Skip Navigation


Bioinformatics Advance Access originally published online on November 25, 2004
Bioinformatics 2005 21(7):1112-1120; doi:10.1093/bioinformatics/bti124
This Article
Right arrow Full Text Freely available
Right arrow FREE Full Text (Print PDF) Freely available
Right arrow All Versions of this Article:
21/7/1112    most recent
bti124v1
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 (14)
Right arrowRequest Permissions
Google Scholar
Right arrow Articles by Reverter, A.
Right arrow Articles by Dalrymple, B. P.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Reverter, A.
Right arrow Articles by Dalrymple, B. P.
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

© The Author 2004. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions{at}oupjournals.org

Validation of alternative methods of data normalization in gene co-expression studies

Antonio Reverter *, Wes Barris , Sean McWilliam , Keren A. Byrne , Yong H. Wang , Siok H. Tan , Nick Hudson and Brian P. Dalrymple

Bioinformatics Group, CSIRO Livestock Industries, Queensland Bioscience Precinct St Lucia, QLD 4067, Australia

*To whom correspondence should be addressed.

Motivation: Clusters of genes encoding proteins with related functions, or in the same regulatory network, often exhibit expression patterns that are correlated over a large number of conditions. Protein associations and gene regulatory networks can be modelled from expression data. We address the question of which of several normalization methods is optimal prior to computing the correlation of the expression profiles between every pair of genes.

Results: We use gene expression data from five experiments with a total of 78 hybridizations and 23 diverse conditions. Nine methods of data normalization are explored based on all possible combinations of normalization techniques according to between and within gene and experiment variation. We compare the resulting empirical distribution of gene x gene correlations with the expectations and apply cross-validation to test the performance of each method in predicting accurate functional annotation. We conclude that normalization methods based on mixed-model equations are optimal.

Contact: tony.reverter-gomez{at}csiro.au


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
 Physiol. GenomicsHome page
E. de la Vega, M. R. Hall, K. J. Wilson, A. Reverter, R. G. Woods, and B. M. Degnan
Stress-induced gene expression profiling in the black tiger shrimp Penaeus monodon
Physiol Genomics, September 11, 2007; 31(1): 126 - 138.
[Abstract] [Full Text] [PDF]

Home page
 Physiol. GenomicsHome page
A. Reverter, N. J. Hudson, Y. Wang, S.-H. Tan, W. Barris, K. A. Byrne, S. M. McWilliam, C. D. K. Bottema, A. Kister, P. L. Greenwood, et al.
A gene coexpression network for bovine skeletal muscle inferred from microarray data
Physiol Genomics, December 13, 2006; 28(1): 76 - 83.
[Abstract] [Full Text] [PDF]

Home page
 J ANIM SCIHome page
S. A. Lehnert, K. A. Byrne, A. Reverter, G. S. Nattrass, P. L. Greenwood, Y. H. Wang, N. J. Hudson, and G. S. Harper
Gene expression profiling of bovine skeletal muscle in response to and during recovery from chronic and severe undernutrition
J Anim Sci, December 1, 2006; 84(12): 3239 - 3250.
[Abstract] [Full Text] [PDF]

Home page
 BioinformaticsHome page
A. Reverter, A. Ingham, S. A. Lehnert, S.-H. Tan, Y. Wang, A. Ratnakumar, and B. P. Dalrymple
Simultaneous identification of differential gene expression and connectivity in inflammation, adipogenesis and cancer
Bioinformatics, October 1, 2006; 22(19): 2396 - 2404.
[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.