Improving detection of driver genes: Power-law null model of copy number variation in cancer

Loes Olde Loohuis, Andreas Witzel, Bud Mishra

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Article number6883143
Pages (from-to)1260-1263
Number of pages4
JournalIEEE/ACM Transactions on Computational Biology and Bioinformatics
Volume11
Issue number6
DOIs
StatePublished - Nov 1 2014

Keywords

  • Copy number variation
  • cancer driver genes detection
  • generative mechanism
  • power-law distribution

ASJC Scopus subject areas

  • Biotechnology
  • Genetics
  • Applied Mathematics

Fingerprint Dive into the research topics of 'Improving detection of driver genes: Power-law null model of copy number variation in cancer'. Together they form a unique fingerprint.

Cite this