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Construction of a reference gene association network from multiple profiling data: application to data analysis.

Duygu Ucar1, Isaac Neuhaus, Petra Ross-MacDonald

  • 1Computer Science and Engineering, The Ohio State University, Columbus, OH 43210, USA.

Bioinformatics (Oxford, England)
|September 12, 2007
PubMed
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This study introduces a novel Reference Gene Association (RGA) network approach for analyzing gene expression data. This method effectively identifies co-regulated genes and pathways, aiding in biological discovery and hypothesis testing.

Area of Science:

  • Bioinformatics
  • Systems Biology
  • Computational Biology

Background:

  • Gene expression profiling generates vast microarray data requiring advanced analysis strategies.
  • Constructing gene association networks from profiling data is crucial for biological insight and functional inference.
  • Existing methods for gene network construction need novel approaches to handle growing data archives.

Purpose of the Study:

  • To develop and investigate a non-parametric approach for translating gene expression profiling data into a gene network.
  • To explore the characteristics and utility of the constructed network.
  • To utilize network information within analysis of variance models for hypothesis testing.

Main Methods:

  • Co-expression network construction using reciprocal ranking criteria and false discovery rate analysis on independent microarray datasets.

Related Experiment Videos

  • Partitioning the Reference Gene Association (RGA) network into densely connected sub-networks via a multilevel graph partitioning algorithm.
  • Developing and applying a MANOVA-based approach for hypothesis testing at the sub-network level using probe expression values.

    • Main Results:

    • Successfully constructed the Reference Gene Association (RGA) network by identifying positively co-regulated probe pairs across multiple datasets.
    • Partitioned the RGA network into biologically relevant sub-networks using graph partitioning.
    • Demonstrated the effectiveness and sensitivity of the MANOVA-based methodology in identifying perturbed transcriptional sub-networks or pathways in published studies.

    Conclusions:

    • The Reference Gene Association (RGA) network provides a robust framework for analyzing gene expression data.
    • The proposed MANOVA-based hypothesis testing approach is effective for identifying biologically significant sub-networks and pathways.
    • This integrated network and statistical approach enhances the analysis of microarray data for biological discovery.