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

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 assess...

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Autores principales: 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.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5003482/
https://www.ncbi.nlm.nih.gov/pubmed/27605185
http://dx.doi.org/10.3390/microarrays2020131
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author 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.
author_facet 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.
author_sort Johnstone, Daniel M.
collection PubMed
description 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.
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spelling pubmed-50034822016-09-06 Evaluation of Different Normalization and Analysis Procedures for Illumina Gene Expression Microarray Data Involving Small Changes 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. Microarrays (Basel) Article 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. MDPI 2013-05-21 /pmc/articles/PMC5003482/ /pubmed/27605185 http://dx.doi.org/10.3390/microarrays2020131 Text en © 2013 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/).
spellingShingle Article
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.
Evaluation of Different Normalization and Analysis Procedures for Illumina Gene Expression Microarray Data Involving Small Changes
title Evaluation of Different Normalization and Analysis Procedures for Illumina Gene Expression Microarray Data Involving Small Changes
title_full Evaluation of Different Normalization and Analysis Procedures for Illumina Gene Expression Microarray Data Involving Small Changes
title_fullStr Evaluation of Different Normalization and Analysis Procedures for Illumina Gene Expression Microarray Data Involving Small Changes
title_full_unstemmed Evaluation of Different Normalization and Analysis Procedures for Illumina Gene Expression Microarray Data Involving Small Changes
title_short Evaluation of Different Normalization and Analysis Procedures for Illumina Gene Expression Microarray Data Involving Small Changes
title_sort evaluation of different normalization and analysis procedures for illumina gene expression microarray data involving small changes
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5003482/
https://www.ncbi.nlm.nih.gov/pubmed/27605185
http://dx.doi.org/10.3390/microarrays2020131
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