title: BOOTSTRAP INFERENCE FOR NETWORK CONSTRUCTION WITH AN APPLICATION TO A BREAST CANCER MICROARRAY STUDY.
abstract: 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.
authors.affiliation: Fred Hutchinson Cancer Research Center, M2-B500, 1100 Fairview Ave N., Seattle, WA 98109, USA.
authors.forename: Shuang
authors.initials: S
authors.lastname: Li
authors.affiliation: Fred Hutchinson Cancer Research Center, M2-B500, 1100 Fairview Ave N., Seattle, WA 98109, USA.
authors.forename: Li
authors.initials: L
authors.lastname: Hsu
authors.affiliation: Department of Statistics, University of California, Davis, Mathematical Sciences Building, One Shields Avenue, Davis, CA 95616.
authors.forename: Jie
authors.initials: J
authors.lastname: Peng
authors.affiliation: Fred Hutchinson Cancer Research Center, M2-B500, 1100 Fairview Ave N., Seattle, WA 98109, USA.
authors.forename: Pei
authors.initials: P
authors.lastname: Wang
keywords: FDR; GGM; high dimensional data; mixture model; model aggregation