A two-stage approach of gene network analysis for high-dimensional heterogeneous data.

ABSTRACT: Gaussian graphical models have been widely used to construct gene regulatory networks from gene expression data. Most existing methods for Gaussian graphical models are designed to model homogeneous data, assuming a single Gaussian distribution. In practice, however, data may consist of gene expression studies with unknown confounding factors, such as study cohort, microarray platforms, experimental batches, which produce heterogeneous data, and hence lead to false positive edges or low detection power in resulting network, due to those unknown factors. To overcome this problem and improve the performance in constructing gene networks, we propose a two-stage approach to construct a gene network from heterogeneous data. The first stage is to perform a clustering analysis in order to assign

SUBMITTER: Lee S 

PROVIDER: S-EPMC5862270 | biostudies-literature | 2018 Apr

REPOSITORIES: biostudies-literature