Single-cell TF-DNA binding prediction and analysis based on transfer learning framework

Abstract

Cell type-specific gene expressions during development or in disease are regulated by interactions between transcription factors (TFs) and their binding sites. Recently, many deep learning approaches have been developed to characterize TF-DNA binding within a population of cells. However, determining TF binding sites (TFBSs) in single cells remains challenging due to the sparsity of data. Here, we propose a multi-stage transfer learning framework called STAPLE for single-cell TF-DNA binding prediction and analysis. Specifically, we design the Cell Type Learning to capture the relationship between different TF-DNA binding events in the same cell type. Meanwhile, we present Individual Learning to extract common motif and chromatin accessibility features of a particular binding event in a cellular population. In addition, we leverage Single-cell Learning to annotate TFBSs in each cell without any supervised label. Extensive experiments based on 570 single-cell datasets validate the effectiveness of our framework for considering cellular heterogeneity, outperforming current methods. This work can provide new insight into the relationship between TF-DNA binding and cellular heterogeneity. The source code of STAPLE can be found at https://github.com/ZhangLab312/STAPLE.