diff --git a/.gitignore b/.gitignore
index 4bb9d22..fe47eb5 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,50 +1,22 @@
-_build/
-data*
-results*
-logs*
-_issues.ipynb
-*/figures/*
-figures/*
-data/*
-write/*
-
-*.egg-info/
-.pytest_cache/
-.coverage
-cache/
-coverage.xml
-test.h5ad
-test_compr.h5ad
-test_no_compr.h5ad
-docs/_build
-archive/
-data/
-README.html
-scripts/
-test_notebooks.txt
-upload_to_pypi.sh
+/_build/
+/dist/
+*.h5ad
+*.zip
 
-# Ignore biomart file
-.pybiomart.sqlite
+/cache/
+/archive/
+/data/
+/write/
+/figures/
+/scripts/
 
-# always-ignore extensions
+# caches
 *~
-
-# Python / Byte-compiled / optimized / DLL
 __pycache__/
-*.py[cod]
-*.so
-.cache
+.ipynb_checkpoints/
+.*cache/
 
 # OS or Editor files and folders
 .DS_Store
 Thumbs.db
-.ipynb_checkpoints/
 .directory
-/.idea/
-/.vscode/
-/hatch.toml
-
-# always-ignore directories
-/dist/
-/build/
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 3e63860..de519a9 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -15,15 +15,14 @@ repos:
   - repo: https://github.com/astral-sh/ruff-pre-commit
     rev: v0.12.12
     hooks:
-      - id: ruff
-        types_or: [python, pyi, jupyter]
+      - id: ruff-check
         args: ["--fix"]
       - id: ruff-format
-        types_or: [python, pyi, jupyter]
-  - repo: https://github.com/pre-commit/mirrors-prettier
-    rev: v4.0.0-alpha.8
+  - repo: https://github.com/tox-dev/pyproject-fmt
+    rev: v2.6.0
     hooks:
-      - id: prettier
-        exclude_types:
-          - markdown
-        language_version: 21.5.0
+      - id: pyproject-fmt
+  - repo: https://github.com/biomejs/pre-commit
+    rev: v2.2.3
+    hooks:
+      - id: biome-format
diff --git a/.prettierrc.yaml b/.prettierrc.yaml
deleted file mode 100644
index b10f6cc..0000000
--- a/.prettierrc.yaml
+++ /dev/null
@@ -1,7 +0,0 @@
-overrides:
-  # JSON with comments and trailing commas
-  - files: benchmarks/asv.conf.json
-    options:
-      parser: json5
-      quoteProps: preserve
-      singleQuote: false
diff --git a/.vscode/launch.json b/.vscode/launch.json
new file mode 100644
index 0000000..15b4d13
--- /dev/null
+++ b/.vscode/launch.json
@@ -0,0 +1,14 @@
+{
+	// https://go.microsoft.com/fwlink/?linkid=830387
+	"version": "0.2.0",
+	"configurations": [
+		{
+			"name": "Build docs",
+			"type": "debugpy",
+			"request": "launch",
+			"module": "sphinx",
+			"args": ["-M", "html", ".", "_build"],
+			"console": "internalConsole",
+		},
+	],
+}
diff --git a/.vscode/settings.json b/.vscode/settings.json
new file mode 100644
index 0000000..a098bed
--- /dev/null
+++ b/.vscode/settings.json
@@ -0,0 +1,13 @@
+{
+	"[python]": {
+		"editor.formatOnSave": true,
+		"editor.defaultFormatter": "charliermarsh.ruff",
+		"editor.codeActionsOnSave": {
+			"source.fixAll": "always",
+			"source.organizeImports": "always",
+		},
+	},
+	"notebook.formatOnSave.enabled": true,
+	"python.terminal.activateEnvironment": true,
+	"python.analysis.typeCheckingMode": "basic",
+}
diff --git a/_static/css/custom.css b/_static/css/custom.css
index 03b7b0a..766febc 100644
--- a/_static/css/custom.css
+++ b/_static/css/custom.css
@@ -1,15 +1,15 @@
 body {
-  background-image: repeating-linear-gradient(
-    45deg,
-    var(--pst-color-danger-bg) 0,
-    var(--pst-color-danger-bg) 20px,
-    transparent 20px,
-    transparent 40px
-  );
+	background-image: repeating-linear-gradient(
+		45deg,
+		var(--pst-color-danger-bg) 0,
+		var(--pst-color-danger-bg) 20px,
+		transparent 20px,
+		transparent 40px
+	);
 }
 
 .bd-sidebar-primary,
 .bd-sidebar-secondary,
 body:not(.scrolled) .bd-header-article {
-  background-color: initial !important;
+	background-color: initial !important;
 }
diff --git a/basic-scrna-tutorial.ipynb b/basic-scrna-tutorial.ipynb
index 19d3c49..d5ea25d 100644
--- a/basic-scrna-tutorial.ipynb
+++ b/basic-scrna-tutorial.ipynb
@@ -25,11 +25,13 @@
    "outputs": [],
    "source": [
     "# Core scverse libraries\n",
-    "import scanpy as sc\n",
+    "from __future__ import annotations\n",
+    "\n",
     "import anndata as ad\n",
     "\n",
     "# Data retrieval\n",
-    "import pooch"
+    "import pooch\n",
+    "import scanpy as sc"
    ]
   },
   {
@@ -185,9 +187,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "sc.pp.calculate_qc_metrics(\n",
-    "    adata, qc_vars=[\"mt\", \"ribo\", \"hb\"], inplace=True, log1p=True\n",
-    ")"
+    "sc.pp.calculate_qc_metrics(adata, qc_vars=[\"mt\", \"ribo\", \"hb\"], inplace=True, log1p=True)"
    ]
   },
   {
@@ -604,12 +604,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": null,
    "id": "11bb19be-3df1-4a17-ad84-16b50a9348ea",
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Using the igraph implementation and a fixed number of iterations can be significantly faster, especially for larger datasets\n",
+    "# Using the igraph implementation and a fixed number of iterations can be significantly faster,\n",
+    "# especially for larger datasets\n",
     "sc.tl.leiden(adata, flavor=\"igraph\", n_iterations=2)"
    ]
   },
@@ -764,9 +765,7 @@
    "outputs": [],
    "source": [
     "for res in [0.02, 0.5, 2.0]:\n",
-    "    sc.tl.leiden(\n",
-    "        adata, key_added=f\"leiden_res_{res:4.2f}\", resolution=res, flavor=\"igraph\"\n",
-    "    )"
+    "    sc.tl.leiden(adata, key_added=f\"leiden_res_{res:4.2f}\", resolution=res, flavor=\"igraph\")"
    ]
   },
   {
@@ -1052,9 +1051,7 @@
     }
    ],
    "source": [
-    "sc.pl.rank_genes_groups_dotplot(\n",
-    "    adata, groupby=\"leiden_res_0.50\", standard_scale=\"var\", n_genes=5\n",
-    ")"
+    "sc.pl.rank_genes_groups_dotplot(adata, groupby=\"leiden_res_0.50\", standard_scale=\"var\", n_genes=5)"
    ]
   },
   {
@@ -1214,7 +1211,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "notebook",
    "language": "python",
    "name": "python3"
   },
@@ -1228,7 +1225,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.8"
+   "version": "3.13.7"
   },
   "mystnb": {
    "execution_mode": "off"
diff --git a/biome.jsonc b/biome.jsonc
new file mode 100644
index 0000000..60f4c36
--- /dev/null
+++ b/biome.jsonc
@@ -0,0 +1,18 @@
+{
+	"$schema": "https://biomejs.dev/schemas/2.1.1/schema.json",
+	"formatter": { "useEditorconfig": true },
+	"overrides": [
+		{
+			"includes": ["./.vscode/*.json", "**/*.jsonc", "**/asv.conf.json"],
+			"json": {
+				"formatter": {
+					"trailingCommas": "all",
+				},
+				"parser": {
+					"allowComments": true,
+					"allowTrailingCommas": true,
+				},
+			},
+		},
+	],
+}
diff --git a/conf.py b/conf.py
index 475f11a..ecd7d99 100644
--- a/conf.py
+++ b/conf.py
@@ -1,8 +1,9 @@
-from collections.abc import Mapping
-from datetime import datetime
+from __future__ import annotations
+
+from datetime import UTC, datetime
 from importlib.metadata import metadata
 from types import MappingProxyType
-from typing import TYPE_CHECKING, Sequence
+from typing import TYPE_CHECKING, ClassVar
 
 from docutils import nodes
 from sphinx import addnodes
@@ -11,7 +12,10 @@
 from sphinx.ext.intersphinx import resolve_reference_in_inventory
 from sphinx.util.docutils import SphinxDirective
 
+
 if TYPE_CHECKING:
+    from collections.abc import Mapping, Sequence
+
     from docutils.parsers.rst.states import Inliner
     from sphinx.application import Sphinx
     from sphinx.environment import BuildEnvironment
@@ -20,7 +24,7 @@
 meta = metadata("scanpy-tutorials")
 project = meta["Name"]
 author = meta["Author"]
-copyright = f"{datetime.now():%Y}, {author}"
+copyright = f"{datetime.now(UTC):%Y}, {author}"  # noqa: A001
 release = version = meta["Version"]
 
 extensions = [
@@ -48,7 +52,6 @@
     anndata=("https://anndata.readthedocs.io/en/stable/", None),
     scanpy=("https://scanpy.readthedocs.io/en/stable/", None),
 )
-# TODO: move images here from scanpy
 suppress_warnings = ["image.not_readable"]
 
