BMix provides univariate Binomial and Beta-Binomial mixture models.
Count-based mixtures can be used in a variety of settings, for instance
to model genome sequencing data of somatic mutations in cancer. BMix
fits these mixtures by maximum likelihood exploiting the Expectation
Maximization algorithm. Model selection for the number of mixture
components is by the Integrated Classification Likelihood, an extension
of the Bayesian Information Criterion that includes the entropy of the
latent variables.
If you use BMix, 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).
You can install the released version of BMix from
GitHub with:
# install.packages("devtools")
devtools::install_github("caravagnalab/BMix")Giulio Caravagna. Cancer Data Science (CDS) Laboratory.
