Skip Navigation


Bioinformatics Advance Access originally published online on February 15, 2006
Bioinformatics 2006 22(9):1096-1102; doi:10.1093/bioinformatics/btl056
This Article
Right arrow Full Text Freely available
Right arrow FREE Full Text (Print PDF) Freely available
Right arrow All Versions of this Article:
22/9/1096    most recent
btl056v1
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in ISI Web of Science
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Add to My Personal Archive
Right arrow Download to citation manager
Right arrow Search for citing articles in:
ISI Web of Science (4)
Right arrowRequest Permissions
Google Scholar
Right arrow Articles by Conesa, A.
Right arrow Articles by Talón, M.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Conesa, A.
Right arrow Articles by Talón, M.
Social Bookmarking
 Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

© The Author 2006. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

maSigPro: a method to identify significantly differential expression profiles in time-course microarray experiments

Ana Conesa 1,{dagger},*, María José Nueda 2,{dagger}, Alberto Ferrer 3 and Manuel Talón 1

1 Centro de Genómica. Instituto Valenciano de Investigaciones Agrarias, Apartado Oficial 46113 Moncada, Valencia, Spain
2 Departamento de Estadística e Investigación Operativa. Universidad de Alicante Apartado 03080, Alicante Spain
3 Departamento de Estadística e Investigación Operativa Aplicadas y Calidad, Universidad Politécnica de Valencia Apartado 46022, Valencia, Spain

*To whom correspondence should be addressed.

Motivation: Multi-series time-course microarray experiments are useful approaches for exploring biological processes. In this type of experiments, the researcher is frequently interested in studying gene expression changes along time and in evaluating trend differences between the various experimental groups. The large amount of data, multiplicity of experimental conditions and the dynamic nature of the experiments poses great challenges to data analysis.

Results: In this work, we propose a statistical procedure to identify genes that show different gene expression profiles across analytical groups in time-course experiments. The method is a two-regression step approach where the experimental groups are identified by dummy variables. The procedure first adjusts a global regression model with all the defined variables to identify differentially expressed genes, and in second a variable selection strategy is applied to study differences between groups and to find statistically significant different profiles. The methodology is illustrated on both a real and a simulated microarray dataset.

Availability: The method has been implemented in the statistical language R and is freely available from the Bioconductor contributed packages repository and from http://www.ivia.es/centrogenomica/bioinformatics.htm

Contact: aconesa{at}ivia.es; mj.nueda{at}ua.es


Received on November 9, 2005; revised on February 1, 2006; accepted on February 10, 2006

Add to CiteULike CiteULike   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us    What's this?


This article has been cited by other articles:


Home page
Brief Funct Genomic ProteomicHome page
F. Cordero, M. Botta, and R. A. Calogero
Microarray data analysis and mining approaches
Brief Funct Genomic Proteomic, January 22, 2008; (2008) elm034v1.
[Abstract] [Full Text] [PDF]


Home page
BioinformaticsHome page
R. Sanges, F. Cordero, and R. A. Calogero
oneChannelGUI: a graphical interface to Bioconductor tools, designed for life scientists who are not familiar with R language
Bioinformatics, December 15, 2007; 23(24): 3406 - 3408.
[Abstract] [Full Text] [PDF]


Home page
Eukaryot CellHome page
A. M. Levin, R. P. de Vries, A. Conesa, C. de Bekker, M. Talon, H. H. Menke, N. N. M. E. van Peij, and H. A. B. Wosten
Spatial Differentiation in the Vegetative Mycelium of Aspergillus niger
Eukaryot. Cell, December 1, 2007; 6(12): 2311 - 2322.
[Abstract] [Full Text] [PDF]


Home page
BioinformaticsHome page
M. J. Nueda, A. Conesa, J. A. Westerhuis, H. C. J. Hoefsloot, A. K. Smilde, M. Talon, and A. Ferrer
Discovering gene expression patterns in time course microarray experiments by ANOVA SCA
Bioinformatics, July 15, 2007; 23(14): 1792 - 1800.
[Abstract] [Full Text] [PDF]



Disclaimer:
Please note that abstracts for content published before 1996 were created through digital scanning and may therefore not exactly replicate the text of the original print issues. All efforts have been made to ensure accuracy, but the Publisher will not be held responsible for any remaining inaccuracies. If you require any further clarification, please contact our Customer Services Department.