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Rtreemix: an R package for estimating evolutionary pathways and genetic progression scores

MPS-Authors
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Bogojeska,  Jasmina
Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society;
International Max Planck Research School, MPI for Informatics, Max Planck Society;

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Alexa,  Adrian
Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society;
International Max Planck Research School, MPI for Informatics, Max Planck Society;

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Altmann,  André
Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society;

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Lengauer,  Thomas
Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society;

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Rahnenführer,  Jörg
Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society;

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Citation

Bogojeska, J., Alexa, A., Altmann, A., Lengauer, T., & Rahnenführer, J. (2008). Rtreemix: an R package for estimating evolutionary pathways and genetic progression scores. Bioinformatics, 24(20), 2391-2392. doi:10.1093/bioinformatics/btn410.


Cite as: https://hdl.handle.net/11858/00-001M-0000-000F-1CDD-1
Abstract
Summary: In genetics, many evolutionary pathways can be modeled by the ordered accumulation of permanent changes. Mixture models of mutagenetic trees have been used to describe disease progression in cancer and in HIV. In cancer, progression is modeled by the accumulation of chromosomal gains and losses in tumor cells; in HIV, the accumulation of drug resistance-associated mutations in the viral genome is known to be associated with disease progression. From such evolutionary models, genetic progression scores can be derived that assign measures for the disease state to single patients. Rtreemix is an R package for estimating mixture models of evolutionary pathways from observed cross-sectional data and for estimating associated genetic progression scores. The package also provides extended functionality for estimating confidence intervals for estimated model parameters and for evaluating the stability of the estimated evolutionary mixture models.