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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:

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