Dictys: dynamic gene regulatory network dissects developmental continuum with single-cell multiomics

Gene regulatory networks (GRNs) are key determinants of cell function and identity and are dynamically rewired during development and disease. Despite decades of advancement, challenges remain in GRN inference, including dynamic rewiring, causal inference, feedback loop modeling and context specificity. To address these challenges, we develop Dictys, a dynamic GRN inference and analysis method that leverages multiomic single-cell assays of chromatin accessibility and gene expression, context-specific transcription factor footprinting, stochastic process network and efficient probabilistic modeling of single-cell RNA-sequencing read counts. Dictys improves GRN reconstruction accuracy and reproducibility and enables the inference and comparative analysis of context-specific and dynamic GRNs across developmental contexts. Dictys' network analyses recover unique insights in human blood and mouse skin development with cell-type-specific and dynamic GRNs. Its dynamic network visualizations enable time-resolved discovery and investigation of developmental driver transcription factors and their regulated targets. Dictys is available as a free, open-source and user-friendly Python package. By probabilistic modeling of gene regulation and expression kinetics, Dictys infers dynamic and context-specific gene regulatory networks using single-cell multiomics data.

Automated summary

Gene regulatory networks (GRNs) play crucial roles in cell function and identity, and undergo dynamic rewiring in development and disease. However, challenges in GRN inference persist, including dynamic rewiring, causal inference, feedback loop modeling, and context specificity. To tackle these challenges, researchers develop Dictys, a dynamic GRN inference and analysis method that combines various single-cell assays and probabilistic modeling to improve accuracy and reproducibility in GRN reconstruction. Dictys allows the inference and comparative analysis of context-specific and dynamic GRNs, providing unique insights into development and uncovering regulatory relationships. It is a freely available Python package that facilitates the probabilistic modeling of gene regulatory networks using single-cell multiomics data.