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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

Official Date:

11 September 2007

Dates:

Date	Event
11 September 2007	Published

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

References:

1. Brown M, Grundy W, Lin D, Cristianini N, Sugnet C, Furey T, Ares
M, Haussler D: Knowledge-based analysis of microarray gene
expression data using support vector machines. Proc Nat Acad
of Sci 1999, 97:262-267.
2. Feng JF, (editor): Computational Neuroscience: A Comprehensive
Approach Chapman and Hall/CRC: Boca Raton; 2004.
3. Horton PM, Bonny L, Nicol A, Kendrick K, Feng JF: Applictions of
multi-variate analysis of variances (MANOVA) to multi-electrode
array data. J Neurosci Meth 2005, 146:22-41.
4. Marchini JL, Ripley BD: A new statistical approach to detect significant
activation in functional MRI. Neuoimage 2000,
12:366-380.
5. Mehring C, Rickert J, Vaadia E, Oliveira S, Aertsen A, Rotter S: Inference
of hand movements from local field potentials in monkey
motor cortex. Nat Neurosci 2003, 6:1253-1254.
6. Destexhe A, Contreras D, Steriade M: Spatiotemporal Analysis
of Local Field Potentials and Unit Discharges in Cat Cerebral
Cortex during Natural Wake and Sleep States. J Neurosci
1999, 19:4595-4608.
7. Li WT: The-more-the-better and the-less-the-better. Bioinformatics
2006, 18:2187-2188.
8. Nielsen K, Logothetis N, Rainer G: Dissociation between Local
Field Potentials and Spiking Activity in Macaque Inferior
Temporal Cortex Reveals Diagnosticity-Based Encoding of
Complex Objects. J Neurosci 2006, 26:9639-9645.
9. Johnson RA, Wichern DW: Applied Multivariate Statistical Analysis Fifth
edition. Prentice Hall: New Jersey; 2002.
10. Feng JF: Correlated Neuronal Computation, in Unsolved
Problems of Noise in Biology. AIP conference proceedings
2003:516-534.
11. Durrant S, Feng JF: Negatively-correlated firing: the functional
meaning of lateral inhibition within cortical columns. Biol
Cybern 2006, 95:431-453.
12. Nicol AU, Magnusson MS, Segonds-Pichon A, Tate A, Feng JF, Kendrick
KM: Local and global encoding of odor stimuli by olfactory
bulb neural networks. Society for Neuroscience Abstracts 2005,
6:476.
13. Lasko TA, Bhagwat JG, Zou KH, Ohno-Machado L: The use of
receiver operating characteristic curves in biomedical informatics.
Journal of Biomedical Informatics 2005, 38:404-415.
14. Mason SJ, Graham NE: Areas beneath the relative operating
characteristics (ROC) and relative operating levels (ROL)
curves: Statistical significance and interpretation. QJR Meteorol
Soc 2002, 128:2145-2166.
15. Lock C, Hermans G, Pedotti R, Brendolan A, Schadt E, Garren E,
Langer-Gould A, Strober S, Cannella B, Allard J, Klonowski P, Austin
A, Lad N, Kaminski N, Galli S, Oksenberg J, Raine C, Heller R, Steinman
L: Gene-microarray analysis of multiple sclerosis lesions
yields new targets validated in autoimmune encephalomyelitis.
NatureMedicine 2002, 8(5):500-508.
16. Qin J, Lewis D, Noble W: Kernel hierarchical gene clustering
from microarray expression data. Bioinformatics 2003,
19:2097-2104.
17. Yeung KY, Bumgarner RE, Raftery AE: Bayesian model averaging:
development of an improved multi-class, gene selection and
classification tool for microarray data. Bioinformatics 2005,
21:2394-2402.
18. Microarray dataset [ftp://ftp.afcs.org/pub/datacenter/microarray]
19. Gene name [http://www.dcs.warwick.ac.uk/~jianhua/doc/
genename.xls]
20. Belliveau JW, Kennedy DNJ, McKinstry RC, Buchbinder BR,
Weisskoff RM, Cohen MS, Vevea JM, Brady TJ, Rosen BR: Functional
mapping of the human visual cortex by magnetic resonance
imaging. Science 1991, 254:716-719.
21. Thulborn KR, Chang SY, Shen GX, Voyvodic JT: High resolution
echo-planar fMRI of human visual cortex at 3.0 Tesla. NMR
Biomed 1997, 10:183-190.
22. Aron A, Fisher H, Mashek D, Strong G, Li HF, Brown L: Reward,
motivation, and emotion systems associated with earlystage
intense romantic love. J Neurophysiol 2005, 94:327-337.
23. Hottor homepage [http://www.dcs.warwick.ac.uk/~jianhua/doc/
hottor.html]