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A novel approach to detect hot-spots in large-scale multivariate data

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Wu, Jianhua, Kendrick, Keith M. and Feng, Jianfeng. (2007) A novel approach to detect hot-spots in large-scale multivariate data. BMC Bioinformatics, Vol.8 (No.331). ISSN 1471-2105

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Official URL: http://dx.doi.org/10.1186/1471-2105-8-331

Abstract

Background: Progressive advances in the measurement of complex multifactorial components of biological processes involving both spatial and temporal domains have made it difficult to identify the variables (genes, proteins, neurons etc.) significantly changed activities in response to a stimulus within large data sets using conventional statistical approaches. The set of all changed variables is termed hot-spots. The detection of such hot spots is considered to be an NP hard problem, but by first establishing its theoretical foundation we have been able to develop an algorithm that provides a solution. Results: Our results show that a first-order phase transition is observable whose critical point separates the hot-spot set from the remaining variables. Its application is also found to be more successful than existing approaches in identifying statistically significant hot-spots both with simulated data sets and in real large-scale multivariate data sets from gene arrays, electrophysiological recording and functional magnetic resonance imaging experiments. Conclusion: In summary, this new statistical algorithm should provide a powerful new analytical tool to extract the maximum information from complex biological multivariate data.

Item Type:	Journal Article
Subjects:	Q Science > QR Microbiology
Divisions:	Faculty of Science > Centre for Scientific Computing Faculty of Science > Computer Science
Library of Congress Subject Headings (LCSH):	Multivariate analysis, Statistics
Journal or Publication Title:	BMC Bioinformatics
Publisher:	BioMed Central Ltd.
ISSN:	1471-2105
Date:	11 September 2007
Volume:	Vol.8
Number:	No.331
Identification Number:	10.1186/1471-2105-8-331
Status:	Peer Reviewed
Access rights to Published version:	Open Access
Funder:	Engineering and Physical Sciences Research Council (EPSRC), Biotechnology and Biological Sciences Research Council (Great Britain) (BBSRC)
Grant number:	GR/R54569, GR/ S20574, and GR/S30443 (EPSRC)
Related URLs:	Organisation
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URI:	http://wrap.warwick.ac.uk/id/eprint/540