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    Optimizing the noise versus bias trade-off for Illumina whole genome expression BeadChips

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    Author
    Shi, W; Oshlack, A; Smyth, GK
    Date
    2010-12-01
    Source Title
    Nucleic Acids Research
    Publisher
    OXFORD UNIV PRESS
    University of Melbourne Author/s
    Smyth, Gordon; Oshlack, Alicia; Shi, Wei
    Affiliation
    Medical Biology (W.E.H.I.)
    School of Mathematics and Statistics
    School of Physics
    Metadata
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    Document Type
    Journal Article
    Citations
    Shi, W., Oshlack, A. & Smyth, G. K. (2010). Optimizing the noise versus bias trade-off for Illumina whole genome expression BeadChips. NUCLEIC ACIDS RESEARCH, 38 (22), https://doi.org/10.1093/nar/gkq871.
    Access Status
    Open Access
    URI
    http://hdl.handle.net/11343/255783
    DOI
    10.1093/nar/gkq871
    Abstract
    Five strategies for pre-processing intensities from Illumina expression BeadChips are assessed from the point of view of precision and bias. The strategies include a popular variance stabilizing transformation and model-based background corrections that either use or ignore the control probes. Four calibration data sets are used to evaluate precision, bias and false discovery rate (FDR). The original algorithms are shown to have operating characteristics that are not easily comparable. Some tend to minimize noise while others minimize bias. Each original algorithm is shown to have an innate intensity offset, by which unlogged intensities are bounded away from zero, and the size of this offset determines its position on the noise-bias spectrum. By adding extra offsets, a continuum of related algorithms with different noise-bias trade-offs is generated, allowing direct comparison of the performance of the strategies on equivalent terms. Adding a positive offset is shown to decrease the FDR of each original algorithm. The potential of each strategy to generate an algorithm with an optimal noise-bias trade-off is explored by finding the offset that minimizes its FDR. The use of control probes as part of the background correction and normalization strategy is shown to achieve the lowest FDR for a given bias.

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