From 2aeb573dac310e10f0d02d64a54eaea58a630064 Mon Sep 17 00:00:00 2001
From: "Philipp A." <flying-sheep@web.de>
Date: Tue, 9 Sep 2025 13:03:53 +0200
Subject: [PATCH 1/5] Add cell cycle score

---
 .gitignore              |  54 +---
 .pre-commit-config.yaml |  17 +-
 .prettierrc.yaml        |   7 -
 .vscode/launch.json     |  14 +
 .vscode/settings.json   |  12 +
 _static/css/custom.css  |  16 +-
 biome.jsonc             |  18 ++
 how-to/cell_cycle.ipynb | 625 ++++++++++++++++++++++++++++++++++++++++
 pyproject.toml          |  41 +--
 9 files changed, 721 insertions(+), 83 deletions(-)
 delete mode 100644 .prettierrc.yaml
 create mode 100644 .vscode/launch.json
 create mode 100644 .vscode/settings.json
 create mode 100644 biome.jsonc
 create mode 100644 how-to/cell_cycle.ipynb

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..794decc
--- /dev/null
+++ b/.vscode/settings.json
@@ -0,0 +1,12 @@
+{
+	"[python]": {
+		"editor.formatOnSave": true,
+		"editor.defaultFormatter": "charliermarsh.ruff",
+		"editor.codeActionsOnSave": {
+			"source.fixAll": "always",
+			"source.organizeImports": "always",
+		},
+	},
+	"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/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/how-to/cell_cycle.ipynb b/how-to/cell_cycle.ipynb
new file mode 100644
index 0000000..5e7e2a1
--- /dev/null
+++ b/how-to/cell_cycle.ipynb
@@ -0,0 +1,625 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Cell-Cycle Scoring and Regression\n",
+    "\n",
+    "This is a _Scanpy_ demo that shows how to regress cell cycle effect, following the approach showed in [Seurat's vignette](http://satijalab.org/seurat/cell_cycle_vignette.html#assign-cell-cycle-scores).\n",
+    "As for the R example, toy dataset consists of murine hematopoietic progenitors from [Nestorowa et al., Blood 2016](https://doi.org/10.1182/blood-2016-05-716480).\n",
+    "The files of the Seurat tutorial – used here for reasons of benchmarking – can be downloaded [here](https://www.dropbox.com/s/3dby3bjsaf5arrw/cell_cycle_vignette_files.zip?dl=1).\n",
+    "A more recent version of the dataset can be downloaded [here](http://blood.stemcells.cam.ac.uk/data/nestorowa_corrected_log2_transformed_counts.txt.gz)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2462ca47040e4885b0698ff2ff1183fa",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/html": [
+       "<table class=table>\n",
+       "            <thead style=\"position: sticky; top: 0; background-color: var(--jp-layout-color0, var(--vscode-editor-background, white));\">\n",
+       "        <tr><th>Package</th><th>Version</th></tr>\n",
+       "    </thead>\n",
+       "    <tbody>\n",
+       "        <tr><td><strong>scanpy</strong></td><td>1.11.4</td></tr>\n",
+       "    </tbody>\n",
+       "    <thead style=\"position: sticky; top: 0; background-color: var(--jp-layout-color0, var(--vscode-editor-background, white));\">\n",
+       "        <tr><th>Component</th><th>Info</th></tr>\n",
+       "    </thead>\n",
+       "    <tbody>\n",
+       "        <tr><td>Python</td><td>3.13.7 (main, Aug 15 2025, 12:34:02) [GCC 15.2.1 20250813]</td></tr>\n",
+       "        <tr><td>OS</td><td>Linux-6.16.5-zen1-1-zen-x86_64-with-glibc2.42</td></tr>\n",
+       "        <tr><td>CPU</td><td>16 logical CPU cores</td></tr>\n",
+       "        <tr><td>GPU</td><td>No GPU found</td></tr>\n",
+       "        <tr><td>Updated</td><td>2025-09-09 10:58</td></tr>\n",
+       "    </tbody>\n",
+       "        </table>\n",
+       "\n",
+       "        <details>\n",
+       "        <summary>Dependencies</summary>\n",
+       "                <div style=\"max-height: min(500px, 80vh); overflow-y: auto;\">\n",
+       "    <table class=table>\n",
+       "            <thead style=\"position: sticky; top: 0; background-color: var(--jp-layout-color0, var(--vscode-editor-background, white));\">\n",
+       "    <tr><th>Dependency</th><th>Version</th></tr>\n",
+       "</thead>\n",
+       "<tbody>\n",
+       "    <tr><td>scikit-learn</td><td>1.7.2</td></tr>\n",
+       "    <tr><td>packaging</td><td>25.0</td></tr>\n",
+       "    <tr><td>charset-normalizer</td><td>3.4.3</td></tr>\n",
+       "    <tr><td>sphinxcontrib-jsmath</td><td>1.0.1</td></tr>\n",
+       "    <tr><td>scipy</td><td>1.16.1</td></tr>\n",
+       "    <tr><td>crc32c</td><td>2.7.1</td></tr>\n",
+       "    <tr><td>zarr</td><td>3.1.2</td></tr>\n",
+       "    <tr><td>sphinxcontrib-qthelp</td><td>2.0.0</td></tr>\n",
+       "    <tr><td>matplotlib</td><td>3.10.6</td></tr>\n",
+       "    <tr><td>stack-data</td><td>0.6.3</td></tr>\n",
+       "    <tr><td>comm</td><td>0.2.3</td></tr>\n",
+       "    <tr><td>pandas</td><td>2.3.2</td></tr>\n",
+       "    <tr><td>prompt_toolkit</td><td>3.0.52</td></tr>\n",
+       "    <tr><td>session-info2</td><td>0.2.1</td></tr>\n",
+       "    <tr><td>jupyter_client</td><td>8.6.3</td></tr>\n",
+       "    <tr><td>h5py</td><td>3.14.0</td></tr>\n",
+       "    <tr><td>pure_eval</td><td>0.2.3</td></tr>\n",
+       "    <tr><td>pyzmq</td><td>27.1.0</td></tr>\n",
+       "    <tr><td>texttable</td><td>1.7.0</td></tr>\n",
+       "    <tr><td>jedi</td><td>0.19.2</td></tr>\n",
+       "    <tr><td>leidenalg</td><td>0.10.2</td></tr>\n",
+       "    <tr><td>decorator</td><td>5.2.1</td></tr>\n",
+       "    <tr><td>pillow</td><td>11.3.0</td></tr>\n",
+       "    <tr><td>numcodecs</td><td>0.16.2</td></tr>\n",
+       "    <tr><td>PyYAML</td><td>6.0.2</td></tr>\n",
+       "    <tr><td>Pygments</td><td>2.19.2</td></tr>\n",
+       "    <tr><td>wcwidth</td><td>0.2.13</td></tr>\n",
+       "    <tr><td>executing</td><td>2.2.1</td></tr>\n",
+       "    <tr><td>ipykernel</td><td>6.30.1</td></tr>\n",
+       "    <tr><td>igraph</td><td>0.11.9</td></tr>\n",
+       "    <tr><td>numba</td><td>0.61.2</td></tr>\n",
+       "    <tr><td>numpy</td><td>2.2.6</td></tr>\n",
+       "    <tr><td>sphinxcontrib-htmlhelp</td><td>2.1.0</td></tr>\n",
+       "    <tr><td>asttokens</td><td>3.0.0</td></tr>\n",
+       "    <tr><td>jupyter_core</td><td>5.8.1</td></tr>\n",
+       "    <tr><td>ipywidgets</td><td>8.1.7</td></tr>\n",
+       "    <tr><td>sphinxcontrib-devhelp</td><td>2.0.0</td></tr>\n",
+       "    <tr><td>pyparsing</td><td>3.2.3</td></tr>\n",
+       "    <tr><td>donfig</td><td>0.8.1.post1</td></tr>\n",
+       "    <tr><td>kiwisolver</td><td>1.4.9</td></tr>\n",
+       "    <tr><td>tornado</td><td>6.5.2</td></tr>\n",
+       "    <tr><td>sphinxcontrib-serializinghtml</td><td>2.0.0</td></tr>\n",
+       "    <tr><td>parso</td><td>0.8.5</td></tr>\n",
+       "    <tr><td>legacy-api-wrap</td><td>1.4.1</td></tr>\n",
+       "    <tr><td>debugpy</td><td>1.8.16</td></tr>\n",
+       "    <tr><td>anndata</td><td>0.12.2</td></tr>\n",
+       "    <tr><td>traitlets</td><td>5.14.3</td></tr>\n",
+       "    <tr><td>pytz</td><td>2025.2</td></tr>\n",
+       "    <tr><td>six</td><td>1.17.0</td></tr>\n",
+       "    <tr><td>sphinxcontrib-applehelp</td><td>2.0.0</td></tr>\n",
+       "    <tr><td>psutil</td><td>7.0.0</td></tr>\n",
+       "    <tr><td>cycler</td><td>0.12.1</td></tr>\n",
+       "    <tr><td>llvmlite</td><td>0.44.0</td></tr>\n",
+       "    <tr><td>natsort</td><td>8.4.0</td></tr>\n",
+       "    <tr><td>platformdirs</td><td>4.4.0</td></tr>\n",
+       "    <tr><td>ipython</td><td>9.5.0</td></tr>\n",
+       "    <tr><td>typing_extensions</td><td>4.15.0</td></tr>\n",
+       "    <tr><td>python-dateutil</td><td>2.9.0.post0</td></tr>\n",
+       "    <tr><td>threadpoolctl</td><td>3.6.0</td></tr>\n",
+       "    <tr><td>joblib</td><td>1.5.2</td></tr>\n",
+       "</tbody>\n",
+       "    </table>\n",
+       "</div>\n",
+       "    </details>\n",
+       "        <details>\n",
+       "            <summary>Copyable Markdown</summary>\n",
+       "            <pre>| Package | Version |\n",
+       "| ------- | ------- |\n",
+       "| scanpy  | 1.11.4  |\n",
+       "\n",
+       "| Dependency                    | Version     |\n",
+       "| ----------------------------- | ----------- |\n",
+       "| scikit-learn                  | 1.7.2       |\n",
+       "| packaging                     | 25.0        |\n",
+       "| charset-normalizer            | 3.4.3       |\n",
