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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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Local false discovery rate facilitates comparison of different microarray experiments.

Wan-Jen Hong1, Robert Tibshirani, Gilbert Chu

  • 1Department of Medicine, Department of Biochemistry, Department of Statistics and Health Research and Policy, Stanford University Medical Center, Stanford, CA 94305, USA.

Nucleic Acids Research
|October 15, 2009
PubMed
Summary

The local false discovery rate (LFDR) effectively identifies gene expression changes in microarray data. LFDR analysis quantitatively compares experiments and complements functional assessments, revealing subtle biological differences.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Molecular Biology

Background:

  • The local false discovery rate (LFDR) is a statistical measure to assess the probability of false positives in gene expression analysis.
  • Accurate identification of differentially expressed genes is crucial for understanding biological responses and experimental outcomes.

Purpose of the Study:

  • To evaluate the utility of LFDR for quantitative comparison of microarray experiments.
  • To demonstrate how LFDR can complement functional enrichment analyses and reveal nuanced biological differences between experimental conditions.

Main Methods:

  • Computer simulations were used to validate LFDR thresholds for identifying true and false gene expression changes.
  • LFDR was applied to compare different microarray data pre-processing methods using Venn diagrams, scatter plots, correlation coefficients, and gene function distributions.
  • LFDR was used to analyze gene expression responses to ultraviolet radiation (UV), ionizing radiation (IR), and tobacco smoke exposure.

Main Results:

  • LFDR thresholds of <10% and >90% accurately distinguished between genes with and without expression changes in simulations.
  • While pre-processing methods showed high correlations (r=0.84-0.92), LFDR revealed differences in the magnitude and discordance of gene responses.
  • Genes responding to both UV and IR were enriched in cell cycle and DNA repair pathways, while UV-specific responders were depleted in cell adhesion.
  • Genes responding to tobacco smoke were enriched for detoxification functions.

Conclusions:

  • LFDR provides a robust method for quantitatively comparing microarray experiments and assessing the reliability of gene expression findings.
  • LFDR analysis effectively complements functional assessments, highlighting specific biological pathway enrichments and depletions related to different stimuli.
  • LFDR reveals both similarities and differences in biological responses across various experimental conditions and data processing strategies.