Wu, J orcid.org/0000-0001-6093-599X, Kendrick, KM and Feng, J (2007) A novel approach to detect hot-spots in large-scale multivariate data. BMC Bioinformatics, 8. 331. ISSN 1471-2105
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.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2007 Wu et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
Keywords: | Local Field Potential; Significance Score; Fluothane; Wilks Lambda; Multielectrode Array |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Medicine and Health (Leeds) > School of Dentistry (Leeds) > Applied Health and Clinical Translation (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 18 Sep 2019 09:03 |
Last Modified: | 18 Sep 2019 09:03 |
Status: | Published |
Publisher: | BMC |
Identification Number: | 10.1186/1471-2105-8-331 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:143966 |
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