--- name: spatial-phenotyping description: Spatial statistics on a segmented multiplex dataset — a cell-by-marker AnnData with coordinates, from CODEX, MxIF, IMC or MIBI. Phenotyping, neighborhood enrichment, cellular neighborhoods, Ripley's L and co-occurrence, with the per-slide graph construction and per-patient aggregation that keep the statistics honest. category: analysis license: CC-BY-4.0 author: Heureka Labs version: 1.0.0 tags: [spatial, single-cell, multiplex-imaging, h5ad, statistics] datasets: [https://exampledata.scverse.org/squidpy/mibitof.h5ad] allowed-tools: Read, Write, Edit, Bash verified: date: 2026-08-26 against: squidpy 1.8.3 / scanpy 1.12.3 / spac 0.9.3 (git, plus parmap) / anndata 0.13.x / numpy 2.5.2 / pandas 3.x / Python 3.12.8 / squidpy MIBI-TOF example dataset (3309 cells, 36 markers, 3 ROIs, 2 donors) executed: 22 unverified: 0 --- # Spatial statistics on segmented multiplex data This starts where segmentation ends: a cell-by-marker matrix with an `x` and a `y` for every cell. The image is gone; what is left is a point pattern with attributes, and the questions are about arrangement — which cell types sit next to which, whether a type clusters or disperses, whether any of it differs between patients. The statistics are the easy part. Every library below computes neighborhood enrichment correctly. What goes wrong is upstream of the statistic: a graph built across slides that are not adjacent, a permutation null that permutes the wrong thing, and a p-value computed over cells when the samples are patients. All three produce a number, a figure, and a conclusion. Nothing here needs an account or a GPU. ## The object One `AnnData`. Cells in `obs`, markers in `var`, intensities in `X`, coordinates in `obsm["spatial"]` as an `(n_cells, 2)` array, and — this is the part that is usually missing — a column in `obs` naming the **image, ROI or slide** each cell came from, and another naming the **patient or specimen**. ```python import anndata as ad a = ad.read_h5ad("mibitof.h5ad") # or whatever your segmentation step wrote print(a) # AnnData object with n_obs x n_vars = 3309 x 36 print(a.obsm["spatial"].shape) # (3309, 2) print(a.obs[["library_id", "donor"]].value_counts()) ``` Every block on this page runs against that file, which `## Try it` at the end downloads — so you can read the page straight through with a live object in front of you rather than substituting your own as you go. Keep every annotation in `.obs` and every embedding in `.obsm`. That is what makes the object portable to any scverse tool and what makes the file the analysis rather than a step in it. If the coordinates are in pixels, convert them to micrometres once, at the point you write the object, using the pixel size out of the acquisition file. Every distance below — Ripley's radii, co-occurrence intervals, neighbourhood radii — is in the units of `obsm["spatial"]`, and a radius of 50 means something different in every dataset otherwise. ## Build the graph per image. This is the one that ruins results silently `squidpy` builds a neighbour graph from the coordinates. Every ROI in a multi-sample object typically starts its coordinate system at the origin, so a single graph over the whole object connects cells that were never within a millimetre of each other. Measured on the squidpy MIBI-TOF example — three colorectal ROIs, all spanning roughly x 1-1021, y 1-1021: ```python import squidpy as sq wrong = a.copy() sq.gr.spatial_neighbors(wrong, coord_type="generic", delaunay=True) right = a.copy() sq.gr.spatial_neighbors(right, library_key="library_id", coord_type="generic", delaunay=True) ``` | | Edges | Edges joining cells from different ROIs | |---|---|---| | no `library_key` | 19,792 | **13,276 (67%)** | | `library_key="library_id"` | 19,720 | 0 | Two thirds of the graph is fictional. And the consequence is not noise — it is a systematically different answer that still looks reasonable: | | Epithelial self-enrichment z-score | |---|---| | no `library_key` | 19.0 | | `library_key="library_id"` | **72.3** | Nothing raises, nothing warns, and the wrong