bio-workflows-imc-pipeline

name	bio-workflows-imc-pipeline
description	Orchestrates imaging mass cytometry from raw MCD acquisitions to patient-level spatial analysis, chaining steinbock preprocessing, Mesmer/Cellpose segmentation, single-cell quantification, phenotyping, and squidpy spatial statistics. Use when committing the panel + segmentation frame + pixel size (every per-cell number is a mask-bounded pixel average), compensating channel spillover on PIXELS before segmentation but running REDSEA lateral-spillover on the per-cell table AFTER segmentation, using arcsinh cofactor 1 (not the suspension-CyTOF 5), and aggregating to the PATIENT before any cross-condition test (cells and ROIs from one patient are not independent replicates). Hands mechanism to the imaging-mass-cytometry component skills; not a re-teach of any single step.
tool_type	python
primary_tool	steinbock
goal_approach_exempt	true
workflow	true
depends_on	["imaging-mass-cytometry/data-preprocessing","imaging-mass-cytometry/cell-segmentation","imaging-mass-cytometry/phenotyping","imaging-mass-cytometry/spatial-analysis","imaging-mass-cytometry/differential-analysis","imaging-mass-cytometry/interactive-annotation","imaging-mass-cytometry/quality-metrics"]

Version Compatibility

Reference examples tested with: Cellpose 4.0+ (cpsam model), anndata 0.10+, matplotlib 3.8+, numpy 1.26+, pandas 2.2+, scanpy 1.10+, scvi-tools 1.1+, squidpy 1.3+, steinbock 0.16+

Before using code patterns, verify installed versions match. If versions differ:

• Python: pip show <package> then help(module.function) to check signatures
• R: packageVersion('<pkg>') then ?function_name to verify parameters
• CLI: <tool> --version then <tool> --help to confirm flags

If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.

Imaging Mass Cytometry Pipeline

"Process my imaging mass cytometry data from images to spatial analysis" -> Orchestrate image preprocessing (steinbock), cell segmentation (Cellpose), phenotyping (FlowSOM/scanpy), spatial neighborhood analysis (squidpy), and tissue community detection.

This is a workflow skill: it owns the chaining decisions and hand-offs, not the internals of any one step.

The governing principle

Segmentation is the largest irreversible error source, and it is spatial: every per-cell number is a mask-bounded pixel average, so a wrong boundary fabricates cell types before any expression QC can see them. The seam ORDER — and the patient-level unit — is therefore what decides trustworthiness.

1. The comparison frame (panel + segmentation frame + pixel size) is committed once and inherited by every per-cell number. The summed membrane channel encodes a cell-type bias (sum BROADLY-expressed markers, or segmentation under-performs on cell types lacking a strong membrane marker); Mesmer was trained at model_mpp ~0.5 um and rescales the input to it, so passing the wrong pixel size (Mesmer's image_mpp defaults to None = NO rescaling, assuming the input is already at model resolution — the true pixel size must be passed explicitly; steinbock's --pixelsize flag wraps image_mpp and defaults to 1.0) rescales cells to the wrong learned size and degrades every boundary. No downstream step recovers a merged or split cell.
2. Channel spillover is compensated on PIXELS before segmentation; lateral spillover (REDSEA) runs on the per-cell table AFTER segmentation — they are DIFFERENT problems. Metal-isotope crosstalk is a pixel-level NNLS correction whose compensated value must be what gets averaged into the per-cell mean (post-aggregation is wrong). REDSEA corrects real signal leaking across shared cell boundaries at ~1 um even with perfect segmentation and zero channel spillover — it is defined on segmented neighbors, so it must run after segmentation. Running REDSEA pre-segmentation, or channel comp post-aggregation, is a category error.