VeTra

VeTra infers cellular trajectories from single-cell RNA sequencing (scRNAseq) data using RNA velocity to elucidate dynamic processes such as cell cycle progression and development.

Key Features:

• RNA velocity integration: Incorporates RNA velocity vectors to capture directionality of cell state transitions in scRNAseq data.
• Cosine similarity-based directionality: Applies cosine similarity to assess the directionality of cellular transitions between velocity vectors.
• Cell grouping and clustering: Identifies and groups cells into clusters representing distinct stages or states along inferred trajectories.
• Merging weakly connected components: Merges weakly connected components to delineate cell groups according to transition directions.
• Regulatory gene suggestion: Suggests key regulatory genes associated with the inferred trajectories.
• Visualization of state transitions: Enables visualization of cell state transitions without requiring prior knowledge of trajectories.

Scientific Applications:

• Cell cycle progression analysis: Dissects dynamic cell cycle transitions in single-cell datasets.
• Developmental biology: Resolves developmental trajectories and stages from scRNAseq profiles.
• Regulator identification: Identifies candidate regulatory genes that may guide cellular transitions.
• Single-cell transcriptomics interpretation: Interprets complex cellular dynamics in single-cell transcriptomic studies.

Methodology:

Uses RNA velocity vectors, computes cosine similarity to determine transition directions, groups cells into clusters and merges weakly connected components to delineate trajectories, and performs suggestion of key regulatory genes.