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marker-dominance-mapper

Assigns tissue-region labels to spot-level marker count CSVs by dominant marker expression.

1.1k|257|Updated Feb 25, 2026
Search Tags:#bioinformatics#svg#spatial transcriptomics#CSV#region mapping#tissue regions#marker dominance
One-click install
npx skills add https://github.com/ClawBio/ClawBio --skill marker-dominance-mapper
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: marker-dominance-mapper
Source: https://github.com/ClawBio/ClawBio/tree/main/skills/marker-dominance-mapper
Command: npx skills add https://github.com/ClawBio/ClawBio --skill marker-dominance-mapper

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Manually inspecting marker columns spot by spot to assign tissue regions is slow and error-prone. This Skill deterministically maps each spot in a local CSV to a region label (immune_edge, tumor_core, stromal_zone, proliferative_core) based on the dominant marker among EPCAM, PTPRC, COL1A1, and MKI67, then produces a report, tables, and an SVG map.

Core Features & Use Cases

  • Deterministic region assignment: Maps each spot to a region using the highest of four marker counts, with no external APIs or uploads.
  • Hotspot flagging: Flags tumor-core and MKI67-dominant proliferative-core spots for review.
  • Structured outputs: Writes report.md, result.json, mapped_spots.csv, region_summary.csv, an SVG spot map, and a reproducibility command log.
  • Use Case: A researcher with a spot-level marker count table runs the demo or their own CSV to get a labeled region map and summary tables for downstream figure integration.

Quick Start

Run the marker dominance mapper on your spot-count CSV by asking the agent to map marker-dominance spots from your file, or use the built-in demo mode to see a synthetic six-spot region map.

Frequently Asked Questions about marker-dominance-mapper

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I map marker-dominance regions from a spot count CSV?▼

Run the marker dominance mapper script with --input pointing to your CSV and --output for the result directory. The CSV must contain spot_id, x, y, total_counts, EPCAM, PTPRC, COL1A1, and MKI67 columns, and each spot is labeled by its highest marker.

What input format does marker dominance mapping require?▼

A CSV file with the columns spot_id, x, y, total_counts, EPCAM, PTPRC, COL1A1, and MKI67. All columns except spot_id must be numeric, and the file must contain at least one spot row.

Does marker dominance mapping need external Python packages?▼

No, it uses only the Python 3.10+ standard library. There are no third-party package dependencies, and all processing runs locally without network access.

Can this tool do spatial neighbor analysis or clustering?▼

No, it only assigns regions by dominant marker expression. The x and y coordinates are used solely to draw the SVG map, not for spatial-neighbour analysis, autocorrelation, clustering, or label transfer.

Why does the mapper reject my CSV file?▼

The script raises an error if required columns are missing, if any required column contains non-numeric values, or if the file has no spot rows. Check that all eight required columns exist and contain valid numbers.

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