Pseudotime Trajectory Visualization

Visualize single-cell developmental trajectories showing cellular differentiation processes using pseudotime analysis.

Function

• Infer developmental trajectories from single-cell RNA-seq data
• Calculate pseudotime values representing cellular differentiation progress
• Visualize trajectory trees and lineage branching
• Overlay gene expression dynamics along pseudotime
• Identify lineage-specific marker genes
• Generate publication-ready trajectory plots

Technical Difficulty

High - Requires understanding of single-cell analysis, dimensionality reduction, trajectory inference algorithms, and Python visualization libraries.

Usage

# Basic trajectory analysis from AnnData file
python scripts/main.py --input data.h5ad --output ./results

# Specify starting cells and lineage inference method
python scripts/main.py --input data.h5ad --start-cell stem_cell_cluster --method diffusion --output ./results

# Visualize specific gene expression along trajectories
python scripts/main.py --input data.h5ad --genes SOX2,OCT4,NANOG --plot-genes --output ./results

# Full analysis with custom parameters
python scripts/main.py --input data.h5ad \
    --embedding umap \
    --method slingshot \
    --start-cell-type progenitor \
    --n-lineages 3 \
    --genes MARKER1,MARKER2,MARKER3 \
    --output ./results \
    --format pdf

Parameters

Input Format

Required AnnData (.h5ad) structure:

AnnData object with n_obs × n_vars = n_cells × n_genes
    obs: 'leiden', 'cell_type'  # Cluster and cell type annotations
    var: 'highly_variable'       # Highly variable gene marker
    obsm: 'X_umap', 'X_pca'      # Pre-computed embeddings (optional)
    layers: 'spliced', 'unspliced'  # For RNA velocity (optional)

Output Files

output_directory/
├── trajectory_plot.{format}          # Main trajectory visualization
├── pseudotime_distribution.{format}  # Pseudotime value distribution
├── lineage_tree.{format}             # Branching lineage structure
├── gene_expression_heatmap.{format}  # Gene dynamics heatmap (if --plot-genes)
├── gene_trends/
│   ├── {gene_name}_trend.{format}    # Individual gene expression trends
│   └── ...
├── pseudotime_values.csv             # Cell-level pseudotime values
├── lineage_assignments.csv           # Cell lineage assignments
└── analysis_report.json              # Analysis parameters and statistics

Output Format Example

analysis_report.json

{
  "analysis_date": "2026-02-06T06:00:00",
  "method": "diffusion",
  "n_cells": 5000,
  "n_lineages": 3,
  "root_cell": "cell_1234",
  "pseudotime_range": [0.0, 1.0],
  "lineages": {
    "lineage_1": {
      "cell_count": 1500,
      "terminal_state": "mature_type_A",
      "mean_pseudotime": 0.75
    },
    "lineage_2": {
      "cell_count": 1200,
      "terminal_state": "mature_type_B",
      "mean_pseudotime": 0.68
    }
  }
}

pseudotime_values.csv

cell_id,cluster,cell_type,pseudotime,lineage,branch_probability
cell_001,0,progenitor,0.05,lineage_1,0.95
cell_002,1,intermediate,0.42,lineage_1,0.88
...

Dependencies

• Python 3.9+
• scanpy>=1.9.0 - Single-cell analysis framework
• scvelo>=0.2.5 - RNA velocity analysis
• palantir - Trajectory inference and pseudotime
• scikit-learn - Dimensionality reduction and clustering
• matplotlib>=3.5.0 - Plotting
• seaborn - Statistical visualization
• pandas, numpy - Data manipulation
• anndata - Single-cell data structure

Optional:

• slingshot (R) via rpy2 - Alternative trajectory method

Implementation Notes

1. Preprocessing: Assumes input data is already normalized and log-transformed
2. Root Detection: If start cell not specified, uses cell cycle or marker gene expression to infer progenitors
3. Diffusion Pseudotime: Default method using diffusion maps for robust trajectory inference
4. Palantir: Used for soft lineage assignments and fate probability estimation
5. Memory: Large datasets (>50k cells) may require 16GB+ RAM

Methods

Diffusion Pseudotime (DPT)

• Uses diffusion maps to capture non-linear cell relationships
• Robust to noise and dataset size
• Good for complex branching trajectories

Slingshot

• Principal curve-based approach
• Simultaneous inference of multiple lineages
• Requires R installation with rpy2 bridge

PAGA (Partition-based Graph Abstraction)

• Connects clusters based on transcriptome similarity
• Provides coarse-grained trajectory overview
• Fast and scalable

Palantir

• Diffusion-based fate probability estimation
• Soft lineage assignments
• Best for fate bias analysis

Limitations

• Requires high-quality single-cell data with good cell type coverage
• Assumes differentiation is the main source of variation
• May not capture rare transitional states with few cells
• Circular or cyclic processes not well represented by linear pseudotime
• RNA velocity requires spliced/unspliced counts in AnnData layers

Safety & Best Practices

• Validate trajectories with known marker genes and biological knowledge
• Multiple methods recommended for critical analyses
• Batch effects should be corrected before trajectory inference
• Cell cycle effects may confound differentiation trajectories
• Do not overinterpret precise pseudotime values as absolute time

Example Workflow

# Preprocess data with scanpy (before using this tool)
import scanpy as sc

adata = sc.read_h5ad('raw_data.h5ad')
sc.pp.normalize_total(adata)
sc.pp.log1p(adata)
sc.pp.highly_variable_genes(adata, n_top_genes=2000)
sc.pp.scale(adata)
sc.tl.pca(adata)
sc.pp.neighbors(adata)
sc.tl.umap(adata)
sc.tl.leiden(adata)
adata.write('data.h5ad')

# Then run this skill
# python scripts/main.py --input data.h5ad --start-cell-type progenitor

References

• Haghverdi et al. (2016) - Diffusion pseudotime
• Street et al. (2018) - Slingshot
• Wolf et al. (2019) - PAGA
• Setty et al. (2019) - Palantir
• La Manno et al. (2018) - RNA velocity

Version

• Created: 2026-02-06
• Status: Functional
• Version: 1.0.0

Risk Assessment

Security Checklist

• No hardcoded credentials or API keys
• No unauthorized file system access (../)
• Output does not expose sensitive information
• Prompt injection protections in place
• Input file paths validated (no ../ traversal)
• Output directory restricted to workspace
• Script execution in sandboxed environment
• Error messages sanitized (no stack traces exposed)
• Dependencies audited

Prerequisites

# Python dependencies
pip install -r requirements.txt

Evaluation Criteria

Success Metrics

• Successfully executes main functionality
• Output meets quality standards
• Handles edge cases gracefully
• Performance is acceptable

Test Cases

1. Basic Functionality: Standard input → Expected output
2. Edge Case: Invalid input → Graceful error handling
3. Performance: Large dataset → Acceptable processing time

Lifecycle Status

• Current Stage: Draft
• Next Review Date: 2026-03-06
• Known Issues: None
• Planned Improvements:
  • Performance optimization
  • Additional feature support