This article is part of the supplement: Proceedings of the Fifth Annual MCBIOS Conference. Systems Biology: Bridging the Omics
Testing for treatment effects on gene ontology
1 Department of Information and Mathematics, Korea University, Jochiwon, Chungnam 339-700, Korea
2 Center for Functional Genomics, Division of Systems Toxicology, National Center for Toxicological Research, 3900 NCTR Road, Jefferson, AR 72079, USA
3 Louisiana State University Health Sciences Center, New Orleans, LA 70112
4 Depts. of Geriatrics. Biochemistry & Molecular Biology, and Pharmacology/Toxicology, University of Arkansas for Medical Sciences, and VA Medical Center, Little Rock, AR 72205, USA
5 Department of Epidemiology, University of Arkansas for Medical Sciences, College of Public Health, 4301 W Markham St, #820, Little Rock, AR 72205, USA
BMC Bioinformatics 2008, 9(Suppl 9):S20 doi:10.1186/1471-2105-9-S9-S20Published: 12 August 2008
In studies that use DNA arrays to assess changes in gene expression, it is preferable to measure the significance of treatment effects on a group of genes from a pathway or functional category such as gene ontology terms (GO terms, http://www.geneontology.org webcite) because this facilitates the interpretation of effects and may markedly increase significance. A modified meta-analysis method to combine p-values was developed to measure the significance of an overall treatment effect on such functionally-defined groups of genes, taking into account the correlation structure among genes. For hypothesis testing that allows gene expression to change in both directions, p-values are calculated under the null distribution generated by a Monte Carlo method.
As a test of this procedure, we attempted to distinguish altered pathways in microarray studies performed with Mitochips, oligonucleotide microarrays specific to mitochondrial DNA-encoded transcripts. We found that our analytic method improves the specificity of selection for altered pathways, due to incorporation of the inter-gene correlation structure in each pathway. It is thus a practical method to measure treatment effects on GO groups. In many actual applications, microarray experiments measure treatment effects under complicated design structures and with small sample sizes. For such applications to real data of limited statistical power, and also in computer simulations, we demonstrate that our method gives reasonable test results.