Evaluation of Differential Normalization and Analysis Procedures for Illumina Gene Expression Microarray Data Involving Small Changes
Evaluation of different normalization and analysis procedures for illumina gene expression microarray data involving small changesWhile Illumina microarrays can be used successfully for detecting small gene expression changes due to their high degree of technical replicability, there is little infor...
| Main Authors: | , , , , , , , , , |
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| Format: | Journal Article |
| Published: |
MDPI AG
2013
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| Online Access: | http://hdl.handle.net/20.500.11937/17835 |
| _version_ | 1848749572521721856 |
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| author | Johnstone, D. Riveros, C. Heidari, M. Graham, Ross Trinder, D. Berretta, R. Olynyk, John Scott, R. Moscato, P. Milward, E. |
| author_facet | Johnstone, D. Riveros, C. Heidari, M. Graham, Ross Trinder, D. Berretta, R. Olynyk, John Scott, R. Moscato, P. Milward, E. |
| author_sort | Johnstone, D. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Evaluation of different normalization and analysis procedures for illumina gene expression microarray data involving small changesWhile 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. |
| first_indexed | 2025-11-14T07:23:04Z |
| format | Journal Article |
| id | curtin-20.500.11937-17835 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T07:23:04Z |
| publishDate | 2013 |
| publisher | MDPI AG |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-178352017-09-13T15:42:43Z Evaluation of Differential Normalization and Analysis Procedures for Illumina Gene Expression Microarray Data Involving Small Changes Johnstone, D. Riveros, C. Heidari, M. Graham, Ross Trinder, D. Berretta, R. Olynyk, John Scott, R. Moscato, P. Milward, E. Evaluation of different normalization and analysis procedures for illumina gene expression microarray data involving small changesWhile 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. 2013 Journal Article http://hdl.handle.net/20.500.11937/17835 10.3390/microarrays2020131 MDPI AG unknown |
| spellingShingle | Johnstone, D. Riveros, C. Heidari, M. Graham, Ross Trinder, D. Berretta, R. Olynyk, John Scott, R. Moscato, P. Milward, E. Evaluation of Differential Normalization and Analysis Procedures for Illumina Gene Expression Microarray Data Involving Small Changes |
| title | Evaluation of Differential Normalization and Analysis Procedures for Illumina Gene Expression Microarray Data Involving Small Changes |
| title_full | Evaluation of Differential Normalization and Analysis Procedures for Illumina Gene Expression Microarray Data Involving Small Changes |
| title_fullStr | Evaluation of Differential Normalization and Analysis Procedures for Illumina Gene Expression Microarray Data Involving Small Changes |
| title_full_unstemmed | Evaluation of Differential Normalization and Analysis Procedures for Illumina Gene Expression Microarray Data Involving Small Changes |
| title_short | Evaluation of Differential Normalization and Analysis Procedures for Illumina Gene Expression Microarray Data Involving Small Changes |
| title_sort | evaluation of differential normalization and analysis procedures for illumina gene expression microarray data involving small changes |
| url | http://hdl.handle.net/20.500.11937/17835 |