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Novel Sequence Discovery by Subtractive Genomics
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The functional false discovery rate with applications to genomics.

Xiongzhi Chen1, David G Robinson1, John D Storey1

  • 1Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ 08544, USA.

Biostatistics (Oxford, England)
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Summary

This study introduces a new framework to improve the estimation of the false discovery rate (FDR) using an "informative variable." This method enhances accuracy in statistical testing across various scientific applications, including genomics.

Keywords:
q-valueFDRFunctional data analysisGenetics of gene expressionKernel density estimationLocal false discovery rateMultiple hypothesis testingRNA-seqSequencing deptheQTL

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Area of Science:

  • Statistical genetics
  • Bioinformatics
  • Genomics

Background:

  • The false discovery rate (FDR) quantifies false positives in hypothesis testing.
  • Traditional FDR estimation relies on p-values or test statistics.
  • Additional information can potentially improve FDR estimation accuracy.

Purpose of the Study:

  • To develop a novel framework for estimating FDRs and q-values incorporating an
  • informative variable.
  • To treat FDR as a function of this informative variable for more precise statistical inference.

Main Methods:

  • Developed a generalizable framework for FDR and q-value estimation.
  • Incorporated an
  • informative variable
  • that provides prior probability or power information.
  • Applied the framework to two distinct genomics applications.

Main Results:

  • Demonstrated the framework's utility in a yeast eQTL study using genetic marker-gene distance as the informative variable.
  • Showcased its application in a mouse RNA-seq study for differential gene expression analysis, using per-gene read depth as the informative variable.
  • The proposed method offers a flexible approach to enhance statistical testing.

Conclusions:

  • The developed framework provides a robust method for estimating FDRs and q-values when auxiliary information is available.
  • This approach is broadly applicable across diverse scientific fields, particularly in complex biological data analysis.
  • The incorporation of informative variables leads to more accurate statistical assessments in genomics and beyond.