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Related Experiment Videos

Analysis of oligonucleotide array experiments with repeated measures using mixed models.

Hao Li1, Constance L Wood, Thomas V Getchell

  • 1Department of Statistics, 815 Patterson Office Tower, University of Kentucky, Lexington, Kentucky 40506-0027, USA. lhao@uky.edu

BMC Bioinformatics
|January 1, 2005
PubMed
Summary
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Linear mixed models effectively analyze microarray data in Alzheimer's disease studies with repeated measures. This approach identifies differentially expressed genes and controls for multiple testing, enhancing reliability in complex experiments.

Area of Science:

  • Genomics
  • Biostatistics
  • Neuroscience

Background:

  • Mixed factorial experiments are increasingly used in microarray analysis.
  • Alzheimer's disease research involves factors like disease presence and specific brain regions (olfactory bulb, cerebellum).
  • Repeated measures and correlations within subjects require specialized analytical methods.

Purpose of the Study:

  • To analyze oligonucleotide array experiments with repeated measures using a linear mixed model.
  • To identify differentially expressed genes in Alzheimer's disease research.
  • To address multiple testing complexities in multi-level treatment experiments.

Main Methods:

  • Utilized a linear mixed model for oligonucleotide array data with repeated measures.
  • Constructed a generalized F test for selecting differentially expressed genes.

Related Experiment Videos

  • Applied the Benjamini and Hochberg (BH) procedure to control false discovery rate (FDR).
  • Employed protected Fisher's least significant difference (LSD) test for significant interaction effects.

    • Main Results:

    • A linear mixed model proved suitable for analyzing complex microarray data.
    • The generalized F test successfully identified differentially expressed genes.
    • The BH procedure effectively controlled the false discovery rate at 5%.
    • Specific tests were applied to categorize genes based on interaction significance.

    Conclusions:

    • Linear mixed models are appropriate for analyzing repeated measures in oligonucleotide array experiments.
    • A generalized F test combined with sequential testing controls gene-based family-wise error rate (FWER).
    • This methodology enhances the identification of significant genes in complex experimental designs.