| DNA copy number gains and losses are commonly found in tumor tissue, and some of these aberrations play a role in tumor genesis and development. Although high resolution DNA copy number data can be obtained using array-based techniques, no single method is widely used to distinguish between recurrent and sporadic copy number aberrations. Here we introduce Discovering Copy Number Aberrations Manifested In Cancer (DiNAMIC), a novel method for assessing the statistical significance of recurrent copy number aberrations. DiNAMIC uses two resampling schemes - a permutation method and a bootstrap procedure - both of which largely preserve the correlation structure found in the underlying DNA copy number data. It is important to maintain as much of the correlation structure as possible when resampling, and we believe this may yield additional power to detect recurrent aberrations. Extensive simulation studies show that DiNAMIC controls false positive discoveries in a variety of realistic scenarios. We use DiNAMIC to analyze two publicly available tumor datasets, and our results show that DiNAMIC detects multiple loci that have biological relevance. Although DiNAMIC provides methods for detecting CNAs, loci that exhibit aberrant copy number do not always lie in genes related to the tumor phenotype. Therefore we introduce methods for computing confidence intervals around CNAs. Copy number datasets often contain data obtained from subjects with different subtypes of a given tumor type, and the tumor subtypes can harbor distinct genetic mutations. Because groups of subjects may have similar copy number profiles, we present methods for determining which subjects contribute to a given CNA. Some studies collect both DNA copy number and clinical data from subjects, but no methods for jointly analyzing both data types are widely used. We describe a preliminary testing procedure for comparing the locations of CNAs and loci whose copy number values are highly associated with a given clinical variable. |
