diff --git a/pixi.lock b/pixi.lock
index 8661dcb..cec533b 100644
--- a/pixi.lock
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   sha256: 2d1b7d9308288661f56672b1b157d75fc536714d3638487bbea17b6318a78248
   requires_python: '>=3.9'
+- pypi: https://files.pythonhosted.org/packages/cc/88/5c8819f232448d52514b76a64cf285bc608cff9179f3eeacf58750612323/statsmodels-0.15.0-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl
+  name: statsmodels
+  version: 0.15.0
+  sha256: bcaa1e4499697c4e023e5fa6fb04459a34e4f4a449c45ae441992e933bde24db
+  requires_dist:
+  - numpy>=1.23.5,<3
+  - scipy>=1.8,!=1.9.2
+  - pandas>=1.4,!=2.1.0
+  - patsy>=0.5.6
+  - packaging>=21.3
+  - formulaic>=1.1.0
+  - pytest>=8.4.1,<10 ; extra == 'test'
+  - pytest-randomly ; extra == 'test'
+  - pytest-run-parallel ; extra == 'test'
+  - pytest-xdist ; extra == 'test'
+  - pytest-cov ; extra == 'test'
+  - coverage[toml] ; extra == 'test'
+  - meson ; extra == 'test'
+  - ninja ; sys_platform != 'emscripten' and extra == 'test'
+  - sphinx ; extra == 'doc'
+  - pandas!=2.1.0,>=2.2.2 ; extra == 'doc'
+  - pydata-sphinx-theme ; extra == 'doc'
+  - jupyter ; extra == 'doc'
+  - notebook ; extra == 'doc'
+  - nbsphinx ; extra == 'doc'
+  - nbconvert ; extra == 'doc'
+  - pyyaml ; extra == 'doc'
+  - pandas-datareader ; extra == 'doc'
+  - simplegeneric ; extra == 'doc'
+  - seaborn ; extra == 'doc'
+  - numpydoc ; extra == 'doc'
+  - pymc ; os_name != 'nt' and extra == 'doc'
+  - arviz ; os_name != 'nt' and extra == 'doc'
+  - jinja2 ; extra == 'doc'
+  - pickleshare ; extra == 'doc'
+  - pytest>=8.4.1,<10 ; extra == 'doc'
+  - cython>=3.0.10,<4 ; extra == 'dev'
+  - setuptools-scm~=9.0 ; extra == 'dev'
+  - meson-python>=0.18.0 ; extra == 'dev'
+  - meson ; extra == 'dev'
+  - ninja ; extra == 'dev'
+  - matplotlib>=3 ; extra == 'dev'
+  - colorama ; extra == 'dev'
+  - joblib ; extra == 'dev'
+  - pywinpty ; os_name == 'nt' and extra == 'dev'
+  - ruff ; extra == 'dev'
+  - polars>=1.0.0 ; extra == 'polars'
+  requires_python: '>=3.10'
 - pypi: https://files.pythonhosted.org/packages/cc/e3/357baa8d2a9164a98dfd7ef9d3a58125df0ed981be909945bdd337be7194/msgspec-0.22.0-cp313-cp313-macosx_11_0_arm64.whl
   name: msgspec
   version: 0.22.0
@@ -2774,6 +3768,27 @@ packages:
   version: 6.0.3
   sha256: 8da9669d359f02c0b91ccc01cac4a67f16afec0dac22c2ad09f46bee0697eba8
   requires_python: '>=3.8'
+- pypi: https://files.pythonhosted.org/packages/d1/89/5d4c86da1130d9059681e5b6cd7645df5c10279a6a079c5c37dcb2cc6f3f/narwhals-2.27.1-py3-none-any.whl
+  name: narwhals
+  version: 2.27.1
+  sha256: d057df13f5852b8e157596e82eb5e955fad267425df5e420e0ee9863da483b31
+  requires_dist:
+  - cudf-cu12>=24.10.0 ; sys_platform == 'linux' and extra == 'cudf'
+  - dask[dataframe]>=2024.8 ; extra == 'dask'
+  - duckdb>=1.1 ; extra == 'duckdb'
+  - ibis-framework>=6.0.0 ; extra == 'ibis'
+  - packaging>=21.3 ; extra == 'ibis'
+  - pyarrow-hotfix>=0.7 ; extra == 'ibis'
+  - modin>=0.22.0 ; extra == 'modin'
+  - pandas>=1.3.4 ; extra == 'pandas'
+  - polars>=0.20.4 ; extra == 'polars'
+  - pyarrow>=13.0.0 ; extra == 'pyarrow'
+  - pyspark>=3.5.0 ; extra == 'pyspark'
+  - pyspark[connect]>=3.5.0 ; extra == 'pyspark-connect'
+  - narwhals[duckdb] ; extra == 'sql'
+  - sqlparse>=0.5.5 ; extra == 'sql'
+  - sqlframe>=3.22.0,!=3.39.3 ; extra == 'sqlframe'
+  requires_python: '>=3.10'
 - pypi: https://files.pythonhosted.org/packages/d1/d6/3965ed04c63042e047cb6a3e6ed1a63a35087b6a609aa3a15ed8ac56c221/colorama-0.4.6-py2.py3-none-any.whl
   name: colorama
   version: 0.4.6
@@ -2802,6 +3817,27 @@ packages:
   - python-discovery>=1.6
   - typing-extensions>=4.13.2 ; python_full_version < '3.11'
   requires_python: '>=3.9'
+- pypi: https://files.pythonhosted.org/packages/e4/6d/cfe55762435f36107815d56a2cfbebe7e3129b593c47a670c6eb1d7917d3/wrapt-2.5.1-cp313-cp313-macosx_10_13_x86_64.whl
+  name: wrapt
+  version: 2.5.1
+  sha256: f98eaf784cd12bc69c77af398084174531007cd81849c962163ccfc6e791f3ea
+  requires_dist:
+  - pytest ; extra == 'dev'
+  - setuptools ; extra == 'dev'
+  requires_python: '>=3.9'
+- pypi: https://files.pythonhosted.org/packages/e7/05/c19819d5e3d95294a6f5947fb9b9629efb316b96de511b418c53d245aae6/cycler-0.12.1-py3-none-any.whl
+  name: cycler
+  version: 0.12.1
+  sha256: 85cef7cff222d8644161529808465972e51340599459b8ac3ccbac5a854e0d30
+  requires_dist:
+  - ipython ; extra == 'docs'
+  - matplotlib ; extra == 'docs'
+  - numpydoc ; extra == 'docs'
+  - sphinx ; extra == 'docs'
+  - pytest ; extra == 'tests'
+  - pytest-cov ; extra == 'tests'
+  - pytest-xdist ; extra == 'tests'
+  requires_python: '>=3.8'
 - pypi: https://files.pythonhosted.org/packages/ea/ca/87f8548f73d452aab35e4a90f8b39ae303295e0f2ef0b4055c44d6b3f1be/pandas-3.0.6-cp313-cp313-macosx_11_0_arm64.whl
   name: pandas
   version: 3.0.6
@@ -2939,6 +3975,45 @@ packages:
   - numpy
   - pyarrow ; extra == 'fast-io'
   requires_python: '>=3.12'
+- pypi: https://files.pythonhosted.org/packages/ed/c2/5e9d0773f1589397a3ea3dcfa4bbee36e2855ad938d738dd6ff9f505a59b/llvmlite-0.50.0-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl
+  name: llvmlite
+  version: 0.50.0
+  sha256: 425845f415a06dc50db08db033c6b568e0d85c4937e932c605a4d49e1514b2da
+  requires_python: '>=3.10'
+- pypi: https://files.pythonhosted.org/packages/ef/36/04d74f0c71d93829657a703d680a54968253bbb5c93babc34378eae2087a/fonttools-4.67.0-cp313-cp313-macosx_10_13_x86_64.whl
+  name: fonttools
+  version: 4.67.0
+  sha256: 1be99c1f07fca59510d657ef3eae584b5273fa4e203aff2383b3520744e19536
+  requires_dist:
+  - lxml>=4.0 ; extra == 'lxml'
+  - brotli>=1.0.1 ; platform_python_implementation == 'CPython' and extra == 'woff'
+  - brotlicffi>=0.8.0 ; platform_python_implementation != 'CPython' and extra == 'woff'
+  - zopfli>=0.1.4 ; extra == 'woff'
+  - unicodedata2>=18.0.0 ; python_full_version < '3.16' and extra == 'unicode'
+  - lz4>=1.7.4.2 ; extra == 'graphite'
+  - scipy ; platform_python_implementation != 'PyPy' and extra == 'interpolatable'
+  - munkres ; platform_python_implementation == 'PyPy' and extra == 'interpolatable'
+  - pycairo ; extra == 'interpolatable'
+  - matplotlib ; extra == 'plot'
+  - sympy ; extra == 'symfont'
+  - xattr ; sys_platform == 'darwin' and extra == 'type1'
+  - skia-pathops>=0.5.0 ; extra == 'pathops'
+  - uharfbuzz>=0.45.0 ; extra == 'repacker'
+  - lxml>=4.0 ; extra == 'all'
+  - brotli>=1.0.1 ; platform_python_implementation == 'CPython' and extra == 'all'
+  - brotlicffi>=0.8.0 ; platform_python_implementation != 'CPython' and extra == 'all'
+  - zopfli>=0.1.4 ; extra == 'all'
+  - unicodedata2>=18.0.0 ; python_full_version < '3.16' and extra == 'all'
+  - lz4>=1.7.4.2 ; extra == 'all'
+  - scipy ; platform_python_implementation != 'PyPy' and extra == 'all'
+  - munkres ; platform_python_implementation == 'PyPy' and extra == 'all'
+  - pycairo ; extra == 'all'
+  - matplotlib ; extra == 'all'
+  - sympy ; extra == 'all'
+  - xattr ; sys_platform == 'darwin' and extra == 'all'
+  - skia-pathops>=0.5.0 ; extra == 'all'
+  - uharfbuzz>=0.45.0 ; extra == 'all'
+  requires_python: '>=3.11'
 - pypi: https://files.pythonhosted.org/packages/ef/82/7a9d0550484a62c6da82858ee9419f3dd1ccc9aa1c26a1e43da3ecd20b0d/natsort-8.4.0-py3-none-any.whl
   name: natsort
   version: 8.4.0
@@ -2988,6 +4063,37 @@ packages:
   - pyranges1[docs] ; extra == 'all'
   - pyranges1[cli] ; extra == 'all'
   requires_python: '>=3.12.0'
+- pypi: https://files.pythonhosted.org/packages/f7/62/de5bdd77d935331f4f802edc11e4d82950f642caad6cb2f949837b8560e2/pillow-12.3.0-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl
+  name: pillow
+  version: 12.3.0
+  sha256: 0847a763afefb695bc912d7c131e7e0632d4edc1d8698f58ddabec8e46b8b6d3
+  requires_dist:
+  - furo ; extra == 'docs'
+  - olefile ; extra == 'docs'
+  - sphinx>=8.2 ; extra == 'docs'
+  - sphinx-autobuild ; extra == 'docs'
+  - sphinx-copybutton ; extra == 'docs'
+  - sphinx-inline-tabs ; extra == 'docs'
+  - sphinxext-opengraph ; extra == 'docs'
+  - olefile ; extra == 'fpx'
+  - olefile ; extra == 'mic'
+  - arro3-compute ; extra == 'test-arrow'
+  - arro3-core ; extra == 'test-arrow'
+  - nanoarrow ; extra == 'test-arrow'
+  - pyarrow ; extra == 'test-arrow'
+  - coverage>=7.4.2 ; extra == 'tests'
+  - defusedxml ; extra == 'tests'
+  - markdown2 ; extra == 'tests'
+  - olefile ; extra == 'tests'
+  - packaging ; extra == 'tests'
+  - pytest ; extra == 'tests'
+  - pytest-cov ; extra == 'tests'
+  - pytest-timeout ; extra == 'tests'
+  - pytest-xdist ; extra == 'tests'
+  - setuptools ; extra == 'tests'
+  - trove-classifiers>=2024.10.12 ; extra == 'tests'
+  - defusedxml ; extra == 'xmp'
+  requires_python: '>=3.10'
 - pypi: https://files.pythonhosted.org/packages/f7/b9/c720e56858d4e1539503297ed37063e0c08e0f3541c41b777f6a800f75fa/pydantic_core-2.50.1-cp313-cp313-macosx_11_0_arm64.whl
   name: pydantic-core
   version: 2.50.1
@@ -3012,6 +4118,23 @@ packages:
   requires_dist:
   - ukkonen ; extra == 'license'
   requires_python: '>=3.10'
+- pypi: https://files.pythonhosted.org/packages/fc/75/0576b03f7889ad25b5385b4f6c69e0543713425a6f193b056c9b0a2e65ce/formulaic-1.2.2-py3-none-any.whl
+  name: formulaic
+  version: 1.2.2
+  sha256: 0f84ff49e3fc9dc0e68ab08a0a9427874021aa6c558e66b44dc634a35739b09b
+  requires_dist:
+  - interface-meta>=1.2.0
+  - narwhals>=1.17
+  - numpy>=1.20.0
+  - pandas>=1.3
+  - scipy>=1.6
+  - typing-extensions>=4.2.0
+  - wrapt>=1.0 ; python_full_version < '3.13'
+  - wrapt>=1.17.0rc1 ; python_full_version >= '3.13'
+  - pyarrow>=1 ; extra == 'arrow'
+  - sympy>=1.3,!=1.10 ; extra == 'calculus'
+  - polars>=1 ; extra == 'polars'
+  requires_python: '>=3.9'
 - pypi: https://files.pythonhosted.org/packages/fd/b6/e76e80fed5f2558273c7839e622f98095c9b36c719c7147e38e3c055cb70/google_crc32c-1.9.0-cp313-cp313-manylinux2014_x86_64.manylinux_2_17_x86_64.whl
   name: google-crc32c
   version: 1.9.0
diff --git a/pyproject.toml b/pyproject.toml
index d01b48a..322589d 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -45,6 +45,12 @@ dependencies = [
     # inside functions, like pysam, so `cau --help` loads neither.
     "anndata>=0.10",
     "mudata>=0.3",
+    # QC figures. Imported inside functions, so a run that draws nothing
+    # never loads it.
+    "matplotlib>=3.8",
+    # QC metrics. scanpy names them as the IGVF pipeline does, so ours are
+    # directly comparable with the pipeline's own.
+    "scanpy>=1.10",
 ]
 
 [project.scripts]
diff --git a/src/crispr_analysis_utils/cli/mudata_builder.py b/src/crispr_analysis_utils/cli/mudata_builder.py
index fd931a8..59529a9 100644
--- a/src/crispr_analysis_utils/cli/mudata_builder.py
+++ b/src/crispr_analysis_utils/cli/mudata_builder.py
@@ -78,6 +78,14 @@ def add_arguments(parser: argparse.ArgumentParser) -> None:
             "channel, joined to every cell's obs and kept in uns."
         ),
     )
+    run_parser.add_argument(
+        "--readout-genes",
+        help=(
+            "The panel a targeted assay reads out, as a table with a column "
+            "of ENSEMBL ids. Adds a boolean readout column to gene.var; "
+            "without it the column is absent."
+        ),
+    )
     run_parser.add_argument(
         "--covariates",
         help=(
@@ -115,6 +123,7 @@ def run(args: argparse.Namespace) -> int:
             genome_accession=args.genome_accession,
             covariates=args.covariates,
             metadata=_channel(args.channel),
+            readout_genes=args.readout_genes,
             guide_annotation=args.guide_annotation,
             element_mapping=args.element_mapping,
         )
diff --git a/src/crispr_analysis_utils/mudata_builder.py b/src/crispr_analysis_utils/mudata_builder.py
index afa7d44..84eb5bd 100644
--- a/src/crispr_analysis_utils/mudata_builder.py
+++ b/src/crispr_analysis_utils/mudata_builder.py
@@ -91,7 +91,40 @@ def read_counts(path: str | os.PathLike[str]):
         raise BuildError(f"{path} is not a readable h5ad: {error}") from error
 
 
-def gene_var(store, ids: pd.Index) -> pd.DataFrame:
+def read_readout_genes(path: str | os.PathLike[str]) -> pd.Series:
+    """The genes a targeted assay reads out, as unversioned ENSEMBL ids.
