Bioinformatics Advance Access published online on April 29, 2004
Bioinformatics, doi:10.1093/bioinformatics/bth285
Bioinformatics © Oxford University Press 2004; all rights reserved
| ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
1 INSERMU472, 16 Avenue Paul Vaillant Couturier 94807 Villejuif Cedex, France; Institut Curie, 26 rue d'Ulm, 75248 Paris Cedex, France
* To whom correspondence should be addressed. E-mail: broet{at}vjf.inserm.fr.
Motivation: Multiclass response (MCR) experiments are those in which there are more than two classes to be compared. In these experiments, though the null hypothesis is simple, there are typically many patterns of gene expression changes across the different classes that lead to complex alternatives. In this paper, we propose a new strategy for selecting genes in MCR that is based on a flexible mixture model for the marginal distribution of a modified F statistic. Using this model, false positive and negative discovery rates can be estimated and combined to produce a rule for selecting a subset of genes. Moreover, the method proposed allows calculation of these rates for any predefined subset of genes. Results: We illustrate the performance our approach using simulated datasets and a real breast cancer microarray dataset from Hedenfalk et al. (2001). In this latter study, we investigate predefined subset of genes and point out interesting differences between three distinct biological pathways. Availability: http://www.bgx.org.uk/software.html.
Revised March 22, 2004
Accepted April 13, 2004
Article
A mixture model-based strategy for selecting sets of genes in multiclass response microarray experiments
2 Department of Epidemiology and Public Health, Imperial College, Norfolk Place, London W2 1PG, United Kingdom
3 Institut Curie, 26 rue d'Ulm, 75248 Paris Cedex, France
Abstract 
CiteULike 
Connotea 
Del.icio.us What's this?
This article has been cited by other articles:
|
|
 |
|
  P. Broet, S. Camilleri-Broet, S. Zhang, M. Alifano, D. Bangarusamy, M. Battistella, Y. Wu, M. Tuefferd, J.-F. Regnard, E. Lim, et al. Prediction of Clinical Outcome in Multiple Lung Cancer Cohorts by Integrative Genomics: Implications for Chemotherapy Selection Cancer Res., February 1, 2009; 69(3): 1055 - 1062. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 W. Pan, X. Shen, A. Jiang, and R. P. Hebbel Semi-supervised learning via penalized mixture model with application to microarray sample classification Bioinformatics, October 1, 2006; 22(19): 2388 - 2395. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
G.J. McLachlan, R.W. Bean, and L. B.-T. Jones A simple implementation of a normal mixture approach to differential gene expression in multiclass microarrays Bioinformatics, July 1, 2006; 22(13): 1608 - 1615. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
P. Broet and S. Richardson Detection of gene copy number changes in CGH microarrays using a spatially correlated mixture model Bioinformatics, April 15, 2006; 22(8): 911 - 918. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
Y. Xie, W. Pan, and A. B. Khodursky A note on using permutation-based false discovery rate estimates to compare different analysis methods for microarray data Bioinformatics, December 1, 2005; 21(23): 4280 - 4288. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
M. A. van de Wiel, J. L. Costa, K. Smid, C. B.M. Oudejans, A. M. Bergman, G. A. Meijer, G. J. Peters, and B. Ylstra Expression Microarray Analysis and Oligo Array Comparative Genomic Hybridization of Acquired Gemcitabine Resistance in Mouse Colon Reveals Selection for Chromosomal Aberrations Cancer Res., November 15, 2005; 65(22): 10208 - 10213. [Abstract] [Full Text] [PDF]
|
|
|
|
|
|
Y. Pawitan, K. R. K. Murthy, S. Michiels, and A. Ploner Bias in the estimation of false discovery rate in microarray studies Bioinformatics, October 15, 2005; 21(20): 3865 - 3872. [Abstract] [Full Text] [PDF]
|
|