Transcription factors play a crucial role in gene expression regulation, directly interpreting the genome to influence cellular functions and processes. Understanding Transcriptional Regulatory Networks (TRNs) provides insights into gene expression dynamics and regulatory mechanisms, critical for comprehending biological processes and disease states. Through single-cell RNA sequencing (scRNA-seq), analyzing gene expression at single-cell resolution offers opportunities to elucidate cellular heterogeneity and expression at a higher resolution. This paper presents preliminary validation of scRegNet, a novel network inference pipeline designed for single-cell transcriptomic data analysis, integrating correlative and causal inference methods to construct TRNs. Here, the pipeline incorporates Pearson correlation and Granger causality testing along with a comprehensive TF-target gene database and trajectory inference algorithms. We demonstrate scRegNet's efficacy using a mouse pancreatic endocrinogenesis dataset, identifying key TFs associated with endocrine cell differentiation. Comparison with literature-based TFs validates scRegNet's accuracy, while novel TFs offer hypotheses for further experimental validation. Our results reveal benefits of integrating multiple causal inference methods and trajectory analyses for robust TRN inference across unique datasets. This study highlights scRegNet's potential as a versatile tool for deciphering gene regulatory mechanisms in diverse biological contexts, paving the way for future applications in single-cell transcriptomic research.