number is the smaller one, so it reads as a conservative result. **Pass `library_key` whenever the object holds more than one image**, and check it afterwards rather than trusting it: ```python import numpy as np lib = a.obs["library_id"].to_numpy() edges = right.obsp["spatial_connectivities"].tocoo() assert (lib[edges.row] != lib[edges.col]).sum() == 0 ``` Three lines, run once, and they close the failure mode entirely. `wrong` and `right` above are copies, made so the two can be compared. Everything from here on uses `a`, so build the graph on `a` itself before going further — otherwise the next call fails with `KeyError: Spatial connectivity key 'spatial_connectivities' not found in adata.obsp`: ```python sq.gr.spatial_neighbors(a, library_key="library_id", coord_type="generic", delaunay=True) ``` One forward-compatibility note, because this page will outlive the version it was written against: squidpy 1.8.3 emits `FutureWarning: Calling spatial_neighbors is deprecated and will be removed in squidpy v1.9.0`, directing you to `spatial_neighbors_delaunay`, `spatial_neighbors_knn`, `spatial_neighbors_radius`, `spatial_neighbors_grid` or `spatial_neighbors_from_builder`. The `library_key` argument and everything above about it carry over to the replacements — what changes is that the graph construction moves from a keyword into the function name, which is an improvement, because it stops the construction being an unstated default. The same rule covers functions that have no `library_key` argument. `sq.gr.ripley` and `sq.gr.co_occurrence` compute over the whole object, so subset per image and loop: ```python for roi in a.obs["library_id"].cat.categories: sub = a[a.obs["library_id"] == roi].copy() sq.gr.ripley(sub, cluster_key="Cluster", mode="L", n_simulations=100, seed=0) ... ``` `.copy()` matters, and not for the reason you would guess. `sq.gr.ripley` on an AnnData *view* does not raise: it runs, writes `Cluster_ripley_L` into the **view's** `uns`, and leaves the parent object untouched. The view is then discarded at the end of the loop iteration and the result goes nowhere. Verified on the dataset below — the parent's `uns` never gains the key. It is not quite silent: the write emits an anndata `ImplicitModificationWarning` alongside a couple of pandas copy warnings, and the view actualizes into a real object in the process (`sub.is_view` flips to `False`). But a warning in a loop over twenty ROIs is not what stops anybody, and the missing result only shows up much later as an empty `uns`. Copy, keep the copy, and read the result off it. ## Transform intensities before you cluster them Marker intensities are not counts and are not log-normal. They span orders of magnitude between markers, they have a nonzero background, and a handful of cells are far brighter than the rest. **Check first whether somebody already transformed it.** A published `.h5ad` frequently arrives normalised, and arcsinh on top of a z-score is meaningless. The MIBI-TOF example used in `## Try it` below has `X` running -0.599 to 0.717 — plainly not raw intensity. One line, before anything else: ```python print(float(a.X.min()), float(a.X.max())) ``` Raw multiplex intensity is non-negative and skewed, typically spanning two or three orders of magnitude. Anything centred on zero has been processed, and the processing is usually undocumented in the file. **arcsinh** is the standard in the cytometry lineage that CODEX and IMC inherit from. It is linear near zero — so background stays background rather than being blown up the way `log1p` does — and logarithmic above the cofactor: ```python import numpy as np a.layers["raw"] = a.X.copy() a.X = np.arcsinh(a.X / 5.0) # cofactor 5 for fluorescence; 150 is typical for CyTOF ``` The cofactor is a real parameter. Too small and background noise is expanded into structure; too large and everything is squashed linear. Pick it by looking at where each marker's negative population sits, and record it — a clustering result is not reproducible without it. Then z-score **per marker**, so no single bright channel dominates the distance