The breakpoint package implements variants of the Cross-Entropy (CE) method proposed in Priyadarshana and Sofronov (2015, 2012a and 2012b) to estimate both the number and the corresponding locations of break-points in biological sequences of continuous and discrete measurements. The proposed method primarily built to detect multiple break-points in genomic sequences. However, it can be easily extended and applied to other problems.
"breakpoint"" package provides estimates on both the number as well as the corresponding locations of break-points. The algorithms utilize the Cross-Entropy (CE) method, which is a model-based stochastic optimization procedure to obtain the estimates on locations. Model selection procedures are used to obtain the number of break-points. Current implementation of the methodology works as an exact search method in estimating the number of break-points. However, it supports calculations if the initial locations are provided. A parallel implementation of the procedures can be carried-out in Unix/Linux/MAC OSX and WINDOWS OS with the use of "parallel" and "doParallel" packages.
Priyadarshana, W.J.R.M. and Sofronov, G.
Maintainer: Priyadarshana, W.J.R.M. <email@example.com>
Priyadarshana, W. J. R. M., Sofronov G. (2015). Multiple Break-Points Detection in Array CGH Data via the Cross-Entropy Method, IEEE/ACM Transactions on Computational Biology and Bioinformatics, 12 (2), pp.487-498.
Priyadarshana, W. J. R. M. and Sofronov, G. (2012a). A Modified Cross- Entropy Method for Detecting Multiple Change-Points in DNA Count Data. In Proc. of the IEEE Conference on Evolutionary Computation (CEC), 1020-1027, DOI: 10.1109/CEC.2012.6256470.
Priyadarshana, W. J. R. M. and Sofronov, G. (2012b). The Cross-Entropy Method and Multiple Change-Points Detection in Zero-Inflated DNA read count data. In: Y. T. Gu, S. C. Saha (Eds.) The 4th International Conference on Computational Methods (ICCM2012), 1-8, ISBN 978-1-921897-54-2.
Rubinstein, R., and Kroese, D. (2004) The Cross-Entropy Method: A Unified Approach to Combinatorial Optimization, Monte-Carlo Simulation and Machine Learning. Springer-Verlag, New York.
Zhang, N.R., and Siegmund, D.O. (2007) A modified Bayes information criterion with applications to the analysis of comparative genomic hybridization data. Biometrics, 63, 22-32.