Cell Dynamics Analysis

Overview

This skill answers: How do cells change over time, and what are their fate decisions?

Two complementary approaches:

1. RNA Velocity: Infers directionality from spliced/unspliced ratios
2. Pseudotime/Trajectory: Orders cells along developmental paths

Decision Tree: Which Approach?

Q: Does your data have spliced/unspliced information?
│
├─ YES (velocyto/kallisto processed)
│   │
│   └─ RNA Velocity Analysis
│       ├─ scVelo (deterministic) - Fast, good default
│       ├─ scVelo (dynamical) - More accurate, slower
│       └─ VeloVI - Deep learning, handles noise
│       │
│       └─ Then: CellRank for fate probability
│
└─ NO (standard scRNA-seq/spatial)
    │
    └─ Pseudotime Analysis
        ├─ Palantir - Multi-lineage, fate probabilities
        ├─ DPT (Diffusion Pseudotime) - Classic, fast
        └─ PAGA - Trajectory + clustering

RNA Velocity Workflow

Prerequisites

Data must contain:

• adata.layers['spliced']: Spliced counts
• adata.layers['unspliced']: Unspliced counts

Check with: 'spliced' in adata.layers and 'unspliced' in adata.layers

Step 1: Preprocessing for Velocity

Velocity-specific preprocessing:
1. Filter genes by spliced/unspliced detection
2. Normalize spliced and unspliced separately
3. Compute moments (first/second order)

Step 2: Compute Velocity

Use analyze_velocity_data tool with method selection:

Step 3: Visualize Velocity

Use visualize_data with plot_type="velocity":

• subtype="stream": Velocity streamlines on embedding
• subtype="phase": Phase portraits for specific genes
• subtype="proportions": Spliced/unspliced ratios
• subtype="heatmap": Velocity across pseudotime

Step 4: Fate Analysis with CellRank

After velocity computation:

1. Use analyze_trajectory_data with method="cellrank"
2. Identify initial/terminal states
3. Compute fate probabilities
4. Identify lineage driver genes

Pseudotime Workflow (No Velocity Data)

Method Selection

Step 1: Define Root Cell

Critical for pseudotime:

Options for defining root:
1. Known marker gene expression (e.g., stem cell markers)
2. Specific cluster known to be progenitor
3. User-specified cell barcode

Step 2: Compute Trajectory

Use analyze_trajectory_data tool with:

• method: "palantir", "dpt", or "cellrank"
• root_cell or root_cluster: Starting point

Step 3: Visualize Trajectory

Use visualize_data with plot_type="trajectory":

• subtype="pseudotime": Cells colored by pseudotime
• subtype="fate_map": Fate probability heatmap
• subtype="gene_trends": Expression along trajectory
• subtype="circular": Circular trajectory plot

Biological Interpretation

What Velocity Tells You

• Direction of differentiation: Where cells are heading
• Dynamic genes: Genes actively changing
• State transitions: Intermediate cell states
• Terminal states: End points of differentiation

What Pseudotime Tells You

• Ordering of cells: Relative developmental time
• Branching points: Fate decision locations
• Lineage relationships: Which states lead to which
• Driver genes: Genes driving transitions

Spatial Context

Combining Velocity with Space

Questions to explore:

• Do velocity vectors align with spatial gradients?
• Are terminal states localized to specific regions?
• How does spatial organization relate to developmental time?

Visualization Tips

• Overlay velocity arrows on spatial coordinates
• Color spatial plots by pseudotime
• Identify spatial zones of active differentiation

Common Analysis Scenarios

Tumor Heterogeneity

Focus on:
- Cancer stem cell identification
- Differentiation hierarchies within tumor
- EMT transitions

Development

Focus on:
- Lineage specification
- Progenitor identification
- Temporal gene programs

Regeneration/Wound Healing

Focus on:
- Activation states
- Re-differentiation paths
- Spatial gradients of repair

Troubleshooting

Output Summary

Successful dynamics analysis provides:

• Pseudotime values: Ordering of cells
• Velocity vectors: Direction of change
• Fate probabilities: Likelihood of terminal states
• Driver genes: Key regulators of transitions
• Spatial mapping: Where dynamics occur in tissue