metric: ```python import scanpy as sc sc.pp.scale(a, max_value=10) ``` Scale after arcsinh, not instead of it. And keep `raw` — every annotation decision below is made by looking at untransformed intensities, because that is the scale on which "positive" means anything. ### Batch effects between runs are not the same as between patients Marker intensity drifts between acquisition runs: antibody lot, exposure, scanner calibration. If you correct it, correct on the **run**, not the patient, and check what you removed. Cross-slide correction that uses the patient as the batch variable removes the between-patient difference you are trying to measure. If run and patient are confounded — one slide per patient, run in order — no correction can separate them, and the honest move is to say so rather than to apply one. ## Phenotyping: gate or cluster, and check either **Gating** applies per-marker thresholds and is defensible when the panel is well characterised and the thresholds come from a control. It is reproducible, it is auditable, and it puts every cell into exactly one place you chose. **Clustering** finds structure without thresholds and is what most workflows use. It is also where cell types get invented. Measured on a Cellpose-segmented Vectra 7-colour field — 1,334 cells, six markers — at three Leiden resolutions, everything else held fixed: | Resolution | Clusters | Highest cluster mean: CD8 / FoxP3 / CD68 | |---|---|---| | 0.2 | 6 | 3.87 / 0.97 / 0.66 | | 0.5 | 11 | 3.75 / 2.96 / 1.30 | | 1.0 | 14 | 3.71 / 2.80 / 1.21 | Those are one run's figures. The segmentation that produced the object is not reproducible run to run, so neither is the clustering on top of it: repeating the same pipeline gives 11 or 12 clusters at resolution 0.5 and moves each mean in the second significant figure — FoxP3 between 2.75 and 3.29, CD68 between 1.21 and 1.30. The comparison between resolutions holds; the individual numbers are a snapshot. At 0.2 the FoxP3 cells are absorbed into other clusters and no cluster's FoxP3 mean gets above 1. At 0.5 a FoxP3-high cluster separates cleanly at 2.96. Going on to 1.0 adds three more clusters and no new marker separation at all — it splits the CK-high tumour compartment into four clusters whose means differ in nothing nameable. None of that is visible in the clustering. You have to look at the marker means: ```python import scanpy as sc sc.pp.neighbors(a, n_neighbors=15, random_state=0) sc.tl.leiden(a, resolution=0.5, key_added="cluster", flavor="igraph", n_iterations=2, random_state=0) ``` Then look at what each cluster contains, before naming anything: ```python import numpy as np, pandas as pd from scipy.sparse import issparse layer = a.layers["raw"] raw = layer.toarray() if issparse(layer) else np.asarray(layer) means = (pd.DataFrame(raw, index=a.obs_names, columns=a.var_names) .groupby(a.obs["cluster"].values, observed=True).mean()) print(means.round(2).to_string()) ``` The `issparse` branch is not defensive padding. Anything that has been through scanpy holds `X` — and therefore `layers["raw"]` — as a `csr_matrix`, and `np.asarray` on one of those does not densify it: it returns a 0-dimensional object array wrapping the matrix, and `pd.DataFrame` then fails with `ValueError: Must pass 2-d input. shape=()`. The MIBI-TOF object used below is sparse, so this is the default case, not the exception. `"cluster"` here is whatever you passed as `key_added` to `sc.tl.leiden`. A dataset that arrives with cell types already assigned uses whatever column the depositor chose — the MIBI-TOF example in `## Try it` calls it `Cluster` — so read the column name off `a.obs` rather than assuming this one. **A marker in the panel is not a cell type in the data.