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

Chris D. Greenman, Graham Bignell, Adam Butler, Sarah Edkins, Jon Hinton, Dave Beare, Sajani Swamy, Thomas Santarius, Lina Chen, Sara Widaa, P. Andy Futreal, Michael R. Stratton

Research output: Contribution to journalArticlepeer-review

152 Citations (Scopus)


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.
Original languageEnglish
Pages (from-to)164-175
Number of pages12
Issue number1
Publication statusPublished - 1 Jan 2010

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