title: Improving Detection of Driver Genes: Power-Law Null Model of Copy Number Variation in Cancer.
abstract: In this paper, we study Copy Number Variation (CNV) data. The underlying process generating CNV segments is generally assumed to be memory-less, giving rise to an exponential distribution of segment lengths. In this paper, we provide evidence from cancer patient data, which suggests that this generative model is too simplistic, and that segment lengths follow a power-law distribution instead. We conjecture a simple preferential attachment generative model that provides the basis for the observed power-law distribution. We then show how an existing statistical method for detecting cancer driver genes can be improved by incorporating the power-law distribution in the null model.
authors.forename: Loes Olde
authors.initials: LO
authors.lastname: Loohuis
authors.forename: Andreas
authors.initials: A
authors.lastname: Witzel
authors.forename: Bud
authors.initials: B
authors.lastname: Mishra
mesh_terms: D000465:Algorithms; D014408:Biomarkers, Tumor / Q000235:genetics*; D019295:Computational Biology / Q000379:methods*; D056915:DNA Copy Number Variations / Q000235:genetics*; D030541:Databases, Genetic; D006801:Humans; D015233:Models, Statistical; D009369:Neoplasms / Q000235:genetics*
chemical_list: D014408:Biomarkers, Tumor