CGHnormaliter Package (Version 1.2.0)

This package contains an implementation of the CGHnormaliter strategy for improved normalization of dual-channel array Comparative Genomic Hybridization (aCGH) data displaying many copy number imbalances. The key idea of our method is that temporary exclusion of aberrations from the data allows for a more appropriate calculation of the LOWESS regression curve. As a result, after normalization, the log2 intensity ratios of the normals will generally be closer to zero and better reflect the biological reality. We coin this normalization strategy ‘local-LOWESS’ since only a subset of the log2 ratios is considered in the LOWESS regression. The strategy can be summarized as follows. Initially the log2 intensity ratios are segmented using DNAcopy [4]. The segmented data are then given as input to a calling tool named CGHcall [2] to discriminate the normals from gains and losses. These normals are subsequently used for normalization based on LOWESS. These steps are then iterated to refine the normalization. An overview is given in Figure 1. For more detailed information we refer to the publication of our method [3].