| Summary: | Abstract
Motivation: Recent advances in single-cell RNA-sequencing (scRNA-seq) technologies promise to enable the study
of gene regulatory associations at unprecedented resolution in diverse cellular contexts. However, identifying
unique regulatory associations observed only in specific cell types or conditions remains a key challenge; this is particularly so for rare transcriptional states whose sample sizes are too small for existing gene regulatory network inference methods to be effective.
Results: We present ShareNet, a Bayesian framework for boosting the accuracy of cell type-specific gene regulatory
networks by propagating information across related cell types via an information sharing structure that is adaptively
optimized for a given single-cell dataset. The techniques we introduce can be used with a range of general network
inference algorithms to enhance the output for each cell type. We demonstrate the enhanced accuracy of our approach on three benchmark scRNA-seq datasets. We find that our inferred cell type-specific networks also uncover
key changes in gene associations that underpin the complex rewiring of regulatory networks across cell types, tissues and dynamic biological processes. Our work presents a path toward extracting deeper insights about cell typespecific gene regulation in the rapidly growing compendium of scRNA-seq datasets.
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