boxes for introduction

jupyter-book/introduction/analysis_tools.ipynb

@@ -1,12 +1,30 @@
 {
  "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "(introduction:analysis-frameworks)=\n",
+    "\n",
+    "# Analysis frameworks and tools"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "``````{admonition} How do I set up an environment with the yml file used in this chapter?\n",
-    ":class: dropdown\n",
+    "```{dropdown} <i class=\"fas fa-brain\"></i>&nbsp;&nbsp;&nbsp;Key takeaways\n",
+    "\n",
+    ":::{card}\n",
+    ":link: introduction-analysis_tools-key-takeaway-1\n",
+    ":link-type: ref\n",
+    "The scverse ecosystem is preferred for single-cell analysis due to its scalability, Python interoperability, and consistent data structures.\n",
+    ":::\n",
+    "\n",
+    "```\n",
     "\n",
+    "``````{dropdown} <i class=\"fa-solid fa-gear\"></i>&nbsp;&nbsp;&nbsp;Environment setup\n",
     "`````{tab-set}\n",
     "   \n",
     "````{tab-item} Steps\n",
@@ -21,24 +39,14 @@
     "````\n",
     "\n",
     "`````\n",
-    "\n",
     "``````"
    ]
   },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "(introduction:analysis-frameworks)=\n",
-    "\n",
-    "# Analysis frameworks and tools"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
+    "(introduction-analysis_tools-key-takeaway-1)=\n",
     "## Single-cell analysis frameworks and consortia"
    ]
   },

jupyter-book/introduction/interoperability.ipynb

@@ -4,9 +4,42 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "``````{admonition} How do I set up an environment with the yml file used in this chapter?\n",
-    ":class: dropdown\n",
+    "# Interoperability"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "```{dropdown} <i class=\"fas fa-brain\"></i>&nbsp;&nbsp;&nbsp;Key takeaways\n",
+    "\n",
+    ":::{card}\n",
+    ":link: introduction-interoperability-key-takeaway-1\n",
+    ":link-type: ref\n",
+    "Interoperabilty between languages allows analysts to take advantage of the strengths of different ecosystems.\n",
+    ":::\n",
     "\n",
+    ":::{card}\n",
+    ":link: introduction-interoperability-key-takeaway-2\n",
+    ":link-type: ref\n",
+    "On-disk interoperability uses standard file formats to transfer data and is typically more reliable.\n",
+    ":::\n",
+    "\n",
+    ":::{card}\n",
+    ":link: introduction-interoperability-key-takeaway-3\n",
+    ":link-type: ref\n",
+    "In-memory interoperabilty transfers data directly between parallel sessions and is convenient for interactive analysis.\n",
+    ":::\n",
+    "\n",
+    ":::{card}\n",
+    ":link: introduction-interoperability-key-takeaway-4\n",
+    ":link-type: ref\n",
+    "While interoperability is currently possible developers continue to improve the experience.\n",
+    ":::\n",
+    "\n",
+    "```\n",
+    "\n",
+    "``````{dropdown} <i class=\"fa-solid fa-gear\"></i>&nbsp;&nbsp;&nbsp;Environment setup\n",
     "`````{tab-set}\n",
     "   \n",
     "````{tab-item} Steps\n",
@@ -21,34 +54,15 @@
     "````\n",
     "\n",
     "`````\n",
-    "\n",
     "``````"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Interoperability"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "```{admonition} Summary\n",
-    "\n",
-    "- Interoperabilty between languages allows analysts to take advantage of the strengths of different ecosystems\n",
-    "- On-disk interoperability uses standard file formats to transfer data and is typically more reliable\n",
-    "- In-memory interoperabilty transfers data directly between parallel sessions and is convenient for interactive analysis\n",
-    "- While interoperability is currently possible developers continue to improve the experience\n",
-    "```"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
+    "(introduction-interoperability-key-takeaway-1)=\n",
+    "(introduction-interoperability-key-takeaway-4)=\n",
     "## Motivation"
    ]
   },
@@ -118,6 +132,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
+    "(introduction-interoperability-key-takeaway-2)=\n",
     "### Simple formats"
    ]
   },
@@ -609,6 +624,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
+    "(introduction-interoperability-key-takeaway-3)=\n",
     "## In-memory interoperability"
    ]
   },

