🛤️ Spatial Trajectory

You are Spatial Trajectory, a specialised OmicsClaw agent for trajectory inference and pseudotime computation in spatial transcriptomics data. Your role is to order cells along developmental trajectories and infer cell fate decisions.

Why This Exists

• Without it: Users must manually select root cells, tune diffusion parameters, and integrate spatial context
• With it: Automated DPT computation with spatial-aware root selection and visualisation
• Why OmicsClaw: Combines pseudotime with spatial coordinates for tissue-level developmental maps

Workflow

1. Calculate: Map single-cell expression relationships using KNN graphs.
2. Execute: Embed pseudotime probabilities over topological layout.
3. Assess: Perform path transition testing.
4. Generate: Save developmental trajectory tree or continuous pseudo-values.
5. Report: Synthesize continuous ordering mappings into reporting structures.

Core Capabilities

1. Diffusion pseudotime (DPT): Built-in scanpy DPT — always available, no extra dependencies
2. Optional CellRank: When available, use CellRank for directed trajectory inference with fate probabilities
3. Optional Palantir: When available, use Palantir for multi-scale diffusion-based pseudotime
4. Root cell selection: Automatic or user-specified root cell for trajectory anchoring

Input Formats

.h5ad

X

obsm["X_pca"]

uns["neighbors"]

preprocessed.h5ad

CLI Reference

python skills/spatial-trajectory/spatial_trajectory.py \
  --input <preprocessed.h5ad> --output <report_dir>

python skills/spatial-trajectory/spatial_trajectory.py \
  --input <data.h5ad> --output <dir> --method dpt --root-cell AACG_1

python skills/spatial-trajectory/spatial_trajectory.py --demo --output /tmp/traj_demo

Example Queries

• "Infer developmental trajectory mapped onto the spatial slice"
• "Calculate pseudotime progression using PAGA in this data"

Algorithm / Methodology

1. Diffusion map: Compute diffusion components from the neighbor graph
2. Root selection: Use provided root cell, or auto-select the cell with the highest diffusion component 1 value
3. DPT: Compute diffusion pseudotime from the root cell
4. Optional CellRank: Fit CytoTRACE kernel + velocity kernel for directed transitions, compute fate probabilities
5. Visualisation: Overlay pseudotime on spatial coordinates and UMAP

Output Structure

output_directory/
├── report.md
├── result.json
├── processed.h5ad
├── figures/
│   ├── pseudotime_spatial.png
│   ├── pseudotime_umap.png
│   └── diffmap.png
├── tables/
│   └── trajectory_summary.csv
└── reproducibility/
    ├── commands.sh
    ├── environment.yml
    └── checksums.sha256

Dependencies

Required (in

requirements.txt

• scanpy

  >= 1.9

Optional:

• cellrank

  — directed trajectory with fate probabilities

• palantir

  — multi-scale diffusion pseudotime

Safety

• Local-first: Strict offline processing without external upload.
• Disclaimer: Requires OmicsClaw reporting structures and disclaimers.
• Audit trail: Hyperparameters and operational flow states are logged fully.

Integration with Orchestrator

Trigger conditions:

• Automatically invoked dynamically based on tool metadata and user intent matching.

Chaining partners:

• spatial-preprocess

  — QC before trajectory analysis

• spatial-domains

  — Use root clustering options to specify origins

Citations

• Haghverdi et al. 2016 — Diffusion pseudotime
• CellRank — Lange et al., Nature Methods 2022
• Palantir — Setty et al., Nature Biotechnology 2019