Spatial Transcriptomics

Spatial Transcriptomics

Version Compatibility

Reference examples assume recent stable releases of the preferred tools, especially scanpy-like and the other tools listed below.

Before using code or command patterns, verify installed versions match the environment:

• Python: python -c "import <module>; print(<module>.__version__)"
• CLI: <tool> --version
• If signatures differ, inspect the installed help or API and adapt the pattern instead of retrying unchanged.

Overview

Workflow for spatial transcriptomics preprocessing, domain detection, deconvolution, neighborhood analysis, and publication-ready maps.

When To Use This Skill

• use when the task is spatial transcriptomics analysis or spatially aware visualization
• use when coordinates, images, or spot-level expression are part of the dataset
• use when the user needs domains, deconvolution, or neighborhood summaries

Quick Route

• If the input is raw or minimally processed data, start with validation and QC before any modeling.
• If the input is already processed, skip directly to the first workflow step that matches the user goal.
• If the user asks for a biological conclusion, always produce at least one QC or confidence artifact alongside the final result.

Progressive Disclosure

• Read references/technical_reference.md when you need deeper tool-selection rules, environment adaptation notes, or extra validation guidance.
• Keep SKILL.md as the main execution path and load the reference file only when the task or failure mode needs the extra detail.

Default Rules

• Prefer Python-first workflows unless the task explicitly requires something else.
• Keep intermediate and final outputs separated.
• Record software versions, reference builds, and key parameters when they affect interpretation.
• Favor reproducible tables and figures over one-off interactive-only outputs.

Expected Inputs

• spatial expression data
• coordinates or histology images
• optional single-cell reference

Expected Outputs

• spatial domains
• deconvolution tables
• spatial maps and neighborhood results

Preferred Tools

• scanpy-like spatial tooling
• image analysis utilities
• matplotlib
• seaborn

Starter Pattern

Preferred starting point: scanpy-like
Inputs: spatial expression data, coordinates or histology images, optional single-cell reference
Outputs: spatial domains, deconvolution tables, spatial maps and neighborhood results

Workflow

1. Validate spatial assets

Confirm coordinate systems, image registration, and barcode alignment where applicable.

2. Preprocess expression and spatial structure

Normalize expression while preserving spatial coordinates and neighborhood information.

3. Choose a task path

Run domain detection, deconvolution, communication, or neighborhood analysis according to the question.

4. Visualize spatial biology

Generate maps that preserve physical context, legends, and scale.

5. Export interpretable artifacts

Save spatial labels, coordinates, and figure-ready outputs.

Output Artifacts

• Recommended output layout:
  • results/ for final tables and serialized objects
  • figures/ for plots and static visual exports
  • qc/ for checks that justify downstream interpretation
• Minimum expected outputs for this skill:
• spatial domains
• deconvolution tables
• spatial maps and neighborhood results

Quality Review

• Confirm identifiers and metadata join correctly before modeling or summarizing.
• Generate at least one QC artifact before final biological interpretation.
• Keep raw or minimally processed inputs separate from transformed outputs.
• Review embeddings together with QC metrics and batch structure before labeling biology.
• Preserve the processed object with metadata and embeddings for downstream reuse.

Anti-Patterns

• dropping coordinate integrity during preprocessing
• treating deconvolution outputs as ground truth cell counts
• using overcrowded spatial plots without readable legends

Related Skills

• scRNA Preprocessing And Clustering
• Cell Annotation
• Cell Communication
• Trajectory And Lineage

Optional Supplements

• scanpy