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Related Experiment Videos

A comparison of parametric versus permutation methods with applications to general and temporal microarray gene

Ronghui Xu1, Xiaochun Li

  • 1Department of Biostatistics, Harvard School of Public Health and Dana-Farber Cancer Institute, 44 Binney Street, Boston, MA 02115, USA. rxu@jimmy.harvard.edu

Bioinformatics (Oxford, England)
|July 2, 2003
PubMed
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Permutation methods are less accurate than parametric methods for ranking genes in microarray analysis, despite their common use. Parametric methods offer better gene ranking accuracy, especially when dealing with distribution distances.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Microarray data analysis often requires ranking genes by differential importance to identify biologically relevant genes.
  • Permutation methods are frequently recommended for gene ranking in microarray studies due to small sample sizes and potential non-normality.
  • These recommendations stem from classical hypothesis testing principles.

Purpose of the Study:

  • To investigate the relationship between hypothesis testing and gene ranking in microarray data analysis.
  • To evaluate the accuracy and properties of permutation methods compared to parametric methods for gene ranking.

Main Methods:

  • Exploration of the theoretical relationship between hypothesis testing and gene ranking.
  • Simulation studies comparing permutation and parametric methods for gene ranking accuracy.

Related Experiment Videos

  • Assessment of gene ranking variability using bootstrap methods.

    • Main Results:

    • Permutation methods do not provide a metric for the distance between distributions, limiting their effectiveness in gene ranking.
    • Simulation studies indicated permutation methods were often less accurate than parametric methods for ranking genes.
    • Permutation methods showed lower variability in gene ranking, consistent with their robustness to outliers.

    Conclusions:

    • Parametric methods may be more accurate for gene ranking in microarray data than commonly recommended permutation methods.
    • The discreteness and non-metric properties of permutation distributions contribute to their lower accuracy.
    • While robust to outliers, permutation methods' limitations in capturing distributional distances impact their utility for precise gene ranking.