bio-spatial-transcriptomics-spatial-proteomics

name	bio-spatial-transcriptomics-spatial-proteomics
description	Analyzes multiplexed antibody-imaging data (CODEX/PhenoCycler, MIBI-TOF, IMC, CyCIF, Opal/Vectra mIF) as continuous protein intensity rather than transcript counts, using scimap and squidpy. Use when choosing an intensity transform/normalization (arcsinh cofactor vs z-score vs percentile -- NOT log1p-of-counts) and correcting channel spillover and antibody-batch effects; deciding whether to phenotype by gating or by clustering on intensities; recognizing that a bounded antibody panel makes marker absence uninformative; treating whole-cell segmentation (Mesmer) as the dominant error source; and knowing which platform applies and when to defer to the imaging-mass-cytometry skills for the IMC pipeline.
origin	openai4s
category	bioskills/spatial-transcriptomics
metadata	{"tool_type":"python","primary_tool":"scimap","third_party":{"name":"GPTomics/bioSkills","repository":"https://github.com/GPTomics/bioSkills","commit":"d91ed3d563019e649dc854c56ccd62551359488a","license":"MIT"}}

Version Compatibility

Reference examples tested with: scimap 2.0+, scanpy 1.10+, anndata 0.10+, squidpy 1.4+

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

• Python: pip show <package> then help(module.function) to check signatures

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

Spatial Proteomics Analysis

"Analyze my CODEX/MIBI/IMC multiplexed-imaging data" -> Turn a cell-by-marker protein-intensity matrix into phenotyped cells and spatial neighborhoods, while treating intensity as a continuous, confounded signal.

• Python: per-marker arcsinh/z-score transform -> spillover/batch correction -> scimap.tl.phenotype_cells() (gating) or scimap.tl.cluster() (clustering) -> squidpy.gr.nhood_enrichment()

Governing Principle

Protein intensity is continuous with antibody, batch, and staining confounds -- it is not a molecule count, and treating it like one is a category error that propagates through every downstream result.

A multiplexed-imaging measurement is the reporter signal (fluorescence photons or secondary-ion/metal counts) for an antibody bound to its epitope. That signal scales with antibody affinity, conjugation efficiency, staining-day conditions, fixation, and detector response -- none of which are the abundance of the protein, and all of which differ between markers and between samples. Consequences that separate this from RNA-based spatial omics: there is no Poisson/NB count model, so the variance-stabilizing transform is arcsinh (or z-score/percentile), NEVER log1p applied as if the values were UMIs; metal/fluor channels leak into each other (spillover) and must be compensated; and antibody-batch and staining variation must be normalized before any cross-sample comparison. Hickey 2021 (Front Immunol 12:727626) made the cost concrete: crossing 5 normalizations x 4 clustering methods on ONE CODEX dataset produced 20 different cell-type annotations -- the normalization-and-clustering choice, not the biology, dominated the phenotype calls.

The antibody panel is targeted, so absence is uninformative. A 20-100 marker panel is chosen a priori; the phenotype space is bounded by it exactly as an imaging RNA panel (Xenium/MERFISH/CosMx) bounds detectable transcripts. A cell type whose defining markers are off-panel is invisible or silently mis-assigned to the nearest panel-defined type -- there is no de-novo discovery. "Marker X is absent" usually means "X was not stained," not "the protein is not there."

Segmentation is the dominant downstream error source -- the per-cell intensity vector is only as good as the cell mask. There is no native cell in an image; a cell-by-marker matrix exists only after an algorithm draws boundaries. Lateral spillover of membrane/cytoplasmic signal into neighboring masks fabricates phantom double-positive cells (a CD3+CD20+ "cell" is usually a T cell touching a B cell), and every neighborhood, niche, and proximity result inherits that error. Whole-cell segmentation on a membrane/boundary stain (Mesmer/DeepCell, trained on the ~1M-cell TissueNet, is the multiplexed-imaging standard) is the highest-leverage decision; nucleus-only loses cytoplasmic signal and nucleus-expansion assumes round equal cells. The IMC/MIBI pipeline mechanics live in the imaging-mass-cytometry category -- this skill owns the platform breadth and the intensity reframe; defer the deep pipeline there.

The Platform-Breadth Decision

This skill owns BREADTH across antibody-based platforms; IMC pipeline DEPTH lives in imaging-mass-cytometry. The first question is which platform produced the data, because chemistry sets the confounds.

