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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...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2013
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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. |
format | Online Article Text |
id | pubmed-5003482 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2013 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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