 # -- Options for HTML output ----------------------------------------------
@@ -88,6 +91,7 @@ def fake_cite(
     options: Mapping[str, object] = MappingProxyType({}),
     content: Sequence[str] = (),
 ) -> tuple[list[nodes.Node], list[str]]:
+    del name, lineno, options, content
     msg = f"cite:{text}"
     return [
         inliner.document.reporter.info(msg),
@@ -96,16 +100,15 @@ def fake_cite(
 
 
 class FakeDomain(Domain):
-    name = "cite"
-    roles = dict(p=fake_cite, t=fake_cite)
+    name: ClassVar = "cite"
+    roles: ClassVar = dict(p=fake_cite, t=fake_cite)
 
 
 # Role linking to the canonical location in scanpy’s docs
 
 
 MSG = (
-    "Please access this document in its canonical location "
-    "as the currently accessed page may not be rendered correctly"
+    "Please access this document in its canonical location as the currently accessed page may not be rendered correctly"
 )
 
 
@@ -144,6 +147,7 @@ def missing_reference(
     node: addnodes.pending_xref,
     contnode: nodes.TextElement,
 ) -> nodes.Node | None:
+    del app, env, contnode
     # ignore known scanpy labels
     if node["reftarget"] in {
         "external-data-integration",
diff --git a/dask.ipynb b/dask.ipynb
index 50a862f..e83c669 100644
--- a/dask.ipynb
+++ b/dask.ipynb
@@ -63,12 +63,16 @@
     }
    ],
    "source": [
+    "from __future__ import annotations\n",
+    "\n",
     "from pathlib import Path\n",
     "\n",
-    "import dask.distributed as dd\n",
-    "import scanpy as sc\n",
     "import anndata as ad\n",
+    "import dask.distributed as dd\n",
     "import h5py\n",
+    "import pooch\n",
+    "import scanpy as sc\n",
+    "\n",
     "\n",
     "sc.logging.print_header()"
    ]
@@ -80,45 +84,20 @@
     "Here, we'll be working with a moderately large dataset of 1.4 million cells taken from: [COVID-19 immune features revealed by a large-scale single-cell transcriptome atlas](https://cellxgene.cziscience.com/collections/0a839c4b-10d0-4d64-9272-684c49a2c8ba)\n"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def download(url: str, path: Path) -> None:\n",
-    "    \"\"\"Download a file from `url` and save it to `path`, showing a progress bar.\"\"\"\n",
-    "    from tqdm.autonotebook import tqdm\n",
-    "    from urllib.request import urlretrieve\n",
-    "\n",
-    "    pb = tqdm(unit=\"B\", unit_scale=True, unit_divisor=1024)\n",
-    "\n",
-    "    def update(b: int = 1, bsize: int = 1, tsize: int | None = None):\n",
-    "        if tsize is not None:\n",
-    "            pb.total = tsize\n",
-    "        return pb.update(b * bsize - pb.n)\n",
-    "\n",
-    "    try:\n",
-    "        with pb:\n",
-    "            urlretrieve(url, path, reporthook=update)\n",
-    "    except BaseException:\n",
-    "        path.unlink(missing_ok=True)\n",
-    "        raise"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
-    "cell_atlas_path = Path(\"data/cell_atlas.h5ad\")\n",
-    "cell_atlas_path.parent.mkdir(exist_ok=True)\n",
-    "if not cell_atlas_path.exists():\n",
-    "    download(\n",
+    "cell_atlas_path = Path(\n",
+    "    pooch.retrieve(\n",
     "        \"https://datasets.cellxgene.cziscience.com/82eac9c1-485f-4e21-ab21-8510823d4f6e.h5ad\",\n",
-    "        cell_atlas_path,\n",
-    "    )"
+    "        known_hash=\"sha256:0b24babfb34b4af87a76806039afa3513c2c04c9045e2a9fb31a6e9350b1fabe\",\n",
+    "        fname=\"cell_atlas.h5ad\",\n",
+    "        path=\"data\",\n",
+    "    )\n",
+    ")"
    ]
   },
   {
@@ -497,9 +476,7 @@
     "        obs=ad.io.read_elem(f[\"obs\"]),\n",
     "        var=ad.io.read_elem(f[\"var\"]),\n",
     "    )\n",
-    "    adata.X = ad.experimental.read_elem_as_dask(\n",
-    "        f[\"raw/X\"], chunks=(SPARSE_CHUNK_SIZE, adata.shape[1])\n",
-    "    )"
+    "    adata.X = ad.experimental.read_elem_as_dask(f[\"raw/X\"], chunks=(SPARSE_CHUNK_SIZE, adata.shape[1]))"
    ]
   },
   {
@@ -707,7 +684,8 @@
    ],
    "source": [
     "%%time\n",
-    "from sklearn_ann.kneighbors.annoy import AnnoyTransformer  # noqa: E402\n",
+    "from sklearn_ann.kneighbors.annoy import AnnoyTransformer\n",
+    "\n",
     "\n",
     "transformer = AnnoyTransformer(n_neighbors=15)\n",
     "sc.pp.neighbors(adata, transformer=transformer)"
@@ -772,7 +750,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "sc",
+   "display_name": "notebook",
    "language": "python",
    "name": "python3"
   },
@@ -786,7 +764,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.7"
+   "version": "3.13.7"
   }
  },
  "nbformat": 4,
diff --git a/how-to/knn-transformers.ipynb b/how-to/knn-transformers.ipynb
index dd7371b..0e847db 100644
--- a/how-to/knn-transformers.ipynb
+++ b/how-to/knn-transformers.ipynb
@@ -39,6 +39,9 @@
     }
    ],
    "source": [
+    "from __future__ import annotations\n",
+    "\n",
+    "\n",
     "%pip install -qU \"pip\"\n",
     "%pip install -q \"scanpy\" \"sklearn-ann[annoy]\""
    ]
@@ -60,6 +63,7 @@
     "import scanpy as sc\n",
     "from sklearn_ann.kneighbors.annoy import AnnoyTransformer  # noqa: F401\n",
     "\n",
+    "\n",
     "sc.logging.print_header()"
    ]
   },
@@ -167,6 +171,7 @@
    "source": [
     "import matplotlib.pyplot as plt\n",
     "\n",
+    "\n",
     "with plt.ioff():\n",
     "    fig, axs = plt.subplots(1, 3, figsize=(18, 5), layout=\"constrained\")\n",
     "sc.pl.umap(adata_default, color=\"leiden\", ax=axs[0], show=False, title=\"Default\")\n",
diff --git a/how-to/plotting-with-marsilea.ipynb b/how-to/plotting-with-marsilea.ipynb
index c39e3ab..684713f 100644
--- a/how-to/plotting-with-marsilea.ipynb
+++ b/how-to/plotting-with-marsilea.ipynb
@@ -52,9 +52,12 @@
     }
    ],
    "source": [
+    "from __future__ import annotations\n",
+    "\n",
     "import numpy as np\n",
     "import scanpy as sc\n",
     "\n",
+    "\n",
     "pbmc = sc.datasets.pbmc3k_processed().raw.to_adata()\n",
     "pbmc"
    ]
@@ -69,7 +72,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "id": "bbc7e9ca-7e9a-4240-a8b1-bf438c2c82e4",
    "metadata": {},
    "outputs": [],
@@ -101,7 +104,7 @@
     "    \"#58DADA\",\n",
     "    \"#F85959\",\n",
     "]\n",
-    "cmapper = dict(zip(uni_cells, cell_colors))"
+    "cmapper = dict(zip(uni_cells, cell_colors, strict=True))"
    ]
   },
   {
@@ -493,9 +496,7 @@
     "m.group_cols(cells, order=uni_cells)\n",
     "\n",
     "m.add_right(mp.Labels(agg.obs[\"louvain\"], align=\"center\"), pad=0.1)\n",
-    "m.add_left(\n",
-    "    mp.Numbers(agg_cell_counts, color=\"#EEB76B\", label=\"Count\"), size=0.5, pad=0.1\n",
-    ")\n",
+    "m.add_left(mp.Numbers(agg_cell_counts, color=\"#EEB76B\", label=\"Count\"), size=0.5, pad=0.1)\n",
     "m.add_dendrogram(\"right\", pad=0.1)\n",
     "m.add_legends()\n",
     "m.render()"
@@ -511,7 +512,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "id": "6f46dc1f-c2fe-4483-9a5d-d351798ef581",
    "metadata": {},
    "outputs": [
@@ -532,7 +533,7 @@
     "\n",
     "tp.group_cols(pbmc.obs[\"louvain\"], order=uni_cells, spacing=0.005)\n",
     "tp.add_dendrogram(\"top\", add_base=False, size=1)\n",
-    "for row, gene_name in zip(exp.T, markers):\n",
+    "for row in exp.T:\n",
     "    area = mp.Area(\n",
     "        row,\n",
     "        add_outline=False,\n",
@@ -556,7 +557,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "id": "5b2f1772-b1fa-44dd-87cf-8921ba1782f4",
    "metadata": {},
    "outputs": [
@@ -576,12 +577,13 @@
     "from matplotlib.cm import ScalarMappable\n",
     "from matplotlib.colors import Normalize\n",
     "\n",
+    "\n",
     "gene_data = []\n",
     "cdata = []\n",
-    "for row, gene_name in zip(exp.T, markers[:5]):\n",
+    "for row, gene_name in zip(exp.T, markers[:5], strict=True):\n",
     "    # Transform data to wide-format, marsilea only supports wide-format\n",
     "    pdata = (\n",
-    "        pd.DataFrame({\"exp\": row, \"cell_type\": pbmc.obs[\"louvain\"]})\n",
+    "        pd.DataFrame({\"exp\": row, \"cell_type\": pbmc.obs[\"louvain\"]})  # noqa: PD010\n",
     "        .reset_index(drop=True)\n",
     "        .pivot(columns=\"cell_type\", values=\"exp\")\n",
     "    )\n",
@@ -788,6 +790,7 @@
    "source": [
     "import session_info\n",
     "\n",
+    "\n",
     "session_info.show(dependencies=True)"
    ]
   }
diff --git a/integrating-data-using-ingest.ipynb b/integrating-data-using-ingest.ipynb
index 52e303d..d631a84 100644
