Identifying gene regulatory network rewiring using latent differential graphical models.

ABSTRACT: Gene regulatory networks (GRNs) are highly dynamic among different tissue types. Identifying tissue-specific gene regulation is critically important to understand gene function in a particular cellular context. Graphical models have been used to estimate GRN from gene expression data to distinguish direct interactions from indirect associations. However, most existing methods estimate GRN for a specific cell/tissue type or in a tissue-naive way, or do not specifically focus on network rewiring between different tissues. Here, we describe a new method called Latent Differential Graphical Model (LDGM). The motivation of our method is to estimate the differential network between two tissue types directly without inferring the network for individual tissues, which has the advantage of utilizin

SUBMITTER: Tian D 

PROVIDER: S-EPMC5041474 | biostudies-literature | 2016 Sep

REPOSITORIES: biostudies-literature