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A Statistical Change-Point Analysis Approach for Modeling the Ratio of Next Generation Sequencing Reads

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Advances in the Mathematical Sciences

Part of the book series: Association for Women in Mathematics Series ((AWMS,volume 6))

Abstract

One of the key features of statistical change-point analysis is to estimate the unknown change-point locations for various statistical models imposed on the sample data. This analysis can be done through a hypothesis testing process, a model selection perspective, or a Bayesian approach, among other methods. Change-point analysis has a wide range of applications in research fields such as statistical quality control, finance and economics, climate study, medicine, genetics, etc. In this paper, a change-point analysis motivated by the modeling of genomic data will be provided. The high throughput next generation sequencing (NGS) technology is now frequently used in profiling tumor and control samples for the study of DNA copy number variants (CNVs). In particular, the ratio of the read count of the tumor sample to that of the control sample is popularly used for identifying CNV regions. To identify CNV regions is equivalent to finding change-points that potentially exist in the NGS reads ratio data. We present a change-point model and a Bayesian solution for the estimation of the change-point locations in NGS reads ratio data. Simulation studies of the proposed method indicate the effectiveness of the proposed method in identifying change-point locations. Applications of the proposed change point model for identifying boundaries of DNA copy number variation (CNV) regions using the next generation sequencing data of breast cancer/tumor cell lines and lung cancer cell line will be presented.

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Author information

Authors and Affiliations

  1. Department of Biostatistics and Epidemiology, Augusta University, 1120 15th Street, Augusta, GA, 30912, USA

    Jie Chen

  2. Stowers Institute for Medical Research, 1000 E 50th Street, Kansas City, MO, 64110, USA

    Hua Li

Authors
  1. Jie Chen
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  2. Hua Li
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Corresponding author

Correspondence to Jie Chen .

Editor information

Editors and Affiliations

  1. National Security Agency , Fort Meade, Maryland, USA

    Gail Letzter

  2. Microsoft Research, Redmond, Washington, USA

    Kristin Lauter

  3. Department of Mathematics & Comp. Sci., Saint Louis University Department of Mathematics & Comp. Sci., Saint Louis, Missouri, USA

    Erin Chambers

  4. Department of Statistics, University of Missouri–Columbia, Columbia, Missouri, USA

    Nancy Flournoy

  5. Department of Mathematics , Boston College Department of Mathematics, Chestnut Hill, Massachusetts, USA

    Julia Elisenda Grigsby

  6. National Security Agency , Fort Meade, Maryland, USA

    Carla Martin

  7. Department of Mathematics & Comp. Sci., DeSales University Department of Mathematics & Comp. Sci., Center Valley, Pennsylvania, USA

    Kathleen Ryan

  8. Department of Mathematics, University of Maryland Department of Mathematics, College Park, Maryland, USA

    Konstantina Trivisa

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Chen, J., Li, H. (2016). A Statistical Change-Point Analysis Approach for Modeling the Ratio of Next Generation Sequencing Reads. In: Letzter, G., et al. Advances in the Mathematical Sciences. Association for Women in Mathematics Series, vol 6. Springer, Cham. https://doi.org/10.1007/978-3-319-34139-2_13

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