Platform	Chemistry	Markers	Strengths	Dominant confounds
CODEX / PhenoCycler (Goltsev 2018)	DNA-barcoded antibodies, iterative fluorescent reporter cycles	~50-60+	High-plex on a standard fluorescence microscope; sub-um optical	Cycle-to-cycle registration drift, photobleaching/tissue degradation over many cycles; continuous intensity
MIBI-TOF (Angelo 2014; Keren 2019)	Lanthanide-metal antibodies, ion beam + TOF mass spec	~40 metal channels	High resolution (~260-500 nm); low autofluorescence	Slow, small FOV; semi-quantitative (secondary-ion yield, detector); isotopic/channel crosstalk (spillover)
IMC (Giesen 2014)	Metal-isotope antibodies, UV laser ablation + CyTOF	~40 metal channels	Metal multiplexing, no autofluorescence	~1 um, slow ablation; metal-channel spillover (Chevrier 2018); conjugation-efficiency bias. Pipeline -> imaging-mass-cytometry
CyCIF / t-CyCIF (Lin 2018)	Cyclic IF: stain ~4 dyes, image, bleach, restain	up to ~60	Conventional optical microscope, accessible	Bleach/restain degrades antigenicity; registration drift; autofluorescence
Opal / Vectra mIF (Parra 2017)	Tyramide-amplified multispectral IF	~6-8	Clinical-grade, FFPE-validated	Low plex; spectral unmixing artifacts; amplification nonlinearity

CODEX/CyCIF/Opal yield continuous FLUORESCENCE intensity; MIBI/IMC yield semi-quantitative METAL counts (still not transcript counts -- they carry detector and spillover effects, not Poisson sampling). All five share the targeted-panel and segmentation traps above. When the data is specifically IMC or MIBI and the question is the end-to-end processing workflow (spillover compensation, segmentation execution, FlowSOM phenotyping on metal channels), defer to imaging-mass-cytometry rather than reimplementing it here.

Transform and Normalize Intensities

Goal: Put marker intensities on a comparable, variance-stabilized scale and remove antibody-batch and staining confounds before phenotyping -- without imposing a count model.

Approach: Apply arcsinh with a per-dataset-tuned cofactor (or z-score/percentile), then correct channel spillover and batch; choose the transform deliberately, because this choice dominates the cell-type calls.

Transform	Form	Best when	Fails / caveat
arcsinh (cofactor)	arcsinh(x / cofactor)	Mass-cytometry-like intensities (CyTOF/IMC/MIBI); compresses high values, near-linear near zero	Cofactor ~5 is a CyTOF CONVENTION (Bendall 2011), NOT auto-optimal for imaging -- too small over-expands near-zero noise into spurious populations; tune and sanity-check per dataset
z-score (per marker)	(x - mean) / sd	Cross-marker comparability for clustering	Sensitive to outliers; assumes roughly symmetric post-transform spread
percentile / min-max (per marker)	clip to e.g. 1st-99th pct, scale 0-1	Gating-style cutoffs; robust to extreme bright pixels	Throws away absolute scale; per-image rescaling can erase real cross-sample differences
log1p-of-counts	log(1 + x) with NB/Poisson tooling	RNA UMI counts	WRONG for intensity -- there is no count process; imposes a model the data does not follow

scimap's pp.rescale fits a per-marker two/three-component Gaussian mixture to set the 0-1 gating scale (an intensity-aware step, distinct from log1p-as-counts); for clustering, an explicit arcsinh or z-score on adata.X is the transparent choice.

import numpy as np
import scimap as sm

# Tune the cofactor: start at 5 (CyTOF convention) but verify the near-zero
# population is not split into a phantom 'positive' cluster for each marker.
cofactor = 5
adata.layers['intensity'] = adata.X.copy()              # stash raw intensities
adata.X = np.arcsinh(adata.X / cofactor)                # variance-stabilize; NOT log1p-of-counts

# Antibody/staining-batch correction across images or staining days.
# Intensity differences between batches masquerade as biology -- correct before merging.
sm.pp.combat(adata, batch_key='imageid')                # batch_key names the confound column in .obs

Channel spillover (isotopic impurity and oxide/abundance-sensitivity crosstalk for metals; spectral bleed for fluorophores) creates false double-positive cells. Estimate a spillover matrix from single-stain bead controls and correct by non-negative least squares (Chevrier 2018, implemented in CATALYST/spillR). For IMC/MIBI specifically, run compensation through the imaging-mass-cytometry/data-preprocessing skill rather than reimplementing the matrix here.

Phenotype Cells: Gating vs Clustering

Goal: Assign each cell a cell-type label from its marker-intensity vector.

