From d398bb530254a31554cb3a92947ed31078ed8a36 Mon Sep 17 00:00:00 2001
From: Quarto GHA Workflow Runner <quarto-github-actions-publish@example.com>
Date: Tue, 30 Apr 2024 21:42:37 +0000
Subject: [PATCH] Built site for gh-pages

---
 .nojekyll                                     |   2 +-
 index-preview.html                            |   2 +-
 index.embed.ipynb                             |  44 +++---
 index.html                                    |   2 +-
 index.out.ipynb                               |  44 +++---
 .../samples_with_mut.jpeg                     | Bin 0 -> 628128 bytes
 notebooks/TCGA-preview.html                   |  64 ++++++++-
 notebooks/TCGA.embed.ipynb                    | 130 +++++++++++++-----
 notebooks/TCGA.out.ipynb                      | 127 ++++++++++++-----
 notebooks/TCGA.qmd                            |  28 +++-
 .../TCGA_files/figure-html/fig-genesmut-1.png | Bin 168636 -> 120579 bytes
 notebooks/images/samples_with_mut.jpeg        | Bin 0 -> 628128 bytes
 12 files changed, 324 insertions(+), 119 deletions(-)
 create mode 100644 index_files/figure-html/71803f22-7a18-4d2c-a605-e1fab7827635-1-images/samples_with_mut.jpeg
 create mode 100644 notebooks/images/samples_with_mut.jpeg

diff --git a/.nojekyll b/.nojekyll
index 33b45e1..5b7d9e0 100644
--- a/.nojekyll
+++ b/.nojekyll
@@ -1 +1 @@
-36ac1e8f
\ No newline at end of file
+9f041e84
\ No newline at end of file
diff --git a/index-preview.html b/index-preview.html
index e675e8b..446577c 100644
--- a/index-preview.html
+++ b/index-preview.html
@@ -1986,7 +1986,7 @@ <h2 class="unnumbered anchored" data-anchor-id="references">References</h2>
     }
   }
 });
-</script>  </div> <!-- /content -->  <script>var lightboxQuarto = GLightbox({"openEffect":"zoom","loop":false,"closeEffect":"zoom","descPosition":"bottom","selector":".lightbox"});
+</script>  </div> <!-- /content -->  <script>var lightboxQuarto = GLightbox({"selector":".lightbox","loop":false,"openEffect":"zoom","closeEffect":"zoom","descPosition":"bottom"});
 window.onload = () => {
   lightboxQuarto.on('slide_before_load', (data) => {
     const { slideIndex, slideNode, slideConfig, player, trigger } = data;
diff --git a/index.embed.ipynb b/index.embed.ipynb
index 7f2814f..7413215 100644
--- a/index.embed.ipynb
+++ b/index.embed.ipynb
@@ -17,7 +17,7 @@
     "\n",
     "Here, we are gonna analyze the relation between transcription factor binding (ESRRA binding data) from a ChIP-Seq experiment and the genome-wide associations between DNA variants and phenotypes like diseases. For this task, we are gonna use a the `gwascat` package distributed by the **EMBL** (European Molecular Biology Laboratories)."
    ],
-   "id": "6d48891b-0b50-4e37-b75f-1be41679e65c"
+   "id": "c9542c03-479e-4eb8-9c4c-72c2d488d5ad"
   },
   {
    "cell_type": "code",
@@ -286,7 +286,7 @@
    "source": [
     "library(tidyverse)"
    ],
-   "id": "afefebd8-90ea-40e7-8e37-0fe2a3a29aa5"
+   "id": "f9491474-86c9-400d-852f-31418e2c06e6"
   },
   {
    "cell_type": "markdown",
@@ -294,7 +294,7 @@
    "source": [
     "First, we need to download the data, keep the 24 chromosomes (from 1 to Y) and, specify the sequence information from the GRCh38 human genome annotation."
    ],
-   "id": "06766732-09bb-41ee-8f2c-12153d291af9"
+   "id": "36a09b02-03f8-491d-be8c-a333f8f358e0"
   },
   {
    "cell_type": "code",
@@ -328,7 +328,7 @@
     "\n",
     "gg = gwcat |> as_GRanges()"
    ],
-   "id": "1a80f3df-dff0-4a54-8bdb-decccb4528ea"
+   "id": "cc6c4e03-4cb2-4dca-97de-f6cd9a526dd5"
   },
   {
    "cell_type": "markdown",
@@ -336,7 +336,7 @@
    "source": [
     "Now, let’s plot a karyogram that will show the SNP’s identified with significant associations with a phenotype. The SNP’s in the GWAS catalog have a stringent criterion of significance and there has been a replication of the finding from a independent population."
