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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 publication

DOI

10.1186/gb-2010-11-9-r92

Type

Journal article

Journal

Genome biology

Publication Date

01/2010

Volume

11

Addresses

Department of Statistics, University of Oxford, South Parks Road, Oxford, OX1 3TG, UK. yau@stats.ox.ac.uk

Keywords

Cell Line, Tumor, Humans, Neoplasms, Microarray Analysis, Data Interpretation, Statistical, Bayes Theorem, Genotype, Polyploidy, Genetic Heterogeneity, Mutation, Polymorphism, Single Nucleotide, Genome, Human, Algorithms, Models, Genetic, DNA Copy Number Variations, DNA Contamination