hctsa: A Computational Framework for Automated Time-Series Phenotyping Using Massive Feature Extraction
hctsa: A Computational Framework for Automated Time-Series Phenotyping Using Massive Feature Extraction
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DOI：
10.1016/j.cels.2017.10.001
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发表时间：
2017-11-22
期刊：
影响因子：
9.3
通讯作者：
Jones, Nick S.
中科院分区：
文献类型：
--
作者：
Fulcher, Ben D.;Jones, Nick S.
表型测量通常采用时间序列的形式，但我们目前缺乏一种系统的方法，将这些复杂的数据流与科学上有意义的结果联系起来，例如将生物体的运动动力学与其基因型或患者的脑动力学测量与其疾病诊断联系起来。以前的工作解决了这个问题，通过比较成千上万的不同的科学时间序列分析方法的实现方式称为高度比较的时间序列分析。在这里，我们介绍hctsa，一个软件工具，用于应用这种方法的数据。hctsa包括用于计算超过7，700个时间序列特征的架构和一套分析和可视化算法，以自动选择用于给定应用的有用和可解释的时间序列特征。使用高通量表型实验的范例应用，我们展示了hctsa如何让研究人员利用数十年的时间序列研究来量化和理解时间序列数据中的信息结构。
Phenotype measurements frequently take the form of time series, but we currently lack a systematic method for relating these complex data streams to scientifically meaningful outcomes, such as relating the movement dynamics of organisms to their genotype or measurements of brain dynamics of a patient to their disease diagnosis. Previous work addressed this problem by comparing implementations of thousands of diverse scientific time-series analysis methods in an approach termed highly comparative time-series analysis. Here, we introduce hctsa, a software tool for applying this methodological approach to data. hctsa includes an architecture for computing over 7,700 time-series features and a suite of analysis and visualization algorithms to automatically select useful and interpretable time-series features for a given application. Using exemplar applications to high-throughput phenotyping experiments, we show how hctsa allows researchers to leverage decades of time-series research to quantify and understand informative structure in time-series data.