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Preprocessing and Computational Analysis of Single-Cell Epigenomic Datasets

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Computational Methods for Single-Cell Data Analysis

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1935))

Abstract

Recent technological developments have enabled the characterization of the epigenetic landscape of single cells across a range of tissues in normal and diseased states and under various biological and chemical perturbations. While analysis of these profiles resembles methods from single-cell transcriptomic studies, unique challenges are associated with bioinformatics processing of single-cell epigenetic data, including a much larger (10–1,000×) feature set and significantly greater sparsity, requiring customized solutions. Here, we discuss the essentials of the computational methodology required for analyzing common single-cell epigenomic measurements for DNA methylation using bisulfite sequencing and open chromatin using ATAC-Seq.

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Acknowledgments

We are grateful to Jason Buenrostro for useful feedback in the discussion of the scATAC-seq computational analyses.

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Authors and Affiliations

  1. Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA

    Caleb Lareau, Divy Kangeyan & Martin J. Aryee

  2. Department of Pathology, Massachusetts General Hospital, Boston, MA, USA

    Caleb Lareau, Divy Kangeyan & Martin J. Aryee

  3. Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Martin J. Aryee

Authors
  1. Caleb Lareau
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  2. Divy Kangeyan
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  3. Martin J. Aryee
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Corresponding author

Correspondence to Martin J. Aryee .

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Editors and Affiliations

  1. Dana–Farber Cancer Institute and Harvard Chan, School of Public Health, Boston, MA, USA

    Guo-Cheng Yuan

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© 2019 Springer Science+Business Media, LLC, part of Springer Nature

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Lareau, C., Kangeyan, D., Aryee, M.J. (2019). Preprocessing and Computational Analysis of Single-Cell Epigenomic Datasets. In: Yuan, GC. (eds) Computational Methods for Single-Cell Data Analysis. Methods in Molecular Biology, vol 1935. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-9057-3_13

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  • DOI: https://doi.org/10.1007/978-1-4939-9057-3_13

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  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-9056-6

  • Online ISBN: 978-1-4939-9057-3

  • eBook Packages: Springer Protocols

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