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Related Experiment Videos

[Multiple comparison procedures: principles, limits. Applications to microarray phenotype-genotype analysis].

C Dalmasso1, P Broët, T Moreau

  • 1INSERM U472, 16, avenue Paul-Vaillant-Couturier, 94807 Villejuif, France.

Revue D'Epidemiologie Et De Sante Publique
|March 3, 2005
PubMed
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This study addresses the challenge of multiple statistical tests in high-throughput data analysis. It introduces methods like Family Wise Error Rate (FWER) and False Discovery Rate (FDR) for accurate gene expression analysis.

Area of Science:

  • Statistical analysis
  • Bioinformatics
  • Genomics

Context:

  • High-throughput studies, such as those using microarrays, involve numerous statistical tests.
  • The multiplicity of these tests presents a significant challenge in data analysis.
  • This problem is prevalent in fields including psychometry, epidemiology, and genetics.

Purpose:

  • To introduce the framework for multiple comparisons.
  • To present statistical procedures based on Family Wise Error Rate (FWER) and False Discovery Rate (FDR).
  • To apply and discuss these procedures using a real-world breast carcinoma dataset.

Summary:

  • The study details statistical methods for handling multiple comparisons, focusing on FWER and FDR.
  • It applies these methods to a breast cancer dataset, evaluating their effectiveness.

Related Experiment Videos

  • Guidelines for selecting and using appropriate multiple comparison procedures are provided.

    • Impact:

    • Provides researchers with robust methods for analyzing large-scale genomic data.
    • Enhances the reliability of findings in gene expression studies.
    • Offers practical guidance for applying multiple testing corrections across various scientific disciplines.