Imaging mass cytometry

Imaging Mass Cytometry

Version Compatibility

Reference examples assume recent stable releases of the preferred tools, especially image 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 multiplexed imaging or IMC segmentation, phenotyping, and spatial summarization.

When To Use This Skill

• use when the task is imaging mass cytometry or related multiplexed tissue imaging
• use when segmentation, cell phenotyping, and spatial summaries are needed
• use when the deliverable includes cell-level features plus tissue-level maps

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

• marker images
• panel metadata
• segmentation masks or raw images

Expected Outputs

• cell-level feature tables
• phenotype assignments
• spatial plots

Preferred Tools

• image analysis utilities
• pandas
• numpy
• matplotlib

Starter Pattern

Preferred starting point: image
Inputs: marker images, panel metadata, segmentation masks or raw images
Outputs: cell-level feature tables, phenotype assignments, spatial plots

Workflow

1. Validate panel and images

Confirm marker-channel mapping, image integrity, and segmentation assets.

2. Segment and quantify cells

Produce cell-level intensities and morphological features.

3. Phenotype cells

Assign cell states using marker panels and thresholding or clustering logic.

4. Summarize spatial organization

Compute neighborhood or region-level patterns when the question requires them.

5. Export image-linked outputs

Save cell tables, masks, and visualization overlays.

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:
• cell-level feature tables
• phenotype assignments
• spatial plots

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.
• Check missingness, batch effects, and identification or annotation confidence before differential interpretation.
• Keep feature-level and summarized entity-level outputs distinct.

Anti-Patterns

• using poorly validated segmentation as if it were exact
• hiding threshold assumptions in phenotype calls
• reporting only heatmaps without spatial context

Related Skills

• Proteomics
• Metabolomics
• Structural Biology

Optional Supplements

• None required for the first pass.