** In the same run CD68's best cluster mean is 1.30 in this run — 1.21 to 1.30 across runs — against a 99th percentile of per-cell mean intensity of 1.65 across all cells — the dimmest channel in the panel. CD8 by contrast reaches 3.75 in one cluster while sitting at 0.11-0.22 in every other. There is no macrophage cluster to find here, at any resolution, even though CD68 is in the panel and macrophages are in the tissue. Reporting "no macrophages" from that would be wrong; naming a CD68-high cluster the data does not support would be worse. The honest output is that the panel included CD68 and the clustering did not resolve it. So: print the table, name clusters from the markers that are actually elevated, leave the ones you cannot name unnamed, and say in the write-up which is which. An unnamed cluster is honest. A cluster named from the argmax of six markers is not — the argmax is defined for every cluster whether or not any marker is elevated. Derive the map from the marker table rather than typing cluster ids, because the ids are an artefact of the run — they move when the seed, the library version or the accelerator changes, so a map written out by hand silently mislabels the next run: A raw threshold does not travel — it depends on the scale, and half the objects you meet have already been transformed by somebody. Score the *separation* instead, which does: ```python def separation(means, marker): # how far the top cluster sits above the rest, in across-cluster SDs col = means[marker] top = col.idxmax() rest = col.drop(top) return top, (col[top] - rest.mean()) / rest.std() naming = {} for marker, label in [("CD8", "CD8 T cell"), ("CD68", "Macrophage"), ("CD4", "CD4 T cell"), ("CK", "Epithelial")]: cid, sep = separation(means, marker) print(f"{marker:5s} top cluster {cid} separation {sep:.2f} SD") if sep >= 2: # your bar, stated once, applied to all naming.setdefault(cid, label) a.obs["cell_type"] = (a.obs["cluster"] .map(naming).astype("object").fillna("unassigned").astype("category")) print(naming) # record which ids this run used ``` On the MIBI-TOF object that prints: ``` CD8 top cluster 4 separation 2.06 SD CD68 top cluster 6 separation 1.89 SD CD4 top cluster 6 separation 1.93 SD CK top cluster 8 separation 1.83 SD ``` **One cluster of nine gets a name, and 2,989 of 3,309 cells stay unassigned.** That is this section's argument made concrete rather than asserted. All four markers have a top cluster — `idxmax` always does — and three of the four fall short of a bar set before looking. Naming from `idxmax` alone would have shipped four cell types, three of them unsupported. **And then check the one name you did give.** This dataset ships a published annotation in `obs["Cluster"]`, so the claim is testable: ```python ct = pd.crosstab(a.obs["cluster"], a.obs["Cluster"]) for cid, label in naming.items(): counts = ct.loc[cid].sort_values(ascending=False) print(f'{label} (cluster {cid}) is {counts.max() / counts.sum():.1%} {counts.idxmax()}') print(counts.to_string()) # CD8 T cell (cluster 4) is 31.6% Tcell_CD8 # Tcell_CD8 101 · Tcell_CD4 96 · Imm_other 40 · Endothelial 37 # Myeloid_CD68 19 · Myeloid_CD11c 16 · Fibroblast 7 · Epithelial 4 ``` The cluster that cleared the bar is **31.6% CD8 T cells**. It is not a CD8 T cell population; it is a mixed immune cluster whose CD8 mean happens to lead. So the separation score ranked correctly and validated nothing — which is the honest limit of it, and the reason to cross-tabulate against a published annotation wherever one exists rather than replacing it. Three caveats on the score itself, none of which the bar fixes. It is a heuristic effect-size over unweighted cluster means, not a statistical test. It depends on how many clusters you have, so changing the Leiden resolution changes every separation value. And it ignores cluster size, so a tiny cluster of outliers scores like a real population. Use it to rank candidates for inspection; do not use it to decide. The 2 SD bar is likewise a choice, not a law — state yours and apply it to every marker rather than per marker after seeing the answer. And this object arrives pre-normalised, so its separations are compressed relative to raw data from a well-designed panel. Substitute your own panel's markers, and if a name you expect raises `KeyError`, that is the panel telling you it does not contain that marker — worth knowing before you go looking for the cell type. ## Neighborhood enrichment Are cells of type A found