bio-imaging-mass-cytometry-phenotyping

name	bio-imaging-mass-cytometry-phenotyping
description	Assign cell types from marker expression in IMC/MIBI data using clustering (PhenoGraph/FlowSOM/Leiden/Pixie), marker-based probabilistic classifiers (Astir), or image-context CNNs (CellSighter), covering the double-positive segmentation artifact, lineage-vs-state markers, the two spillover types, and why a "cell type" in imaging is conditioned on a segmentation guess. Use when phenotyping segmented IMC cells, choosing clustering vs classification, diagnosing implausible double-positive populations, separating lineage from functional markers, or transferring labels across a cohort.
origin	openai4s
category	bioskills/imaging-mass-cytometry
metadata	{"tool_type":"python","primary_tool":"scanpy","third_party":{"name":"GPTomics/bioSkills","repository":"https://github.com/GPTomics/bioSkills","commit":"d91ed3d563019e649dc854c56ccd62551359488a","license":"MIT"}}

Version Compatibility

Reference examples tested with: scanpy 1.10+, anndata 0.10+, astir 0.1.4+, numpy 1.26+, scikit-learn 1.4+, FlowSOM 2.10+ (R)

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

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

Notes specific to this skill: arcsinh cofactor for IMC single-cell means is ~1, not the suspension-CyTOF 5 -- do not hard-code 5. Astir assigns a per-cell probability and routes below-threshold cells (default 0.7) to "Unknown" rather than forcing a call. CellSighter consumes raw multi-channel image crops + masks (not a mean matrix). FlowSOM consensus metaclustering can override set.seed() via ConsensusClusterPlus.

Cell Phenotyping for IMC

"Assign cell types to my segmented IMC cells" -> Map each cell's marker profile to an identity, while distinguishing real co-expression from segmentation/spillover artifacts.

• Python: scanpy.tl.leiden (cluster then annotate), astir (marker-dictionary classifier)
• R: FlowSOM (self-organizing-map clustering)

The Single Most Important Modern Insight -- a "cell type" in imaging is an inference conditioned on a segmentation guess

In suspension CyTOF each event is one physically isolated cell; in imaging, every cell-by-marker row is the integral of pixels inside a polygon a segmentation algorithm drew, and that polygon is wrong at a non-trivial fraction of cells -- so the most dangerous phenotypes are not biology but boundary artifacts. The canonical case is the CD3+CD20+ ("T/B") double-positive, also CD3+CD68+ and panCK+CD45+. It arises by two distinct mechanisms that are indistinguishable in the mean matrix: segmentation merging (one polygon spans a T cell and a B cell) and lateral spillover (a neighbor's membrane bleeds across the boundary even with perfect masks). The diagnostic tell that separates artifact from biology is spatial: artifactual double-positives localize to cell BORDERS and to high-density regions, so a suspect population must be mapped back onto the image before it is believed (CellSighter authors state the matrix cannot separate the two). The asymmetry that drives method choice: clustering CREATES the artifact as a named population, while a marker-dictionary classifier (Astir) REFUSES it -- a true double-positive vector matches no defined type and is quarantined as "Unknown" rather than crowned a new lineage. This is why imaging-aware groups increasingly prefer (semi-)supervised phenotyping for the lineage layer, and why mean-expression clustering imported wholesale from CyTOF inherits none of the spatial information that would let it notice the polygon was wrong.

Phenotyping Approach Taxonomy

Approach	Tools	Input	Robust to bad segmentation?	Failure signature
Unsupervised clustering	PhenoGraph, FlowSOM, Leiden	cell x marker mean matrix	No -- averages spilled signal into a fake type	phantom double-positive clusters; resolution-dependent type count
Pixel-then-cell clustering	Pixie (ark-analysis)	pixel x marker, then cell	More -- avoids committing to a segmentation mean early	parameter-sensitive; still unsupervised
Marker-based probabilistic	Astir	mean matrix + marker->type YAML	Partially -- ambiguous cells -> "Unknown"	high Unknown rate if dictionary/markers wrong
Image-context CNN	CellSighter, MAPS	raw image crops + masks + labels	Yes -- sees where the signal sits	needs representative labels not harvested from clustering
Segmentation-aware mixture	STARLING	mean matrix + doublet prior	Yes -- models a cell as a mixture of two	newer; verify priors

Decision Tree by Scenario

Scenario	Recommended	Why
Can write marker->celltype rules, no training labels	Astir (lineage layer)	deterministic, fast, "Unknown" for ambiguous, separates type from state
Have expert-labeled cells, segmentation/spillover is a known problem	CellSighter	image context rejects border/spillover double-positives
Severe segmentation doubt	STARLING	explicitly models doublet/contamination mixtures
Annotated reference cohort, want label transfer	STELLAR	graph model using neighborhood + expression
Exploratory, no priors, accept manual annotation	Pixie (most robust) or Leiden/FlowSOM (least)	always run the double-positive image-diagnostic first
Any across-condition comparison of the resulting types	hand off to differential-analysis	phenotyping and statistical-unit choice are orthogonal

Load and Transform

Goal: Build the single-cell matrix on the correct count scale.

