A Poisson Log-Normal Model for Constructing Gene Covariation Network Using RNA-seq Data.

ABSTRACT: Constructing expression networks using transcriptomic data is an effective approach for studying gene regulation. A popular approach for constructing such a network is based on the Gaussian graphical model (GGM), in which an edge between a pair of genes indicates that the expression levels of these two genes are conditionally dependent, given the expression levels of all other genes. However, GGMs are not appropriate for non-Gaussian data, such as those generated in RNA-seq experiments. We propose a novel statistical framework that maximizes a penalized likelihood, in which the observed count data follow a Poisson log-normal distribution. To overcome the computational challenges, we use Laplace's method to approximate the likelihood and its gradients, and apply the alternating directions m

SUBMITTER: Choi Y 

PROVIDER: S-EPMC5510689 | biostudies-literature | 2017 Jul

REPOSITORIES: biostudies-literature