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Discrete Multiple Testing in Detecting Differential Methylation Using Sequencing Data

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Statistical Modeling in Biomedical Research

Part of the book series: Emerging Topics in Statistics and Biostatistics ((ETSB))

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Abstract

DNA methylation, as one of the most important epigenetic mechanisms, is critical for deciding cell fate, and hence tightly relevant to understanding disease processes, such as cancer. We will discuss the multiple testing issue in detecting differential methylation in next generation sequencing studies. The detection requires comparing DNA methylation levels at millions of genomic loci across different genomic samples and can be viewed as a large-scale multiple testing problem. Due to low read counts at individual CpG sites, discreteness in the test statistics is nonignorable and brings up many intriguing statistical issues on proper control of false discovery rates (FDRs). Popular FDR control procedures are often underpowered in methylation sequencing data analysis due to the discreteness. We will discuss FDR control methods that accommodate such discreteness.

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Notes

  1. 1.

    We only include the aBHH method out of the aBH and the aBHH methods, and the AHSU method out of the HSU and the AHSU methods, as those two have been shown yielding better performance than their non-adaptive counterparts in their original references. Besides, due to high computational cost, we do not include the grouping algorithm in our simulation study.

  2. 2.

    When multiple samples are available in each group, other approaches like those based on t-test or the beta-binomial model may also be used, as discussed in Sect. 3. Our focus is not to address how to model the replicates, but using the FET to demonstrate the performance of various methods in the context of multiple testing of discrete hypotheses.

  3. 3.

    As mentioned in Sect. 3.1.1 Gilbert [17], recognized this issue and proposed a grid search method. However, the grid search method is computationally too costly to be included in the simulation study.

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Authors and Affiliations

  1. Washington University in St. Louis, St. Louis, MO, USA

    Guanshengrui Hao & Nan Lin

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  1. Guanshengrui Hao
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  2. Nan Lin
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Correspondence to Nan Lin .

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  1. Math and Statistics, 1342, Georgia State University, Atlanta, GA, USA

    Yichuan Zhao

  2. School of Social Work, University of North Carolina, Chapel Hill, NC, USA

    Ding-Geng (Din) Chen

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Hao, G., Lin, N. (2020). Discrete Multiple Testing in Detecting Differential Methylation Using Sequencing Data. In: Zhao, Y., Chen, DG. (eds) Statistical Modeling in Biomedical Research. Emerging Topics in Statistics and Biostatistics . Springer, Cham. https://doi.org/10.1007/978-3-030-33416-1_4

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