BELMM:贝叶斯模型选择和随机步行平滑在时间序列集群中的时间序列集群
Olli Sarala1, Tanja Pyhäjärvi2, Mikko J Sillanpää1
1Research Unit of Mathematical Sciences, University of Oulu, FI-90014 Oulu, Finland.
我们开发了贝叶斯隐藏混合模型估计 (BELMM) 来聚类时间序列的奥米克数据. BELMM有效地建模复杂的生物数据,识别基因表达和表型中的模式.
科学领域:
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
- 统计遗传学 统计遗传学
背景情况:
- 奥米克技术的进步产生了大量的时间课程数据集.
- 聚类分析对于揭示这些复杂数据集中的结构至关重要.
研究的目的:
- 介绍用于时间序列数据分析的潜在混合模型 (BELMM) 的贝叶斯估计.
- 为时间序列数据的聚类和建模提供灵活的贝叶斯框架.
主要方法:
- 使用混合模型与随机步行平滑先验用于平均曲线.
- 用可逆跳马尔科夫链蒙特卡罗来选择模型和确定混合物组件的数量.
- 根据与潜在的随机步行衍生趋势的相似性,将时间序列分配给集群.
主要成果:
- 展示BELMM在模拟和现实世界奥米克时间序列数据上的应用.
- 展示BELMM框架的快速和缓慢实施方式.
- 成功聚集了法国死亡率和Drosophila melanogaster基因表达数据.
结论:
- BELMM提供了一个强大的贝叶斯方法,用于时间序列聚类.
- 该框架在R和Stan中实现,可用于可复制性的代码.
- 贝尔姆 (BELMM) 便于分析复杂的生物时间过程数据.
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