Neurochemical Research

, Volume 29, Issue 6, pp 1213–1222 | Cite as

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

  • Paul Pavlidis
  • Jie Qin
  • Victoria Arango
  • John J. Mann
  • Etienne Sibille


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.”

Age brain gene expression microarray prefrontal cortex statistics 


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Copyright information

© Plenum Publishing Corporation 2004

Authors and Affiliations

  • Paul Pavlidis
    • 2
  • Jie Qin
    • 1
  • Victoria Arango
    • 3
    • 4
    • 5
  • John J. Mann
    • 3
    • 5
  • Etienne Sibille
    • 3
    • 5
  1. 1.Columbia Genome Center, Columbia UniversityNew York
  2. 2.Department of Biomedical InformaticsColumbia UniversityNew York
  3. 3.Department of PsychiatryColumbia UniversityNew York
  4. 4.Department of Anatomy & Cell BiologyNew York State Psychiatric InstituteNew York
  5. 5.Department of NeuroscienceNew York State Psychiatric InstituteNew York

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