rna-velocity-agent

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.

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:

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

Author

AI Group - Biomedical AI Platform