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Related Experiment Videos

Assessing stability of gene selection in microarray data analysis.

Xing Qiu1, Yuanhui Xiao, Alexander Gordon

  • 1Department of Biostatistics and Computational Biology, University of Rochester, 601 Elmwood Avenue, Rochester, New York 14642, USA. xqiu@bst.rochester.edu

BMC Bioinformatics
|February 3, 2006
PubMed
Summary
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Gene selection stability is crucial for reliable results. This study uses resampling to identify consistently selected genes, improving the robustness of differential gene expression analysis.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Statistical Genetics

Background:

  • Differential gene expression analysis involves identifying genes with significant expression changes.
  • The number of differentially expressed genes is a random variable, necessitating stability assessment.
  • Gene selection procedure stability is a critical factor for reliable biological interpretation.

Purpose of the Study:

  • To assess the stability and properties of various gene selection procedures.
  • To introduce a method for quantifying gene selection instability.
  • To evaluate the impact of gene expression correlation on multiple testing procedures.

Main Methods:

  • Utilized resampling techniques (e.g., bootstrapping) to evaluate gene selection stability.

Related Experiment Videos

  • Employed biological and simulated datasets for comprehensive analysis.
  • Conducted computer simulations to study the effect of correlation on multiple testing.

    • Main Results:

    • Identified significant variability in gene selection frequency across subsamples, even for genes with similar adjusted p-values.
    • Developed and applied a novel method to assess the extent of gene selection instability.
    • Demonstrated that gene expression correlation influences the performance of multiple testing procedures.

    Conclusions:

    • Resampling techniques can refine gene sets by selecting genes with high and consistent selection frequencies.
    • Resampling provides a robust framework for assessing the variability of performance indicators in gene selection.
    • The study elucidates the stability characteristics of multiple testing procedures, offering insights for improved data analysis.