DIRECT-NET: An efficient method to discover cis-regulatory elements and construct regulatory networks from single-cell multiomics data

The emergence of single-cell multiomics data provides unprecedented opportunities to scrutinize the transcriptional regulatory mechanisms controlling cell identity. However, how to use those datasets to dissect the cis-regulatory element (CRE)-to-gene relationships at a single-cell level remains a major challenge. Here, we present DIRECT-NET, a machine-learning method based on gradient boosting, to identify genome-wide CREs and their relationship to target genes, either from parallel single-cell gene expression and chromatin accessibility data or from single-cell chromatin accessibility data alone. By extensively evaluating and characterizing DIRECT-NET's predicted CREs using independent functional genomics data, we find that DIRECT-NET substantially improves the accuracy of inferring CRE-to-gene relationships in comparison to existing methods. DIRECT-NET is also capable of revealing cell subpopulation-specific and dynamic regulatory linkages. Overall, DIRECT-NET provides an efficient tool for predicting transcriptional regulation codes from single-cell multiomics data.

Automated summary

The emergence of single-cell multiomics data provides unprecedented opportunities to study the transcriptional regulatory mechanisms controlling cell identity. DIRECT-NET, a machine-learning method based on gradient boosting, allows us to identify genome-wide cis-regulatory elements (CREs) and their relationship to target genes from single-cell data. DIRECT-NET substantially improves the accuracy of inferring CRE-to-gene relationships and reveals cell subpopulation-specific and dynamic regulatory linkages.