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

Greenman, Chris D., Bignell, Graham, Butler, Adam, Edkins, Sarah, Hinton, Jon, Beare, Dave, Swamy, Sajani, Santarius, Thomas, Chen, Lina, Widaa, Sara, Futreal, P. Andy and Stratton, Michael R. (2010) PICNIC: an algorithm to predict absolute allelic copy number variation with microarray cancer data. Biostatistics, 11 (1). pp. 164-175. ISSN 1465-4644

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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.

Item Type: Article
Uncontrolled Keywords: sdg 3 - good health and well-being ,/dk/atira/pure/sustainabledevelopmentgoals/good_health_and_well_being
Faculty \ School: Faculty of Science > School of Computing Sciences

Faculty of Science > School of Biological Sciences
UEA Research Groups: Faculty of Science > Research Groups > Computational Biology
Depositing User: Christopher Greenman
Date Deposited: 22 Jun 2011 11:01
Last Modified: 22 Apr 2023 00:18
URI: https://ueaeprints.uea.ac.uk/id/eprint/30590
DOI: 10.1093/biostatistics/kxp045

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