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    A cross-package Bioconductor workflow for analysing methylation array data.

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    29
    Author
    Maksimovic, J; Phipson, B; Oshlack, A
    Date
    2016
    Source Title
    F1000Research
    Publisher
    F1000 Research Ltd
    University of Melbourne Author/s
    Oshlack, Alicia; Maksimovic, Jovana; Phipson, Belinda
    Affiliation
    School of Physics
    Paediatrics (RCH)
    Metadata
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    Document Type
    Journal Article
    Citations
    Maksimovic, J., Phipson, B. & Oshlack, A. (2016). A cross-package Bioconductor workflow for analysing methylation array data.. F1000Res, 5, pp.1281-. https://doi.org/10.12688/f1000research.8839.3.
    Access Status
    Open Access
    URI
    http://hdl.handle.net/11343/256973
    DOI
    10.12688/f1000research.8839.3
    Open Access at PMC
    http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4916993
    Abstract
    Methylation in the human genome is known to be associated with development and disease. The Illumina Infinium methylation arrays are by far the most common way to interrogate methylation across the human genome. This paper provides a Bioconductor workflow using multiple packages for the analysis of methylation array data. Specifically, we demonstrate the steps involved in a typical differential methylation analysis pipeline including: quality control, filtering, normalization, data exploration and statistical testing for probe-wise differential methylation. We further outline other analyses such as differential methylation of regions, differential variability analysis, estimating cell type composition and gene ontology testing. Finally, we provide some examples of how to visualise methylation array data.

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