+
+    A TAP-seq panel measures a few hundred genes on purpose; every other
+    feature in the matrix is off-panel and near-empty. Which is which is not
+    in the counts, so it comes from the panel's own table: the first column
+    holding ENSEMBL ids is taken as the gene.
+
+    Returns
+    -------
+    pandas.Series
+        How many rows of the panel name each gene, indexed by unversioned
+        ENSEMBL id. Usually one; a gene measured through two amplicons has
+        two, and its counts are a sum of both.
+    """
+    path = Path(path)
+    if not path.is_file():
+        raise BuildError(f"The readout gene table {path} does not exist.")
+    frame = pd.read_csv(path, sep="\t" if path.suffix == ".tsv" else ",")
+    ensembl = [
+        column
+        for column in frame.columns
+        if frame[column].astype(str).str.startswith("ENSG").any()
+    ]
+    if not ensembl:
+        raise BuildError(
+            f"{path} has no column of ENSEMBL gene ids, so it cannot say "
+            "which genes are read out."
+        )
+    genes = _unversioned(frame[ensembl[0]].astype("string"))
+    return genes.value_counts()
+
+
+def gene_var(store, ids: pd.Index, readout: pd.Series | None = None) -> pd.DataFrame:
     """The annotation of every gene in the matrix, in the matrix's own order.
 
     Parameters
@@ -101,6 +134,10 @@ def gene_var(store, ids: pd.Index) -> pd.DataFrame:
         only the ones guides are annotated against.
     ids
         The matrix's gene ids, versioned or not.
+    readout
+        The genes the assay reads out, as `read_readout_genes` returns them.
+        Adds ``readout`` and ``readout_primers``; without it both are
+        absent, since a whole-transcriptome run reads out everything.
 
     Returns
     -------
@@ -125,8 +162,15 @@ def gene_var(store, ids: pd.Index) -> pd.DataFrame:
     # kb indexes genes as the GTF names them, with the version; the store
     # strips it. The rule has to be the store's own, or a PAR_Y gene, whose
     # id ends in neither, comes back annotated with nothing.
-    out = annotation.reindex(_unversioned(pd.Series(ids, dtype="string")))
+    bare = _unversioned(pd.Series(ids, dtype="string"))
+    out = annotation.reindex(bare)
     out.index = pd.Index(ids, name="gene_id")
+    if readout is not None:
+        counts = readout.reindex(bare).fillna(0).astype(int)
+        out["readout"] = (counts > 0).to_numpy()
+        # Above one, the panel measures the gene through two amplicons and
+        # the counts are their sum, as n_oligos means on the guide side.
+        out["readout_primers"] = counts.to_numpy()
     return out
 
 
@@ -403,6 +447,7 @@ def run(
     guide_annotation: str | os.PathLike[str] | None = None,
     element_mapping: str | os.PathLike[str] | None = None,
     metadata: dict[str, str] | None = None,
+    readout_genes: str | os.PathLike[str] | None = None,
 ) -> RunResult:
     """Assemble one channel's matrices into a MuData, and write it.
 
@@ -506,6 +551,13 @@ def run(
             state["inputs"][name] = str(path)
             state["inputs"][f"{name}_md5"] = file_md5(path)
 
+        readout = None
+        if readout_genes is not None:
+            readout = read_readout_genes(readout_genes)
+            state["inputs"]["readout_genes"] = str(readout_genes)
+            state["inputs"]["readout_genes_md5"] = file_md5(readout_genes)
+            logger.info("%d genes are read out by the assay", len(readout))
+
         reference = reference_block(
             loaded,
             gtf_accession=gtf_accession,
@@ -517,7 +569,7 @@ def run(
         data = build(
             rna_counts,
             guide_counts,
-            gene_table=gene_var(loaded, rna_counts.var_names),
+            gene_table=gene_var(loaded, rna_counts.var_names, readout=readout),
             guide_table=guide_var(guide_metadata, guide_counts.var_names),
             measurement_set=measurement_set,
             reference=reference,
@@ -539,6 +591,16 @@ def run(
             "genes": int(data["gene"].n_vars),
             "guides": int(data["guide"].n_vars),
             "genes_unannotated": int(data["gene"].var["symbol"].isna().sum()),
+            "genes_readout": (
+                int(data["gene"].var["readout"].sum())
+                if "readout" in data["gene"].var
+                else None
+            ),
+            "genes_multi_primer": (
+                int((data["gene"].var["readout_primers"] > 1).sum())
+                if "readout_primers" in data["gene"].var
+                else None
+            ),
             "guides_unannotated": int(data["guide"].var["spacer"].isna().sum()),
             "uns_tables": {k: int(len(v)) for k, v in tables.items()},
         }
diff --git a/src/crispr_analysis_utils/qc.py b/src/crispr_analysis_utils/qc.py
new file mode 100644
index 0000000..b1c057e
--- /dev/null
+++ b/src/crispr_analysis_utils/qc.py
@@ -0,0 +1,1404 @@
+"""Quality-control figures, each published as three files.
+
+A figure nobody can check is an assertion. Every one here writes the numbers
+it was drawn from, a caption that says what it shows, and a PDF at print
+resolution, under one stem:
+
+    <stem>.pdf      the figure, 300 dpi, vector text
+    <stem>.tsv.gz   the numbers, one row per plotted point
+    <stem>.txt      the caption, as it would appear under the figure
+
+The data file is the point: a reviewer can redraw the figure, and a reader
+can find the number behind a claim without rerunning anything.
+"""
+
+from __future__ import annotations
+
+import gzip
+import os
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+
+DPI = 300
+"""Print resolution, for the raster parts of an otherwise vector PDF."""
+
+OKABE_ITO = (
+    "#E69F00",
+    "#56B4E9",
+    "#009E73",
+    "#F0E442",
+    "#0072B2",
+    "#D55E00",
+    "#CC79A7",
+    "#000000",
+)
+"""Categorical colours, colourblind-safe. `cividis` for continuous scales."""
+
+
+class FigureError(RuntimeError):
+    """The figure cannot be drawn from what was given."""
+
+
+class FilterError(RuntimeError):
+    """The filter cannot be applied to what was given."""
+
+
+def style():
+    """Set the colours and the defaults every figure here shares.
+
+    Returns
+    -------
+    module
+        `matplotlib.pyplot`, so a caller need not import it separately.
+    """
+    import matplotlib as mpl
+    import matplotlib.pyplot as plt
+
+    mpl.rcParams.update(
+        {
+            "axes.prop_cycle": mpl.cycler(color=list(OKABE_ITO)),
+            "image.cmap": "cividis",
+            "axes.spines.top": False,
+            "axes.spines.right": False,
+            "axes.grid": True,
+            "grid.color": "0.8",
+            "grid.linewidth": 0.6,
+            "axes.axisbelow": True,
+            "figure.dpi": 140,
+            "savefig.dpi": DPI,
+            "savefig.facecolor": "white",
+            # Text stays text in the PDF, so a figure can be relabelled
+            # without redrawing it.
+            "pdf.fonttype": 42,
+        }
+    )
+    return plt
+
+
+def publish(
+    stem: str | os.PathLike[str],
+    figure,
+    data: pd.DataFrame,
+    caption: str,
+) -> dict[str, Path]:
+    """Write a figure, its numbers and its caption under one stem.
+
+    Parameters
+    ----------
+    stem
+        Path without a suffix. Three files are written beside it.
+    figure
+        A matplotlib figure.
+    data
+        The numbers the figure was drawn from, one row per plotted point.
+    caption
+        One or two sentences saying what the figure shows. Written as given,
+        so it can go straight under the figure.
+
+    Returns
+    -------
+    dict
+        The three paths, by suffix.
+
+    Raises
+    ------
+    FigureError
+        The data is empty, or the caption is blank. Either makes the figure
+        unpublishable, and failing here is cheaper than finding out later.
+    """
+    if data.empty:
+        raise FigureError("A figure must be published with the numbers behind it.")
+    caption = caption.strip()
+    if not caption:
+        raise FigureError("A figure must be published with a caption.")
+
+    stem = Path(stem)
+    stem.parent.mkdir(parents=True, exist_ok=True)
+    written = {
+        ".pdf": stem.with_suffix(".pdf"),
+        ".tsv.gz": stem.with_name(stem.name + ".tsv.gz"),
+        ".txt": stem.with_suffix(".txt"),
+    }
+    figure.savefig(written[".pdf"], dpi=DPI, bbox_inches="tight")
+    with gzip.open(written[".tsv.gz"], "wt", encoding="utf-8") as handle:
+        data.to_csv(handle, sep="\t", index=False)
+    written[".txt"].write_text(caption + "\n", encoding="utf-8")
+    return written
+
+
+def umis_per_cell(data, modality: str) -> np.ndarray:
+    """Total UMIs for every cell of the object, zero where it has none.
+
+    A cell the modality does not hold has no UMIs in it, which is not the
+    same as being absent from the object: the union keeps both, and a knee
+    plot that dropped them would overstate how deep the channel is.
+    """
+    held = np.asarray(data.obsm[modality]).ravel()
+    umis = np.zeros(data.n_obs)
+    umis[held] = np.asarray(data[modality].X.sum(axis=1)).ravel()
+    return umis
+
+
+def filter_cells(data, min_umis: int, modality: str = "gene"):
+    """Keep the barcodes whose `modality` UMI count reaches `min_umis`.
+
+    Parameters
+    ----------
+    data : mudata.MuData
+        The object to filter.
+    min_umis : int
+        The floor, read off the knee of this channel. There is no default:
+        where the knee falls depends on the channel's depth, so a floor
+        carried over from another one is a guess.
+    modality : str
+        The modality whose UMIs decide, `"gene"` by default. A barcode with
+        real RNA and no guide is kept, since an unassigned cell is a result.
+
+    Returns
+    -------
+    tuple of (mudata.MuData, numpy.ndarray)
+        The filtered copy, and the boolean mask over the original barcodes.
+    """
+    keep = umis_per_cell(data, modality) >= min_umis
+    filtered = data[keep].copy()
+    # The step that cuts writes down what it cut, so the record cannot be
+    # left to whoever writes the file.
+    record = dict(filtered.uns.get("qc", {}))
+    record["floor"] = {
+        "min_umis": int(min_umis),
+        "modality": modality,
+        "n_cells_in": int(data.n_obs),
+        "n_cells_out": int(filtered.n_obs),
+    }
+    filtered.uns["qc"] = record
+    return filtered, keep
+
+
+def knee(data, modalities: tuple[str, ...] = ("gene", "guide")):
+    """Barcodes ranked by UMI count, log on both axes.
+
+    Parameters
+    ----------
+    data
+        A MuData as `mudata_builder` assembles it.
+    modalities
+        Which modalities to draw, each its own curve.
+
+    Returns
+    -------
+    tuple
+        The figure, the numbers, and the caption, ready for `publish`.
+
+    Raises
+    ------
+    FigureError
+        No modality has a barcode with a UMI.
+    """
+    plt = style()
+    figure, axes = plt.subplots(figsize=(6, 4.5))
+
+    rows = []
+    for modality in modalities:
+        ranked = np.sort(umis_per_cell(data, modality))[::-1]
+        ranked = ranked[ranked > 0]
+        if not len(ranked):
+            continue
+        rank = np.arange(1, len(ranked) + 1)
+        axes.plot(rank, ranked, label=modality, linewidth=1.5)
+        rows.append(
+            pd.DataFrame(
+                {"modality": modality, "rank": rank, "umis": ranked.astype(int)}
+            )
+        )
+    if not rows:
+        raise FigureError("No modality has a barcode with a UMI to rank.")
+
+    axes.set(
+        xscale="log",
+        yscale="log",
+        xlabel="barcode rank",
+        ylabel="UMIs",
+    )
+    axes.legend(frameon=False)
+    figure.tight_layout()
+
+    numbers = pd.concat(rows, ignore_index=True)
+    counted = ", ".join(
+        f"{int((numbers['modality'] == m).sum()):,} {m}"
+        for m in numbers["modality"].unique()
+    )
+    caption = (
+        "Barcodes ranked by total UMIs, both axes logarithmic. Every barcode "
+        f"the object holds is ranked ({counted} barcodes with at least one "
+        "UMI); a barcode a modality does not hold counts as zero and so "
+        "falls outside the curve. The knee is where real cells give way to "
+        "ambient background, and its position is what a count threshold has "
+        "to justify itself against."
+    )
+    return figure, numbers, caption
+
+
+def mad_outlier(
+    values,
+    nmads: float = 5.0,
+    side: str = "both",
+    log: bool = False,
+) -> np.ndarray:
+    """Flag the values that sit more than `nmads` MADs from the median.
+
+    The median absolute deviation is the spread of the channel measured
+    against itself, so a threshold built from it moves with the channel's
+    own depth instead of being carried over from another one. The MAD is
+    rescaled to a standard deviation (`scale="normal"`), so `nmads` reads
+    as sigmas on a normal distribution.
+
+    Parameters
+    ----------
+    values : array-like
+        One number per cell. `NaN` is never flagged: a cell the modality
+        does not hold was not measured.
+    nmads : float
+        How many MADs from the median is still acceptable, 5 by default.
+    side : {"both", "upper", "lower"}
+        Which tail to flag. A count metric is two-sided; a contamination
+        fraction only has an upper tail worth cutting.
+    log : bool
+        Work on `log10(x + 1)` rather than the raw value. Off by default:
+        a count distribution wants it, a percentage does not.
+
+    Returns
+    -------
+    numpy.ndarray
+        Boolean, true where the value is an outlier.
+
+    Raises
+    ------
+    FigureError
+        The MAD is zero, so every value equals the median and no threshold
+        can be read off it. This is what an unfiltered barcode space looks
+        like: pick the cells first, then measure their spread.
+    """
+    numbers = np.asarray(values, dtype=float)
+    measured = ~np.isnan(numbers)
+    low, high = mad_bounds(numbers[measured], nmads=nmads, side=side, log=log)
+
+    flagged = np.zeros(numbers.shape, dtype=bool)
+    flagged[measured] |= numbers[measured] < low
+    flagged[measured] |= numbers[measured] > high
+    return flagged
+
+
+def mad_bounds(
+    values,
+    nmads: float = 5.0,
+    side: str = "both",
+    log: bool = False,
+) -> tuple[float, float]:
+    """The thresholds `mad_outlier` applies, in the values' own units.
+
+    A figure draws these and colours by them, so the line on the plot and
+    the cut in the table cannot disagree.
+
+    Parameters
+    ----------
+    values : array-like
+        One number per cell, with no `NaN`.
+    nmads, side, log
+        As `mad_outlier`.
+
+    Returns
+    -------
+    tuple of float
+        Lower and upper threshold. A tail that `side` does not cut comes
+        back as `-inf` or `inf`.
+
+    Raises
+    ------
+    FigureError
+        `side` is not one of the three, or the MAD is zero.
+    """
+    from scipy.stats import median_abs_deviation
+
+    if side not in ("both", "lower", "upper"):
+        raise FigureError(f"side must be both, upper or lower, not {side!r}")
+    numbers = np.asarray(values, dtype=float)
+    scale = np.log10(numbers + 1) if log else numbers
+    middle = np.median(scale)
+    spread = median_abs_deviation(scale, scale="normal")
+    if spread == 0:
+        raise FigureError(
+            "The MAD is zero, so the threshold would be the median itself. "
+            "Run this on cells, not on the whole barcode space."
+        )
+
+    low, high = middle - nmads * spread, middle + nmads * spread
+    if log:
+        low, high = 10**low - 1, 10**high - 1
+    return (
+        float(low) if side in ("both", "lower") else -np.inf,
+        float(high) if side in ("both", "upper") else np.inf,
+    )
+
+
+def _label(value: float) -> str:
+    """A number short enough to sit on a threshold line."""
+    return f"{value:,.0f}" if abs(value) >= 100 else f"{value:.3g}"
+
+
+KEPT = OKABE_ITO[4]
+"""Blue, for what a threshold keeps: the cells the analysis goes on with."""
