Bootstrap inference for network construction with an application to a breast cancer microarray study

TL;DR

This paper introduces BINCO, a bootstrap-based method for constructing genetic networks that effectively controls false discovery rates, demonstrated on breast cancer microarray data to identify key genes and interactions.

Contribution

The paper presents a novel bootstrap inference method, BINCO, for network construction that directly controls FDRs, addressing challenges in high-dimensional, unsupervised settings.

Findings

BINCO accurately controls FDR in network inference.

Application to breast cancer data identified key genes.

Method is broadly applicable beyond genetics.

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.