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Evaluation of Different Normalization and Analysis Procedures for Illumina Gene Expression Microarray Data Involving Small Changes
Centre for Bioinformatics, Biomarker Discovery and Information-Based Medicine, The University of Newcastle, Callaghan, NSW 2308, Australia
School of Biomedical Sciences and Pharmacy, The University of Newcastle, Callaghan, NSW 2308, Australia
Discipline of Physiology and Bosch Institute, University of Sydney, Sydney, NSW 2006, Australia
Australian Research Council Centre of Excellence in Bioinformatics, Callaghan, NSW 2308, Australia
School of Electrical Engineering and Computer Science, the University of Newcastle, Callaghan, NSW 2308, Australia
School of Biomedical Sciences, CHIRI Biosciences Research Precinct, Faculty of Health Sciences, Curtin University, Bentley, WA 6102, Australia
School of Medicine and Pharmacology, University of Western Australia, Fremantle, WA 6160, Australia
Western Australian Institute for Medical Research, Perth, WA 6000, Australia
Department of Gastroenterology, Fremantle Hospital, Fremantle, WA 6160, Australia
Curtin Health Innovation Research Institute, Curtin University, Bentley, WA 6102, Australia
Institute for Immunology & Infectious Diseases, Murdoch University, Murdoch, WA 6153, Australia
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
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.
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.
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.