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 th...

• Type: Journal
• Year: 2002
• Where: BMCBI
• Authors: David M. Mutch, Alvin Berger, Robert Mansourian, Andreas Rytz, Matthew-Alan Roberts
• Keywords: Absolute Expression, BMCBI 2002, Fold Change, Model