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Related Experiment Videos

Comparing functional annotation analyses with Catmap.

Thomas Breslin1, Patrik Edén, Morten Krogh

  • 1Complex Systems Division, Department of Theoretical Physics, Lund University, Lund, Sweden. thomas@thep.lu.se <thomas@thep.lu.se>

BMC Bioinformatics
|December 14, 2004
PubMed
Summary
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Gene category analysis in microarray experiments requires careful consideration of scoring methods and null hypotheses. Cutoff-independent scores and appropriate null hypothesis testing, like sample label permutations, are crucial for reliable significance assignment.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Microarray experiments generate ranked gene lists for biological interpretation.
  • Traditional gene category analysis often relies on cutoff-based scores and random gene permutations for significance.
  • Existing tools may introduce arbitrariness due to cutoff dependency and suboptimal null hypothesis choices.

Purpose of the Study:

  • To compare different scoring methods and null hypotheses in gene category analysis.
  • To evaluate the impact of cutoff-based versus cutoff-independent scores.
  • To assess the suitability of random gene permutations versus sample label permutations as null hypotheses.

Main Methods:

  • Analysis of three public microarray datasets with two-class sample divisions.

Related Experiment Videos

  • Genes ranked by correlation to class labels.
  • Development and use of the Catmap program for comparing scores and null hypotheses.
  • Utilized Gene Ontology annotations for defining gene categories.

    • Main Results:

    • Cutoff-based scores demonstrated strong dependence on cutoff selection, introducing arbitrariness.
    • The choice of null hypothesis significantly impacted category significance.
    • Random gene permutations yielded considerably smaller p-values compared to sample label permutations, especially for large, coexpressed gene categories.

    Conclusions:

    • Cutoff-independent scoring is preferred for robust gene category analysis of ranked gene lists.
    • Random gene permutations are not a reliable approximation for sample label permutations as a null hypothesis.
    • Careful selection of the null hypothesis is critical for accurate significance assessment in gene set enrichment analysis.