--- a/integrating-data-using-ingest.ipynb
+++ b/integrating-data-using-ingest.ipynb
@@ -42,9 +42,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "from __future__ import annotations\n",
+    "\n",
     "import anndata\n",
-    "import scanpy as sc\n",
-    "import pandas as pd"
+    "import pandas as pd\n",
+    "import scanpy as sc"
    ]
   },
   {
@@ -327,9 +329,7 @@
    "outputs": [],
    "source": [
     "adata_concat.obs[\"louvain\"] = (\n",
-    "    adata_concat.obs[\"louvain\"]\n",
-    "    .astype(\"category\")\n",
-    "    .cat.reorder_categories(adata_ref.obs[\"louvain\"].cat.categories)\n",
+    "    adata_concat.obs[\"louvain\"].astype(\"category\").cat.reorder_categories(adata_ref.obs[\"louvain\"].cat.categories)\n",
     ")\n",
     "# fix category colors\n",
     "adata_concat.uns[\"louvain_colors\"] = adata_ref.uns[\"louvain_colors\"]"
@@ -713,9 +713,7 @@
     "# actually subset\n",
     "adata_all = adata_all[~adata_all.obs[\"celltype\"].isin(minority_classes)].copy()\n",
     "# reorder according to abundance\n",
-    "adata_all.obs[\"celltype\"] = adata_all.obs[\"celltype\"].cat.reorder_categories(\n",
-    "    counts.index[:-5].tolist()\n",
-    ")"
+    "adata_all.obs[\"celltype\"] = adata_all.obs[\"celltype\"].cat.reorder_categories(counts.index[:-5].tolist())"
    ]
   },
   {
@@ -765,9 +763,7 @@
     }
    ],
    "source": [
-    "sc.pl.umap(\n",
-    "    adata_all, color=[\"batch\", \"celltype\"], palette=sc.pl.palettes.vega_20_scanpy\n",
-    ")"
+    "sc.pl.umap(adata_all, color=[\"batch\", \"celltype\"], palette=sc.pl.palettes.vega_20_scanpy)"
    ]
   },
   {
@@ -1030,9 +1026,7 @@
    "outputs": [],
    "source": [
     "adata_concat.obs[\"celltype\"] = (\n",
-    "    adata_concat.obs[\"celltype\"]\n",
-    "    .astype(\"category\")\n",
-    "    .cat.reorder_categories(adata_ref.obs[\"celltype\"].cat.categories)\n",
+    "    adata_concat.obs[\"celltype\"].astype(\"category\").cat.reorder_categories(adata_ref.obs[\"celltype\"].cat.categories)\n",
     ")\n",
     "# fix category coloring\n",
     "adata_concat.uns[\"celltype_colors\"] = adata_ref.uns[\"celltype_colors\"]"
@@ -1151,13 +1145,10 @@
     ")\n",
     "# intersect categories\n",
     "obs_query_conserved = adata_query.obs.loc[\n",
-    "    adata_query.obs[\"celltype\"].isin(conserved_categories)\n",
-    "    & adata_query.obs[\"celltype_orig\"].isin(conserved_categories)\n",
+    "    adata_query.obs[\"celltype\"].isin(conserved_categories) & adata_query.obs[\"celltype_orig\"].isin(conserved_categories)\n",
     "].copy()\n",
     "# remove unused categories\n",
-    "obs_query_conserved[\"celltype\"] = obs_query_conserved[\n",
-    "    \"celltype\"\n",
-    "].cat.remove_unused_categories()\n",
+    "obs_query_conserved[\"celltype\"] = obs_query_conserved[\"celltype\"].cat.remove_unused_categories()\n",
     "# remove unused categories and fix category ordering\n",
     "obs_query_conserved[\"celltype_orig\"] = (\n",
     "    obs_query_conserved[\"celltype_orig\"]\n",
@@ -1720,7 +1711,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -1742,8 +1733,9 @@
    "source": [
     "import matplotlib.pyplot as plt\n",
     "\n",
+    "\n",
     "fig, axes = plt.subplots(1, 3, figsize=(15, 5))\n",
-    "for batch, ax in zip([\"1\", \"2\", \"3\"], axes):\n",
+    "for batch, ax in zip([\"1\", \"2\", \"3\"], axes, strict=True):\n",
     "    sc.pl.umap(adata_concat, color=\"batch\", groups=[batch], ax=ax, show=False)"
    ]
   }
diff --git a/paga-paul15.ipynb b/paga-paul15.ipynb
index 41ca519..0419191 100644
--- a/paga-paul15.ipynb
+++ b/paga-paul15.ipynb
@@ -44,12 +44,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
+    "from __future__ import annotations\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
-    "import matplotlib.pyplot as pl\n",
     "import scanpy as sc"
    ]
   },
@@ -751,9 +753,7 @@
     }
    ],
    "source": [
-    "sc.pl.draw_graph(\n",
-    "    adata, color=[\"louvain_anno\", \"Itga2b\", \"Prss34\", \"Cma1\"], legend_loc=\"on data\"\n",
-    ")"
+    "sc.pl.draw_graph(adata, color=[\"louvain_anno\", \"Itga2b\", \"Prss34\", \"Cma1\"], legend_loc=\"on data\")"
    ]
   },
   {
@@ -778,9 +778,7 @@
     }
    ],
    "source": [
-    "sc.pl.draw_graph(\n",
-    "    adata, color=[\"louvain_anno\", \"Itga2b\", \"Prss34\", \"Cma1\"], legend_loc=\"on data\"\n",
-    ")"
+    "sc.pl.draw_graph(adata, color=[\"louvain_anno\", \"Itga2b\", \"Prss34\", \"Cma1\"], legend_loc=\"on data\")"
    ]
   },
   {
@@ -792,7 +790,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -812,10 +810,10 @@
     }
    ],
    "source": [
-    "pl.figure(figsize=(8, 2))\n",
+    "plt.figure(figsize=(8, 2))\n",
     "for i in range(28):\n",
-    "    pl.scatter(i, 1, c=sc.pl.palettes.zeileis_28[i], s=200)\n",
-    "pl.show()"
+    "    plt.scatter(i, 1, c=sc.pl.palettes.zeileis_28[i], s=200)\n",
+    "plt.show()"
    ]
   },
   {
@@ -1074,7 +1072,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -1104,10 +1102,8 @@
     }
    ],
    "source": [
-    "_, axs = pl.subplots(\n",
-    "    ncols=3, figsize=(6, 2.5), gridspec_kw={\"wspace\": 0.05, \"left\": 0.12}\n",
-    ")\n",
-    "pl.subplots_adjust(left=0.05, right=0.98, top=0.82, bottom=0.2)\n",
+    "_, axs = plt.subplots(ncols=3, figsize=(6, 2.5), gridspec_kw={\"wspace\": 0.05, \"left\": 0.12})\n",
+    "plt.subplots_adjust(left=0.05, right=0.98, top=0.82, bottom=0.2)\n",
     "for ipath, (descr, path) in enumerate(paths):\n",
     "    data = sc.pl.paga_path(\n",
     "        adata,\n",
@@ -1119,18 +1115,18 @@
     "        left_margin=0.15,\n",
     "        n_avg=50,\n",
     "        annotations=[\"distance\"],\n",
-    "        show_yticks=True if ipath == 0 else False,\n",
+    "        show_yticks=ipath == 0,\n",
     "        show_colorbar=False,\n",
     "        color_map=\"Greys\",\n",
     "        groups_key=\"clusters\",\n",
     "        color_maps_annotations={\"distance\": \"viridis\"},\n",
-    "        title=\"{} path\".format(descr),\n",
+    "        title=f\"{descr} path\",\n",
     "        return_data=True,\n",
     "        show=False,\n",
     "    )\n",
-    "    data.to_csv(\"./write/paga_path_{}.csv\".format(descr))\n",
-    "pl.savefig(\"./figures/paga_path_paul15.pdf\")\n",
-    "pl.show()"
+    "    data.to_csv(f\"./write/paga_path_{descr}.csv\")\n",
+    "plt.savefig(\"./figures/paga_path_paul15.pdf\")\n",
+    "plt.show()"
    ]
   }
  ],
diff --git a/pbmc3k.ipynb b/pbmc3k.ipynb
old mode 100755
new mode 100644
index f277981..c07797a
--- a/pbmc3k.ipynb
+++ b/pbmc3k.ipynb
@@ -21,9 +21,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "from __future__ import annotations\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
     "import pandas as pd\n",
-    "import scanpy as sc\n",
-    "import matplotlib.pyplot as plt"
+    "import scanpy as sc"
    ]
   },
   {
@@ -484,7 +486,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -500,7 +502,7 @@
     }
    ],
    "source": [
-    "# adata.X = adata.X.astype(\"int32\") # needed to match R results\n",
+    "# To match R results, do: `adata.X = adata.X.astype(\"int32\")`\n",
     "adata"
    ]
   },
@@ -615,9 +617,7 @@
    "source": [
     "# annotate the group of mitochondrial genes as \"mt\"\n",
     "adata.var[\"mt\"] = adata.var_names.str.startswith(\"MT-\")\n",
-    "sc.pp.calculate_qc_metrics(\n",
-    "    adata, qc_vars=[\"mt\"], percent_top=None, log1p=False, inplace=True\n",
-    ")"
+    "sc.pp.calculate_qc_metrics(adata, qc_vars=[\"mt\"], percent_top=None, log1p=False, inplace=True)"
    ]
   },
   {
@@ -709,9 +709,7 @@
    "outputs": [],
    "source": [
     "adata = adata[\n",
-    "    (adata.obs.n_genes_by_counts < 2500)\n",
-    "    & (adata.obs.n_genes_by_counts > 200)\n",
-    "    & (adata.obs.pct_counts_mt < 5),\n",
+    "    (adata.obs.n_genes_by_counts < 2500) & (adata.obs.n_genes_by_counts > 200) & (adata.obs.pct_counts_mt < 5),\n",
     "    :,\n",
     "].copy()\n",
     "adata.layers[\"counts\"] = adata.X.copy()"
@@ -1419,9 +1417,7 @@
     }
    ],
    "source": [
-    "sc.tl.rank_genes_groups(\n",
-    "    adata, \"leiden\", mask_var=\"highly_variable\", method=\"logreg\", max_iter=1000\n",
-    ")\n",
+    "sc.tl.rank_genes_groups(adata, \"leiden\", mask_var=\"highly_variable\", method=\"logreg\", max_iter=1000)\n",
     "sc.pl.rank_genes_groups(adata, n_genes=25, sharey=False)"
