diff --git a/.github/workflows/docs.yml b/.github/workflows/docs.yml
index c4c3568..33d4892 100644
--- a/.github/workflows/docs.yml
+++ b/.github/workflows/docs.yml
@@ -63,7 +63,7 @@ jobs:
     - name: Check that saved notebook outputs still match the package
       # Runs pages that need no downloaded data, without saving them, and fails
       # when what they print has changed. Add such pages to PAGES.
-      run: make -C docs check-outputs PAGES="quickstart differential_expression guides/export_cubes guides/bulk_rna_seq guides/composition"
+      run: make -C docs check-outputs PAGES="quickstart differential_expression guides/export_cubes guides/bulk_rna_seq guides/composition guides/combine_studies guides/replication_across_studies"
     - name: Store the site
       if: ${{ !inputs.pages }}
       uses: actions/upload-artifact@v7
diff --git a/THIRD_PARTY_NOTICES.md b/THIRD_PARTY_NOTICES.md
index 473d9f9..c863c7f 100644
--- a/THIRD_PARTY_NOTICES.md
+++ b/THIRD_PARTY_NOTICES.md
@@ -273,7 +273,7 @@ the dataset below, their inputs are generated with fixed random seeds. Reference
 in those files come from the packages each script names.
 
 The pages of the documentation (`docs/source/`) read the datasets that `docs/datasets.py`
-prepares in a cache outside the repository: two simulated with fixed random seeds by
+prepares in a cache outside the repository: three simulated with fixed random seeds by
 that script, and the two below, GEO series GSE96583 and the MSigDB Hallmark
 interferon-alpha response set. No file of these datasets is in the repository; the
 script records the source, licence, citation and checksum of each. Two guides also
diff --git a/cytepool/replication.py b/cytepool/replication.py
index bb01a44..dc04b1c 100644
--- a/cytepool/replication.py
+++ b/cytepool/replication.py
@@ -46,9 +46,9 @@
 
 Not done:
     The r-values of Heller, Bogomolov and Benjamini (2014), which claim replication
-    in a follow-up study of the genes a primary study selects, and held-out
-    prediction of case status from the genes the studies find (Nakatsuka et al.
-    2025).
+    in a follow-up study of the genes a primary study selects. Held-out prediction
+    of case status from the genes the studies find (Nakatsuka et al. 2025) is
+    :func:`cytepool.held_out_prediction.held_out_prediction`.
 
 References:
     Nakatsuka N, Adler D, Jiang L, Hartman A, Cheng E, Klann E, Satija R (2025).
diff --git a/docs/coverage_exemptions.toml b/docs/coverage_exemptions.toml
index 35d213b..3120d61 100644
--- a/docs/coverage_exemptions.toml
+++ b/docs/coverage_exemptions.toml
@@ -64,8 +64,6 @@
 "cytepool.table.Table.filter" = "guides/pool_cells: sample and group tables"
 "cytepool.table.Table.with_columns" = "guides/pool_cells: sample and group tables"
 "cytepool.plotting.plot_dispersions" = "guides/bulk_rna_seq: DESeq2's dispersion estimates"
-"cytepool.plotting.plot_forest" = "guides/combine_studies: one gene across studies"
-"cytepool.cube.CubeCollection.select" = "guides/combine_studies: the studies, cell types and genes of an analysis"
 "cytepool.plotting.plot_pca" = "guides/normalization_and_quality and guides/bulk_rna_seq: samples on their principal components"
 "cytepool.plotting.plot_dotplot" = "guides/pool_cells: the markers of each cell type as a dot plot, from the cube"
 "cytepool.plotting.plot_quasi_dispersions" = "guides/bulk_rna_seq: edgeR's quasi-dispersions"
diff --git a/docs/datasets.py b/docs/datasets.py
index 1bcbdd8..568a6e2 100644
--- a/docs/datasets.py
+++ b/docs/datasets.py
@@ -1,13 +1,13 @@
 """The datasets of the documentation, prepared once and kept outside the repository.
 
 Several pages read the same data: a simulated single-cell study (its cells, and the
-cube pooled from them), a simulated bulk RNA-seq experiment, the published
-interferon-beta stimulation of Kang et al. (2018), and the MSigDB Hallmark gene set
-of the interferon-alpha response. This script makes them deterministically into a
-cache outside the repository and records, for each, where it comes from, its
-licence, how to cite it, the SHA-256 of every file it downloads, and what it must
-contain: the fingerprint of every cube it writes and the digest of every other file.
-Pages open the cubes with ``read_cube``.
+cube pooled from them), a simulated bulk RNA-seq experiment, six simulated cohorts of
+one disease, the published interferon-beta stimulation of Kang et al. (2018), and the
+MSigDB Hallmark gene set of the interferon-alpha response. This script makes them
+deterministically into a cache outside the repository and records, for each, where it
+comes from, its licence, how to cite it, the SHA-256 of every file it downloads, and
+what it must contain: the fingerprint of every cube it writes and the digest of every
+other file. Pages open the cubes with ``read_cube``.
 
 Usage, from the repository root::
 
@@ -46,7 +46,7 @@
 
 import numpy as np
 
-from cytepool.cube import from_bulk, from_cells
+from cytepool.cube import Cube, from_bulk, from_cells
 from cytepool.io import read_cube, write_cube
 
 REPOSITORY = Path(__file__).resolve().parents[1]
@@ -262,6 +262,169 @@ def simulated_bulk(downloads: Path, output: Path) -> None:
     )
 
 
+# Six case-control cohorts of one disease: name, cases, controls, cells per sample
+# relative to the others, counts per cell, and the standard deviation of a gene's log2
+# expression between donors.
+COHORTS = (
+    ("cohort_1", 30, 30, 1.0, 3_000, 0.50),
+    ("cohort_2", 16, 16, 0.8, 2_500, 0.50),
+    ("cohort_3", 12, 12, 1.2, 2_000, 0.55),
+    ("cohort_4", 8, 8, 0.7, 3_500, 0.50),
+    ("cohort_5", 6, 6, 0.5, 1_500, 0.65),
+    ("cohort_6", 10, 10, 1.0, 2_500, 0.55),
+)
+# The cell types by their Cell Ontology ids, with their mean cells per sample: B cell,
+# CD4-positive alpha-beta T cell, natural killer cell, CD14-positive monocyte.
+COHORT_CELL_TYPES = (
+    ("CL:0000236", 60),
+    ("CL:0000624", 250),
+    ("CL:0000623", 50),
+    ("CL:0001054", 180),
+)
+# Each cohort's own names of the cell types. cohort_4 annotated its CD4 T cells as
+# naive (CL:0000895) and memory (CL:0000897) cells, and cohort_6 has no NK cells.
+COHORT_GROUP_NAMES = {
+    "cohort_1": ("B cells", "CD4 T cells", "NK cells", "CD14 monocytes"),
+    "cohort_2": ("B", "CD4", "NK", "CD14 Mono"),
+    "cohort_3": ("B cell", "CD4+ T cell", "NK cell", "CD14+ monocyte"),
+    "cohort_4": ("B", ("CD4 naive", "CD4 memory"), "NK", "Mono CD14"),
+    "cohort_5": ("Bcell", "T_CD4", "NK", "Mono_CD14"),
+    "cohort_6": ("B cells", "CD4+ T cells", None, "CD14+ monocytes"),
+}
+CD4_SUBSETS = ("CL:0000895", "CL:0000897")
+# The genes of each cell type that the disease changes in every cohort alike, that it
+# changes in every cohort by amounts that vary between cohorts, and that each cohort
+# alone changes, per cohort.
+COHORT_CHANGES = {
+    "CL:0000236": (10, 0, 0),
+    "CL:0000624": (30, 10, 5),
+    "CL:0000623": (0, 0, 0),
+    "CL:0001054": (80, 20, 10),
+}
+
+
+def simulated_cohorts(downloads: Path, output: Path) -> None:
+    """Six case-control cohorts of one disease, each a cube as its source exported it.
+
+    12 to 60 donors per cohort, one sample each, with the pseudobulk counts of four
+    cell types over 2,000 genes. Each cell type has its own profile; each cohort
+    shifts every gene a little; a gene's log2 expression varies between donors by a
+    standard deviation of 0.5 to 0.65, more in the small cohort_5. The disease
+    changes some genes in every cohort, by 0.3 to 0.9 on the log2 scale alike in
+    every cohort or by 0.3 to 0.8 with a standard deviation of 0.4 between cohorts,
+    each case by an activity of its own (gamma-distributed, mean 1); in every cohort
+    it also changes a few genes of its own by 0.8 to 1.5, in every case alike, as a
+    confounder of that cohort would.
+    The cohorts differ as cohorts from different sources do: donors are numbered
+    D01, D02, ... in each; cohort_3's annotation lacks 200 genes and cohort_5 wrote
+    zeros for 150 genes it did not measure, marked in its feature column
+    ``measured``; the cell types carry Cell Ontology ids and names of each cohort's
+    own. Writes a cube per cohort and the true log2 fold changes.
+    """
+    rng = np.random.default_rng(20261028)
+    n_genes = 2_000
+    genes = np.array([f"gene_{g:04d}" for g in range(1, n_genes + 1)])
+    n_cohorts, n_types = len(COHORTS), len(COHORT_CELL_TYPES)
+    profiles = rng.normal(0.0, 1.6, n_genes) + rng.normal(0.0, 0.6, (n_types, n_genes))
+    log2_fc = np.zeros((n_cohorts, n_types, n_genes))
+    own_change = np.zeros((n_cohorts, n_types, n_genes), dtype=bool)
+    for t, (ontology_id, _) in enumerate(COHORT_CELL_TYPES):
+        alike, varying, single = COHORT_CHANGES[ontology_id]
+        chosen = rng.permutation(n_genes)[: alike + varying + n_cohorts * single]
+        signs = rng.choice([-1.0, 1.0], chosen.size)
+        shared = chosen[:alike]
+        log2_fc[:, t, shared] = signs[:alike] * rng.uniform(0.3, 0.9, alike)
+        log2_fc[:, t, shared] += rng.normal(0.0, 0.1, (n_cohorts, alike))
+        apart = chosen[alike : alike + varying]
+        log2_fc[:, t, apart] = signs[alike : alike + varying] * rng.uniform(
+            0.3, 0.8, varying
+        ) + rng.normal(0.0, 0.4, (n_cohorts, varying))
+        for c in range(n_cohorts):
+            start = alike + varying + c * single
+            own = chosen[start : start + single]
+            log2_fc[c, t, own] = signs[start : start + single] * rng.uniform(
+                0.8, 1.5, single
+            )
+            own_change[c, t, own] = True
+    order = rng.permutation(n_genes)
+    absent = {"cohort_3": np.sort(order[:200]), "cohort_5": np.sort(order[200:350])}
+    cohort_shift = rng.normal(0.0, 0.3, (n_cohorts, n_genes))
+    for c, (name, cases, controls, cell_scale, depth, spread) in enumerate(COHORTS):
+        n = cases + controls
+        condition = rng.permutation(np.array(["control"] * controls + ["case"] * cases))
+        activity = np.where(condition == "case", rng.gamma(2.0, 0.5, n), 0.0)
+        group_ids, ontology_ids, parts = [], [], []
+        for t, ((ontology_id, mean_cells), names) in enumerate(
+            zip(COHORT_CELL_TYPES, COHORT_GROUP_NAMES[name], strict=True)
+        ):
+            if names is None:
+                continue
+            subsets = isinstance(names, tuple)
+            for k, group in enumerate(names if subsets else (names,)):
+                profile = profiles[t]
+                if subsets:
+                    profile = profile + rng.normal(0.0, 0.3, n_genes)
+                group_ids.append(group)
+                ontology_ids.append(CD4_SUBSETS[k] if subsets else ontology_id)
+                parts.append(
+                    (t, profile, mean_cells * ((0.45, 0.55)[k] if subsets else 1))
+                )
+        counts = np.zeros((len(group_ids), n, n_genes), dtype=np.int64)
+        n_cells = np.zeros((n, len(group_ids)), dtype=np.int64)
+        for g, (t, profile, mean_cells) in enumerate(parts):
+            for j in range(n):
+                effect = np.where(
+                    own_change[c, t],
+                    (condition[j] == "case") * log2_fc[c, t],
+                    activity[j] * log2_fc[c, t],
+                )
+                log2_mean = (
+                    profile
+                    + cohort_shift[c]
+                    + effect
+                    + rng.normal(0.0, spread, n_genes)
+                )
+                proportion = 2.0**log2_mean / np.sum(2.0**log2_mean)
+                n_cells[j, g] = rng.poisson(mean_cells * cell_scale)
+                library = n_cells[j, g] * rng.lognormal(np.log(depth), 0.2)
+                counts[g, j] = rng.poisson(library * proportion)
+        features: dict[str, Any] = {"feature_id": genes}
+        if name == "cohort_3":
+            kept = np.setdiff1d(np.arange(n_genes), absent[name])
+            counts, features = counts[:, :, kept], {"feature_id": genes[kept]}
+        elif name == "cohort_5":
+            measured = ~np.isin(np.arange(n_genes), absent[name])
+            counts[:, :, ~measured] = 0
+            features = {"feature_id": genes, "measured": measured}
+        cube = Cube(
+            {"counts": counts.astype(np.uint32)},
+            samples={
+                "sample_id": [f"D{j + 1:02d}" for j in range(n)],
+                "condition": condition,
+            },
+            groups={"group_id": group_ids, "cell_ontology_id": ontology_ids},
+            features=features,
+            n_cells=n_cells,
+            provenance={
+                "source": {
+                    "dataset": "simulated_cohorts",
+                    "cohort": name,
+                    "generator": "docs/datasets.py, numpy.random.default_rng(20261028)",
+                }
+            },
+        )
+        write_cube(cube, output / f"simulated_{name}.cube.zarr", overwrite=True)
+    np.savez_compressed(
+        output / "simulated_cohorts_truth.npz",
+        feature_id=genes,
+        studies=np.array([name for name, *_ in COHORTS]),
+        cell_ontology_ids=np.array(
+            [ontology_id for ontology_id, _ in COHORT_CELL_TYPES]
+        ),
+        true_log2_fold_change=log2_fc,
+    )
+
+
 # Kang et al. (2018): GEO series GSE96583, batch 2.
 