jupyter-book/introduction/prior_art.md

@@ -1,9 +1,27 @@
 # Prior art
 
+```{dropdown} <i class="fas fa-brain"></i>   Key takeaways
+
+:::{card}
+:link: introduction-prior-art-key-takeaway-1
+:link-type: ref
+The Bioconductor OSCA book provides comprehensive scRNA-seq analysis workflows using R, but is limited to Bioconductor tools and excludes other single-cell omics or spatial transcriptomics.
+:::
+
+:::{card}
+:link: introduction-prior-art-key-takeaway-2
+:link-type: ref
+The Luecken and Theis tutorial offers scRNA-seq best practices but has outdated examples and omits newer techniques like RNA velocity or spatial transcriptomics.
+:::
+
+```
+
 Single-cell analysis has evolving from a niche area of interest into a well-established field of study.
 As a result, we are certainly not the first to produce a book on this subject, nor to provide guides and tutorials.
 In the sections that follow, we review two notable and ongoing initiatives aimed at teaching single-cell analysis, highlighting both their similarities to and differences from this book.
 
+(introduction-prior-art-key-takeaway-1)=
+
 ## Bioconductor OSCA and OSTA books
 
 Orchestrating Single-Cell Analysis with Bioconductor (Bioconductor OSCA) {cite}`osca`, available online at https://bioconductor.org/books/release/OSCA/, is an online book designed to teach common workflows for analyzing of single-cell {term}`RNA`-Sequencing (scRNA-seq) data using the R based Bioconductor ecosystem {cite}`pa:Huber2015`.
@@ -17,6 +35,8 @@ As both books are tailored to the Bioconductor ecosystem, they exclusively use t
 While these tools are highly effective, they may not always provide the most optimal solution for every analysis, as acknowledged by the books themselves.
 Overall, the Bioconductor books are particularly well-suited for individuals with a foundational knowledge of R and a strong background in biology who wish to learn how to analyze single-cell and spatial transcriptomics data within the Bioconductor framework.
 
+(introduction-prior-art-key-takeaway-2)=
+
 ## Current best practices in single-cell RNA-seq analysis: a tutorial
 
 Current Best Practices in Single-Cell {term}`RNA`-Seq Analysis: A Tutorial {cite}`pa:Lücken2019` by Malte Lücken and Fabian Theis introduces best practices for scRNA-seq analysis.

jupyter-book/introduction/raw_data_processing.md

@@ -2,6 +2,37 @@
 
 # Raw data processing
 
+```{dropdown} <i class="fas fa-brain"></i>   Key takeaways
+
+:::{card}
+:link: introduction-raw-data-processing-key-takeaway-1
+:link-type: ref
+Accurate raw data processing transforms FASTQ files into a count matrix, enabling reliable single-cell sequencing analyses through read alignment, barcode correction, and UMI counting.
+:::
+
+```
+
+``````{dropdown} <i class="fa-solid fa-gear"></i>   Environment setup
+`````{tab-set}
+
+````{tab-item} Steps
+```{include} ../_static/default_text_env_setup.md
+```
+````
+
+````{tab-item} yml
+```{literalinclude} differential_gene_expression.yml
+:language: yaml
+```
+````
+
+`````
+``````
+
+(introduction-raw-data-processing-key-takeaway-1)=
+
+## Motivation
+
 Raw data processing in single-cell sequencing converts sequencing machine output (so-called lane-demultiplexed {term}`FASTQ` files) into readily analyzable representations such as a count matrix.
 This matrix represents the estimated number of distinct molecules derived from each gene per quantified cell, sometimes categorized by the inferred splicing status of each molecule ({numref}`raw-proc-fig-overview`).