Abstract 2342: BARTsc: A transcription factor analysis suite for single-cell omics data

Abstract In Eukaryotic cells, the expression of a small set of transcription factors (TFs) is usually sufficient to set up a cell-type-specific gene expression program. The underlying complicated epigenetic mechanisms that explain how cells change their states in response to intra- and extra-cellular signals in different scenarios remain to be fully understood. Importantly, epigenetic regulation of gene expression has been considered a central regulatory mechanism of cell fate determination and cellular plasticity. Insights into it will also profoundly impact our understanding of misregulation of gene expression in diseases such as cancer. However, the functional TFs of many cell types remain unclear, which greatly hinders the study of epigenetic regulatory mechanisms. The rapid development of single-cell technology is a promising opportunity that enables us to infer functional TFs for different cell types. Some computational methods have been published in this area. However, existing methods have various pitfalls. For instance, some methods use transcription factor binding motifs (TFBMs) as the reference of TF binding, overlooking widespread non-specific binding. Some other methods hold strict criteria for training data and thus can only predict a small number of TFs. To overcome these issues, we propose to leverage publicly available high-quality ChIP-seq data to predict TFs functional in single-cell omics data. Here, we present BARTsc, a comprehensive suite for a series of TF analysis functionalities, including inference of signature TFs of given cell types/clusters, cross-cell-type/cluster TF activity comparative analysis, and TF clustering based on their cross cell-type distributions. The input of BARTsc can be either scRNA-seq, scATAC-seq, or scMultiome data. We demonstrate that BARTsc outperforms several existing tools in both sensitivity and specificity in identifying key cell type regulators in single-cell datasets of well-studied systems. Applying BARTsc to a scRNA-seq dataset for cancer-associated fibroblasts (CAFs), we identified distinct regulatory activities of some TFs in different subtypes of CAFs, but the TF genes are universally expressed in these subtypes. In addition, by time course BARTsc analysis, we identified a list of TFs that may either promote or suppress CAF differentiation, offering a new approach to find potential candidate therapeutic targets. Citation Format: Hongpan Zhang, Jingyi Wang, Zhenjia Wang, Chongzhi Zang. BARTsc: A transcription factor analysis suite for single-cell omics data abstract. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts) ; 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84 (6Suppl): Abstract nr 2342.