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Open Access Highly Accessed Methodology article

Iterative rank-order normalization of gene expression microarray data

Eric A Welsh*, Steven A Eschrich, Anders E Berglund and David A Fenstermacher

Author Affiliations

H. Lee Moffitt Cancer Center and Research Institute, University of South Florida, Tampa, FL, 33612, USA

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BMC Bioinformatics 2013, 14:153  doi:10.1186/1471-2105-14-153

Published: 7 May 2013

Abstract

Background

Many gene expression normalization algorithms exist for Affymetrix GeneChip microarrays. The most popular of these is RMA, primarily due to the precision and low noise produced during the process. A significant strength of this and similar approaches is the use of the entire set of arrays during both normalization and model-based estimation of signal. However, this leads to differing estimates of expression based on the starting set of arrays, and estimates can change when a single, additional chip is added to the set. Additionally, outlier chips can impact the signals of other arrays, and can themselves be skewed by the majority of the population.

Results

We developed an approach, termed IRON, which uses the best-performing techniques from each of several popular processing methods while retaining the ability to incrementally renormalize data without altering previously normalized expression. This combination of approaches results in a method that performs comparably to existing approaches on artificial benchmark datasets (i.e. spike-in) and demonstrates promising improvements in segregating true signals within biologically complex experiments.

Conclusions

By combining approaches from existing normalization techniques, the IRON method offers several advantages. First, IRON normalization occurs pair-wise, thereby avoiding the need for all chips to be normalized together, which can be important for large data analyses. Secondly, the technique does not require similarity in signal distribution across chips for normalization, which can be important for maintaining biologically relevant differences in a heterogeneous background. Lastly, IRON introduces fewer post-processing artifacts, particularly in data whose behavior violates common assumptions. Thus, the IRON method provides a practical solution to common needs of expression analysis. A software implementation of IRON is available at [http://gene.moffitt.org/libaffy/ webcite].

Keywords:
Microarray; Expression; Normalization; Affymetrix; GeneChip