first two boxes for chromatin-accessibility

jupyter-book/chromatin_accessibility/gene_regulatory_networks_atac.ipynb

@@ -2,12 +2,34 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "99c06074",
+   "id": "9b419960",
    "metadata": {},
    "source": [
-    "``````{admonition} How do I set up an environment with the yml file used in this chapter?\n",
-    ":class: dropdown\n",
+    "# Gene regulatory networks using chromatin accessibility"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6dedf1bb",
+   "metadata": {},
+   "source": [
+    "```{dropdown} <i class=\"fas fa-brain\"></i>&nbsp;&nbsp;&nbsp;Key takeaways\n",
     "\n",
+    ":::{card}\n",
+    ":link: chromatin-accessibility-gene-regulatory-networks-atac-key-takeaway-1\n",
+    ":link-type: ref\n",
+    "We prepared an RNA and ATAC object using R, for processing with FigR and CisTopic.\n",
+    ":::\n",
+    "\n",
+    ":::{card}\n",
+    ":link: chromatin-accessibility-gene-regulatory-networks-atac-key-takeaway-2\n",
+    ":link-type: ref\n",
+    "We calculated DORC scores with FigR, and visualized those as scatter, heatmap and networks.\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",
@@ -22,7 +44,6 @@
     "````\n",
     "\n",
     "`````\n",
-    "\n",
     "``````"
    ]
   },
@@ -36,8 +57,6 @@
     }
    },
    "source": [
-    "# Gene regulatory networks using chromatin accessibility\n",
-    "\n",
     "## Motivation\n",
     "\n",
     "Analyzing chromatin accessibility and gene expression together to understand gene regulation is helpful due to the mechanistic relationship between those two during the control of gene regulation, mediated through transcription factors (TFs) and other epigenetic modulators {cite}`atac:Spitz2012-sw`. Briefly, regulatory regions annotated as promoters and local/distal enhancers are engaged during the early phases of gene expression regulation, and chromatin accessibility increase, or decrease, can be used as a proxy for changes in their activity. Hence, the global positive or negative correlation between proximal and distal accessible elements (measured by ATAC-seq) and target genes (measured by RNA-seq) within a genome neighborhood distance (e.g. less than 200 Kbp), serves to annotate genomic regulatory relationships during the inference of Gene Regulatory Networks (GRNs). Using sequencing data describing gene (RNA) and peak (ATAC) features, tools that build correlation matrices between peaks and matrices help summarize strong peak-gene interactions.\n",
@@ -116,6 +135,7 @@
     }
    },
    "source": [
+    "(chromatin-accessibility-gene-regulatory-networks-atac-key-takeaway-1)=\n",
     "### Use zellkonverter to convert h5ad to SingleCellExperiment"
    ]
   },
@@ -488,6 +508,7 @@
     }
    },
    "source": [
+    "(chromatin-accessibility-gene-regulatory-networks-atac-key-takeaway-2)\n",
     "### Preparation and execution of cisTopic"
    ]
   },
@@ -2875,23 +2896,6 @@
     "sessionInfo()"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "id": "5665b4da",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "## Takeaways\n",
-    "\n",
-    "In this notebook, we have:\n",
-    "\n",
-    "1. Prepared an RNA and ATAC object using R, for processing with FigR and CisTopic.\n",
-    "2. Calculated DORC scores with FigR, and visualized those as scatter, heatmap and networks."
-   ]
-  },
   {
    "attachments": {},
    "cell_type": "markdown",

jupyter-book/chromatin_accessibility/introduction.ipynb

@@ -8,13 +8,36 @@
     "# Single-cell ATAC sequencing"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "b3502848",
+   "metadata": {},
+   "source": [
+    "```{dropdown} <i class=\"fas fa-brain\"></i>&nbsp;&nbsp;&nbsp;Key takeaways\n",
+    "\n",
+    ":::{card}\n",
+    ":link: chromatin-accessibility-introductions-atac-key-takeaway-1\n",
+    ":link-type: ref\n",
+    "Chromatin accessibility is a key determinant of cell identity, providing an orthogonal layer of information to gene expression profiles. It reflects the combined regulatory state of a cell, including DNA methylation, histone modifications, and transcription factor activity, which collectively influence gene expression and cell differentiation processes. \n",
+    ":::\n",
+    "\n",
+    ":::{card}\n",
+    ":link: chromatin-accessibility-introduction-atac-key-takeaway-2\n",
+    ":link-type: ref\n",
+    "ScATAC-seq data is highly sparse due to the limited number of DNA copies per cell, leading to many features with zero counts. Defining biologically meaningful features, such as peaks or genome bins, is crucial for analysis, but it can be challenging to capture cell-type-specific accessibility. \n",
+    ":::\n",
+    "\n",
+    "```"
+   ]
+  },
   {
    "cell_type": "markdown",
    "id": "c59fb48c-e6f5-470b-831a-3018c1b108f9",
    "metadata": {
     "tags": []
    },
    "source": [
+    "(chromatin-accessibility-introductions-atac-key-takeaway-1)=\n",
     "## Motivation\n",
     "\n",
     "Every cell of an organism shares the same DNA with the same set of functional units referred to as genes. With this in mind, what determines the tremendous diversity of cells reaching from natural killer cells of the immune system to neurons transmitting electrochemical signals throughout the body? In the previous chapters, we saw that cell identity and function can be inferred from gene expression profiles in each cell. The control of gene expression is driven by a complex interplay of regulatory mechanisms such as DNA methylation, histone modifications, and transcription factor activity. {term}`Chromatin` accessibility largely reflects the combined regulatory state of a cell, serving as an orthogonal layer of information to mRNA levels describing cell identity. Furthermore, exploring the chromatin accessibility profile enables additional insights into gene regulatory mechanisms and cell differentiation processes that might not be captured by scRNA-seq data."
@@ -88,6 +111,7 @@
    "id": "bca4f6e7-bc0d-4c2e-a284-a274637dad7f",
    "metadata": {},
    "source": [
+    "(chromatin-accessibility-introduction-atac-key-takeaway-2)=\n",
     "## Data characteristics - feature definition and sparsity \n",
     "\n",
     "Single-cell ATAC-seq data measures chromatin accessibility across the entire genome. Since this includes coding and non-coding regions, genes can not be used as pre-defined features, as is the case for scRNA-seq data. Instead, the most common approach to define biologically meaningful features is detecting regions of high accessibility compared to a background - i.e. peaks in the distribution of fragment counts along the genome. Peaks in coding regions indicate that a gene might be transcribed, while in non-coding regions, accessibility is seen as a prerequisite or result for the binding of regulatory proteins such as transcription factors. However, calling peaks on all cells of a dataset can hide cell-type specific accessibility or accessibility profiles of rare cell types. Therefore, a proposed solution is to call cluster-specific peaks which requires prior peak-independent clustering of the cells. SnapATAC{cite}`atac:fang_comprehensive_2021` and ArchR{cite}`atac:granja_archr_2021` suggest a binning strategy, that creates features by dividing the entire genome into uniformly sized windows and using this feature set for clustering of the cells. \n",