---
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
1. **Splicing-Based Velocity**: Calculate RNA velocity from spliced/unspliced transcript ratios.
2. **Dynamic Modeling**: Deep learning models (scVelo dynamical mode) for accurate velocity estimation.
3. **Trajectory Inference**: Project velocity vectors onto UMAP/PCA for differentiation flow visualization.
4. **Driver Gene Identification**: Identify genes driving cell state transitions.
5. **Latent Time Estimation**: Reconstruct cellular pseudotime from velocity fields.
6. **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
1. **Input**: scRNA-seq data with spliced/unspliced counts (from STARsolo, velocyto, kallisto-bustools).
2. **Quality Control**: Filter genes by splice detection rates and expression levels.
3. **Velocity Computation**: Calculate velocity using steady-state or dynamical models.
4. **Embedding Projection**: Project velocity onto low-dimensional representations.
5. **Trajectory Analysis**: Identify root cells, terminal states, and differentiation paths.
6. **Driver Analysis**: Rank genes by velocity-based contribution to transitions.
7. **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**:
```bash
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
1. **Velocity Stream Plot**: Arrows on UMAP showing differentiation flow
2. **Phase Portraits**: Spliced vs unspliced for individual genes
3. **Latent Time Coloring**: Cells colored by differentiation progress
4. **Driver Gene Heatmaps**: Top genes driving each transition