scRegulocity

scRegulocity analyzes local RNA velocity patterns in single-cell RNA sequencing (scRNA-seq) data to identify transitioning transcriptional states and link them to genes, pathways, and regulatory networks.

Key Features:

• Integration of RNA velocity and cell embeddings: Combines RNA velocity estimates with locality information from cell embedding coordinates to provide spatially informed velocity analyses.
• Detection of local velocity-switching patterns: Identifies local regions where nearby cells exhibit abrupt changes in RNA velocity indicative of transitioning states.
• Annotation with genes and pathways: Associates detected velocity-switching patterns with relevant genes and enriched pathways to provide biological context.
• Visualization at the regulatory network level: Visualizes velocity-switching patterns within regulatory networks to aid interpretation of regulatory changes.
• Integrated workflow for velocity estimation, pattern detection, and visualization: Combines steps for RNA velocity estimation, local pattern detection, annotation, and network-level visualization into a single analytical workflow.

Scientific Applications:

• Inference of dynamic cell states: Models transcriptional dynamics via RNA velocity to identify distinct and transitioning cellular states.
• Identification of regulatory drivers: Detects abrupt local velocity changes to pinpoint candidate drivers of cellular transitions.
• Pathway enrichment analysis: Links velocity-switching patterns to enriched pathways to reveal molecular mechanisms underlying transcriptional dynamics.

Methodology:

Integrates RNA velocity estimates with cell embedding coordinates to detect local abrupt changes in velocities (velocity-switching patterns), annotates these patterns with genes and enriched pathways, and visualizes them at the regulatory network level.