    ],
-   "id": "9029301e-45aa-4a4d-b47a-eb6b80ac90d7"
+   "id": "da1e2503-e905-44aa-a794-71795dc1462e"
   },
   {
    "cell_type": "code",
@@ -373,7 +373,7 @@
    "source": [
     "ggbio::autoplot(gg, layout=\"karyogram\")"
    ],
-   "id": "86fad0db-f30c-499d-980c-d45b0d765f35"
+   "id": "a320a4ee-9be4-4a7f-aee2-c88810dfa505"
   },
   {
    "cell_type": "markdown",
@@ -381,7 +381,7 @@
    "source": [
     "We can see the peak data as a `GRanges` object:"
    ],
-   "id": "cdfc9bd1-b9d4-4fe6-bc61-8c4ddaa92741"
+   "id": "e06215a6-5ccc-4508-b45c-47ddee34bf11"
   },
   {
    "cell_type": "code",
@@ -429,7 +429,7 @@
     "\n",
     "GM12878"
    ],
-   "id": "675c80c3-02be-4dff-8ccc-742a2026d3cb"
+   "id": "a1e90532-61db-4d43-b185-21abfae6323b"
   },
   {
    "cell_type": "markdown",
@@ -437,7 +437,7 @@
    "source": [
     "If we see the bottom of the `GRanges` table, this experiment have the hg19 annotation from the human genome. To work on the GRCh38 annotation we need to lift-over with a `.chain` file. For this we can use the `AnnotationHub` package."
    ],
-   "id": "8193a756-fe42-4d3c-9c7b-20d79253d0ec"
+   "id": "a750841d-6759-48b5-b987-606a796ccc7d"
   },
   {
    "cell_type": "code",
@@ -555,7 +555,7 @@
    "source": [
     "library(AnnotationHub)"
    ],
-   "id": "6809a5d8-babb-42a4-b1f8-6b939eca8966"
+   "id": "355858ef-b251-43fa-b072-5cce8176f216"
   },
   {
    "cell_type": "code",
@@ -574,7 +574,7 @@
     "seqlevelsStyle(GM12878) <- \"UCSC\"\n",
     "seqlevelsStyle(gg) <- \"UCSC\""
    ],
-   "id": "2c36d2a0-bd55-407b-bfb7-0fef593a6a92"
+   "id": "f8ec39e6-c645-4c32-9a88-1c9efed74ab2"
   },
   {
    "cell_type": "markdown",
@@ -584,7 +584,7 @@
     "\n",
     "We can see the duplications with the `reduce` function from `IRanges` package:"
    ],
-   "id": "35c4706f-6c4c-4d23-93ea-44a2ede5e86c"
+   "id": "3393032a-f01e-4406-8f95-88f91997849d"
   },
   {
    "cell_type": "code",
@@ -603,7 +603,7 @@
     "# duplicated loci\n",
     "length(gg) - length(reduce(gg))"
    ],
-   "id": "f7814240-0afd-4cf3-ad13-7d2761a59b5d"
+   "id": "c52b71c7-1d63-4277-99f4-47b951b75078"
   },
   {
    "cell_type": "markdown",
@@ -611,7 +611,7 @@
    "source": [
     "We can see that there are `261160` duplicated loci. Let’s find the overlap between the *reduced* catalog and the ChIP-Seq experiment:"
    ],
-   "id": "c7e6f250-e419-4b42-948a-61baa107c57b"
+   "id": "5d481a56-eeab-4466-b13d-5f68b20a844a"
   },
   {
    "cell_type": "code",
@@ -646,7 +646,7 @@
     "fo = findOverlaps(GM12878, reduce(gg))\n",
     "fo"
    ],
-   "id": "808066c3-160a-4dd6-8b70-edc34533775b"
+   "id": "4a785737-da11-4031-a821-ab5cc6460214"
   },
   {
    "cell_type": "markdown",
@@ -654,7 +654,7 @@
    "source": [
     "We can see 613 hits. Then, we are gonna eobtain the ranges from those hits, retrieve the phenotypes (DISEASE/TRAIT) and show the top 20 most common phenotypes with association to SNPs that lies on the ESRRA binding peaks."
