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    Evaluation of Differential Normalization and Analysis Procedures for Illumina Gene Expression Microarray Data Involving Small Changes

    Access Status
    Open access via publisher
    Authors
    Johnstone, D.
    Riveros, C.
    Heidari, M.
    Graham, Ross
    Trinder, D.
    Berretta, R.
    Olynyk, John
    Scott, R.
    Moscato, P.
    Milward, E.
    Date
    2013
    Type
    Journal Article
    
    Metadata
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    Citation
    Johnstone, D. and Riveros, C. and Heidari, M. and Graham, R. and Trinder, D. and Berretta, R. and Olynyk, J. et al. 2013. Evaluation of Differential Normalization and Analysis Procedures for Illumina Gene Expression Microarray Data Involving Small Changes. Microarrays. 2 (2): pp. 131-152.
    Source Title
    Microarrays
    DOI
    10.3390/microarrays2020131
    ISSN
    20763905
    URI
    http://hdl.handle.net/20.500.11937/17835
    Collection
    • Curtin Research Publications
    Abstract

    Evaluation of different normalization and analysis procedures for illumina gene expression microarray data involving small changesWhile Illumina microarrays can be used successfully for detecting small gene expression changes due to their high degree of technical replicability, there is little information on how different normalization and differential expression analysis strategies affect outcomes. To evaluate this, we assessed concordance across gene lists generated by applying different combinations of normalization strategy and analytical approach to two Illumina datasets with modest expression changes. In addition to using traditional statistical approaches, we also tested an approach based on combinatorial optimization. We found that the choice of both normalization strategy and analytical approach considerably affected outcomes, in some cases leading to substantial differences in gene lists and subsequent pathway analysis results. Our findings suggest that important biological phenomena may be overlooked when there is a routine practice of using only one approach to investigate all microarray datasets. Analytical artefacts of this kind are likely to be especially relevant for datasets involving small fold changes, where inherent technical variation—if not adequately minimized by effective normalization—may overshadow true biological variation. This report provides some basic guidelines for optimizing outcomes when working with Illumina datasets involving small expression changes.

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