    ]
   },
@@ -2077,13 +2073,7 @@
    "source": [
     "result = adata.uns[\"rank_genes_groups\"]\n",
     "groups = result[\"names\"].dtype.names\n",
-    "pd.DataFrame(\n",
-    "    {\n",
-    "        f\"{group}_{key[:1]}\": result[key][group]\n",
-    "        for group in groups\n",
-    "        for key in [\"names\", \"pvals\"]\n",
-    "    }\n",
-    ").head(5)"
+    "pd.DataFrame({f\"{group}_{key[:1]}\": result[key][group] for group in groups for key in [\"names\", \"pvals\"]}).head(5)"
    ]
   },
   {
@@ -2294,9 +2284,7 @@
     }
    ],
    "source": [
-    "sc.pl.umap(\n",
-    "    adata, color=\"leiden\", legend_loc=\"on data\", title=\"\", frameon=False, save=\".pdf\"\n",
-    ")"
+    "sc.pl.umap(adata, color=\"leiden\", legend_loc=\"on data\", title=\"\", frameon=False, save=\".pdf\")"
    ]
   },
   {
@@ -2432,7 +2420,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.13.5"
+   "version": "3.13.7"
   }
  },
  "nbformat": 4,
diff --git a/plotting/advanced.ipynb b/plotting/advanced.ipynb
index 8453dda..35b1653 100644
--- a/plotting/advanced.ipynb
+++ b/plotting/advanced.ipynb
@@ -21,22 +21,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "id": "641629fa",
    "metadata": {},
    "outputs": [],
    "source": [
-    "import scanpy as sc\n",
-    "import pandas as pd\n",
-    "import numpy as np\n",
+    "from __future__ import annotations\n",
+    "\n",
+    "from typing import TYPE_CHECKING\n",
     "\n",
+    "import matplotlib.colors as mcolors\n",
     "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import scanpy as sc\n",
     "import seaborn as sns\n",
-    "import matplotlib.colors as mcolors\n",
     "\n",
     "# Inital setting for plot size\n",
     "from matplotlib import rcParams\n",
     "\n",
+    "\n",
+    "rng = np.random.default_rng()\n",
+    "\n",
     "FIGSIZE = (3, 3)\n",
     "rcParams[\"figure.figsize\"] = FIGSIZE"
    ]
@@ -217,9 +223,7 @@
     "# finished plotting on all Axes and making all plot adjustments\n",
     "sc.pl.umap(adata, color=\"louvain\", ax=axs[0], show=False)\n",
     "# Example zoom-in into a subset of louvain clusters\n",
-    "sc.pl.umap(\n",
-    "    adata[adata.obs.louvain.isin([\"0\", \"3\", \"9\"]), :], color=\"S_score\", ax=axs[1]\n",
-    ")"
+    "sc.pl.umap(adata[adata.obs.louvain.isin([\"0\", \"3\", \"9\"]), :], color=\"S_score\", ax=axs[1])"
    ]
   },
   {
@@ -438,9 +442,7 @@
     "wspace = 1\n",
     "# Adapt figure size based on number of rows and columns and added space between them\n",
     "# (e.g. wspace between columns)\n",
-    "fig, axs = plt.subplots(\n",
-    "    nrow, ncol, figsize=(ncol * figsize + (ncol - 1) * wspace * figsize, nrow * figsize)\n",
-    ")\n",
+    "fig, axs = plt.subplots(nrow, ncol, figsize=(ncol * figsize + (ncol - 1) * wspace * figsize, nrow * figsize))\n",
     "plt.subplots_adjust(wspace=wspace)\n",
     "sc.pl.umap(adata, color=\"louvain\", ax=axs[0], show=False)\n",
     "sc.pl.umap(adata, color=\"phase\", ax=axs[1])"
@@ -665,7 +667,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "id": "7112d43f",
    "metadata": {},
    "outputs": [
@@ -685,12 +687,10 @@
     "# To ensure that the ordering corresponds to cell cycle define order of categories;\n",
     "# this should include all categories in the corresponding pandas table column\n",
     "phases = [\"G1\", \"S\", \"G2M\"]\n",
-    "adata.obs[\"phase_ordered\"] = pd.Categorical(\n",
-    "    values=adata.obs.phase, categories=phases, ordered=True\n",
-    ")\n",
+    "adata.obs[\"phase_ordered\"] = pd.Categorical(values=adata.obs.phase, categories=phases, ordered=True)\n",
     "sc.pl.umap(adata, color=[\"phase\", \"phase_ordered\"], wspace=0.5)\n",
     "# This just removes the newly added ordered column from adata as we do not need it below\n",
-    "adata.obs.drop(\"phase_ordered\", axis=1, inplace=True)"
+    "del adata.obs[\"phase_ordered\"]"
    ]
   },
   {
@@ -738,7 +738,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": null,
    "id": "7d9b11fb",
    "metadata": {},
    "outputs": [
@@ -756,6 +756,7 @@
    "source": [
     "from matplotlib.lines import Line2D\n",
     "\n",
+    "\n",
     "fig = sc.pl.umap(adata, color=[\"bulk_labels\"], return_fig=True)\n",
     "ax = fig.axes[0]\n",
     "# Remove original Legend\n",
@@ -776,9 +777,7 @@
     "            markersize=7,\n",
     "        )\n",
     "        # Color groups in adata\n",
-    "        for l, c in zip(\n",
-    "            list(adata.obs.bulk_labels.cat.categories), adata.uns[\"bulk_labels_colors\"]\n",
-    "        )\n",
+    "        for l, c in zip(list(adata.obs.bulk_labels.cat.categories), adata.uns[\"bulk_labels_colors\"], strict=True)\n",
     "    ],\n",
     "    # Customize Legend outline\n",
     "    # Remove background\n",
@@ -883,7 +882,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": null,
    "id": "de3b2707",
    "metadata": {},
    "outputs": [
@@ -909,13 +908,12 @@
     "# Package used for adding well aligned labels on the plot\n",
     "from adjustText import adjust_text\n",
     "\n",
+    "\n",
     "with plt.rc_context({\"figure.figsize\": (5, 5)}):\n",
     "    x = \"means\"\n",
     "    y = \"dispersions\"\n",
     "    color = \"is_highly_variable\"\n",
-    "    adata.var[\"is_highly_variable\"] = (\n",
-    "        adata.var[\"highly_variable\"].astype(bool).astype(str)\n",
-    "    )\n",
+    "    adata.var[\"is_highly_variable\"] = adata.var[\"highly_variable\"].astype(bool).astype(str)\n",
     "    ax = sc.pl.scatter(adata, x=x, y=y, color=color, show=False)\n",
     "    print(\"Axes:\", ax)\n",
     "    # Move plot title from Axes to Legend\n",
@@ -940,8 +938,8 @@
     "    ]\n",
     "    for gene in genes:\n",
     "        # Position of object to be marked\n",
-    "        x_loc = adata.var.at[gene, x]\n",
-    "        y_loc = adata.var.at[gene, y]\n",
+    "        x_loc = adata.var.loc[gene, x]\n",
+    "        y_loc = adata.var.loc[gene, y]\n",
     "        # Text color\n",
     "        color_point = \"k\"\n",
     "        texts.append(ax.text(x_loc, y_loc, gene, color=color_point, fontsize=10))\n",
@@ -1042,7 +1040,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": null,
    "id": "7f79cb3d",
    "metadata": {},
    "outputs": [
@@ -1062,16 +1060,16 @@
     "\n",
     "# Make mock column for plotting, here we use random values from normal distribution\n",
     "loc = 0\n",
-    "adata.obs[\"normal\"] = np.random.normal(loc=loc, size=adata.shape[0])\n",
+    "adata.obs[\"normal\"] = rng.normal(loc=loc, size=adata.shape[0])\n",
     "\n",
     "# Center at mean (loc) of the distribution with vcenter parameter\n",
     "sc.pl.umap(adata, color=\"normal\", cmap=\"coolwarm\", s=20, vcenter=loc)\n",
-    "adata.obs.drop(\"normal\", axis=1, inplace=True)"
+    "del adata.obs[\"normal\"]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": null,
    "id": "7ba212d0",
    "metadata": {},
    "outputs": [
@@ -1095,10 +1093,8 @@
     "# To make a symmetric palette centerd around 0 we set vmax to maximal absolut value and vmin to\n",
     "# the negative value of maxabs\n",
     "maxabs = max(abs(adata.obs[\"B_cell_score\"]))\n",
-    "sc.pl.umap(\n",
-    "    adata, color=\"B_cell_score\", cmap=\"coolwarm\", s=20, vmin=-maxabs, vmax=maxabs\n",
-    ")\n",
-    "adata.obs.drop(\"B_cell_score\", axis=1, inplace=True)"
+    "sc.pl.umap(adata, color=\"B_cell_score\", cmap=\"coolwarm\", s=20, vmin=-maxabs, vmax=maxabs)\n",
+    "del adata.obs[\"B_cell_score\"]"
    ]
   },
   {
@@ -1111,7 +1107,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": null,
    "id": "bdedcfc0",
    "metadata": {},
    "outputs": [
@@ -1130,18 +1126,18 @@
     "# Log-scaled palette\n",
     "\n",
     "# Make mock column with log-normally distirbuited values\n",
-    "adata.obs[\"lognormal\"] = np.random.lognormal(3, 1, adata.shape[0])\n",
+    "adata.obs[\"lognormal\"] = rng.lognormal(3, 1, adata.shape[0])\n",
     "\n",
     "# Log scaling of the palette\n",
     "norm = mcolors.LogNorm()\n",
     "sc.pl.umap(adata, color=\"lognormal\", s=20, norm=norm)\n",
     "\n",
-    "adata.obs.drop(\"lognormal\", axis=1, inplace=True)"
+    "del adata.obs[\"lognormal\"]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": null,
    "id": "31890a47",
    "metadata": {},
    "outputs": [
@@ -1157,6 +1153,9 @@
     }
    ],
    "source": [
