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

Reverter, A, Barris, W, McWilliam, S, Byrne, KA, Wang, YH, Tan, SH, Hudson, N and Dalrymple, BP (2005) Validation of alternative methods of data normalization in gene co-expression studies. Bioinformatics, 21 7: 1112-1120. doi:10.1093/bioinformatics/bti124


Author Reverter, A
Barris, W
McWilliam, S
Byrne, KA
Wang, YH
Tan, SH
Hudson, N
Dalrymple, BP
Title Validation of alternative methods of data normalization in gene co-expression studies
Journal name Bioinformatics   Check publisher's open access policy
ISSN 1367-4803
Publication date 2005-04-01
Sub-type Article (original research)
DOI 10.1093/bioinformatics/bti124
Volume 21
Issue 7
Start page 1112
End page 1120
Total pages 9
Language eng
Subject 1308 Clinical Biochemistry
1706 Computer Science Applications
1703 Computational Theory and Mathematics
Abstract Motivation: Clusters of genes encoding proteins with related function, 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.
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status Unknown

Document type: Journal Article
Sub-type: Article (original research)
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Citation counts: TR Web of Science Citation Count  Cited 32 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 37 times in Scopus Article | Citations
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