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Drug Discovery: Overview

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An Integrated Workflow of Identification and Quantification on FDR Control-Based Untargeted Metabolome
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False Discovery Rate Control With Groups.

James X Hu1, Hongyu Zhao, Harrison H Zhou

  • 1Department of Statistics, Yale University, New Haven, CT 06511.

Journal of the American Statistical Association
|September 21, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces a new p-value weighting method for large-scale hypothesis testing that leverages group structures. The data-driven procedure offers increased statistical power compared to traditional methods while controlling false discovery rates.

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Area of Science:

  • Biostatistics
  • Genomics
  • Statistical Genetics

Background:

  • Large-scale hypothesis testing is common in genomics.
  • Hypotheses often have inherent group structures (e.g., Gene Ontology, phenotypes).
  • Existing methods may not fully utilize this group information.

Purpose of the Study:

  • To develop a p-value weighting procedure that incorporates group structure.
  • To increase statistical power in multiple hypothesis testing.
  • To control the false discovery rate (FDR) under weak conditions.

Main Methods:

  • A novel p-value weighting procedure is proposed.
  • The procedure assigns weights based on the relative importance of hypothesis groups.
  • It estimates the proportion of true null hypotheses for FDR control.

Main Results:

  • The proposed procedure is theoretically and computationally more powerful than the Benjamini-Hochberg procedure.
  • It effectively controls the false discovery rate asymptotically.
  • Demonstrated favorable performance on a breast cancer dataset.

Conclusions:

  • Incorporating group structure enhances statistical power in large-scale hypothesis testing.
  • The proposed data-driven method offers an improvement over classical approaches.
  • This approach is beneficial for analyzing complex biological data.