+
+REMOVED = "0.7"
+"""Grey, for what it removes. Neutral, so the eye reads the kept cells first."""
+
+EDGE = "0.2"
+"""Near-black, for the outline a light fill needs to be seen on a white page."""
+
+EDGE_WIDTH = 0.4
+"""Points, thin enough that it outlines the fill rather than replacing it."""
+
+LOG_METRICS = ("total_counts", "n_genes_by_counts")
+"""Metrics whose distribution is read on a log axis, because they are counts."""
+
+
+def _band(values, metric, nmads, log, sides):
+    """One metric's MAD band, `(-inf, inf)` when it has none.
+
+    The side rule and the NaN drop live here rather than in each caller,
+    so the panel and the filter cut at the same place.
+    """
+    on_log = metric in log
+    side = sides.get(metric, "both" if on_log else "upper")
+    measured = values[np.isfinite(values)]
+    if measured.size == 0:
+        return -np.inf, np.inf
+    try:
+        return mad_bounds(measured, nmads=nmads, side=side, log=on_log)
+    except FigureError:
+        # No spread, so no threshold. That is a metric to report, not a
+        # reason to take the figure or the filter down.
+        return -np.inf, np.inf
+
+
+def metric_distributions(
+    frame: pd.DataFrame,
+    nmads: float | None = 5.0,
+    limits: dict[str, tuple[float | None, float | None]] | None = None,
+    band: tuple[str, ...] | None = None,
+    log: tuple[str, ...] = LOG_METRICS,
+    sides: dict[str, str] | None = None,
+    bins: int = 60,
+):
+    """One histogram per QC metric, split at the rules it is cut on.
+
+    The rules come from `cell_filters`, so pass this the same `nmads`,
+    `limits` and `band` as the filter: the lines on the figure are then
+    the cuts the file was made with, not a second opinion.
+
+    Parameters
+    ----------
+    frame : pandas.DataFrame
+        One column per metric, one row per cell, as `cell_fractions`
+        returns. Rows that are entirely `NaN` are cells the modality does
+        not hold, and are dropped rather than drawn as zero.
+    nmads : float, optional
+        Width of each band, in MADs. `None` draws only `limits`.
+    limits : dict, optional
+        `{metric: (low, high)}`, as `cell_filters` takes them.
+    band : tuple of str, optional
+        The metrics to measure a band for, every column by default.
+    log : tuple of str
+        Metrics binned, thresholded and drawn on a log axis. A count
+        distribution spans orders of magnitude; a percentage does not.
+    sides : dict, optional
+        Which tail each band cuts. The default is `"both"` for a count and
+        `"upper"` for everything else, since a cell with unusually little
+        mitochondrial signal is not a problem.
+    bins : int
+        Bins per panel.
+
+    Returns
+    -------
+    tuple of (matplotlib.figure.Figure, pandas.DataFrame, str)
+        The figure, one row per bin per metric carrying both thresholds,
+        and the caption.
+
+    Raises
+    ------
+    FigureError
+        The frame has no columns, or no cell survives the `NaN` drop.
+    """
+    from matplotlib import ticker
+
+    plt = style()
+
+    if frame.shape[1] == 0:
+        raise FigureError("No metric to draw.")
+    measured = frame.dropna(how="all")
+    if measured.empty:
+        raise FigureError("No measured cell to draw.")
+
+    metrics = list(measured.columns)
+    # The same call the filter makes, so a panel cannot draw a line the
+    # file was not cut on.
+    passes, rules = cell_filters(
+        measured, nmads=nmads, limits=limits, band=band, log=log, sides=sides
+    )
+    columns = min(3, len(metrics))
+    rows = -(-len(metrics) // columns)
+    figure, axes = plt.subplots(
+        rows, columns, figsize=(4.0 * columns, 3.0 * rows), squeeze=False
+    )
+
+    records = []
+    unthresholded: list[str] = []
+    for axis, metric in zip(axes.ravel(), metrics, strict=False):
+        values = measured[metric].to_numpy(dtype=float)
+        on_log = metric in log
+        mine = rules[rules["metric"] == metric]
+        if mine.empty:
+            unthresholded.append(metric)
+            within = np.ones(len(values), dtype=bool)
+        else:
+            within = passes[list(mine["rule"])].to_numpy(dtype=bool).all(axis=1)
+        # What the panel cut on, which is the tightest of its rules.
+        low = float(mine["low"].max()) if not mine.empty else -np.inf
+        high = float(mine["high"].min()) if not mine.empty else np.inf
+
+        if on_log:
+            # A zero has no place on a log axis, so the range starts at the
+            # smallest value actually present.
+            positive = values[values > 0]
+            floor = float(positive.min()) if positive.size else 1.0
+            edges = np.logspace(np.log10(floor), np.log10(np.nanmax(values)), bins + 1)
+            axis.set_xscale("log")
+            # Over a span this narrow matplotlib labels minor ticks too, and
+            # four superscripts in an inch run into each other. Label 1, 2
+            # and 5 per decade instead, in the digits the caption uses.
+            decades = np.log10(edges[-1]) - np.log10(edges[0])
+            axis.xaxis.set_major_locator(
+                ticker.LogLocator(subs=(1.0,) if decades > 3 else (1.0, 2.0, 5.0))
+            )
+            axis.xaxis.set_major_formatter(
+                ticker.FuncFormatter(lambda value, _: _label(value))
+            )
+            axis.xaxis.set_minor_formatter(ticker.NullFormatter())
+        else:
+            floor = float(np.nanmin(values))
+            edges = np.linspace(floor, np.nanmax(values), bins + 1)
+        # logspace rounds its endpoints, so a cell sitting exactly on the
+        # smallest or largest value can fall outside its own range and go
+        # uncounted. Pin the outer edges back onto the data.
+        edges[0] = min(edges[0], floor)
+        edges[-1] = max(edges[-1], float(np.nanmax(values)))
+
+        kept_counts, _ = np.histogram(values[within], bins=edges)
+        cut_counts, _ = np.histogram(values[~within], bins=edges)
+        axis.stairs(kept_counts, edges, fill=True, color=KEPT, label="kept")
+        if (~within).any():
+            # Stacked, so the top of the grey is kept + cut. Passing the cut
+            # count alone fills between it and the baseline, which paints
+            # over the kept cells instead of sitting on them.
+            axis.stairs(
+                kept_counts + cut_counts,
+                edges,
+                baseline=kept_counts,
+                fill=True,
+                color=REMOVED,
+                label="removed",
+            )
+        edges_of = [
+            (bound, upper, rule.source)
+            for rule in mine.itertuples()
+            for bound, upper in ((rule.low, False), (rule.high, True))
+        ]
+        for threshold, upper, source in edges_of:
+            if not np.isfinite(threshold):
+                continue
+            axis.axvline(threshold, color="black", linewidth=1.0, linestyle="--")
+            # A line nobody can read is a line nobody can check. The label
+            # sits inside the band, so it never leaves the panel.
+            axis.annotate(
+                f"{source}  {_label(threshold)}",
+                xy=(threshold, 1.0),
+                xycoords=axis.get_xaxis_transform(),
+                xytext=(-3 if upper else 3, -3),
+                textcoords="offset points",
+                rotation=90,
+                ha="right" if upper else "left",
+                va="top",
+                fontsize="x-small",
+                clip_on=True,
+            )
+        # The panel's own result, since a cut this small leaves no grey
+        # tall enough to see: with no count, one colour says nothing.
+        axis.set_title(
+            f"{int(cut_counts.sum()):,} of {int(kept_counts.sum() + cut_counts.sum()):,} beyond"
+            if metric not in unthresholded
+            else "no rule",
+            loc="right",
+            fontsize="small",
+            color="0.35",
+        )
+        axis.set(xlabel=metric, ylabel="cells")
+        axis.grid(which="major", color="0.85", linewidth=0.6)
+        axis.set_axisbelow(True)
+
+        records.append(
+            pd.DataFrame(
+                {
+                    "metric": metric,
+                    "bin_left": edges[:-1],
+                    "bin_right": edges[1:],
+                    "n_kept": kept_counts,
+                    "n_removed": cut_counts,
+                    "threshold_low": low,
+                    "threshold_high": high,
+                }
+            )
+        )
+
+    for axis in axes.ravel()[len(metrics) :]:
+        axis.set_visible(False)
+    figure.tight_layout()
+
+    numbers = pd.concat(records, ignore_index=True)
+    thresholds = ", ".join(
+        f"{rule.metric} {rule.source} "
+        + " to ".join(
+            f"{bound:,.2f}" for bound in (rule.low, rule.high) if np.isfinite(bound)
+        )
+        for rule in rules.itertuples()
+    )
+    removed = int(numbers["n_removed"].sum())
+    caption = (
+        f"Distribution of each quality-control metric over {len(measured):,} "
+        "cells. Counts are binned and drawn on a log axis, fractions on a "
+        "linear one. The dashed lines are the rules each panel is cut on, "
+        "a band measured from this channel or a limit given to it "
+        f"({thresholds}); cells beyond them are grey, and each panel's "
+        "title counts its own. Each panel cuts on its own metric, so those "
+        f"counts add to {removed:,}, which double-counts a cell that "
+        "several metrics reject."
+    )
+    if unthresholded:
+        caption += (
+            " No threshold is drawn for "
+            + ", ".join(unthresholded)
+            + ", which no rule covers."
+        )
+    return figure, numbers, caption
+
+
+def metric_scatter(
+    frame: pd.DataFrame,
+    x: str = "total_counts",
+    y: str = "pct_counts_targeted",
+    x_threshold: float | None = None,
+    y_threshold: float | None = None,
+    log: tuple[str, ...] = LOG_METRICS,
+    marginals: bool = True,
+    unit: str = "cells",
+    groups=None,
+):
+    """Two QC metrics against each other, cut into quadrants by thresholds.
+
+    A cell can be poor on either metric alone, and the pair says which.
+    The thresholds are given, not derived: a MAD band is one way to reach
+    them and a library that has been looked at closely is another.
+
+    Parameters
+    ----------
+    frame : pandas.DataFrame
+        One row per cell, carrying at least the two columns named.
+    x, y : str
+        Columns to draw. Rows with `NaN` in either are dropped.
+    x_threshold, y_threshold : float, optional
+        Cuts, drawn as dashed lines. A cell is kept when it is at or above
+        both; a threshold left out keeps that axis whole.
+    log : tuple of str
+        Metrics drawn on a log axis.
+    marginals : bool
+        Draw each metric's own histogram against its axis.
+    unit : str
+        What a point is, for the caption. Cells by default; the same figure
+        serves a per-gene comparison.
+    groups : array-like, optional
+        A label per row. Given, the colour carries the label rather than
+        whether the point clears the thresholds, which stay as lines. A row
+        with no label is drawn grey and behind the rest. Use it where
+        nothing is being filtered and the thresholds only mark where things
+        were expected to fall.
+
+    Returns
+    -------
+    tuple of (matplotlib.figure.Figure, pandas.DataFrame, str)
+        The figure, one row per cell with its quadrant, and the caption.
+
+    Raises
+    ------
+    FigureError
+        A column is missing, or no cell has both metrics.
+    """
+    plt = style()
+
+    for name in (x, y):
+        if name not in frame:
+            raise FigureError(f"{name!r} is not a column of the frame.")
+    measured = frame[[x, y]].dropna()
+    if measured.empty:
+        raise FigureError(
+            f"No {unit[:-1] if unit.endswith('s') else unit} has both metrics."
+        )
+
+    xs = measured[x].to_numpy(dtype=float)
+    ys = measured[y].to_numpy(dtype=float)
+    above_x = xs >= x_threshold if x_threshold is not None else np.ones(len(xs), bool)
+    above_y = ys >= y_threshold if y_threshold is not None else np.ones(len(ys), bool)
+    kept = above_x & above_y
+    # A log axis cannot show a zero, and matplotlib drops one without
+    # saying so. Keep it in the numbers and account for it in the caption.
+    drawn = np.ones(len(xs), dtype=bool)
+    for name, values in ((x, xs), (y, ys)):
+        if name in log:
+            drawn &= values > 0
+
+    # A few hundred genes and ten thousand cells want different points: one
+    # needs to be seen, the other needs not to be a solid block.
+    size, alpha = (2.0, 0.4) if len(measured) > 2_000 else (14.0, 0.7)
+
+    if marginals:
+        figure = plt.figure(figsize=(6.0, 6.0))
+        grid = figure.add_gridspec(
+            2, 2, width_ratios=(5, 1), height_ratios=(1, 5), wspace=0.05, hspace=0.05
+        )
+        axis = figure.add_subplot(grid[1, 0])
+        top = figure.add_subplot(grid[0, 0], sharex=axis)
+        right = figure.add_subplot(grid[1, 1], sharey=axis)
+    else:
+        figure, axis = plt.subplots(figsize=(5.5, 4.5))
+        top = right = None
+
+    if groups is None:
+        layers = [
+            (kept & drawn, KEPT, "kept", alpha, 0.0),
+            (~kept & drawn, REMOVED, "removed", alpha, 0.0),
+        ]
+    else:
+        labels = pd.Series(groups, index=frame.index).reindex(measured.index)
+        # Deterministic, so a label keeps its colour across figures. A
+        # Categorical says the order it wants; anything else is sorted,
+        # since row order is not a meaning.
+        named = (
+            [str(name) for name in labels.cat.categories]
+            if isinstance(labels.dtype, pd.CategoricalDtype)
+            else sorted(str(name) for name in pd.unique(labels.dropna()))
+        )
+        # Unlabelled first and see-through, so the classes being compared
+        # sit on top of it. Those are drawn solid: a handful of points at
+        # the alpha that keeps a cloud of ten thousand readable is barely
+        # visible.
+        # The outline is what makes a pale fill findable: Okabe-Ito's yellow
+        # on a white page has almost no edge of its own.
+        layers = [(labels.isna().to_numpy() & drawn, REMOVED, None, alpha, 0.0)]
+        layers += [
+            (
+                (labels == name).to_numpy() & drawn,
+                OKABE_ITO[i % len(OKABE_ITO)],
+                name,
+                1.0,
+                EDGE_WIDTH,
+            )
+            for i, name in enumerate(named)
+        ]
+
+    # Rasterised: one point per cell stays a vector object otherwise, and a
+    # channel of 10,000 cells turns into a PDF nobody can open.
+    for mask, colour, label, opacity, edge in layers:
+        if mask.any():
+            axis.scatter(
+                xs[mask],
+                ys[mask],
+                s=size,
+                alpha=opacity,
+                linewidths=edge,
+                edgecolors=EDGE,
+                color=colour,
+                label=label,
+                rasterized=True,
+            )
+    if x in log:
+        axis.set_xscale("log")
+    if y in log:
+        axis.set_yscale("log")
+    if x_threshold is not None:
+        axis.axvline(x_threshold, color="black", linewidth=1.0, linestyle="--")
+    if y_threshold is not None:
+        axis.axhline(y_threshold, color="black", linewidth=1.0, linestyle="--")
+    axis.set(xlabel=x, ylabel=y)
+    axis.grid(which="major", color="0.85", linewidth=0.6)
+    axis.set_axisbelow(True)
+    # Only worth a legend when there are two groups to tell apart.