next to cells of type B more often than chance? `squidpy` answers it by permuting cluster labels over the fixed graph and comparing the observed adjacency count to the null: ```python sq.gr.nhood_enrichment(a, cluster_key="cell_type", library_key="library_id", seed=0, n_perms=1000) z = a.uns["cell_type_nhood_enrichment"]["zscore"] ``` The matrix is symmetric, and its diagonal is self-enrichment — how strongly a type sits with its own kind. On the MIBI-TOF data, with the per-ROI graph *and* the per-ROI null: Epithelial 63.3, other immune 24.8, CD8 T 24.2, Fibroblast 23.3. **`library_key` belongs here too, and it is a separate fix from the graph.** Building the graph per ROI stops cells on different slides from being neighbours. It does not stop the *permutation* from moving a label out of one ROI and into another — and when the ROIs have different compositions, as they do here (Epithelial runs 0.102 to 0.391 across the three), a pooled null is more dispersed than any single ROI and the z-scores inflate. Measured on the same per-ROI graph, changing only the null: | Permutation | Epithelial | Fibroblast | CD4 T | CD8 T | |---|---|---|---|---| | pooled (no `library_key`) | 72.3 | 31.7 | 26.6 | 26.4 | | within each ROI | **63.3** | **23.3** | **17.8** | **24.2** | Up to 9.6 of z across the matrix — the largest single change is Myeloid_CD68's self-enrichment — and the ranking moves too: CD4 T outranks CD8 T under the pooled null and falls below it under the correct one. Two things to be precise about, because the obvious summary of this is wrong. **The direction is consistent on self-enrichment and not across the whole matrix.** Seven of the eight diagonal entries are larger under the pooled null; Endothelial is the exception, at 13.5 pooled against 13.8 per-ROI. Across all 64 entries it is close to a coin flip — 31 larger, 33 smaller. So "the pooled null inflates everything" is not a claim you can make; "it inflates self-enrichment, which is usually what gets quoted" is. **The mechanism is the expected count, not the spread.** It is tempting to say the pooled null is more dispersed, and for the Epithelial self term it is not: the permutation standard deviation is 37.4 pooled against 38.2 per-ROI. What moves is the null *mean* — 1000.5 pooled against 1285.6 per-ROI, against an observed count of 3702. Shuffling labels across ROIs of different composition lowers the expected number of same-type adjacencies, so the observed count sits further above a null that was never the right one. Pass `library_key` to both calls. Three things to hold onto. **The null permutes labels, not positions.** The tissue architecture is held fixed and only the cell-type assignment is shuffled, which is the right null for "given where the cells are, is this labelling non-random". It is not a null for "is this tissue organised", and the z-score is not comparable across datasets with different densities or different graph constructions. **The z-score is not a p-value and it is not calibrated.** With thousands of cells and a dense graph, z-scores in the tens are ordinary. Use them to rank pairs within one dataset, not to declare significance. **It inherits every graph decision.** Delaunay versus k-nearest versus a fixed radius changes the answer, because it changes what "next to" means. State the construction with the result. A fixed radius in micrometres is the most interpretable of the three and the most sensitive to segmentation density. ## Cellular neighborhoods A neighborhood is a *region* type rather than a cell type: describe each cell by the composition of cells around it, then cluster those composition vectors. ```python import numpy as np, pandas as pd from sklearn.cluster import KMeans A = a.obsp["spatial_connectivities"] onehot = pd.get_dummies(a.obs["cell_type"]).to_numpy(float) comp = A @ onehot # neighbour counts per cell comp = comp / np.clip(comp.sum(1, keepdims=True), 1, None) a.obs["neighborhood"] = pd.Categorical( KMeans(n_clusters=8, n_init=10, random_state=0).fit_predict(comp)) ``` The number of neighborhoods is as arbitrary as a Leiden resolution