A statistical approach for detecting genomic aberrations in heterogeneous tumor samples from single nucleotide polymorphism genotyping data

Christopher Yau, Dmitri Mouradov, Robert N. Jorissen, Stefano Colella, Ghazala K. Mirza, Graham Steers, Adrian Harris, Ioannis Ragoussis, Oliver M. Sieber, Chris C. Holmes

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107 Citations (Scopus)

Abstract

We describe a statistical method for the characterization of genomic aberrations in single nucleotide polymorphism microarray data acquired from cancer genomes. Our approach allows us to model the joint effect of polyploidy, normal DNA contamination and intra-tumour heterogeneity within a single unified Bayesian framework. We demonstrate the efficacy of our method on numerous datasets including laboratory generated mixtures of normal-cancer cell lines and real primary tumours.

Original languageEnglish
Article numberR92
Number of pages15
JournalGenome Biology
Volume11
Issue number9
DOIs
Publication statusPublished - 21 Sep 2010

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    Yau, C., Mouradov, D., Jorissen, R. N., Colella, S., Mirza, G. K., Steers, G., Harris, A., Ragoussis, I., Sieber, O. M., & Holmes, C. C. (2010). A statistical approach for detecting genomic aberrations in heterogeneous tumor samples from single nucleotide polymorphism genotyping data. Genome Biology, 11(9), [R92]. https://doi.org/10.1186/gb-2010-11-9-r92