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Related Experiment Videos

A variable fold change threshold determines significance for expression microarrays.

Thomas J Mariani1, Vikram Budhraja, Brigham H Mecham

  • 1Division of Pulmonary and Critical Care, Department of Medicine, Brigham and Women's Hospital at Harvard Medical School, Boston, Massachusetts 02115, USA. tmariani@rics.bwh.harvard.edu

FASEB Journal : Official Publication of the Federation of American Societies for Experimental Biology
|December 12, 2002
PubMed
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This study quantifies gene expression variability in microarrays, developing a method to identify significant changes. The approach reduces false positives, improving accuracy even without replicates.

Area of Science:

  • Genomics
  • Bioinformatics
  • Molecular Biology

Background:

  • Gene expression analysis using microarrays is prone to measurement noise and variability.
  • Variability in transcript hybridization confounds the identification of true biological changes.
  • Oligonucleotide-based microarrays are widely used but require robust methods to interpret results.

Purpose of the Study:

  • To assess and quantify measurement variability in oligonucleotide-based microarrays.
  • To develop a method for identifying statistically significant gene expression changes.
  • To reduce false-positive results in microarray data analysis.

Main Methods:

  • Generated replicate hybridization data using Affymetrix Human U95 GeneChip set.
  • Analyzed intensity-specific variability in gene expression levels.

Related Experiment Videos

  • Applied LOESS fitting to quantify standard deviation and calculate a "variable fold-change" threshold.

    • Main Results:

    • Gene expression variability is intensity-specific.
    • The "variable fold-change" method effectively quantifies variability.
    • The developed method removes intensity-specific bias.
    • A 5- to 10-fold reduction in false-positive changes was observed.

    Conclusions:

    • The "variable fold-change" approach enhances the prediction of significant gene expression changes.
    • This method improves the reliability of oligonucleotide-based microarray experiments.
    • The approach is valuable for reducing false leads, even without experimental replicates.