Spatial Transcriptomics Mapper (ID: 196)#

When to Use#

• Use this skill when the task is to Map spatial transcriptomics data from 10x Genomics Visium/Xenium onto.
• 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#

See ## Features above for related details.

• Scope-focused workflow aligned to: Map spatial transcriptomics data from 10x Genomics Visium/Xenium onto.
• Packaged executable path(s): scripts/__init__.py plus 1 additional script(s).
• Structured execution path designed to keep outputs consistent and reviewable.

Dependencies#

See ## Prerequisites above for related details.

• Python: 3.10+. Repository baseline for current packaged skills.
• h5py: unspecified. Declared in requirements.txt.
• matplotlib: unspecified. Declared in requirements.txt.
• numpy: unspecified. Declared in requirements.txt.
• pandas: unspecified. Declared in requirements.txt.
• pil: unspecified. Declared in requirements.txt.
• pyarrow: unspecified. Declared in requirements.txt.
• scanpy: unspecified. Declared in requirements.txt.
• seaborn: unspecified. Declared in requirements.txt.
• squidpy: unspecified. Declared in requirements.txt.
• tifffile: unspecified. Declared in requirements.txt.

Example Usage#

See ## Usage above for related details.

bash

cd "20260318/scientific-skills/Data Analytics/spatial-transcriptomics-mapper"
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/__init__.py with additional helper scripts under scripts/.
• 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.

bash

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.

bash

python -m py_compile scripts/main.py
python scripts/main.py --help

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.

Description#

Spatial Transcriptomics analysis tool for processing 10x Genomics Visium or Xenium data, projecting gene expression data back onto tissue section images to draw "gene-space" distribution maps. Supports gene expression visualization, spatial clustering analysis, and morphological feature correlation.

Features#

• Visium Data Processing: Supports Space Ranger output (filtered_feature_bc_matrix.h5, spatial/tissue_positions_list.csv, spatial/tissue_lowres_image.png)
• Xenium Data Processing: Supports Xenium Explorer output (.h5, transcripts.parquet, nucleus_boundaries.parquet)
• Gene Expression Mapping: Projects expression of specified genes onto tissue images
• Spatial Clustering Visualization: Displays spatial distribution of Seurat/Scanpy clustering results
• Multi-gene Joint Analysis: Supports combined visualization of multiple genes
• High-resolution Output: Supports high-resolution image export

Installation#

text

# Required dependencies
pip install scanpy squidpy matplotlib seaborn pillow numpy pandas h5py

# Optional: For Xenium data processing
pip install pyarrow dask

# Optional: For advanced image processing
pip install opencv-python scikit-image

Quick Start - Test Data#

Generate sample Visium data to test the tool:

text

# Generate test data
python scripts/generate_test_data.py \
  --platform visium \
  --output ./test_data/visium_sample \
  --n-spots 500 \
  --n-genes 1000

# Run analysis on test data
python scripts/main.py \
  --platform visium \
  --data-dir ./test_data/visium_sample \
  --gene GENE_0000 \
  --output ./test_output/

Usage#

Basic - Visium Data#

text

python scripts/main.py \
  --platform visium \
  --data-dir /path/to/spaceranger/outs/ \
  --gene PIK3CA \
  --output ./output/

Basic - Xenium Data#

text

python scripts/main.py \
  --platform xenium \
  --data-dir /path/to/xenium/outs/ \
  --gene PIK3CA \
  --output ./output/

Multiple Genes#

text

python scripts/main.py \
  --platform visium \
  --data-dir /path/to/data/ \
  --genes PIK3CA,PTEN,EGFR \
  --mode overlay \
  --output ./output/

With Clustering Results#

text

python scripts/main.py \
  --platform visium \
  --data-dir /path/to/data/ \
  --cluster-file ./clusters.csv \
  --output ./output/

Input File Structure#

Visium (Space Ranger output)#

text

outs/
├── filtered_feature_bc_matrix.h5    # Gene expression matrix
├── raw_feature_bc_matrix.h5         # Raw counts (optional)
├── spatial/
│   ├── tissue_positions_list.csv    # Spot positions
│   ├── tissue_lowres_image.png      # Low-res H&E image
│   ├── tissue_hires_image.png       # High-res H&E image
│   └── scalefactors_json.json       # Scale factors
└── web_summary.html

Xenium#

text

outs/
├── cell_feature_matrix.h5           # Cell x gene matrix
├── transcripts.parquet              # Transcript coordinates
├── nucleus_boundaries.parquet       # Cell boundaries
├── cell_boundaries.parquet
├── morphology_focus.ome.tif         # Morphology image
└── experiment.xenium

