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

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 ane...

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Bibliographic Details
Published in:Biostatistics (Oxford, England) England), 2010-01, Vol.11 (1), p.164-175
Main Authors: Greenman, Chris D., Bignell, Graham, Butler, Adam, Edkins, Sarah, Hinton, Jon, Beare, Dave, Swamy, Sajani, Santarius, Thomas, Chen, Lina, Widaa, Sara, Futreal, P. Andy, Stratton, Michael R.
Format: Article
Language:English
Subjects:
Algorithms
Alleles
Aneuploidy
Bayes Theorem
Bias
Biostatistics
Cancer
Cell Line, Tumor
Cells
copy number
Data processing
Diploids
DNA Copy Number Variations - genetics
Genes, Tumor Suppressor
Genetic algorithms
Genetic disorders
Genetic Testing
Genomes
Genotype
Genotype & phenotype
Genotypes
Hereditary diseases
hidden Markov models
Humans
Internet
Loss of heterozygosity
Loss of Heterozygosity - genetics
Markov analysis
Markov Chains
Models, Statistical
Neoplasms - diagnosis
Neoplasms - genetics
Oligonucleotide Array Sequence Analysis - methods
Oligonucleotides
Polymorphism, Single Nucleotide - genetics
Polyploidy
Reproducibility of Results
Segmentation
Sensitivity and Specificity
Software
Transformation
Tumor cell lines
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Description
Summary: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.
ISSN:1465-4644
1468-4357
DOI:10.1093/biostatistics/kxp045