Open AccessThe Global Error Assessment (GEA) model for the selection of differentially expressed genes in microarray data
Author(s) -
Robert Mansourian,
David M. Mutch,
Nicolas Antille,
Jacques Aubert,
Paul Fogel,
Jean-Marc Le Goff,
Julie Moulin,
Anton Petrov,
Andréas Rytz,
Johannes J. Voegel,
Matthew-Alan Roberts
Publication year - 2004
Publication title -
bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/bth319
Subject(s) - selection (genetic algorithm) , microarray , microarray analysis techniques , gene , computational biology , biology , microarray databases , genetics , gene selection , dna microarray , computer science , gene expression , artificial intelligence
Microarray technology has become a powerful research tool in many fields of study; however, the cost of microarrays often results in the use of a low number of replicates (k). Under circumstances where k is low, it becomes difficult to perform standard statistical tests to extract the most biologically significant experimental results. Other more advanced statistical tests have been developed; however, their use and interpretation often remain difficult to implement in routine biological research. The present work outlines a method that achieves sufficient statistical power for selecting differentially expressed genes under conditions of low k, while remaining as an intuitive and computationally efficient procedure.