and gets less scrutiny. Report the composition matrix — mean fraction of each cell type per neighborhood — beside the count, so a reader can see whether eight neighborhoods describe eight distinguishable regions or three regions and five variations on one. Cells at the edge of an ROI have fewer neighbours than cells in the middle, so their composition vectors are noisier and biased toward whatever is inside the boundary. Either exclude a border margin or carry the neighbour count as a covariate; do not ignore it. ## Ripley's L and co-occurrence Ripley's L asks whether one cell type clusters or disperses relative to complete spatial randomness, as a function of radius. Above the CSR line means clustered; below means regularly spaced. ```python for roi in a.obs["library_id"].cat.categories: sub = a[a.obs["library_id"] == roi].copy() sq.gr.ripley(sub, cluster_key="cell_type", mode="L", n_simulations=100, seed=0) stat = sub.uns["cell_type_ripley_L"]["L_stat"] ``` **Edge correction is not optional.** A cell near the boundary of an ROI has part of its neighbourhood outside the imaged region, so the uncorrected count is low and every type looks more dispersed than it is. The effect grows with the radius, so the bias is largest exactly where the interesting structure usually is. Cap the radii you interpret at a fraction of the ROI's short side — a quarter is a common rule — and say what you capped at. **Simulations are per-ROI.** The CSR envelope depends on the ROI's shape and cell density, so an envelope computed on a pooled object is not the envelope for any of the ROIs in it. Co-occurrence answers a related question — the probability of finding type B within a distance of type A, relative to B's overall frequency — and is less sensitive to the graph because it works from distances directly: ```python sq.gr.co_occurrence(sub, cluster_key="cell_type") ``` ## The unit of replication is the patient, not the cell This is the error that survives peer review most often, and it is arithmetic rather than biology. The MIBI-TOF example holds **3,309 cells**, from **3 ROIs**, from **2 donors**: ``` donor library_id 21d7 point23 1241 90de point16 1023 point8 1045 ``` A test comparing cell-level values between conditions with n = 3,309 is answering "do these two collections of cells differ", which they always do, because cells within one patient are not independent draws. The n that a clinical claim rests on is 2. So aggregate first, then test: ```python frac = (a.obs.groupby(["library_id", "cell_type"], observed=True).size() .unstack(fill_value=0) .pipe(lambda d: d.div(d.sum(axis=1), axis=0))) frac.to_csv("per_roi_composition.csv") ``` On this dataset that yields three rows — one per ROI — and the between-ROI spread is large: Epithelial runs 0.102, 0.206, 0.391 and CD4 T runs 0.078, 0.220, 0.396. Two of those ROIs are the same donor. Any statistic that treated the 3,309 cells as independent would have called differences of that size overwhelmingly significant. Then: - **Multiple ROIs per patient are nested, not replicated.** Average them to the patient first, or fit a mixed model with patient as a random effect. Treating three ROIs from one patient as n = 3 is the same error one level up. - **The same rule applies to the spatial statistics**, not only to composition. Compute neighborhood enrichment per ROI, aggregate to the patient, and compare those. A z-score from a graph pooled over both groups has no replication structure at all. - **Say the n out loud in the write-up**, as patients and as ROIs. A figure captioned "n = 3,309 cells" and a figure captioned "n = 2 patients" describe the same experiment, and only one of them lets a reader judge it. ## SPAC, scimap and squidpy - **squidpy** (BSD-3-Clause, scverse) is what this page is written against. It is on PyPI, it operates on `AnnData` in place, and it covers the graph, neighborhood enrichment, Ripley, co-occurrence and centrality. - **scimap** (MIT) is on PyPI and adds a gating-first phenotyping workflow, spatial LDA and spatial-context maps, on the same `AnnData`. - **SPAC** (BSD-3-Clause, Frederick