A novel approach to detect hot-spots in large-scale multivariate data
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
WRAP_Feng-NOVEL-approach.pdf - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader
Official URL: http://dx.doi.org/10.1186/1471-2105-8-331
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.|
|Official Date:||11 September 2007|
|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)|
1. Brown M, Grundy W, Lin D, Cristianini N, Sugnet C, Furey T, Ares
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