Inferring transcription factor regulatory networks from single-cell ATAC-seq data based on graph neural networks

Transcription factor regulatory networks underlie major features of cellular identity and complex function such as pluripotency, development and differentiation. Li and colleagues develop a graph neural network to predict transcription factor regulatory networks based on single-cell ATAC-seq data. Sequence-specific transcription factors (TFs) are the key effectors of eukaryotic gene control and they regulate hundreds to thousands of downstream genes. Of particular interest are interactions in which a given TF regulates other TFs; these interactions define the TF regulatory networks (TRNs) that underlie cellular identity and major function. Chromatin accessibility depicts whether or not a DNA sequence is physically accessible and provides a direct measurement of transcriptional regulation. Benefiting from the accumulating chromatin accessibility data and deep learning advances, we developed a new computational method named DeepTFni to infer TRNs from the single-cell assay for transposase-accessible chromatin using sequencing (scATAC-seq) data. By implementing a graph neural network, which is more suitable for network representation, DeepTFni shows outstanding performance in TRN inference, which it supports with limited numbers of cells. Furthermore, by applying DeepTFni we identified hub TFs in tissue development and tumorigenesis and revealed that many mixed-phenotype acute leukemia associated genes undergo a prominent alteration to the TRN while there is moderate difference in messenger RNA level. The DeepTFni webserver is easy to use and has provided the predicted TRNs for several popular cell lines.