Estimating single-sample networks to model disruption of gene regulation in cancer

Abstract

The biological state of the cell is characterized by a complex network of interacting genes, proteins, microRNAs, as well as other molecules. Gene regulatory network reconstruction algorithms estimate the likelihood of these molecular interactions by drawing on large numbers of expression data to model an "aggregate" network. However, while informative in highlighting overall differences between two or more conditions, aggregate network models fail to capture the heterogeneity represented in a disease population. I will introduce a computational framework for single-sample network reconstruction that allows us to “extract” individual patient networks from aggregate networks. I will demonstrate the strengths of this method in multiple datasets and will highlight newly identified gene regulatory interactions that play a role in cancer.