VIBER

The VIBER package implements a variational Bayesian framework to fit multi-variate Binomial mixture models, described in Caravagna et al; Nature Genetics 52, 898–907 (2020). The statistical model is semi-parametric and fitted by a variational mean-field approximation to the model posterior; mixture components are Binomial distributions that can model multi-dimensional count data, for instance sequencing read counts in cancer genomics. The package provides functions to fit the model and visualise clustering results.

Citation

If you use VIBER, please cite:

  • G. Caravagna, T. Heide, M.J. Williams, L. Zapata, D. Nichol, K. Chkhaidze, W. Cross, G.D. Cresswell, B. Werner, A. Acar, L. Chesler, C.P. Barnes, G. Sanguinetti, T.A. Graham, A. Sottoriva. Subclonal reconstruction of tumors by using machine learning and population genetics. Nature Genetics 52, 898–907 (2020).

Help and support

Installation

You can install the released version of VIBER from GitHub with:

# install.packages("devtools")
devtools::install_github("caravagnalab/VIBER")

Giulio Caravagna. Cancer Data Science (CDS) Laboratory.