+       "| sphinxcontrib-jsmath          | 1.0.1       |\n",
+       "| scipy                         | 1.16.1      |\n",
+       "| crc32c                        | 2.7.1       |\n",
+       "| zarr                          | 3.1.2       |\n",
+       "| sphinxcontrib-qthelp          | 2.0.0       |\n",
+       "| matplotlib                    | 3.10.6      |\n",
+       "| stack-data                    | 0.6.3       |\n",
+       "| comm                          | 0.2.3       |\n",
+       "| pandas                        | 2.3.2       |\n",
+       "| prompt_toolkit                | 3.0.52      |\n",
+       "| session-info2                 | 0.2.1       |\n",
+       "| jupyter_client                | 8.6.3       |\n",
+       "| h5py                          | 3.14.0      |\n",
+       "| pure_eval                     | 0.2.3       |\n",
+       "| pyzmq                         | 27.1.0      |\n",
+       "| texttable                     | 1.7.0       |\n",
+       "| jedi                          | 0.19.2      |\n",
+       "| leidenalg                     | 0.10.2      |\n",
+       "| decorator                     | 5.2.1       |\n",
+       "| pillow                        | 11.3.0      |\n",
+       "| numcodecs                     | 0.16.2      |\n",
+       "| PyYAML                        | 6.0.2       |\n",
+       "| Pygments                      | 2.19.2      |\n",
+       "| wcwidth                       | 0.2.13      |\n",
+       "| executing                     | 2.2.1       |\n",
+       "| ipykernel                     | 6.30.1      |\n",
+       "| igraph                        | 0.11.9      |\n",
+       "| numba                         | 0.61.2      |\n",
+       "| numpy                         | 2.2.6       |\n",
+       "| sphinxcontrib-htmlhelp        | 2.1.0       |\n",
+       "| asttokens                     | 3.0.0       |\n",
+       "| jupyter_core                  | 5.8.1       |\n",
+       "| ipywidgets                    | 8.1.7       |\n",
+       "| sphinxcontrib-devhelp         | 2.0.0       |\n",
+       "| pyparsing                     | 3.2.3       |\n",
+       "| donfig                        | 0.8.1.post1 |\n",
+       "| kiwisolver                    | 1.4.9       |\n",
+       "| tornado                       | 6.5.2       |\n",
+       "| sphinxcontrib-serializinghtml | 2.0.0       |\n",
+       "| parso                         | 0.8.5       |\n",
+       "| legacy-api-wrap               | 1.4.1       |\n",
+       "| debugpy                       | 1.8.16      |\n",
+       "| anndata                       | 0.12.2      |\n",
+       "| traitlets                     | 5.14.3      |\n",
+       "| pytz                          | 2025.2      |\n",
+       "| six                           | 1.17.0      |\n",
+       "| sphinxcontrib-applehelp       | 2.0.0       |\n",
+       "| psutil                        | 7.0.0       |\n",
+       "| cycler                        | 0.12.1      |\n",
+       "| llvmlite                      | 0.44.0      |\n",
+       "| natsort                       | 8.4.0       |\n",
+       "| platformdirs                  | 4.4.0       |\n",
+       "| ipython                       | 9.5.0       |\n",
+       "| typing_extensions             | 4.15.0      |\n",
+       "| python-dateutil               | 2.9.0.post0 |\n",
+       "| threadpoolctl                 | 3.6.0       |\n",
+       "| joblib                        | 1.5.2       |\n",
+       "\n",
+       "| Component | Info                                                       |\n",
+       "| --------- | ---------------------------------------------------------- |\n",
+       "| Python    | 3.13.7 (main, Aug 15 2025, 12:34:02) [GCC 15.2.1 20250813] |\n",
+       "| OS        | Linux-6.16.5-zen1-1-zen-x86_64-with-glibc2.42              |\n",
+       "| CPU       | 16 logical CPU cores                                       |\n",
+       "| GPU       | No GPU found                                               |\n",
+       "| Updated   | 2025-09-09 10:58                                           |</pre>\n",
+       "        </details>"
+      ],
+      "text/markdown": [
+       "| Package | Version |\n",
+       "| ------- | ------- |\n",
+       "| scanpy  | 1.11.4  |\n",
+       "\n",
+       "| Dependency                    | Version     |\n",
+       "| ----------------------------- | ----------- |\n",
+       "| scikit-learn                  | 1.7.2       |\n",
+       "| packaging                     | 25.0        |\n",
+       "| charset-normalizer            | 3.4.3       |\n",
+       "| sphinxcontrib-jsmath          | 1.0.1       |\n",
+       "| scipy                         | 1.16.1      |\n",
+       "| crc32c                        | 2.7.1       |\n",
+       "| zarr                          | 3.1.2       |\n",
+       "| sphinxcontrib-qthelp          | 2.0.0       |\n",
+       "| matplotlib                    | 3.10.6      |\n",
+       "| stack-data                    | 0.6.3       |\n",
+       "| comm                          | 0.2.3       |\n",
+       "| pandas                        | 2.3.2       |\n",
+       "| prompt_toolkit                | 3.0.52      |\n",
+       "| session-info2                 | 0.2.1       |\n",
+       "| jupyter_client                | 8.6.3       |\n",
+       "| h5py                          | 3.14.0      |\n",
+       "| pure_eval                     | 0.2.3       |\n",
+       "| pyzmq                         | 27.1.0      |\n",
+       "| texttable                     | 1.7.0       |\n",
+       "| jedi                          | 0.19.2      |\n",
+       "| leidenalg                     | 0.10.2      |\n",
+       "| decorator                     | 5.2.1       |\n",
+       "| pillow                        | 11.3.0      |\n",
+       "| numcodecs                     | 0.16.2      |\n",
+       "| PyYAML                        | 6.0.2       |\n",
+       "| Pygments                      | 2.19.2      |\n",
+       "| wcwidth                       | 0.2.13      |\n",
+       "| executing                     | 2.2.1       |\n",
+       "| ipykernel                     | 6.30.1      |\n",
+       "| igraph                        | 0.11.9      |\n",
+       "| numba                         | 0.61.2      |\n",
+       "| numpy                         | 2.2.6       |\n",
+       "| sphinxcontrib-htmlhelp        | 2.1.0       |\n",
+       "| asttokens                     | 3.0.0       |\n",
+       "| jupyter_core                  | 5.8.1       |\n",
+       "| ipywidgets                    | 8.1.7       |\n",
+       "| sphinxcontrib-devhelp         | 2.0.0       |\n",
+       "| pyparsing                     | 3.2.3       |\n",
+       "| donfig                        | 0.8.1.post1 |\n",
+       "| kiwisolver                    | 1.4.9       |\n",
+       "| tornado                       | 6.5.2       |\n",
+       "| sphinxcontrib-serializinghtml | 2.0.0       |\n",
+       "| parso                         | 0.8.5       |\n",
+       "| legacy-api-wrap               | 1.4.1       |\n",
+       "| debugpy                       | 1.8.16      |\n",
+       "| anndata                       | 0.12.2      |\n",
+       "| traitlets                     | 5.14.3      |\n",
+       "| pytz                          | 2025.2      |\n",
+       "| six                           | 1.17.0      |\n",
+       "| sphinxcontrib-applehelp       | 2.0.0       |\n",
+       "| psutil                        | 7.0.0       |\n",
+       "| cycler                        | 0.12.1      |\n",
+       "| llvmlite                      | 0.44.0      |\n",
+       "| natsort                       | 8.4.0       |\n",
+       "| platformdirs                  | 4.4.0       |\n",
+       "| ipython                       | 9.5.0       |\n",
+       "| typing_extensions             | 4.15.0      |\n",
+       "| python-dateutil               | 2.9.0.post0 |\n",
+       "| threadpoolctl                 | 3.6.0       |\n",
+       "| joblib                        | 1.5.2       |\n",
+       "\n",
+       "| Component | Info                                                       |\n",
+       "| --------- | ---------------------------------------------------------- |\n",
