Academy of Mathematics and Systems Science, CAS
Colloquia & Seminars

Speaker:	张庆昭副教授，厦门大学
Inviter:	王启华研究员
Title:	Assisted Estimation of Gene Expression Graphical Models
Time & Venue:	2019.12.06 16:00 N213
Abstract:	In the study of gene expression data, network analysis has played a uniquely important role. To accommodate the high dimensionality and low sample size and generate interpretable results, regularized estimation is usually conducted in the construction of gene expression Gaussian Graphical Models (GGMs). Gene expressions are regulated by regulators. To better decipher the interconnections among gene expressions, conditional GGMs (cGGMs), which accommodate gene expressions as well as their regulators, have been constructed. In practical data analysis, the construction of both GGMs and cGGMs is often unsatisfactory, mainly caused by the large number of model parameters and limited sample size. In this article, we recognize that, with the regulation between gene expressions and regulators, the sparsity structures of the GGMs and cGGMs satisfy a hierarchy. Accordingly, we propose a joint estimation which reinforces the hierarchical structure and use GGMs to assist the construction of cGGMs and vice versa. Consistency properties are rigorously established, and an effective computational algorithm is developed. In simulation, the assisted construction outperforms the separation construction of GGMs and cGGMs. Two TCGA datasets are analyzed, leading to findings different from the direct competitors. Beyond gene expression data, the proposed approach can be potentially applied to a variety of other high dimensional network analysis.