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Related Experiment Videos

Identification of gene expression patterns using planned linear contrasts.

Hao Li1, Constance L Wood, Yushu Liu

  • 1Department of Statistics, University of Kentucky, Lexington, KY 40536-0027, USA. lhao@uky.edu

BMC Bioinformatics
|May 9, 2006
PubMed
Summary
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This study introduces a novel method for analyzing gene expression patterns in time-course experiments, focusing on the timing and shape of gene responses to identify co-expressed genes in biological networks.

Area of Science:

  • Genomics
  • Bioinformatics
  • Systems Biology

Background:

  • Gene expression timing is critical in time-course profiles for understanding gene networks.
  • Identifying co-expressed genes requires analyzing similar expression patterns over sampling intervals.
  • Multi-level treatment experiments with numerous genes present significant multiple testing challenges.

Purpose of the Study:

  • To develop a method for analyzing time-course gene expression data that accounts for the timing and patterns of gene regulation.
  • To address challenges in identifying co-expressed genes and managing multiple testing issues in complex experiments.
  • To characterize gene expression profiles based on temporal dynamics and response shapes.

Main Methods:

  • Utilized ANOVA F-tests to identify significantly regulated genes.

Related Experiment Videos

  • Applied Benjamini and Hochberg (BH) procedure for false discovery rate (FDR) control.
  • Employed sequential testing of orthogonal contrasts (reverse Helmert series) to categorize genes by response timing.
  • Characterized expression 'fluctuation' shapes using stepwise Studentized Maximum Modulus tests to control family-wise error rate (MFWER).
  • Demonstrated the method on murine olfactory sensory epithelia microarray data.

    • Main Results:

    • Successfully categorized genes into four classes based on the timing of initial expression changes.
    • Characterized the general shapes of gene expression patterns along subsequent time points within each class.
    • The method was effectively applied to analyze time-course microarray data.

    Conclusions:

    • Planned linear contrasts provide a robust approach for analyzing time-course microarray experiments.
    • The method accurately characterizes gene expression patterns by temporal order, initial response timing, and subsequent shape.
    • This approach is well-suited for experiments with infrequent or non-uniform sampling intervals.