CT-CBN

Continuous Time Conjunctive Bayesian Networks

Current version

0.1.04 Oct 2011

Description

CT-CBN is a collection of two C programs, ct-cbn and h-cbn, that implement algorithms for model selection and maximum likelihood parameter estimation for continuous time conjunctive Bayesian networks. These graphical models have been designed to model the accumulation of mutations subject to constraints on the order in which the mutations can occur. The order constraints are encoded as a partially ordered set (poset) of genetic events. The waiting time for each event follows an independent exponential distribution.

The original ct-cbn program by Beerenwinkel and Sullivant (2009) implements selection of an optimal poset and estimation of exponential rates from censored data consisting only of the observed mutational patterns.

The hidden conjunctive Bayesian network model h-cbn accounts for noisy genotype observations (Gerstung et al., 2009). It also implemented a simulated annealing algorithm for structure search, a denoising of the genotypes via the maximum a posteriori (MAP) estimates, and the computation of the most likely progression.

Download

CT-CBN is distributed under the GNU General Public License.

• ct-cbn-0.1.04 Oct 2011
• ct-cbn-0.1.01 Apr 2009
• ct-cbn-0.0.03 May 2008
• ct-cbn-0.0.02 Mar 2008
• ct-cbn-0.0.01 Aug 2007

For changes between versions please see the file CHANGELOG.

Installation

Please consult the README file. The software requires the GSL library.

Data sets

We provide a collection of genetic data sets from three cancer studies conducted by the Vogelstein lab. The data consists of three tables with mutations and a mapping of genes to 12 core pathways (Parsons et al, 2008). The package also contains the python module cbn.py for parsing the raw data and for bootstrap and permutation analyses. The module requires the numpy python module.

• Cancer data with core pathways
  

Authors and Contributors

• Niko Beerenwinkel
• Moritz Gerstung
• Seth Sullivant

Citations

• Beerenwinkel N and Sullivant S (2009). Markov models for accumulating mutations. Biometrika 96(3):645-861.
• Gerstung M, Baudis M, Moch H, Beerenwinkel N (2009). Quantifying cancer progression with conjunctive Bayesian networks. Bioinformatics 25(21):2809-2815.
• Gerstung M, Eriksson N, Lin J, Vogelstein B, and Beerenwinkel N (2011). The temporal order of genetic and pathway alterations in tumorigenesis. PLoS ONE 6:e27136.
  

Contact

Niko Beerenwinkel