Inference of transcriptional regulatory mechanisms from single-cell (sc) omics data, such as scRNA-seq, scATAC-seq, and scMultiome, remains an important problem in single-cell biology and functional genomics. Most existing methods for predicting functional transcriptional regulators (TRs) from single-cell data rely on co-expression between regulator and target genes and/or sequence motif enrichment, holding inherent limitations. Here, we present BARTsc, a computational method that accurately predicts functional TRs from clustered single-cell omics data by leveraging a large collection of public ChIP-seq profiles. BARTsc implements a novel framework to infer a cis-regulatory profile from differential genomic features from either unimodal (RNA or ATAC) or bimodal (scMultiome) single-cell profiling data and identify TRs whose binding profiles most associate with the cis-regulatory profile. BARTsc can quantify TR activity across cell clusters and predict key regulators for each cell cluster. We demonstrate that BARTsc can successfully identify active TRs in each cell type and cell-type-defining key regulators across diverse biological systems, including mouse cortex, human peripheral blood mononuclear cells (PBMCs), and human pancreatic ductal adenocarcinoma (PDAC). Using a generative-AI-assisted, literature-supported collection of cell-type key regulators as benchmarks, we show that BARTsc consistently outperforms existing state-of-the-art methods. We apply BARTsc to identify critical regulators in PDAC, including NEFLA, a novel PDAC key regulator, and validate its function in pancreatic tumor proliferation by experiments. As a robust and versatile computational method, BARTsc provides deeper insights into cell-type-specific regulatory programs, facilitating the discovery