Linked Life Data

15564293

Source:http://linkedlifedata.com/resource/pubmed/id/15564293

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
7
pubmed:dateCreated
2005-3-24
pubmed:abstractText
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.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Apr
pubmed:issn
1367-4803
pubmed:author
pubmed:issnType
Print
pubmed:day
1
pubmed:volume
21
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
1112-20
pubmed:dateRevised
2006-11-15
pubmed:meshHeading
pubmed:year
2005
pubmed:articleTitle
Validation of alternative methods of data normalization in gene co-expression studies.
pubmed:affiliation
Bioinformatics Group, CSIRO Livestock Industries, Queensland Bioscience Precinct, St Lucia, QLD 4067, Australia. tony.reverter-gomez@csiro.au
pubmed:publicationType
Journal Article, Comparative Study, Evaluation Studies, Validation Studies