Approach: Arcsinh with cofactor ~1 for IMC means (not 5), and keep raw counts available. Treat zeros as genuine low ion counts plus Poisson noise, not technical dropout -- scRNA-style imputation hallucinates expression.

import scanpy as sc
import anndata as ad
import numpy as np

adata = ad.read_h5ad('imc_segmented.h5ad')
adata.layers['counts'] = adata.X.copy()
adata.X = np.arcsinh(adata.X / 1.0)   # cofactor ~1 for IMC single-cell means, not 5

Marker-Based Classification with Astir

Goal: Assign lineage with a principled abstention instead of a forced call.

Approach: Encode marker->celltype rules in a YAML with separate cell_type and cell_state blocks; Astir returns a per-cell probability and labels below-threshold cells "Unknown". The Unknown rate is itself QC -- 40% Unknown means the dictionary or panel is mis-specified, not that the cells are exotic.

from astir.data import from_anndata_yaml

# inputs are PATHS: an .h5ad and a marker YAML with a cell_type block (CD3->T, CD20->B,
# CD68->Macrophage; no type is both) and an optional cell_state block (Ki67, PD-1)
ast = from_anndata_yaml('imc_segmented.h5ad', 'markers.yaml')
ast.fit_type()
celltypes = ast.get_celltypes(threshold=0.7)   # per-cell labels; < 0.7 -> 'Unknown' (information, not failure)

Cluster on Lineage Markers Only

Goal: Discover structure without splitting one type into activation states.

Approach: Cluster on lineage markers only; mixing continuous state markers (Ki67, PD-1) fragments one type into proliferating/resting pseudo-types. Validating clusters with the same markers used to cluster is circular -- confirm with held-out evidence (spatial context, independent markers).

lineage = ['CD45', 'CD3', 'CD8', 'CD4', 'CD20', 'CD68', 'E-cadherin']   # lineage only, no Ki67/PD-1
sub = adata[:, lineage]
sc.pp.pca(sub, n_comps=min(15, len(lineage)))
sc.pp.neighbors(sub, n_neighbors=15)
sc.tl.leiden(sub, resolution=0.5)
adata.obs['leiden'] = sub.obs['leiden']
# report cluster stability across resolutions/seeds rather than one hand-picked setting

Diagnose Double-Positive Populations

Goal: Decide whether an implausible co-expressing population is biology or artifact.

Approach: A real co-expressing cell has the second marker over its own membrane/cytoplasm; an artifact has it concentrated on the border adjacent to a donor neighbor. Quantify how often the suspect cells sit next to a cell of the donor type -- border + donor-adjacency means spillover/merge, not a lineage.

import squidpy as sq

sq.gr.spatial_neighbors(adata, coord_type='generic', delaunay=True)
suspect = adata.obs['cell_type'] == 'CD3+CD20+?'
# if suspect cells are overwhelmingly adjacent to true B cells (the CD20 donor), the CD20
# is spillover/merge, not endogenous -- treat the population as a QC failure, not a discovery

Per-Method Failure Modes

Clustering -- arbitrary resolution invents types

Trigger: tuning Leiden resolution / FlowSOM metacluster count until clusters match expectation. Mechanism: the resolution directly sets the type count; it is an identifiability hole, not a tuning knob. Symptom: unreproducible type counts; clusters drift across samples. Fix: fix the type set with a dictionary/classifier, or report stability across resolutions and seeds.

FlowSOM -- seed override

Trigger: set.seed() then consensus metaclustering, expecting reproducibility. Mechanism: ConsensusClusterPlus resets the seed internally. Symptom: cluster identities differ between runs. Fix: set the seed inside the consensus call; assess label stability across runs.

"I compensated, so no double-positives"

Trigger: running CATALYST channel compensation and assuming spatial spillover is handled. Mechanism: channel/isotope spillover and lateral/optical spillover are different physical problems. Symptom: double-positives persist after channel compensation. Fix: channel compensation early (pixel level), REDSEA boundary compensation after segmentation; neither fixes a merged segment -- improve segmentation first.