+    "if TYPE_CHECKING:\n",
+    "    from numpy.typing import ArrayLike\n",
+    "\n",
     "# Centered non-symmetric palette\n",
     "\n",
     "# Make mock column for plotting, here we use B cell score\n",
@@ -1167,21 +1166,26 @@
     "# the distance of vmin and vmax from the cenetr\n",
     "# Adapted from https://stackoverflow.com/a/50003503\n",
     "class MidpointNormalize(mcolors.Normalize):\n",
-    "    def __init__(self, vmin=None, vmax=None, midpoint=0, clip=False):\n",
+    "    vmin: float\n",
+    "    vmax: float\n",
+    "    midpoint: float\n",
+    "\n",
+    "    def __init__(\n",
+    "        self, vmin: float | None = None, vmax: float | None = None, *, midpoint: float = 0, clip: bool = False\n",
+    "    ) -> None:\n",
     "        self.midpoint = midpoint\n",
-    "        mcolors.Normalize.__init__(self, vmin, vmax, clip)\n",
+    "        super().__init__(vmin, vmax, clip=clip)\n",
     "\n",
-    "    def __call__(self, value, clip=None):\n",
+    "    def __call__(self, value: ArrayLike, clip: object = None) -> np.ma.MaskedArray:\n",
+    "        del clip\n",
     "        value = np.array(value).astype(float)\n",
     "        normalized_min = max(\n",
     "            0.0,\n",
-    "            0.5\n",
-    "            * (1.0 - abs((self.midpoint - self.vmin) / (self.midpoint - self.vmax))),\n",
+    "            0.5 * (1.0 - abs((self.midpoint - self.vmin) / (self.midpoint - self.vmax))),\n",
     "        )\n",
     "        normalized_max = min(\n",
     "            1.0,\n",
-    "            0.5\n",
-    "            * (1.0 + abs((self.vmax - self.midpoint) / (self.midpoint - self.vmin))),\n",
+    "            0.5 * (1.0 + abs((self.vmax - self.midpoint) / (self.midpoint - self.vmin))),\n",
     "        )\n",
     "        normalized_mid = 0.5\n",
     "        x, y = (\n",
@@ -1213,13 +1217,13 @@
     ")\n",
     "# Adjust Colorbar ylim to be just outside of vmin,vmax and not far outside of this range\n",
     "# as the padding we set initially may be too broad\n",
-    "cmap_yticklabels = np.array([t._y for t in fig.axes[1].get_yticklabels()])\n",
+    "cmap_yticklabels = np.array([t.get_position()[1] for t in fig.axes[1].get_yticklabels()])\n",
     "fig.axes[1].set_ylim(\n",
     "    max(cmap_yticklabels[cmap_yticklabels < vmin]),\n",
     "    min(cmap_yticklabels[cmap_yticklabels > vmax]),\n",
     ")\n",
     "\n",
-    "adata.obs.drop(\"B_cell_score\", axis=1, inplace=True)"
+    "del adata.obs[\"B_cell_score\"]"
    ]
   },
   {
@@ -1334,7 +1338,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": null,
    "id": "d117c8cb",
    "metadata": {},
    "outputs": [
@@ -1351,20 +1355,15 @@
    ],
    "source": [
     "# Make two batches in the adata object for the plot example\n",
-    "adata.obs[\"batch\"] = [\"a\"] * int(adata.shape[0] / 2) + [\"b\"] * (\n",
-    "    adata.shape[0] - int(adata.shape[0] / 2)\n",
-    ")\n",
+    "adata.obs[\"batch\"] = [\"a\"] * int(adata.shape[0] / 2) + [\"b\"] * (adata.shape[0] - int(adata.shape[0] / 2))\n",
     "\n",
     "fig, axs = plt.subplots(1, 2, figsize=(9, 3))\n",
     "plt.subplots_adjust(wspace=1)\n",
     "sc.pl.umap(adata, color=\"batch\", ax=axs[0], title=\"Default ordering\", show=False)\n",
     "# Randomly order cells by making a random index and subsetting AnnData based on it\n",
     "# Set a random seed to ensure that the cell ordering will be reproducible\n",
-    "np.random.seed(0)\n",
-    "random_indices = np.random.permutation(list(range(adata.shape[0])))\n",
-    "sc.pl.umap(\n",
-    "    adata[random_indices, :], color=\"batch\", ax=axs[1], title=\"Random re-ordering\"\n",
-    ")"
+    "random_indices = np.random.default_rng(0).permutation(list(range(adata.shape[0])))\n",
+    "sc.pl.umap(adata[random_indices, :], color=\"batch\", ax=axs[1], title=\"Random re-ordering\")"
    ]
   },
   {
@@ -1415,15 +1414,14 @@
    "source": [
     "from itertools import product\n",
     "\n",
+    "\n",
     "# Copy adata not to modify UMAP in the original adata object\n",
     "adata_temp = adata.copy()\n",
     "# Loop through different umap parameters, recomputting and replotting UMAP for each of them\n",
     "MIN_DISTS = [0.1, 1, 2]\n",
     "SPREADS = [0.5, 1, 5]\n",
     "# Create grid of plots, with a little extra room for the legends\n",
-    "fig, axes = plt.subplots(\n",
-    "    len(MIN_DISTS), len(SPREADS), figsize=(len(SPREADS) * 3 + 2, len(MIN_DISTS) * 3)\n",
-    ")\n",
+    "fig, axes = plt.subplots(len(MIN_DISTS), len(SPREADS), figsize=(len(SPREADS) * 3 + 2, len(MIN_DISTS) * 3))\n",
     "\n",
     "for (i, min_dist), (j, spread) in product(enumerate(MIN_DISTS), enumerate(SPREADS)):\n",
     "    ax = axes[i][j]\n",
@@ -1565,9 +1563,7 @@
    "source": [
     "# Compare UMAP and PAGA layouts\n",
     "fig, axs = plt.subplots(1, 2, figsize=(6, 3))\n",
-    "sc.pl.umap(\n",
-    "    adata, color=\"louvain\", ax=axs[0], show=False, title=\"UMAP\", legend_loc=\"on data\"\n",
-    ")\n",
+    "sc.pl.umap(adata, color=\"louvain\", ax=axs[0], show=False, title=\"UMAP\", legend_loc=\"on data\")\n",
     "sc.pl.paga(adata, ax=axs[1], title=\"PAGA\")"
    ]
   },
diff --git a/plotting/core.ipynb b/plotting/core.ipynb
index e465b98..7854c40 100644
--- a/plotting/core.ipynb
+++ b/plotting/core.ipynb
@@ -70,6 +70,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "from __future__ import annotations\n",
+    "\n",
     "import scanpy as sc\n",
     "from matplotlib.pyplot import rc_context"
    ]
@@ -571,9 +573,7 @@
     }
    ],
    "source": [
-    "ax = sc.pl.stacked_violin(\n",
-    "    pbmc, marker_genes_dict, groupby=\"clusters\", swap_axes=False, dendrogram=True\n",
-    ")"
+    "ax = sc.pl.stacked_violin(pbmc, marker_genes_dict, groupby=\"clusters\", swap_axes=False, dendrogram=True)"
    ]
   },
   {
@@ -715,17 +715,12 @@
    "source": [
     "import matplotlib.pyplot as plt\n",
     "\n",
+    "\n",
     "fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(20, 4), gridspec_kw={\"wspace\": 0.9})\n",
     "\n",
-    "ax1_dict = sc.pl.dotplot(\n",
-    "    pbmc, marker_genes_dict, groupby=\"bulk_labels\", ax=ax1, show=False\n",
-    ")\n",
-    "ax2_dict = sc.pl.stacked_violin(\n",
-    "    pbmc, marker_genes_dict, groupby=\"bulk_labels\", ax=ax2, show=False\n",
-    ")\n",
-    "ax3_dict = sc.pl.matrixplot(\n",
-    "    pbmc, marker_genes_dict, groupby=\"bulk_labels\", ax=ax3, show=False, cmap=\"viridis\"\n",
-    ")"
+    "ax1_dict = sc.pl.dotplot(pbmc, marker_genes_dict, groupby=\"bulk_labels\", ax=ax1, show=False)\n",
+    "ax2_dict = sc.pl.stacked_violin(pbmc, marker_genes_dict, groupby=\"bulk_labels\", ax=ax2, show=False)\n",
+    "ax3_dict = sc.pl.matrixplot(pbmc, marker_genes_dict, groupby=\"bulk_labels\", ax=ax3, show=False, cmap=\"viridis\")"
    ]
   },
   {
@@ -764,9 +759,7 @@
     }
    ],
    "source": [
-    "ax = sc.pl.heatmap(\n",
-    "    pbmc, marker_genes_dict, groupby=\"clusters\", cmap=\"viridis\", dendrogram=True\n",
-    ")"
+    "ax = sc.pl.heatmap(pbmc, marker_genes_dict, groupby=\"clusters\", cmap=\"viridis\", dendrogram=True)"
    ]
   },
   {
@@ -1038,9 +1031,7 @@
     }
    ],
    "source": [
-    "sc.pl.rank_genes_groups_matrixplot(\n",
-    "    pbmc, n_genes=3, use_raw=False, vmin=-3, vmax=3, cmap=\"bwr\", layer=\"scaled\"\n",
-    ")"
+    "sc.pl.rank_genes_groups_matrixplot(pbmc, n_genes=3, use_raw=False, vmin=-3, vmax=3, cmap=\"bwr\", layer=\"scaled\")"
    ]
   },
   {
diff --git a/pyproject.toml b/pyproject.toml
index e537954..2a378a1 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,23 +1,33 @@
+[build-system]
+build-backend = "hatchling.build"
+requires = [ "hatch-vcs", "hatchling" ]
+
 [project]
 name = "scanpy-tutorials"
-dynamic = ["version"]
 authors = [
-    { name = "Alex Wolf" },
-    { name = "Fidel Ramirez" },
-    { name = "Sergei Rybakov" },
+  { name = "Alex Wolf" },
+  { name = "Fidel Ramirez" },
+  { name = "Sergei Rybakov" },
+]
+requires-python = ">=3.11"
+classifiers = [
+  "Programming Language :: Python :: 3 :: Only",
+  "Programming Language :: Python :: 3.11",
+  "Programming Language :: Python :: 3.12",
+  "Programming Language :: Python :: 3.13",
 ]
-requires-python = ">=3.9"
+dynamic = [ "version" ]
 dependencies = [
-    "sphinx",
-    "myst-nb",
-    "scanpydoc[theme]>=0.13.4",
+  "myst-nb",
+  "scanpydoc[theme]>=0.13.4",
+  "sphinx",
 ]
 
