标题:Statistical significance approximation for local similarity analysis of dependent time series data
作者:Zhang, Fang; Sun, Fengzhu; Luan, Yihui
作者机构:[Zhang, Fang; Luan, Yihui] Shandong Univ, Sch Math, Jinan 250100, Shandong, Peoples R China.; [Sun, Fengzhu] Univ Southern Calif, Dept Biol Sci, Qua 更多
通讯作者:Luan, Yihui;Luan, YH
通讯作者地址:[Luan, YH]Shandong Univ, Sch Math, Jinan 250100, Shandong, Peoples R China.
来源:BMC BIOINFORMATICS
出版年:2019
卷:20
期:1
DOI:10.1186/s12859-019-2595-x
关键词:Data-driven local similarity analysis; Long-run variance; Nonparametric; kernel estimate; Statistical significance
摘要:BackgroundLocal similarity analysis (LSA) of time series data has been extensively used to investigate the dynamics of biological systems in a wide range of environments. Recently, a theoretical method was proposed to approximately calculate the statistical significance of local similarity (LS) scores. However, the method assumes that the time series data are independent identically distributed, which can be violated in many problems.ResultsIn this paper, we develop a novel approach to accurately approximate statistical significance of LSA for dependent time series data using nonparametric kernel estimated long-run variance. We also investigate an alternative method for LSA statistical significance approximation by computing the local similarity score of the residuals based on a predefined statistical model. We show by simulations that both methods have controllable type I errors for dependent time series, while other approaches for statistical significance can be grossly oversized. We apply both methods to human and marine microbial datasets, where most of possible significant associations are captured and false positives are efficiently controlled.ConclusionsOur methods provide fast and effective approaches for evaluating statistical significance of dependent time series data with controllable type I error. They can be applied to a variety of time series data to reveal inherent relationships among the different factors.
收录类别:EI;SCOPUS;SCIE
资源类型:期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85060631585&doi=10.1186%2fs12859-019-2595-x&partnerID=40&md5=730bf8b9a70fab05bcadf1d6592dd9cb
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