+    if groups is not None or (kept.any() and not kept.all()):
+        axis.legend(frameon=False, fontsize="small", loc="lower right")
+
+    for panel, values, name, orientation in (
+        (top, xs, x, "vertical"),
+        (right, ys, y, "horizontal"),
+    ):
+        if panel is None:
+            continue
+        shown = values[drawn]
+        edges = (
+            np.logspace(np.log10(shown.min()), np.log10(shown.max()), 60)
+            if name in log
+            else 60
+        )
+        panel.hist(shown, bins=edges, color="0.8", orientation=orientation)
+        panel.axis("off")
+
+    counts = {
+        "kept": int(kept.sum()),
+        "low_" + x: int((~above_x & above_y).sum()),
+        "low_" + y: int((above_x & ~above_y).sum()),
+        "low_both": int((~above_x & ~above_y).sum()),
+    }
+    numbers = pd.DataFrame(
+        {
+            unit.rstrip("s"): measured.index,
+            x: xs,
+            y: ys,
+            "above_" + x: above_x,
+            "above_" + y: above_y,
+            "kept": kept,
+            "drawn": drawn,
+        }
+    )
+    if groups is not None:
+        numbers["group"] = labels.to_numpy()
+    stated = ", ".join(
+        f"{name} at {value:,.4g}"
+        for name, value in ((x, x_threshold), (y, y_threshold))
+        if value is not None
+    )
+    caption = (
+        f"{x} against {y} for {len(measured):,} {unit}, one point each. "
+        + (f"Dashed lines are the thresholds ({stated}). " if stated else "")
+        + (
+            "Colour is the group each point belongs to, and a point in no "
+            "group is grey. Counts by quadrant: "
+            if groups is not None
+            else "Points at or above every threshold are blue and the rest grey: "
+        )
+        + ", ".join(f"{label} {number:,}" for label, number in counts.items())
+        + ". Histograms on the margins are each metric's own distribution "
+        + f"over the same {unit}."
+    )
+    if not drawn.all():
+        caption += (
+            f" {int((~drawn).sum()):,} of the {unit} hold a zero on a log "
+            "axis and so cannot be drawn; they are in the data file, marked "
+            "by the drawn column, and are counted in the quadrants above."
+        )
+    return figure, numbers, caption
+
+
+def mean_upm(counts) -> np.ndarray:
+    """Mean UMIs per million per gene, over the cells of one modality.
+
+    Each cell is scaled to a million UMIs first, so a deep cell does not
+    outweigh a shallow one, and the mean is taken across cells.
+
+    `normalization.counts_per_million` is the same arithmetic for the dense
+    guide-by-sample tables of a bulk screen; a single-cell matrix is sparse
+    and far too large to densify, so it is done here instead.
+
+    Parameters
+    ----------
+    counts : anndata.AnnData
+        One modality. Cells in rows, genes in columns.
+
+    Returns
+    -------
+    numpy.ndarray
+        One value per gene. A cell with no UMIs contributes nothing rather
+        than dividing by zero.
+    """
+    import scipy.sparse as sp
+
+    matrix = counts.X
+    totals = np.asarray(matrix.sum(axis=1)).ravel()
+    # A cell with no UMIs has no composition to measure; scaling it by zero
+    # keeps it in the denominator without inventing counts for it.
+    scale = np.divide(
+        1e6, totals, out=np.zeros_like(totals, dtype=float), where=totals > 0
+    )
+    if sp.issparse(matrix):
+        scaled = sp.diags(scale) @ matrix
+    else:
+        scaled = np.asarray(matrix) * scale[:, None]
+    return np.asarray(scaled.mean(axis=0)).ravel()
+
+
+def panel_expression(data, modality: str = "gene", flag: str = "readout"):
+    """How much more a targeted panel's genes are seen than everything else.
+
+    A targeted assay works when its panel dominates. This compares the two
+    groups gene by gene rather than cell by cell, which is what says whether
+    the enrichment comes from the panel as a whole or from a few genes.
+
+    Parameters
+    ----------
+    data
+        A MuData as `mudata_builder` assembles it.
+    modality : str
+        Which modality holds the genes.
+    flag : str
+        The boolean column of `var` marking the panel.
+
+    Returns
+    -------
+    tuple of (matplotlib.figure.Figure, pandas.DataFrame, str)
+        The figure, one row per gene, and the caption.
+
+    Raises
+    ------
+    FigureError
+        `var` has no such column, or it marks no gene.
+    """
+    plt = style()
+
+    counts = data[modality]
+    if flag not in counts.var:
+        raise FigureError(f"{flag!r} is not a column of {modality}.var.")
+    in_panel = counts.var[flag].fillna(False).to_numpy().astype(bool)
+    if not in_panel.any():
+        raise FigureError(f"{flag!r} marks no gene, so there is no panel.")
+
+    values = mean_upm(counts)
+    numbers = pd.DataFrame(
+        {
+            "gene": counts.var_names,
+            "symbol": counts.var.get("symbol", pd.Series(index=counts.var_names)),
+            "mean_upm": values,
+            "in_panel": in_panel,
+        }
+    ).reset_index(drop=True)
+
+    figure, axis = plt.subplots(figsize=(4.0, 4.5))
+    groups = [values[in_panel], values[~in_panel]]
+    # +1 so a gene seen in no cell stays on a log axis instead of vanishing.
+    boxes = axis.boxplot(
+        [group + 1 for group in groups],
+        tick_labels=["panel", "background"],
+        widths=0.5,
+        patch_artist=True,
+        flierprops={"markersize": 2, "markeredgewidth": 0, "alpha": 0.3},
+    )
+    for patch, colour in zip(boxes["boxes"], (KEPT, REMOVED), strict=False):
+        patch.set_facecolor(colour)
+    for median in boxes["medians"]:
+        median.set_color("black")
+    axis.set_yscale("log")
+    axis.set_ylabel("mean UMIs per million + 1")
+    axis.grid(axis="y", which="major", color="0.85", linewidth=0.6)
+    axis.set_axisbelow(True)
+    figure.tight_layout()
+
+    panel_median = float(np.median(values[in_panel]))
+    background_median = float(np.median(values[~in_panel]))
+    seen = int((values[~in_panel] > 0).sum())
+    difference = (
+        f"a {panel_median / background_median:,.0f}-fold difference"
+        if background_median
+        # Half the reference is never seen, so its median is zero and a
+        # ratio says nothing. The share that is seen does.
+        else f"the background median being zero, with {seen:,} of "
+        f"{int((~in_panel).sum()):,} background genes seen at all"
+    )
+    caption = (
+        f"Mean expression per gene, in units of UMIs per million, for the "
+        f"{int(in_panel.sum()):,} genes of the panel against the "
+        f"{int((~in_panel).sum()):,} others. Medians are "
+        f"{panel_median:,.1f} and {background_median:,.1f}, {difference}. "
+        "One is added before taking the log so a gene seen in no cell still "
+        "appears. Comparing genes rather than cells is what separates "
+        "enrichment across the whole panel from a few genes carrying it."
+    )
+    return figure, numbers, caption
+
+
+MITO_CONTIG = "chrM"
+"""Mitochondrial genes are the ones on the mitochondrial contig."""
+
+RIBO_PREFIXES = ("RPS", "RPL")
+"""Cytoplasmic ribosomal protein genes, by symbol."""
+
+
+def gene_classes(var: pd.DataFrame) -> dict[str, np.ndarray]:
+    """Which genes are mitochondrial, ribosomal and read out by the assay.
+
+    Mitochondrial genes are taken from the contig, not an ``MT`` symbol
+    prefix. On GENCODE v43 the contig and the ``MT-`` prefix agree exactly,
+    37 genes, but a prefix without the hyphen also matches 529 nuclear genes,
+    mostly metallothioneins, and the MTRNR2L family: nuclear copies of
+    MT-RNR2 that are highly expressed and would inflate the mitochondrial
+    share of exactly the cells one would then discard as dying.
+
+    Ribosomal genes are the RPS and RPL families by symbol, where no contig
+    helps, split by whether the gene is protein-coding: the pseudogenes share
+    the naming, are transcribed, and belong under their own label rather than
+    swelling the ribosomal share.
+
+    Parameters
+    ----------
+    var
+        ``mod['gene'].var``, needing ``gene_chr`` and ``symbol``, and
+        ``readout`` for the targeted class.
+
+    Returns
+    -------
+    dict
+        A boolean mask per class, over the genes in order.
+    """
+    symbol = (
+        var.get("symbol", pd.Series(index=var.index, dtype="string"))
+        .astype("string")
+        .fillna("")
+    )
+    contig = (
+        var.get("gene_chr", pd.Series(index=var.index, dtype="string"))
+        .astype("string")
+        .fillna("")
+    )
+    # An RPS or RPL name is shared by the ribosomal proteins and by their
+    # many pseudogenes, which are transcribed and would otherwise inflate the
+    # ribosomal share. Counted, but under their own label.
+    ribosomal = symbol.str.upper().str.startswith(RIBO_PREFIXES).to_numpy()
+    kind = (
+        var.get("gene_type", pd.Series(index=var.index, dtype="string"))
+        .astype("string")
+        .fillna("")
+    )
+    coding = (kind == "protein_coding").to_numpy()
+    classes = {
+        "mito": (contig == MITO_CONTIG).to_numpy(),
+        "ribo": ribosomal & coding,
+        "ribo_pseudogene": ribosomal & ~coding,
+    }
+    if "readout" in var:
+        classes["targeted"] = var["readout"].fillna(False).to_numpy().astype(bool)
+    return classes
+
+
+def cell_fractions(
+    data,
+    modality: str = "gene",
+    extra: dict[str, np.ndarray] | None = None,
+) -> pd.DataFrame:
+    """The share of each cell's UMIs coming from each class of gene.
+
+    The arithmetic is scanpy's `calculate_qc_metrics`, so the columns are
+    named as the IGVF pipeline names them and the two can be compared
+    directly. What this adds is which genes belong to which class, and the
+    handling of a cell the modality does not hold.
+
+    Parameters
+    ----------
+    data
+        A MuData as `mudata_builder` assembles it.
+    modality
+        Which modality to measure; the gene counts by default.
+    extra
+        Further classes, as a mapping of name to a boolean mask over the
+        modality's genes. A class defined elsewhere -- an older analysis's
+        own definition of ribosomal, say -- is measured alongside the
+        built-in ones, so a threshold set against it still applies.
+
+    Returns
+    -------
+    pandas.DataFrame
+        One row per cell of the whole object, indexed by cell:
+        ``total_counts``, ``n_genes_by_counts`` and a ``pct_counts_`` column
+        per class. A cell the modality does not hold has nothing measured,
+        so its percentages are NaN rather than zero.
+    """
+    import scanpy as sc
+
+    counts = data[modality]
+    classes = gene_classes(counts.var)
+    for name, mask in (extra or {}).items():
+        mask = np.asarray(mask, dtype=bool)
+        if mask.shape != (counts.n_vars,):
+            raise FigureError(
+                f"The mask for {name!r} covers {mask.shape[0]:,} genes, "
+                f"but the modality holds {counts.n_vars:,}."
+            )
+        classes[name] = mask
+
+    # scanpy reads the class membership off var, by the names given here.
+    measured = counts.copy()
+    for name, mask in classes.items():
+        measured.var[name] = mask
+    sc.pp.calculate_qc_metrics(
+        measured, qc_vars=list(classes), percent_top=None, log1p=False, inplace=True
+    )
+
+    wanted = ["total_counts", "n_genes_by_counts"] + [
+        f"pct_counts_{name}" for name in classes
+    ]
+    frame = measured.obs[wanted]
+    # Back onto every cell of the object: one the modality does not hold has
+    # no measurement, which is not a measurement of zero.
+    return frame.reindex(pd.Index(data.obs_names))
+
+
+def cell_filters(
+    frame: pd.DataFrame,
+    nmads: float | None = 5.0,
+    limits: dict[str, tuple[float | None, float | None]] | None = None,
+    band: tuple[str, ...] | None = None,
+    log: tuple[str, ...] = LOG_METRICS,
+    sides: dict[str, str] | None = None,
+):
+    """Which cells pass each quality-control rule.
+
+    Two kinds of rule: a band this channel's own spread decides, and a
+    limit someone else did. A metric can carry both, so a threshold the
+    screen settled on elsewhere sits next to the band measured here.
+
+    Parameters
+    ----------
+    frame : pandas.DataFrame
+        One column per metric, one row per cell, as `cell_fractions`
+        returns, measured on cells already past the count floor. The MAD
+        describes whatever population it is given, and on raw barcodes
+        that population is ambient.
+    nmads : float, optional
+        Width of each band, in MADs. `None` measures no band, leaving only
+        `limits`.
+    limits : dict, optional
+        `{metric: (low, high)}` in the metric's own units, `None` for an
+        open side. A threshold from outside this channel goes here.
+    band : tuple of str, optional
+        The metrics to measure a band for. Every column by default; name a
+        subset when only some of them are worth cutting on.
+    log : tuple of str
+        Metrics whose band is measured on a log scale.
+    sides : dict, optional
+        Which tail each band cuts. The default is `"both"` for a count and
+        `"upper"` for everything else.
+
+    Returns
+    -------
+    tuple of (pandas.DataFrame, pandas.DataFrame)
+        A boolean frame, one row per cell and one column per rule, `True`
+        where the cell passes it; and the rules themselves, one row each,
+        carrying the metric, where the bounds came from and how many cells
+        the rule rejected.
+
+    Raises
+    ------
+    FilterError
+        A limit or a band names a metric the frame does not hold.
+
+    Notes
+    -----
+    A cell whose metric is `NaN` fails that rule: the modality did not
+    measure it, and a metric a cell was never measured for is not one it
+    passed.
+    """
+    sides = sides or {}
+    limits = limits or {}
+    unknown = [
+        metric
+        for metric in list(limits) + list(band or ())
+        if metric not in frame.columns
+    ]
+    if unknown:
+        raise FilterError(
+            "No such metric: "
+            + ", ".join(unknown)
+            + ". The frame holds "
+            + ", ".join(frame.columns)
+            + "."
+        )
+
+    passes: dict[str, np.ndarray] = {}
+    rules = []
+    for metric in frame.columns:
+        values = frame[metric].to_numpy(dtype=float)
+        bounds = []
+        if nmads is not None and (band is None or metric in band):
+            low, high = _band(values, metric, nmads, log, sides)
+            if (low, high) != (-np.inf, np.inf):
+                bounds.append((f"{metric}_mad", f"{nmads:g} MAD", low, high))
+        if metric in limits:
+            low, high = limits[metric]
+            bounds.append(
+                (
+                    f"{metric}_limit",
+                    "limit",
+                    -np.inf if low is None else float(low),
+                    np.inf if high is None else float(high),
+                )
+            )
+        for name, source, low, high in bounds:
+            within = (values >= low) & (values <= high)
+            passes[name] = within
+            rules.append(
+                {
+                    "rule": name,
+                    "metric": metric,
+                    "source": source,
+                    "low": low,
+                    "high": high,
+                    "n_failed": int((~within).sum()),
+                }
+            )
+
+    return (
+        pd.DataFrame(passes, index=frame.index),
+        pd.DataFrame(
+            rules, columns=["rule", "metric", "source", "low", "high", "n_failed"]
+        ),
+    )
+
+
+def apply_filters(data, passes: pd.DataFrame, rules: pd.DataFrame, path=None):
+    """Keep the cells that pass every rule, and record the rules in `uns`.
+
+    Parameters
+    ----------
+    data : mudata.MuData
+        The object the rules were measured on.
+    passes, rules : pandas.DataFrame
+        What `cell_filters` returned.
+    path : str or pathlib.Path, optional
+        Where to write the filtered object. Nothing is written without one.
+
+    Returns
+    -------
+    mudata.MuData
+        The filtered copy, with every threshold that decided it under
+        `uns["qc"]`, so the file says how it was made.
+
+    Raises
+    ------
+    FilterError
+        There is no rule, the rules do not cover exactly this object's
+        cells, or no cell passes them all.
+    """
+    if list(passes.index) != list(data.obs_names):
+        raise FilterError(
+            f"The rules cover {len(passes.index):,} cells and the object "
+            f"holds {data.n_obs:,}. Measure them on the object being "
+            "filtered: a cell no rule saw would pass every one of them."
+        )
+    if passes.shape[1] == 0:
+        raise FilterError(
+            "No rule to apply: no metric has the spread to measure a band, "
+            "and no limit was given. Filtering on nothing would keep every "
+            "cell and say it passed."
+        )
+    keep = passes.to_numpy(dtype=bool).all(axis=1)
+    if not keep.any():
+        raise FilterError("No cell passes every rule.")
