A0282
Title: Detecting hub variables in large Gaussian graphical models
Authors: Jose Sanchez Gomez - UC-Riverside (United States) [presenting]
Abstract: In modern scientific applications, identifying small sets of variables in a dataset with a strong influence over the rest is often vital. For example, when studying the gene expression levels of cancer patients, estimating the most influential genes can be a first step towards understanding underlying gene dynamics and proposing new treatments. A popular approach for representing variable influence is through a Gaussian graphical model (GGM), where each variable corresponds to a node, and a link between two nodes represents relationships among pairs of variables. In a GGM, influential variables correspond to nodes with a high degree of connectivity, also known as hub variables. A new method is presented for estimating hub variables in GGMs. To this end, a connection is established between the presence of hubs in a GGM and the concentration of principal component vectors on the hub variables. Probabilistic guarantees of convergence for the method are provided, even in high-dimensional data where the number of variables can be arbitrarily large. An application of this new method is also discussed in a prostate cancer gene-expression dataset, through which several hub genes are detected with close connections to tumor development.
