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    Removing unwanted variation in a differential methylation analysis of Illumina HumanMethylation450 array data

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    Author
    Maksimovic, J; Gagnon-Bartsch, JA; Speed, TP; Oshlack, A
    Date
    2015-09-18
    Source Title
    Nucleic Acids Research
    Publisher
    OXFORD UNIV PRESS
    University of Melbourne Author/s
    Maksimovic, Jovana; Speed, Terence; Oshlack, Alicia
    Affiliation
    School of Mathematics and Statistics
    School of Physics
    Paediatrics (RCH)
    Metadata
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    Document Type
    Journal Article
    Citations
    Maksimovic, J., Gagnon-Bartsch, J. A., Speed, T. P. & Oshlack, A. (2015). Removing unwanted variation in a differential methylation analysis of Illumina HumanMethylation450 array data. NUCLEIC ACIDS RESEARCH, 43 (16), https://doi.org/10.1093/nar/gkv526.
    Access Status
    Open Access
    URI
    http://hdl.handle.net/11343/257913
    DOI
    10.1093/nar/gkv526
    Abstract
    Due to their relatively low-cost per sample and broad, gene-centric coverage of CpGs across the human genome, Illumina's 450k arrays are widely used in large scale differential methylation studies. However, by their very nature, large studies are particularly susceptible to the effects of unwanted variation. The effects of unwanted variation have been extensively documented in gene expression array studies and numerous methods have been developed to mitigate these effects. However, there has been much less research focused on the appropriate methodology to use for accounting for unwanted variation in methylation array studies. Here we present a novel 2-stage approach using RUV-inverse in a differential methylation analysis of 450k data and show that it outperforms existing methods.

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