Imputing IMC zeros

Trigger: scRNA-style dropout imputation on the count matrix. Mechanism: IMC zeros are largely genuine low counts, not a capture-dropout mechanism. Symptom: hallucinated expression, inflated positivity. Fix: model low counts as low counts; do not impute.

Quantitative Thresholds

Threshold	Source	Rationale
arcsinh cofactor ~1 (IMC means)	Hunter 2024 Cytometry A 105:36	preserves positive/negative separation; 5 over-compresses
Astir assignment threshold 0.7 (package default)	Geuenich 2021 Cell Syst 12:1173	principled abstention; the Unknown rate is a QC metric
~40 markers, no redundancy	panel design	one channel can decide a fate -- verify the load-bearing channel per type
CellSighter labels NOT from clustering	Amitay 2023 Nat Commun 14:4302	clustering-derived labels re-import the double-positive artifact

Common Errors

Error / symptom	Cause	Solution
Tidy CD3+CD20+ cluster reported as a lineage	clustering legitimized a segmentation/spillover artifact	diagnose border-localization on the image; treat as QC failure
One T-cell type split into two clusters	state markers (Ki67) mixed into lineage clustering	cluster lineage on lineage markers; profile state within type
~40% of cells "Unknown" in Astir	mis-specified dictionary or missing type	inspect Unknown cells; iterate the YAML; tune 0.7 consciously
Cluster identities drift between analyses	stochastic clustering / FlowSOM seed	pin seeds, assess stability; do not assume "cluster 7" is stable
"Disease has more Tregs" with p~0	cell-level testing (pseudoreplication)	aggregate to per-patient proportions; see differential-analysis

References

• Levine JH, Simonds EF, Bendall SC, et al. 2015. Data-Driven Phenotypic Dissection of AML Reveals Progenitor-like Cells that Correlate with Prognosis. Cell 162(1):184-197. — PhenoGraph.
• Van Gassen S, Callebaut B, Van Helden MJ, et al. 2015. FlowSOM: Using self-organizing maps for visualization and interpretation of cytometry data. Cytometry A 87(7):636-645. — FlowSOM.
• Traag VA, Waltman L, van Eck NJ. 2019. From Louvain to Leiden: guaranteeing well-connected communities. Sci Rep 9(1):5233. — Leiden.
• Geuenich MJ, Hou J, Lee S, et al. 2021. Automated assignment of cell identity from single-cell multiplexed imaging and proteomic data. Cell Syst 12(12):1173-1186.e5. — Astir.
• Amitay Y, Bussi Y, Feinstein B, Bagon S, Milo I, Keren L. 2023. CellSighter: a neural network to classify cells in highly multiplexed images. Nat Commun 14:4302. — image-context classification.
• Liu CC, Greenwald NF, et al. 2023. Robust phenotyping of highly multiplexed tissue imaging data using pixel-level clustering. Nat Commun 14:4618. — Pixie.
• Brbic M, Cao K, Hickey JW, et al. 2022. Annotation of spatially resolved single-cell data with STELLAR. Nat Methods 19(11):1411-1418. — label transfer.
• Campbell KR, et al. 2025. Segmentation aware probabilistic phenotyping of single-cell spatial protein expression data. Nat Commun 16:389. — STARLING.
• Bai Y, Zhu B, Rovira-Clave X, et al. 2021. Adjacent Cell Marker Lateral Spillover Compensation and Reinforcement for Multiplexed Images. Front Immunol 12:652631. — REDSEA.
• Chevrier S, Crowell HL, Zanotelli VRT, et al. 2018. Compensation of Signal Spillover in Suspension and Imaging Mass Cytometry. Cell Syst 6(5):612-620.e5. — channel spillover.
• Hunter B, Nicorescu I, Foster E, et al. 2024. OPTIMAL: An OPTimized Imaging Mass cytometry AnaLysis framework for benchmarking segmentation and data exploration. Cytometry A 105(1):36-53. — arcsinh cofactor 1 for IMC single-cell means.

Related Skills

• cell-segmentation - double-positives diagnose the segmentation/spillover that phenotyping inherits
• data-preprocessing - arcsinh cofactor and channel spillover compensation
• spatial-analysis - phenotype labels feed neighborhood and niche analysis
• differential-analysis - comparing cell-type proportions across conditions at the patient level
• interactive-annotation - mapping clusters back onto tissue to confirm they are real
• flow-cytometry/clustering-phenotyping - FlowSOM/PhenoGraph background for suspension data