Optimal Sparse Segment Identification with Application in Copy Number Variation Analysis

• Activity Number: 664
• Type: Topic Contributed
• Date/Time: Thursday, August 5, 2010 : 10:30 AM to 12:20 PM
• Sponsor: Biometrics Section
• Author(s): Jessie Jeng*+ and Tony Cai and Hongzhe Li
• Companies: University of Pennsylvania and University of Pennsylvania and University of Pennsylvania
• Keywords: likelihood ratio selection ; signal detection ; multiple testing ; DNA copy number

Abstract

Motivated by DNA copy number variation (CNV) analysis based on high-density single nucleotide polymorphism (SNP) data, we consider the problem of detecting and identifying sparse short segments in a long one-dimensional sequence of data with additive Gaussian white noise, where the number, length and location of the segments are unknown. We present a statistical characterization of the identifiable region of a segment where it is possible to reliably separate the segment from the noise. An efficient likelihood ratio selection (LRS) procedure is developed, and the asymptotic optimality of this method is presented. The proposed method is demonstrated with simulations and analysis of a real data set on identification of copy number variants based on high-density SNP data. The results show that the LRS procedure can yield greater gain in power than some standard methods.