SCRAT

SCRAT analyzes single-cell regulome data to summarize regulatory activities and identify cell subpopulations from sparse, discrete single-cell ATAC-seq, DNase-seq, and ChIP-seq datasets.

Key Features:

• Supported data types: Processes single-cell ATAC-seq, DNase-seq, and ChIP-seq regulome data.
• Data summarization: Summarizes regulatory activities based on features such as gene sets and transcription factor binding motif sites.
• Cell subpopulation identification: Identifies distinct cell subpopulations within heterogeneous biological samples.
• Discovery of distinguishing features: Detects gene sets and transcription factors that exhibit differential activities among identified cell subpopulations.

Scientific Applications:

• Cell heterogeneity studies: Enables characterization of regulatory differences across individual cells within heterogeneous samples.
• Developmental biology: Supports analysis of regulatory dynamics underlying cellular differentiation and development.
• Cancer research: Facilitates identification of regulatory programs and cell subpopulations relevant to tumor biology.
• Immunology: Assists in profiling regulatory states of immune cell types and subpopulations.
• Regulatory element discovery: Aids in identifying novel regulatory elements and mechanisms driving cellular diversity and function.

Methodology:

Summarization of regulatory activities using gene sets and transcription factor binding motif sites, identification of cell subpopulations, and detection of differentially active gene sets and transcription factors.