PeakSeg: Constrained Optimal Segmentation and Supervised Penalty Learning for Peak Detection in Count Data
Abstract
Peak detection is a central problem in genomic data analysis, and current algorithms for this task are unsupervised and mostly effective for a single data type and pattern (e.g. H3K4me3 data with a sharp peak pattern). We propose PeakSeg, a new constrained maximum likelihood segmentation model for peak detection with an efficient inference algorithm: constrained dynamic programming. We investigate unsupervised and supervised learning of penalties for the critical model selection problem. We show that the supervised method has state-of-the-art peak detection across all data sets in a benchmark that includes both sharp H3K4me3 and broad H3K36me3 patterns.
Cite
Text
Hocking et al. "PeakSeg: Constrained Optimal Segmentation and Supervised Penalty Learning for Peak Detection in Count Data." International Conference on Machine Learning, 2015.Markdown
[Hocking et al. "PeakSeg: Constrained Optimal Segmentation and Supervised Penalty Learning for Peak Detection in Count Data." International Conference on Machine Learning, 2015.](https://mlanthology.org/icml/2015/hocking2015icml-peakseg/)BibTeX
@inproceedings{hocking2015icml-peakseg,
title = {{PeakSeg: Constrained Optimal Segmentation and Supervised Penalty Learning for Peak Detection in Count Data}},
author = {Hocking, Toby and Rigaill, Guillem and Bourque, Guillaume},
booktitle = {International Conference on Machine Learning},
year = {2015},
pages = {324-332},
volume = {37},
url = {https://mlanthology.org/icml/2015/hocking2015icml-peakseg/}
}