A fast Bayesian change point analysis for the segmentation of microarray data

The ability to detect regions of genetic alteration is of great importance in cancer research. These alterations can take the form of large chromosomal gains and losses as well as smaller amplifications and deletions. The detection of such regions allows researchers to identify genes involved in cancer progression, and to fully understand differences between cancer and non-cancer tissue. The Bayesian method proposed by Barry and Hartigan is well suited for the analysis of such change point problems. In our previous article we introduced the R package bcp (Bayesian change point), an MCMC implementation of Barry and Hartigan's method. In a simulation study and real data examples, bcp is shown to both accurately detect change points and estimate segment means. Earlier versions of bcp (prior to 2.0) are O(n(2)) in speed and O(n) in memory (where n is the number of observations), and run in approximately 45 min for a sequence of length 10 000. With the high resolution of newer microarrays, the number of computations in the O(n(2)) algorithm is prohibitively time-intensive.