mobster is a package that implements a model-based approach for
subclonal deconvolution of cancer genome sequencing data (Caravagna
et al; PMID:
32879509).
The package integrates evolutionary theory (i.e., population) and Machine-Learning to analyze (e.g., whole-genome) bulk data from cancer samples. This analysis relates to clustering; we approach it via a maximum-likelihood formulation of Dirichlet mixture models, and use bootstrap routines to assess the confidence of the parameters. The package implements S3 objects to visualize the data and the fits.
If you use mobster, 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 mobster from
GitHub with:
# install.packages("devtools")
devtools::install_github("caravagnalab/mobster")
