Abstract 6294: Motif-independent Identification of functional transcription factors from single-cell multiomics data

Abstract Transcription factors (TF) are proteins that control when and how genes are turned on or off in a cell by binding to specific DNA sequences. The activation of one TF may induce gene expression cascade of other TFs or genes, suggesting the critical role of TFs in cell differentiation and cancer development. Techniques such as ChIP-seq, CUT&RUN and CUT&Tag are widely used to examine TFs’ genomic binding activity, but only one or a few TFs can be examined at once, making them low-efficient considering that hundreds of TFs can be typically expressed in a cell. Recently emerging single-cell genomic technologies provide an alternative approach, using computational tools to predict functional TFs from single-cell transcriptomics or chromatin accessibility assays. However, most existing computational methods share similar limitations, including the use of static TF binding motifs and the inability of considering distal enhancer regulation. We developed BARTsc, a precise and time-efficient TF prediction tool for clustered single-cell genomics data and it overcomes aforementioned pitfalls. BARTsc leverages differential features between cell clusters and does model-based predictions of cis-regulatory profiles based on given feature sets. Association scores were then calculated between the predicted cis-regulatory profiles and a collection of TF binding profiles derived from published ChIP-seq datasets. BARTsc implemented a set of handy downstream analysis modules that reveal the TFs that lead to signature gene expression in each cell type, the relative TF activity across cell types and the key regulators for each cell type. By benchmarking against other methods in predicting key regulators, we showed that BARTsc exhibits superior performances in various contexts. A promising impact of this work is that BARTsc can facilitate more precise identification of key regulators driving cancer development and can lead to discovery of new therapeutic targets. Citation Format: Hongpan Zhang, Jingyi Wang, Zhenjia Wang, Chongzhi Zang. Motif-independent Identification of functional transcription factors from single-cell multiomics data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 6294.