title: Evaluation of calling algorithms for array-CGH.
abstract: Copy number variation (CNV) detection has become an integral part many of genetic studies and new technologies promise to revolutionize our ability to detect and link them to disease. However, recent studies highlight discrepancies in the genome wide CNV profile when measured by different technologies and even by the same technology. Furthermore, the change point algorithms used to call CNVs can have substantial disagreement on the same data set. We focus this article on comparative genomic hybridization (CGH) arrays because this platform lends itself well to accurate statistical modeling. We describe some newer methodological developments in local statistics that are well suited for CNV detection and calling on CGH arrays. Then we use both simulation studies and public data to compare these new local methods with the global methods that currently dominate literature. These results offer suggestions for choosing a particular method and provide insight to the lack of reproducibility that has been seen in the field so far.
authors.affiliation: Department of Statistics, College of Physical and Mathematical Sciences, North Carolina State University Raleigh, NC, USA.
authors.forename: Siddharth
authors.initials: S
authors.lastname: Roy
authors.forename: Alison
authors.initials: A
authors.lastname: Motsinger Reif
keywords: array CGH; change point model; copy number variation; methods comparison; scan statistics