Evaluation of different normalization and analysis procedures for illumina gene expression microarray data involving small changes

D. Johnstone; C. Riveros; M. Heidari; R. Graham; D. Trinder; R. Berretta; J. Olynyk; R. Scott; P. Moscato; E. Milward

doi:10.3390/microarrays2020131

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Evaluation of different normalization and analysis procedures for illumina gene expression microarray data involving small changes

Journal article

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Evaluation of different normalization and analysis procedures for illumina gene expression microarray data involving small changes

D. Johnstone, C. Riveros, M. Heidari, R. Graham, D. Trinder, R. Berretta, J. Olynyk, R. Scott, P. Moscato and E. Milward

Microarrays, Vol.2(2), pp.131-152

2013

DOI: https://doi.org/10.3390/microarrays2020131

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Abstract

While 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.

Details

Title: Evaluation of different normalization and analysis procedures for illumina gene expression microarray data involving small changes
Authors/Creators: D. Johnstone (Author/Creator) - University of Newcastle Australia
C. Riveros (Author/Creator) - University of Newcastle Australia
M. Heidari (Author/Creator) - University of Newcastle Australia
R. Graham (Author/Creator) - Curtin University
D. Trinder (Author/Creator) - The University of Western Australia
R. Berretta (Author/Creator) - Australian Research Council
J. Olynyk (Author/Creator) - Fremantle Hospital
R. Scott (Author/Creator) - University of Newcastle Australia
P. Moscato (Author/Creator) - University of Newcastle Australia
E. Milward (Author/Creator) - University of Newcastle Australia
Publication Details: Microarrays, Vol.2(2), pp.131-152
Publisher: MDPI AG
Identifiers: 991005542703507891
Murdoch Affiliation: Institute for Immunology and Infectious Diseases
Language: English
Resource Type: Journal article

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