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A smoothed EM-algorithm for DNA methylation profiles from sequencing-based methods in cell lines or for a single cell type

Author

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  • Lakhal-Chaieb Lajmi

    (Département de mathématiques et statistique, Université Laval, Québec, Québec G1V 0A6, Canada)

  • Greenwood Celia M.T.

    (Lady Davis Research Institute, Montréal, Québec H3T 1E2, Canada; and Departments of Oncology, Epidemiology, Biostatistics and Occupational Health, and Human Genetics, McGill University, Montréal, Québec H3A 1A2, Canada)

  • Ouhourane Mohamed

    (Département de mathématiques, Université de Québec À Montréal, Québec H2X 3Y7, Canada)

  • Zhao Kaiqiong

    (Lady Davis Research Institute, Montréal, Québec H3T 1E2, Canada; and Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montréal, Québec H3A 1A2, Canada)

  • Abdous Belkacem

    (Département de médecine sociale et préventive, Université Laval, Québec, Québec G1V 0A6, Canada)

  • Oualkacha Karim

    (Département de mathématiques, Université de Québec À Montréal, Montréal, Québec H2X 3Y7, Canada)

Abstract

We consider the assessment of DNA methylation profiles for sequencing-derived data from a single cell type or from cell lines. We derive a kernel smoothed EM-algorithm, capable of analyzing an entire chromosome at once, and to simultaneously correct for experimental errors arising from either the pre-treatment steps or from the sequencing stage and to take into account spatial correlations between DNA methylation profiles at neighbouring CpG sites. The outcomes of our algorithm are then used to (i) call the true methylation status at each CpG site, (ii) provide accurate smoothed estimates of DNA methylation levels, and (iii) detect differentially methylated regions. Simulations show that the proposed methodology outperforms existing analysis methods that either ignore the correlation between DNA methylation profiles at neighbouring CpG sites or do not correct for errors. The use of the proposed inference procedure is illustrated through the analysis of a publicly available data set from a cell line of induced pluripotent H9 human embryonic stem cells and also a data set where methylation measures were obtained for a small genomic region in three different immune cell types separated from whole blood.

Suggested Citation

  • Lakhal-Chaieb Lajmi & Greenwood Celia M.T. & Ouhourane Mohamed & Zhao Kaiqiong & Abdous Belkacem & Oualkacha Karim, 2017. "A smoothed EM-algorithm for DNA methylation profiles from sequencing-based methods in cell lines or for a single cell type," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 16(5-6), pages 333-347, December.
    • Handle: RePEc:bpj:sagmbi:v:16:y:2017:i:5-6:p:333-347:n:3
    DOI: 10.1515/sagmb-2016-0062
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    References listed on IDEAS

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    1. Fiona Allum & Xiaojian Shao & Frédéric Guénard & Marie-Michelle Simon & Stephan Busche & Maxime Caron & John Lambourne & Julie Lessard & Karolina Tandre & Åsa K. Hedman & Tony Kwan & Bing Ge & Lars Rö, 2015. "Characterization of functional methylomes by next-generation capture sequencing identifies novel disease-associated variants," Nature Communications, Nature, vol. 6(1), pages 1-12, November.
    2. Ryan Lister & Mattia Pelizzola & Yasuyuki S. Kida & R. David Hawkins & Joseph R. Nery & Gary Hon & Jessica Antosiewicz-Bourget & Ronan O’Malley & Rosa Castanon & Sarit Klugman & Michael Downes & Ruth , 2011. "Hotspots of aberrant epigenomic reprogramming in human induced pluripotent stem cells," Nature, Nature, vol. 471(7336), pages 68-73, March.
    3. Kjell Doksum & Derick Peterson & Alex Samarov, 2000. "On variable bandwidth selection in local polynomial regression," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 62(3), pages 431-448.
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