+       "| Python    | 3.13.7 (main, Aug 15 2025, 12:34:02) [GCC 15.2.1 20250813] |\n",
+       "| OS        | Linux-6.16.5-zen1-1-zen-x86_64-with-glibc2.42              |\n",
+       "| CPU       | 16 logical CPU cores                                       |\n",
+       "| GPU       | No GPU found                                               |\n",
+       "| Updated   | 2025-09-09 10:58                                           |"
+      ],
+      "text/plain": [
+       "scanpy\t1.11.4\n",
+       "----\t----\n",
+       "scikit-learn\t1.7.2\n",
+       "packaging\t25.0\n",
+       "charset-normalizer\t3.4.3\n",
+       "sphinxcontrib-jsmath\t1.0.1\n",
+       "scipy\t1.16.1\n",
+       "crc32c\t2.7.1\n",
+       "zarr\t3.1.2\n",
+       "sphinxcontrib-qthelp\t2.0.0\n",
+       "matplotlib\t3.10.6\n",
+       "stack-data\t0.6.3\n",
+       "comm\t0.2.3\n",
+       "pandas\t2.3.2\n",
+       "prompt_toolkit\t3.0.52\n",
+       "session-info2\t0.2.1\n",
+       "jupyter_client\t8.6.3\n",
+       "h5py\t3.14.0\n",
+       "pure_eval\t0.2.3\n",
+       "pyzmq\t27.1.0\n",
+       "texttable\t1.7.0\n",
+       "jedi\t0.19.2\n",
+       "leidenalg\t0.10.2\n",
+       "decorator\t5.2.1\n",
+       "pillow\t11.3.0\n",
+       "numcodecs\t0.16.2\n",
+       "PyYAML\t6.0.2\n",
+       "Pygments\t2.19.2\n",
+       "wcwidth\t0.2.13\n",
+       "executing\t2.2.1\n",
+       "ipykernel\t6.30.1\n",
+       "igraph\t0.11.9\n",
+       "numba\t0.61.2\n",
+       "numpy\t2.2.6\n",
+       "sphinxcontrib-htmlhelp\t2.1.0\n",
+       "asttokens\t3.0.0\n",
+       "jupyter_core\t5.8.1\n",
+       "ipywidgets\t8.1.7\n",
+       "sphinxcontrib-devhelp\t2.0.0\n",
+       "pyparsing\t3.2.3\n",
+       "donfig\t0.8.1.post1\n",
+       "kiwisolver\t1.4.9\n",
+       "tornado\t6.5.2\n",
+       "sphinxcontrib-serializinghtml\t2.0.0\n",
+       "parso\t0.8.5\n",
+       "legacy-api-wrap\t1.4.1\n",
+       "debugpy\t1.8.16\n",
+       "anndata\t0.12.2\n",
+       "traitlets\t5.14.3\n",
+       "pytz\t2025.2\n",
+       "six\t1.17.0\n",
+       "sphinxcontrib-applehelp\t2.0.0\n",
+       "psutil\t7.0.0\n",
+       "cycler\t0.12.1\n",
+       "llvmlite\t0.44.0\n",
+       "natsort\t8.4.0\n",
+       "platformdirs\t4.4.0\n",
+       "ipython\t9.5.0\n",
+       "typing_extensions\t4.15.0\n",
+       "python-dateutil\t2.9.0.post0\n",
+       "threadpoolctl\t3.6.0\n",
+       "joblib\t1.5.2\n",
+       "----\t----\n",
+       "Python\t3.13.7 (main, Aug 15 2025, 12:34:02) [GCC 15.2.1 20250813]\n",
+       "OS\tLinux-6.16.5-zen1-1-zen-x86_64-with-glibc2.42\n",
+       "CPU\t16 logical CPU cores\n",
+       "GPU\tNo GPU found\n",
+       "Updated\t2025-09-09 10:58"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from __future__ import annotations\n",
+    "\n",
+    "from pathlib import Path\n",
+    "\n",
+    "import scanpy as sc\n",
+    "\n",
+    "sc.logging.print_header()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Load data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Archive:  cell_cycle_vignette_files.zip\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%bash\n",
+    "test -f cell_cycle_vignette_files.zip || wget 'https://www.dropbox.com/s/3dby3bjsaf5arrw/cell_cycle_vignette_files.zip'\n",
+    "unzip -fd ../data cell_cycle_vignette_files.zip"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "adata = sc.read_csv(\n",
+    "    \"../data/nestorawa_forcellcycle_expressionMatrix.txt\", delimiter=\"\\t\"\n",
+    ").T"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Load cell cycle genes defined in [Tirosh et al, 2015](https://doi.org/10.1126/science.aad0501).\n",
+    "It is a list of 97 genes, represented by their gene symbol. The list here is for humans, in case of alternate organism, a list of ortologues should be compiled.\n",
+    "There are major differences in the way _Scanpy_ and _Seurat_ manage data, in particular we need to filter out cell cycle genes that are not present in our dataset to avoid errors."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "cell_cycle_genes = (\n",
+    "    Path(\"../data/regev_lab_cell_cycle_genes.txt\").read_text().splitlines()\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here we define two lists, genes associated to the S phase and genes associated to the G2M phase"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "s_genes = cell_cycle_genes[:43]\n",
+    "g2m_genes = cell_cycle_genes[43:]\n",
+    "cell_cycle_genes = [x for x in cell_cycle_genes if x in adata.var_names]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Standard filters applied. Note that we do not extract variable genes and work on the whole dataset, instead. This is because, for this demo, almost 70 cell cycle genes would not be scored as variable. Cell cycle scoring on ~20 genes is ineffective. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "sc.pp.filter_cells(adata, min_genes=200)\n",
+    "sc.pp.filter_genes(adata, min_cells=3)\n",
+    "sc.pp.normalize_total(adata, target_sum=1e4)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Log-transformation of data and scaling should always be performed before scoring"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "sc.pp.log1p(adata)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We here perform cell cycle scoring.\n",
+    "The function is actually a wrapper to {func}`scanpy.tl.score_gene_list`, which is launched twice, to score separately S and G2M phases.\n",
+    "Both {func}`scanpy.tl.score_gene_list` and {func}`scanpy.tl.score_cell_cycle_genes` are a port from _Seurat_ and are supposed to work in a very similar way. \n",
+    "To score a gene list, the algorithm calculates the difference of mean expression of the given list and the mean expression of reference genes.\n",
+    "To build the reference, the function randomly chooses a bunch of genes matching the distribution of the expression of the given list.\n",
+    "Cell cycle scoring adds three slots in data, a score for S phase, a score for G2M phase and the predicted cell cycle phase."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING: genes are not in var_names and ignored: Index(['MLF1IP', 'GMNN'], dtype='object')\n"
+     ]
+    }
+   ],
+   "source": [
+    "sc.tl.score_genes_cell_cycle(adata, s_genes=s_genes, g2m_genes=g2m_genes)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Here comes another difference from _Seurat_.\n",
+    "The R package stores raw data, scaled data and variable genes information in separate slots, _Scanpy_ instead keeps only one snapshot of the data.\n",
+    "This implies that PCA is always calculated on the entire dataset. In order to calculate PCA reduction using only a subset of genes (like `cell_cycle_genes`), a trick should be used.\n",
+    "Basically we create a dummy object to store information of PCA projection, which is then reincorporated into original dataset."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "adata_cc_genes = adata[:, cell_cycle_genes].copy()\n",
+    "sc.tl.pca(adata_cc_genes)\n",
+    "sc.pl.pca_scatter(adata_cc_genes, color=\"phase\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As in the original vignette, cells can be easily separated by their cell cycle status when cell cycle genes are used.\n",
+    "Now we can regress out both S score and G2M score."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "sc.pp.regress_out(adata, [\"S_score\", \"G2M_score\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Finally, we reproject dataset using cell cycle genes again. Since we regressed the scores, no effect of cell cycle is now evident."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "adata_cc_genes = adata[:, cell_cycle_genes].copy()\n",
+    "sc.tl.pca(adata_cc_genes)\n",
+    "sc.pl.pca_scatter(adata_cc_genes, color=\"phase\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Author: *Davide Cittaro*"
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "notebook",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.13.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/pyproject.toml b/pyproject.toml
index e537954..43d5ed6 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"
@@ -35,13 +45,8 @@ extra-dependencies = [
 ]
 
