Monash Institute of Cognitive and Clinical Neurosciences (MICCN), Monash University, Wellington Road, Clayton, VIC, 3800, Australia; School of Physics, Sydney University, Physics Road, Camperdown, NSW, 2006, Australia.
Mathematics Department, Imperial College London, Huxley Building, Queen's Gate, London SW7 2AZ, UK.
Cell Syst. 2017 Nov 22;5(5):527-531.e3. doi: 10.1016/j.cels.2017.10.001. Epub 2017 Nov 1.
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.
表型测量通常采用时间序列的形式，但我们目前缺乏一种将这些复杂数据流与具有科学意义的结果（例如，将生物体的运动动态与其基因型相关联，或者将患者大脑动态的测量与其疾病诊断相关联）相关联的系统方法。先前的工作通过在一种称为高度比较时间序列分析的方法中比较数千种不同科学时间序列分析方法的实现来解决这个问题。在这里，我们引入了 hctsa，这是一种用于将这种方法应用于数据的软件工具。hctsa 包括一个用于计算超过 7700 个时间序列特征的架构，以及一套用于自动选择给定应用程序中有用且可解释的时间序列特征的分析和可视化算法。通过使用高通量表型实验的示例应用程序，我们展示了 hctsa 如何使研究人员能够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。
