title: Robust Detection and Identification of Sparse Segments in Ultra-High Dimensional Data Analysis.
abstract: Copy number variants (CNVs) are alternations of DNA of a genome that results in the cell having a less or more than two copies of segments of the DNA. CNVs correspond to relatively large regions of the genome, ranging from about one kilobase to several megabases, that are deleted or duplicated. Motivated by CNV analysis based on next generation sequencing data, we consider the problem of detecting and identifying sparse short segments hidden in a long linear sequence of data with an unspecified noise distribution. We propose a computationally efficient method that provides a robust and near-optimal solution for segment identification over a wide range of noise distributions. We theoretically quantify the conditions for detecting the segment signals and show that the method near-optimally estimates the signal segments whenever it is possible to detect their existence. Simulation studies are carried out to demonstrate the efficiency of the method under different noise distributions. We present results from a CNV analysis of a HapMap Yoruban sample to further illustrate the theory and the methods.
authors: Department of Statistics, University of Pennsylvania, Philadelphia, USA.
authors: T Tony
authors: TT
authors: Cai
authors: X Jessie
authors: XJ
authors: Jeng
authors: Hongzhe
authors: H
authors: Li
references: Nat Methods. 2009 Jan;6(1):99-103
references: 19043412
references: J Am Stat Assoc. 2010 Apr 1;105(491):1156-1166
references: 23543902
references: Annu Rev Genomics Hum Genet. 2009;10:451-81
references: 19715442
references: Nature. 2009 Jun 18;459(7249):987-91
references: 19536264
references: Nat Rev Genet. 2006 Feb;7(2):85-97
references: 16418744
references: Science. 2008 Apr 25;320(5875):539-43
references: 18369103
references: Nat Biotechnol. 2008 Oct;26(10):1135-45
references: 18846087
references: Bioinformatics. 2010 Dec 15;26(24):3051-8
references: 20966003
references: Biostatistics. 2004 Oct;5(4):557-72
references: 15475419
references: Nature. 2011 Feb 3;470(7332):59-65
references: 21293372
references: PLoS One. 2011 Jan 31;6(1):e16327
references: 21305028
references: Biometrics. 2010 Sep;66(3):665-74
references: 19912177
references: Nature. 2006 Nov 23;444(7118):444-54
references: 17122850
references: BMC Bioinformatics. 2010 Aug 18;11:432
references: 20718989
references: Nature. 2008 Sep 11;455(7210):237-41
references: 18668038
references: BMC Bioinformatics. 2009 Mar 06;10:80
references: 19267900
references: Nat Methods. 2009 Sep;6(9):677-81
references: 19668202
references: Genome Biol. 2010;11(5):R50
references: 20459815
references: Genome Res. 2009 Sep;19(9):1586-92
references: 19657104
references: Science. 2004 Jul 23;305(5683):525-8
references: 15273396
references: Nat Methods. 2009 Nov;6(11 Suppl):S13-20
references: 19844226
references: Genome Res. 2011 Jun;21(6):974-84
references: 21324876
references: Nat Genet. 2007 Jul;39(7 Suppl):S37-42
references: 17597780
references: BMC Genomics. 2011 Apr 12;12:184
references: 21486468
references: Nat Biotechnol. 2011 Jul 24;29(8):723-30
references: 21785424
references: Nucleic Acids Res. 2011 Aug;39(15):e103
references: 21646344
references: Nat Rev Genet. 2011 May;12(5):363-76
references: 21358748
references: Nat Genet. 2002 Dec;32 Suppl:496-501
references: 12454644
references: Proc Natl Acad Sci U S A. 2006 Mar 21;103(12):4534-9
references: 16537408