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Competitive Genomic Screens of Barcoded Yeast Libraries
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Published on: August 11, 2011

Discrete nonparametric algorithms for outlier detection with genomic data.

Debashis Ghosh1

  • 1Department of Statistics, Penn State University, University Park, Pennsylvania, USA. ghoshd@psu.edu

Journal of Biopharmaceutical Statistics
|March 24, 2010
PubMed
Summary
This summary is machine-generated.

This study focuses on selecting appropriate test statistics for differential gene expression analysis in high-throughput studies. It proposes using q-value estimation for discrete p-values, enhancing the analysis of outlying expression values.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Statistical Genetics

Background:

  • Differential expression analysis is crucial for high-throughput genetic studies, particularly with gene expression microarrays.
  • The choice of test statistic in multiple comparisons for differential expression analysis is often overlooked.
  • Discrete p-values present a challenge in standard multiple comparison procedures.

Purpose of the Study:

  • To investigate the impact of test statistic choice on differential expression analysis.
  • To adapt multiple comparison procedures for assessing outlying gene expression values.
  • To explore theoretical properties of sequential testing with discrete p-values.

Main Methods:

  • Recasting multiple-comparison procedures to assess outlying expression values.
  • Theoretical exploration of sequential testing procedures for discrete p-values.
  • Application of q-value estimation procedures to differential expression analysis.

Main Results:

  • The study highlights the importance of test statistic selection in identifying significant differential expression.
  • Proposed methods address the complexities arising from discrete p-values in gene expression data.
  • Demonstrated utility of q-value estimation in a prostate cancer gene expression profiling experiment.

Conclusions:

  • The choice of test statistic significantly impacts differential gene expression analysis outcomes.
  • Q-value estimation provides a robust approach for handling discrete p-values in this context.
  • The methodology offers improved analytical tools for high-throughput genetic studies.