Approach: Choose GATING (flow-cytometry-style positive/negative thresholds encoded as a marker workflow) when the panel has canonical lineage markers and the types are known a priori, or CLUSTERING (Leiden/PhenoGraph/FlowSOM on transformed intensities) for unsupervised discovery within the bounded panel; gating and clustering can give materially different calls, and cluster boundaries shift with the transform, cofactor, k, and segmentation spillover.

scimap gating expects a phenotype-workflow DataFrame, not a dict: first column = group, second = cell-type name, remaining columns = marker names holding pos/neg/allpos/allneg/anypos/anyneg.

import pandas as pd
import scimap as sm

# Build the gating workflow (or load a CSV). 'allpos' = all listed markers must clear the gate.
workflow = pd.DataFrame([
    ['lineage', 'T_cell',     'allpos', 'allpos', 'neg',    'neg'],
    ['lineage', 'B_cell',     'allpos', 'neg',    'allpos', 'neg'],
    ['lineage', 'Macrophage', 'allpos', 'neg',    'neg',    'allpos'],
    ['lineage', 'Tumor',      'neg',    'neg',    'neg',    'neg'],
], columns=['group', 'phenotype', 'CD45', 'CD3', 'CD20', 'CD68'])

sm.pp.rescale(adata, gate=None, method='by_image')       # per-marker GMM sets the 0-1 scale; 'by_image' rescales each image separately
sm.tl.phenotype_cells(adata, phenotype=workflow, gate=0.5, label='phenotype')   # gate=0.5 after rescale

# Unsupervised alternative: cluster the transformed intensities, then annotate clusters by marker means.
sm.tl.cluster(adata, method='leiden', resolution=1.0, label='leiden')
# A 'protein absent' cluster may simply lack the marker on the panel -- annotate against the panel, not the transcriptome.

Phenotyping on a targeted panel cannot discover a type whose markers are off-panel; an unexpected "negative-for-everything" cluster is often an unstained type, not a novel state. Audit phantom double-positives (e.g. CD3+CD20+) as likely segmentation spillover before treating them as biology.

Spatial Neighborhood and Interaction Analysis

Goal: Quantify which phenotypes are spatial neighbors more or less than chance, and summarize recurrent cellular neighborhoods.

Approach: Build a spatial graph on cell centroids, then run a permutation-based neighborhood-enrichment test; for niches, summarize each cell's k-nearest-neighbor window by composition and cluster the windows (Schurch 2020 cellular-neighborhoods logic). Co-occurrence is not communication, and a "neighborhood" inherits every segmentation/normalization error upstream.

import squidpy as sq

sq.gr.spatial_neighbors(adata, coord_type='generic', n_neighs=10)   # imaging cells are a point cloud, not a grid -> 'generic'
sq.gr.nhood_enrichment(adata, cluster_key='phenotype')              # label-permutation null; z-scores in adata.uns
sq.pl.nhood_enrichment(adata, cluster_key='phenotype')

# Recurrent cellular neighborhoods (niches): per-cell composition of the local window, then cluster.
sm.tl.spatial_count(adata, phenotype='phenotype', method='knn', knn=10, label='neighborhood_counts')
sm.tl.cluster(adata, method='kmeans', k=8, use_raw=False, label='neighborhood')   # k is a biological choice; report k +/- 1 sensitivity

The neighbor count k and the upstream phenotype calls both define the result; report the window size and show sensitivity. On a 20-100 marker panel the relevant ligand AND receptor AND cofactors are rarely all present, so multiplexed-imaging "communication" is almost always cell-type PROXIMITY (niche co-occurrence), not measured ligand-receptor co-localization -- a weaker inference than transcriptomic LR, because the LR pair was never measured.

