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Summary
This summary is machine-generated.

This study introduces a new R package to accurately estimate False Discovery Rates (FDR) and adjusted p-values. It clarifies the distinction between these metrics, crucial for interpreting statistical findings.

Keywords:
R Packageadjusted p-valuefalse discovery ratemultiple comparisonsnull proportion estimation

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Area of Science:

  • Statistics
  • Computational Biology
  • Bioinformatics

Background:

  • False discovery rates (FDR) are critical for statistical inference, quantifying the likelihood of erroneous findings.
  • Accurate estimation and reporting of FDRs are essential for contextualizing research results and their impact.
  • Existing software often conflates FDR estimates with adjusted p-values, leading to misinterpretation.

Purpose of the Study:

  • Introduce a user-friendly R package for estimating FDRs and computing adjusted p-values.
  • Clarify the distinction between FDR estimates and adjusted p-values in statistical analysis.
  • Provide advanced algorithms for FDR estimation, including improved methods for estimating the null proportion.

Main Methods:

  • Development of a novel R package with functions for FDR estimation and adjusted p-value computation.
  • Implementation of a variety of refined algorithms for FDR estimation and control.
  • Inclusion of plotting functions for clear visualization of results.

Main Results:

  • The R package effectively distinguishes between FDR estimates and adjusted p-values.
  • The package offers a comprehensive suite of algorithms for FDR estimation and control.
  • Illustrative examples demonstrate the utility and ease of use of the package.

Conclusions:

  • Wider reporting of False Discovery Rates alongside observed findings is strongly encouraged.
  • The new R package facilitates accurate FDR estimation and interpretation, enhancing statistical rigor.
  • This tool aims to improve the reliability and understanding of scientific results in various fields.