dc.contributor.author	Jacobson, Dan
dc.contributor.author	Emerton, Guy
dc.date.accessioned	2013-06-26T12:41:06Z
dc.date.available	2013-06-26T12:41:06Z
dc.date.issued	2012-08
dc.date.updated	2013-04-15T11:08:53Z
dc.description	The original publication is available at http://www.biomedcentral.com/1471-2105/13/197	en_ZA
dc.description	Publication of this article was funded by the Stellenbosch University Open Access Fund.
dc.description.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.	en_ZA
dc.description.version	Publishers' Version	en_ZA
dc.format.extent	23 p. : ill.
dc.identifier.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.	en_ZA
dc.identifier.issn	1471-2105 (online)
dc.identifier.issn	1471-2105 (print)
dc.identifier.other	doi.org/10.1186/1471-2105-13-197
dc.identifier.uri	http://hdl.handle.net/10019.1/80938
dc.language.iso	en_ZA	en_ZA
dc.language.rfc3066	en
dc.publisher	BioMed Central	en_ZA
dc.rights.holder	Dan Jacobson et al.; licensee BioMed Central Ltd.	en_ZA
dc.subject	Gene Set Analysis (GSA)	en_ZA
dc.subject	Set analysis methods	en_ZA
dc.subject	Microarray analysis	en_ZA
dc.title	GSA-PCA : gene set generation by principal component analysis of the Laplacian matrix of a metabolic network	en_ZA
dc.type	Article	en_ZA

GSA-PCA : gene set generation by principal component analysis of the Laplacian matrix of a metabolic network

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