Analysis of a simulated microarray dataset: Comparison of methods for data normalisation and detection of differential expression (Open Access publication)
Microarrays allow researchers to measure the expression of thousands of genes in a single experiment. Before statistical comparisons can be made, the data must be assessed for quality and normalisation procedures must be applied, of which many have been proposed. Methods of comparing the normalised...
Autores principales: | , , , , , , , , , , , , , , , , , , , , |
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Formato: | Texto |
Lenguaje: | English |
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BioMed Central
2007
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2682813/ https://www.ncbi.nlm.nih.gov/pubmed/18053575 http://dx.doi.org/10.1186/1297-9686-39-6-669 |
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author | Watson, Michael Pérez-Alegre, Mónica Baron, Michael Denis Delmas, Céline Dovč, Peter Duval, Mylène Foulley, Jean-Louis Garrido-Pavón, Juan José Hulsegge, Ina Jaffrézic, Florence Jiménez-Marín, Ángeles Lavrič, Miha Lê Cao, Kim-Anh Marot, Guillemette Mouzaki, Daphné Pool, Marco H Robert-Granié, Christèle San Cristobal, Magali Tosser-Klopp, Gwenola Waddington, David de Koning, Dirk-Jan |
author_facet | Watson, Michael Pérez-Alegre, Mónica Baron, Michael Denis Delmas, Céline Dovč, Peter Duval, Mylène Foulley, Jean-Louis Garrido-Pavón, Juan José Hulsegge, Ina Jaffrézic, Florence Jiménez-Marín, Ángeles Lavrič, Miha Lê Cao, Kim-Anh Marot, Guillemette Mouzaki, Daphné Pool, Marco H Robert-Granié, Christèle San Cristobal, Magali Tosser-Klopp, Gwenola Waddington, David de Koning, Dirk-Jan |
author_sort | Watson, Michael |
collection | PubMed |
description | Microarrays allow researchers to measure the expression of thousands of genes in a single experiment. Before statistical comparisons can be made, the data must be assessed for quality and normalisation procedures must be applied, of which many have been proposed. Methods of comparing the normalised data are also abundant, and no clear consensus has yet been reached. The purpose of this paper was to compare those methods used by the EADGENE network on a very noisy simulated data set. With the a priori knowledge of which genes are differentially expressed, it is possible to compare the success of each approach quantitatively. Use of an intensity-dependent normalisation procedure was common, as was correction for multiple testing. Most variety in performance resulted from differing approaches to data quality and the use of different statistical tests. Very few of the methods used any kind of background correction. A number of approaches achieved a success rate of 95% or above, with relatively small numbers of false positives and negatives. Applying stringent spot selection criteria and elimination of data did not improve the false positive rate and greatly increased the false negative rate. However, most approaches performed well, and it is encouraging that widely available techniques can achieve such good results on a very noisy data set. |
format | Text |
id | pubmed-2682813 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2007 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-26828132009-05-16 Analysis of a simulated microarray dataset: Comparison of methods for data normalisation and detection of differential expression (Open Access publication) Watson, Michael Pérez-Alegre, Mónica Baron, Michael Denis Delmas, Céline Dovč, Peter Duval, Mylène Foulley, Jean-Louis Garrido-Pavón, Juan José Hulsegge, Ina Jaffrézic, Florence Jiménez-Marín, Ángeles Lavrič, Miha Lê Cao, Kim-Anh Marot, Guillemette Mouzaki, Daphné Pool, Marco H Robert-Granié, Christèle San Cristobal, Magali Tosser-Klopp, Gwenola Waddington, David de Koning, Dirk-Jan Genet Sel Evol Research Microarrays allow researchers to measure the expression of thousands of genes in a single experiment. Before statistical comparisons can be made, the data must be assessed for quality and normalisation procedures must be applied, of which many have been proposed. Methods of comparing the normalised data are also abundant, and no clear consensus has yet been reached. The purpose of this paper was to compare those methods used by the EADGENE network on a very noisy simulated data set. With the a priori knowledge of which genes are differentially expressed, it is possible to compare the success of each approach quantitatively. Use of an intensity-dependent normalisation procedure was common, as was correction for multiple testing. Most variety in performance resulted from differing approaches to data quality and the use of different statistical tests. Very few of the methods used any kind of background correction. A number of approaches achieved a success rate of 95% or above, with relatively small numbers of false positives and negatives. Applying stringent spot selection criteria and elimination of data did not improve the false positive rate and greatly increased the false negative rate. However, most approaches performed well, and it is encouraging that widely available techniques can achieve such good results on a very noisy data set. BioMed Central 2007-11-15 /pmc/articles/PMC2682813/ /pubmed/18053575 http://dx.doi.org/10.1186/1297-9686-39-6-669 Text en Copyright © 2007 INRA, EDP Sciences |
spellingShingle | Research Watson, Michael Pérez-Alegre, Mónica Baron, Michael Denis Delmas, Céline Dovč, Peter Duval, Mylène Foulley, Jean-Louis Garrido-Pavón, Juan José Hulsegge, Ina Jaffrézic, Florence Jiménez-Marín, Ángeles Lavrič, Miha Lê Cao, Kim-Anh Marot, Guillemette Mouzaki, Daphné Pool, Marco H Robert-Granié, Christèle San Cristobal, Magali Tosser-Klopp, Gwenola Waddington, David de Koning, Dirk-Jan Analysis of a simulated microarray dataset: Comparison of methods for data normalisation and detection of differential expression (Open Access publication) |
title | Analysis of a simulated microarray dataset: Comparison of methods for data normalisation and detection of differential expression (Open Access publication) |
title_full | Analysis of a simulated microarray dataset: Comparison of methods for data normalisation and detection of differential expression (Open Access publication) |
title_fullStr | Analysis of a simulated microarray dataset: Comparison of methods for data normalisation and detection of differential expression (Open Access publication) |
title_full_unstemmed | Analysis of a simulated microarray dataset: Comparison of methods for data normalisation and detection of differential expression (Open Access publication) |
title_short | Analysis of a simulated microarray dataset: Comparison of methods for data normalisation and detection of differential expression (Open Access publication) |
title_sort | analysis of a simulated microarray dataset: comparison of methods for data normalisation and detection of differential expression (open access publication) |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2682813/ https://www.ncbi.nlm.nih.gov/pubmed/18053575 http://dx.doi.org/10.1186/1297-9686-39-6-669 |
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