+
+    filtered = data[keep].copy()
+    record = dict(filtered.uns.get("qc", {}))
+    record["n_cells_in"] = int(data.n_obs)
+    record["n_cells_out"] = int(filtered.n_obs)
+    record["rules"] = {
+        row.rule: {
+            "metric": row.metric,
+            "source": row.source,
+            "low": float(row.low),
+            "high": float(row.high),
+            "n_failed": int(row.n_failed),
+        }
+        for row in rules.itertuples()
+    }
+    filtered.uns["qc"] = record
+
+    if path is not None:
+        path = Path(path)
+        path.parent.mkdir(parents=True, exist_ok=True)
+        filtered.write_h5mu(path)
+    return filtered
+
+
+def guide_counts(data, modality: str = "guide") -> pd.DataFrame:
+    """Every guide's UMIs and the number of cells it was seen in.
+
+    Parameters
+    ----------
+    data
+        A MuData as `mudata_builder` assembles it.
+    modality
+        The modality holding the guide counts.
+
+    Returns
+    -------
+    pandas.DataFrame
+        One row per guide, indexed by guide id, with `umis` and `n_cells`.
+        `attrs` carries the modality and how many cells it was summed over,
+        for a caption to quote.
+
+    Notes
+    -----
+    Both numbers are summed over the cells the modality holds, which under a
+    union is not every cell of the object.
+    """
+    counts = data[modality]
+    matrix = counts.X
+    frame = pd.DataFrame(
+        {
+            "umis": np.asarray(matrix.sum(axis=0)).ravel(),
+            "n_cells": np.asarray((matrix > 0).sum(axis=0)).ravel(),
+        },
+        index=pd.Index(counts.var_names, name="guide_id"),
+    )
+    frame.attrs = {"modality": modality, "n_cells": int(counts.n_obs)}
+    return frame
+
+
+#: What each `guide_counts` column is called on an axis.
+GUIDE_LABELS = {"umis": "UMIs per guide", "n_cells": "cells per guide"}
+
+
+def guide_abundance(
+    counts: pd.DataFrame,
+    column: str = "umis",
+    groups=None,
+    bins: int = 60,
+):
+    """How much of the library this channel saw, and how often.
+
+    The guides never seen get a bar of their own left of the axis. A zero
+    has no place on a log scale, and dropping it would answer a different
+    question than the one the figure is asked.
+
+    Parameters
+    ----------
+    counts : pandas.DataFrame
+        One row per guide, as `guide_counts` returns.
+    column : str
+        Which of its columns to bin.
+    groups : optional
+        A label per guide, aligned to `counts.index`. Each class is drawn
+        as its own outline, over the share of that class rather than the
+        count: a library's classes differ in size by two orders of
+        magnitude, and raw counts would show only the largest.
+    bins : int
+        Logarithmic bins between one and the largest count, rounded to
+        integer edges so no bin is narrower than a count.
+
+    Returns
+    -------
+    tuple
+        The figure, the numbers, and the caption, ready for `publish`.
+
+    Raises
+    ------
+    FigureError
+        No guide was seen at all, so there is nothing to bin.
+    """
+    from matplotlib import ticker
+
+    plt = style()
+    if column not in counts.columns:
+        raise FigureError(f"The frame has no {column!r} to bin.")
+    values = counts[column].to_numpy(dtype=float)
+    seen = values[values > 0]
+    if seen.size == 0:
+        raise FigureError(f"No guide has a {column} above zero to bin.")
+
+    labels = pd.Series("every guide" if groups is None else groups, index=counts.index)
+    named = (
+        [str(name) for name in labels.cat.categories]
+        if isinstance(labels.dtype, pd.CategoricalDtype)
+        else sorted(str(name) for name in pd.unique(labels.dropna()))
+    )
+    labels = labels.astype(str)
+
+    # A count is an integer, so a log bin narrower than one alternates
+    # full and empty and draws a comb. Rounding the edges collapses those
+    # into single-count bins and leaves the rest logarithmic.
+    edges = np.unique(np.round(np.logspace(0.0, np.log10(seen.max()), bins + 1)))
+    edges[-1] = max(edges[-1], float(seen.max()))
+
+    figure, axis = plt.subplots(figsize=(6.5, 4.0))
+    # The never-seen bars share the linear strip the symlog axis keeps for
+    # zero, side by side so one class does not hide another.
+    span = 0.8 / len(named)
+    records = []
+    for index, name in enumerate(named):
+        mine = values[(labels == name).to_numpy()]
+        if mine.size == 0:
+            continue
+        colour = KEPT if groups is None else OKABE_ITO[index % len(OKABE_ITO)]
+        scale = float(mine.size) if groups is not None else 1.0
+        binned = np.histogram(mine[mine > 0], bins=edges)[0] / scale
+        never = float((mine == 0).sum()) / scale
+        if groups is None:
+            axis.stairs(binned, edges, fill=True, color=colour)
+        else:
+            axis.stairs(binned, edges, color=colour, linewidth=1.4, label=name)
+        axis.bar(
+            0.1 + span * (index + 0.5),
+            never,
+            width=span * 0.9,
+            color=REMOVED if groups is None else colour,
+            edgecolor=EDGE,
+            linewidth=EDGE_WIDTH,
+        )
+        drawn = np.concatenate([[never], binned]) * scale
+        records.append(
+            pd.DataFrame(
+                {
+                    "class": name,
+                    "panel": ["never seen"] + ["binned"] * (len(edges) - 1),
+                    "bin_left": np.concatenate([[np.nan], edges[:-1]]),
+                    "bin_right": np.concatenate([[np.nan], edges[1:]]),
+                    "n_guides": drawn.astype(int),
+                    "share": drawn / mine.size,
+                }
+            )
+        )
+
+    axis.set_xscale("symlog", linthresh=1.0, linscale=0.35)
+    axis.set_yscale("log")
+    axis.set(
+        xlabel=GUIDE_LABELS.get(column, column.replace("_", " ")),
+        ylabel="guides" if groups is None else "share of the class",
+    )
+    axis.xaxis.set_major_formatter(ticker.FuncFormatter(lambda v, _: _label(v)))
+    axis.xaxis.set_minor_formatter(ticker.NullFormatter())
+    if groups is not None:
+        axis.legend(frameon=False, fontsize="small")
+
+    never = int((values == 0).sum())
+    axis.set_title(
+        f"{never:,} of {len(values):,} never seen",
+        loc="right",
+        fontsize="small",
+        color="0.35",
+    )
+    figure.tight_layout()
+
+    numbers = pd.concat(records, ignore_index=True)
+    over = counts.attrs.get("n_cells")
+    what = GUIDE_LABELS.get(column, column.replace("_", " "))
+    caption = (
+        f"{what[0].upper()}{what[1:]}, binned logarithmically"
+        + (f" over {over:,} cells" if over else "")
+        + f". The bar left of the axis is the {never:,} of {len(values):,} "
+        "guides never seen, which a log scale cannot place at zero. A "
+        "channel holds far fewer cells than a library holds guides, so a "
+        "guide missing here is not a guide missing from the library: that "
+        "question needs the channels pooled."
+    )
+    return figure, numbers, caption
diff --git a/src/crispr_analysis_utils/skills/cau-qc/SKILL.md b/src/crispr_analysis_utils/skills/cau-qc/SKILL.md
new file mode 100644
index 0000000..363c05d
--- /dev/null
+++ b/src/crispr_analysis_utils/skills/cau-qc/SKILL.md
@@ -0,0 +1,268 @@
+---
+name: cau-qc
+description: >-
+  Draw quality-control figures for a CRISPR screen with crispr-analysis-utils
+  (`crispr_analysis_utils.qc`), each published as a PDF, the numbers behind
+  it and a caption. Use when a user wants a knee plot or other QC figure from
+  an assembled MuData, asks how deep a channel is, asks which guides a
+  channel saw, wants a figure ready for a paper, or asks where the numbers
+  behind a figure are. Covers the three
+  files every figure writes, the house style, and why a figure is refused
+  without its data.
+---
+
+# QC figures (`crispr_analysis_utils.qc`)
+
+One pass over a channel: find the floor, measure the cells, cut, write.
+
+```python
+import mudata as md
+from crispr_analysis_utils import qc
+
+data = md.read_h5mu("results/mudata/mudata.h5mu")
+qc.publish("results/qc/knee", *qc.knee(data))
+cells, _ = qc.filter_cells(data, min_umis=1000)  # read off that knee
+fractions = qc.cell_fractions(cells)
+qc.publish("results/qc/metrics", *qc.metric_distributions(fractions))
+passes, rules = qc.cell_filters(fractions, nmads=5)
+clean = qc.apply_filters(cells, passes, rules, path="results/qc/filtered.h5mu")
+qc.publish("results/qc/guides", *qc.guide_abundance(qc.guide_counts(clean)))
+```
+
+## Every figure is three files
+
+| File | Holds |
+| --- | --- |
+| `<stem>.pdf` | the figure at 300 dpi, text kept as text |
+| `<stem>.tsv.gz` | the numbers it was drawn from, one row per plotted point |
+| `<stem>.txt` | the caption, as it would read under the figure |
+
+**The data file is the point.** A reviewer can redraw the figure and a reader
+can find the number behind a claim without rerunning anything. `publish`
+refuses an empty table or a blank caption, because a figure without either is
+an assertion rather than a result, and failing at write time is cheaper than
+finding out in review.
+
+## The style is set once
+
+`qc.style()` puts Okabe-Ito on the colour cycle, `cividis` on images, a grey
+grid behind the data, and drops the top and right spines. A figure that names
+no colour is already right; reach for `qc.OKABE_ITO` only to override one.
+`pdf.fonttype 42` keeps text editable in the PDF, so a figure can be
+relabelled without redrawing it.
+
+## The knee plot
+
+`qc.knee(data)` ranks every barcode by total UMIs, both axes logarithmic, one
+curve per modality.
+
+- **A barcode a modality does not hold counts as zero**, not as missing. The
+  assembled object is the union of the two matrices, and dropping the
+  absentees would make a channel look deeper than it is.
+- **The caption carries the real counts**, generated with the figure, so it
+  cannot drift from what was drawn.
+- The knee is where real cells give way to ambient background. Its position
+  is what a count threshold has to justify itself against; this step draws
+  it and sets no threshold.
+
+## The count floor
+
+`qc.filter_cells(data, min_umis=..., modality="gene")` keeps the barcodes
+whose UMI count reaches the floor, returning the filtered copy and the mask.
+
+- **`min_umis` has no default.** Where the knee falls depends on the
+  channel's depth, so read it off that channel's own knee plot. A floor
+  carried over from another channel is a guess.
+- **The floor is applied to one modality**, `gene` by default. A barcode with
+  real RNA and no guide counts is kept: an unassigned cell is a result, not a
+  failure.
+- Fractions computed before filtering describe the whole barcode space, which
+  is mostly ambient. A mitochondrial median of 0 on an unfiltered object means
+  the median barcode holds almost no UMIs, not that the channel is clean.
+
+## Per-cell fractions
+
+`qc.cell_fractions(data)` returns `total_counts`, `n_genes_by_counts` and a
+`pct_counts_<class>` column per class, over `scanpy`'s
+`calculate_qc_metrics`, so the names match the IGVF pipeline's own.
+
+`qc.gene_classes(var)` decides the classes:
+
+| Class | From |
+| --- | --- |
+| `mito` | `gene_chr == "chrM"` |
+| `ribo` | an `RPS`/`RPL` symbol on a protein-coding gene |
+| `ribo_pseudogene` | the same prefixes on anything else |
+| `targeted` | the `readout` column, present only for a targeted panel |
+
+- **Mitochondrial means the contig, not the symbol.** The `MT` prefix also
+  names the nuclear metallothioneins and the `MTRNR2L` family, which are not
+  mitochondrial. On GENCODE v43 the contig gives 37 genes and the prefix 566.
+- **Ribosomal pseudogenes are counted apart**, since they share the `RPS` and
+  `RPL` prefixes with the ~80 real ribosomal proteins and are transcribed.
+- A cell the modality does not hold gets `NaN`, not zero: it was not
+  measured.
+- **`extra={name: mask}` adds a class of someone else's definition.** A
+  threshold set against an older analysis's idea of ribosomal only applies
+  to that definition, so measure it rather than assume the two agree.
+
+## The metric distributions
+
+`qc.metric_distributions(frame, nmads=5)` draws one histogram per column
+of a `cell_fractions` frame, with matplotlib directly rather than through
+scanpy's plotting.
+
+- **Give it the same rules as the filter.** `nmads`, `limits` and `band`
+  are `cell_filters`' own arguments, and the panel calls it to get them, so
+  the lines are the cuts the file was made with rather than a second
+  opinion. Blue (`qc.KEPT`) passes every rule the metric carries, grey
+  (`qc.REMOVED`) fails one.
+- **Each line is labelled with the rule and the value it drew** -- `5 MAD
+  13,063`, `limit  6` -- and each panel is titled with how many cells it
+  put beyond them. A metric can carry several lines. A cut that
+  removes a handful leaves no grey tall enough to see, so without the count
+  a one-colour panel looks like a panel that failed to say anything.
+- **Counts are two-sided and go on a log axis**, fractions are upper-tail
+  only and linear (`LOG_METRICS`, overridable with `sides`).
+- **The grey bars do not sum to the cells a filter drops.** Each panel
+  counts its own rejects, so a cell that two metrics reject is grey twice.
+  `mad_outlier` per metric, combined with `any`, is what gives the cells.
+- A metric no rule covers loses its lines, not the figure: the panel is
+  drawn without them and the caption says so.
+- A cell the modality does not hold is dropped, never binned as zero.
+
+## Two metrics at once
+
+`qc.metric_scatter(frame, x, y, x_threshold=..., y_threshold=...)` draws one
+point per cell with the thresholds as dashed lines, each metric's histogram
+on its margin, and the quadrant counts in the caption.
+
+- **The thresholds are given, not derived.** A MAD band is one way to reach
+  them; a library someone has QCed closely is another, and a fixed number
+  chosen that way is a legitimate input.
+- **Blue is at or above every threshold**, grey is everything else, so the
+  kept quadrant reads at a glance.
+- **The points are rasterised** and the text is not. One vector point per
+  cell makes a PDF that will not open: the same figure in R came to 39 MB.
+- A cell missing either metric is dropped, not drawn at zero.
+- **A zero cannot sit on a log axis**, and matplotlib drops one without
+  saying so. Such a point stays in the data file with `drawn` false, still
+  counts in its quadrant, and the caption says how many there were.
+- `unit="genes"` makes the same figure a per-gene comparison against a
+  reference, thresholds and quadrant counts included.
+- **`groups=` colours by a label instead of by the thresholds.** Where
+  nothing is being filtered, calling the points below a line "removed" is
+  wrong: the lines then only mark where things were expected to fall, and
+  the colour is free to carry what the point is. A point with no label is
+  grey, see-through and drawn behind the rest; a labelled one is solid
+  and thinly outlined, since the alpha that keeps a cloud of ten thousand
+  readable makes a handful of points invisible, and Okabe-Ito's yellow is
+  a pale fill on a white page.
+
+## Does the panel dominate?
+
+`qc.panel_expression(data)` boxplots the mean expression per gene, in UMIs
+per million, for the genes `var["readout"]` marks against every other gene.
+
+- **Gene by gene, not cell by cell.** `pct_counts_targeted` says how much of
+  a cell is the panel; this says whether that comes from the panel as a
+  whole or from a few genes carrying it.
+- `qc.mean_upm(counts)` is the number behind it: each cell scaled to a
+  million UMIs, then averaged across cells, computed on the sparse matrix.
+  `normalization.counts_per_million` is the same arithmetic for the dense
+  guide-by-sample table of a bulk screen and would densify this one.
+- One is added before the log, so a gene seen in no cell still appears.
+
+## Thresholds from the channel itself
+
+`qc.mad_outlier(values, nmads=5, side="both", log=False)` flags the cells
+more than `nmads` median absolute deviations from the median. The spread is
+the channel's own, so the threshold moves with its depth rather than being
+carried over.
+
+- **The MAD is rescaled to a sigma** (`scale="normal"`, a factor of 1.4826),
+  so `nmads` reads as standard deviations. A raw MAD would make the same
+  number cut a third more tightly.
+- **`log=True` for a count**, which is roughly log-normal; off for a
+  percentage. The base does not matter: a median and a MAD rescale together,
+  so the band in the original units is the same either way.
