PhyliCS

PhyliCS analyzes multi-sample copy-number variation (CNV) and single-cell copy-number aberrations (scCNA/CNA) from single-cell DNA sequencing to quantify intra-tumor heterogeneity and assess the spatial organization of tumor subclones.

Key Features:

• Multi-sample scCNA processing: Processes single-cell CNA profiles from multiple samples of the same tumor to integrate multi-regional data.
• Spatial Heterogeneity score: Computes a Spatial Heterogeneity score to distinguish spatially intermixed versus spatially segregated cell populations.
• CNV/CNA analysis: Analyzes copy-number variation (CNV) and copy number aberrations (CNA) at single-cell resolution.
• Feature Selection: Identifies relevant genomic features from scCNA datasets.
• Dimensionality Reduction: Applies dimensionality reduction methods to simplify scCNA data while preserving key variation.
• Flexible Clustering Module: Groups similar cell populations based on their genetic profiles through clustering.
• Visualization Tools: Produces graphical representations of the spatial distribution and heterogeneity of tumor subclones.

Scientific Applications:

• Quantification of intra-tumor heterogeneity (ITH): Quantifies ITH using CNA and scCNA data derived from single-cell DNA sequencing.
• Spatial organization of subclones: Investigates the spatial distribution and mixing versus segregation of tumor subclones across multiple samples.
• Multi-regional tumor sampling analysis: Assesses spatial heterogeneity using scCNA profiles from multi-regional sampling of the same tumor.
• Interpretation of CNAs at single-cell level: Characterizes subclonal structure through analysis of copy number aberrations in individual cells.

Methodology:

Processes single-cell CNA (scCNA) profiles from multiple tumor samples, performs feature selection, applies dimensionality reduction, conducts clustering, computes the Spatial Heterogeneity score, and generates visualizations.