Multiple testing of local maxima for detection of peaks in ChIP-Seq data

Armin Schwartzman, Andrew Jaffe, Yulia Gavrilov, Clifford A. Meyer

Research output: Contribution to journalArticle

Abstract

A topological multiple testing approach to peak detection is proposed for the problem of detecting transcription factor binding sites in ChIP-Seq data. After kernel smoothing of the tag counts over the genome, the presence of a peak is tested at each observed local maximum, followed by multiple testing correction at the desired false discovery rate level. Valid p-values for candidate peaks are computed via Monte Carlo simulations of smoothed Poisson sequences, whose background Poisson rates are obtained via linear regression from a Control sample at two different scales. The proposed method identifies nearby binding sites that other methods do not.

Original languageEnglish (US)
Pages (from-to)471-494
Number of pages24
JournalAnnals of Applied Statistics
Volume7
Issue number1
DOIs
StatePublished - Mar 2013

Keywords

  • False discovery rate
  • Kernel smoothing
  • Matched filter
  • Poisson sequence
  • Topological inference

ASJC Scopus subject areas

  • Statistics and Probability
  • Modeling and Simulation
  • Statistics, Probability and Uncertainty

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