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Evaluating test statistics to select interesting genes in microarray experiments.

Charles Kooperberg1, Simonetta Sipione, Michael LeBlanc

  • 1Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, PO Box 19024, MP 1002, Seattle, WA 98109-1024, USA. clk@fhcrc.org

Human Molecular Genetics
|September 10, 2002
PubMed
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A new randomization procedure helps assess significance and false-discovery rates in complex microarray experiments. This method, visualized with a graph similar to ROC curves, offers an alternative to the Significance Analysis of Microarrays (SAM) procedure.

Area of Science:

  • Genomics and Bioinformatics
  • Statistical Genetics
  • Computational Biology

Background:

  • Complex microarray experiments require robust statistical methods for significance testing and false-discovery rate control.
  • Existing methods like Significance Analysis of Microarrays (SAM) have limitations in certain experimental designs.
  • Receiver Operator Characteristic (ROC) curves are valuable for evaluating classifier performance, offering insights into statistical test performance.

Purpose of the Study:

  • To propose a novel randomization procedure for evaluating significance levels and false-discovery rates in complex microarray data.
  • To introduce a graphical tool for comparing diverse test statistics within the same experimental context.
  • To assess the performance of the proposed method against the established SAM procedure using real and simulated data.

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Main Methods:

  • Development of a randomization procedure tailored for complex microarray experiments.
  • Utilization of a graph, analogous to ROC curves, to compare the efficacy of different statistical tests.
  • Application of the proposed method to a subset of cell-line data from a Huntington's disease study.
  • Conducting a simulation study to benchmark the proposed procedure against SAM.

Main Results:

  • The proposed randomization procedure effectively evaluates significance levels and false-discovery rates.
  • The associated graph provides a useful tool for comparing test statistics, similar to ROC curve analysis.
  • Initial application to Huntington's disease-related data demonstrates the method's practical utility.
  • Simulation results indicate competitive or improved performance compared to the SAM procedure.

Conclusions:

  • The proposed randomization procedure offers a valuable alternative for analyzing complex microarray experiments.
  • The graphical comparison tool enhances the selection and evaluation of appropriate statistical tests.
  • This approach provides a robust framework for assessing statistical significance and controlling false discoveries in genomic studies.