PeakSegJoint: fast supervised peak detection via joint segmentation of multiple count data samples
This addresses the need for efficient and interpretable peak detection in genomic data analysis, offering a supervised approach for multiple samples, though it appears incremental as it builds on existing segmentation models.
The authors tackled the problem of joint peak detection in genomic data analysis, which was limited to unsupervised algorithms for at most two sample types, by proposing PeakSegJoint, a supervised model for any number of samples that achieved similar accuracy and faster speeds compared to state-of-the-art methods.
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.