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

@inproceedings{Chan2017GeneRN,
  title={Gene Regulatory Network Inference from Single-Cell Data Using Multivariate Information Measures},
  author={Thalia E. Chan and Michael P.H. Stumpf and Ann C. Babtie},
  booktitle={Cell systems},
  year={2017}
}
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… CONTINUE READING