TY - JOUR
T1 - hctsa
T2 - A Computational Framework for Automated Time-Series Phenotyping Using Massive Feature Extraction
AU - Fulcher, Ben D.
AU - Jones, Nick S.
PY - 2017/11/22
Y1 - 2017/11/22
N2 - 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. A new software tool, hctsa, uses massive feature extraction to automatically identify informative and interpretable quantitative phenotypes from time-series data.
AB - 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. A new software tool, hctsa, uses massive feature extraction to automatically identify informative and interpretable quantitative phenotypes from time-series data.
KW - high-throughput phenotyping
KW - time-series analysis
UR - http://www.scopus.com/inward/record.url?scp=85032903635&partnerID=8YFLogxK
U2 - 10.1016/j.cels.2017.10.001
DO - 10.1016/j.cels.2017.10.001
M3 - Article
AN - SCOPUS:85032903635
SN - 2405-4712
VL - 5
SP - 527
EP - 531
JO - Cell Systems
JF - Cell Systems
IS - 5
ER -