    ],
-   "id": "8f2d4db1-4a15-4660-9a76-5f6802ec2175"
+   "id": "beffa472-15b4-4dda-a5ab-13991dfd9b0f"
   },
   {
    "cell_type": "code",
@@ -714,7 +714,7 @@
     "\n",
     "bslib::card(plotly::ggplotly(p), full_screen = T)"
    ],
-   "id": "9e3bf479-dc47-4746-9337-cfe15cb23f08"
+   "id": "c5a0a33e-21ed-491e-9e55-c3743705ba6f"
   },
   {
    "cell_type": "markdown",
@@ -722,7 +722,7 @@
    "source": [
     "Distinct phenotypes identified on the peaks:"
    ],
-   "id": "1f83d7e9-a886-4191-82fc-5febc0b85f86"
+   "id": "2f9761cd-a5d0-4e80-b60d-66446d59103c"
   },
   {
    "cell_type": "code",
@@ -740,7 +740,7 @@
    "source": [
     "length(phset)"
    ],
-   "id": "12f09563-f492-4057-b629-af92d278c961"
+   "id": "41f9c4a1-da3c-4682-982f-0d5cd16183d6"
   },
   {
    "cell_type": "markdown",
@@ -748,7 +748,7 @@
    "source": [
     "Now, how to do the inference of these phenotype on peaks of these b cells? We can use permutation on the genomic positions to test if the number of phenotypes found is due to chance or not."
    ],
-   "id": "98c76c90-adf7-4128-8874-c273749fb235"
+   "id": "c4a9d73f-aa30-41b5-85e7-3a746176a058"
   },
   {
    "cell_type": "code",
@@ -1020,7 +1020,7 @@
    "source": [
     "library(ph525x)"
    ],
-   "id": "a42538fb-3ee9-490d-8dfa-4fd60b495115"
+   "id": "383db3ba-3530-4594-bbdc-ae1b53cc7918"
   },
   {
    "cell_type": "markdown",
@@ -1674,7 +1674,7 @@
      "image/png": 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WuKwYCgFNUPoH2Xic5vrb1v0V7f4D9EPismIoBDTBZskyelkhoE1LXFYMhYAm2CxZRi8r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\nFCBIQAGC/j/qlDmd8aZwbQAAAABJRU5ErkJggg==\n"
     }
    },
-   "id": "39a8e811-1479-45d9-aee0-914a726ae94d"
+   "id": "71a2ff27-275c-4e68-be40-090285dcf1b0"
   }
  ],
  "nbformat": 4,
diff --git a/index.html b/index.html
index a8f40ee..8b28519 100644
--- a/index.html
+++ b/index.html
@@ -974,7 +974,7 @@ <h2 class="unnumbered anchored" data-anchor-id="references">References</h2>
 });
 </script>
 </div> <!-- /content -->
-<script>var lightboxQuarto = GLightbox({"selector":".lightbox","closeEffect":"zoom","loop":false,"openEffect":"zoom","descPosition":"bottom"});
+<script>var lightboxQuarto = GLightbox({"selector":".lightbox","loop":false,"closeEffect":"zoom","openEffect":"zoom","descPosition":"bottom"});
 window.onload = () => {
   lightboxQuarto.on('slide_before_load', (data) => {
     const { slideIndex, slideNode, slideConfig, player, trigger } = data;
diff --git a/index.out.ipynb b/index.out.ipynb
index 84136f5..8d4aa16 100644
--- a/index.out.ipynb
+++ b/index.out.ipynb
@@ -17,7 +17,7 @@
     "\n",
     "Here, we are gonna analyze the relation between transcription factor binding (ESRRA binding data) from a ChIP-Seq experiment and the genome-wide associations between DNA variants and phenotypes like diseases. For this task, we are gonna use a the `gwascat` package distributed by the **EMBL** (European Molecular Biology Laboratories)."
    ],
-   "id": "6096894f-6f1d-47a5-b3f6-60871a9219d7"
+   "id": "85fa3512-7c8d-4edd-a2dd-30538301e1e9"
   },
   {
    "cell_type": "code",
@@ -286,7 +286,7 @@
    "source": [
     "library(tidyverse)"
    ],
-   "id": "d30fd4f8-4a89-4398-97bc-6783b1987f71"
+   "id": "39efe127-75c7-401f-9f42-543e8cd959b9"
   },
   {
    "cell_type": "markdown",
@@ -294,7 +294,7 @@
    "source": [
     "First, we need to download the data, keep the 24 chromosomes (from 1 to Y) and, specify the sequence information from the GRCh38 human genome annotation."
    ],
-   "id": "57b84dab-9eb7-4b71-990c-ab61eee51658"
+   "id": "c19cf706-c969-4b3e-a34a-11c61720d0d2"
   },
   {
    "cell_type": "code",
@@ -328,7 +328,7 @@
     "\n",
     "gg = gwcat |> as_GRanges()"
    ],
-   "id": "bb316333-8772-49d1-a168-07705b659202"
+   "id": "f6029167-3a86-4621-97ab-8ae0d933aacc"
   },
   {
    "cell_type": "markdown",
@@ -336,7 +336,7 @@
    "source": [
     "Now, let’s plot a karyogram that will show the SNP’s identified with significant associations with a phenotype. The SNP’s in the GWAS catalog have a stringent criterion of significance and there has been a replication of the finding from a independent population."
