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Using linear mixed models for normalization of cDNA microarrays.

Philippe Haldermans1, Ziv Shkedy, Suzy Van Sanden

  • 1Hasselt University, Belgium. philippe.haldermans@uhasselt.be

Statistical Applications in Genetics and Molecular Biology
|August 4, 2007
PubMed
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Linear mixed models offer a robust method for normalizing microarray data by addressing systematic variations. This approach improves gene expression comparisons across experiments, outperforming traditional LOWESS normalization.

Area of Science:

  • Genomics
  • Bioinformatics
  • Statistical Modeling

Background:

  • Microarrays enable simultaneous measurement of numerous gene expression levels.
  • Systematic variations in microarray experiments complicate accurate gene expression comparisons.
  • Effective normalization is crucial for reliable cross-experiment analysis.

Purpose of the Study:

  • To introduce and evaluate linear mixed models for normalizing two-color spotted microarrays.
  • To address various sources of variation, including print-tip effects.
  • To compare the performance of linear mixed models against existing normalization methods.

Main Methods:

  • Application of linear mixed models to normalize two-color spotted microarray data.
  • Inclusion of factors like print-tip variation within the model.

Related Experiment Videos

  • Comparison with intensity-dependent normalization methods, specifically LOWESS.

    • Main Results:

    • Linear mixed models provide a parametric approach to microarray normalization.
    • The proposed method demonstrates favorable comparison to LOWESS normalization.
    • Validation on two real-world datasets and a simulation study.

    Conclusions:

    • Linear mixed models offer a powerful and effective tool for microarray data normalization.
    • This technique enhances the accuracy of gene expression comparisons.
    • The method is suitable for handling complex variation sources in genomic studies.