|
Activity Number:
|
526
|
|
Type:
|
Contributed
|
|
Date/Time:
|
Wednesday, August 5, 2009 : 2:00 PM to 3:50 PM
|
|
Sponsor:
|
Biometrics Section
|
| Abstract - #304187 |
|
Title:
|
Noise Reduction for Array CGH Data Using Technical Covariates and Probe-Level Information
|
|
Author(s):
|
Tobias Guennel*+ and Mark Reimers
|
|
Companies:
|
Virginia Commonwealth University and Virginia Commonwealth University
|
|
Address:
|
P.O. Box 980032, Richmond, VA, 23298-0032,
|
|
Keywords:
|
CGH ; normalization ; noise reduction ; microarray ; DNA copy number ; tumor
|
|
Abstract:
|
Motivation: Many studies investigating copy number aberrations at the DNA level as one of the main causes for tumorigenesis use comparative genomic hybridization (CGH) on cDNA arrays. Array CGH data often suffer from low signal to noise ratios resulting in poor resolution of fine features. Results: Bilke et al. (2005) showed that the commonly used running average noise reduction strategy performs poorly when errors are dominated by systematic components. We propose a method that significantly reduces noise using a nonparametric regression on technical covariates of probes to estimate systematic bias. We also modify the topological statistics method introduced by Bilke et al. (2005). The two methods are demonstrated on two CGH data sets from the NCI60 cell lines, and we achieve a nominal error variance reduction of 52%--56%.
|
