Pseudotime Trajectory Viz

Source: https://github.com/aipoch/medical-research-skills

Pseudotime Trajectory Visualization

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

When to Use

• Use this skill when the task needs Visualize single-cell developmental trajectories showing cellular differentiation processes using pseudotime analysis.
• Use this skill for data analysis tasks that require explicit assumptions, bounded scope, and a reproducible output format.
• Use this skill when you need a documented fallback path for missing inputs, execution errors, or partial evidence.

Key Features

• Scope-focused workflow aligned to: Analyze data with pseudotime-trajectory-viz using a reproducible workflow, explicit validation, and structured outputs for review-ready interpretation.
• Packaged executable path(s): scripts/main.py.
• Reference material available in references/ for task-specific guidance.
• Structured execution path designed to keep outputs consistent and reviewable.

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

Example Usage

See ## Usage above for related details.

cd "20260318/scientific-skills/Data Analytics/pseudotime-trajectory-viz"
python -m py_compile scripts/main.py
python scripts/main.py --help

Example run plan:

1. Confirm the user input, output path, and any required config values.
2. Edit the in-file CONFIG block or documented parameters if the script uses fixed settings.
3. Run python scripts/main.py with the validated inputs.
4. Review the generated output and return the final artifact with any assumptions called out.

Implementation Details

See ## Workflow above for related details.

• Execution model: validate the request, choose the packaged workflow, and produce a bounded deliverable.
• Input controls: confirm the source files, scope limits, output format, and acceptance criteria before running any script.
• Primary implementation surface: scripts/main.py.
• Reference guidance: references/ contains supporting rules, prompts, or checklists.
• Parameters to clarify first: input path, output path, scope filters, thresholds, and any domain-specific constraints.
• Output discipline: keep results reproducible, identify assumptions explicitly, and avoid undocumented side effects.

Quick Check

Use this command to verify that the packaged script entry point can be parsed before deeper execution.

python -m py_compile scripts/main.py

Audit-Ready Commands

Use these concrete commands for validation. They are intentionally self-contained and avoid placeholder paths.

python -m py_compile scripts/main.py
python scripts/main.py --help
python scripts/main.py --input "Audit validation sample with explicit symptoms, history, assessment, and next-step plan." --format json

Workflow

1. Confirm the user objective, required inputs, and non-negotiable constraints before doing detailed work.
2. Validate that the request matches the documented scope and stop early if the task would require unsupported assumptions.
3. Use the packaged script path or the documented reasoning path with only the inputs that are actually available.
4. Return a structured result that separates assumptions, deliverables, risks, and unresolved items.
5. If execution fails or inputs are incomplete, switch to the fallback path and state exactly what blocked full completion.

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
...

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