National Laboratory) wraps this ground with a consistent API and a Shiny front end, and is the package behind the platform paper at doi 10.1186/s12859-025-06339-2 and the software paper at doi 10.21105/joss.08787. It is **not on PyPI or conda-forge** — confirmed 26 Aug 2026 — so it installs from the repository. Its README documents a conda environment built from a feature branch against a custom package channel. That is more machinery than it needs — pip installs it straight from the repository into an ordinary Python 3.12 environment: ```bash pip install "git+https://github.com/FNLCR-DMAP/SCSAWorkflow.git" pip install parmap # not declared, and two submodules need it ``` The second line is not optional and is not documented upstream. Without it, `spac.transformations` and `spac.utag_functions` both raise `ModuleNotFoundError: No module named 'parmap'` on import, while the other six submodules load fine — so you discover it partway through a workflow rather than at install time. Treat the README's version numbers with suspicion while you are there: it states "Latest released version is v0.9.0" where the repository tags v0.9.2 and `setup.py` declares 0.9.3. **SPAC has the same per-image trap, under a different keyword.** Its neighbourhood profiler takes `regions=`, which does what `library_key` does in squidpy, and is equally optional: ```python import anndata as ad, numpy as np import spac.spatial_analysis as sa sp = ad.read_h5ad("mibitof.h5ad") # a separate object, so `a` above is untouched sa.neighborhood_profile(sp, phenotypes="Cluster", spatial_key="spatial", distances=[0, 25, 50, 100], regions="library_id") profile = np.asarray(sp.obsm["neighborhood_profile"]) # (3309, 8, 3) cells x types x shells ``` Drop `regions="library_id"` on this dataset and the profiler counts **328,638** neighbours instead of **121,268** — 207,370 of them across ROI boundaries, and **every one of the 3,309 cells** gets a different profile. Same failure as the graph section above, same silence, a different parameter name to remember. That is the argument for reading this page rather than any one library's docs: three packages, three spellings of the same guard, all of them opt-in. Because all three read and write the same `AnnData`, the choice between them is about ergonomics rather than capability, and they compose — but the guard is yours to remember in every one. ## What to write out ```python cats = list(a.obs["cell_type"].cat.categories) # the axis order of the z matrix z = a.uns["cell_type_nhood_enrichment"]["zscore"] a.write_h5ad("spatial_analysis.h5ad") # every annotation in .obs, portable frac.to_csv("per_roi_composition.csv") # tidy, one row per ROI pd.DataFrame(z, index=cats, columns=cats).to_csv("nhood_enrichment_z.csv") ``` `squidpy` returns the enrichment matrix as a bare array, and its rows are in the order of the cluster key's *categories* — not the order they appear in `obs`, and not sorted. Label the axes from `.cat.categories` at the point you write the file, or the CSV is a matrix of numbers whose rows nobody can identify later. Plus a short written summary saying which thresholds and resolutions were chosen and on what basis, which clusters were named and which were left unassigned, what the graph construction was, and what the n is in patients. Those are the decisions a reader needs to disagree with, and none of them is recoverable from the `.h5ad`. ## Try it **Data.** The `squidpy` MIBI-TOF example — 3,309 cells, 36 markers, three ROIs from colorectal carcinoma, published by Hartmann et al (doi 10.1101/2020.01.17.909796) and redistributed by scverse. 20 MB, no account, confirmed reachable on 25 Aug 2026. It is the right test case here precisely because it has more ROIs than donors, which is the shape that makes the replication question concrete. **Run.