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Ann Appl Stat. 2013;7(1):471-494.

MULTIPLE TESTING OF LOCAL MAXIMA FOR DETECTION OF PEAKS IN CHIP-SEQ DATA.

Author information

1
Department of Biostatistics, Harvard School of Public Health and Department of Biostatistics and Computational Biology Dana-Farber Cancer Institute, 450 Brookline Ave., CLS-11007, Boston, MA 02115, USA.
2
Lieber Institute for Brain Development, 855 North Wolfe Street, Baltimore, MD 21205, USA and Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, 615 North Wolfe Street, Baltimore, MD 21205, USA.

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.

KEYWORDS:

Poisson sequence; false discovery rate; kernel smoothing; matched filter; topological inference

PMID:
25411587
PMCID:
PMC4233463

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