Assisted graphical model for gene expression data analysis.

The analysis of gene expression data has been playing a pivotal role in recent biomedical research. For gene expression data, network analysis has been shown to be more informative and powerful than individual-gene and geneset-based analysis. Despite promising successes, with the high dimensionality of gene expression data and often low sample sizes, network construction with gene expression data is still often challenged. In recent studies, a prominent trend is to conduct multidimensional profiling, under which data are collected on gene expressions as well as their regulators (copy number variations, methylation, microRNAs, SNPs, etc). With the regulation relationship, regulators contain information on gene expressions and can potentially assist in estimating their characteristics. In this study, we develop an assisted graphical model (AGM) approach, which can effectively use information in regulators to improve the estimation of gene expression graphical structure. The proposed approach has an intuitive formulation and can adaptively accommodate different regulator scenarios. Its consistency properties are rigorously established. Extensive simulations and the analysis of a breast cancer gene expression data set demonstrate the practical effectiveness of the AGM.