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Open Access Software article

Getting DNA copy numbers without control samples

Maria Ortiz-Estevez12, Ander Aramburu1 and Angel Rubio1*

Author Affiliations

1 Group of Bioinformatics, CEIT and TECNUN, University of Navarra, San Sebastian, Spain

2 Computational Biology Group, Celgene Institute for Translational Research Europe (CITRE), Sevilla, Spain

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Algorithms for Molecular Biology 2012, 7:19  doi:10.1186/1748-7188-7-19

Published: 16 August 2012

Abstract

Background

The selection of the reference to scale the data in a copy number analysis has paramount importance to achieve accurate estimates. Usually this reference is generated using control samples included in the study. However, these control samples are not always available and in these cases, an artificial reference must be created. A proper generation of this signal is crucial in terms of both noise and bias.

We propose NSA (Normality Search Algorithm), a scaling method that works with and without control samples. It is based on the assumption that genomic regions enriched in SNPs with identical copy numbers in both alleles are likely to be normal. These normal regions are predicted for each sample individually and used to calculate the final reference signal. NSA can be applied to any CN data regardless the microarray technology and preprocessing method. It also finds an optimal weighting of the samples minimizing possible batch effects.

Results

Five human datasets (a subset of HapMap samples, Glioblastoma Multiforme (GBM), Ovarian, Prostate and Lung Cancer experiments) have been analyzed. It is shown that using only tumoral samples, NSA is able to remove the bias in the copy number estimation, to reduce the noise and therefore, to increase the ability to detect copy number aberrations (CNAs). These improvements allow NSA to also detect recurrent aberrations more accurately than other state of the art methods.

Conclusions

NSA provides a robust and accurate reference for scaling probe signals data to CN values without the need of control samples. It minimizes the problems of bias, noise and batch effects in the estimation of CNs. Therefore, NSA scaling approach helps to better detect recurrent CNAs than current methods. The automatic selection of references makes it useful to perform bulk analysis of many GEO or ArrayExpress experiments without the need of developing a parser to find the normal samples or possible batches within the data. The method is available in the open-source R package NSA, which is an add-on to the aroma.cn framework. http://www.aroma-project.org/addons webcite.

Keywords:
SNPs; Normalization; Scaling; High-throughput data; Batch effects