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Comparative analysis of microarray normalization procedures: effects on reverse engineering gene networks.

Wei Keat Lim1, Kai Wang, Celine Lefebvre

  • 1Department of Biomedical Informatics, Columbia University, 622 West 168th Street, Vanderbilt Clinic 5th Floor, New York, NY 10032, USA.

Bioinformatics (Oxford, England)
|July 25, 2007
PubMed
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Normalization methods for gene expression data significantly impact cellular network reconstruction. MAS5 normalization surprisingly yielded the most accurate network models, outperforming other common methods for gene expression analysis.

Area of Science:

  • Bioinformatics
  • Systems Biology
  • Computational Biology

Background:

  • Gene expression profile data is increasingly used for reverse engineering cellular networks.
  • Existing normalization methods for Affymetrix GeneChips were optimized for differential gene expression, not network inference.
  • Current evaluation lacks metrics to assess normalization suitability for network reconstruction and gene pair correlation.

Purpose of the Study:

  • To benchmark four common normalization procedures (MAS5, RMA, GCRMA, Li-Wong) for gene expression data.
  • To evaluate their suitability for reverse engineering cellular networks, including protein-protein and protein-DNA interactions.
  • To identify normalization-induced artifacts affecting correlation measurements.

Main Methods:

  • Benchmarking of MAS5, RMA, GCRMA, and Li-Wong normalization procedures.

Related Experiment Videos

  • Application of normalization methods to replicate sample, randomized, and human B-cell datasets.
  • Evaluation in the context of established network reverse engineering algorithms.

    • Main Results:

    • MAS5 normalization demonstrated the most faithful cellular network reconstruction among the tested methods.
    • A specific step in GCRMA was identified as a source of artifacts, causing systematic overestimation of pairwise correlation.
    • Findings have implications for network inference, hierarchical clustering, and other correlation-dependent analyses.

    Conclusions:

    • The choice of normalization procedure critically impacts the accuracy of reverse-engineered cellular networks.
    • MAS5 emerges as a robust method for network reconstruction from gene expression data.
    • Addressing GCRMA artifacts is crucial for reliable correlation measurements in gene expression analysis.