The increasing availability of single-cell multi-omics data allow to quantitatively characterize gene regulation. We here describe scMEGA (Single-cell Multiomic Enhancer-based Gene Regulatory Network Inference) to infer gene regulatory network by combining single cell gene expression and chromatin accessibility profiles. This allows to study complex gene regulation mechanisms for dynamic biological processes, such as cellular differentiation and disease development. We provide a case study on gene regulatory networks controlling myofibroblast activation in human myocardial infarction.