Multiple testing of local maxima for detection of peaks in ChIP-Seq data
arXiv:1305.6372 · doi:10.1214/12-AOAS594
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.
Published in at http://dx.doi.org/10.1214/12-AOAS594 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org)