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README.Rmd
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README.Rmd
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---
output: github_document
---
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
```
# mobster <a href='https://caravagnalab.github.io/mobster'><img src='man/figures/logo.png' align="right" height="139" /></a>
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[](https://www.tidyverse.org/lifecycle/#stable)
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`mobster` is a package that implements a model-based approach for _subclonal deconvolution_ of cancer genome sequencing data ([Caravagna et al; PMID: 32879509](https://www.nature.com/articles/s41588-020-0675-5#:~:text=Subclonal%20reconstruction%20methods%20based%20on,and%20infer%20their%20evolutionary%20history.&text=We%20present%20a%20novel%20approach,learning%20with%20theoretical%20population%20genetics.)).
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.
#### Citation
[](https://doi.org/10.1038/s41588-020-0675-5)
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).
#### Help and support
[](https://caravagnalab.github.io/mobster)
### Installation
You can install the released version of `mobster` from
[GitHub](https://github.com/) with:
``` r
# install.packages("devtools")
devtools::install_github("caravagnalab/mobster")
```
-----
#### Copyright and contacts
Giulio Caravagna. Cancer Data Science (CDS) Laboratory.
[](https://github.com/caravagnalab)
[](https://www.caravagnalab.org/)