Fig. 4
From: Data analysis guidelines for single-cell RNA-seq in biomedical studies and clinical applications
Different strategies and approaches developed for regulon inference and TF activity prediction with scRNA-seq. To achieve regulon and TF activity prediction, the TF databases and TF-target databases are important resources, and the computational strategies include co-expression gene module identification, dynamic and stochastic modeling of TF versus target expression changes, and application of machine learning approaches. TF transcription factor, scRNA-seq single-cell RNA sequencing, AnimalTFDB Animal Transcription Factor DataBase, Cistrome DB Cistrome Data Browser, WGCNA weighted gene co-expression network analysis, SCENIC single cell regulatory network information and clustering, TRRUST transcriptional regulatory relationships unravelled by sentence-based text-mining