 GEO = "https://ftp.ncbi.nlm.nih.gov/geo/series/GSE96nnn/GSE96583/suppl/"
@@ -478,6 +641,33 @@ def hallmark_interferon_alpha(downloads: Path, output: Path) -> None:
             },
             build=simulated_bulk,
         ),
+        Dataset(
+            id="simulated_cohorts",
+            title="Simulated case-control cohorts",
+            description="6 cohorts of one disease, 12 to 60 donors each, 4 cell types "
+            "and 2,000 genes, with changes of every cohort and of one cohort only",
+            source="simulated by simulated_cohorts() in this script, numpy's PCG64 "
+            "generator with seed 20261028",
+            licence=SIMULATION_LICENCE,
+            citation=None,
+            files={
+                "simulated_cohort_1.cube.zarr": "sha256:"
+                "e97a0a97353376a5ac3a5c1e34f7e5f6fad6b0ac07c09b738b808fba6ccedb9e",
+                "simulated_cohort_2.cube.zarr": "sha256:"
+                "f5644603a625381e2eb95ad6e62d8f52780c59b6679d58a10d43f5295965ce96",
+                "simulated_cohort_3.cube.zarr": "sha256:"
+                "281cc4e81aecec198fd3c357271f95c4041ea33fbed711222acb6ad0846709ed",
+                "simulated_cohort_4.cube.zarr": "sha256:"
+                "a03544c081ee531297c9ff7aef0b6e073e626a932364ff33fb4b0746ffea0632",
+                "simulated_cohort_5.cube.zarr": "sha256:"
+                "7eb6775b5b70f8d8f691308aaca864792ddecd76c0e17921f275880897ad124b",
+                "simulated_cohort_6.cube.zarr": "sha256:"
+                "3fb7291bb67093b8883e2dfe177f2e88efe4c014d8b77b16dd6d7615decdb733",
+                "simulated_cohorts_truth.npz": "sha256:"
+                "6599ea11dddb183f630183846f7ece9991003a4cc77875e4e7822317f66346ec",
+            },
+            build=simulated_cohorts,
+        ),
         Dataset(
             id="kang2018",
             title="Interferon-beta stimulated PBMCs (Kang et al. 2018)",
diff --git a/docs/source/guides/combine_studies.ipynb b/docs/source/guides/combine_studies.ipynb
new file mode 100644
index 0000000..ec9f612
--- /dev/null
+++ b/docs/source/guides/combine_studies.ipynb
@@ -0,0 +1,601 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "1c4ebba4",
+   "metadata": {},
+   "source": [
+    "(combine-studies)=\n",
+    "# Combine studies\n",
+    "\n",
+    "Cohorts of one disease ask the same question of different donors, and each answers it\n",
+    "with its own samples, genes and names of cell types. To answer it with all of them,\n",
+    "align the cohorts' cubes in a {api}`CubeCollection`, analyse each cohort on its own,\n",
+    "and combine the results gene by gene by meta-analysis, with the forest plots and the\n",
+    "heterogeneity that metafor reports [@viechtbauer2010], or stack the cohorts into one\n",
+    "cube for a joint model.\n",
+    "[Meta-analysis](../statistics/meta_analysis.md) gives the statistics, and\n",
+    "[Replication across studies](replication_across_studies.ipynb) asks which changes\n",
+    "replicate.\n",
+    "\n",
+    "The cohorts are simulated, so that the truth is known: the `simulated_cohorts`\n",
+    "dataset of the [Datasets](../reference/datasets.md) page, 164 donors in six\n",
+    "case-control cohorts of 12 to 60 donors, with the pseudobulk counts of four blood\n",
+    "cell types over 2,000 genes. The disease changes 100 genes of the monocytes in every\n",
+    "cohort, each case by an activity of its own, and fewer genes of the CD4 T cells and\n",
+    "B cells; each cohort also changes 10 monocyte genes of its own, in every case alike,\n",
+    "as a confounder of that cohort would. `python docs/datasets.py prepare simulated_cohorts`\n",
+    "writes a cube file for every cohort, and the code below opens them by name."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "8102c38a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CytePool 0.1.dev55+g941b81fb5, numpy 2.5.4\n",
+      "cohort_1: 60 donors (30 cases), 2,000 genes\n",
+      "    B cells, CD4 T cells, NK cells, CD14 monocytes\n",
+      "cohort_2: 32 donors (16 cases), 2,000 genes\n",
+      "    B, CD4, NK, CD14 Mono\n",
+      "cohort_3: 24 donors (12 cases), 1,800 genes\n",
+      "    B cell, CD4+ T cell, NK cell, CD14+ monocyte\n",
+      "cohort_4: 16 donors (8 cases), 2,000 genes\n",
+      "    B, CD4 naive, CD4 memory, NK, Mono CD14\n",
+      "cohort_5: 12 donors (6 cases), 2,000 genes (1,850 measured)\n",
+      "    Bcell, T_CD4, NK, Mono_CD14\n",
+      "cohort_6: 20 donors (10 cases), 2,000 genes\n",
+      "    B cells, CD4+ T cells, CD14+ monocytes\n"
+     ]
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "\n",
+    "import cytepool\n",
+    "from cytepool.cube import CubeCollection\n",
+    "from cytepool.design import Design\n",
+    "from cytepool.differential_expression import limma_voom\n",
+    "from cytepool.io import read_cube\n",
+    "from cytepool.meta_analysis import (\n",
+    "    align_study_results,\n",
+    "    differential_expression_meta_analysis,\n",
+    ")\n",
+    "from cytepool.plotting import plot_forest, plot_significant_genes, plot_volcano\n",
+    "\n",
+    "studies = [f\"cohort_{i}\" for i in range(1, 7)]\n",
+    "cubes = {study: read_cube(f\"simulated_{study}.cube.zarr\") for study in studies}\n",
+    "print(f\"CytePool {cytepool.__version__}, numpy {np.__version__}\")\n",
+    "for study, cube in cubes.items():\n",
+    "    cases = np.count_nonzero(cube.samples[\"condition\"] == \"case\")\n",
+    "    genes = f\"{cube.n_features:,} genes\"\n",
+    "    if \"measured\" in cube.features:\n",
+    "        measured = np.count_nonzero(cube.features[\"measured\"])\n",
+    "        genes += f\" ({measured:,} measured)\"\n",
+    "    print(f\"{study}: {cube.n_samples} donors ({cases} cases), {genes}\")\n",
+    "    print(\"    \" + \", \".join(cube.groups[\"group_id\"].tolist()))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "02045e79",
+   "metadata": {},
+   "source": [
+    "## Align the cohorts\n",
+    "\n",
+    "The cubes cannot be compared as they are. Every cohort numbers its donors D01, D02,\n",
+    "..., so donor D01 of one cohort is not donor D01 of another. cohort_3's annotation\n",
+    "lacks 200 genes, and cohort_5's exporter wrote zeros for 150 genes it did not\n",
+    "measure, which its feature column `measured` marks: a zero there is not a count. And\n",
+    "every cohort names its cell types in its own words.\n",
+    "\n",
+    "{api}`CubeCollection` aligns them. It names every sample `study::sample_id`, aligns\n",
+    "the genes by `feature_id` and records which cohort measured which\n",
+    "({api}`CubeCollection.measured`), and aligns the cell types by their Cell Ontology\n",
+    "ids [@diehl2016], which every cube's group table holds in `cell_ontology_id`:\n",
+    "CL:0000236 (B cell), CL:0000624 (CD4-positive, alpha-beta T cell), CL:0000623\n",
+    "(natural killer cell) and CL:0001054 (CD14-positive monocyte). cohort_4 annotated\n",
+    "its CD4 T cells more finely, as naive (CL:0000895) and memory (CL:0000897) cells,\n",
+    "which the ontology places under CL:0000624; `group_mapping` rolls them up to it, and\n",
+    "the collection merges the two, exactly, since a cube's layers are sums\n",
+    "([The cube and its additive layers](../statistics/cubes.md#several-studies))."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "a8713a98",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CubeCollection of 6 studies: 164 samples, 4 groups, 2,000 features\n",
+      "  groups aligned by 'cell_ontology_id'\n",
+      "  cohort_1: 60 samples, 4 groups, 2,000 features measured\n",
+      "  cohort_2: 32 samples, 4 groups, 2,000 features measured\n",
+      "  cohort_3: 24 samples, 4 groups, 1,800 features measured\n",
+      "  cohort_4: 16 samples, 4 groups (1 merged), 2,000 features measured\n",
+      "  cohort_5: 12 samples, 4 groups, 1,850 features measured\n",
+      "  cohort_6: 20 samples, 3 groups, 2,000 features measured\n",
+      "  in every study: 3 groups, 1,650 features\n",
+      "  samples: cohort_1::D01, cohort_1::D02, ..., cohort_6::D20\n",
+      "\n",
+      "CL:0000624 holds\n",
+      "  cohort_1: CD4 T cells\n",
+      "  cohort_2: CD4\n",
+      "  cohort_3: CD4+ T cell\n",
+      "  cohort_4: CD4 naive, CD4 memory\n",
+      "  cohort_5: T_CD4\n",
+      "  cohort_6: CD4+ T cells\n"
+     ]
+    }
+   ],
+   "source": [
+    "cd4_subsets = {\"CD4 naive\": \"CL:0000624\", \"CD4 memory\": \"CL:0000624\"}\n",
+    "collection = CubeCollection(cubes, group_mapping={\"cohort_4\": cd4_subsets})\n",
+    "print(collection)\n",
+    "ids = collection.samples[\"sample_id\"]\n",
+    "print(f\"  samples: {ids[0]}, {ids[1]}, ..., {ids[-1]}\")\n",
+    "members = collection.group_members\n",
+    "cd4 = members[\"group_id\"] == \"CL:0000624\"\n",
+    "print(\"\\nCL:0000624 holds\")\n",
+    "for study in collection.studies:\n",
+    "    names = members[\"study_group_id\"][cd4 & (members[\"study\"] == study)]\n",
+    "    print(f\"  {study}: {', '.join(names)}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9baab815",
+   "metadata": {},
+   "source": [
+    "## Analyse each cohort on its own\n",
+    "\n",
+    "{api}`CubeCollection.aligned_cube` gives a cohort's cube on the collection's axes: its\n",
+    "cell types under their aligned ids, merged where several share one, the genes it\n",
+    "measured, and its own samples, so that a design built from its own sample table\n",
+    "applies. Each cohort is tested by {api}`limma_voom`, cases against controls, and its\n",
+    "results are keyed by the aligned cell types. {api}`plot_significant_genes` shows what\n",
+    "each cohort finds."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "8b7e8560",
+   "metadata": {
+    "figure_descriptions": [
+     "Six panels, one per cohort, each with a horizontal bar per aligned cell type for the genes found up (red, to the right) and down (blue, to the left) at an adjusted p-value below 0.05. In CL:0001054, the CD14-positive monocytes, cohort_1 (60 donors) finds 36 genes up and 37 down, cohort_2 (32 donors) 27 and 25, cohort_3 (24 donors) 19 and 11, cohort_4 (16 donors) 14 and 10, cohort_5 (12 donors) 14 and 14, and cohort_6 (20 donors) 15 and 16. In CL:0000624, the CD4 T cells, the cohorts find 5 to 34 genes; in the B cells (CL:0000236) at most 2; in the NK cells (CL:0000623) none, and cohort_6 has no row for them."
+    ]
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x850 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "contrast = \"case_vs_control\"\n",
+    "results = {}\n",
+    "for study in collection.studies:\n",
+    "    cube = collection.aligned_cube(study)\n",
+    "    design = Design(\n",
+    "        cube.samples,\n",
+    "        \"~ condition\",\n",
+    "        levels={\"condition\": [\"control\", \"case\"]},\n",
+    "        contrasts={contrast: \"conditioncase\"},\n",
+    "    )\n",
+    "    results[study] = limma_voom(cube, design)\n",
+    "\n",
+    "figure, axes = plt.subplots(3, 2, figsize=(8, 8.5), layout=\"constrained\")\n",
+    "for ax, (study, result) in zip(axes.flat, results.items()):\n",
+    "    plot_significant_genes(result, ax=ax)\n",
+    "    ax.set_title(f\"{study}, {cubes[study].n_samples} donors\", fontsize=10)\n",
+    "    if ax is not axes.flat[1]:\n",
+    "        ax.get_legend().remove()\n",
+    "for ax in axes[:-1].flat:\n",
+    "    ax.set_xlabel(\"\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2e2d2708",
+   "metadata": {},
+   "source": [
+    "The larger cohorts find more: cohort_1, with 60 donors, finds 73 monocyte genes, and\n",
+    "the cohorts of 12 to 32 donors 24 to 52. None finds a gene of the NK cells, which the\n",
+    "disease does not change, and cohort_6 has no NK cells to test.\n",
+    "\n",
+    "{api}`align_study_results` lines up the results of one cell type and contrast gene by\n",
+    "gene, with a row for every cohort and every gene of the collection, and says why a\n",
+    "cohort has no result for a gene: it did not measure the gene, or measured it and left\n",
+    "it out, as its expression filter does with genes too rarely detected. For the\n",
