We introduce scKINETICS (Key regulatory Interaction NETwork for Inferring Cell Speed), a dynamical model of gene expression change which is fit with the simultaneous learning of per-cell transcriptional velocities and a governing gene regulatory network. This is accomplished through an expectation-maximization approach derived to learn the impact of each regulator on its target genes, leveraging biologically-motivated priors from epigenetic data, gene-gene co-expression, and constraints on cells’ future states imposed by the phenotypic manifold.

The demo shows the main functionalities of the package including input file generation, GRN construction, EM estimation, visualizations, and the TF ablation experiment.

https://academic.oup.com/bioinformatics/article/39/Supplement_1/i394/7210448