A single-sample method for normalizing and combining full-resolution copy numbers from multiple platforms, labs and analysis methods

Motivation: The rapid expansion of whole-genome copy number (CN) studies brings a demand for increased precision and resolution of CN estimates. Recent studies have obtained CN estimates from more than one platform for the same set of samples, and it is natural to want to combine the different estimates in order to meet this demand. Estimates from different platforms show different degrees of attenuation of the true CN changes. Similar differences can be observed in CNs from the same platform run in different labs, or in the same lab, with different analytical methods. This is the reason why it is not straightforward to combine CN estimates from different sources (platforms, labs and analysis methods). Results: We propose a single-sample multi source normalization that brings full-resolution CN estimates to the same scale across sources. The normalized CNs are such that for any underlying CN level, their mean level is the same regardless of the source, which make them better suited for being combined across sources, e.g. existing segmentation methods may be used to identify aberrant regions. We use microarray-based CN estimates from The Cancer Genome Atlas (TCGA) project to illustrate and validate the method. We show that the normalized and combined data better separate two CN states at a given resolution. We conclude that it is possible to combine CNs from multiple sources such that the resolution becomes effectively larger, and when multiple platforms are combined, they also enhance the genome coverage by complementing each other in different regions.