BioTender-max name: 'rna-velocity-agent' description: 'AI-powered RNA velocity analysis for predicting cellular state transitions, differentiation trajectories, and dynamic gene regulation from single-cell RNA sequencing data.' measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools: - read_file - run_shell_command
RNA Velocity Agent
The RNA Velocity Agent analyzes RNA velocity from single-cell RNA sequencing to predict cellular state transitions, differentiation trajectories, and dynamic transcriptional regulation. It implements velocyto, scVelo, and deep learning approaches for trajectory inference.
When to Use This Skill
- When inferring cell fate decisions and differentiation trajectories from scRNA-seq.
- To identify driver genes of cellular transitions.
- For predicting future cell states from current transcriptional profiles.
- When analyzing developmental processes or disease progression dynamics.
- To study cell cycle dynamics and quiescence transitions.
Core Capabilities
Splicing-Based Velocity: Calculate RNA velocity from spliced/unspliced transcript ratios.
Dynamic Modeling: Deep learning models (scVelo dynamical mode) for accurate velocity estimation.
Trajectory Inference: Project velocity vectors onto UMAP/PCA for differentiation flow visualization.
Driver Gene Identification: Identify genes driving cell state transitions.
Latent Time Estimation: Reconstruct cellular pseudotime from velocity fields.
Multi-Modal Velocity: Integrate protein (CITE-seq) or chromatin (ATAC) velocity.
RNA Velocity Fundamentals
Transcription → Unspliced RNA → Splicing → Spliced (mature) mRNA → Degradation
Velocity = d[spliced]/dt = β[unspliced] - γ[spliced]
- Positive velocity: Gene upregulating
- Negative velocity: Gene downregulating
- Zero velocity: Steady state
Workflow
Input: scRNA-seq data with spliced/unspliced counts (from STARsolo, velocyto, kallisto-bustools).
Quality Control: Filter genes by splice detection rates and expression levels.
Velocity Computation: Calculate velocity using steady-state or dynamical models.
Embedding Projection: Project velocity onto low-dimensional representations.
Trajectory Analysis: Identify root cells, terminal states, and differentiation paths.
Driver Analysis: Rank genes by velocity-based contribution to transitions.
Output: Velocity vectors, trajectory plots, driver genes, latent time estimates.
Example Usage
User: "Analyze RNA velocity in this hematopoiesis scRNA-seq dataset to map differentiation trajectories."
Agent Action:
python3 Skills/Genomics/RNA_Velocity_Agent/velocity_analyzer.py \
--adata hematopoiesis.h5ad \
--spliced_layer spliced \
--unspliced_layer unspliced \
--model dynamical \
--n_top_genes 2000 \
--identify_roots true \
--output velocity_results/
Model Comparison
| Model | Method | Best For | Limitations |
|---|---|---|---|
| velocyto (steady-state) | Linear regression | Quick overview | Assumes equilibrium |
| scVelo stochastic | Moment-based | General use | Limited dynamics |
| scVelo dynamical | Likelihood-based | Complex trajectories | Computationally intensive |
| UniTVelo | Deep learning | Multi-lineage | Training requirements |
| veloVI | Variational inference | Uncertainty quantification | Complex |
Key Analyses
1. Root Cell Identification
- Cells with high unspliced fractions
- Beginning of differentiation trajectories
- Stem/progenitor populations
2. Terminal State Detection
- Cells approaching steady state
- End of velocity streams
- Differentiated cell types
3. Driver Gene Analysis
- Genes with high velocity contributions
- Transition-specific regulators
- Transcription factors driving fate decisions
4. Latent Time
- Continuous measure of differentiation progress
- Aligns with biological time
- Enables dynamic gene expression modeling
Quality Control Metrics
| Metric | Threshold | Interpretation |
|---|---|---|
| Fraction unspliced | >10% of reads | Adequate capture |
| Genes with velocity | >1000 genes | Sufficient coverage |
| Velocity confidence | >0.8 | Reliable estimates |
| Coherence score | >0.3 | Consistent trajectories |
Advanced Applications
Cell Cycle Analysis:
- Separate velocity due to cell cycle from differentiation
- Identify cycling vs quiescent populations
Perturbation Effects:
- Compare velocity between conditions
- Identify acceleration/deceleration of differentiation
Disease Dynamics:
- Track progression in tumor samples
- Identify aberrant differentiation paths
Prerequisites
- Python 3.10+
- scVelo, velocyto
- Scanpy for preprocessing
- GPU recommended for deep learning models
Related Skills
- Single_Cell - For general scRNA-seq analysis
- Single_Cell_Foundation_Models - For cell annotation
- Spatial_Transcriptomics - For spatial velocity
Output Visualizations
- Velocity Stream Plot: Arrows on UMAP showing differentiation flow
- Phase Portraits: Spliced vs unspliced for individual genes
- Latent Time Coloring: Cells colored by differentiation progress
- Driver Gene Heatmaps: Top genes driving each transition
Author
AI Group - Biomedical AI Platform