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Bayesian variable selection in clustering high-dimensional data with substructure

Content Provider	Scilit
Author	Swartz, Michael D. Mo, Qianxing Murphy, Mary E. Lupton, Joanne R. Turner, Nancy D. Hong, Mee Young Vannucci, Marina
Copyright Year	2008
Description	In this article we focus on clustering techniques recently proposed for high-dimensional data that incorporate variable selection and extend them to the modeling of data with a known substructure, such as the structure imposed by an experimental design. Our method essentially approximates the within-group covariance by facilitating clustering without disrupting the groups defined by the experimenter. The method we adopt simultaneously determines which expression patterns are important, and which genes contribute to such patterns. We evaluate performance on simulated data and on microarray data from a colon carcinogenesis study. Selected genes are biologically consistent with current research and provide strong biological validation of the cluster configuration identified by the method.
Related Links	http://www.stat.rice.edu/~marina/papers/jabes08.pdf http://link.springer.com/content/pdf/10.1198%2F108571108X378317.pdf
Ending Page	423
Page Count	17
Starting Page	407
ISSN	10857117
e-ISSN	15372693
DOI	10.1198/108571108x378317
Journal	Journal of Agricultural, Biological and Environmental Statistics
Issue Number	4
Volume Number	13
Language	English
Publisher	Informa UK Limited
Publisher Date	2008-12-01
Access Restriction	Open
Subject Keyword	Journal: Journal of Agricultural, Biological and Environmental Statistics Mathematical and Computational Biology Statistics and Probability Bayesian Inference Designed Experiments Microarray Analysis
Content Type	Text
Resource Type	Article
Subject	Applied Mathematics Statistics and Probability Environmental Science Agricultural and Biological Sciences Statistics, Probability and Uncertainty

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