Lag penalized weighted correlation for time series clustering
Lag penalized weighted correlation for time series clustering
复制标题
DOI：
10.1186/s12859-019-3324-1
复制
发表时间：
2020-01-16
影响因子：
3
通讯作者：
Gitter, Anthony
中科院分区：
文献类型：
--
作者：
Chandereng, Thevaa;Gitter, Anthony
背景用于聚类的相似性或距离度量在根据数据的独特特征进行定制时可以生成直观和可解释的聚类。在用高通量生物测定生成的时间序列数据集中，随着时间的推移按顺序收集诸如基因表达水平或蛋白质磷酸化强度等测量值，并且相似性得分应该捕获这种特殊的时间结构。结果我们提出了一个聚类相似性度量称为滞后惩罚加权相关（LPWC）组对时间序列，表现出密切相关的行为随着时间的推移，即使时间不完全同步。LPWC将时间序列配置文件对齐，以识别常见的时间模式。它根据引入的时间滞后的长度对对齐的配置文件进行降权。我们证明了LPWC与现有的时间序列和一般聚类算法的优势。在基于生物激励脉冲模型的模拟数据集中，LPWC是唯一可以恢复几乎所有模拟基因的真实聚类的方法。LPWC还确定了我们的酵母渗透应激反应和蝾螈肢体再生案例研究中具有不同时间模式的集群。结论LPWC实现了时间序列聚类的两个目标。它将时间序列与随时间的相关变化进行分组，即使这些模式在某些时间序列中出现得更早或更晚。此外，它避免在搜索时间模式时通过应用滞后惩罚来引入大的时间偏移。LPWC R软件包可在MIT许可证下获得。
Background The similarity or distance measure used for clustering can generate intuitive and interpretable clusters when it is tailored to the unique characteristics of the data. In time series datasets generated with high-throughput biological assays, measurements such as gene expression levels or protein phosphorylation intensities are collected sequentially over time, and the similarity score should capture this special temporal structure. Results We propose a clustering similarity measure called Lag Penalized Weighted Correlation (LPWC) to group pairs of time series that exhibit closely-related behaviors over time, even if the timing is not perfectly synchronized. LPWC aligns time series profiles to identify common temporal patterns. It down-weights aligned profiles based on the length of the temporal lags that are introduced. We demonstrate the advantages of LPWC versus existing time series and general clustering algorithms. In a simulated dataset based on the biologically-motivated impulse model, LPWC is the only method to recover the true clusters for almost all simulated genes. LPWC also identifies clusters with distinct temporal patterns in our yeast osmotic stress response and axolotl limb regeneration case studies. Conclusions LPWC achieves both of its time series clustering goals. It groups time series with correlated changes over time, even if those patterns occur earlier or later in some of the time series. In addition, it refrains from introducing large shifts in time when searching for temporal patterns by applying a lag penalty. The LPWC R package is available at and under a MIT license.