 [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
+extend-exclude = [ "scanpy_workshop/" ]
+lint.ignore = [
+  "E741", # Fonts these days don’t display I and l in a confusable way
+  "F821", # R cells can sometimes define variables
 ]
-
-[build-system]
-build-backend = "hatchling.build"
-requires = ["hatchling", "hatch-vcs"]

From 71436a42445232d2f25364f9cfdf6493f0f34eb3 Mon Sep 17 00:00:00 2001
From: "Philipp A." <flying-sheep@web.de>
Date: Tue, 9 Sep 2025 13:04:58 +0200
Subject: [PATCH 2/5] rm nb

---
 how-to/cell_cycle.ipynb | 625 ----------------------------------------
 1 file changed, 625 deletions(-)
 delete mode 100644 how-to/cell_cycle.ipynb

diff --git a/how-to/cell_cycle.ipynb b/how-to/cell_cycle.ipynb
deleted file mode 100644
index 5e7e2a1..0000000
--- a/how-to/cell_cycle.ipynb
+++ /dev/null
@@ -1,625 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Cell-Cycle Scoring and Regression\n",
-    "\n",
-    "This is a _Scanpy_ demo that shows how to regress cell cycle effect, following the approach showed in [Seurat's vignette](http://satijalab.org/seurat/cell_cycle_vignette.html#assign-cell-cycle-scores).\n",
-    "As for the R example, toy dataset consists of murine hematopoietic progenitors from [Nestorowa et al., Blood 2016](https://doi.org/10.1182/blood-2016-05-716480).\n",
-    "The files of the Seurat tutorial – used here for reasons of benchmarking – can be downloaded [here](https://www.dropbox.com/s/3dby3bjsaf5arrw/cell_cycle_vignette_files.zip?dl=1).\n",
-    "A more recent version of the dataset can be downloaded [here](http://blood.stemcells.cam.ac.uk/data/nestorowa_corrected_log2_transformed_counts.txt.gz)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "2462ca47040e4885b0698ff2ff1183fa",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/html": [
-       "<table class=table>\n",
-       "            <thead style=\"position: sticky; top: 0; background-color: var(--jp-layout-color0, var(--vscode-editor-background, white));\">\n",
-       "        <tr><th>Package</th><th>Version</th></tr>\n",
-       "    </thead>\n",
-       "    <tbody>\n",
-       "        <tr><td><strong>scanpy</strong></td><td>1.11.4</td></tr>\n",
-       "    </tbody>\n",
-       "    <thead style=\"position: sticky; top: 0; background-color: var(--jp-layout-color0, var(--vscode-editor-background, white));\">\n",
-       "        <tr><th>Component</th><th>Info</th></tr>\n",
-       "    </thead>\n",
-       "    <tbody>\n",
-       "        <tr><td>Python</td><td>3.13.7 (main, Aug 15 2025, 12:34:02) [GCC 15.2.1 20250813]</td></tr>\n",
-       "        <tr><td>OS</td><td>Linux-6.16.5-zen1-1-zen-x86_64-with-glibc2.42</td></tr>\n",
-       "        <tr><td>CPU</td><td>16 logical CPU cores</td></tr>\n",
-       "        <tr><td>GPU</td><td>No GPU found</td></tr>\n",
-       "        <tr><td>Updated</td><td>2025-09-09 10:58</td></tr>\n",
-       "    </tbody>\n",
-       "        </table>\n",
-       "\n",
-       "        <details>\n",
-       "        <summary>Dependencies</summary>\n",
-       "                <div style=\"max-height: min(500px, 80vh); overflow-y: auto;\">\n",
-       "    <table class=table>\n",
-       "            <thead style=\"position: sticky; top: 0; background-color: var(--jp-layout-color0, var(--vscode-editor-background, white));\">\n",
-       "    <tr><th>Dependency</th><th>Version</th></tr>\n",
-       "</thead>\n",
-       "<tbody>\n",
-       "    <tr><td>scikit-learn</td><td>1.7.2</td></tr>\n",
-       "    <tr><td>packaging</td><td>25.0</td></tr>\n",
-       "    <tr><td>charset-normalizer</td><td>3.4.3</td></tr>\n",
-       "    <tr><td>sphinxcontrib-jsmath</td><td>1.0.1</td></tr>\n",
-       "    <tr><td>scipy</td><td>1.16.1</td></tr>\n",
-       "    <tr><td>crc32c</td><td>2.7.1</td></tr>\n",
-       "    <tr><td>zarr</td><td>3.1.2</td></tr>\n",
-       "    <tr><td>sphinxcontrib-qthelp</td><td>2.0.0</td></tr>\n",
-       "    <tr><td>matplotlib</td><td>3.10.6</td></tr>\n",
-       "    <tr><td>stack-data</td><td>0.6.3</td></tr>\n",
-       "    <tr><td>comm</td><td>0.2.3</td></tr>\n",
-       "    <tr><td>pandas</td><td>2.3.2</td></tr>\n",
-       "    <tr><td>prompt_toolkit</td><td>3.0.52</td></tr>\n",
-       "    <tr><td>session-info2</td><td>0.2.1</td></tr>\n",
-       "    <tr><td>jupyter_client</td><td>8.6.3</td></tr>\n",
-       "    <tr><td>h5py</td><td>3.14.0</td></tr>\n",
-       "    <tr><td>pure_eval</td><td>0.2.3</td></tr>\n",
-       "    <tr><td>pyzmq</td><td>27.1.0</td></tr>\n",
-       "    <tr><td>texttable</td><td>1.7.0</td></tr>\n",
-       "    <tr><td>jedi</td><td>0.19.2</td></tr>\n",
-       "    <tr><td>leidenalg</td><td>0.10.2</td></tr>\n",
-       "    <tr><td>decorator</td><td>5.2.1</td></tr>\n",
-       "    <tr><td>pillow</td><td>11.3.0</td></tr>\n",
-       "    <tr><td>numcodecs</td><td>0.16.2</td></tr>\n",
-       "    <tr><td>PyYAML</td><td>6.0.2</td></tr>\n",
-       "    <tr><td>Pygments</td><td>2.19.2</td></tr>\n",
-       "    <tr><td>wcwidth</td><td>0.2.13</td></tr>\n",
-       "    <tr><td>executing</td><td>2.2.1</td></tr>\n",
-       "    <tr><td>ipykernel</td><td>6.30.1</td></tr>\n",
-       "    <tr><td>igraph</td><td>0.11.9</td></tr>\n",
-       "    <tr><td>numba</td><td>0.61.2</td></tr>\n",
-       "    <tr><td>numpy</td><td>2.2.6</td></tr>\n",
-       "    <tr><td>sphinxcontrib-htmlhelp</td><td>2.1.0</td></tr>\n",
-       "    <tr><td>asttokens</td><td>3.0.0</td></tr>\n",
-       "    <tr><td>jupyter_core</td><td>5.8.1</td></tr>\n",
-       "    <tr><td>ipywidgets</td><td>8.1.7</td></tr>\n",
-       "    <tr><td>sphinxcontrib-devhelp</td><td>2.0.0</td></tr>\n",
-       "    <tr><td>pyparsing</td><td>3.2.3</td></tr>\n",
-       "    <tr><td>donfig</td><td>0.8.1.post1</td></tr>\n",
-       "    <tr><td>kiwisolver</td><td>1.4.9</td></tr>\n",
-       "    <tr><td>tornado</td><td>6.5.2</td></tr>\n",
-       "    <tr><td>sphinxcontrib-serializinghtml</td><td>2.0.0</td></tr>\n",
-       "    <tr><td>parso</td><td>0.8.5</td></tr>\n",
-       "    <tr><td>legacy-api-wrap</td><td>1.4.1</td></tr>\n",
-       "    <tr><td>debugpy</td><td>1.8.16</td></tr>\n",
-       "    <tr><td>anndata</td><td>0.12.2</td></tr>\n",
-       "    <tr><td>traitlets</td><td>5.14.3</td></tr>\n",
-       "    <tr><td>pytz</td><td>2025.2</td></tr>\n",
-       "    <tr><td>six</td><td>1.17.0</td></tr>\n",
-       "    <tr><td>sphinxcontrib-applehelp</td><td>2.0.0</td></tr>\n",
-       "    <tr><td>psutil</td><td>7.0.0</td></tr>\n",
-       "    <tr><td>cycler</td><td>0.12.1</td></tr>\n",
-       "    <tr><td>llvmlite</td><td>0.44.0</td></tr>\n",
-       "    <tr><td>natsort</td><td>8.4.0</td></tr>\n",
-       "    <tr><td>platformdirs</td><td>4.4.0</td></tr>\n",
-       "    <tr><td>ipython</td><td>9.5.0</td></tr>\n",
-       "    <tr><td>typing_extensions</td><td>4.15.0</td></tr>\n",
-       "    <tr><td>python-dateutil</td><td>2.9.0.post0</td></tr>\n",
-       "    <tr><td>threadpoolctl</td><td>3.6.0</td></tr>\n",
-       "    <tr><td>joblib</td><td>1.5.2</td></tr>\n",
-       "</tbody>\n",
-       "    </table>\n",
-       "</div>\n",
-       "    </details>\n",
-       "        <details>\n",
-       "            <summary>Copyable Markdown</summary>\n",
-       "            <pre>| Package | Version |\n",
-       "| ------- | ------- |\n",
-       "| scanpy  | 1.11.4  |\n",
-       "\n",
-       "| Dependency                    | Version     |\n",
-       "| ----------------------------- | ----------- |\n",
-       "| scikit-learn                  | 1.7.2       |\n",
-       "| packaging                     | 25.0        |\n",
-       "| charset-normalizer            | 3.4.3       |\n",
-       "| sphinxcontrib-jsmath          | 1.0.1       |\n",
-       "| scipy                         | 1.16.1      |\n",
-       "| crc32c                        | 2.7.1       |\n",
-       "| zarr                          | 3.1.2       |\n",
-       "| sphinxcontrib-qthelp          | 2.0.0       |\n",
-       "| matplotlib                    | 3.10.6      |\n",
