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FDR-FET: an optimizing gene set enrichment analysis method.

Rui-Ru Ji1, Karl-Heinz Ott, Roumyana Yordanova

  • 1Applied Genomics, Research and Development, Bristol-Myers Squibb, Pennington, NJ, USA.

Advances and Applications in Bioinformatics and Chemistry : AABC
|September 16, 2011
PubMed
Summary

This study introduces FDR-FET, a novel gene set enrichment analysis method that dynamically optimizes thresholds. This approach enhances sensitivity and selectivity for identifying significant biological processes in large-scale experiments.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Systems Biology

Background:

  • Gene set enrichment analysis (GSEA) is crucial for interpreting large-scale biological experiments.
  • Existing GSEA methods often rely on arbitrary fold-change or P-value cutoffs, impacting results.
  • Threshold selection significantly influences the outcome and interpretation of GSEA.

Purpose of the Study:

  • To develop a novel GSEA method, FDR-FET, that dynamically optimizes threshold selection.
  • To improve the sensitivity and selectivity of GSEA for identifying biological processes.
  • To provide a more robust approach for analyzing transcriptome, proteome, and other high-throughput data.

Main Methods:

  • Developed FDR-FET, a method that generates regulated gene lists across multiple False Discovery Rate (FDR) cutoffs.
Keywords:
Fisher’s exact testfalse discovery rategene set enrichment analysismicroarray profilingprotease inhibitors

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  • Employs Fisher's Exact Test (FET) to compute P-values for gene sets at each FDR cutoff.
  • Retains the lowest P-value to determine gene set significance and implements improved global reference set definition.

    • Main Results:

    • FDR-FET dynamically optimizes threshold choices, enhancing GSEA sensitivity and selectivity.
    • The method effectively controls errors while retaining informative genes by combining FDR with multiple cutoffs.
    • Demonstrated validity using a HIV protease inhibitor microarray study, comparing favorably with existing algorithms.

    Conclusions:

    • FDR-FET selectively identifies significantly affected biological processes by optimizing threshold selection.
    • The method offers improved accuracy and interpretability for GSEA in various biological applications.
    • FDR-FET is applicable to diverse user-generated gene lists across different 'omics' fields.