SCENIC

SCENIC reconstructs gene regulatory networks and identifies cell states from single-cell RNA-seq (scRNA-seq) data by integrating cis-regulatory motif analysis with transcription factor activity inference.

Key Features:

• Gene regulatory network reconstruction: Reconstructs gene regulatory networks (GRNs) from scRNA-seq data.
• Cell-state identification: Identifies distinct cell states and clusters based on single-cell transcriptional profiles.
• Cis-regulatory motif analysis: Identifies potential regulatory motifs within the genome to associate cis-regulatory elements with regulation.
• Transcription factor activity inference: Assesses motif activity across individual cells to infer transcription factor activities.
• Linking TFs to targets: Reconstructs networks by linking active transcription factors with their target genes.
• Integration of activity and network analysis: Integrates transcription factor activity inference with network analysis to pinpoint key regulators driving phenotypes.
• Designed for scRNA-seq data: Operates specifically on single-cell RNA sequencing datasets.

Scientific Applications:

• Tumor regulatory heterogeneity: Applied to tumor datasets to uncover regulatory mechanisms contributing to cellular heterogeneity.
• Brain tissue regulatory analysis: Applied to brain datasets to elucidate transcriptional regulation in neural cell types.
• Cell-state transition analysis: Identifies transcription factors that orchestrate cell-state transitions.
• Cell-type classification: Aids classification of cell types based on unique transcriptional and regulatory profiles.

Methodology:

Computational steps explicitly include identifying potential regulatory motifs in the genome, assessing motif activity across cells to infer transcription factor activities, and reconstructing gene networks by linking active transcription factors to their target genes while integrating TF activity inference with network analysis.