scDAPA

scDAPA detects and visualizes dynamic alternative polyadenylation (APA) events from single-cell RNA-seq data, including 3' enriched 10× Genomics libraries, to characterize APA-mediated regulation across cell populations.

Key Features:

• Input Requirements: Accepts BAM/SAM files and cell cluster labels as input.
• Compatibility: Supports 3' enriched scRNA-seq library construction strategies, including 10× Genomics.
• APA detection algorithm: Employs a histogram-based method to summarize polyadenylation site usage and identify candidate APA events.
• Statistical testing: Applies the Wilcoxon rank-sum test to detect genes with dynamic APA across cellular groups.
• Visualization: Provides visualizations to display candidate genes with dynamic APA.
• Implementation: Implemented in Shell and R.

Scientific Applications:

• Cellular differentiation: Detects APA dynamics associated with cellular differentiation processes.
• Developmental biology: Characterizes APA changes during development.
• Disease-state analysis: Investigates APA alterations in disease states.
• Gene regulation in heterogeneous populations: Uncovers how alternative polyadenylation contributes to gene expression diversity and functional specialization within heterogeneous cell populations.

Methodology:

Processes BAM/SAM files with provided cell cluster labels, uses a histogram-based method to summarize polyadenylation site usage, and applies the Wilcoxon rank-sum test to identify genes with dynamic APA; implemented in Shell and R.