Normalization of array-CGH data: influence of copy number imbalances

Background: High-resolution microarray-based comparative genomic hybridization (CGH) techniques have successfully been applied to study copy number imbalances in a number of settings such as the analysis of cancer genomes. For normalization of array-CGH data, methods initially developed for gene expression microarray analysis have, in general, been directly adopted and used. However, these methods are designed to work under assumptions that may not be valid for array-CGH data when copy number imbalances are present. We therefore sought to investigate the effect on normalization imposed by copy number imbalances. Results: Here we demonstrate that copy number imbalances correlate with intensity in array-CGH data thereby causing problems for conventional normalization methods. We propose a strategy to circumvent these problems by taking copy number imbalances into account during normalization, and we test the proposed strategy using several data sets from the analysis of cancer genomes. In addition, we show how the strategy can be applied to conveniently define adaptive sample-specific boundaries between balanced copy number, losses, and gains to facilitate management of variation in tissue heterogeneity when calling copy number changes. Conclusion: We highlight the importance of considering copy number imbalances during normalization of array-CGH data, and show how failure to do so can deleteriously affect data and hamper interpretation.