NDLI: Using the Gene Ontology for Microarray Data Mining: A Comparison of Methods and Application to Age Effects in Human Prefrontal Cortex

Content Provider	Springer Nature Link
Author	Pavlidis, Paul Qin, Jie Arango, Victoria Mann, John J. Sibille, Etienne
Copyright Year	2004
Abstract	One of the challenges in the analysis of gene expression data is placing the results in the context of other data available about genes and their relationships to each other. Here, we approach this problem in the study of gene expression changes associated with age in two areas of the human prefrontal cortex, comparing two computational methods. The first method, “overrepresentation analysis” (ORA), is based on statistically evaluating the fraction of genes in a particular gene ontology class found among the set of genes showing age-related changes in expression. The second method, “functional class scoring” (FCS), examines the statistical distribution of individual gene scores among all genes in the gene ontology class and does not involve an initial gene selection step. We find that FCS yields more consistent results than ORA, and the results of ORA depended strongly on the gene selection threshold. Our findings highlight the utility of functional class scoring for the analysis of complex expression data sets and emphasize the advantage of considering all available genomic information rather than sets of genes that pass a predetermined “threshold of significance.”
Starting Page	1213
Ending Page	1222
Page Count	10
File Format	PDF
ISSN	03643190
Journal	Neurochemical Research
Volume Number	29
Issue Number	6
e-ISSN	15736903
Language	English
Publisher	Kluwer Academic Publishers-Plenum Publishers
Publisher Date	2004-01-01
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Neurosciences Neurology Biochemistry
Content Type	Text
Resource Type	Article
Subject	Medicine Biochemistry Cellular and Molecular Neuroscience

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Using the Gene Ontology for Microarray Data Mining: A Comparison of Methods and Application to Age Effects in Human Prefrontal Cortex

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