+    "monocytes:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "e071e6cd",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "            tested  not tested  not measured\n",
+      "cohort_1     1,973          27             0\n",
+      "cohort_2     1,942          58             0\n",
+      "cohort_3     1,753          47           200\n",
+      "cohort_4     1,960          40             0\n",
+      "cohort_5     1,554         296           150\n",
+      "cohort_6     1,971          29             0\n"
+     ]
+    }
+   ],
+   "source": [
+    "monocytes = \"CL:0001054\"\n",
+    "aligned = align_study_results(collection, results, monocytes, contrast)\n",
+    "statuses = (\"tested\", \"not_tested\", \"not_measured\")\n",
+    "print(f\"{'':<10}{'tested':>8}{'not tested':>12}{'not measured':>14}\")\n",
+    "for study in collection.studies:\n",
+    "    status = aligned[\"status\"][aligned[\"study\"] == study]\n",
+    "    counts = [np.count_nonzero(status == kind) for kind in statuses]\n",
+    "    print(f\"{study:<10}{counts[0]:>8,}{counts[1]:>12,}{counts[2]:>14,}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "add5104c",
+   "metadata": {},
+   "source": [
+    "cohort_3 did not measure 200 genes and cohort_5 150; their expression filters left out\n",
+    "47 and 296 more, cohort_5's the most, with the fewest donors and counts.\n",
+    "\n",
+    "## Meta-analysis\n",
+    "\n",
+    "{api}`differential_expression_meta_analysis` combines, for every cell type and gene,\n",
+    "the log2 fold changes of the cohorts that tested it, each with its standard error,\n",
+    "into one estimate. By default it fits the random-effects model, which lets the\n",
+    "cohorts' true effects vary around their mean by the between-study variance τ²\n",
+    "[@dersimonian1986], estimated by restricted maximum likelihood (REML) as metafor\n",
+    "estimates it by default, and tests the mean with a z-test. A gene that two cohorts or\n",
+    "more tested is analysed over those cohorts, so the genes that cohort_3 and cohort_5\n",
+    "did not measure are analysed over the other cohorts. {api}`plot_volcano` draws the\n",
+    "monocytes' table."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "ddc243a2",
+   "metadata": {
+    "figure_descriptions": [
+     "Volcano plot of the meta-analysis of the monocytes: the pooled log2 fold change, from about -1.1 to 1.1, against the -log10 p-value, up to 23. 42 genes are significant up (red) and 40 down (blue), with log2 fold changes beyond about 0.3 in either direction; the 1,890 others are grey, around zero. The ten genes with the smallest p-values are named, gene_0481, down, the highest."
+    ]
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 650x450 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "meta = differential_expression_meta_analysis(results)\n",
+    "table = meta.table(monocytes, contrast)\n",
+    "plot_volcano(table)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1dff10b7",
+   "metadata": {},
+   "source": [
+    "## One gene across the cohorts\n",
+    "\n",
+    "{api}`plot_forest` draws one gene's log2 fold change in every cohort with its 95%\n",
+    "confidence interval, each square sized by the cohort's weight, and the pooled\n",
+    "estimate as a diamond, as metafor's `forest` does [@lewis2001]. The upper plot is the\n",
+    "monocyte gene the meta-analysis finds most clearly. The lower one is the gene that\n",
+    "cohort_1 finds most clearly among those the simulation changed in cohort_1 alone; the\n",
+    "fixed-effect model, which assumes one true effect for every cohort, is fitted too\n",
+    "(`method=\"fixed\"`)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "487660ad",
+   "metadata": {
+    "figure_descriptions": [
+     "Two forest plots of monocyte genes, a row per cohort with its log2 fold change as a square and its 95% confidence interval, and the pooled random-effects estimate as an orange diamond at the bottom. Above, gene_0481, changed in every cohort: five cohorts between -1.19 and -0.84, every interval below zero, and cohort_3 marked not tested; the pooled estimate is -0.96 [-1.15, -0.78], with I-squared 0%. Below, gene_1312, changed in cohort_1 only: cohort_1 at 1.47 [1.20, 1.73] and the other five cohorts between -0.43 and 0.18, their intervals covering zero; the pooled estimate is 0.16 [-0.43, 0.74], with I-squared 91%, a wide diamond across zero."
+    ]
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "gene_1312, pooled log2 fold change [95% interval]:\n",
+      "  fixed effect: 0.50 [0.33, 0.66], adjusted p 9e-08\n",
+      "  random effects: 0.16 [-0.43, 0.74], adjusted p 1\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 750x800 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "truth = np.load(\"simulated_cohorts_truth.npz\")\n",
+    "genes = truth[\"feature_id\"]\n",
+    "cell_type = list(truth[\"cell_ontology_ids\"]).index(monocytes)\n",
+    "changed = truth[\"true_log2_fold_change\"][:, cell_type] != 0  # cohorts x genes\n",
+    "everywhere = genes[changed.all(axis=0)]\n",
+    "one_cohort = genes[changed.sum(axis=0) == 1]\n",
+    "unchanged = genes[~changed.any(axis=0)]\n",
+    "truth_sets = (everywhere, one_cohort, unchanged)\n",
+    "\n",
+    "strongest = table[\"feature_id\"][np.argmin(table[\"p_value\"])]\n",
+    "first = results[\"cohort_1\"].table(monocytes, contrast)\n",
+    "cohort_1_only = genes[changed[0] & (changed.sum(axis=0) == 1)]\n",
+    "own = np.isin(first[\"feature_id\"], cohort_1_only)\n",
+    "own_gene = first[\"feature_id\"][own][np.argmin(first[\"p_value\"][own])]\n",
+    "fixed = differential_expression_meta_analysis(results, method=\"fixed\")\n",
+    "print(f\"{own_gene}, pooled log2 fold change [95% interval]:\")\n",
+    "for name, analysis in ((\"fixed effect\", fixed), (\"random effects\", meta)):\n",
+    "    found = analysis.table(monocytes, contrast)\n",
+    "    i = np.flatnonzero(found[\"feature_id\"] == own_gene)[0]\n",
+    "    change, p = found[\"log2_fold_change\"][i], found[\"adjusted_p_value\"][i]\n",
+    "    low, high = found[\"ci_lower\"][i], found[\"ci_upper\"][i]\n",
+    "    print(f\"  {name}: {change:.2f} [{low:.2f}, {high:.2f}], adjusted p {p:.2g}\")\n",
+    "\n",
+    "figure, axes = plt.subplots(2, 1, figsize=(7.5, 8), layout=\"constrained\")\n",
+    "for ax, gene in zip(axes, (strongest, own_gene)):\n",
+    "    plot_forest(meta, results, gene, group=monocytes, contrast=contrast, ax=ax)\n",
+    "axes[0].set_title(f\"{strongest}: changed in every cohort\", fontsize=10)\n",
+    "axes[1].set_title(f\"{own_gene}: changed in cohort_1 only\", fontsize=10)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "be49bef9",
+   "metadata": {},
+   "source": [
+    "In the upper plot the cohorts agree: every estimate lies below zero, I² is 0%, and\n",
+    "the pooled log2 fold change, -0.96, has a narrow interval; cohort_3 did not measure\n",
+    "the gene and keeps its row, marked not tested. In the lower one, cohort_1 changes the\n",
+    "gene by 1.47 and the other cohorts' estimates lie around zero. Cochran's Q\n",
+    "[@cochran1954] finds the cohorts far more different than their standard errors allow,\n",
+    "and I² [@higgins2002] puts 91% of the variation down to heterogeneity: the\n",
+    "random-effects model widens the pooled interval to [-0.43, 0.74] and does not find the\n",
+    "gene. The fixed-effect model gives cohort_1, the most precise cohort, the largest\n",
+    "weight and finds it, with an adjusted p-value of 9e-08.\n",
+    "\n",
+    "## Stack the cohorts into one cube\n",
+    "\n",
+    "{api}`CubeCollection.stack` puts the cohorts into one cube for a joint model, with\n",
+    "every cohort's samples and the cohort as a column of the sample table. A cube holds\n",
+    "no missing values, so the stacked cube keeps the genes that every cohort measured and\n",
+    "the cell types that every cohort has; each cohort keeps its own counts and library\n",
+    "sizes. {api}`CubeCollection.select` chooses the cohorts, cell types and genes of an\n",
+    "analysis, such as the cohorts that measured every gene. The design\n",
+    "`~ study + condition` gives every gene a baseline in each cohort and one difference\n",
+    "between cases and controls, shared by all."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "17bee8ed",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Cube with 164 samples x 3 groups x 1,650 features\n",
+      "  layers: counts (int64)\n",
+      "  filled: 492 of 492 sample x group entries have cells (100.0%)\n",
+      "  samples: sample_id, study, condition\n",
+      "  groups: group_id, cell_ontology_id\n",
+      "  features: feature_id\n",
+      "  4 cohorts measured every gene: 2,000 genes\n"
+     ]
+    }
+   ],
+   "source": [
+    "stacked = collection.stack()\n",
+    "print(stacked)\n",
+    "complete = collection.select(studies=collection.measured.all(axis=1))\n",
+    "n_genes = complete.stack().n_features\n",
+    "print(f\"  {complete.n_studies} cohorts measured every gene: {n_genes:,} genes\")\n",
+    "design = Design(\n",
+    "    stacked.samples,\n",
+    "    \"~ study + condition\",\n",
+    "    levels={\"condition\": [\"control\", \"case\"]},\n",
+    "    contrasts={contrast: \"conditioncase\"},\n",
+    ")\n",
+    "joint = limma_voom(stacked, design)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f905689f",
+   "metadata": {},
+   "source": [
+    "## The approaches against the truth\n",
+    "\n",
+    "The simulation knows which genes each approach should find. The monocyte genes found\n",
+    "at a false discovery rate of 5% by cohort_1 alone, by the meta-analyses and by the\n",
+    "joint model, split by what the simulation did to them:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "705e8505",
+   "metadata": {
+    "figure_descriptions": [
+     "Stacked horizontal bars of the monocyte genes found at an adjusted p-value below 0.05 by four approaches, split into genes the simulation changed in every cohort (blue), in one cohort only (orange) and in none (grey): cohort_1 alone 62, 10 and 1; the fixed-effect meta-analysis 91, 23 and 2; the random-effects meta-analysis 80, 0 and 2; the stacked joint model 76, 18 and 4."
+    ]
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "genes changed everywhere: 100, in the stack: 82\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 750x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def by_truth(table):\n",
+    "    called = table[\"feature_id\"][table[\"adjusted_p_value\"] < 0.05]\n",
+    "    return [np.isin(called, kind).sum() for kind in truth_sets]\n",
+    "\n",
+    "\n",
+    "approaches = {\n",
+    "    \"cohort_1 alone\": first,\n",
+    "    \"Meta-analysis, fixed effect\": fixed.table(monocytes, contrast),\n",
+    "    \"Meta-analysis, random effects\": table,\n",
+    "    \"Stacked, ~ study + condition\": joint.table(monocytes, contrast),\n",
+    "}\n",
+    "counts = np.array([by_truth(found) for found in approaches.values()])\n",
+    "in_stack = np.isin(everywhere, stacked.features[\"feature_id\"]).sum()\n",
+    "print(f\"genes changed everywhere: {everywhere.size}, in the stack: {in_stack}\")\n",
+    "\n",
+    "labels = (\"changed in every cohort\", \"changed in one cohort only\", \"unchanged\")\n",
+    "colors = (\"#2a78d6\", \"#eb6834\", \"#898781\")\n",
+    "figure, ax = plt.subplots(figsize=(7.5, 3), layout=\"constrained\")\n",
+    "names, left = list(approaches), np.zeros(len(approaches))\n",
+    "for column, (label, color) in enumerate(zip(labels, colors)):\n",
+    "    values = counts[:, column]\n",
+    "    bars = ax.barh(names, values, left=left, color=color, label=label)\n",
+    "    shown = [f\"{n}\" if n >= 4 else \"\" for n in values]\n",
+    "    ax.bar_label(bars, shown, label_type=\"center\", color=\"white\", fontsize=8)\n",
+    "    left += values\n",
+    "ax.invert_yaxis()\n",
+    "ax.set_xlabel(\"Monocyte genes at adjusted p < 0.05\")\n",
+    "ax.spines[[\"top\", \"right\"]].set_visible(False)\n",
+    "figure.legend(loc=\"outside lower center\", ncols=3, frameon=False)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d23662f6",
