PICNIC: an algorithm to predict absolute allelic copy number variation with microarray cancer data

被引：149

作者：

Greenman, Chris D. ^{[1
]}

Bignell, Graham ^{[1
]}

Butler, Adam ^{[1
]}

Edkins, Sarah ^{[1
]}

Hinton, Jon ^{[1
]}

Beare, Dave ^{[1
]}

Swamy, Sajani ^{[1
]}

Santarius, Thomas ^{[1
]}

Chen, Lina ^{[1
]}

Widaa, Sara ^{[1
]}

Futreal, P. Andy ^{[1
]}

Stratton, Michael R. ^{[1
]}

机构：

[1] Wellcome Trust Sanger Inst, Canc Genome Project, Cambridge CB10 1SA, England

来源：

BIOSTATISTICS | 2010年 / 11卷 / 01期

基金：

英国惠康基金;

关键词：

Allelic; Cancer; Copy; Number; Somatic; Variation; AFFYMETRIX SNP ARRAYS; HIDDEN MARKOV-MODELS; GENOTYPING ALGORITHM; CGH DATA; GENOME; POLYMORPHISMS; POPULATION; MIXTURE;

D O I：

10.1093/biostatistics/kxp045

中图分类号：

Q [生物科学];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

High-throughput oligonucleotide microarrays are commonly employed to investigate genetic disease, including cancer. The algorithms employed to extract genotypes and copy number variation function optimally for diploid genomes usually associated with inherited disease. However, cancer genomes are aneuploid in nature leading to systematic errors when using these techniques. We introduce a preprocessing transformation and hidden Markov model algorithm bespoke to cancer. This produces genotype classification, specification of regions of loss of heterozygosity, and absolute allelic copy number segmentation. Accurate prediction is demonstrated with a combination of independent experimental techniques. These methods are exemplified with affymetrix genome-wide SNP6.0 data from 755 cancer cell lines, enabling inference upon a number of features of biological interest. These data and the coded algorithm are freely available for download.

引用

页码：164 / 175

页数：12

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