Open Access Highly Accessed Open Badges Method

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

Christopher Yau1*, Dmitri Mouradov2, Robert N Jorissen2, Stefano Colella36, Ghazala Mirza3, Graham Steers4, Adrian Harris4, Jiannis Ragoussis3, Oliver Sieber2 and Christopher C Holmes15

Author Affiliations

1 Department of Statistics, University of Oxford, South Parks Road, Oxford, OX1 3TG, UK

2 Ludwig Colon Cancer Initiative Laboratory, Ludwig Institute for Cancer Research, Royal Melbourne Hospital, Victoria 3050, Australia

3 Wellcome Trust Centre for Human Genetics, University of Oxford, Roosevelt Drive, Oxford, OX3 7BN, UK

4 Molecular Oncology Laboratories, Department of Medical Oncology, University of Oxford, Weatherall institute of Molecular Medicine, Headington, Oxford OX3 9DS, UK

5 MRC Harwell, Harwell Science and Innovation Campus, Oxfordshire, OX11 0RD, UK

6 Current Address: UMR203 INRA INSA-Lyon BF2I, Biologie Fonctionnelle Insectes et Interactions, Bat. L. Pasteur, 20 ave. A. Einstein, F-69621 Villeurbanne Cedex, France

For all author emails, please log on.

Genome Biology 2010, 11:R92  doi:10.1186/gb-2010-11-9-r92

Published: 21 September 2010


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.