Ontology highlight
ABSTRACT:
SUBMITTER: Chan TE
PROVIDER: S-EPMC5624513 | biostudies-literature | 2017 Sep
REPOSITORIES: biostudies-literature
Chan Thalia E TE Stumpf Michael P H MPH Babtie Ann C AC
Cell systems 20170901 3
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 g ...[more]