Clonal deconvolution in cancer using longitudinal NGS data
Project description
Introduction
Tumours are mixtures of phylogenetically related cancer cell populations or clones, which are subject to a process of Darwinian evolution in response to selective pressures in their local micro-environment. clonosGP
is a statistical methodology for tracking this latent heterogeneity continuously in time based on longitudinally collected tumour samples. In technical terms, it combines Dirichlet Process Mixture Models with Gaussian Process Priors to identify clusters of mutations and track their cellular prevalence continuously in time. If only cross-sectional data are available, then it performs standard non-parametric clustering of mutations based on their observed frequency, similarly to PyClone
and other software in the same category. The statistical models underlying clonosGP
were implemented in the excellent probabilistic programming system PyMC3
on which we also rely for inference using variational methods.
Installation
clonosGP
requires Python 3.7 or later. It can be easilly installed as follows:
- Create a virtual environment:
python3 -m venv myenv
- Activate the newly created environment:
source myenv/bin/activate
- Install
clonosGP
as follows:pip install -U clonosGP
All necessary dependencies will also be installed.
Usage
A guide to start using clonosGP
quickly is available here. A more thorough tutorial can be found here.
Citation
For citation information check http://github.com/dvav/clonosGP
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