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Related Experiment Videos

A practical false discovery rate approach to identifying patterns of differential expression in microarray data.

Gregory R Grant1, Junmin Liu, Christian J Stoeckert

  • 1Center for Bioinformatics, University of Pennsylvania, 1429 Blockley Hall, 423 Guardian Drive, Philadelphia, PA 19104-6021, USA. ggrant@pcbi.upenn.edu

Bioinformatics (Oxford, England)
|March 31, 2005
PubMed
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This study refines statistical methods for identifying differentially expressed genes using microarrays. It proposes an improved approach to controlling the false discovery rate (FDR) for more reliable gene expression analysis.

Area of Science:

  • Genomics
  • Bioinformatics
  • Statistical Genetics

Background:

  • Microarray analysis commonly involves searching for differentially expressed genes.
  • Statistical rigor and practicality present significant challenges in gene expression studies.
  • False discovery rate (FDR) control is standard but debated in its definition and application.

Purpose of the Study:

  • To improve permutation estimation methods for gene expression analysis.
  • To define and estimate the false discovery rate (FDR) in a more straightforward manner.
  • To address practical issues in statistical analysis, including the choice of statistic and data transformation.

Main Methods:

  • Utilizing and refining permutation estimation approaches for statistical analysis.

Related Experiment Videos

  • Developing a convenient definition and estimation method for the false discovery rate (FDR).
  • Examining the impact of data transformations and the SAM 'fudge factor' parameter.

    • Main Results:

    • A practical definition for the false discovery rate (FDR) that is straightforward to estimate.
    • Analysis of how data transformations (e.g., log transform) affect statistical power for different genes.
    • Insights into optimizing the SAM 'fudge factor' parameter with respect to statistical power.

    Conclusions:

    • The study offers an improved permutation-based method for estimating and controlling the false discovery rate (FDR) in microarray studies.
    • Practical considerations, such as data transformation and parameter optimization, are crucial for enhancing the power and reliability of gene expression analysis.
    • The proposed methods aim to increase the rigor and practicality of identifying differentially expressed genes.