Open Access
March 2013 Bootstrap inference for network construction with an application to a breast cancer microarray study
Shuang Li, Li Hsu, Jie Peng, Pei Wang
Ann. Appl. Stat. 7(1): 391-417 (March 2013). DOI: 10.1214/12-AOAS589


Gaussian Graphical Models (GGMs) have been used to construct genetic regulatory networks where regularization techniques are widely used since the network inference usually falls into a high–dimension–low–sample–size scenario. Yet, finding the right amount of regularization can be challenging, especially in an unsupervised setting where traditional methods such as BIC or cross-validation often do not work well. In this paper, we propose a new method—Bootstrap Inference for Network COnstruction (BINCO)—to infer networks by directly controlling the false discovery rates (FDRs) of the selected edges. This method fits a mixture model for the distribution of edge selection frequencies to estimate the FDRs, where the selection frequencies are calculated via model aggregation. This method is applicable to a wide range of applications beyond network construction. When we applied our proposed method to building a gene regulatory network with microarray expression breast cancer data, we were able to identify high-confidence edges and well-connected hub genes that could potentially play important roles in understanding the underlying biological processes of breast cancer.


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Shuang Li. Li Hsu. Jie Peng. Pei Wang. "Bootstrap inference for network construction with an application to a breast cancer microarray study." Ann. Appl. Stat. 7 (1) 391 - 417, March 2013.


Published: March 2013
First available in Project Euclid: 9 April 2013

zbMATH: 06171277
MathSciNet: MR3086424
Digital Object Identifier: 10.1214/12-AOAS589

Keywords: FDR , GGM , high dimensional data , mixture model , model aggregation

Rights: Copyright © 2013 Institute of Mathematical Statistics

Vol.7 • No. 1 • March 2013
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