+   "metadata": {},
+   "source": [
+    "cohort_1 alone finds 62 of the 100 genes that the disease changes in every cohort. The\n",
+    "fixed-effect meta-analysis finds the most of them, 91, and 23 genes of single cohorts\n",
+    "with them. The random-effects meta-analysis finds 80 and none of a single cohort: a\n",
+    "change in one cohort alone makes the cohorts disagree, which raises τ² and widens the\n",
+    "interval. The joint model assumes one change for every cohort, as the fixed effect\n",
+    "does, and finds 76 and 18; it tests only the genes that every cohort measured, which\n",
+    "hold 82 of the 100.\n",
+    "\n",
+    "## Choices\n",
+    "\n",
+    "- **Meta-analysis or a joint model.** A meta-analysis needs only each cohort's\n",
+    "  results, keeps the genes some cohorts did not measure, lets each cohort keep its own\n",
+    "  design, normalization and variances, and measures how far the cohorts disagree. A\n",
+    "  joint model of the stacked cohorts suits cohorts made alike, with one protocol and\n",
+    "  platform, that are too small to estimate their variances alone; it estimates one\n",
+    "  difference for every cohort, so a change in one cohort enters the estimate for all.\n",
+    "- **Fixed or random effects.** The fixed-effect model (`method=\"fixed\"`) answers for\n",
+    "  the cohorts at hand and assumes they estimate one effect; the random-effects model,\n",
+    "  the default, allows the effect to vary between cohorts. τ² can also be estimated by\n",
+    "  DerSimonian and Laird's method (`\"dl\"`) or Paule and Mandel's (`\"pm\"`), and\n",
+    "  `test=\"knapp_hartung\"` gives the t-tests that metafor's documentation recommends for\n",
+    "  few studies ([Meta-analysis](../statistics/meta_analysis.md#tests-and-heterogeneity)).\n",
+    "- **Genes that some cohorts did not test.** `min_studies`, two by default, sets how\n",
+    "  many cohorts must test a gene; {api}`align_study_results` says whether a cohort did\n",
+    "  not measure a gene or left it out.\n",
+    "- **Cell types and ids.** Cell types are aligned by `cell_ontology_id` when every\n",
+    "  cohort has it, by `group_id` otherwise, or by another column (`align_groups_by`);\n",
+    "  `group_mapping` names the aligned id of any group, or None to leave it out. Columns\n",
+    "  of ids other than `sample_id`, such as donors with several samples, are kept apart\n",
+    "  by cohort with `within_study`."
+   ]
+  }
+ ],
+ "metadata": {
+  "datasets": [
+   "simulated_cohorts"
+  ],
+  "executed_at": "2026-10-26T21:28:59+00:00",
+  "executed_code_sha256": "deba02883cab4b6b05bc7a5808e0573f453613d3e37d64804183954b8783d956",
+  "executed_datasets": {
+   "simulated_cohorts": {
+    "simulated_cohort_1.cube.zarr": "sha256:e97a0a97353376a5ac3a5c1e34f7e5f6fad6b0ac07c09b738b808fba6ccedb9e",
+    "simulated_cohort_2.cube.zarr": "sha256:f5644603a625381e2eb95ad6e62d8f52780c59b6679d58a10d43f5295965ce96",
+    "simulated_cohort_3.cube.zarr": "sha256:281cc4e81aecec198fd3c357271f95c4041ea33fbed711222acb6ad0846709ed",
+    "simulated_cohort_4.cube.zarr": "sha256:a03544c081ee531297c9ff7aef0b6e073e626a932364ff33fb4b0746ffea0632",
+    "simulated_cohort_5.cube.zarr": "sha256:7eb6775b5b70f8d8f691308aaca864792ddecd76c0e17921f275880897ad124b",
+    "simulated_cohort_6.cube.zarr": "sha256:3fb7291bb67093b8883e2dfe177f2e88efe4c014d8b77b16dd6d7615decdb733",
+    "simulated_cohorts_truth.npz": "sha256:6599ea11dddb183f630183846f7ece9991003a4cc77875e4e7822317f66346ec"
+   }
+  },
+  "executed_with": "0.1.dev55+g941b81fb5",
+  "execution_seconds": 6.3,
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/source/guides/replication_across_studies.ipynb b/docs/source/guides/replication_across_studies.ipynb
new file mode 100644
index 0000000..c1bacb7
--- /dev/null
+++ b/docs/source/guides/replication_across_studies.ipynb
@@ -0,0 +1,644 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "681835ad",
+   "metadata": {},
+   "source": [
+    "(replication-across-studies)=\n",
+    "# Replication across studies\n",
+    "\n",
+    "Most genes that one study finds are not found by the next: of the genes that one\n",
+    "dataset of Alzheimer's disease found differentially expressed, over 85% were not\n",
+    "found in any of 16 others [@nakatsuka2025]. Some are changes the other studies were\n",
+    "too small to detect, others belong to one study alone. This page measures how well\n",
+    "six cohorts replicate each other, and which of their genes replicate: the genes found\n",
+    "in k cohorts, π₁ between cohorts, combined p-values against the p-value that a change\n",
+    "replicates, SumRank [@nakatsuka2025], and the prediction of cases in a cohort left\n",
+    "out. [Meta-analysis](../statistics/meta_analysis.md) explains the statistics.\n",
+    "\n",
+    "The cohorts are the six simulated ones of [Combine studies](combine_studies.ipynb),\n",
+    "aligned and analysed as there, each by {api}`limma_voom` on its own. The simulation\n",
+    "knows which monocyte genes the disease changes in every cohort, and which genes one\n",
+    "cohort alone changes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "0b25cd2a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CytePool 0.1.dev55+g941b81fb5, numpy 2.5.4\n",
+      "monocyte genes, adjusted p < 0.05    every cohort  one cohort  unchanged\n",
+      "cohort_1 (60 donors)                           62          10          1\n",
+      "cohort_2 (32 donors)                           38          10          4\n",
+      "cohort_3 (24 donors)                           22           7          1\n",
+      "cohort_4 (16 donors)                           17           7          0\n",
+      "cohort_5 (12 donors)                           26           2          0\n",
+      "cohort_6 (20 donors)                           22           7          2\n",
+      "changed by the simulation                     100          60           \n"
+     ]
+    }
+   ],
+   "source": [
+    "import textwrap\n",
+    "import warnings\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "\n",
+    "import cytepool\n",
+    "from cytepool.cube import CubeCollection\n",
+    "from cytepool.design import Design\n",
+    "from cytepool.differential_expression import limma_voom\n",
+    "from cytepool.held_out_prediction import held_out_prediction\n",
+    "from cytepool.io import read_cube\n",
+    "from cytepool.meta_analysis import (\n",
+    "    align_study_results,\n",
+    "    differential_expression_meta_analysis,\n",
+    ")\n",
+    "from cytepool.plotting import (\n",
+    "    plot_held_out_auc,\n",
+    "    plot_replicated_genes,\n",
+    "    plot_study_agreement,\n",
+    "    plot_sumrank,\n",
+    ")\n",
+    "from cytepool.replication import differential_expression_replication\n",
+    "from cytepool.stats.combined_p_values import (\n",
+    "    combine_p_values,\n",
+    "    partial_conjunction,\n",
+    ")\n",
+    "from cytepool.stats.multiple_testing import adjust_p_values\n",
+    "from cytepool.sumrank import differential_expression_sumrank\n",
+    "\n",
+    "studies = [f\"cohort_{i}\" for i in range(1, 7)]\n",
+    "cubes = {study: read_cube(f\"simulated_{study}.cube.zarr\") for study in studies}\n",
+    "cd4_subsets = {\"CD4 naive\": \"CL:0000624\", \"CD4 memory\": \"CL:0000624\"}\n",
+    "collection = CubeCollection(cubes, group_mapping={\"cohort_4\": cd4_subsets})\n",
+    "contrast, monocytes = \"case_vs_control\", \"CL:0001054\"\n",
+    "results = {}\n",
+    "for study in collection.studies:\n",
+    "    cube = collection.aligned_cube(study)\n",
+    "    design = Design(\n",
+    "        cube.samples,\n",
+    "        \"~ condition\",\n",
+    "        levels={\"condition\": [\"control\", \"case\"]},\n",
+    "        contrasts={contrast: \"conditioncase\"},\n",
+    "    )\n",
+    "    results[study] = limma_voom(cube, design)\n",
+    "\n",
+    "truth = np.load(\"simulated_cohorts_truth.npz\")\n",
+    "genes = truth[\"feature_id\"]\n",
+    "cell_type = list(truth[\"cell_ontology_ids\"]).index(monocytes)\n",
+    "changed = truth[\"true_log2_fold_change\"][:, cell_type] != 0  # cohorts x genes\n",
+    "everywhere = genes[changed.all(axis=0)]\n",
+    "one_cohort = genes[changed.sum(axis=0) == 1]\n",
+    "unchanged = genes[~changed.any(axis=0)]\n",
+    "truth_sets = (everywhere, one_cohort, unchanged)\n",
+    "columns = (\"every cohort\", \"one cohort\", \"unchanged\")\n",
+    "\n",
+    "\n",
+    "def by_truth(feature_ids, adjusted):\n",
+    "    called = np.asarray(feature_ids)[np.asarray(adjusted) < 0.05]\n",
+    "    return [np.isin(called, kind).sum() for kind in truth_sets]\n",
+    "\n",
+    "\n",
+    "def row(label, values):\n",
+    "    print(f\"{label:<36}{values[0]:>13}{values[1]:>12}{values[2]:>11}\")\n",
+    "\n",
+    "\n",
+    "print(f\"CytePool {cytepool.__version__}, numpy {np.__version__}\")\n",
+    "row(\"monocyte genes, adjusted p < 0.05\", columns)\n",
+    "for study, result in results.items():\n",
+    "    table = result.table(monocytes, contrast)\n",
+    "    label = f\"{study} ({cubes[study].n_samples} donors)\"\n",
+    "    row(label, by_truth(table[\"feature_id\"], table[\"adjusted_p_value\"]))\n",
+    "row(\"changed by the simulation\", (everywhere.size, one_cohort.size, \"\"))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ac47b4ce",
+   "metadata": {},
+   "source": [
+    "Each cohort finds 17 to 62 of the 100 genes that the disease changes in every cohort,\n",
+    "the most in the largest, and 2 to 10 of the 10 genes it changes alone.\n",
+    "\n",
+    "## Genes found in k cohorts\n",
+    "\n",
+    "{api}`differential_expression_replication` reads one cell type and contrast of every\n",
+    "cohort, lined up gene by gene, and counts the cohorts that find each gene (adjusted\n",
+    "p-value below 0.05) and in which direction. {api}`plot_replicated_genes` draws how\n",
+    "many genes each number of cohorts finds, as the paper that introduced SumRank counts\n",
+    "them [@nakatsuka2025]."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "779274c0",
+   "metadata": {
+    "figure_descriptions": [
+     "Bars of the monocyte genes found at an adjusted p-value below 0.05 by exactly k of the six cohorts: 73 by one, 32 by two, 15 by three, 9 by four, 4 by five and none by all six. Every gene is found in one direction by the cohorts that find it; no bar has a part for genes found up in some cohorts and down in others."
+    ]
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "monocyte genes found: 133\n",
+      "  by one cohort only: 73\n",
+      "    changed in every cohort: 22\n",
+      "    changed in that cohort alone: 43\n",
+      "    unchanged: 8\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 720x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "replication = differential_expression_replication(\n",
+    "    collection, results, group=monocytes, contrast=contrast\n",
+    ")\n",
+    "found = replication.genes\n",
+    "once = found[\"feature_id\"][found[\"n_found\"] == 1]\n",
+    "every, own, none = (np.isin(once, kind).sum() for kind in truth_sets)\n",
+    "print(f\"monocyte genes found: {np.count_nonzero(found['n_found'])}\")\n",
+    "print(f\"  by one cohort only: {once.size}\")\n",
+    "print(f\"    changed in every cohort: {every}\")\n",
+    "print(f\"    changed in that cohort alone: {own}\")\n",
+    "print(f\"    unchanged: {none}\")\n",
+    "plot_replicated_genes(replication)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f898c8d6",
+   "metadata": {},
+   "source": [
+    "Most genes are found by one cohort only, 73 of the 133 found, and none by all six. Of\n",
+    "those 73, 43 belong to that cohort alone, but 22 change in every cohort and the other\n",
+    "cohorts missed them: a gene that one cohort finds and the next does not is not\n",
+    "necessarily a change of one cohort.\n",
+    "\n",
+    "## How well each cohort replicates the others\n",
+    "\n",
+    "A gene that one cohort finds and another does not may still change in the other, by\n",
