Interpreting Microarray Experiments Via Co-expressed Gene Groups Analysis (CGGA)

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4265)


Microarray technology produces vast amounts of data by measuring simultaneously the expression levels of thousands of genes under hundreds of biological conditions. Nowadays, one of the principal challenges in bioinformatics is the interpretation of huge data using different sources of information.

We propose a novel data analysis method named CGGA (Co-expressed Gene Groups Analysis) that automatically finds groups of genes that are functionally enriched, i.e. have the same functional annotations, and are co-expressed.

CGGA automatically integrates the information of microarrays, i.e. gene expression profiles, with the functional annotations of the genes obtained by the genome-wide information sources such as Gene Ontology (GO).

By applying CGGA to well-known microarray experiments, we have identified the principal functionally enriched and co-expressed gene groups, and we have shown that this approach enhances and accelerates the interpretation of DNA microarray experiments.


Gene Ontology Microarray Experiment Aerobic Respiration Respiratory Chain Complex Filamentous Growth 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Attwood, T., Miller, C.J.: Which craft is best in bioinformatics? Computer Chemistry 25, 329–339 (2001)CrossRefGoogle Scholar
  2. 2.
    Breitling, R., Amtmann, A., Herzyk, P.: IGA: A simple tool to enhance sensitivity and facilitate interpretation of microarray experiments. BMC Bioinformatics 5, 34 (2004)CrossRefGoogle Scholar
  3. 3.
    Chuaqui, R.: Post-analysis follow-up and validation of microarray experiments. Nature Genetics 32, 509–514 (2002)CrossRefGoogle Scholar
  4. 4.
    DeRisi, J., Iyer, L., Brown, V.: Exploring the metabolic and genetic control of gene expression on a genomic scale. Science 278, 680–686 (1997)CrossRefGoogle Scholar
  5. 5.
    Draghici, S., et al.: Global functional profiling of gene expression. Genomics 81, 1–7 (2003)CrossRefGoogle Scholar
  6. 6.
    Gibbons, D., Roth, F., et al.: Judging the quality of gene expression-Based Clustering Methods Using Gene Annotation. Genome Research 12, 1574–1581 (2002)CrossRefGoogle Scholar
  7. 7.
    Hosack, D., Dennis, G., et al.: Identifying biological themes within lists of genes with EASE. Genome Biology 4, R70 (2003)Google Scholar
  8. 8.
    Kim, S., Volsky, D., et al.: PAGE: Parametric Analysis of Gene Set Enrichment. BMC Bioinformatics 6, 144 (2005)CrossRefGoogle Scholar
  9. 9.
    Masys, D., et al.: Use of keyword hierarchies to interpret gene expressions patterns. BMC Bioinformatics 17, 319–326 (2001)Google Scholar
  10. 10.
    Mootha, V., et al.: PGC-l α-reponsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes. Nature Genetics 34(3), 267–273 (2003)CrossRefGoogle Scholar
  11. 11.
    Pasquier, C., Girardot, F., Jevardat, K., Christen, R.: THEA: Ontology-driven analysis of microarray data. Bioinformatics 20(16) (2004)Google Scholar
  12. 12.
    Quackenbush, J.: Microarray data normalization and transformation. Nature Genetics 32(suppl.), 496–501 (2002)CrossRefGoogle Scholar
  13. 13.
    Riva, A., Carpentier, A., Torresani, B., Henaut, A.: Comments on selected fundamental aspects of microarray analysis. Computational Bio. and Chem. 29, 319–336 (2005)zbMATHCrossRefGoogle Scholar
  14. 14.
    Robinson, M., et al.: FunSpec: A web based cluster interpreter for yeast. BMC Bioinformatics 3, 35 (2002)CrossRefGoogle Scholar
  15. 15.
    Sung, G., Jung, U., Yang, K.: A graph theoretic modeling on GO space for biological interpretation of gene clusters. BMC Bioinformatics 3, 381–386 (2004)Google Scholar
  16. 16.
    Tusher, V., Tibshirani, R., Chu, G., et al.: Significance analysis of microarrays applied to the ionizing radiation response. In: Proc. Nat. Acad. Sci. USA, vol. 98(9), pp. 5116–5121 (2001)Google Scholar
  17. 17.
    Martinez, R., et al.: CGGA: An automatic tool for the interpretation of gene expression experiments. Accepted on the Journal of Integrative Bioinformatics (to appear, 2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  1. 1.Laboratoire I3SSophia-AntipolisFrance
  2. 2.Laboratoire Biologie VirtuelleCentre de Biochimie, Parc ValroseNiceFrance
  3. 3.INRIA Sophia AntipolisSophia-AntipolisFrance

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