Causal gene regulatory analysis with RNA velocity reveals an interplay between slow and fast transcription factors

Abstract

Single-cell expression dynamics from differentiation trajectories or RNA velocity have the potential to reveal causal links between transcription factors (TFs) and their target genes in gene regulatory networks (GRNs). However, existing methods either neglect these expression dynamics or require cells to be ordered along a linear pseudotemporal axis, which is incompatible with branching trajectories. We introduce Velorama, an approach to causal GRN inference that represents single-cell differentiation dynamics as a directed acyclic graph (DAG) of cells constructed from pseudotime or RNA velocity measurements. In contrast to previous approaches, Velorama is able to work directly with RNA velocity-based cell-to-cell transition probabilities and enables estimates of TF interaction speeds with their target genes. On a set of synthetic datasets, Velorama substantially outperforms existing approaches, improving area under the precision-recall curve (AUPRC) by 3.7–4.8x over the next best method. Applying Velorama to four RNA velocity datasets, we uncover evidence that the speed of a TF’s interactions is tied to its regulatory function. For human corticogenesis, we find slow TFs to be linked to gliomas and co-regulate preferentially with fast TFs, while fast TFs are associated with neuropsychiatric diseases. We expect Velorama to be a critical part of the RNA velocity toolkit for investigating the causal drivers of differentiation and disease.