Gene Regulatory Network Inference from Single-Cell Data Using Multivariate Information Measures.

ABSTRACT: While single-cell gene expression experiments present new challenges for data processing, the cell-to-cell variability observed also reveals statistical relationships that can be used by information theory. Here, we use multivariate information theory to explore the statistical dependencies between triplets of genes in single-cell gene expression datasets. We develop PIDC, a fast, efficient algorithm that uses partial information decomposition (PID) to identify regulatory relationships between genes. We thoroughly evaluate the performance of our algorithm and demonstrate that the higher-order information captured by PIDC allows it to outperform pairwise mutual information-based algorithms when recovering true relationships present in simulated data. We also infer gene regulatory networks f

SUBMITTER: Chan TE 

PROVIDER: S-EPMC5624513 | biostudies-literature | 2017 Sep

REPOSITORIES: biostudies-literature