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.
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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.
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 via ConsensusClusterPlus.
set.seed()
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
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.
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
