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Multiple confidence intervals for selected parameters adjusted for the false coverage rate in monotone dose-response

Jianan Peng1, Wei Liu2, Frank Bretz3,4

  • 1Department of Mathematics and Statistics, Acadia University, Wolfville, NS, Canada B4P 2R6.

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|December 28, 2016
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Summary
This summary is machine-generated.

This study introduces a new method for analyzing dose-response microarray data, adjusting for the false coverage-statement rate (FCR) in selected genes. This approach improves the reliability of confidence intervals in gene expression analysis.

Keywords:
Dose-response studyFCRFDROrder-restricted inferenceSelective inference

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Area of Science:

  • Bioinformatics
  • Statistical Genetics
  • Microarray Analysis

Background:

  • Dose-response microarray experiments involve selecting genes with a monotone relationship.
  • Traditional confidence intervals (CIs) may be unreliable when applied only to selected parameters.
  • The false coverage-statement rate (FCR) concept addresses selection bias in statistical inference.

Purpose of the Study:

  • To develop and evaluate a method for constructing multiple confidence intervals (CIs) for selected parameters in dose-response microarray experiments.
  • To adjust these CIs for the false coverage-statement rate (FCR) to account for selection bias.
  • To assess the performance of the proposed FCR-adjusted method through simulation and real data analysis.

Main Methods:

  • Application of the Benjamini and Yekutieli's false coverage-statement rate (FCR) concept.
  • Construction of multiple confidence intervals for the mean gene expression difference between the highest dose and control.
  • Focus on genes exhibiting a monotone dose-response relationship in microarray experiments.
  • Validation through a simulation study and application to a large-scale real microarray dataset.

Main Results:

  • The proposed method provides FCR-adjusted confidence intervals for selected genes in dose-response microarray experiments.
  • Simulation studies demonstrate the performance characteristics of the FCR-adjusted method.
  • The method was successfully applied to a real microarray experiment involving 16,998 genes.

Conclusions:

  • The FCR-adjusted method offers a statistically sound approach for analyzing selected parameters in dose-response microarray data.
  • This method enhances the reliability of confidence intervals by controlling for selection bias.
  • The findings are relevant for accurate gene expression analysis in biological research.