+    "too little for it to detect. π₁ of the second cohort's p-values for the genes the\n",
+    "first finds, one minus Storey's estimate of the proportion of them without an effect\n",
+    "[@storey2003], estimates the share that change in the second cohort, whether it finds\n",
+    "them or not. {api}`plot_study_agreement` draws it for every pair of cohorts. Each\n",
+    "entry is computed from the few dozen genes that one cohort finds; on so few p-values\n",
+    "qvalue's default estimate of π₀, a smoother read at λ = 0.95, is unsteady, and a\n",
+    "single λ of 0.5 is steadier (`null_proportion={\"lambdas\": 0.5}`)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "ebdc14ed",
+   "metadata": {
+    "figure_descriptions": [
+     "Heatmap of the six cohorts, discovery cohorts in rows and replication cohorts in columns, each entry with its value and the number of genes it is computed from, 20 to 73: pi1 of the genes the discovery cohort finds, in the replication cohort's p-values, from 0.47 (the genes of cohort_3 in cohort_4 and in cohort_6) to 1.00 (the genes of cohort_5 in cohort_2 and in cohort_3), most entries between 0.6 and 0.9."
+    ]
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "pi1 by qvalue's smoother: 0.00 to 1.00, mean 0.72\n",
+      "pi1 by lambda 0.5: 0.47 to 1.00, mean 0.73\n",
+      "share of the genes found by both cohorts: mean 0.32\n",
+      "share with the same sign: 0.73 to 1.00\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 750x580 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "steady = differential_expression_replication(\n",
+    "    collection,\n",
+    "    results,\n",
+    "    group=monocytes,\n",
+    "    contrast=contrast,\n",
+    "    null_proportion={\"lambdas\": 0.5},\n",
+    ")\n",
+    "pairs = steady.study_pairs\n",
+    "for name, pi1 in (\n",
+    "    (\"qvalue's smoother\", replication.study_pairs[\"pi1\"]),\n",
+    "    (\"lambda 0.5\", pairs[\"pi1\"]),\n",
+    "):\n",
+    "    low, high, mean = pi1.min(), pi1.max(), pi1.mean()\n",
+    "    print(f\"pi1 by {name}: {low:.2f} to {high:.2f}, mean {mean:.2f}\")\n",
+    "both = pairs[\"n_found_in_both\"] / pairs[\"n_found\"]\n",
+    "signs = pairs[\"sign_agreement\"]\n",
+    "print(f\"share of the genes found by both cohorts: mean {both.mean():.2f}\")\n",
+    "print(f\"share with the same sign: {signs.min():.2f} to {signs.max():.2f}\")\n",
+    "figure, ax = plt.subplots(figsize=(7.5, 5.8), layout=\"constrained\")\n",
+    "plot_study_agreement(steady, ax=ax)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "45c69a6b",
+   "metadata": {},
+   "source": [
+    "π₁ lies between 0.47 and 1.00, 0.73 on average: of the genes one cohort finds, most\n",
+    "change in each other cohort, although the other cohort finds only a third of them\n",
+    "itself, on average, and their signs agree for 73% to 100% of them. qvalue's smoother\n",
+    "gave π₁ from 0 to 1 on the same genes. π₁ is not symmetric: it reads the genes of the\n",
+    "discovery cohort in the p-values of the replication cohort, and the genes of the small\n",
+    "cohort_5, its clearest changes, replicate best.\n",
+    "\n",
+    "## Combined p-values and replicated changes\n",
+    "\n",
+    "{api}`combine_p_values` combines each gene's p-values over the cohorts that tested it\n",
+    "into the p-value of the hypothesis that no cohort changes it: Fisher's method responds\n",
+    "to the strongest cohort [@fisher1932], and Stouffer's, weighted by the square roots of\n",
+    "the cohorts' sizes, to the evidence they share [@stouffer1949; @zaykin2011]; the\n",
+    "replication result holds the latter. A small combined p-value says that some cohort\n",
+    "changes the gene, not that the change replicates. {api}`partial_conjunction` tests\n",
+    "instead whether at least u cohorts change it, in one direction [@benjamini2008]; the\n",
+    "replication result holds it for u = 2. The monocyte genes each finds at a false\n",
+    "discovery rate of 5%, with the random-effects meta-analysis (REML) of\n",
+    "[Combine studies](combine_studies.ipynb) for comparison:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "be8696bb",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "genes at adjusted p < 0.05           every cohort  one cohort  unchanged\n",
+      "Fisher                                         94          36          3\n",
+      "Stouffer, weighted                             93          19          2\n",
+      "in at least 2 cohorts                          41           0          0\n",
+      "in at least 3 cohorts                           7           0          0\n",
+      "meta-analysis (REML)                           80           0          2\n"
+     ]
+    }
+   ],
+   "source": [
+    "aligned = align_study_results(collection, results, monocytes, contrast)\n",
+    "shape = (collection.n_studies, collection.n_features)\n",
+    "p_values = aligned[\"p_value\"].reshape(shape)\n",
+    "log2_fc = aligned[\"log2_fold_change\"].reshape(shape)\n",
+    "ids, found_ids = collection.features[\"feature_id\"], found[\"feature_id\"]\n",
+    "fisher = combine_p_values(p_values, method=\"fisher\", directions=log2_fc)\n",
+    "in_three = partial_conjunction(p_values, 3, directions=log2_fc)\n",
+    "meta = differential_expression_meta_analysis(results).table(monocytes, contrast)\n",
+    "methods = {\n",
+    "    \"Fisher\": (ids, adjust_p_values(fisher.p_value, \"bh\")),\n",
+    "    \"Stouffer, weighted\": (found_ids, found[\"adjusted_p_value\"]),\n",
+    "    \"in at least 2 cohorts\": (found_ids, found[\"replication_adjusted_p_value\"]),\n",
+    "    \"in at least 3 cohorts\": (ids, adjust_p_values(in_three.p_value, \"bh\")),\n",
+    "    \"meta-analysis (REML)\": (meta[\"feature_id\"], meta[\"adjusted_p_value\"]),\n",
+    "}\n",
+    "row(\"genes at adjusted p < 0.05\", columns)\n",
+    "for name, (feature_ids, adjusted) in methods.items():\n",
+    "    row(name, by_truth(feature_ids, adjusted))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1e06f3e4",
+   "metadata": {},
+   "source": [
+    "Fisher's method finds 94 of the 100 genes changed in every cohort, and 36 of the 60\n",
+    "genes of single cohorts: one cohort's strong change is enough. Stouffer's method,\n",
+    "which weighs the evidence of every cohort and its direction, keeps 19 of those. The\n",
+    "partial conjunction tests find no gene of a single cohort, at a cost in power: 41\n",
+    "genes in at least 2 cohorts and 7 in at least 3, since the change must be clear in\n",
+    "that many cohorts on its own. The random-effects meta-analysis finds 80, none of a\n",
+    "single cohort.\n",
+    "\n",
+    "## SumRank\n",
+    "\n",
+    "SumRank asks instead that a gene rank high in most cohorts [@nakatsuka2025]. Each\n",
+    "cohort ranks the genes that every cohort tested by their signed -log10 p-values, and a\n",
+    "gene's normalized ranks are summed over the cohorts, for genes up and, from the other\n",
+    "end, for genes down. If the cohorts ranked the genes at random, the sum of a gene's\n",
+    "ranks over every cohort would follow the Irwin-Hall distribution\n",
+    "[@irwin1927; @hall1927], which gives its p-value.\n",
+    "{api}`differential_expression_sumrank` computes it for one cell type and contrast, and\n",
+    "{api}`plot_sumrank` draws every gene's p-value, the genes in alphabetical order, as the\n",
+    "authors of SumRank draw it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "a34e9df8",
+   "metadata": {
+    "figure_descriptions": [
+     "Two Manhattan plots of the 1,380 genes ranked, in alphabetical order, at the -log10 Irwin-Hall p-value of SumRank for genes up (top) and down (bottom). 29 genes up (red) and 27 down (blue) pass an adjusted p-value of 0.05, at -log10 p-values from about 3 to 12, above a dotted line at the highest of the other genes, about 2.5 up and 2.9 down; the other genes, black, lie mostly below 2. The four strongest genes of each direction are named."
+    ]
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1,380 genes ranked, by all six cohorts\n",
+      "adjusted p < 0.05                    every cohort  one cohort  unchanged\n",
+      "genes up                                       28           0          1\n",
+      "genes down                                     27           0          0\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x650 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sumrank = differential_expression_sumrank(\n",
+    "    collection, results, group=monocytes, contrast=contrast\n",
+    ")\n",
+    "table = sumrank.table\n",
+    "print(f\"{len(table):,} genes ranked, by all six cohorts\")\n",
+    "row(\"adjusted p < 0.05\", columns)\n",
+    "for direction in (\"up\", \"down\"):\n",
+    "    adjusted = table[f\"{direction}_adjusted_p_value\"]\n",
+    "    row(f\"genes {direction}\", by_truth(table[\"feature_id\"], adjusted))\n",
+    "figure, axes = plt.subplots(2, 1, figsize=(8, 6.5), layout=\"constrained\")\n",
+    "plot_sumrank(sumrank, n_labels=4, ax=axes[0])\n",
+    "plot_sumrank(sumrank, direction=\"down\", n_labels=4, ax=axes[1])\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1069af8b",
+   "metadata": {},
+   "source": [
+    "SumRank finds 56 genes, 29 up and 27 down, all but one changed in every cohort, and no\n",
+    "gene of a single cohort, which ranks high in that cohort only. It ranks the 1,380\n",
+    "genes that all six cohorts tested: genes that cohort_3 or cohort_5 did not measure,\n",
+    "or that the expression filter of the small cohort_5 left out, are not ranked.\n",
+    "\n",
+    "## Summing the best ranks\n",
+    "\n",
+    "A cohort can miss a change for reasons of its own, so SumRank can sum each gene's best\n",
+    "ranks only, those of the cohorts that rank it highest: the paper summed every study of\n",
+    "Parkinson's disease, Huntington's disease and schizophrenia, of which there were 3 to\n",
+    "6, and 65% of the 17 studies of Alzheimer's disease and 55% of the 16 of COVID-19,\n",
+    "proportions it chose by cross-validation [@nakatsuka2025]. A sum of the best ranks is\n",
+    "smaller than a sum of uniform ranks, so its Irwin-Hall p-values are too small, as\n",
+    "{api}`differential_expression_sumrank` warns. Its p-values come instead from\n",
+    "permutations: the case and control labels are permuted within each cohort, each\n",
+    "cohort's analysis is run again, and the statistics of every gene in every permutation\n",
+    "make one null distribution [@phipson2010]. Here the best 4 of the 6 cohorts are\n",
+    "summed, with 30 permutations."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "d6b26d0c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "RuntimeWarning: the Irwin-Hall p-values of a sum of the best 4 of 6 studies'\n",
+      "ranks are too small, as the sum of the best ranks is smaller than a sum of\n",
+      "uniform ranks; the p_value columns hold them, so take p-values from\n",
+      "permutations (n_permutations and permute)\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "up or down, adjusted p < 0.05        every cohort  one cohort  unchanged\n",
+      "all 6 cohorts, Irwin-Hall                      55           0          1\n",
+      "best 4, Irwin-Hall                             62           7         16\n",
+      "best 4, 30 permutations                        59           0          0\n"
+     ]
+    }
+   ],
+   "source": [
+    "with warnings.catch_warnings(record=True) as caught:\n",
+    "    warnings.simplefilter(\"always\")\n",
+    "    irwin_hall = differential_expression_sumrank(\n",
+    "        collection, results, monocytes, contrast, proportion_of_studies=4 / 6\n",
+    "    )\n",
+    "print(textwrap.fill(f\"RuntimeWarning: {caught[0].message}\", 78), end=\"\\n\\n\")\n",
+    "permuted = differential_expression_sumrank(\n",
+    "    collection,\n",
+    "    results,\n",
+    "    monocytes,\n",
+    "    contrast,\n",
+    "    proportion_of_studies=4 / 6,\n",
+    "    n_permutations=30,\n",
+    "    permute=\"condition\",\n",
+    "    rng=1,\n",
+    ")\n",
+    "row(\"up or down, adjusted p < 0.05\", columns)\n",