+
+- **It cannot pick the first cut.** The MAD describes whatever population
+  it is given, and on an unfiltered barcode space that population is ambient
+  droplets. On one real channel the median barcode held 2 UMIs and the
+  5-MAD band on `log10` reached 131, so every actual cell was an outlier.
+  The order is: knee for the floor, then MAD on the cells that survive it.
+- **`log=True` for a count, always.** On the raw scale the band is symmetric
+  around the median and its lower bound goes negative, so only the upper
+  tail ever cuts anything.
+- **`side="upper"` for a contamination fraction.** A cell with unusually
+  little mitochondrial signal is not a problem; one with a lot is.
+- 5 MADs is the usual starting point. 3 is aggressive, and on a healthy
+  channel cuts cells that are only deep.
+
+## The rules, and the file they make
+
+`qc.cell_filters(frame, nmads=5, limits=...)` turns the metric frame into one
+boolean column per rule, and a table of the rules themselves.
+`qc.apply_filters(data, passes, rules, path=...)` keeps the cells that pass
+every one, records them, and writes the object.
+
+- **Two kinds of rule, kept apart.** A band is measured from this channel's
+  own spread; a limit is a number someone decided, `{metric: (low, high)}`
+  with `None` for an open side. `total_counts_mad` and `total_counts_limit`
+  are separate columns, so a threshold the screen settled on elsewhere sits
+  next to the band measured here, and the record says which one cut what.
+- **The panel draws these rules.** `metric_distributions` calls
+  `cell_filters` for them, so pass both the same arguments and the figure
+  cannot disagree with the file.
+- **Measure the rules on the object being filtered.** `apply_filters`
+  refuses a frame that does not cover exactly its cells: a cell no rule saw
+  would otherwise pass every one of them.
+- **A `NaN` fails its rule.** The modality did not measure that cell, and a
+  metric a cell was never measured for is not a metric it passed.
+- **`uns["qc"]` is the record** the file carries: `floor`, written by
+  `filter_cells` itself; `rules`, every threshold with its source and how
+  many cells it rejected; and the counts in and out. An open bound is an
+  infinity, since `None` does not survive HDF5.
+- **`band=` names the metrics worth a band**, every column by default. Not
+  every metric deserves one: a ribosomal fraction with a long tail loses
+  real cells to a band nobody chose.
+- `nmads=None` leaves only the limits, for a screen whose thresholds were
+  settled elsewhere.
+
+## The guides the channel saw
+
+`qc.guide_counts(data)` gives every guide its UMIs and the cells it reached;
+`qc.guide_abundance(counts)` bins them.
+
+- **A guide missing from a channel is not a guide missing from the library.**
+  Ten thousand cells cannot sample forty thousand guides, so most of a
+  library is absent from any one channel by arithmetic. Pool the channels
+  before calling a guide lost, and compare the count against what the cell
+  count and the MOI predict.
+- **The guides never seen get a bar of their own**, on the linear strip of a
+  symlog axis. A log scale has no place for a zero, and leaving them out
+  would answer a different question than the one the figure is asked.
+- **The bins are rounded to integer edges**, so no bin is narrower than a
+  count. Without that the low end draws a comb rather than a distribution.
+- **Two modes are the shape to expect**: ambient at a handful of UMIs, and
+  the guides really in cells at thousands. Where the trough falls is what a
+  per-cell assignment has to separate.
+- **`groups=` compares the classes as shares**, not counts, since a library
+  is mostly targeting guides and its few hundred controls would otherwise be
+  a sliver.
+- `column="n_cells"` bins the cells a guide reached instead of its UMIs.
+
+## Ask before running
+
+- **Which object?** One channel's MuData, or several concatenated. A knee
+  plot over pooled channels hides a channel that failed.
+- **Is this a targeted assay, and is the panel flagged?** `cell_fractions`
+  adds `pct_counts_targeted` only when `var` carries the `readout` column,
+  and `panel_expression` needs it too. Without the flag a TAP channel is
+  QCed as if it were whole-transcriptome, and a `pct_counts_targeted` limit
+  is a rule that cannot be written.
+- **What is the floor?** `min_umis` has no default; read it off this
+  channel's knee.
+- **Where do the figures and the filtered object go?** Nothing is written
+  outside the stem's folder, and `apply_filters` writes one `.h5mu`.
diff --git a/tests/test_qc.py b/tests/test_qc.py
new file mode 100644
index 0000000..0386eec
--- /dev/null
+++ b/tests/test_qc.py
@@ -0,0 +1,929 @@
+"""QC: what the classes mean, what a floor keeps, and what a figure writes."""
+
+from __future__ import annotations
+
+import gzip
+
+import numpy as np
+import pandas as pd
+import pytest
+
+from crispr_analysis_utils.qc import (
+    FigureError,
+    cell_fractions,
+    filter_cells,
+    gene_classes,
+    knee,
+    publish,
+    umis_per_cell,
+)
+
+BARCODES = ["AAA", "CCC", "GGG"]
+
+
+@pytest.fixture(autouse=True)
+def _close_figures():
+    """pyplot keeps every figure until it is closed, and this file draws many."""
+    yield
+    import matplotlib.pyplot as plt
+
+    plt.close("all")
+
+
+def mudata():
+    """Three barcodes: the first gene-only, the last guide-only."""
+    import anndata as ad
+    import mudata as md
+
+    gene_var = pd.DataFrame(
+        {
+            # MT-CO1 is on chrM; MT1A is a nuclear metallothionein that the
+            # symbol prefix would sweep in and the contig does not.
+            "gene_chr": ["chrM", "chr16", "chr19", "chr1", "chr11"],
+            "symbol": ["MT-CO1", "MT1A", "RPS2", "RPL5P1", "VWF"],
+            "gene_type": [
+                "protein_coding",
+                "protein_coding",
+                "protein_coding",
+                "processed_pseudogene",
+                "protein_coding",
+            ],
+            "readout": [False, False, False, False, True],
+        },
+        index=pd.Index([f"ENSG{n}" for n in range(5)], name="gene_id"),
+    )
+    gene = ad.AnnData(
+        X=np.array([[10, 10, 20, 10, 50], [0, 0, 0, 0, 100]], dtype="float32"),
+        obs=pd.DataFrame(index=pd.Index(BARCODES[:2], name="barcode")),
+        var=gene_var,
+    )
+    guide = ad.AnnData(
+        X=np.array([[3, 0], [0, 7]], dtype="float32"),
+        obs=pd.DataFrame(index=pd.Index(BARCODES[1:], name="barcode")),
+        var=pd.DataFrame(index=pd.Index(["g1", "g2"], name="guide_id")),
+    )
+    return md.MuData({"gene": gene, "guide": guide})
+
+
+def test_a_barcode_the_modality_does_not_hold_counts_as_zero_umis():
+    data = mudata()
+    assert umis_per_cell(data, "gene").tolist() == [100.0, 100.0, 0.0]
+    assert umis_per_cell(data, "guide").tolist() == [0.0, 3.0, 7.0]
+
+
+def test_mito_is_the_contig_not_the_symbol():
+    classes = gene_classes(mudata()["gene"].var)
+    # MT-CO1 only: MT1A shares the prefix and is nuclear.
+    assert classes["mito"].tolist() == [True, False, False, False, False]
+
+
+def test_ribosomal_pseudogenes_are_counted_under_their_own_label():
+    classes = gene_classes(mudata()["gene"].var)
+    assert classes["ribo"].tolist() == [False, False, True, False, False]
+    assert classes["ribo_pseudogene"].tolist() == [False, False, False, True, False]
+
+
+def test_the_readout_panel_becomes_a_class_only_when_var_declares_one():
+    var = mudata()["gene"].var
+    assert gene_classes(var)["targeted"].tolist() == [False] * 4 + [True]
+    assert "targeted" not in gene_classes(var.drop(columns="readout"))
+
+
+def test_fractions_are_percentages_and_a_cell_outside_the_modality_has_none():
+    frame = cell_fractions(mudata())
+    assert frame.index.tolist() == BARCODES
+    assert frame.loc["AAA", "total_counts"] == 100.0
+    assert frame.loc["AAA", "pct_counts_mito"] == pytest.approx(10.0)
+    assert frame.loc["AAA", "pct_counts_targeted"] == pytest.approx(50.0)
+    # Not measured, rather than measured as zero.
+    assert frame.loc["GGG"].isna().all()
+
+
+def test_a_floor_keeps_a_cell_with_rna_and_no_guide():
+    filtered, keep = filter_cells(mudata(), min_umis=50)
+    assert keep.tolist() == [True, True, False]
+    assert filtered.n_obs == 2
+    # AAA has no guide counts at all and is still a cell.
+    assert filtered["guide"].n_obs == 1
+
+
+def test_the_floor_has_no_default_because_the_knee_is_per_channel():
+    with pytest.raises(TypeError):
+        filter_cells(mudata())
+
+
+def test_a_figure_writes_its_numbers_and_its_caption_beside_it(tmp_path):
+    figure, numbers, caption = knee(mudata())
+    written = publish(tmp_path / "knee", figure, numbers, caption)
+    assert set(written) == {".pdf", ".tsv.gz", ".txt"}
+    assert all(path.exists() for path in written.values())
+    with gzip.open(written[".tsv.gz"], "rt") as handle:
+        assert pd.read_csv(handle, sep="\t").equals(numbers)
+    assert written[".txt"].read_text().strip() == caption.strip()
+
+
+def test_a_figure_without_numbers_or_a_caption_is_refused(tmp_path):
+    figure, numbers, caption = knee(mudata())
+    with pytest.raises(FigureError):
+        publish(tmp_path / "a", figure, numbers.iloc[:0], caption)
+    with pytest.raises(FigureError):
+        publish(tmp_path / "b", figure, numbers, "   ")
+
+
+def test_mad_flags_both_tails_and_never_an_unmeasured_cell():
+    from crispr_analysis_utils.qc import mad_outlier
+
+    values = np.array([100.0, 110.0, 105.0, 95.0, 100.0, 1e6, np.nan])
+    flagged = mad_outlier(values, nmads=3)
+    assert flagged[5]  # the deep outlier
+    assert not flagged[6]  # not measured, so not an outlier
+    assert not flagged[:5].any()
+
+
+def test_mad_can_be_held_to_one_tail():
+    from crispr_analysis_utils.qc import mad_outlier
+
+    values = np.array([100.0, 110.0, 105.0, 95.0, 100.0, 1.0, 1e6])
+    assert mad_outlier(values, nmads=3, side="upper").tolist()[-2:] == [False, True]
+    assert mad_outlier(values, nmads=3, side="lower").tolist()[-2:] == [True, False]
+
+
+def test_a_zero_mad_is_refused_because_it_is_the_unfiltered_barcode_space():
+    from crispr_analysis_utils.qc import mad_outlier
+
+    # What the whole barcode space looks like: the median barcode is ambient.
+    with pytest.raises(FigureError, match="not on the whole barcode space"):
+        mad_outlier(np.array([2.0] * 1000 + [5000.0]))
+
+
+def test_the_mad_is_rescaled_to_a_sigma_so_the_band_is_wider_than_the_raw_mad():
+    from crispr_analysis_utils.qc import mad_outlier
+
+    # Median 0, raw MAD 1, so scale="normal" puts one MAD at 1.4826.
+    values = np.array([-1.2] + [-1.0] * 500 + [0.0] + [1.0] * 500 + [1.2])
+    flagged = mad_outlier(values, nmads=1)
+    # A raw MAD would flag both probes; a sigma-scaled one does not.
+    assert not flagged.any()
+    assert mad_outlier(values, nmads=0.8).tolist()[0] is True
+
+
+def test_the_distributions_draw_one_panel_per_metric_and_bin_every_cell(tmp_path):
+    from crispr_analysis_utils.qc import metric_distributions
+
+    frame = cell_fractions(mudata())
+    figure, numbers, caption = metric_distributions(frame, bins=4)
+
+    metrics = list(frame.columns)
+    assert sorted(numbers["metric"].unique()) == sorted(metrics)
+    # The unmeasured cell is dropped, the other two are binned exactly once.
+    per_metric = numbers.groupby("metric")[["n_kept", "n_removed"]].sum().sum(axis=1)
+    assert (per_metric == 2).all()
+    assert "MAD" in caption
+    written = publish(tmp_path / "metrics", figure, numbers, caption)
+    assert written[".pdf"].exists()
+
+
+def test_the_distributions_need_a_metric_and_a_measured_cell():
+    from crispr_analysis_utils.qc import metric_distributions
+
+    frame = cell_fractions(mudata())
+    with pytest.raises(FigureError, match="No metric"):
+        metric_distributions(frame[[]])
+    with pytest.raises(FigureError, match="No measured cell"):
+        metric_distributions(frame.loc[["GGG"]])
+
+
+def test_a_log_panel_bins_from_the_smallest_value_not_from_one():
+    from crispr_analysis_utils.qc import metric_distributions
+
+    frame = pd.DataFrame({"total_counts": [1000.0, 2000.0, 8000.0]})
+    _, numbers, _ = metric_distributions(frame, bins=4)
+    # Otherwise three of the four bins would sit under the data.
+    assert numbers["bin_left"].min() == pytest.approx(1000.0)
+    assert numbers["n_kept"].sum() == 3
+
+
+def test_a_cell_on_the_smallest_or_largest_value_is_still_counted():
+    from crispr_analysis_utils.qc import metric_distributions
+
+    # logspace rounds these endpoints just inside the data.
+    frame = pd.DataFrame({"total_counts": [271.0, 1000.0, 2321.0, 12150.0]})
+    _, numbers, _ = metric_distributions(frame, bins=60)
+    assert numbers["n_kept"].sum() == 4
+
+
+def test_the_removed_layer_sits_on_the_kept_one_rather_than_over_it():
+    from matplotlib.patches import StepPatch
+
+    from crispr_analysis_utils.qc import metric_distributions
+
+    # One outlier among many: the grey must be a sliver on top.
+    rng = np.random.default_rng(0)
+    frame = pd.DataFrame({"pct_counts_mito": np.r_[rng.normal(1.0, 0.1, 50), 9.0]})
+    figure, numbers, _ = metric_distributions(frame, bins=4)
+
+    axis = figure.axes[0]
+    patches = [a for a in axis.get_children() if isinstance(a, StepPatch)]
+    assert len(patches) == 2
+    kept = patches[0].get_data().values
+    stacked = patches[1].get_data().values
+    baseline = patches[1].get_data().baseline
+    # The grey starts where the blue ends and only ever goes up from there.
+    assert np.allclose(baseline, kept)
+    assert (stacked >= kept).all()
+    assert np.allclose(stacked - kept, numbers["n_removed"].to_numpy())
+
+
+def test_the_line_is_the_threshold_and_the_colour_changes_at_it():
+    from crispr_analysis_utils.qc import mad_bounds, metric_distributions
+
+    rng = np.random.default_rng(0)
+    values = np.r_[rng.normal(1.0, 0.1, 50), 9.0]
+    frame = pd.DataFrame({"pct_counts_mito": values})
+    figure, numbers, caption = metric_distributions(frame, bins=4)
+
+    # Upper tail only for a fraction, so one line, at the upper bound.
+    _, high = mad_bounds(values, side="upper")
+    lines = [x.get_xdata()[0] for x in figure.axes[0].get_lines()]
+    assert lines == pytest.approx([high])
+    assert numbers["threshold_high"].iloc[0] == pytest.approx(high)
+    assert not np.isfinite(numbers["threshold_low"].iloc[0])
+    # Everything grey lies beyond the line, everything blue short of it.
