Multi-Scale Multiple Testing for Region Detection with Application to Genomic Copy Number Change in Population Analyses

• Activity Number: 629
• Type: Topic Contributed
• Date/Time: Thursday, August 8, 2013 : 8:30 AM to 10:20 AM
• Sponsor: Mental Health Statistics Section
• Author(s): Nezamoddin N Kachouie*+ and Armin Schwartzman and Xihong Lin
• Companies: Florida Institute of Technology and Harvard School of Public Health and Harvard School of Public Health
• Keywords: Feature extraction ; Region detection ; Local polynomial regression ; Kernel smoothing ; Copy number alterations (CNAs)

Abstract

Feature extraction from observed noisy samples is a common important problem in statistics and engineering. This paper presents a novel general statistical approach to the problem of detecting non-zero regions in a long data sequence. Specifically, this method is used to solve the problem of finding non-zero genomic regions with significant copy number alterations (CNAs) between two cell populations. The proposed technique employs multi-scale kernel smoothing in conjunction with statistical multiple testing to detect the non-zero regions while controlling their false discovery rate and maximizing their signal to noise ratio (SNR) via matched filtering. This is achieved by translating the one-dimensional (1D) spatial region detection problem to an equivalent zero dimensional (0D) peak detection problem. In addition, if the shape of the non-zero regions is known a priori, e.g. rectangular pulse, the signal regions can also be reconstructed from the detected peaks, seen as their topological point representatives. The proposed method is applied to synthetic data and a real data set of lung cancer DNA copy numbers.