Functional Comparison of Microarray Data Across Multiple Platforms Using the Method of Percentage of Overlapping Functions

  • Zhiguang LiEmail author
  • Joshua C. Kwekel
  • Tao Chen
Part of the Methods in Molecular Biology book series (MIMB, volume 802)


Functional comparison across microarray platforms is used to assess the comparability or similarity of the biological relevance associated with the gene expression data generated by multiple microarray platforms. Comparisons at the functional level are very important considering that the ultimate purpose of microarray technology is to determine the biological meaning behind the gene expression changes under a specific condition, not just to generate a list of genes. Herein, we present a method named percentage of overlapping functions (POF) and illustrate how it is used to perform the functional comparison of microarray data generated across multiple platforms. This method facilitates the determination of functional differences or similarities in microarray data generated from multiple array platforms across all the functions that are presented on these platforms. This method can also be used to compare the functional differences or similarities between experiments, projects, or laboratories.

Key words

Microarray Biological pathway database Functional comparison Percentage of overlapping functions Gene expression 



The authors would like to thank Drs. Minjun Chen and Zhihua Xu in Division of Systems Biology, National Center for Toxicological Research, U.S. Food and Drug Administration for their enlightening comments and hearty discussions in reviewing the manuscript, and Dr. Lin Xie in Department of Aquaculture and Fisheries, University of Arkansas at Pine Bluff for her advice on the statistical methods used in this manuscript. The views presented in this chapter do not necessarily reflect those of the Food and Drug Administration.


  1. 1.
    Barrett JC, Kawasaki ES (2003) Microarrays: the use of oligonucleotides and cDNA for the analysis of gene expression. Drug Discov Today 8:134–141.PubMedCrossRefGoogle Scholar
  2. 2.
    Holloway AJ, van Laar RK, Tothill RW et al (2002) Options available--from start to finish--for obtaining data from DNA microarrays II. Nat Genet 32:481–489.PubMedCrossRefGoogle Scholar
  3. 3.
    Shi L, Reid LH, Jones WD et al (2006) The MicroArray Quality Control (MAQC) project shows inter- and intraplatform reproducibility of gene expression measurements. Nat Biotechnol 24:1151–1161.PubMedCrossRefGoogle Scholar
  4. 4.
    Yauk CL, Berndt ML, Williams A et al (2004) Comprehensive comparison of six microarray technologies. Nucleic Acids Res 32:e124.PubMedCrossRefGoogle Scholar
  5. 5.
    Tan PK, Downey TJ, Spitznagel EL Jr et al (2003) Evaluation of gene expression measurements from commercial microarray platforms. Nucleic Acids Res 31:5676–5684.PubMedCrossRefGoogle Scholar
  6. 6.
    Barrett T, Troup DB, Wilhite SE et al (2009) NCBI GEO: archive for high-throughput functional genomic data. Nucleic Acids Res 37:D885-890.PubMedCrossRefGoogle Scholar
  7. 7.
    Barrett T, Suzek TO, Troup DB et al (2005) NCBI GEO: mining millions of expression profiles – database and tools. Nucleic Acids Res 33:D562-566.PubMedCrossRefGoogle Scholar
  8. 8.
    Li Z, Su Z, Wen Z et al (2009) Microarray platform consistency is revealed by biologically functional analysis of gene expression profiles. BMC Bioinformatics 10:S12.PubMedCrossRefGoogle Scholar
  9. 9.
    Chen L, Mei N, Yao L et al (2006) Mutations induced by carcinogenic doses of aristolochic acid in kidney of Big Blue transgenic rats. Toxicol Lett 165:250–256.PubMedCrossRefGoogle Scholar
  10. 10.
    Mei N, Arlt VM, Phillips DH et al (2006) DNA adduct formation and mutation induction by aristolochic acid in rat kidney and liver. Mutat Res 602:83–91.PubMedCrossRefGoogle Scholar
  11. 11.
    Mengs U, Lang W, Poch J-A (1982) The carcinogenic action of aristolochic acid in rats. Archives of Toxicology 51:107–119.CrossRefGoogle Scholar
  12. 12.
    Guo L, Lobenhofer EK, Wang C et al (2006) Rat toxicogenomic study reveals analytical consistency across microarray platforms. Nat Biotechnol 24:1162–1169.PubMedCrossRefGoogle Scholar
  13. 13.
  14. 14.
  15. 15.
    IPA. (2009) Calculating and Interpreting the p-values for Functions, Pathways, and Lists in Ingenuity Pathways Analysis. Ingenuity Systems, Redwood City, CA, 94063, USA.Google Scholar
  16. 16.
    Sun H, Fang H, Chen T et al (2006) GOFFA: gene ontology for functional analysis – a FDA gene ontology tool for analysis of genomic and proteomic data. BMC Bioinformatics 7:S23.PubMedCrossRefGoogle Scholar
  17. 17.
  18. 18.

Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  1. 1.Division of Genetic and Molecular ToxicologyNational Center for Toxicological Research, U.S. Food and Drug AdministrationJeffersonUSA
  2. 2.Division of System BiologyNational Center for Toxicological Research, U.S. Food and Drug AdministrationJeffersonUSA

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