** Self-contained: ```bash pip install squidpy scanpy anndata igraph curl -L -O https://exampledata.scverse.org/squidpy/mibitof.h5ad ``` ```python import anndata as ad, numpy as np, pandas as pd, squidpy as sq a = ad.read_h5ad("mibitof.h5ad") lib = a.obs["library_id"].to_numpy() print("cells", a.n_obs, "| markers", a.n_vars, "| ROIs", a.obs.library_id.nunique(), "| donors", a.obs.donor.nunique()) print(a.obs.groupby(["donor", "library_id"], observed=True).size().to_string()) for roi in a.obs.library_id.cat.categories: m = lib == roi xy = a.obsm["spatial"][m] print(f" {roi}: x {xy[:,0].min():.0f}-{xy[:,0].max():.0f} " f"y {xy[:,1].min():.0f}-{xy[:,1].max():.0f}") def cross_roi(adata): e = adata.obsp["spatial_connectivities"].tocoo() return int((lib[e.row] != lib[e.col]).sum()), int(e.nnz) pooled = a.copy() sq.gr.spatial_neighbors(pooled, coord_type="generic", delaunay=True) per_roi = a.copy() sq.gr.spatial_neighbors(per_roi, library_key="library_id", coord_type="generic", delaunay=True) print("pooled graph : %d/%d edges cross an ROI boundary" % cross_roi(pooled)) print("per-ROI graph: %d/%d edges cross an ROI boundary" % cross_roi(per_roi)) cats = list(a.obs.Cluster.cat.categories) epi = cats.index("Epithelial") for name, obj in [("pooled", pooled), ("per-ROI", per_roi)]: sq.gr.nhood_enrichment(obj, cluster_key="Cluster", seed=0, show_progress_bar=False) z = obj.uns["Cluster_nhood_enrichment"]["zscore"] print(f"{name:8s} Epithelial self-z = {z[epi][epi]:.1f}") frac = (a.obs.groupby(["library_id", "Cluster"], observed=True).size() .unstack(fill_value=0).pipe(lambda d: d.div(d.sum(axis=1), axis=0))) print("\nper-ROI composition:\n", frac.round(3).to_string()) # The graph must not join cells from different images. assert cross_roi(per_roi)[0] == 0 assert cross_roi(pooled)[0] > 0.5 * cross_roi(pooled)[1] # Enrichment is symmetric, and the pooled graph understates self-enrichment here. zp = per_roi.uns["Cluster_nhood_enrichment"]["zscore"] assert np.allclose(zp, zp.T, equal_nan=True) assert zp[epi][epi] > pooled.uns["Cluster_nhood_enrichment"]["zscore"][epi][epi] # More ROIs than donors: the point of the whole section above. assert a.obs.library_id.nunique() > a.obs.donor.nunique() assert np.allclose(frac.sum(axis=1).to_numpy(), 1.0) print("OK") ``` **Expect.** Invariants — a failure means the skill is wrong: - With `library_key`, **zero** edges join cells from different `library_id` values. Without it, most edges do — the three ROIs share one coordinate box, so a pooled Delaunay triangulation stitches them together. - `zscore` is symmetric, because adjacency is. - Every row of the per-ROI composition table sums to 1. - The dataset has more ROIs than donors, so ROI-level rows are not independent samples. Observed values, from a run on 25 Aug 2026 against squidpy 1.8.3 — these move if scverse rebuilds the example or squidpy changes its default graph: - 3,309 cells, 36 markers, 3 ROIs, 2 donors. `point23` is donor `21d7`; `point8` and `point16` are both donor `90de`. - All three ROIs span roughly x 1-1021, y 1-1021. - Pooled graph 19,792 edges, **13,276 crossing an ROI boundary**. Per-ROI graph 19,720 edges, 0 crossing. - Epithelial self-enrichment z: 19.0 pooled graph, **72.3** per-ROI graph. Both hold the *null* pooled, so this pair isolates the graph effect — which is what the block is testing. Fixing the null as well, as *Neighborhood enrichment* above says you should, takes the per-ROI-graph figure from 72.3 down to 63.3. The two corrections are independent and both point the same way. - Epithelial fraction by ROI: point16 0.206, point23 0.102, point8 0.391. **Across other data.** The graph check, the arcsinh transform, the resolution comparison and the aggregation were re-run on a Cellpose-segmented Vectra 7-colour field — a single ROI, six markers, 1,334 cells — and on the squidpy IMC example (4,668 cells, 34 markers, one ROI, cell types already assigned). What generalises is all of it except the graph check, which is vacuous on a single-ROI object; the failure it catches only exists once an object holds more than one image, which is exactly when nobody remembers to look. What changes is the clustering: six markers do not support the resolution that thirty-six do, and the IMC object's marker names arrive as instrument channel strings like `1021522Tm169Di EGFR`, which need cleaning before any marker-based annotation is legible.