Single-cell RNA sequencing (scRNA-seq) has emerged as a powerful experimental approach to study cellular heterogeneity. One of the challenges in scRNA-seq data analysis is integrating different types of biological data to consistently recognize discrete biological functions and regulatory mechanisms of cells, such as transcription factor activities and gene regulatory networks in distinct cell populations. We have developed an approach to infer transcription factor activities from scRNA-seq data that leverages existing biological data on transcription factor binding sites. We show that the inferred transcription factor activities for key cell types identify regulatory transcription factors that are known to mechanistically control cell function and cell fate. The BITFAM approach not only identifies biologically meaningful transcription factor activities, but also provides valuable insights into underlying transcription factor regulatory mechanisms.

This Agreement shall begin on the date hereof. Certain parts of this website offer the opportunity for users to post opinions, information and material including without limitation academic papers and data ('Material') in areas of the website. FACULTY OPINIONS does not claim any ownership in the Material that you or any other user posts. FACULTY OPINIONS does not screen, edit, publish or review Material prior to its appearance on the website and is not responsible for it. Material does not reflect the views or opinions of FACULTY OPINIONS, its agents or affiliates.