+    beyond = numbers[numbers["n_removed"] > 0]
+    assert (beyond["bin_right"] > high).all()
+    assert f"{high:,.2f}" in caption
+
+
+def test_every_line_is_labelled_with_the_rule_and_the_value_it_drew():
+    from crispr_analysis_utils.qc import mad_bounds, metric_distributions
+
+    rng = np.random.default_rng(0)
+    values = np.r_[rng.normal(1.0, 0.1, 50), 9.0]
+    figure, _, _ = metric_distributions(
+        pd.DataFrame({"pct_counts_mito": values}), nmads=3.0, bins=4
+    )
+
+    _, high = mad_bounds(values, nmads=3.0, side="upper")
+    labels = [t.get_text() for t in figure.axes[0].texts]
+    assert labels == [f"3 MAD  {high:.3g}"]
+
+
+def test_a_line_label_sits_inside_the_band_not_against_the_panel_edge():
+    from crispr_analysis_utils.qc import metric_distributions
+
+    rng = np.random.default_rng(0)
+    figure, _, _ = metric_distributions(
+        pd.DataFrame({"total_counts": rng.lognormal(7.0, 0.4, 500)}), bins=10
+    )
+    axis = figure.axes[0]
+    figure.canvas.draw()
+    lower, upper = sorted(line.get_xdata()[0] for line in axis.get_lines())
+    # The loop draws the lower line first, so the labels come in that order.
+    below, above = (text.get_window_extent() for text in axis.texts)
+    # Both fall between the lines, where the kept cells are: outside them
+    # the panel edge is a few points away and the label is cut in half.
+    assert below.x0 >= axis.transData.transform((lower, 0))[0]
+    assert above.x1 <= axis.transData.transform((upper, 0))[0]
+
+
+def test_the_panel_says_how_many_it_put_beyond_the_band():
+    from crispr_analysis_utils.qc import metric_distributions
+
+    rng = np.random.default_rng(0)
+    frame = pd.DataFrame({"pct_counts_mito": np.r_[rng.normal(1.0, 0.1, 50), 9.0, 9.1]})
+    figure, numbers, _ = metric_distributions(frame, bins=4)
+
+    assert figure.axes[0].get_title(loc="right") == "2 of 52 beyond"
+    assert int(numbers["n_removed"].sum()) == 2
+
+
+def test_a_panel_with_one_layer_carries_no_legend_naming_it():
+    from crispr_analysis_utils.qc import metric_distributions
+
+    # Nothing is beyond the band, so a legend would name only the blue.
+    figure, _, _ = metric_distributions(
+        pd.DataFrame({"pct_counts_mito": [1.0, 1.1, 1.2, 1.3]}), bins=4
+    )
+    assert all(axis.get_legend() is None for axis in figure.axes)
+
+
+def test_a_metric_with_no_spread_loses_its_threshold_not_the_whole_figure():
+    from crispr_analysis_utils.qc import metric_distributions
+
+    frame = pd.DataFrame(
+        {"pct_counts_mito": [1.0] * 50, "pct_counts_ribo": [1.0, 2.0] * 25}
+    )
+    figure, numbers, caption = metric_distributions(frame, bins=4)
+
+    assert len(figure.axes) >= 2
+    assert figure.axes[0].get_title(loc="right") == "no rule"
+    flat = numbers[numbers["metric"] == "pct_counts_mito"]
+    assert not np.isfinite(flat["threshold_high"]).any()
+    assert "pct_counts_mito" in caption.split("No threshold is drawn for")[1]
+
+
+def test_a_limit_is_drawn_and_labelled_beside_the_band_it_joins():
+    from crispr_analysis_utils.qc import metric_distributions
+
+    rng = np.random.default_rng(3)
+    frame = pd.DataFrame(
+        {
+            "total_counts": rng.lognormal(7.0, 0.3, 300),
+            "pct_counts_ribo": rng.normal(15.0, 3.0, 300),
+        }
+    )
+    figure, numbers, caption = metric_distributions(
+        frame,
+        band=("total_counts",),
+        limits={"pct_counts_ribo": (6.0, None)},
+        bins=8,
+    )
+    counts, ribo = figure.axes[:2]
+
+    # A band on the one named, a limit on the one given, nothing doubled up.
+    assert [t.get_text().split()[0] for t in counts.texts] == ["5", "5"]
+    assert [t.get_text() for t in ribo.texts] == ["limit  6"]
+    assert numbers.loc[numbers["metric"] == "pct_counts_ribo", "threshold_low"].iloc[
+        0
+    ] == pytest.approx(6.0)
+    assert "pct_counts_ribo limit 6.00" in caption
+
+
+def test_the_scatter_cuts_into_quadrants_and_keeps_only_the_upper_right():
+    from crispr_analysis_utils.qc import metric_scatter
+
+    frame = pd.DataFrame(
+        {
+            "total_counts": [500.0, 2000.0, 500.0, 2000.0],
+            "pct_counts_targeted": [10.0, 10.0, 60.0, 60.0],
+        }
+    )
+    figure, numbers, caption = metric_scatter(frame, x_threshold=1000, y_threshold=50)
+    assert numbers["kept"].tolist() == [False, False, False, True]
+    assert "kept 1" in caption
+    assert "low_both 1" in caption
+    # One line per threshold, on the scatter panel.
+    scatter_axis = figure.axes[0]
+    assert len(scatter_axis.get_lines()) == 2
+
+
+def test_the_scatter_drops_a_cell_missing_either_metric_and_refuses_an_empty_one():
+    from crispr_analysis_utils.qc import metric_scatter
+
+    frame = pd.DataFrame(
+        {"total_counts": [1000.0, np.nan], "pct_counts_targeted": [60.0, 60.0]}
+    )
+    _, numbers, _ = metric_scatter(frame, marginals=False)
+    assert len(numbers) == 1
+
+    with pytest.raises(FigureError, match="not a column"):
+        metric_scatter(frame, x="missing")
+    with pytest.raises(FigureError, match="No cell has both"):
+        metric_scatter(frame.iloc[1:2], y="total_counts", x="pct_counts_targeted")
+
+
+def test_the_scatter_drops_the_legend_when_nothing_is_removed():
+    from crispr_analysis_utils.qc import metric_scatter
+
+    frame = pd.DataFrame(
+        {"total_counts": [2000.0, 3000.0], "pct_counts_targeted": [60.0, 61.0]}
+    )
+    figure, _, _ = metric_scatter(frame, x_threshold=1000, y_threshold=25)
+    assert figure.axes[0].get_legend() is None
+
+
+def test_an_extra_gene_class_is_measured_alongside_the_built_in_ones():
+    data = mudata()
+    # Another analysis's own definition, wider than this module's.
+    wider = np.array([False, True, True, True, False])
+    frame = cell_fractions(data, extra={"ribo_wide": wider})
+
+    assert "pct_counts_ribo_wide" in frame
+    # AAA: 10 + 20 + 10 of 100 UMIs.
+    assert frame.loc["AAA", "pct_counts_ribo_wide"] == pytest.approx(40.0)
+    # The built-in classes are untouched.
+    assert frame.loc["AAA", "pct_counts_ribo"] == pytest.approx(20.0)
+
+
+def test_an_extra_mask_of_the_wrong_length_is_refused():
+    with pytest.raises(FigureError, match="covers 2 genes"):
+        cell_fractions(mudata(), extra={"wrong": np.array([True, False])})
+
+
+def test_mean_upm_scales_each_cell_before_averaging():
+    from crispr_analysis_utils.qc import mean_upm
+
+    data = mudata()
+    values = mean_upm(data["gene"])
+    # AAA is 10/10/20/10/50 of 100; CCC is 0/0/0/0/100 of 100.
+    assert values.tolist() == pytest.approx(
+        [50_000.0, 50_000.0, 100_000.0, 50_000.0, 750_000.0]
+    )
+
+
+def test_a_cell_with_no_umis_does_not_divide_by_zero():
+    import anndata as ad
+    import mudata as md
+
+    from crispr_analysis_utils.qc import mean_upm
+
+    empty = ad.AnnData(
+        X=np.array([[1.0, 1.0], [0.0, 0.0]], dtype="float32"),
+        obs=pd.DataFrame(index=pd.Index(["a", "b"], name="barcode")),
+        var=pd.DataFrame(index=pd.Index(["g1", "g2"], name="gene_id")),
+    )
+    values = mean_upm(md.MuData({"gene": empty})["gene"])
+    # The empty cell contributes zero, so the mean is halved, not infinite.
+    assert values.tolist() == pytest.approx([250_000.0, 250_000.0])
+
+
+def test_the_panel_boxplot_splits_on_the_readout_flag(tmp_path):
+    from crispr_analysis_utils.qc import panel_expression
+
+    figure, numbers, caption = panel_expression(mudata())
+    assert numbers["in_panel"].sum() == 1
+    assert len(numbers) == 5
+    assert "1 genes of the panel against the 4 others" in caption
+    written = publish(tmp_path / "panel", figure, numbers, caption)
+    assert written[".tsv.gz"].exists()
+
+
+def test_the_panel_boxplot_needs_a_flag_that_marks_something():
+    from crispr_analysis_utils.qc import panel_expression
+
+    data = mudata()
+    with pytest.raises(FigureError, match="not a column"):
+        panel_expression(data, flag="missing")
+    data["gene"].var["readout"] = False
+    with pytest.raises(FigureError, match="marks no gene"):
+        panel_expression(data)
+
+
+def test_the_panel_caption_says_what_a_zero_background_median_means():
+    from crispr_analysis_utils.qc import panel_expression
+
+    data = mudata()
+    # Most of an unfiltered reference is never seen, so its median is zero.
+    data["gene"].X = np.array([[0, 0, 0, 0, 50], [0, 0, 0, 0, 100]], dtype="float32")
+    _, _, caption = panel_expression(data)
+    assert "inf" not in caption
+    assert "background median being zero" in caption
+    assert "0 of 4 background genes seen" in caption
+
+
+def test_the_scatter_serves_a_per_gene_comparison_too():
+    from crispr_analysis_utils.qc import metric_scatter
+
+    frame = pd.DataFrame(
+        {"reference_upm": [5.0, 100.0], "mean_umi": [0.2, 3.0]},
+        index=pd.Index(["ENSG1", "ENSG2"], name="gene"),
+    )
+    _, numbers, caption = metric_scatter(
+        frame,
+        x="reference_upm",
+        y="mean_umi",
+        x_threshold=30,
+        y_threshold=1,
+        log=("reference_upm", "mean_umi"),
+        unit="genes",
+    )
+    assert "2 genes, one point each" in caption
+    assert "over the same genes" in caption
+    assert numbers.columns[0] == "gene"
+    assert numbers["kept"].tolist() == [False, True]
+
+
+def test_a_zero_on_a_log_axis_is_reported_rather_than_silently_dropped():
+    from crispr_analysis_utils.qc import metric_scatter
+
+    frame = pd.DataFrame(
+        {
+            "total_counts": [0.0, 1000.0, 3000.0],
+            "pct_counts_targeted": [60.0, 60.0, 60.0],
+        }
+    )
+    _, numbers, caption = metric_scatter(frame, x_threshold=500, y_threshold=25)
+
+    assert numbers["drawn"].tolist() == [False, True, True]
+    # Still counted: it is below the threshold, and the quadrant says so.
+    assert numbers["kept"].tolist() == [False, True, True]
+    assert "1 of the cells hold a zero on a log axis" in caption
+
+
+def test_the_point_size_follows_how_many_points_there_are():
+    from crispr_analysis_utils.qc import metric_scatter
+
+    few = pd.DataFrame(
+        {"total_counts": [1000.0] * 10, "pct_counts_targeted": [60.0] * 10}
+    )
+    many = pd.DataFrame(
+        {"total_counts": [1000.0] * 3000, "pct_counts_targeted": [60.0] * 3000}
+    )
+    small = metric_scatter(many, marginals=False)[0].axes[0].collections[0].get_sizes()
+    large = metric_scatter(few, marginals=False)[0].axes[0].collections[0].get_sizes()
+    assert large[0] > small[0]
+
+
+def test_groups_colour_the_points_instead_of_the_threshold():
+    from crispr_analysis_utils.qc import OKABE_ITO, REMOVED, metric_scatter
+
+    frame = pd.DataFrame(
+        {"reference_upm": [5.0, 100.0, 200.0], "mean_umi": [0.2, 3.0, 4.0]},
+        index=pd.Index(["a", "b", "c"], name="gene"),
+    )
+    groups = pd.Series(["Day 0", "Day 4", None], index=frame.index)
+    figure, numbers, caption = metric_scatter(
+        frame,
+        x="reference_upm",
+        y="mean_umi",
+        x_threshold=30,
+        y_threshold=1,
+        log=("reference_upm", "mean_umi"),
+        unit="genes",
+        groups=groups,
+        marginals=False,
+    )
+
+    assert numbers["group"].tolist()[:2] == ["Day 0", "Day 4"]
+    assert pd.isna(numbers["group"].iloc[2])
+    labels = [t.get_text() for t in figure.axes[0].get_legend().get_texts()]
+    assert labels == ["Day 0", "Day 4"]
+    # Nothing is being filtered, so the caption must not say so.
+    assert "removed" not in caption
+    assert "Colour is the group" in caption
+    # The unlabelled point is grey and drawn first, behind the classes.
+    collections = figure.axes[0].collections
+    assert collections[0].get_facecolor()[0][0] == pytest.approx(float(REMOVED))
+    assert len(collections) == 3
+    assert OKABE_ITO[0].lstrip("#")
+
+
+def test_groups_still_report_the_quadrant_counts():
+    from crispr_analysis_utils.qc import metric_scatter
+
+    frame = pd.DataFrame({"reference_upm": [5.0, 100.0], "mean_umi": [0.2, 3.0]})
+    _, _, caption = metric_scatter(
+        frame,
+        x="reference_upm",
+        y="mean_umi",
+        x_threshold=30,
+        y_threshold=1,
+        log=("reference_upm", "mean_umi"),
+        unit="genes",
+        groups=["low", "high"],
+        marginals=False,
+    )
+    assert "kept 1" in caption and "low_both 1" in caption
+
+
+def test_a_label_keeps_its_colour_whatever_order_the_rows_arrive_in():
+    from crispr_analysis_utils.qc import metric_scatter
+
+    def legend(order):
+        frame = pd.DataFrame(
+            {"reference_upm": [10.0, 20.0, 30.0], "mean_umi": [1.0, 2.0, 3.0]}
+        )
+        figure, _, _ = metric_scatter(
+            frame,
+            x="reference_upm",
+            y="mean_umi",
+            log=("reference_upm", "mean_umi"),
+            groups=order,
+            marginals=False,
+        )
+        return [t.get_text() for t in figure.axes[0].get_legend().get_texts()]
+
+    assert legend(["Day 4", "Day 0", "Day 2"]) == legend(["Day 0", "Day 2", "Day 4"])
+
+
+def test_a_categorical_sets_its_own_legend_order():
+    from crispr_analysis_utils.qc import metric_scatter
+
+    frame = pd.DataFrame({"reference_upm": [10.0, 20.0], "mean_umi": [1.0, 2.0]})
+    wanted = ["Housekeeping", "Day 0"]
+    groups = pd.Categorical(["Day 0", "Housekeeping"], categories=wanted)
+    figure, _, _ = metric_scatter(
+        frame,
+        x="reference_upm",
+        y="mean_umi",
+        log=("reference_upm", "mean_umi"),
+        groups=groups,
+        marginals=False,
+    )
+    assert [t.get_text() for t in figure.axes[0].get_legend().get_texts()] == wanted
+
+
+def test_a_grouped_point_is_drawn_solid_and_the_background_is_not():
+    from crispr_analysis_utils.qc import metric_scatter
+
+    frame = pd.DataFrame({"reference_upm": [10.0, 20.0], "mean_umi": [1.0, 2.0]})
+    figure, _, _ = metric_scatter(
+        frame,
+        x="reference_upm",
+        y="mean_umi",
+        log=("reference_upm", "mean_umi"),
+        groups=[None, "Day 4"],
+        marginals=False,
+    )
+    background, named = figure.axes[0].collections
+    assert background.get_alpha() < 1.0
+    assert named.get_alpha() == 1.0
+
+
+def test_a_grouped_point_is_outlined_so_a_pale_fill_still_reads():
+    from crispr_analysis_utils.qc import metric_scatter
+
+    frame = pd.DataFrame({"reference_upm": [10.0, 20.0], "mean_umi": [1.0, 2.0]})
+    figure, _, _ = metric_scatter(
+        frame,
+        x="reference_upm",
+        y="mean_umi",
+        log=("reference_upm", "mean_umi"),
+        groups=[None, "Day 4"],
+        marginals=False,
+    )
+    background, named = figure.axes[0].collections
+    assert background.get_linewidths()[0] == 0
+    assert named.get_linewidths()[0] > 0
+    assert named.get_edgecolors()[0].tolist() != named.get_facecolors()[0].tolist()
+
+
+def test_a_rule_is_measured_from_the_channel_or_given_as_a_limit():
+    from crispr_analysis_utils.qc import cell_filters
+
+    rng = np.random.default_rng(0)
+    frame = pd.DataFrame(
+        {
+            "total_counts": rng.lognormal(7.0, 0.3, 200),
+            "pct_counts_mito": rng.normal(5.0, 1.0, 200),
+        }
+    )
+    passes, rules = cell_filters(frame, limits={"pct_counts_mito": (None, 6.0)})
+
+    # Both kinds on one metric, and the band's own on the other.
