},
year={2023},
doi={https://doi.org/10.1016/j.crmeth.2023.100417},
publisher={Elsevier}
}