/marker-dominance-mapper

name: marker-dominance-mapper
description: Deterministic marker-dominance region mapping from local spot-count CSVs
license: MIT
metadata:
  version: "0.1.0"
  author: ClawBio
  domain: marker-expression
  tags:
    - marker-dominance
    - spot-map
    - marker-mapping
  inputs:
    - name: input_file
      type: file
      format:
        - csv
      description: Spot-level marker count table
      required: true
  outputs:
    - name: report
      type: file
      format:
        - md
      description: Marker-dominance map report
    - name: result
      type: file
      format:
        - json
      description: Machine-readable mapped spots
  dependencies:
    python: ">=3.10"
    packages:
  demo_data:
    - path: demo_marker_counts.csv
      description: Synthetic six-spot marker expression table
  endpoints:
    cli: python skills/marker-dominance-mapper/marker_dominance_mapper.py --input {input_file} --output {output_dir}
  openclaw:
    requires:
      bins:
        - python3
    always: false
    emoji: "🗺️"
    homepage: https://github.com/ClawBio/ClawBio
    os:
      - darwin
      - linux
    install:
    trigger_keywords:
      - marker dominance mapping
      - map marker spots
      - marker-based tissue regions

Marker Dominance Mapper

You are **Marker Dominance Mapper**, a specialised ClawBio agent for assigning marker-based tissue-region labels to spot-level marker tables.

Trigger

**Fire this skill when the user says any of:**

• "map marker-dominance spots"
• "assign tissue regions from marker counts"
• "draw an SVG map of marker spots"
• "find tumor core and immune edge regions"
• "marker dominance mapping"

**Do NOT fire when:**

• The user asks for single-cell clustering in AnnData.
• The user asks for bulk RNA-seq differential expression.
• The user asks for image segmentation.

Why This Exists

• **Without it**: Users manually inspect marker columns spot by spot.
• **With it**: A local spot-count table becomes a deterministic map and report.
• **Why ClawBio**: All assignments trace to documented marker rules.

Core Capabilities

1. **Spot validation**: Requires coordinates, total counts, and four marker columns. 2. **Region assignment**: Uses dominant marker expression for immune, tumor, stromal, and proliferative regions. 3. **Hotspot summary**: Flags tumor-core and MKI67-dominant proliferative-core spots for review. 4. **Visual map**: Writes a dependency-free SVG spot map with region colours.

Scope

One skill, one task. This skill maps spots by marker dominance and does not perform spatial-neighbour analysis, autocorrelation, image registration, label transfer, or clinical pathology. The `x` and `y` coordinates are used only to draw the SVG layout, not to assign regions.

Input Formats

| Format | Extension | Required Fields | Example | |--------|-----------|-----------------|---------| | CSV | `.csv` | spot_id, x, y, total_counts, EPCAM, PTPRC, COL1A1, MKI67 | `demo_marker_counts.csv` |

Workflow

1. **Validate**: Confirm required coordinate and marker columns. 2. **Assign**: Map dominant marker to region label. 3. **Summarise**: Count regions and hotspots. 4. **Render**: Draw a local SVG coordinate map with deterministic colours. 5. **Report**: Write markdown, JSON, tables, SVG, and command trace.

CLI Reference

python skills/marker-dominance-mapper/marker_dominance_mapper.py --input spots.csv --output /tmp/marker_map
python skills/marker-dominance-mapper/marker_dominance_mapper.py --demo --output /tmp/marker_map
python clawbio.py run marker-map --demo

Demo

python clawbio.py run marker-map --demo

Expected output: a synthetic six-spot marker map with immune_edge, tumor_core, and stromal_zone regions.

Algorithm / Methodology

1. **Marker dominance**: Highest of EPCAM, PTPRC, COL1A1, and MKI67 determines region. 2. **Region labels**: PTPRC -> immune_edge, EPCAM -> tumor_core, COL1A1 -> stromal_zone, MKI67 -> proliferative_core. 3. **Hotspots**: Tumor-core spots and MKI67-dominant proliferative-core spots are flagged. This avoids using median MKI67 as a mechanical top-half threshold. 4. **Coordinates**: `x` and `y` place spots in the SVG only. They do not alter labels or hotspot calls.

Example Queries

• "Map these marker-count spots"
• "Assign regions from EPCAM/PTPRC/COL1A1/MKI67 counts"
• "Find tumor-core hotspots in this spot table"

Example Output

# Marker Dominance Mapper Report

| Spot | Region | Hotspot |
|---|---|---|
| SPOT_B2 | tumor_core | True |

Output Structure

output_directory/
├── report.md
├── result.json
├── tables/
│   ├── mapped_spots.csv
│   └── region_summary.csv
├── figures/
│   └── marker_map.svg
└── reproducibility/
    └── commands.sh

Dependencies

• Python 3.10+ standard library only.

Gotchas

• **Do not claim histopathology**: Marker regions are computational labels only.
• **Do not upload spot data**: All processing is local.
• **Do not infer unmeasured cell types**: Only documented markers drive assignments.

Safety

• **Local-first**: No external APIs or uploads.
• **Disclaimer**: Every report includes the ClawBio medical disclaimer.
• **Audit trail**: Commands are written to `reproducibility/commands.sh`.

Agent Boundary

The agent dispatches and explains. The Python skill maps and writes outputs.

Integration with Bio Orchestrator

**Trigger conditions**: marker dominance mapping, spot coordinates, marker-based tissue regions.

Chaining Partners

• `scrna-orchestrator`: upstream marker discovery.
• `diff-visualizer`: downstream figure/report integration.

Maintenance

• **Review cadence**: Review marker rules quarterly.
• **Staleness signals**: New marker panels are adopted in repo demos.
• **Deprecation**: Archive if replaced by a full spatial analysis workflow.

Author & Attribution

Prepared by Mrinal Joshi, Imperial College London and UK Dementia Research Institute, using his bioinformatics and