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A novel method to accurately calculate statistical significance of local similarity analysis for high-throughput time series.
Statistical Applications in Genetics and Molecular Biology ( IF 0.9 ) Pub Date : 2018-11-18 , DOI: 10.1515/sagmb-2018-0019
Fang Zhang 1 , Ang Shan 1 , Yihui Luan 1
Affiliation  

In recent years, a large number of time series microbial community data has been produced in molecular biological studies, especially in metagenomics. Among the statistical methods for time series, local similarity analysis is used in a wide range of environments to capture potential local and time-shifted associations that cannot be distinguished by traditional correlation analysis. Initially, the permutation test is popularly applied to obtain the statistical significance of local similarity analysis. More recently, a theoretical method has also been developed to achieve this aim. However, all these methods require the assumption that the time series are independent and identically distributed. In this paper, we propose a new approach based on moving block bootstrap to approximate the statistical significance of local similarity scores for dependent time series. Simulations show that our method can control the type I error rate reasonably, while theoretical approximation and the permutation test perform less well. Finally, our method is applied to human and marine microbial community datasets, indicating that it can identify potential relationship among operational taxonomic units (OTUs) and significantly decrease the rate of false positives.

中文翻译:

一种精确计算高通量时间序列局部相似性分析统计显着性的新方法。

近年来,在分子生物学研究中,特别是在宏基因组学研究中,已经产生了大量的时间序列微生物群落数据。在时间序列的统计方法中,局部相似性分析用于广泛的环境中,以捕获潜在的局部和时移关联,而传统关联分析无法区分这些关联。最初,置换检验广泛用于获得局部相似性分析的统计意义。最近,还开发了一种理论方法来实现该目的。但是,所有这些方法都需要假设时间序列是独立的并且分布均匀。在本文中,我们提出了一种基于移动块自举的新方法,以近似依赖时间序列的局部相似性评分的统计意义。仿真表明,我们的方法可以合理地控制I类错误率,而理论逼近和置换测试的效果不佳。最后,我们的方法被应用于人类和海洋微生物群落数据集,表明它可以识别操作分类单位(OTU)之间的潜在关系,并显着降低假阳性率。
更新日期:2019-11-01
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