PeakSegJoint: fast supervised peak detection via joint segmentation of multiple count data samples

2015/06/03 by Toby Dylan Hocking, Guillaume Bourque, Hocking, Toby Dylan +1
Biochemistry, Genetics and Molecular Biology · Mathematics · #Gene expression and cancer classification #Genomics and Chromatin Dynamics #Genomics and Phylogenetic Studies #q-bio.GN #stat.ML

paper · pdf · doi:10.48550/arxiv.1506.01286

11 pages, 5 figures

arxiv created 2015/06/03 · arxiv updated 2015/06/04

Abstract

Joint peak detection is a central problem when comparing samples in genomic data analysis, but current algorithms for this task are unsupervised and limited to at most 2 sample types. We propose PeakSegJoint, a new constrained maximum likelihood segmentation model for any number of sample types. To select the number of peaks in the segmentation, we propose a supervised penalty learning model. To infer the parameters of these two models, we propose to use a discrete optimization heuristic for the segmentation, and convex optimization for the penalty learning. In comparisons with state-of-the-art peak detection algorithms, PeakSegJoint achieves similar accuracy, faster speeds, and a more interpretable model with overlapping peaks that occur in exactly the same positions across all samples.

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