 [tool.hatch.version]
 source = "vcs"
 
 [tool.hatch.build.targets.wheel]
-bypass-selection = true  # This is not a package
+bypass-selection = true # This is not a package
 
 [tool.hatch.envs.default]
 python = "3.13"
@@ -29,19 +39,31 @@ clean = "git clean -fdX _build"
 [tool.hatch.envs.notebook]
 scripts.install-kernel = "python -m ipykernel install --user --name=scanpy-tutorials --display-name=\"Scanpy Tutorials\""
 extra-dependencies = [
+  "dask[distributed]",
   "ipykernel",
   "ipywidgets",
-  "scanpy[skmisc,leiden]",
+  "scanpy[skmisc,leiden,dask]",
+  "pooch",
 ]
 
 [tool.ruff]
-extend-exclude = ["scanpy_workshop/"]
-[tool.ruff.lint]
-ignore = [
-    "F821",  # R cells can sometimes define variables
-    "E741",  # Fonts these days don’t display I and l in a confusable way
+line-length = 120
+extend-exclude = [ "scanpy_workshop/" ]
+lint.select = [ "ALL" ]
+lint.ignore = [
+  "C408",    # dict() calls are nice
+  "COM812",  # trailing commas handled by black
+  "D",       # no module docstrings for notebooks
+  "E741",    # variable names aren’t really ambiguous
+  "F821",    # `%%R -o` means that ruff doesn’t understand which variables exist
+  "FIX002",  # “TODO” comments
+  "PLR0913", # having many (kw)args is fine
+  "PLR2004", # Magic numbers are sadly common
+  "Q",       # handled by formatter
+  "S",       # no need to worry about security here
+  "T201",    # Allow print in notebooks
 ]
-
-[build-system]
-build-backend = "hatchling.build"
-requires = ["hatchling", "hatch-vcs"]
+lint.allowed-confusables = [ "×", "’", "l" ]
+lint.isort.known-first-party = [ "anndata2ri" ]
+lint.isort.lines-after-imports = 2
+lint.isort.required-imports = [ "from __future__ import annotations" ]
diff --git a/spatial/basic-analysis.ipynb b/spatial/basic-analysis.ipynb
index 1da5603..12de302 100644
--- a/spatial/basic-analysis.ipynb
+++ b/spatial/basic-analysis.ipynb
@@ -37,9 +37,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import scanpy as sc\n",
-    "import pandas as pd\n",
+    "from __future__ import annotations\n",
+    "\n",
     "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "import scanpy as sc\n",
     "import seaborn as sns"
    ]
   },
@@ -442,9 +444,7 @@
     "sc.pp.pca(adata)\n",
     "sc.pp.neighbors(adata)\n",
     "sc.tl.umap(adata)\n",
-    "sc.tl.leiden(\n",
-    "    adata, key_added=\"clusters\", flavor=\"igraph\", directed=False, n_iterations=2\n",
-    ")"
+    "sc.tl.leiden(adata, key_added=\"clusters\", flavor=\"igraph\", directed=False, n_iterations=2)"
    ]
   },
   {
diff --git a/spatial/integration-scanorama.ipynb b/spatial/integration-scanorama.ipynb
index 720c3dd..8bf5242 100644
--- a/spatial/integration-scanorama.ipynb
+++ b/spatial/integration-scanorama.ipynb
@@ -59,15 +59,17 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import scanpy as sc\n",
+    "from __future__ import annotations\n",
+    "\n",
+    "from pathlib import Path\n",
+    "\n",
     "import anndata as an\n",
-    "import pandas as pd\n",
-    "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
-    "import seaborn as sns\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
     "import scanorama\n",
-    "\n",
-    "from pathlib import Path"
+    "import scanpy as sc\n",
+    "import seaborn as sns"
    ]
   },
   {
@@ -275,12 +277,8 @@
     }
    ],
    "source": [
-    "adata_spatial_anterior = sc.datasets.visium_sge(\n",
-    "    sample_id=\"V1_Mouse_Brain_Sagittal_Anterior\"\n",
-    ")\n",
-    "adata_spatial_posterior = sc.datasets.visium_sge(\n",
-    "    sample_id=\"V1_Mouse_Brain_Sagittal_Posterior\"\n",
-    ")"
+    "adata_spatial_anterior = sc.datasets.visium_sge(sample_id=\"V1_Mouse_Brain_Sagittal_Anterior\")\n",
+    "adata_spatial_posterior = sc.datasets.visium_sge(sample_id=\"V1_Mouse_Brain_Sagittal_Posterior\")"
    ]
   },
   {
@@ -491,9 +489,7 @@
    "source": [
     "sc.pp.neighbors(adata_spatial, use_rep=\"X_scanorama\")\n",
     "sc.tl.umap(adata_spatial)\n",
-    "sc.tl.leiden(\n",
-    "    adata_spatial, key_added=\"clusters\", n_iterations=2, flavor=\"igraph\", directed=False\n",
-    ")"
+    "sc.tl.leiden(adata_spatial, key_added=\"clusters\", n_iterations=2, flavor=\"igraph\", directed=False)"
    ]
   },
   {
@@ -525,9 +521,7 @@
     }
    ],
    "source": [
-    "sc.pl.umap(\n",
-    "    adata_spatial, color=[\"clusters\", \"library_id\"], palette=sc.pl.palettes.default_20\n",
-    ")"
+    "sc.pl.umap(adata_spatial, color=[\"clusters\", \"library_id\"], palette=sc.pl.palettes.default_20)"
    ]
   },
   {
@@ -539,13 +533,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "clusters_colors = dict(\n",
-    "    zip([str(i) for i in range(18)], adata_spatial.uns[\"clusters_colors\"])\n",
-    ")"
+    "clusters_colors = dict(zip([str(i) for i in range(18)], adata_spatial.uns[\"clusters_colors\"], strict=True))"
    ]
   },
   {
@@ -580,9 +572,7 @@
    "source": [
     "fig, axs = plt.subplots(1, 2, figsize=(15, 10))\n",
     "\n",
-    "for i, library in enumerate(\n",
-    "    [\"V1_Mouse_Brain_Sagittal_Anterior\", \"V1_Mouse_Brain_Sagittal_Posterior\"]\n",
-    "):\n",
+    "for i, library in enumerate([\"V1_Mouse_Brain_Sagittal_Anterior\", \"V1_Mouse_Brain_Sagittal_Posterior\"]):\n",
     "    ad = adata_spatial[adata_spatial.obs.library_id == library, :].copy()\n",
     "    sc.pl.spatial(\n",
     "        ad,\n",
@@ -590,11 +580,7 @@
     "        library_id=library,\n",
     "        color=\"clusters\",\n",
     "        size=1.5,\n",
-    "        palette=[\n",
-    "            v\n",
-    "            for k, v in clusters_colors.items()\n",
-    "            if k in ad.obs.clusters.unique().tolist()\n",
-    "        ],\n",
+    "        palette=[v for k, v in clusters_colors.items() if k in ad.obs.clusters.unique().tolist()],\n",
     "        legend_loc=None,\n",
     "        show=False,\n",
     "        ax=axs[i],\n",
@@ -632,50 +618,60 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "if not Path(\"./data/adata_processed.h5ad\").exists():\n",
-    "    !wget https://ftp.ncbi.nlm.nih.gov/geo/series/GSE115nnn/GSE115746/suppl/GSE115746_cells_exon_counts.csv.gz -O data/GSE115746_cells_exon_counts.csv.gz\n",
-    "    !gunzip data/GSE115746_cells_exon_counts.csv.gz\n",
-    "    !wget https://ftp.ncbi.nlm.nih.gov/geo/series/GSE115nnn/GSE115746/suppl/GSE115746_complete_metadata_28706-cells.csv.gz -O data/GSE115746_complete_metadata_28706-cells.csv.gz\n",
-    "    !gunzip data/GSE115746_complete_metadata_28706-cells.csv.gz\n",
-    "    %pip install pybiomart\n",
-    "    counts = pd.read_csv(\"data/GSE115746_cells_exon_counts.csv\", index_col=0).T\n",
-    "    meta = pd.read_csv(\n",
-    "        \"data/GSE115746_complete_metadata_28706-cells.csv\", index_col=\"sample_name\"\n",
+    "if not Path(\"cache/adata_processed.h5ad\").exists():\n",
+    "    import pooch\n",
+    "\n",
+    "    p_counts = Path(\n",
+    "        pooch.retrieve(\n",
+    "            \"https://ftp.ncbi.nlm.nih.gov/geo/series/GSE115nnn/GSE115746/suppl/GSE115746_cells_exon_counts.csv.gz\",\n",
+    "            known_hash=\"sha256:5693f546dde28680d49bd7bf1255d42b0f77901aec050b94d56e54be10c00648\",\n",
