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Related Experiment Videos

False discovery rate, sensitivity and sample size for microarray studies.

Yudi Pawitan1, Stefan Michiels, Serge Koscielny

  • 1Department of Medical Epidemiology and Biostatistics, Karolinska Institutet 17177 Stockholm, Sweden. yudi.pawitan@meb.ki.se

Bioinformatics (Oxford, England)
|April 21, 2005
PubMed
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The shift from P-values to false discovery rate (FDR) is crucial for microarray analysis. Controlling FDR is essential, but researchers must also consider the false negative rate (FNR) to optimize study design.

Area of Science:

  • Genomics
  • Bioinformatics
  • Statistical Genetics

Background:

  • Microarray studies face high false positive rates, necessitating a move from P-values to false discovery rate (FDR).
  • The sensitivity and false negative rate (FNR) in microarray data analysis require more attention.
  • Understanding the statistical underpinnings of FDR is critical for accurate interpretation of gene expression data.

Purpose of the Study:

  • To explain the necessity of transitioning from P-values to FDR for statistical assessment in microarray data.
  • To elucidate the key factors influencing the false discovery rate (FDR).
  • To discuss controlling FDR and FNR through sample size determination in two-sample comparative microarray studies.

Main Methods:

  • Utilized a mixture model incorporating differentially expressed (DE) and non-DE genes to address the challenge of identifying DE genes.

Related Experiment Videos

  • Analyzed factors determining FDR, including the proportion of DE genes, distribution of true differences, measurement variability, and sample size.
  • Developed methods to compute sensitivity or FNR curves alongside FDR curves for comprehensive study design evaluation.

    • Main Results:

    • Identified four key factors determining FDR: proportion of DE genes, distribution of true differences, measurement variability, and sample size.
    • Demonstrated that many small microarray studies suffer from high FDR.
    • Showed that controlling FDR alone can result in unacceptably high FNR, highlighting the need for joint consideration.

    Conclusions:

    • The transition to FDR is necessary for robust microarray data analysis.
    • Sample size is a critical factor for controlling both FDR and FNR.
    • Routine computation of FDR and FNR curves is recommended for optimizing microarray study design and ensuring reliable results.