PeakSegJoint：通过多个计数数据样本的联合分割的快速监督峰值检测
PeakSegJoint: fast supervised peak detection via joint segmentation of multiple count data samples
摘要：
联合峰检测是基因组数据分析中比较样本时的一个核心问题，但目前用于此任务的算法是无监督的，并且仅限于最多2种样本类型。我们提出了PeakSegJoint，一个新的约束极大似然分割模型的任何数量的样本类型。为了选择分割中的峰的数量，我们提出了一种监督惩罚学习模型。为了推断这两个模型的参数，我们建议使用离散优化启发式分割，和凸优化的惩罚学习。与最先进的峰值检测算法相比，PeakSegJoint实现了类似的准确性，更快的速度和更可解释的模型，其中重叠的峰值在所有样本中完全相同的位置出现。
英文摘要：
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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