Unraveling causal gene regulation from the RNA velocity graph using Velorama

Abstract

Gene regulatory network (GRN) inference that incorporates single-cell RNA-seq (scRNA-seq) differentiation trajectories or RNA velocity can reveal causal links between transcription factors and their target genes. However, current GRN inference methods require a total ordering of cells along a linear pseudotemporal axis, which is biologically inappropriate since trajectories with branches cannot be reduced to a single time axis. Such orderings are especially difficult to derive from RNA velocity studies since they characterize each cell’s state transition separately. Here, we introduce Velorama, a novel conceptual approach to causal GRN inference that newly represents scRNA-seq differentiation dynamics as a partial ordering of cells and operates on the directed acyclic graph (DAG) of cells constructed from pseudotime or RNA velocity measurements. To our knowledge, Velorama is the first GRN inference method that can work directly with RNA velocity-based cell-to-cell transition probabilities. On a standard set of synthetic datasets, we first demonstrate Velorama’s use with just pseudotime, finding that it improves area under the precision-recall curve (AUPRC) by 1.25-3x over state-of-the-art approaches. Using RNA velocity instead of pseudotime as the input to Velorama further improves AUPRC by an additional 1.75-3x. We also applied Velorama to study cell differentiation in pancreas, dentate gyrus, and bone marrow from real datasets and obtained intriguing evidence for the relationship between regulator interaction speeds and mechanisms of gene regulatory control during differentiation. We expect Velorama to be a critical part of the RNA velocity toolkit for investigating the causal drivers of differentiation and disease.