+    "for name, found_by in (\n",
+    "    (\"all 6 cohorts, Irwin-Hall\", sumrank),\n",
+    "    (\"best 4, Irwin-Hall\", irwin_hall),\n",
+    "    (\"best 4, 30 permutations\", permuted),\n",
+    "):\n",
+    "    table = found_by.table\n",
+    "    up, down = table[\"up_adjusted_p_value\"], table[\"down_adjusted_p_value\"]\n",
+    "    row(name, by_truth(table[\"feature_id\"], np.minimum(up, down)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b3cc6d7f",
+   "metadata": {},
+   "source": [
+    "Summed over the best 4 of the 6 cohorts, the Irwin-Hall p-values find 16 unchanged\n",
+    "genes and 7 of single cohorts, as the warning says they would. With permutations, the\n",
+    "same statistics find 59 genes, all changed in every cohort, more than the sum over all\n",
+    "six.\n",
+    "\n",
+    "## Prediction in held-out cohorts\n",
+    "\n",
+    "Genes that replicate should tell cases from controls in a cohort that had no part in\n",
+    "choosing them. {api}`held_out_prediction` leaves each cohort out in turn, chooses 20\n",
+    "genes up and 20 down in every cell type from the other five, by SumRank and by a\n",
+    "random-effects meta-analysis with the Knapp-Hartung adjustment, as the paper compares\n",
+    "them, scores every donor of the held-out cohort by how high its profiles rank those\n",
+    "genes, as UCell scores them [@andreatta2021], and reports the area under the ROC\n",
+    "curve (AUC) of its cases against its controls [@hanley1982]. {api}`plot_held_out_auc`\n",
+    "draws it for every cohort. The paper chose the proportion of studies summed by\n",
+    "cross-validation; the mean AUC for a few proportions comes first."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "e0583f28",
+   "metadata": {
+    "figure_descriptions": [
+     "The AUC of every held-out cohort's cases against its controls, a row per cohort, by the method that chose the genes from the other five: SumRank (blue circles) 0.86, 0.88, 0.92, 0.94, 1.00 and 0.93 for cohort_1 to cohort_6, mean 0.92; the meta-analysis (orange squares) 0.76, 0.86, 0.83, 0.94, 0.94 and 0.91, mean 0.87. A dotted line marks 0.5, the AUC of a score that does not separate them."
+    ]
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "proportion of the cohorts summed: mean AUC\n",
+      "  1.0: SumRank 0.92, meta-analysis 0.87\n",
+      "  0.8: SumRank 0.89, meta-analysis 0.87\n",
+      "  0.6: SumRank 0.87, meta-analysis 0.87\n",
+      "  0.4: SumRank 0.92, meta-analysis 0.87\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 650x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "lines = [\"proportion of the cohorts summed: mean AUC\"]\n",
+    "for proportion in (1.0, 0.8, 0.6, 0.4):\n",
+    "    auc = held_out_prediction(\n",
+    "        collection,\n",
+    "        results,\n",
+    "        contrast,\n",
+    "        \"condition\",\n",
+    "        \"case\",\n",
+    "        \"control\",\n",
+    "        n_genes=20,\n",
+    "        proportion_of_studies=proportion,\n",
+    "    ).auc\n",
+    "    sumrank_auc = np.nanmean(auc[\"auc\"][auc[\"method\"] == \"sumrank\"])\n",
+    "    meta_auc = np.nanmean(auc[\"auc\"][auc[\"method\"] == \"meta_analysis\"])\n",
+    "    line = f\"SumRank {sumrank_auc:.2f}, meta-analysis {meta_auc:.2f}\"\n",
+    "    lines.append(f\"  {proportion}: {line}\")\n",
+    "print(\"\\n\".join(lines))\n",
+    "prediction = held_out_prediction(\n",
+    "    collection, results, contrast, \"condition\", \"case\", \"control\", n_genes=20\n",
+    ")\n",
+    "plot_held_out_auc(prediction)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0e48d69a",
+   "metadata": {},
+   "source": [
+    "SumRank's genes predict the held-out cases a little better here, with a mean AUC of\n",
+    "0.92 against 0.87, most clearly in cohort_1 and cohort_3; with six cohorts, the\n",
+    "proportion summed moves the mean AUC between 0.87 and 0.92 only. In the 17 studies of\n",
+    "Alzheimer's disease of the paper, SumRank's genes predicted the left-out datasets with\n",
+    "a mean AUC of 0.78, as did those of the datasets merged into one model with the\n",
+    "dataset as a covariate; those of the inverse-variance meta-analysis 0.74, and the\n",
+    "genes of single datasets 0.67 (its Table 1) [@nakatsuka2025].\n",
+    "\n",
+    "## Choices\n",
+    "\n",
+    "- **Which genes replicate.** A meta-analysis estimates each gene's change with its\n",
+    "  uncertainty and weighs the cohorts by their precision; with random effects, their\n",
+    "  disagreement widens the interval. A partial conjunction test claims a change in at\n",
+    "  least u cohorts, with a false discovery rate over the genes. SumRank asks that a\n",
+    "  gene rank high in most cohorts, and weighs every cohort alike.\n",
+    "- **Which combination of p-values.** Fisher's method finds genes that any cohort\n",
+    "  changes; Stouffer's, weighted by the square roots of the cohorts' sizes, the default\n",
+    "  of {api}`differential_expression_replication`, rewards evidence the cohorts share;\n",
+    "  neither says that a change replicates, which {api}`partial_conjunction` tests\n",
+    "  (`at_least`, and `conjunction_method` for Simes's, Fisher's or Stouffer's\n",
+    "  combination).\n",
+    "- **π₁ on few genes.** Estimate π₀ at one λ, such as 0.5, or with qvalue's bootstrap\n",
+    "  (`null_proportion={\"method\": \"bootstrap\"}`), when the cohorts find few genes.\n",
+    "- **SumRank's proportion and p-values.** Summing every cohort, the Irwin-Hall p-values\n",
+    "  serve; summing fewer needs permutations (`n_permutations` and `permute`), each of\n",
+    "  which runs every cohort's analysis again, and designs with random effects cannot be\n",
+    "  permuted. SumRank ranks only the genes that every cohort tested, so a cohort with a\n",
+    "  short panel of genes or a strict expression filter shortens the list for all.\n",
+    "- **Held-out prediction.** `n_genes` sets how many genes up and down are chosen in each\n",
+    "  cell type, and `groups` the cell types scored; an AUC below 0.5 says the genes\n",
+    "  predict the wrong direction."
+   ]
+  }
+ ],
+ "metadata": {
+  "datasets": [
+   "simulated_cohorts"
+  ],
+  "executed_at": "2026-10-26T21:31:43+00:00",
+  "executed_code_sha256": "150d11925767e14113ab6fb25d5b773c4c35c7a7807b2920ed3c0807d9da6454",
+  "executed_datasets": {
+   "simulated_cohorts": {
+    "simulated_cohort_1.cube.zarr": "sha256:e97a0a97353376a5ac3a5c1e34f7e5f6fad6b0ac07c09b738b808fba6ccedb9e",
+    "simulated_cohort_2.cube.zarr": "sha256:f5644603a625381e2eb95ad6e62d8f52780c59b6679d58a10d43f5295965ce96",
+    "simulated_cohort_3.cube.zarr": "sha256:281cc4e81aecec198fd3c357271f95c4041ea33fbed711222acb6ad0846709ed",
+    "simulated_cohort_4.cube.zarr": "sha256:a03544c081ee531297c9ff7aef0b6e073e626a932364ff33fb4b0746ffea0632",
+    "simulated_cohort_5.cube.zarr": "sha256:7eb6775b5b70f8d8f691308aaca864792ddecd76c0e17921f275880897ad124b",
+    "simulated_cohort_6.cube.zarr": "sha256:3fb7291bb67093b8883e2dfe177f2e88efe4c014d8b77b16dd6d7615decdb733",
+    "simulated_cohorts_truth.npz": "sha256:6599ea11dddb183f630183846f7ece9991003a4cc77875e4e7822317f66346ec"
+   }
+  },
+  "executed_with": "0.1.dev55+g941b81fb5",
+  "execution_seconds": 19.3,
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docs/source/index.md b/docs/source/index.md
index 5df3d74..5eabfdc 100644
--- a/docs/source/index.md
+++ b/docs/source/index.md
@@ -36,12 +36,12 @@ what CytePool offers for each today.
 | Which cell types expand or contract? | propeller. Not yet: precision-weighted mixed models or count models of composition | [Cell-type composition](guides/composition.ipynb), [Composition tests](statistics/composition.md) |
 | Which pathways and regulators respond? | camera, fry, roast, mroast and romer on any contrast; gene set enrichment analysis (GSEA) of ranked genes; over-representation analysis of the genes that change; scores of pathways in every sample (GSVA, ssGSEA, combined z-scores, PLAGE); activities of transcription factors and pathways from the networks CollecTRI and PROGENy (decoupler's ULM, MLM and weighted aggregate); gene sets read from GMT files | [Gene sets and pathways](guides/gene_sets.ipynb), [Pathway and regulator activity](guides/pathway_activity.ipynb), [Gene-set tests](statistics/gene_set_tests.md) |
 | What drives the variation between donors, and what must be adjusted for? | Design checks for confounded factors, sample quality weights, mixed models that estimate a variance per random effect for every gene, and variance partitioning: the share of each gene's variance that each variable explains, in every cell type | [Normalization, filtering and sample quality](guides/normalization_and_quality.ipynb), [Repeated measures and donors](guides/repeated_measures.ipynb), [Designs, linear models and contrasts](statistics/linear_models.md), [Mixed models, REML and dream](statistics/mixed_models.md) |
-| Does it replicate, and how should studies be combined? | Studies aligned by sample, gene and cell type, each analysed on its own and lined up gene by gene; meta-analysis of every gene across studies, with the between-study variance, Q and I²; combined p-values, the p-value that a change replicates in at least u studies, sign agreement, pi1 between studies and the genes found in k studies; SumRank, which asks that a gene rank high in most studies, and the prediction of cases in held-out studies. Not yet: shrinkage across studies | [Meta-analysis](statistics/meta_analysis.md), [The cube and its additive layers](statistics/cubes.md) |
+| Does it replicate, and how should studies be combined? | Studies aligned by sample, gene and cell type, each analysed on its own and lined up gene by gene; meta-analysis of every gene across studies, with the between-study variance, Q and I²; combined p-values, the p-value that a change replicates in at least u studies, sign agreement, pi1 between studies and the genes found in k studies; SumRank, which asks that a gene rank high in most studies, and the prediction of cases in held-out studies. Not yet: shrinkage across studies | [Combine studies](guides/combine_studies.ipynb), [Replication across studies](guides/replication_across_studies.ipynb), [Meta-analysis](statistics/meta_analysis.md), [The cube and its additive layers](statistics/cubes.md) |
 | Can patients be stratified from their profiles? | Not yet; cubes convert to AnnData and MuData for other tools | [Capability matrix](reference/capabilities.md) |
 | Which cell-cell interactions change? | Not yet | [Related software](reference/related_software.md) |
 | Do effects depend on genotype, time, sex or context? | Interactions and F-tests in designs, likelihood ratio tests, random intercepts for repeated measures, with F-tests and small-sample (Kenward-Roger) tests in mixed models. Not yet: random slopes. QTL mapping is left to dedicated tools | [Repeated measures and donors](guides/repeated_measures.ipynb), [Designs, linear models and contrasts](statistics/linear_models.md), [Count models](statistics/count_models.md), [Mixed models, REML and dream](statistics/mixed_models.md) |
 | How do cells respond to drugs and doses? | A dose as a factor or a covariate of a design. Not yet: dose-response fits, benchmark doses, synergy | [Capability matrix](reference/capabilities.md) |
-| Can public studies be pooled into one cube? | The cubes of several studies stacked into one, of the genes and cell types that every study has, with sample and donor ids kept apart by study; cubes import the pseudobulk layouts of scanpy, decoupler and LIANA | [The cube and its additive layers](statistics/cubes.md), [Export a cube from another tool](guides/export_cubes.ipynb) |
+| Can public studies be pooled into one cube? | The cubes of several studies stacked into one, of the genes and cell types that every study has, with sample and donor ids kept apart by study; cubes import the pseudobulk layouts of scanpy, decoupler and LIANA | [Combine studies](guides/combine_studies.ipynb), [The cube and its additive layers](statistics/cubes.md), [Export a cube from another tool](guides/export_cubes.ipynb) |
 