-       "| stack-data                    | 0.6.3       |\n",
-       "| comm                          | 0.2.3       |\n",
-       "| pandas                        | 2.3.2       |\n",
-       "| prompt_toolkit                | 3.0.52      |\n",
-       "| session-info2                 | 0.2.1       |\n",
-       "| jupyter_client                | 8.6.3       |\n",
-       "| h5py                          | 3.14.0      |\n",
-       "| pure_eval                     | 0.2.3       |\n",
-       "| pyzmq                         | 27.1.0      |\n",
-       "| texttable                     | 1.7.0       |\n",
-       "| jedi                          | 0.19.2      |\n",
-       "| leidenalg                     | 0.10.2      |\n",
-       "| decorator                     | 5.2.1       |\n",
-       "| pillow                        | 11.3.0      |\n",
-       "| numcodecs                     | 0.16.2      |\n",
-       "| PyYAML                        | 6.0.2       |\n",
-       "| Pygments                      | 2.19.2      |\n",
-       "| wcwidth                       | 0.2.13      |\n",
-       "| executing                     | 2.2.1       |\n",
-       "| ipykernel                     | 6.30.1      |\n",
-       "| igraph                        | 0.11.9      |\n",
-       "| numba                         | 0.61.2      |\n",
-       "| numpy                         | 2.2.6       |\n",
-       "| sphinxcontrib-htmlhelp        | 2.1.0       |\n",
-       "| asttokens                     | 3.0.0       |\n",
-       "| jupyter_core                  | 5.8.1       |\n",
-       "| ipywidgets                    | 8.1.7       |\n",
-       "| sphinxcontrib-devhelp         | 2.0.0       |\n",
-       "| pyparsing                     | 3.2.3       |\n",
-       "| donfig                        | 0.8.1.post1 |\n",
-       "| kiwisolver                    | 1.4.9       |\n",
-       "| tornado                       | 6.5.2       |\n",
-       "| sphinxcontrib-serializinghtml | 2.0.0       |\n",
-       "| parso                         | 0.8.5       |\n",
-       "| legacy-api-wrap               | 1.4.1       |\n",
-       "| debugpy                       | 1.8.16      |\n",
-       "| anndata                       | 0.12.2      |\n",
-       "| traitlets                     | 5.14.3      |\n",
-       "| pytz                          | 2025.2      |\n",
-       "| six                           | 1.17.0      |\n",
-       "| sphinxcontrib-applehelp       | 2.0.0       |\n",
-       "| psutil                        | 7.0.0       |\n",
-       "| cycler                        | 0.12.1      |\n",
-       "| llvmlite                      | 0.44.0      |\n",
-       "| natsort                       | 8.4.0       |\n",
-       "| platformdirs                  | 4.4.0       |\n",
-       "| ipython                       | 9.5.0       |\n",
-       "| typing_extensions             | 4.15.0      |\n",
-       "| python-dateutil               | 2.9.0.post0 |\n",
-       "| threadpoolctl                 | 3.6.0       |\n",
-       "| joblib                        | 1.5.2       |\n",
-       "\n",
-       "| Component | Info                                                       |\n",
-       "| --------- | ---------------------------------------------------------- |\n",
-       "| Python    | 3.13.7 (main, Aug 15 2025, 12:34:02) [GCC 15.2.1 20250813] |\n",
-       "| OS        | Linux-6.16.5-zen1-1-zen-x86_64-with-glibc2.42              |\n",
-       "| CPU       | 16 logical CPU cores                                       |\n",
-       "| GPU       | No GPU found                                               |\n",
-       "| Updated   | 2025-09-09 10:58                                           |</pre>\n",
-       "        </details>"
-      ],
-      "text/markdown": [
-       "| Package | Version |\n",
-       "| ------- | ------- |\n",
-       "| scanpy  | 1.11.4  |\n",
-       "\n",
-       "| Dependency                    | Version     |\n",
-       "| ----------------------------- | ----------- |\n",
-       "| scikit-learn                  | 1.7.2       |\n",
-       "| packaging                     | 25.0        |\n",
-       "| charset-normalizer            | 3.4.3       |\n",
-       "| sphinxcontrib-jsmath          | 1.0.1       |\n",
-       "| scipy                         | 1.16.1      |\n",
-       "| crc32c                        | 2.7.1       |\n",
-       "| zarr                          | 3.1.2       |\n",
-       "| sphinxcontrib-qthelp          | 2.0.0       |\n",
-       "| matplotlib                    | 3.10.6      |\n",
-       "| stack-data                    | 0.6.3       |\n",
-       "| comm                          | 0.2.3       |\n",
-       "| pandas                        | 2.3.2       |\n",
-       "| prompt_toolkit                | 3.0.52      |\n",
-       "| session-info2                 | 0.2.1       |\n",
-       "| jupyter_client                | 8.6.3       |\n",
-       "| h5py                          | 3.14.0      |\n",
-       "| pure_eval                     | 0.2.3       |\n",
-       "| pyzmq                         | 27.1.0      |\n",
-       "| texttable                     | 1.7.0       |\n",
-       "| jedi                          | 0.19.2      |\n",
-       "| leidenalg                     | 0.10.2      |\n",
-       "| decorator                     | 5.2.1       |\n",
-       "| pillow                        | 11.3.0      |\n",
-       "| numcodecs                     | 0.16.2      |\n",
-       "| PyYAML                        | 6.0.2       |\n",
-       "| Pygments                      | 2.19.2      |\n",
-       "| wcwidth                       | 0.2.13      |\n",
-       "| executing                     | 2.2.1       |\n",
-       "| ipykernel                     | 6.30.1      |\n",
-       "| igraph                        | 0.11.9      |\n",
-       "| numba                         | 0.61.2      |\n",
-       "| numpy                         | 2.2.6       |\n",
-       "| sphinxcontrib-htmlhelp        | 2.1.0       |\n",
-       "| asttokens                     | 3.0.0       |\n",
-       "| jupyter_core                  | 5.8.1       |\n",
-       "| ipywidgets                    | 8.1.7       |\n",
-       "| sphinxcontrib-devhelp         | 2.0.0       |\n",
-       "| pyparsing                     | 3.2.3       |\n",
-       "| donfig                        | 0.8.1.post1 |\n",
-       "| kiwisolver                    | 1.4.9       |\n",
-       "| tornado                       | 6.5.2       |\n",
-       "| sphinxcontrib-serializinghtml | 2.0.0       |\n",
-       "| parso                         | 0.8.5       |\n",
-       "| legacy-api-wrap               | 1.4.1       |\n",
-       "| debugpy                       | 1.8.16      |\n",
-       "| anndata                       | 0.12.2      |\n",
-       "| traitlets                     | 5.14.3      |\n",
-       "| pytz                          | 2025.2      |\n",
-       "| six                           | 1.17.0      |\n",
-       "| sphinxcontrib-applehelp       | 2.0.0       |\n",
-       "| psutil                        | 7.0.0       |\n",
-       "| cycler                        | 0.12.1      |\n",
-       "| llvmlite                      | 0.44.0      |\n",
-       "| natsort                       | 8.4.0       |\n",
-       "| platformdirs                  | 4.4.0       |\n",
-       "| ipython                       | 9.5.0       |\n",
-       "| typing_extensions             | 4.15.0      |\n",
-       "| python-dateutil               | 2.9.0.post0 |\n",
-       "| threadpoolctl                 | 3.6.0       |\n",
-       "| joblib                        | 1.5.2       |\n",
-       "\n",
-       "| Component | Info                                                       |\n",
-       "| --------- | ---------------------------------------------------------- |\n",
-       "| Python    | 3.13.7 (main, Aug 15 2025, 12:34:02) [GCC 15.2.1 20250813] |\n",
-       "| OS        | Linux-6.16.5-zen1-1-zen-x86_64-with-glibc2.42              |\n",
