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Related Concept Videos

DNA Microarrays02:34

DNA Microarrays

Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...

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Mixture models for detecting differentially expressed genes in microarrays.

Liat Ben-Tovim Jones1, Richard Bean, Geoffrey J McLachlan

  • 1ARC Centre in Bioinformatics, Institute for Molecular Bioscience, University of Queensland, St. Lucia, Brisbane, 4072, Australia. liatj@maths.uq.edu.au

International Journal of Neural Systems
|November 23, 2006
PubMed
Summary

This study introduces mixture models for identifying differentially expressed genes in microarray data, improving multiple hypothesis testing by calculating local false discovery rates (FDR). This method aids in selecting significant genes and refining decision rules for better accuracy.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Statistical Genetics

Background:

  • Microarray experiments commonly face challenges in detecting differentially expressed genes across multiple classes.
  • Gene selection from large datasets necessitates robust multiple hypothesis testing frameworks.

Purpose of the Study:

  • To address multiplicity issues in gene expression analysis using mixture models.
  • To provide a local false discovery rate (FDR) for each gene.
  • To develop a decision rule incorporating prior probabilities and false negative rates.

Main Methods:

  • Application of mixture models to handle multiplicity in gene expression data.
  • Calculation of local false discovery rate (FDR) for individual genes.
  • Estimation of prior probabilities for non-differential expression.

Main Results:

  • The mixture model approach provides a local FDR measure for each gene.
  • It enables estimation of prior probabilities for non-differential expression.
  • The developed decision rule can account for false negative rates.

Conclusions:

  • Mixture models offer a powerful framework for gene selection in microarray studies.
  • This approach enhances the reliability of identifying significant genes by managing multiplicity.
  • The method was successfully applied to a breast cancer dataset, demonstrating its practical utility.