Microarrays 2013, 2(2), 131-152; doi:10.3390/microarrays2020131
Article

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

1 Centre for Bioinformatics, Biomarker Discovery and Information-Based Medicine, The University of Newcastle, Callaghan, NSW 2308, Australia 2 School of Biomedical Sciences and Pharmacy, The University of Newcastle, Callaghan, NSW 2308, Australia 3 Discipline of Physiology and Bosch Institute, University of Sydney, Sydney, NSW 2006, Australia 4 Australian Research Council Centre of Excellence in Bioinformatics, Callaghan, NSW 2308, Australia 5 School of Electrical Engineering and Computer Science, the University of Newcastle, Callaghan, NSW 2308, Australia 6 School of Biomedical Sciences, CHIRI Biosciences Research Precinct, Faculty of Health Sciences, Curtin University, Bentley, WA 6102, Australia 7 School of Medicine and Pharmacology, University of Western Australia, Fremantle, WA 6160, Australia 8 Western Australian Institute for Medical Research, Perth, WA 6000, Australia 9 Department of Gastroenterology, Fremantle Hospital, Fremantle, WA 6160, Australia 10 Curtin Health Innovation Research Institute, Curtin University, Bentley, WA 6102, Australia 11 Institute for Immunology & Infectious Diseases, Murdoch University, Murdoch, WA 6153, Australia 12 The Division of Molecular Medicine, Hunter Area Pathology Service, New Lambton, NSW 2305, Australia
* Author to whom correspondence should be addressed.
Received: 25 March 2013; in revised form: 8 May 2013 / Accepted: 10 May 2013 / Published: 21 May 2013
(This article belongs to the Special Issue Feature Papers)
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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.
Keywords: gene expression microarray; normalization; Illumina

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MDPI and ACS Style

Johnstone, D.M.; Riveros, C.; Heidari, M.; Graham, R.M.; Trinder, D.; Berretta, R.; Olynyk, J.K.; Scott, R.J.; Moscato, P.; Milward, E.A. Evaluation of Different Normalization and Analysis Procedures for Illumina Gene Expression Microarray Data Involving Small Changes. Microarrays 2013, 2, 131-152.

AMA Style

Johnstone DM, Riveros C, Heidari M, Graham RM, Trinder D, Berretta R, Olynyk JK, Scott RJ, Moscato P, Milward EA. Evaluation of Different Normalization and Analysis Procedures for Illumina Gene Expression Microarray Data Involving Small Changes. Microarrays. 2013; 2(2):131-152.

Chicago/Turabian Style

Johnstone, Daniel M.; Riveros, Carlos; Heidari, Moones; Graham, Ross M.; Trinder, Debbie; Berretta, Regina; Olynyk, John K.; Scott, Rodney J.; Moscato, Pablo; Milward, Elizabeth A. 2013. "Evaluation of Different Normalization and Analysis Procedures for Illumina Gene Expression Microarray Data Involving Small Changes." Microarrays 2, no. 2: 131-152.

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