Identification of cancer genes using a statistical framework for multiexperiment analysis of nondiscretized array CGH data.

ABSTRACT: Tumor formation is in part driven by DNA copy number alterations (CNAs), which can be measured using microarray-based Comparative Genomic Hybridization (aCGH). Multiexperiment analysis of aCGH data from tumors allows discovery of recurrent CNAs that are potentially causal to cancer development. Until now, multiexperiment aCGH data analysis has been dependent on discretization of measurement data to a gain, loss or no-change state. Valuable biological information is lost when a heterogeneous system such as a solid tumor is reduced to these states. We have developed a new approach which inputs nondiscretized aCGH data to identify regions that are significantly aberrant across an entire tumor set. Our method is based on kernel regression and accounts for the strength of a probe's signal, its

SUBMITTER: Klijn C 

PROVIDER: S-EPMC2241875 | biostudies-literature | 2008 Feb

REPOSITORIES: biostudies-literature