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...

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Autores principales: 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
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2007
Materias:
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.
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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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