+    "            path=\"../data\",\n",
+    "        )\n",
     "    )\n",
+    "    p_meta = Path(\n",
+    "        pooch.retrieve(\n",
+    "            \"https://ftp.ncbi.nlm.nih.gov/geo/series/GSE115nnn/GSE115746/suppl/GSE115746_complete_metadata_28706-cells.csv.gz\",\n",
+    "            known_hash=\"sha256:381cc4dd26898016d506394b4cfbcebab38ac88d2f512ccf98216a5487db5bd2\",\n",
+    "            path=\"../data\",\n",
+    "        )\n",
+    "    )\n",
+    "\n",
+    "    counts = pd.read_csv(p_counts, compression=\"gzip\", index_col=0).T\n",
+    "    meta = pd.read_csv(p_meta, compression=\"gzip\", index_col=\"sample_name\")\n",
     "    meta = meta.loc[counts.index]\n",
     "    annot = sc.queries.biomart_annotations(\n",
     "        \"mmusculus\",\n",
     "        [\"mgi_symbol\", \"ensembl_gene_id\"],\n",
     "    ).set_index(\"mgi_symbol\")\n",
     "    annot = annot[annot.index.isin(counts.columns)]\n",
-    "    counts = counts.rename(columns=dict(zip(annot.index, annot[\"ensembl_gene_id\"])))\n",
+    "    counts = counts.rename(columns=dict(zip(annot.index, annot[\"ensembl_gene_id\"], strict=True)))\n",
     "    adata_cortex = an.AnnData(counts, obs=meta)\n",
     "    sc.pp.normalize_total(adata_cortex, inplace=True)\n",
     "    sc.pp.log1p(adata_cortex)\n",
-    "    adata_cortex.write_h5ad(\"data/adata_processed.h5ad\")"
+    "    adata_cortex.write_h5ad(\"cache/adata_processed.h5ad\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "adata_cortex = sc.read(\"./data/adata_processed.h5ad\")"
+    "adata_cortex = sc.read(\"cache/adata_processed.h5ad\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "adata_spatial_anterior.var.set_index(\"gene_ids\", inplace=True)\n",
-    "adata_spatial_posterior.var.set_index(\"gene_ids\", inplace=True)"
+    "adata_spatial_anterior.var_names = adata_spatial_anterior.var[\"gene_ids\"]\n",
+    "adata_spatial_posterior.var_names = adata_spatial_posterior.var[\"gene_ids\"]"
    ]
   },
   {
@@ -691,12 +687,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "adata_anterior_subset = adata_spatial_anterior[\n",
-    "    adata_spatial_anterior.obsm[\"spatial\"][:, 1] < 6000, :\n",
-    "]\n",
+    "adata_anterior_subset = adata_spatial_anterior[adata_spatial_anterior.obsm[\"spatial\"][:, 1] < 6000, :]\n",
     "adata_posterior_subset = adata_spatial_posterior[\n",
-    "    (adata_spatial_posterior.obsm[\"spatial\"][:, 1] < 4000)\n",
-    "    & (adata_spatial_posterior.obsm[\"spatial\"][:, 0] < 6000),\n",
+    "    (adata_spatial_posterior.obsm[\"spatial\"][:, 1] < 4000) & (adata_spatial_posterior.obsm[\"spatial\"][:, 0] < 6000),\n",
     "    :,\n",
     "]"
    ]
@@ -793,21 +786,14 @@
    "source": [
     "from sklearn.metrics.pairwise import cosine_distances\n",
     "\n",
+    "\n",
     "distances_anterior = 1 - cosine_distances(\n",
-    "    adata_cortex_anterior[adata_cortex_anterior.obs.dataset == \"smart-seq\"].obsm[\n",
-    "        \"X_scanorama\"\n",
-    "    ],\n",
-    "    adata_cortex_anterior[adata_cortex_anterior.obs.dataset == \"visium\"].obsm[\n",
-    "        \"X_scanorama\"\n",
-    "    ],\n",
+    "    adata_cortex_anterior[adata_cortex_anterior.obs.dataset == \"smart-seq\"].obsm[\"X_scanorama\"],\n",
+    "    adata_cortex_anterior[adata_cortex_anterior.obs.dataset == \"visium\"].obsm[\"X_scanorama\"],\n",
     ")\n",
     "distances_posterior = 1 - cosine_distances(\n",
-    "    adata_cortex_posterior[adata_cortex_posterior.obs.dataset == \"smart-seq\"].obsm[\n",
-    "        \"X_scanorama\"\n",
-    "    ],\n",
-    "    adata_cortex_posterior[adata_cortex_posterior.obs.dataset == \"visium\"].obsm[\n",
-    "        \"X_scanorama\"\n",
-    "    ],\n",
+    "    adata_cortex_posterior[adata_cortex_posterior.obs.dataset == \"smart-seq\"].obsm[\"X_scanorama\"],\n",
+    "    adata_cortex_posterior[adata_cortex_posterior.obs.dataset == \"visium\"].obsm[\"X_scanorama\"],\n",
     ")"
    ]
   },
@@ -820,29 +806,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "def label_transfer(dist, labels):\n",
+    "def label_transfer(dist: np.ndarray, labels: pd.Series) -> np.ndarray:\n",
     "    lab = pd.get_dummies(labels).to_numpy().T\n",
     "    class_prob = lab @ dist\n",
     "    norm = np.linalg.norm(class_prob, 2, axis=0)\n",
     "    class_prob = class_prob / norm\n",
-    "    class_prob = (class_prob.T - class_prob.min(1)) / class_prob.ptp(1)\n",
-    "    return class_prob"
+    "    return (class_prob.T - class_prob.min(1)) / class_prob.ptp(1)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "class_prob_anterior = label_transfer(distances_anterior, adata_cortex.obs.cell_subclass)\n",
-    "class_prob_posterior = label_transfer(\n",
-    "    distances_posterior, adata_cortex.obs.cell_subclass\n",
-    ")"
+    "class_prob_anterior = label_transfer(distances_anterior, adata_cortex.obs[\"cell_subclass\"])\n",
+    "class_prob_posterior = label_transfer(distances_posterior, adata_cortex.obs[\"cell_subclass\"])"
    ]
   },
   {
@@ -885,14 +868,10 @@
    "outputs": [],
    "source": [
     "adata_anterior_subset_transfer = adata_anterior_subset.copy()\n",
-    "adata_anterior_subset_transfer.obs = pd.concat(\n",
-    "    [adata_anterior_subset.obs, cp_anterior_df], axis=1\n",
-    ")\n",
+    "adata_anterior_subset_transfer.obs = pd.concat([adata_anterior_subset.obs, cp_anterior_df], axis=1)\n",
     "\n",
     "adata_posterior_subset_transfer = adata_posterior_subset.copy()\n",
-    "adata_posterior_subset_transfer.obs = pd.concat(\n",
-    "    [adata_posterior_subset.obs, cp_posterior_df], axis=1\n",
-    ")"
+    "adata_posterior_subset_transfer.obs = pd.concat([adata_posterior_subset.obs, cp_posterior_df], axis=1)"
    ]
   },
   {
@@ -999,12 +978,8 @@
     }
    ],
    "source": [
-    "sc.pl.spatial(\n",
-    "    adata_anterior_subset_transfer, img_key=\"hires\", color=[\"Oligo\", \"Astro\"], size=1.5\n",
-    ")\n",
-    "sc.pl.spatial(\n",
-    "    adata_posterior_subset_transfer, img_key=\"hires\", color=[\"Oligo\", \"Astro\"], size=1.5\n",
-    ")"
+    "sc.pl.spatial(adata_anterior_subset_transfer, img_key=\"hires\", color=[\"Oligo\", \"Astro\"], size=1.5)\n",
+    "sc.pl.spatial(adata_posterior_subset_transfer, img_key=\"hires\", color=[\"Oligo\", \"Astro\"], size=1.5)"
    ]
   },
   {
@@ -1026,7 +1001,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "notebook",
    "language": "python",
    "name": "python3"
   },
@@ -1040,7 +1015,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.6"
+   "version": "3.13.7"
   }
  },
  "nbformat": 4,
diff --git a/tutorial_pearson_residuals.ipynb b/tutorial_pearson_residuals.ipynb
index 1094c3a..56420d1 100644
--- a/tutorial_pearson_residuals.ipynb
+++ b/tutorial_pearson_residuals.ipynb
@@ -76,10 +76,17 @@
     }
    ],
    "source": [
-    "import numpy as np\n",
+    "from __future__ import annotations\n",
+    "\n",
+    "import tarfile\n",
+    "from pathlib import Path\n",
+    "\n",
     "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import pooch\n",
     "import scanpy as sc\n",
