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Related Experiment Videos

Cross-platform comparison and visualisation of gene expression data using co-inertia analysis.

Aedín C Culhane1, Guy Perrière, Desmond G Higgins

  • 1Department of Biochemistry, Biosciences Institute, University College Cork, Cork, Ireland. Aedin.Culhane@ucd.ie

BMC Bioinformatics
|November 25, 2003
PubMed
Summary

Co-inertia analysis (CIA) offers a robust method for comparing gene expression data across different microarray platforms. This approach effectively identifies common trends and relationships, overcoming limitations of traditional subset filtering.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Systems Biology

Background:

  • Microarray technology advancements have led to diverse platforms, hindering data comparability.
  • Current cross-platform analysis methods often exclude significant gene data by focusing on common gene subsets.
  • A novel method is needed to effectively compare gene expression data across different microarray platforms.

Purpose of the Study:

  • To introduce and demonstrate Co-inertia Analysis (CIA) as a powerful method for cross-platform gene expression data comparison.
  • To illustrate CIA's ability to identify common trends and relationships in gene expression profiles from different microarray technologies.

Main Methods:

  • Co-inertia Analysis (CIA), a multivariate statistical technique, was employed.
  • CIA identifies ordinations from multiple datasets with the same samples by maximizing covariance between successive axes.

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  • The method is suitable for high-dimensional data, such as gene expression datasets where genes far exceed samples.

    • Main Results:

    • CIA was applied to gene expression data from 60 tumor cell lines analyzed using Affymetrix and spotted cDNA arrays.
    • The analysis revealed graphical representations (bi-plots) of consensus and divergence between expression profiles from different platforms.
    • Key genes driving the main trends in the analysis were readily identifiable.

    Conclusions:

    • Co-inertia Analysis (CIA) provides a robust and efficient approach for integrating gene expression datasets from various microarray platforms.
    • CIA offers simplified graphical outputs, facilitating the identification of relationships within large-scale biological datasets.
    • This method enhances the comparability and interpretation of gene expression data across diverse experimental setups.