Each developmental lineage that gives rise to a complete organism is created through a unique network of regulatory interactions. While reverse engineering each of these networks offers the potential to discover the mechanisms that produce each specialized lineage, modeling these complex networks is still challenging and prone to noise. In this study, I inferred a gene regulatory network for the skeletal muscle lineage using a publicly available single-nucleus RNA-sequencing atlas of mouse organogenesis and a widely used method for gene regulatory network inference, pySCENIC. Through a combination of edge filtering approaches, I was able to reduce noise and produce a network model that reflected prior knowledge and introduced novel mechanistic hypotheses.
