Binary Segmentation Method Research Articles

A statistical change point model approach for the detection of DNA copy number variations in array CGH data.

Array comparative genomic hybridization (aCGH) provides a high-resolution and high-throughput technique for screening of copy number variations (CNVs) within the entire genome. This technique, compared to the conventional CGH, significantly improves the identification of chromosomal abnormalities. However, due to the random noise inherited in the imaging and hybridization process, identifying statistically significant DNA copy number changes in aCGH data is challenging. We propose a novel approach that uses the mean and variance change point model (MVCM) to detect CNVs or breakpoints in aCGH data sets. We derive an approximate p-value for the test statistic and also give the estimate of the locus of the DNA copy number change. We carry out simulation studies to evaluate the accuracy of the estimate and the p-value formulation. These simulation results show that the approach is effective in identifying copy number changes. The approach is also tested on fibroblast cancer cell line data, breast tumor cell line data, and breast cancer cell line aCGH data sets that are publicly available. Changes that have not been identified by the circular binary segmentation (CBS) method but are biologically verified are detected by our approach on these cell lines with higher sensitivity and specificity than CBS.

HIERARCHICAL SEGMENTED POINT PROCESS MODELS WITH MULTIPLE CHANGE POINTS FOR MAINTAINED SYSTEMS

The failure processes of maintained systems operating in a changing environment may be affected by the changes and exhibit different failure behaviour before and after the changes. Such processes exhibiting abrupt changes in failure intensities at specified times require segmented models with the process domain divided into segments at the points of changes in the environment to represent them. The individual segments can be modeled by any of the usual point process models and combined to form a composite segmented model with multiple change points. This paper proposes such segmented models with multiple change points to represent the failure processes of these systems and uses a hierarchical binary segmentation method to obtain the location of the changes. Its purpose is to quantify the impacts of changes in the environment on the failure intensities. These models are applied to the field data from an industrial setting; parameter estimates obtained and are shown to more accurately describe the failure processes of maintained system in a changing environment than the single point process models usually used. The interpretation and use of these models for maintained systems is also depicted.