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.
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.
Velorama is described in the bioRxiv preprint, Unraveling causal gene regulation from the RNA velocity graph using Velorama by Rohit Singh*, Alexander Wu*, Anish Mudide*, and Bonnie Berger.
This will introduce both the Python package as well as make an executable 'velorama' available that you can call from the command-line.