SCOUT

SCOUT identifies and analyzes outlier cells in single-cell datasets generated by mass cytometry (CyToF) and single-cell RNA sequencing (scRNA-Seq) to characterize cellular heterogeneity relevant to cancer processes such as invasion, metastasis, and therapy resistance.

Key Features:

• Outlier identification: Detects outlier cells from large single-cell datasets derived from mass cytometry (CyToF) and scRNA-Seq.
• SCOUT Selector: Systematically applies outlier analysis across datasets using a broad spectrum of biological markers.
• Comparative analysis: Compares identified outlier cells with non-outlier counterparts across different samples.
• Expression-level detection: Pinpoints outliers based on the expression levels of proteins or RNAs.

Scientific Applications:

• Cancer research: Characterizes tumor cellular heterogeneity and identifies cells potentially involved in metastasis or therapy resistance.
• Biomarker discovery: Identifies candidate protein or RNA biomarkers associated with cancer progression and treatment response.

Methodology:

Analyzes single-cell cancer datasets (CyToF and scRNA-Seq) and applies computational techniques to identify outliers based on protein or RNA expression levels.