Research
Normalization and analysis of DNA microarray data by self-consistency and local regression
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* Corresponding author: Thomas B Kepler kepler@santafe.edu
1 Santa Fe Institute, Santa Fe, NM 87501, USA
2 University of North Carolina Curriculum in Toxicology, US Environmental Protection Agency, Research Triangle Park, NC 27711, USA
3 Toxicogenomics-Mechanisms, Department of Safety Assessment, GlaxoSmithKline, 5 Moore Drive, Research Triangle Park, NC 27709, USA
Genome Biology 2002, 3:research0037-research0037.12 doi:10.1186/gb-2002-3-7-research0037
Published: 28 June 2002Abstract
Background
With the advent of DNA hybridization microarrays comes the remarkable ability, in principle, to simultaneously monitor the expression levels of thousands of genes. The quantiative comparison of two or more microarrays can reveal, for example, the distinct patterns of gene expression that define different cellular phenotypes or the genes induced in the cellular response to insult or changing environmental conditions. Normalization of the measured intensities is a prerequisite of such comparisons, and indeed, of any statistical analysis, yet insufficient attention has been paid to its systematic study. The most straightforward normalization techniques in use rest on the implicit assumption of linear response between true expression level and output intensity. We find that these assumptions are not generally met, and that these simple methods can be improved.
Results
We have developed a robust semi-parametric normalization technique based on the assumption that the large majority of genes will not have their relative expression levels changed from one treatment group to the next, and on the assumption that departures of the response from linearity are small and slowly varying. We use local regression to estimate the normalized expression levels as well as the expression level-dependent error variance.
Conclusions
We illustrate the use of this technique in a comparison of the expression profiles of cultured rat mesothelioma cells under control and under treatment with potassium bromate, validated using quantitative PCR on a selected set of genes. We tested the method using data simulated under various error models and find that it performs well.