Common Errors

Symptom	Cause	Fix
Spurious "positive" populations near zero; clusters that split noise	Treated intensity as counts with log1p, or used an untuned tiny arcsinh cofactor	Use arcsinh with a per-dataset-tuned cofactor (start ~5, verify near-zero is not over-expanded), or z-score/percentile -- never log1p-of-counts
Phantom CD3+CD20+ (or any lineage-incompatible) double-positive cells	Channel spillover and/or segmentation lateral spillover between adjacent cells	Compensate spillover (NNLS, Chevrier 2018) and audit segmentation; treat double-positives as artifacts until proven
Cell types differ wildly between two runs of the same data	Normalization x clustering choice dominates calls (Hickey 2021: 20 annotations from one CODEX dataset)	Fix and report the transform, cofactor, normalization, and clustering; do not present one pipeline's calls as ground truth
Cross-sample comparison shows a "batch" cell type	Antibody-lot/staining/fixation intensity differences not normalized	Correct batch (combat or per-image rescale) before merging or comparing samples
Concluded a cell type or marker is "absent"	Read panel absence as biological absence on a bounded antibody panel	State that absence on a targeted panel is uninformative; the marker was likely not stained
Neighborhood/interaction result looks strong but is not reproducible	Built on bad segmentation masks; enrichment inherits the mask error	Validate segmentation (membrane-stain whole-cell, Mesmer) before trusting any spatial result
sm.tl.spatial_cluster returns one cluster or nonsense	Ran it before building the neighborhood matrix it reads	Compute sm.tl.spatial_count (or sm.tl.spatial_lda) first, then point spatial_cluster(df_name=...) at that result
phenotype_cells errors or mislabels everything	Passed a dict instead of the workflow DataFrame, or did not rescale first	Pass a group/phenotype/marker DataFrame with pos/neg/allpos codes; run sm.pp.rescale before phenotyping

Related Skills

• image-analysis - whole-cell segmentation upstream of every per-cell intensity vector (the dominant error source)
• imaging-mass-cytometry/cell-segmentation - Mesmer/Cellpose segmentation execution and error propagation for IMC/MIBI
• imaging-mass-cytometry/phenotyping - FlowSOM/Phenograph phenotyping on metal channels and the double-positive artifact
• spatial-transcriptomics/spatial-multiomics - integrating spatial proteomics with matched spatial transcriptomics (ADT/CytAssist)
• spatial-transcriptomics/spatial-statistics - permutation nulls, neighborhood enrichment, and co-occurrence shared with squidpy

References

• Goltsev Y, Samusik N, Kennedy-Darling J, et al. (2018) Deep profiling of mouse splenic architecture with CODEX multiplexed imaging. Cell 174(4):968-981. DOI 10.1016/j.cell.2018.07.010
• Angelo M, Bendall SC, Finck R, et al. (2014) Multiplexed ion beam imaging of human breast tumors. Nature Medicine 20(4):436-442. DOI 10.1038/nm.3488
• Keren L, Bosse M, Thompson S, et al. (2019) MIBI-TOF: a multiplexed imaging platform relates cellular phenotypes and tissue structure. Science Advances 5(10):eaax5851. DOI 10.1126/sciadv.aax5851
• Giesen C, Wang HAO, Schapiro D, et al. (2014) Highly multiplexed imaging of tumor tissues with subcellular resolution by mass cytometry. Nature Methods 11(4):417-422. DOI 10.1038/nmeth.2869
• Lin J-R, Izar B, Wang S, et al. (2018) Highly multiplexed immunofluorescence imaging of human tissues and tumors using t-CyCIF and conventional optical microscopes. eLife 7:e31657. DOI 10.7554/eLife.31657
• Parra ER, Uraoka N, Jiang M, et al. (2017) Validation of multiplex immunofluorescence panels using multispectral microscopy for immune-profiling of FFPE human tumor tissues. Scientific Reports 7(1):13380. DOI 10.1038/s41598-017-13942-8
• Greenwald NF, Miller G, Moen E, et al. (2022) Whole-cell segmentation of tissue images with human-level performance using large-scale data annotation and deep learning (Mesmer/DeepCell). Nature Biotechnology 40(4):555-565. DOI 10.1038/s41587-021-01094-0
• Stringer C, Wang T, Michaelos M, Pachitariu M (2021) Cellpose: a generalist algorithm for cellular segmentation. Nature Methods 18(1):100-106. DOI 10.1038/s41592-020-01018-x
• Chevrier S, Crowell HL, Zanotelli VRT, et al. (2018) Compensation of signal spillover in suspension and imaging mass cytometry. Cell Systems 6(5):612-620. DOI 10.1016/j.cels.2018.02.010
• Bendall SC, Simonds EF, Qiu P, et al. (2011) Single-cell mass cytometry of differential immune and drug responses across a human hematopoietic continuum. Science 332(6030):687-696. DOI 10.1126/science.1198704
• Hickey JW, Tan Y, Nolan GP, Goltsev Y (2021) Strategies for accurate cell type identification in CODEX multiplexed imaging data. Frontiers in Immunology 12:727626. DOI 10.3389/fimmu.2021.727626
• Schurch CM, Bhate SS, Barlow GL, et al. (2020) Coordinated cellular neighborhoods orchestrate antitumoral immunity at the colorectal cancer invasive front. Cell 182(5):1341-1359. DOI 10.1016/j.cell.2020.07.005