+    assert list(rules["rule"]) == [
+        "total_counts_mad",
+        "pct_counts_mito_mad",
+        "pct_counts_mito_limit",
+    ]
+    assert list(passes.columns) == list(rules["rule"])
+    assert rules.set_index("rule").loc["pct_counts_mito_limit", "source"] == "limit"
+    assert rules.set_index("rule").loc["total_counts_mad", "source"] == "5 MAD"
+    limit = passes["pct_counts_mito_limit"].to_numpy()
+    assert limit.tolist() == (frame["pct_counts_mito"] <= 6.0).tolist()
+    assert rules["n_failed"].iloc[-1] == int((~limit).sum())
+
+
+def test_the_band_the_filter_cuts_on_is_the_one_the_panel_drew():
+    from crispr_analysis_utils.qc import cell_filters, metric_distributions
+
+    rng = np.random.default_rng(1)
+    frame = pd.DataFrame({"total_counts": rng.lognormal(7.0, 0.3, 300)})
+    _, numbers, _ = metric_distributions(frame, nmads=4.0, bins=8)
+    _, rules = cell_filters(frame, nmads=4.0)
+
+    assert rules["low"].iloc[0] == pytest.approx(numbers["threshold_low"].iloc[0])
+    assert rules["high"].iloc[0] == pytest.approx(numbers["threshold_high"].iloc[0])
+
+
+def test_a_cell_the_modality_never_measured_fails_the_rule():
+    from crispr_analysis_utils.qc import cell_filters
+
+    frame = pd.DataFrame({"pct_counts_mito": [1.0, 1.1, 1.2, np.nan]})
+    passes, rules = cell_filters(frame, limits={"pct_counts_mito": (0.0, 2.0)})
+
+    assert not passes.iloc[3].any()
+    # The NaN is dropped before the band, so it cannot poison the median.
+    assert np.isfinite(rules["high"]).all()
+
+
+def test_a_band_can_be_restricted_to_the_metrics_worth_cutting_on():
+    from crispr_analysis_utils.qc import cell_filters
+
+    rng = np.random.default_rng(2)
+    frame = pd.DataFrame(
+        {
+            "total_counts": rng.lognormal(7.0, 0.3, 200),
+            "pct_counts_ribo": rng.normal(15.0, 3.0, 200),
+        }
+    )
+    _, rules = cell_filters(frame, band=("total_counts",))
+    assert list(rules["rule"]) == ["total_counts_mad"]
+
+
+def test_a_limit_on_a_metric_the_frame_does_not_hold_is_refused():
+    from crispr_analysis_utils.qc import FilterError, cell_filters
+
+    frame = pd.DataFrame({"pct_counts_mito": [1.0, 2.0, 3.0]})
+    with pytest.raises(FilterError, match="pct_counts_targeted"):
+        cell_filters(frame, limits={"pct_counts_targeted": (25.0, None)})
+    with pytest.raises(FilterError, match="n_genes_by_counts"):
+        cell_filters(frame, band=("n_genes_by_counts",))
+
+
+def test_a_flat_metric_with_no_limit_leaves_nothing_to_apply():
+    from crispr_analysis_utils.qc import FilterError, apply_filters, cell_filters
+
+    data = mudata()
+    # A metric with no spread yields no rule. Drawing that is fine; applying
+    # it would keep every cell and call them filtered.
+    passes, rules = cell_filters(
+        pd.DataFrame({"pct_counts_mito": [1.0] * 3}, index=list(data.obs_names))
+    )
+    assert passes.shape[1] == 0 and rules.empty
+    with pytest.raises(FilterError, match="No rule to apply"):
+        apply_filters(data, passes, rules)
+
+
+def test_the_filtered_object_keeps_only_passing_cells_and_says_why(tmp_path):
+    from crispr_analysis_utils.qc import apply_filters, cell_filters
+
+    data = mudata()
+
+    passes, rules = cell_filters(
+        pd.DataFrame({"pct_counts_mito": [1.0, 9.0, 1.0]}, index=list(data.obs_names)),
+        nmads=None,
+        limits={"pct_counts_mito": (None, 5.0)},
+    )
+    path = tmp_path / "qc" / "filtered.h5mu"
+    filtered = apply_filters(data, passes, rules, path=path)
+
+    assert filtered.n_obs == 2
+    assert list(filtered.obs_names) == [BARCODES[0], BARCODES[2]]
+    assert filtered.uns["qc"]["n_cells_in"] == 3
+    assert filtered.uns["qc"]["n_cells_out"] == 2
+    assert filtered.uns["qc"]["rules"]["pct_counts_mito_limit"] == {
+        "metric": "pct_counts_mito",
+        "source": "limit",
+        "low": -np.inf,
+        "high": 5.0,
+        "n_failed": 1,
+    }
+    assert path.is_file()
+
+
+def test_the_written_file_carries_the_thresholds_that_made_it(tmp_path):
+    import mudata as md
+
+    from crispr_analysis_utils.qc import apply_filters, cell_filters, filter_cells
+
+    data = mudata()
+
+    cells, _ = filter_cells(data, min_umis=1)
+    passes, rules = cell_filters(
+        pd.DataFrame({"pct_counts_mito": [1.0, 2.0]}, index=list(cells.obs_names)),
+        nmads=None,
+        limits={"pct_counts_mito": (None, 5.0)},
+    )
+    path = tmp_path / "filtered.h5mu"
+    apply_filters(cells, passes, rules, path=path)
+
+    record = md.read_h5mu(path).uns["qc"]
+    # The floor is the first cut, and it writes itself down.
+    assert record["floor"]["min_umis"] == 1
+    assert record["floor"]["n_cells_in"] == 3
+    assert record["rules"]["pct_counts_mito_limit"]["high"] == 5.0
+
+
+def test_rules_measured_on_another_object_are_refused():
+    from crispr_analysis_utils.qc import FilterError, apply_filters, cell_filters
+
+    data = mudata()
+
+    passes, rules = cell_filters(
+        pd.DataFrame({"pct_counts_mito": [1.0, 2.0]}, index=list(data.obs_names)[:2]),
+        nmads=None,
+        limits={"pct_counts_mito": (None, 5.0)},
+    )
+    with pytest.raises(FilterError, match="would pass every one"):
+        apply_filters(data, passes, rules)
+
+
+def test_a_filter_that_keeps_nothing_is_refused():
+    from crispr_analysis_utils.qc import FilterError, apply_filters, cell_filters
+
+    data = mudata()
+
+    passes, rules = cell_filters(
+        pd.DataFrame({"pct_counts_mito": [9.0, 9.0, 9.0]}, index=list(data.obs_names)),
+        nmads=None,
+        limits={"pct_counts_mito": (None, 5.0)},
+    )
+    with pytest.raises(FilterError, match="No cell passes"):
+        apply_filters(data, passes, rules)
+
+
+def guide_library():
+    """Six guides over four cells: one no cell carries, one in every cell."""
+    import anndata as ad
+    import mudata as md
+
+    counts = np.array(
+        [
+            [0, 1, 0, 40, 900, 0],
+            [0, 0, 2, 0, 800, 0],
+            [0, 0, 0, 50, 0, 0],
+            [0, 1, 0, 0, 700, 0],
+        ],
+        dtype="float32",
+    )
+    var = pd.DataFrame(
+        {"type": ["targeting"] * 4 + ["non-targeting"] * 2},
+        index=pd.Index([f"g{n}" for n in range(6)], name="guide_id"),
+    )
+    guide = ad.AnnData(
+        X=counts,
+        obs=pd.DataFrame(index=pd.Index([f"c{n}" for n in range(4)], name="barcode")),
+        var=var,
+    )
+    return md.MuData({"guide": guide})
+
+
+def test_a_guide_no_cell_carries_is_counted_rather_than_dropped():
+    from crispr_analysis_utils.qc import guide_counts
+
+    counts = guide_counts(guide_library())
+    assert counts.index.tolist() == [f"g{n}" for n in range(6)]
+    assert counts["umis"].tolist() == [0.0, 2.0, 2.0, 90.0, 2400.0, 0.0]
+    assert counts["n_cells"].tolist() == [0, 2, 1, 2, 3, 0]
+
+
+def test_the_counts_remember_how_many_cells_they_were_summed_over():
+    from crispr_analysis_utils.qc import guide_counts
+
+    # A caption cannot say it otherwise: under a union the guide modality
+    # holds fewer cells than the object does.
+    assert guide_counts(guide_library()).attrs == {"modality": "guide", "n_cells": 4}
+
+
+def test_the_guides_never_seen_get_the_bar_a_log_axis_cannot_give_them():
+    from crispr_analysis_utils.qc import guide_abundance, guide_counts
+
+    figure, numbers, caption = guide_abundance(guide_counts(guide_library()))
+    unseen = numbers[numbers["panel"] == "never seen"]
+    assert unseen["n_guides"].tolist() == [2]
+    assert figure.axes[0].get_xscale() == "symlog"
+    assert "2 of 6" in figure.axes[0].get_title(loc="right")
+    assert "2 of 6 guides never seen" in caption
+
+
+def test_every_guide_that_was_seen_lands_in_a_bin():
+    from crispr_analysis_utils.qc import guide_abundance, guide_counts
+
+    _, numbers, _ = guide_abundance(guide_counts(guide_library()))
+    assert int(numbers.loc[numbers["panel"] == "binned", "n_guides"].sum()) == 4
+
+
+def test_a_count_is_never_split_across_two_bins():
+    from crispr_analysis_utils.qc import guide_abundance, guide_counts
+
+    # Logarithmic edges below ten are narrower than one, and would leave
+    # every other bin empty: a comb, not a distribution.
+    _, numbers, _ = guide_abundance(guide_counts(guide_library()), bins=60)
+    binned = numbers[numbers["panel"] == "binned"]
+    widths = binned["bin_right"] - binned["bin_left"]
+    assert (widths >= 1.0).all()
+
+
+def test_a_class_is_drawn_as_its_share_so_a_small_one_still_reads():
+    from crispr_analysis_utils.qc import guide_abundance, guide_counts
+
+    data = guide_library()
+    _, numbers, _ = guide_abundance(
+        guide_counts(data), groups=data["guide"].var["type"]
+    )
+    unseen = numbers[numbers["panel"] == "never seen"].set_index("class")
+    # One of four targeting guides, one of two controls: the same count,
+    # and the share is what separates them.
+    assert unseen.loc["targeting", "n_guides"] == 1
+    assert unseen.loc["non-targeting", "n_guides"] == 1
+    assert unseen.loc["targeting", "share"] == pytest.approx(0.25)
+    assert unseen.loc["non-targeting", "share"] == pytest.approx(0.5)
+
+
+def test_the_same_figure_bins_the_cells_a_guide_reached():
+    from crispr_analysis_utils.qc import guide_abundance, guide_counts
+
+    figure, numbers, caption = guide_abundance(
+        guide_counts(guide_library()), column="n_cells"
+    )
+    assert figure.axes[0].get_xlabel() == "cells per guide"
+    assert caption.startswith("Cells per guide")
+    assert int(numbers.loc[numbers["panel"] == "binned", "n_guides"].sum()) == 4
+
+
+def test_a_library_no_cell_saw_is_refused_rather_than_drawn_empty():
+    from crispr_analysis_utils.qc import guide_abundance, guide_counts
+
+    data = guide_library()
+    data["guide"].X[:] = 0
+    with pytest.raises(FigureError, match="above zero"):
+        guide_abundance(guide_counts(data))
+
+
+def test_the_caption_refuses_to_call_a_channels_gap_a_library_gap():
+    from crispr_analysis_utils.qc import guide_abundance, guide_counts
+
+    _, _, caption = guide_abundance(guide_counts(guide_library()))
+    assert "over 4 cells" in caption
+    assert "channels pooled" in caption
diff --git a/tests/test_skills.py b/tests/test_skills.py
index cb76cfd..2faf5d0 100644
--- a/tests/test_skills.py
+++ b/tests/test_skills.py
@@ -33,6 +33,7 @@
     "cau-element-mapping": ("element_mapping",),
     "cau-igvf-export": ("igvf_export",),
     "cau-mudata-builder": ("mudata_builder",),
+    "cau-qc": ("qc",),
 }
 
 # Modules that are plumbing rather than analysis, so need no skill.
diff --git a/uv.lock b/uv.lock
index aed96ba..4c031af 100644
--- a/uv.lock
+++ b/uv.lock
@@ -5,9 +5,12 @@ resolution-markers = [
     "python_full_version >= '3.14' and sys_platform == 'win32'",
     "python_full_version >= '3.14' and sys_platform == 'emscripten'",
     "python_full_version >= '3.14' and sys_platform != 'emscripten' and sys_platform != 'win32'",
-    "python_full_version < '3.14' and sys_platform == 'win32'",
-    "python_full_version < '3.14' and sys_platform == 'emscripten'",
-    "python_full_version < '3.14' and sys_platform != 'emscripten' and sys_platform != 'win32'",
+    "python_full_version == '3.13.*' and sys_platform == 'win32'",
+    "python_full_version < '3.13' and sys_platform == 'win32'",
+    "python_full_version == '3.13.*' and sys_platform == 'emscripten'",
+    "python_full_version < '3.13' and sys_platform == 'emscripten'",
+    "python_full_version == '3.13.*' and sys_platform != 'emscripten' and sys_platform != 'win32'",
+    "python_full_version < '3.13' and sys_platform != 'emscripten' and sys_platform != 'win32'",
 ]
 
 [[package]]
@@ -230,6 +233,15 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/58/50/6c0d534c5f134586a8e1ba4e330569e32f057e33372ae556463212fb4cd3/click-8.5.0-py3-none-any.whl", hash = "sha256:255bc9599cf7748b4b1a446ccc735421bd08a2ae529a8b88597d3de5664ee360", size = 125251, upload-time = "2026-08-26T13:33:12.928Z" },
 ]
 
+[[package]]
+name = "cloudpickle"
+version = "3.1.2"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/27/fb/576f067976d320f5f0114a8d9fa1215425441bb35627b1993e5afd8111e5/cloudpickle-3.1.2.tar.gz", hash = "sha256:7fda9eb655c9c230dab534f1983763de5835249750e85fbcef43aaa30a9a2414", size = 22330, upload-time = "2025-11-03T09:25:26.604Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/88/39/799be3f2f0f38cc727ee3b4f1445fe6d5e4133064ec2e4115069418a5bb6/cloudpickle-3.1.2-py3-none-any.whl", hash = "sha256:9acb47f6afd73f60dc1df93bb801b472f05ff42fa6c84167d25cb206be1fbf4a", size = 22228, upload-time = "2025-11-03T09:25:25.534Z" },
+]
+
 [[package]]
 name = "colorama"
 version = "0.4.6"
@@ -239,18 +251,100 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/d1/d6/3965ed04c63042e047cb6a3e6ed1a63a35087b6a609aa3a15ed8ac56c221/colorama-0.4.6-py2.py3-none-any.whl", hash = "sha256:4f1d9991f5acc0ca119f9d443620b77f9d6b33703e51011c16baf57afb285fc6", size = 25335, upload-time = "2022-10-25T02:36:20.889Z" },
 ]
 
+[[package]]
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+version = "1.4.0"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "numpy" },
+]
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 [package.metadata.requires-dev]
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