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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




Journal article


Genome Biol

Publication Date





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