GSA-PCA : gene set generation by principal component analysis of the Laplacian matrix of a metabolic network
Date
2012-08
Authors
Jacobson, Dan
Emerton, Guy
Journal Title
Journal ISSN
Volume Title
Publisher
BioMed Central
Abstract
Abstract
Background
Gene Set Analysis (GSA) has proven to be a useful approach to microarray analysis. However, most of the method development for GSA has focused on the statistical tests to be used rather than on the generation of sets that will be tested. Existing methods of set generation are often overly simplistic. The creation of sets from individual pathways (in isolation) is a poor reflection of the complexity of the underlying metabolic network. We have developed a novel approach to set generation via the use of Principal Component Analysis of the Laplacian matrix of a metabolic network. We have analysed a relatively simple data set to show the difference in results between our method and the current state-of-the-art pathway-based sets.
Results
The sets generated with this method are semi-exhaustive and capture much of the topological complexity of the metabolic network. The semi-exhaustive nature of this method has also allowed us to design a hypergeometric enrichment test to determine which genes are likely responsible for set significance. We show that our method finds significant aspects of biology that would be missed (i.e. false negatives) and addresses the false positive rates found with the use of simple pathway-based sets.
Conclusions
The set generation step for GSA is often neglected but is a crucial part of the analysis as it defines the full context for the analysis. As such, set generation methods should be robust and yield as complete a representation of the extant biological knowledge as possible. The method reported here achieves this goal and is demonstrably superior to previous set analysis methods.
Description
The original publication is available at http://www.biomedcentral.com/1471-2105/13/197
Publication of this article was funded by the Stellenbosch University Open Access Fund.
Publication of this article was funded by the Stellenbosch University Open Access Fund.
Keywords
Gene Set Analysis (GSA), Set analysis methods, Microarray analysis
Citation
Jacobson, D. & Emerton, G. 2012. GSA-PCA: gene set generation by principal component analysis of the Laplacian matrix of a metabolic network. BMC Bioinformatics, 13(1):197, doi.org/10.1186/1471-2105-13-197.