Methodology article

The limit fold change model: A practical approach for selecting differentially expressed genes from microarray data

David M Mutch¹ , Alvin Berger¹ , Robert Mansourian¹ , Andreas Rytz² and Matthew-Alan Roberts¹

¹  Metabolic and Genomic Regulation, Nestlé Research Center, Vers-chez-les-Blanc, CH-1000 Lausanne 26, Switzerland

²  Applied Mathematics, Nestlé Research Center, Vers-chez-les-Blanc, CH-1000 Lausanne 26, Switzerland

author email corresponding author email

BMC Bioinformatics 2002, 3:17doi:10.1186/1471-2105-3-17

Published:	21 June 2002

Abstract

Background

The biomedical community is developing new methods of data analysis to more efficiently process the massive data sets produced by microarray experiments. Systematic and global mathematical approaches that can be readily applied to a large number of experimental designs become fundamental to correctly handle the otherwise overwhelming data sets.

Results

The gene selection model presented herein is based on the observation that: (1) variance of gene expression is a function of absolute expression; (2) one can model this relationship in order to set an appropriate lower fold change limit of significance; and (3) this relationship defines a function that can be used to select differentially expressed genes. The model first evaluates fold change (FC) across the entire range of absolute expression levels for any number of experimental conditions. Genes are systematically binned, and those genes within the top X% of highest FCs for each bin are evaluated both with and without the use of replicates. A function is fitted through the top X% of each bin, thereby defining a limit fold change. All genes selected by the 5% FC model lie above measurement variability using a within standard deviation (SD_within) confidence level of 99.9%. Real time-PCR (RT-PCR) analysis demonstrated 85.7% concordance with microarray data selected by the limit function.

Conclusion

The FC model can confidently select differentially expressed genes as corroborated by variance data and RT-PCR. The simplicity of the overall process permits selecting model limits that best describe experimental data by extracting information on gene expression patterns across the range of expression levels. Genes selected by this process can be consistently compared between experiments and enables the user to globally extract information with a high degree of confidence.