Network-based modular latent structure analysis.

ABSTRACT:

Background

High-throughput expression data, such as gene expression and metabolomics data, exhibit modular structures. Groups of features in each module follow a latent factor model, while between modules, the latent factors are quasi-independent. Recovering the latent factors can shed light on the hidden regulation patterns of the expression. The difficulty in detecting such modules and recovering the latent factors lies in the high dimensionality of the data, and the lack of knowledge in module membership.

Methods

Here we describe a method based on community detection in the co-expression network. It consists of inference-based network construction, module detection, and interacting latent factor detection from modules.

Results

In simulations, the method outperforme

SUBMITTER: Yu T 

PROVIDER: S-EPMC4248660 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature