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A novel method to accurately calculate statistical significance of local similarity analysis for high-throughput time series

Author

Listed:
  • Zhang Fang

    (School of Mathematics, Shandong University, Jinan, 250100, P.R. China)

  • Shan Ang

    (School of Mathematics, Shandong University, Jinan, 250100, P.R. China)

  • Luan Yihui

    (School of Mathematics, Shandong University, Jinan, 250100, P.R. China)

Abstract

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.

Suggested Citation

  • Zhang Fang & Shan Ang & Luan Yihui, 2018. "A novel method to accurately calculate statistical significance of local similarity analysis for high-throughput time series," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 17(6), pages 1-14, December.
  • Handle: RePEc:bpj:sagmbi:v:17:y:2018:i:6:p:14:n:2
    DOI: 10.1515/sagmb-2018-0019
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    References listed on IDEAS

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    1. Jeremy Berkowitz & Lutz Kilian, 2000. "Recent developments in bootstrapping time series," Econometric Reviews, Taylor & Francis Journals, vol. 19(1), pages 1-48.
    2. John D. Storey, 2002. "A direct approach to false discovery rates," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(3), pages 479-498, August.
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