The limit fold change model: a practical approach for selecting differentially expressed genes from microarray data.
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 (SDwithin) confidence level of 99.9%. Real time-PCR (RT-PCR) analysis demonstrated 85.7% concordance with microarray data selected by the limit function.


CONCLUSIONS
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.
Mutch
David M
DM
Metabolic and Genomic Regulation, Nestlé Research Center, Vers-chez-les-Blanc, CH-1000 Lausanne 26, Switzerland. david.mutch@rdls.nestle.com
Berger
Alvin
A
Mansourian
Robert
R
Rytz
Andreas
A
Roberts
Matthew-Alan
MA
D000818:Animals; D019295:Computational Biology / Q000379:methods; D004032:Diet; D020869:Gene Expression Profiling / Q000379:methods*; D005796:Genes / Q000235:genetics; D014644:Genetic Variation / Q000235:genetics; D008099:Liver / Q000737:chemistry / Q000378:metabolism; D008297:Male; D051379:Mice; D008815:Mice, Inbred Strains; D008957:Models, Genetic; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D009928:Organ Specificity / Q000235:genetics; D018075:RNA, Complementary / Q000235:genetics; D020133:Reverse Transcriptase Polymerase Chain Reaction / Q000379:methods
D018075:RNA, Complementary