Output Files#

• {gene}_spatial_map.png: Single gene spatial expression map
• {gene}_heatmap.png: Gene expression heatmap
• multi_gene_overlay.png: Multi-gene overlay map (if using --mode overlay)
• cluster_spatial_map.png: Cluster spatial distribution map
• combined_report.html: Comprehensive HTML report

Parameters#

ParameterTypeDefaultDescription--platformstrrequiredPlatform type: visium or xenium--data-dirstrrequiredData directory path--genestroptionalSingle gene name--geneslistoptionalMultiple genes, comma-separated--modestrsingleMode: single/overlay/multi--cluster-filestroptionalClustering result CSV file path--outputstr./outputOutput directory--dpiint300Output image DPI--cmapstrviridisColor map scheme--spot-sizefloat1.0Visium spot size factor--alphafloat0.8Transparency (0-1)--min-countint0Minimum expression filter--cropstroptionalCrop region (x1,y1,x2,y2)

Examples#

Example 1: Single Gene Visualization#

text

python scripts/main.py \
  --platform visium \
  --data-dir ./visium_sample/outs/ \
  --gene EPCAM \
  --cmap Reds \
  --output ./results/

Example 2: Tumor Marker Combination#

text

python scripts/main.py \
  --platform visium \
  --data-dir ./breast_cancer/outs/ \
  --genes PIK3CA,ERBB2,ESR1,PGR \
  --mode multi \
  --cmap plasma \
  --output ./tumor_markers/

Example 3: Xenium Subcellular Resolution#

text

python scripts/main.py \
  --platform xenium \
  --data-dir ./xenium_lung/outs/ \
  --genes SFTPB,SFTPC,SCGB1A1 \
  --dpi 600 \
  --output ./xenium_results/

Example 4: Spatial Clustering Visualization#

text

python scripts/main.py \
  --platform visium \
  --data-dir ./sample/outs/ \
  --cluster-file ./seurat_clusters.csv \
  --output ./clusters/

API Usage#

python

from skills.spatial_transcriptomics_mapper.scripts.main import SpatialMapper

# Initialize
mapper = SpatialMapper(
    platform="visium",
    data_dir="/path/to/data",
    output_dir="./output"
)

# Load data
mapper.load_data()

# Plot single gene
mapper.plot_gene_spatial(
    gene="PIK3CA",
    cmap="viridis",
    save_path="./output/pik3ca.png"
)

# Plot multiple genes
mapper.plot_multi_genes(
    genes=["PIK3CA", "PTEN", "EGFR"],
    mode="grid",
    save_path="./output/multi.png"
)

# Get spatial statistics
stats = mapper.get_spatial_stats(gene="PIK3CA")

Notes#

• Visium data uses low-resolution images by default to improve processing speed, can use --hires parameter to enable high resolution
• For large Xenium datasets, it is recommended to use --crop parameter to specify region of interest
• Color map reference: https://matplotlib.org/stable/tutorials/colors/colormaps.html
• For large samples, consider using --downsample parameter to reduce resolution

References#

• 10x Genomics Visium: https://www.10xgenomics.com/products/spatial-gene-expression
• 10x Genomics Xenium: https://www.10xgenomics.com/platforms/xenium
• Scanpy: https://scanpy.readthedocs.io/
• Squidpy: https://squidpy.readthedocs.io/

Risk Assessment#

Risk IndicatorAssessmentLevelCode ExecutionPython/R scripts executed locallyMediumNetwork AccessNo external API callsLowFile System AccessRead input files, write output filesMediumInstruction TamperingStandard prompt guidelinesLowData ExposureOutput files saved to workspaceLow

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#

text

# 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

Output Requirements#

Every final response should make these items explicit when they are relevant:

• Objective or requested deliverable
• Inputs used and assumptions introduced
• Workflow or decision path
• Core result, recommendation, or artifact
• Constraints, risks, caveats, or validation needs
• Unresolved items and next-step checks

Error Handling#

• If required inputs are missing, state exactly which fields are missing and request only the minimum additional information.
• If the task goes outside the documented scope, stop instead of guessing or silently widening the assignment.
• If scripts/main.py fails, report the failure point, summarize what still can be completed safely, and provide a manual fallback.
• Do not fabricate files, citations, data, search results, or execution outcomes.

Input Validation#

This skill accepts requests that match the documented purpose of spatial-transcriptomics-mapper and include enough context to complete the workflow safely.

Do not continue the workflow when the request is out of scope, missing a critical input, or would require unsupported assumptions. Instead respond:

spatial-transcriptomics-mapper only handles its documented workflow. Please provide the missing required inputs or switch to a more suitable skill.

Response Template#

Use the following fixed structure for non-trivial requests:

1. Objective
2. Inputs Received
3. Assumptions
4. Workflow
5. Deliverable
6. Risks and Limits
7. Next Checks

If the request is simple, you may compress the structure, but still keep assumptions and limits explicit when they affect correctness.