ICML 2015poster26 citations
PeakSeg: constrained optimal segmentation and supervised penalty learning for peak detection in count data
Toby Hocking, Guillem Rigaill, Guillaume Bourque
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.
BibTeX
@InProceedings{pmlr-v37-hocking15,
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 = {Proceedings of the 32nd International Conference on Machine Learning},
pages = {324--332},
year = {2015},
editor = {Bach, Francis and Blei, David},
volume = {37},
series = {Proceedings of Machine Learning Research},
address = {Lille, France},
month = {07--09 Jul},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v37/hocking15.pdf},
url = {https://proceedings.mlr.press/v37/hocking15.html},
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.}
}