-       "| CPU       | 16 logical CPU cores                                       |\n",
-       "| GPU       | No GPU found                                               |\n",
-       "| Updated   | 2025-09-09 10:58                                           |"
-      ],
-      "text/plain": [
-       "scanpy\t1.11.4\n",
-       "----\t----\n",
-       "scikit-learn\t1.7.2\n",
-       "packaging\t25.0\n",
-       "charset-normalizer\t3.4.3\n",
-       "sphinxcontrib-jsmath\t1.0.1\n",
-       "scipy\t1.16.1\n",
-       "crc32c\t2.7.1\n",
-       "zarr\t3.1.2\n",
-       "sphinxcontrib-qthelp\t2.0.0\n",
-       "matplotlib\t3.10.6\n",
-       "stack-data\t0.6.3\n",
-       "comm\t0.2.3\n",
-       "pandas\t2.3.2\n",
-       "prompt_toolkit\t3.0.52\n",
-       "session-info2\t0.2.1\n",
-       "jupyter_client\t8.6.3\n",
-       "h5py\t3.14.0\n",
-       "pure_eval\t0.2.3\n",
-       "pyzmq\t27.1.0\n",
-       "texttable\t1.7.0\n",
-       "jedi\t0.19.2\n",
-       "leidenalg\t0.10.2\n",
-       "decorator\t5.2.1\n",
-       "pillow\t11.3.0\n",
-       "numcodecs\t0.16.2\n",
-       "PyYAML\t6.0.2\n",
-       "Pygments\t2.19.2\n",
-       "wcwidth\t0.2.13\n",
-       "executing\t2.2.1\n",
-       "ipykernel\t6.30.1\n",
-       "igraph\t0.11.9\n",
-       "numba\t0.61.2\n",
-       "numpy\t2.2.6\n",
-       "sphinxcontrib-htmlhelp\t2.1.0\n",
-       "asttokens\t3.0.0\n",
-       "jupyter_core\t5.8.1\n",
-       "ipywidgets\t8.1.7\n",
-       "sphinxcontrib-devhelp\t2.0.0\n",
-       "pyparsing\t3.2.3\n",
-       "donfig\t0.8.1.post1\n",
-       "kiwisolver\t1.4.9\n",
-       "tornado\t6.5.2\n",
-       "sphinxcontrib-serializinghtml\t2.0.0\n",
-       "parso\t0.8.5\n",
-       "legacy-api-wrap\t1.4.1\n",
-       "debugpy\t1.8.16\n",
-       "anndata\t0.12.2\n",
-       "traitlets\t5.14.3\n",
-       "pytz\t2025.2\n",
-       "six\t1.17.0\n",
-       "sphinxcontrib-applehelp\t2.0.0\n",
-       "psutil\t7.0.0\n",
-       "cycler\t0.12.1\n",
-       "llvmlite\t0.44.0\n",
-       "natsort\t8.4.0\n",
-       "platformdirs\t4.4.0\n",
-       "ipython\t9.5.0\n",
-       "typing_extensions\t4.15.0\n",
-       "python-dateutil\t2.9.0.post0\n",
-       "threadpoolctl\t3.6.0\n",
-       "joblib\t1.5.2\n",
-       "----\t----\n",
-       "Python\t3.13.7 (main, Aug 15 2025, 12:34:02) [GCC 15.2.1 20250813]\n",
-       "OS\tLinux-6.16.5-zen1-1-zen-x86_64-with-glibc2.42\n",
-       "CPU\t16 logical CPU cores\n",
-       "GPU\tNo GPU found\n",
-       "Updated\t2025-09-09 10:58"
-      ]
-     },
-     "execution_count": 1,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from __future__ import annotations\n",
-    "\n",
-    "from pathlib import Path\n",
-    "\n",
-    "import scanpy as sc\n",
-    "\n",
-    "sc.logging.print_header()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Load data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Archive:  cell_cycle_vignette_files.zip\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%bash\n",
-    "test -f cell_cycle_vignette_files.zip || wget 'https://www.dropbox.com/s/3dby3bjsaf5arrw/cell_cycle_vignette_files.zip'\n",
-    "unzip -fd ../data cell_cycle_vignette_files.zip"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "adata = sc.read_csv(\n",
-    "    \"../data/nestorawa_forcellcycle_expressionMatrix.txt\", delimiter=\"\\t\"\n",
-    ").T"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Load cell cycle genes defined in [Tirosh et al, 2015](https://doi.org/10.1126/science.aad0501).\n",
-    "It is a list of 97 genes, represented by their gene symbol. The list here is for humans, in case of alternate organism, a list of ortologues should be compiled.\n",
-    "There are major differences in the way _Scanpy_ and _Seurat_ manage data, in particular we need to filter out cell cycle genes that are not present in our dataset to avoid errors."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "cell_cycle_genes = (\n",
-    "    Path(\"../data/regev_lab_cell_cycle_genes.txt\").read_text().splitlines()\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here we define two lists, genes associated to the S phase and genes associated to the G2M phase"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "s_genes = cell_cycle_genes[:43]\n",
-    "g2m_genes = cell_cycle_genes[43:]\n",
-    "cell_cycle_genes = [x for x in cell_cycle_genes if x in adata.var_names]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Standard filters applied. Note that we do not extract variable genes and work on the whole dataset, instead. This is because, for this demo, almost 70 cell cycle genes would not be scored as variable. Cell cycle scoring on ~20 genes is ineffective. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "sc.pp.filter_cells(adata, min_genes=200)\n",
-    "sc.pp.filter_genes(adata, min_cells=3)\n",
-    "sc.pp.normalize_total(adata, target_sum=1e4)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Log-transformation of data and scaling should always be performed before scoring"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "sc.pp.log1p(adata)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We here perform cell cycle scoring.\n",
-    "The function is actually a wrapper to {func}`scanpy.tl.score_gene_list`, which is launched twice, to score separately S and G2M phases.\n",
-    "Both {func}`scanpy.tl.score_gene_list` and {func}`scanpy.tl.score_cell_cycle_genes` are a port from _Seurat_ and are supposed to work in a very similar way. \n",
-    "To score a gene list, the algorithm calculates the difference of mean expression of the given list and the mean expression of reference genes.\n",
-    "To build the reference, the function randomly chooses a bunch of genes matching the distribution of the expression of the given list.\n",
-    "Cell cycle scoring adds three slots in data, a score for S phase, a score for G2M phase and the predicted cell cycle phase."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "WARNING: genes are not in var_names and ignored: Index(['MLF1IP', 'GMNN'], dtype='object')\n"
-     ]
-    }
-   ],
-   "source": [
-    "sc.tl.score_genes_cell_cycle(adata, s_genes=s_genes, g2m_genes=g2m_genes)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here comes another difference from _Seurat_.\n",
-    "The R package stores raw data, scaled data and variable genes information in separate slots, _Scanpy_ instead keeps only one snapshot of the data.\n",
-    "This implies that PCA is always calculated on the entire dataset. In order to calculate PCA reduction using only a subset of genes (like `cell_cycle_genes`), a trick should be used.\n",
-    "Basically we create a dummy object to store information of PCA projection, which is then reincorporated into original dataset."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "adata_cc_genes = adata[:, cell_cycle_genes].copy()\n",
-    "sc.tl.pca(adata_cc_genes)\n",
-    "sc.pl.pca_scatter(adata_cc_genes, color=\"phase\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "As in the original vignette, cells can be easily separated by their cell cycle status when cell cycle genes are used.\n",
-    "Now we can regress out both S score and G2M score."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
-   "source": [
-    "sc.pp.regress_out(adata, [\"S_score\", \"G2M_score\"])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Finally, we reproject dataset using cell cycle genes again. Since we regressed the scores, no effect of cell cycle is now evident."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "adata_cc_genes = adata[:, cell_cycle_genes].copy()\n",
-    "sc.tl.pca(adata_cc_genes)\n",
-    "sc.pl.pca_scatter(adata_cc_genes, color=\"phase\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Author: *Davide Cittaro*"
-   ]
-  }
- ],
- "metadata": {
-  "anaconda-cloud": {},
-  "kernelspec": {
-   "display_name": "notebook",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.13.7"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}

