Iterative Group Analysis (iGA): A simple tool to enhance sensitivity and facilitate interpretation of microarray experiments

Breitling, R., Amtmann, A. and Herzyk, P. (2004) Iterative Group Analysis (iGA): A simple tool to enhance sensitivity and facilitate interpretation of microarray experiments. BMC Bioinformatics, 5(34),

Abstract

BACKGROUND: The biological interpretation of even a simple microarray experiment can be a challenging and highly complex task. Here we present a new method (Iterative Group Analysis) to facilitate, improve, and accelerate this process. RESULTS: Our Iterative Group Analysis approach (iGA) uses elementary statistics to identify those functional classes of genes that are significantly changed in an experiment and at the same time determines which of the class members are most likely to be differentially expressed. iGA does not require that all members of a class change and is therefore robust against imperfect class assignments, which can be derived from public sources (e.g. GeneOntologies) or automated processes (e.g. key word extraction from gene names). In contrast to previous non-iterative approaches, iGA does not depend on the availability of fixed lists of differentially expressed genes, and thus can be used to increase the sensitivity of gene detection especially in very noisy or small data sets. In the extreme, iGA can even produce statistically meaningful results without any experimental replication. The automated functional annotation provided by iGA greatly reduces the complexity of microarray results and facilitates the interpretation process. In addition, iGA can be used as a fast and efficient tool for the platform-independent comparison of a microarray experiment to the vast number of published results, automatically highlighting shared genes of potential interest. CONCLUSIONS: By applying iGA to a wide variety of data from diverse organisms and platforms we show that this approach enhances and accelerates the interpretation of microarray experiments.

• Item Type: Articles
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Amtmann, Professor Anna
• Authors: Breitling, R., Amtmann, A., and Herzyk, P.
• Subjects: Q Science > QH Natural history > QH426 Genetics; Q Science > QH Natural history
• College/School: College of Medical Veterinary and Life Sciences > School of Molecular Biosciences; College of Science and Engineering > School of Computing Science
• Journal Name: BMC Bioinformatics
• Publisher: Biomed Central
• ISSN: 1471-2105
• Copyright Holders: © 2004 Breitling et al; licensee BioMed Central Ltd
• First Published: First published in BMC Bioinformatics 2004, 5:34 (published 29 March 2004)
• Publisher Policy: This is an Open Access article: verbatim copying and redistribution of this article are permitted in all media for any purpose, provided this notice is preserved along with the article's original URL.