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Large-Scale Multiple Testing of Correlations.

T Tony Cai1, Weidong Liu2

  • 1Department of Statistics, The Wharton School, University of Pennsylvania, Philadelphia, PA 19104 ( tcai@wharton.upenn.edu ).

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This study introduces new methods for multiple testing of correlations, crucial for analyzing gene networks and brain connectivity. These procedures effectively control false discoveries and enhance statistical power in large-scale datasets.

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Area of Science:

  • Statistics
  • Bioinformatics
  • Neuroscience

Background:

  • Multiple testing of correlations is common in gene coexpression and brain connectivity analysis.
  • Existing methods may not adequately control error rates in large-scale simultaneous testing.

Purpose of the Study:

  • To develop novel multiple testing procedures for large-scale correlation analysis in one-sample and two-sample settings.
  • To introduce a bootstrap method for estimating false rejections among true null hypotheses.

Main Methods:

  • Proposed new multiple testing procedures for simultaneous correlation testing.
  • Introduced a bootstrap method for error rate estimation.
  • Investigated procedure properties theoretically and numerically.

Main Results:

  • Procedures asymptotically control the overall false discovery rate (FDR) and false discovery proportion (FDP).
  • Simulation results demonstrate good performance in terms of test size and power.
  • The proposed methods significantly outperform two alternative approaches.

Conclusions:

  • The new multiple testing procedures offer reliable control of error rates in large-scale correlation analysis.
  • The methods are effective and outperform existing alternatives, as shown by simulations and a prostate cancer dataset analysis.