 The [Literature](reference/literature.md) page collects studies behind these questions,
 and the [Capability matrix](reference/capabilities.md) lists every method with the
diff --git a/docs/source/myst.yml b/docs/source/myst.yml
index f9c4ae5..f51b467 100644
--- a/docs/source/myst.yml
+++ b/docs/source/myst.yml
@@ -58,6 +58,10 @@ project:
     children:
     - file: guides/bulk_rna_seq.ipynb
       title: Bulk RNA-seq
+    - file: guides/combine_studies.ipynb
+      title: Combine studies
+    - file: guides/replication_across_studies.ipynb
+      title: Replication across studies
   - title: How the statistics work
     children:
     - file: statistics/choosing_a_method.md
diff --git a/docs/source/reference/datasets.md b/docs/source/reference/datasets.md
index f32805c..4c7e265 100644
--- a/docs/source/reference/datasets.md
+++ b/docs/source/reference/datasets.md
@@ -25,6 +25,7 @@ code.
 | --- | --- | --- | --- | --- |
 | `simulated_pbmc` | 12 donors, each sampled before and after a stimulation; about 20,000 cells of six cell types over 2,000 genes, with planted changes in every cell type, a response program shared by all, a rise in the share of monocytes, and dendritic cells too rare in some samples; the cube of all five layers and the cells with the true effects | simulated by the script, seed 20261026 | part of CytePool, BSD 3-Clause | |
 | `simulated_bulk` | 3 treatments x 4 replicates in two batches, 10,000 genes; drug A changes 500 genes, drug B 300; one noisier sample; the cube and the true effects | simulated by the script, seed 20261027 | part of CytePool, BSD 3-Clause | |
+| `simulated_cohorts` | 6 case-control cohorts of one disease, 12 to 60 donors each, with the pseudobulk counts of four blood cell types over 2,000 genes; changes in every cohort, 100 of them in the monocytes, and 10 monocyte genes changed in each cohort alone; donor ids that repeat across cohorts, genes that two cohorts did not measure, and cell types named in each cohort's own words with their Cell Ontology ids; a cube per cohort and the true effects | simulated by the script, seed 20261028 | part of CytePool, BSD 3-Clause | |
 | `kang2018` | Peripheral blood cells of 8 lupus patients, each split into an interferon-beta stimulated and an untreated culture: 24,673 cells pooled into 16 samples x 8 cell types x 35,635 genes (counts and detecting cells); a 77 MB download | [GEO GSE96583](https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE96583), batch 2 | GEO's terms: NCBI places no restrictions on the use of GEO data, though submitters may claim rights in theirs ([disclaimer](https://www.ncbi.nlm.nih.gov/geo/info/disclaimer.html)) | @kang2018 |
 | `hallmark_interferon_alpha` | The 97 genes of the Hallmark interferon-alpha response set (M5911, MSigDB v2026.2.Hs) | [MSigDB](https://www.gsea-msigdb.org/gsea/msigdb/human/geneset/HALLMARK_INTERFERON_ALPHA_RESPONSE) | Creative Commons Attribution 4.0 ([terms](https://www.gsea-msigdb.org/gsea/msigdb_license_terms.jsp)) | @liberzon2015 |
 
