Analyze spatial cell-cell interactions, neighborhoods, and niches in IMC/MIBI data with squidpy and imcRtools, covering neighborhood-enrichment permutation nulls, the abundance-vs-density confound, inhomogeneous Ripley's K, cellular-neighborhood discovery, graph-construction (contact vs proximity), and edge effects. Use when testing whether cell types co-locate, choosing a spatial null, building a neighbor graph, discovering tissue niches, or deciding whether a spatial pattern is real or a density/segmentation artifact.
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Analyze spatial cell-cell interactions, neighborhoods, and niches in IMC/MIBI data with squidpy and imcRtools, covering neighborhood-enrichment permutation nulls, the abundance-vs-density confound, inhomogeneous Ripley's K, cellular-neighborhood discovery, graph-construction (contact vs proximity), and edge effects. Use when testing whether cell types co-locate, choosing a spatial null, building a neighbor graph, discovering tissue niches, or deciding whether a spatial pattern is real or a density/segmentation artifact.
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: squidpy's nhood_enrichment z-score is unbounded and scales with graph degree, so it is NOT comparable across images of different cell counts. squidpy does not implement Ripley's K (its ripley and are loose analogs); the inhomogeneous K that conditions on tissue density lives in R /. Build the graph with before any test.
co_occurrence
spatstat::Kinhom
Kcross.inhom
gr.spatial_neighbors(coord_type='generic')
Spatial Analysis for IMC
"Analyze spatial cell interactions in my IMC data" -> Build a neighbor graph, then test cell-type co-location against an explicitly chosen null, or discover recurrent niches.
The Single Most Important Modern Insight -- a spatial interaction is a hypothesis test, and the null silently decides what is measured
A "spatial interaction" or "niche" is not an observation; it is a test against a null model, and which null is chosen (label-shuffle vs CSR vs density-conditioned vs patient-level) decides whether the result is real biology, a density gradient, a segmentation artifact, or upstream clustering. The label-permutation null (histoCAT, squidpy nhood_enrichment) holds cell positions fixed and shuffles identities: it DOES control global abundance (a rare type cannot show spurious enrichment just from being rare) but it does NOT control local density or tissue architecture -- so two cell types that merely share an anatomical compartment (both enriched in a follicle or at an invasive margin) score as strongly "interacting" with no direct affinity. This density confound is the dominant false-positive engine, and a finding significant under CSR but null under inhomogeneous Ripley's K is a density artifact, not an interaction. Two further facts compound it: the nhood_enrichment z-score is unbounded and graph-degree-dependent, so a z of 30 in a 50k-cell image and a z of 8 in a 5k-cell image are not on the same scale (never threshold a fixed z across heterogeneous images); and the replicate is the patient, not the cell or the image, so a per-cell test over tens of thousands of cells reports p~0 for trivial effects (pseudoreplication). The skill forces two questions onto every analysis: against which null, and at which unit.
Spatial Methods Taxonomy
Method
Measures
Null / inference
Confound it does NOT control
histoCAT / squidpy nhood_enrichment
A neighbors B more/less than chance
within-image label shuffle; z-score
local density, architecture, edges
squidpy interaction_matrix
raw cluster-cluster edge counts
none (descriptive)
everything -- just counts
squidpy co_occurrence
P(B | A at distance d) / P(B)
none (descriptive curve)
needs its own CSR null
Ripley's K/L, cross-K, K_inhom
clustering vs dispersion over radii
CSR; K_inhom conditions on intensity
tissue inhomogeneity (unless K_inhom); ROI shape
Cellular Neighborhoods (CN)
recurrent local compositions (niches)
none -- clustering, not a test
window size = scale; doubly-derived
Spatial-LDA / UTAG
microenvironment topics / tissue domains
generative / unsupervised
topic/domain count arbitrary
Moran's I / Geary's C
spatial autocorrelation of a continuous mark
permutation / analytic
graph definition; global stat masks local
Decision Tree by Scenario
Question
Method
Why
Do two named types co-locate more than chance? (fast screen, one image)
nhood_enrichment / histoCAT
abundance-aware permutation z
Same, but worried about density/architecture
inhomogeneous cross-K (Kcross.inhom)
conditions on the tissue's own intensity surface
At what scale is a type clustered/dispersed?
Ripley's K/L over radii (edge-corrected)
second-order structure across distance
What recurrent multicellular niches exist? (discovery)
Cellular Neighborhoods (sweep window k)
recurrent compositions; no built-in test
Is a continuous marker/score spatially structured?
Moran's I (global) / Geary's C (local)
autocorrelation
Does an interaction/niche differ between conditions?
hand off to differential-analysis
per-image summary -> patient unit -> mixed model + FDR
Build the Spatial Graph (contact vs proximity)
Goal: Construct the graph whose definition matches the biological claim.
Approach: Delaunay approximates physical adjacency (contact/juxtacrine) but invents long edges across lumen/necrosis, so prune by a max distance; fixed radius gives true proximity (paracrine) at a stated micron scale; kNN silently mixes contact and proximity because fixed k spans microns in dense regions and hundreds of microns in sparse ones. Build the graph per image.
import squidpy as sq
import numpy as np
# contact graph: Delaunay, then prune edges longer than a biological max distance (um)
sq.gr.spatial_neighbors(adata, coord_type='generic', delaunay=True)
dist = adata.obsp['spatial_distances']
keep = dist.copy(); keep.data[keep.data > 30] = 0; keep.eliminate_zeros() # cap at ~30 um
adata.obsp['spatial_connectivities'] = (keep > 0).astype(float)
# OR proximity graph: fixed radius at a justified micron scale (paracrine range)# sq.gr.spatial_neighbors(adata, coord_type='generic', radius=30.0)
Neighborhood Enrichment with a Named Null
Goal: Test co-location per image with the abundance-aware permutation null, knowing its blind spot.
