While single-cell gene expression experiments present
new challenges for data processing, the cellto-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 from three
experimental single-cell datasets and illustrate how
network context, choices made during analysis,
and sources of variability affect network inference.
PIDC tutorials and open-source software for estimating
PID are available. PIDC should facilitate the
identification of putative functional relationships
and mechanistic hypotheses from single-cell transcriptomic
data.