diff --git a/docs/source/statistics/cubes.md b/docs/source/statistics/cubes.md
index d66caa0..873a37a 100644
--- a/docs/source/statistics/cubes.md
+++ b/docs/source/statistics/cubes.md
@@ -146,4 +146,5 @@ gene by gene, and {api}`differential_expression_meta_analysis` combines them
 study measured and the cell types that every study has: a cube holds no missing values,
 so it refuses the others rather than fill them with zeros. The collection's provenance
 names each study's cube by its fingerprint, with the alignment, and so does the
-provenance of a stacked cube.
+provenance of a stacked cube. [Combine studies](../guides/combine_studies.ipynb) aligns
+six cohorts this way.
diff --git a/docs/source/statistics/meta_analysis.md b/docs/source/statistics/meta_analysis.md
index bef7adc..ef35c8c 100644
--- a/docs/source/statistics/meta_analysis.md
+++ b/docs/source/statistics/meta_analysis.md
@@ -18,7 +18,10 @@ for every cell type and contrast ({api}`MetaAnalysisResult.table`). Where the st
 give p-values rather than comparable effects, or to ask in how many studies a gene
 changes, their p-values are combined, and
 {api}`differential_expression_replication` says how well the studies replicate each
-other (see [](#combined-p-values) and below).
+other (see [](#combined-p-values) and below). The guides
+[Combine studies](../guides/combine_studies.ipynb) and
+[Replication across studies](../guides/replication_across_studies.ipynb) run them on
+six simulated cohorts whose true changes are known.
 
 ## The model
 
diff --git a/tests/test_documentation.py b/tests/test_documentation.py
index 89ee9f5..7188f0d 100644
--- a/tests/test_documentation.py
+++ b/tests/test_documentation.py
@@ -1288,6 +1288,7 @@ def test_every_dataset_records_its_source_licence_citation_and_contents() -> Non
     assert set(datasets.DATASETS) == {
         "simulated_pbmc",
         "simulated_bulk",
+        "simulated_cohorts",
         "kang2018",
         "hallmark_interferon_alpha",
     }