Approach: Run nhood_enrichment per image (so the shuffle is within-image), keep the z as a per-image summary, and never threshold a fixed z across images of different size. Cross-check density-driven hits against inhomogeneous K.
per_image_z = {}
for img_id, idx in adata.obs.groupby('image_id').groups.items():
sub = adata[idx].copy()
sq.gr.spatial_neighbors(sub, coord_type='generic', delaunay=True)
sq.gr.nhood_enrichment(sub, cluster_key='cell_type', seed=0)
per_image_z[img_id] = sub.uns['cell_type_nhood_enrichment']['zscore']
# aggregate these per-image summaries to the PATIENT unit in differential-analysis, not here
Cellular Neighborhoods (niche discovery)
Goal: Find recurrent local cell-type compositions, treating them as exploratory.
Approach: Per cell, summarize the composition of its window of neighbors, then cluster the windows. The window size IS the spatial scale and is almost always unjustified, so sweep it and report that the biological conclusion survives k in {10, 20, 30}. A CN has no built-in significance test; significance enters only as a cross-condition comparison (differential-analysis).
Trigger: reporting a z-score as a "significant interaction." Mechanism: the label-shuffle null absorbs abundance but not local density, so co-compartmentalized types score as interacting. Symptom: every type pair sharing a region looks attracted. Fix: name the null; cross-check with inhomogeneous cross-K; or shuffle within a compartment, not the whole image.
Fixed z threshold across images
Trigger: calling z>2 "significant" across images of different cell counts. Mechanism: the z is unbounded and scales with graph degree. Symptom: large images dominate; small images never reach threshold. Fix: rank within image or convert to an effect size; aggregate per-image summaries to the patient unit.
Unpruned Delaunay / kNN mislabeled as contact
Trigger: kNN graph results called "contact," or Delaunay across tissue gaps. Mechanism: fixed k mixes contact and proximity by density; Delaunay invents long edges across voids. Symptom: phantom interactions across lumen/necrosis. Fix: prune Delaunay by a max distance; use fixed radius for paracrine claims; state the micron scale.
CSR Ripley's K on inhomogeneous tissue
Trigger: homogeneous-Poisson K on structured tissue. Mechanism: CSR assumes constant intensity, so everything tests as clustered. Symptom: universal "clustering". Fix: inhomogeneous K (Kinhom/Kcross.inhom) that divides by the estimated intensity surface.
Niche taken as a discovered fact
Trigger: treating a CN as established biology. Mechanism: it is doubly-derived (cluster cells -> cluster windows) with an arbitrary window and k, and no significance test. Symptom: different labs report different niches on the same tissue. Fix: sweep window k; report stability (NMI/ARI); validate niche-defining markers against raw images for spillover.
Quantitative Thresholds
Threshold
Source
Rationale
CN window = 10 nearest neighbors, 9 CNs retained
Schurch 2020 Cell 182:1341
the canonical CN convention -- sweep it, do not adopt blindly
CODEX i-niches: Delaunay 1st-tier, k-means = 100
Goltsev 2018 Cell 174:968
window/scale is a choice, not a default
n_perm >= 10,000 for small corrected p
Schapiro 2017 Nat Methods 14:873
n_perm=1000 floors p at ~1/1001, too coarse after FDR
BH-FDR across ~C(C+1)/2 type pairs x radii
multiplicity
~200 pairs at p<0.05 guarantees false positives
Prune Delaunay at a biological max distance (e.g. ~30 um)
graph hygiene
removes edges across acellular voids
Common Errors
Error / symptom
Cause
Solution
"Significant interaction" that is two types in one compartment
density confound under label-shuffle
inhomogeneous cross-K; shuffle within compartment
p~0 across 50k cells
cell-level pseudoreplication
per-image summary -> patient unit (differential-analysis)
Niche changes with the window/k
window IS the scale
sweep k_window and n_cn; report stability
Boundary cells dominate a small ROI
ignored edge effects
edge-corrected K; erode/buffer the ROI interior
Many "significant" pairs
no multiple-testing correction
BH-FDR across all pairs and radii
References
Schapiro D, Jackson HW, Raghuraman G, et al. 2017. histoCAT: analysis of cell phenotypes and interactions in multiplex image cytometry data. Nat Methods 14(9):873-876. — neighborhood permutation test.
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.e15. — cellular neighborhoods (i-niches).
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.e19. — canonical CN convention.
Palla G, Spitzer H, Klein M, et al. 2022. Squidpy: a scalable framework for spatial omics analysis. Nat Methods 19(2):171-178. — squidpy spatial functions.
Chen Z, Soifer I, Hilton H, Keren L, Jojic V. 2020. Modeling Multiplexed Images with Spatial-LDA Reveals Novel Tissue Microenvironments. J Comput Biol 27(8):1204-1218. — Spatial-LDA.
Kim J, Rustam S, Mosquera JM, et al. 2022. Unsupervised discovery of tissue architecture in multiplexed imaging. Nat Methods 19(12):1653-1661. — UTAG domains.
Ripley BD. 1977. Modelling Spatial Patterns. J R Stat Soc Series B 39(2):172-212. — the K-function.
Moran PAP. 1950. Notes on Continuous Stochastic Phenomena. Biometrika 37(1/2):17-23. — Moran's I.
Geary RC. 1954. The Contiguity Ratio and Statistical Mapping. Incorporated Statistician 5(3):115-145. — Geary's C.
Related Skills
phenotyping - cell-type labels are the input to every spatial test
differential-analysis - testing whether interactions/niches differ between conditions at the patient level
cell-segmentation - over-segmentation and lateral spillover create fake niches