From 3646d2d3dfaa9f0cf87d74ea760e1d353bfeb923 Mon Sep 17 00:00:00 2001
From: "Philipp A." <flying-sheep@web.de>
Date: Tue, 9 Sep 2025 15:47:16 +0200
Subject: [PATCH 3/5] WIP lint

---
 .vscode/settings.json               |   1 +
 basic-scrna-tutorial.ipynb          |  22 ++--
 conf.py                             |  11 +-
 dask.ipynb                          |  58 ++++-------
 how-to/knn-transformers.ipynb       |   5 +
 how-to/plotting-with-marsilea.ipynb |  21 ++--
 integrating-data-using-ingest.ipynb |  34 +++----
 paga-paul15.ipynb                   |  20 ++--
 pbmc3k.ipynb                        |  32 ++----
 plotting/advanced.ipynb             |  61 ++++--------
 plotting/core.ipynb                 |  27 ++---
 pyproject.toml                      |  26 ++++-
 spatial/basic-analysis.ipynb        |  10 +-
 spatial/integration-scanorama.ipynb | 149 ++++++++++++----------------
 tutorial_pearson_residuals.ipynb    |  87 ++++++++--------
 15 files changed, 247 insertions(+), 317 deletions(-)

diff --git a/.vscode/settings.json b/.vscode/settings.json
index 794decc..a098bed 100644
--- a/.vscode/settings.json
+++ b/.vscode/settings.json
@@ -7,6 +7,7 @@
 			"source.organizeImports": "always",
 		},
 	},
+	"notebook.formatOnSave.enabled": true,
 	"python.terminal.activateEnvironment": true,
 	"python.analysis.typeCheckingMode": "basic",
 }
diff --git a/basic-scrna-tutorial.ipynb b/basic-scrna-tutorial.ipynb
index 19d3c49..24fe17d 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)"
    ]
   },
   {
@@ -764,9 +764,7 @@
    "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 +1050,7 @@
     }
    ],
    "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 +1210,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "notebook",
    "language": "python",
    "name": "python3"
   },
@@ -1228,7 +1224,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.8"
+   "version": "3.13.7"
   },
   "mystnb": {
    "execution_mode": "off"
diff --git a/conf.py b/conf.py
index 475f11a..2fdeee0 100644
--- a/conf.py
+++ b/conf.py
@@ -1,8 +1,9 @@
-from collections.abc import Mapping
+from __future__ import annotations
+
 from datetime import datetime
 from importlib.metadata import metadata
 from types import MappingProxyType
-from typing import TYPE_CHECKING, Sequence
+from typing import TYPE_CHECKING
 
 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
@@ -104,8 +108,7 @@ class FakeDomain(Domain):
 
 
 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"
 )
 
 
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..0241452 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, gene_name in zip(exp.T, markers, strict=True):\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": [
@@ -576,9 +577,10 @@
     "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",
@@ -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..6df6336 100644
--- a/paga-paul15.ipynb
+++ b/paga-paul15.ipynb
@@ -48,8 +48,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import numpy as np\n",
+    "from __future__ import annotations\n",
+    "\n",
     "import matplotlib.pyplot as pl\n",
+    "import numpy as np\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\")"
    ]
   },
   {
@@ -1104,9 +1102,7 @@
     }
    ],
    "source": [
-    "_, axs = pl.subplots(\n",
-    "    ncols=3, figsize=(6, 2.5), gridspec_kw={\"wspace\": 0.05, \"left\": 0.12}\n",
-    ")\n",
+    "_, axs = pl.subplots(ncols=3, figsize=(6, 2.5), gridspec_kw={\"wspace\": 0.05, \"left\": 0.12})\n",
     "pl.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",
@@ -1124,11 +1120,11 @@
     "        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",
+    "    data.to_csv(f\"./write/paga_path_{descr}.csv\")\n",
     "pl.savefig(\"./figures/paga_path_paul15.pdf\")\n",
     "pl.show()"
    ]
diff --git a/pbmc3k.ipynb b/pbmc3k.ipynb
index f277981..768fc7d 100755
--- 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"
    ]
   },
   {
@@ -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..51a3618 100644
--- a/plotting/advanced.ipynb
+++ b/plotting/advanced.ipynb
@@ -26,17 +26,19 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import scanpy as sc\n",
-    "import pandas as pd\n",
-    "import numpy as np\n",
+    "from __future__ import annotations\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",
     "FIGSIZE = (3, 3)\n",
     "rcParams[\"figure.figsize\"] = FIGSIZE"
    ]
@@ -217,9 +219,7 @@
     "# 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 +438,7 @@
     "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])"
@@ -685,9 +683,7 @@
     "# 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)"
@@ -738,7 +734,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": null,
    "id": "7d9b11fb",
    "metadata": {},
    "outputs": [
@@ -756,6 +752,7 @@
    "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 +773,7 @@
     "            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",
@@ -909,13 +904,12 @@
     "# 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",
@@ -1095,9 +1089,7 @@
     "# 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",
+    "sc.pl.umap(adata, color=\"B_cell_score\", cmap=\"coolwarm\", s=20, vmin=-maxabs, vmax=maxabs)\n",
     "adata.obs.drop(\"B_cell_score\", axis=1, inplace=True)"
    ]
   },
@@ -1175,13 +1167,11 @@
     "        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",
@@ -1351,9 +1341,7 @@
    ],
    "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",
@@ -1362,9 +1350,7 @@
     "# 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",
-    ")"
+    "sc.pl.umap(adata[random_indices, :], color=\"batch\", ax=axs[1], title=\"Random re-ordering\")"
    ]
   },
   {
@@ -1415,15 +1401,14 @@
    "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 +1550,7 @@
    "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 43d5ed6..599d726 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -39,14 +39,34 @@ 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/" ]
+line-length = 120
+lint.select = [ "ALL" ]
 lint.ignore = [
-  "E741", # Fonts these days don’t display I and l in a confusable way
-  "F821", # R cells can sometimes define variables
+  "C408",    # dict() calls are nice
+  "COM812",  # trailing commas handled by black
+  "D203",    # prefer 0 to 1 blank line before class members
+  "D213",    # prefer docstring summary on first line
+  "FIX002",  # “TODO” comments
+  "PLR0913", # having many (kw)args is fine
+  "Q",       # handled by formatter
+  "S",       # no need to worry about security here
 ]
+lint.per-file-ignores.'**/*.ipynb' = [
+  "D",       # no module docstrings for notebooks
+  "F821",    # `%%R -o` means that ruff doesn’t understand which variables exist
+  "PLR2004", # Magic numbers are sadly common
+  "T201",    # Allow print in notebooks
+]
+lint.allowed-confusables = [ "×", "’" ]
+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,

