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Statistical Challenges in Analyzing Methylation and Long-Range Chromosomal Interaction Data

Author

Listed:
  • Zhaohui Qin

    (Emory University)

  • Ben Li

    (Emory University)

  • Karen N. Conneely

    (Emory University School of Medicine)

  • Hao Wu

    (Emory University)

  • Ming Hu

    (New York University School of Medicine)

  • Deepak Ayyala

    (The Ohio State University)

  • Yongseok Park

    (University of Pittsburgh)

  • Victor X. Jin

    (The University of Texas Health Science Center at San Antonio)

  • Fangyuan Zhang

    (Texas Tech University)

  • Han Zhang

    (The Ohio State University)

  • Li Li

    (Emory University)

  • Shili Lin

    (The Ohio State University)

Abstract

With the rapid development of high-throughput technologies such as array and next generation sequencing, genome-wide, nucleotide-resolution epigenomic data are increasingly available. In recent years, there has been particular interest in data on DNA methylation and 3-dimensional (3D) chromosomal organization, which are believed to hold keys to understand biological mechanisms, such as transcription regulation, that are closely linked to human health and diseases. However, small sample size, complicated correlation structure, substantial noise, biases, and uncertainties, all present difficulties for performing statistical inference. In this review, we present an overview of the new technologies that are frequently utilized in studying DNA methylation and 3D chromosomal organization. We focus on reviewing recent developments in statistical methodologies designed for better interrogating epigenomic data, pointing out statistical challenges facing the field whenever appropriate.

Suggested Citation

  • Zhaohui Qin & Ben Li & Karen N. Conneely & Hao Wu & Ming Hu & Deepak Ayyala & Yongseok Park & Victor X. Jin & Fangyuan Zhang & Han Zhang & Li Li & Shili Lin, 2016. "Statistical Challenges in Analyzing Methylation and Long-Range Chromosomal Interaction Data," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 8(2), pages 284-309, October.
    • Handle: RePEc:spr:stabio:v:8:y:2016:i:2:d:10.1007_s12561-016-9145-0
    DOI: 10.1007/s12561-016-9145-0
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    Cited by:

    1. Ben Li & Yunxiao Li & Zhaohui S. Qin, 2017. "Improving Hierarchical Models Using Historical Data with Applications in High-Throughput Genomics Data Analysis," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 9(1), pages 73-90, June.

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