     "\n",
+    "\n",
     "sc.settings.verbosity = 3  # verbosity: errors (0), warnings (1), info (2), hints (3)\n",
     "sc.logging.print_header()\n",
     "sc.settings.set_figure_params(dpi=80, facecolor=\"white\")"
@@ -104,15 +111,25 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# !mkdir tutorial_data\n",
-    "# !mkdir tutorial_data/pbmc3k_v1\n",
-    "# !mkdir tutorial_data/pbmc10k_v3\n",
-    "\n",
-    "# !wget http://cf.10xgenomics.com/samples/cell-exp/1.1.0/pbmc3k/pbmc3k_filtered_gene_bc_matrices.tar.gz -O tutorial_data/pbmc3k_v1.tar.gz\n",
-    "# !cd tutorial_data; tar -xzf pbmc3k_v1.tar.gz -C pbmc3k_v1 --strip-components 2\n",
+    "p_pbmc3k = Path(\n",
+    "    pooch.retrieve(\n",
+    "        \"http://cf.10xgenomics.com/samples/cell-exp/1.1.0/pbmc3k/pbmc3k_filtered_gene_bc_matrices.tar.gz\",\n",
+    "        known_hash=\"sha256:847d6ebd9a1ec9a768f2be7e40ca42cbfe75ebeb6d76a4c24167041699dc28b5\",\n",
+    "        path=\"data/pbmc3k_v1\",\n",
+    "    )\n",
+    ").relative_to(Path.cwd())\n",
+    "with tarfile.open(p_pbmc3k, \"r:gz\") as f:\n",
+    "    f.extractall(p_pbmc3k.parent, filter=\"data\")\n",
     "\n",
-    "# !wget https://cf.10xgenomics.com/samples/cell-exp/3.0.0/pbmc_10k_v3/pbmc_10k_v3_filtered_feature_bc_matrix.tar.gz -O tutorial_data/pbmc10k_v3.tar.gz\n",
-    "# !cd tutorial_data; tar -xzf pbmc10k_v3.tar.gz -C pbmc10k_v3 --strip-components 1"
+    "p_pbmc10k = Path(\n",
+    "    pooch.retrieve(\n",
+    "        \"https://cf.10xgenomics.com/samples/cell-exp/3.0.0/pbmc_10k_v3/pbmc_10k_v3_filtered_feature_bc_matrix.tar.gz\",\n",
+    "        known_hash=\"060af9e4370c6ad877e2987cf8cdc63a6d5c84e57875580f2be6c8e90748cf63\",\n",
+    "        path=\"data/pbmc10k_v3\",\n",
+    "    )\n",
+    ").relative_to(Path.cwd())\n",
+    "with tarfile.open(p_pbmc10k, \"r:gz\") as f:\n",
+    "    f.extractall(p_pbmc10k.parent, filter=\"data\")"
    ]
   },
   {
@@ -138,8 +155,8 @@
     }
    ],
    "source": [
-    "adata_pbmc3k = sc.read_10x_mtx(\"tutorial_data/pbmc3k_v1/\", cache=True)\n",
-    "adata_pbmc10k = sc.read_10x_mtx(\"tutorial_data/pbmc10k_v3/\", cache=True)\n",
+    "adata_pbmc3k = sc.read_10x_mtx(p_pbmc3k.parent / \"filtered_gene_bc_matrices/hg19\", cache=True)\n",
+    "adata_pbmc10k = sc.read_10x_mtx(p_pbmc10k.parent / \"filtered_feature_bc_matrix\", cache=True)\n",
     "\n",
     "adata_pbmc3k.uns[\"name\"] = \"PBMC 3k (v1)\"\n",
     "adata_pbmc10k.uns[\"name\"] = \"PBMC 10k (v3)\""
@@ -244,9 +261,7 @@
    "source": [
     "for adata in [adata_pbmc3k, adata_pbmc10k]:\n",
     "    adata.var[\"mt\"] = adata.var_names.str.startswith(\"MT-\")\n",
-    "    sc.pp.calculate_qc_metrics(\n",
-    "        adata, qc_vars=[\"mt\"], percent_top=None, log1p=False, inplace=True\n",
-    "    )"
+    "    sc.pp.calculate_qc_metrics(adata, qc_vars=[\"mt\"], percent_top=None, log1p=False, inplace=True)"
    ]
   },
   {
@@ -393,12 +408,9 @@
     "adata_pbmc3k.obs[\"outlier_total\"] = adata_pbmc3k.obs.total_counts > 5000\n",
     "adata_pbmc3k.obs[\"outlier_ngenes\"] = adata_pbmc3k.obs.n_genes_by_counts > 2500\n",
     "\n",
-    "print(\n",
-    "    \"%u cells with high %% of mitochondrial genes\"\n",
-    "    % (sum(adata_pbmc3k.obs[\"outlier_mt\"]))\n",
-    ")\n",
-    "print(\"%u cells with large total counts\" % (sum(adata_pbmc3k.obs[\"outlier_total\"])))\n",
-    "print(\"%u cells with large number of genes\" % (sum(adata_pbmc3k.obs[\"outlier_ngenes\"])))\n",
+    "print(f\"{sum(adata_pbmc3k.obs['outlier_mt'])} cells with high % of mitochondrial genes\")\n",
+    "print(f\"{sum(adata_pbmc3k.obs['outlier_total'])} cells with large total counts\")\n",
+    "print(f\"{sum(adata_pbmc3k.obs['outlier_ngenes'])} cells with large number of genes\")\n",
     "\n",
     "adata_pbmc3k = adata_pbmc3k[~adata_pbmc3k.obs[\"outlier_mt\"], :]\n",
     "adata_pbmc3k = adata_pbmc3k[~adata_pbmc3k.obs[\"outlier_total\"], :]\n",
@@ -436,14 +448,9 @@
     "adata_pbmc10k.obs[\"outlier_total\"] = adata_pbmc10k.obs.total_counts > 25000\n",
     "adata_pbmc10k.obs[\"outlier_ngenes\"] = adata_pbmc10k.obs.n_genes_by_counts > 6000\n",
     "\n",
-    "print(\n",
-    "    \"%u cells with high %% of mitochondrial genes\"\n",
-    "    % (sum(adata_pbmc10k.obs[\"outlier_mt\"]))\n",
-    ")\n",
-    "print(\"%u cells with large total counts\" % (sum(adata_pbmc10k.obs[\"outlier_total\"])))\n",
-    "print(\n",
-    "    \"%u cells with large number of genes\" % (sum(adata_pbmc10k.obs[\"outlier_ngenes\"]))\n",
-    ")\n",
+    "print(f\"{sum(adata_pbmc10k.obs['outlier_mt'])} cells with high % of mitochondrial genes\")\n",
+    "print(f\"{sum(adata_pbmc10k.obs['outlier_total'])} cells with large total counts\")\n",
+    "print(f\"{sum(adata_pbmc10k.obs['outlier_ngenes'])} cells with large number of genes\")\n",
     "\n",
     "adata_pbmc10k = adata_pbmc10k[~adata_pbmc10k.obs[\"outlier_mt\"], :]\n",
     "adata_pbmc10k = adata_pbmc10k[~adata_pbmc10k.obs[\"outlier_total\"], :]\n",
@@ -514,9 +521,7 @@
    ],
    "source": [
     "for adata in [adata_pbmc3k, adata_pbmc10k]:\n",
-    "    sc.experimental.pp.highly_variable_genes(\n",
-    "        adata, flavor=\"pearson_residuals\", n_top_genes=2000\n",
-    "    )"
+    "    sc.experimental.pp.highly_variable_genes(adata, flavor=\"pearson_residuals\", n_top_genes=2000)"
    ]
   },
   {
@@ -535,7 +540,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -566,12 +571,10 @@
    ],
    "source": [
     "fig, axes = plt.subplots(1, 2, figsize=(12, 6))\n",
-    "for ax, adata in zip(axes, [adata_pbmc3k, adata_pbmc10k]):\n",
+    "for ax, adata in zip(axes, [adata_pbmc3k, adata_pbmc10k], strict=True):\n",
     "    hvgs = adata.var[\"highly_variable\"]\n",
     "\n",
-    "    ax.scatter(\n",
-    "        adata.var[\"mean_counts\"], adata.var[\"residual_variances\"], s=3, edgecolor=\"none\"\n",
-    "    )\n",
+    "    ax.scatter(adata.var[\"mean_counts\"], adata.var[\"residual_variances\"], s=3, edgecolor=\"none\")\n",
     "    ax.scatter(\n",
     "        adata.var[\"mean_counts\"][hvgs],\n",
     "        adata.var[\"residual_variances\"][hvgs],\n",
@@ -740,14 +743,10 @@
    "source": [
     "# keep raw and depth-normalized counts for later\n",
     "adata_pbmc3k.layers[\"raw\"] = adata_pbmc3k.X.copy()\n",
-    "adata_pbmc3k.layers[\"sqrt_norm\"] = np.sqrt(\n",
-    "    sc.pp.normalize_total(adata_pbmc3k, inplace=False)[\"X\"]\n",
-    ")\n",
+    "adata_pbmc3k.layers[\"sqrt_norm\"] = np.sqrt(sc.pp.normalize_total(adata_pbmc3k, inplace=False)[\"X\"])\n",
     "\n",
     "adata_pbmc10k.layers[\"raw\"] = adata_pbmc10k.X.copy()\n",
-    "adata_pbmc10k.layers[\"sqrt_norm\"] = np.sqrt(\n",
-    "    sc.pp.normalize_total(adata_pbmc10k, inplace=False)[\"X\"]\n",
-    ")"
+    "adata_pbmc10k.layers[\"sqrt_norm\"] = np.sqrt(sc.pp.normalize_total(adata_pbmc10k, inplace=False)[\"X\"])"
    ]
   },
   {
@@ -1041,7 +1040,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "notebook",
    "language": "python",
    "name": "python3"
   },
@@ -1055,7 +1054,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.6"
+   "version": "3.13.7"
   }
  },
  "nbformat": 4,