期刊
STATISTICAL APPLICATIONS IN GENETICS AND MOLECULAR BIOLOGY
卷 4, 期 -, 页码 -出版社
BERKELEY ELECTRONIC PRESS
DOI: 10.2202/1544-6115.1175
关键词
shrinkage; covariance estimation; small n, large p problem; graphical Gaussian model (GGM); genetic network; gene expression
Inferring large-scale covariance matrices from sparse genomic data is an ubiquitous problem in bioinformatics. Clearly, the widely used standard covariance and correlation estimators are ill-suited for this purpose. As statistically efficient and computationally fast alternative we propose a novel shrinkage covariance estimator that exploits the Ledoit-Wolf (2003) lemma for analytic calculation of the optimal shrinkage intensity. Subsequently, we apply this improved covariance estimator (which has guaranteed minimum mean squared error, is well-conditioned, and is always positive definite even for small sample sizes) to the problem of inferring large-scale gene association networks. We show that it performs very favorably compared to competing approaches both in simulations as well as in application to real expression data.
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