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Related Experiment Videos

Selecting differentially expressed genes from microarray experiments.

Margaret Sullivan Pepe1, Gary Longton, Garnet L Anderson

  • 1Department of Biostatistics, University of Washington, Seattle, Washington 98195-7232, USA. mspepe@u.washington.edu

Biometrics
|May 24, 2003
PubMed
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This study introduces statistical methods for ranking genes and proteins to distinguish cancerous from normal tissues. It highlights Receiver Operating Characteristic Curve measures and bootstrap-enhanced selection probability for reliable biomarker discovery and sample-size calculations in high-throughput studies.

Area of Science:

  • Biostatistics
  • Bioinformatics
  • Genomics

Background:

  • High-throughput technologies like gene expression arrays and mass spectrometry enable simultaneous evaluation of thousands of potential biomarkers.
  • Distinguishing between cancerous and normal tissues is a critical application for biomarker discovery.

Purpose of the Study:

  • To evaluate statistical methods for ranking genes/proteins based on differential expression between tissue types.
  • To propose and validate methods for quantifying sampling variability in gene rankings.
  • To develop a procedure for sample-size calculations in exploratory biomarker studies.

Main Methods:

  • Comparison of various statistical measures for gene/protein ranking, focusing on Receiver Operating Characteristic (ROC) Curve-related metrics.

Related Experiment Videos

  • Utilizing the "selection probability function" estimated via the bootstrap to quantify sampling variability.
  • Analysis of a real dataset from ovarian cancer gene expression arrays and simulation studies.

    • Main Results:

    • Two ROC Curve-related measures demonstrated particular suitability for ranking differentially expressed genes/proteins.
    • The bootstrap-estimated selection probability function effectively quantifies sampling variability in rankings.
    • The proposed methods were validated using both real and simulated data.

    Conclusions:

    • The study provides robust statistical methods for identifying differentially expressed genes/proteins, crucial for distinguishing cancerous from normal tissues.
    • Quantifying sampling variability enhances the reliability of biomarker rankings.
    • The developed approach facilitates appropriate sample-size determination for exploratory biomarker research.