From d57e742f8048392cfe21bf1ff161bae1520638c8 Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Tue, 9 Sep 2025 13:47:47 +0000
Subject: [PATCH 4/5] [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci
---
 pyproject.toml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pyproject.toml b/pyproject.toml
index 599d726..0c71ef7 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -47,8 +47,8 @@ extra-dependencies = [
 ]
 
 [tool.ruff]
-extend-exclude = [ "scanpy_workshop/" ]
 line-length = 120
+extend-exclude = [ "scanpy_workshop/" ]
 lint.select = [ "ALL" ]
 lint.ignore = [
   "C408",    # dict() calls are nice

From e6afbe4cbd84b2de87825a1610ec3d8dea14fd2b Mon Sep 17 00:00:00 2001
From: "Philipp A." <flying-sheep@web.de>
Date: Tue, 9 Sep 2025 16:12:27 +0200
Subject: [PATCH 5/5] rest

---
 basic-scrna-tutorial.ipynb          |  5 ++-
 conf.py                             | 13 ++++---
 how-to/plotting-with-marsilea.ipynb |  4 +-
 paga-paul15.ipynb                   | 24 ++++++------
 pbmc3k.ipynb                        |  4 +-
 plotting/advanced.ipynb             | 59 ++++++++++++++++++-----------
 pyproject.toml                      | 13 +++----
 7 files changed, 67 insertions(+), 55 deletions(-)
 mode change 100755 => 100644 pbmc3k.ipynb

diff --git a/basic-scrna-tutorial.ipynb b/basic-scrna-tutorial.ipynb
index 24fe17d..d5ea25d 100644
--- a/basic-scrna-tutorial.ipynb
+++ b/basic-scrna-tutorial.ipynb
@@ -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)"
    ]
   },
diff --git a/conf.py b/conf.py
index 2fdeee0..ecd7d99 100644
--- a/conf.py
+++ b/conf.py
@@ -1,9 +1,9 @@
 from __future__ import annotations
 
-from datetime import datetime
+from datetime import UTC, datetime
 from importlib.metadata import metadata
 from types import MappingProxyType
-from typing import TYPE_CHECKING
+from typing import TYPE_CHECKING, ClassVar
 
 from docutils import nodes
 from sphinx import addnodes
@@ -24,7 +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 = [
@@ -52,7 +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 ----------------------------------------------
@@ -92,6 +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),
@@ -100,8 +100,8 @@ 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
@@ -147,6 +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/how-to/plotting-with-marsilea.ipynb b/how-to/plotting-with-marsilea.ipynb
index 0241452..684713f 100644
--- a/how-to/plotting-with-marsilea.ipynb
+++ b/how-to/plotting-with-marsilea.ipynb
@@ -533,7 +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, strict=True):\n",
+    "for row in exp.T:\n",
     "    area = mp.Area(\n",
     "        row,\n",
     "        add_outline=False,\n",
@@ -583,7 +583,7 @@
     "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",
diff --git a/paga-paul15.ipynb b/paga-paul15.ipynb
index 6df6336..0419191 100644
--- a/paga-paul15.ipynb
+++ b/paga-paul15.ipynb
@@ -44,13 +44,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "from __future__ import annotations\n",
     "\n",
-    "import matplotlib.pyplot as pl\n",
+    "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
     "import scanpy as sc"
    ]
@@ -790,7 +790,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -810,10 +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()"
    ]
   },
   {
@@ -1072,7 +1072,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -1102,8 +1102,8 @@
     }
    ],
    "source": [
-    "_, axs = pl.subplots(ncols=3, figsize=(6, 2.5), gridspec_kw={\"wspace\": 0.05, \"left\": 0.12})\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",
@@ -1115,7 +1115,7 @@
     "        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",
@@ -1125,8 +1125,8 @@
     "        show=False,\n",
     "    )\n",
     "    data.to_csv(f\"./write/paga_path_{descr}.csv\")\n",
-    "pl.savefig(\"./figures/paga_path_paul15.pdf\")\n",
-    "pl.show()"
+    "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 768fc7d..c07797a
--- a/pbmc3k.ipynb
+++ b/pbmc3k.ipynb
@@ -486,7 +486,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -502,7 +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"
    ]
   },
diff --git a/plotting/advanced.ipynb b/plotting/advanced.ipynb
index 51a3618..35b1653 100644
--- a/plotting/advanced.ipynb
+++ b/plotting/advanced.ipynb
@@ -21,13 +21,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "id": "641629fa",
    "metadata": {},
    "outputs": [],
    "source": [
     "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",
@@ -39,6 +41,8 @@
     "from matplotlib import rcParams\n",
     "\n",
     "\n",
+    "rng = np.random.default_rng()\n",
+    "\n",
     "FIGSIZE = (3, 3)\n",
     "rcParams[\"figure.figsize\"] = FIGSIZE"
    ]
@@ -663,7 +667,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "id": "7112d43f",
    "metadata": {},
    "outputs": [
@@ -686,7 +690,7 @@
     "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\"]"
    ]
   },
   {
@@ -878,7 +882,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": null,
    "id": "de3b2707",
    "metadata": {},
    "outputs": [
@@ -934,8 +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",
@@ -1036,7 +1040,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": null,
    "id": "7f79cb3d",
    "metadata": {},
    "outputs": [
@@ -1056,16 +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": [
@@ -1090,7 +1094,7 @@
     "# the negative value of maxabs\n",
     "maxabs = max(abs(adata.obs[\"B_cell_score\"]))\n",
     "sc.pl.umap(adata, color=\"B_cell_score\", cmap=\"coolwarm\", s=20, vmin=-maxabs, vmax=maxabs)\n",
-    "adata.obs.drop(\"B_cell_score\", axis=1, inplace=True)"
+    "del adata.obs[\"B_cell_score\"]"
    ]
   },
   {
@@ -1103,7 +1107,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": null,
    "id": "bdedcfc0",
    "metadata": {},
    "outputs": [
@@ -1122,18 +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": [
@@ -1149,6 +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",
@@ -1159,11 +1166,18 @@
     "# 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",
@@ -1203,13 +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\"]"
    ]
   },
   {
@@ -1324,7 +1338,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": null,
    "id": "d117c8cb",
    "metadata": {},
    "outputs": [
@@ -1348,8 +1362,7 @@
     "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",
+    "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\")"
    ]
   },
diff --git a/pyproject.toml b/pyproject.toml
index 0c71ef7..2a378a1 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -53,20 +53,17 @@ lint.select = [ "ALL" ]
 lint.ignore = [
   "C408",    # dict() calls are nice
   "COM812",  # trailing commas handled by black
-  "D203",    # prefer 0 to 1 blank line before class members
-  "D213",    # prefer docstring summary on first line
+  "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
-]
-lint.per-file-ignores.'**/*.ipynb' = [
-  "D",       # no module docstrings for notebooks
-  "F821",    # `%%R -o` means that ruff doesn’t understand which variables exist
-  "PLR2004", # Magic numbers are sadly common
   "T201",    # Allow print in notebooks
 ]
-lint.allowed-confusables = [ "×", "’" ]
+lint.allowed-confusables = [ "×", "’", "l" ]
 lint.isort.known-first-party = [ "anndata2ri" ]
 lint.isort.lines-after-imports = 2
 lint.isort.required-imports = [ "from __future__ import annotations" ]