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Related Experiment Videos

Improving false discovery rate estimation.

Stan Pounds1, Cheng Cheng

  • 1Department of Biostatistics, MS 262 St Jude Children's Research Hospital, 332 N. Lauderdale Street, Memphis, TN 38105-2794, USA. stanley.pounds@stjude.org

Bioinformatics (Oxford, England)
|February 28, 2004
PubMed
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A new method, SPLOSH, offers a more stable way to estimate false discoveries in microarray data analysis than existing q-value or BUM methods. This approach provides a reliable estimate of the conditional false discovery rate (cFDR).

Area of Science:

  • Bioinformatics
  • Statistical genomics
  • High-throughput data analysis

Background:

  • Controlling the false discovery rate (FDR) in microarray analysis is crucial but often impractical.
  • Existing methods like q-value and beta-uniform mixture (BUM) have limitations in stability and applicability.
  • The q-value's interpretation may be unwarranted due to reliance on an unstable estimator.
  • BUM is only reliable when its model accurately reflects p-value distributions.

Purpose of the Study:

  • To introduce a novel method, spacings LOESS histogram (SPLOSH), for estimating the conditional false discovery rate (cFDR).
  • To provide a more stable and broadly applicable alternative to existing FDR estimation techniques.

Main Methods:

  • The proposed method, SPLOSH, estimates the conditional FDR (cFDR).

Related Experiment Videos

  • cFDR represents the expected proportion of false positives given a specific number of significant findings (k).
  • SPLOSH is designed for enhanced stability compared to q-values and wider applicability than BUM.

    • Main Results:

    • Simulation studies and a data analysis example demonstrate SPLOSH's effectiveness.
    • SPLOSH shows desired characteristics, outperforming both q-value and BUM methods.
    • The results indicate SPLOSH is a more reliable tool for FDR estimation.

    Conclusions:

    • SPLOSH offers a robust and stable method for estimating conditional FDR in microarray data.
    • It addresses limitations of current q-value and BUM approaches.
    • Freely available S-plus code is provided for implementing the SPLOSH procedure.