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

Journal article

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.

Authors: Yau, C; Mouradov, D; Jorissen, R; Colella, S; Mirza, G

• Publication status: Published
• Journal: Genome biology
• Volume: 11
• Issue: 9
• Pages: R92
• Publication date: 2010-01-01
• DOI: 10.1186/gb-2010-11-9-r92
• EISSN: 1465-6914
• ISSN: 1465-6906
• Language: English
• Keywords: Polymorphism, Single Nucleotide; Cell Line, Tumor; Data Interpretation, Statistical; Neoplasms; Microarray Analysis; Mutation; Bayes Theorem; Models, Genetic; Genome, Human; Algorithms; Polyploidy; DNA Copy Number Variations; DNA Contamination; Humans; Genetic Heterogeneity; Genotype