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Related Experiment Videos

Group testing for pathway analysis improves comparability of different microarray datasets.

Theodora Manoli1, Norbert Gretz, Hermann-Josef Gröne

  • 1Theoretical Bioinformatics, German Cancer Reseach Center, 69120 Heidelberg, Germany.

Bioinformatics (Oxford, England)
|August 10, 2006
PubMed
Summary
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Comparing gene expression analysis methods is crucial for reliable results. Group testing approaches, particularly Fisher's exact test followed by global test, offer more consistent findings across different datasets and statistical methods for microarray analysis.

Area of Science:

  • Genomics and Bioinformatics
  • Cancer Research
  • Statistical Modeling

Background:

  • DNA microarrays are widely used for transcriptome analysis, leading to numerous data processing methods.
  • Discrepancies arise when comparing gene lists from different statistical methods or datasets.
  • Investigating these discrepancies at the pathway level is essential for robust biological interpretation.

Purpose of the Study:

  • To evaluate discrepancies in gene expression analysis across different statistical methods and datasets.
  • To examine the consistency of pathway identification using various group testing procedures.
  • To identify reliable biological pathways involved in prostate cancer pathogenesis.

Main Methods:

  • Analysis of three public prostate cancer microarray datasets (HGU95A/HGU95Av2).

Related Experiment Videos

  • Data normalization using variance stabilizing normalization (vsn) and mixed model normalization (MMN).
  • Application of statistical analysis of microarrays and mixed model analysis, with false discovery rate adjustment.
  • Group testing using Fisher's exact test, Gene Set Enrichment Analysis (GSEA), and global test.

    • Main Results:

    • Gene list overlaps varied: 42-52% for the same method across datasets, 63-85% for different methods on the same dataset.
    • Fisher's exact test followed by global test demonstrated higher concordance between methods and datasets compared to GSEA.
    • All group testing methods identified known prostate cancer-associated pathways.
    • Recurrently identified pathways across analyses suggest higher reliability.

    Conclusions:

    • Group testing procedures, especially Fisher's exact test combined with global test, improve consistency in microarray data analysis.
    • Consistent pathway identification across multiple datasets and methods enhances confidence in biological findings.
    • This approach aids in identifying reliable molecular mechanisms in diseases like prostate cancer.