    ],
-   "id": "940cbe64-ca8a-459d-9566-81f9972652d1"
+   "id": "8872de3a-4e89-4d2f-98ff-4d824f61cd08"
   },
   {
    "cell_type": "code",
@@ -373,7 +373,7 @@
    "source": [
     "ggbio::autoplot(gg, layout=\"karyogram\")"
    ],
-   "id": "bc12b642-dff9-4be3-bc03-5f0470e020a3"
+   "id": "fec023d4-db0e-49c3-8bc3-b264a4ed8f7f"
   },
   {
    "cell_type": "markdown",
@@ -381,7 +381,7 @@
    "source": [
     "We can see the peak data as a `GRanges` object:"
    ],
-   "id": "28c8cf00-1bee-4932-a1c7-40bb204b5dbd"
+   "id": "8f6efaf8-3874-438a-b2f1-c1123a9411df"
   },
   {
    "cell_type": "code",
@@ -429,7 +429,7 @@
     "\n",
     "GM12878"
    ],
-   "id": "04d98d45-fa2a-4bdf-b388-56e5b96c6a64"
+   "id": "9b18e2c3-8867-47de-826e-51651ed83052"
   },
   {
    "cell_type": "markdown",
@@ -437,7 +437,7 @@
    "source": [
     "If we see the bottom of the `GRanges` table, this experiment have the hg19 annotation from the human genome. To work on the GRCh38 annotation we need to lift-over with a `.chain` file. For this we can use the `AnnotationHub` package."
    ],
-   "id": "8267f5ef-b221-4e04-9277-3dd53e890946"
+   "id": "de1a55c0-dd3d-47a1-8ac1-d5048cc3c064"
   },
   {
    "cell_type": "code",
@@ -555,7 +555,7 @@
    "source": [
     "library(AnnotationHub)"
    ],
-   "id": "c43932c9-9a8b-429c-9640-17b19282768c"
+   "id": "28e30502-6bd7-4a60-9a12-3abe4fff3eb9"
   },
   {
    "cell_type": "code",
@@ -574,7 +574,7 @@
     "seqlevelsStyle(GM12878) <- \"UCSC\"\n",
     "seqlevelsStyle(gg) <- \"UCSC\""
    ],
-   "id": "c0569879-d3bd-4a4a-b66a-61c12a927785"
+   "id": "334e2fad-6319-459b-81db-62538c3365d0"
   },
   {
    "cell_type": "markdown",
@@ -584,7 +584,7 @@
     "\n",
     "We can see the duplications with the `reduce` function from `IRanges` package:"
    ],
-   "id": "b7304a31-ff70-4221-8e0b-5b471dac487a"
+   "id": "6c41a903-0483-4c84-b2ce-0d735dd7ea21"
   },
   {
    "cell_type": "code",
@@ -603,7 +603,7 @@
     "# duplicated loci\n",
     "length(gg) - length(reduce(gg))"
    ],
-   "id": "feff87c4-cfbd-4e70-b3a1-1d7823674c48"
+   "id": "41a00344-9e2c-4293-b056-6f5670b2a4ec"
   },
   {
    "cell_type": "markdown",
@@ -611,7 +611,7 @@
    "source": [
     "We can see that there are `261160` duplicated loci. Let’s find the overlap between the *reduced* catalog and the ChIP-Seq experiment:"
    ],
-   "id": "79fede56-38ad-4333-86a6-dc8fa99ec81e"
+   "id": "08b21c6b-7a93-48b2-b456-f4716e7fba32"
   },
   {
    "cell_type": "code",
@@ -646,7 +646,7 @@
     "fo = findOverlaps(GM12878, reduce(gg))\n",
     "fo"
    ],
-   "id": "76afe7c0-65e5-4f89-9d13-f11f6793c369"
+   "id": "fb0303e3-4606-4ba7-a8c5-de8966da7461"
   },
   {
    "cell_type": "markdown",
@@ -654,7 +654,7 @@
    "source": [
     "We can see 613 hits. Then, we are gonna eobtain the ranges from those hits, retrieve the phenotypes (DISEASE/TRAIT) and show the top 20 most common phenotypes with association to SNPs that lies on the ESRRA binding peaks."
    ],
-   "id": "fbd711a1-5921-4737-a7d9-1b2223e7ad8a"
+   "id": "92a3bf61-b7d7-4b1f-9915-1d8a97bd9ca4"
   },
   {
    "cell_type": "code",
@@ -714,7 +714,7 @@
     "\n",
     "bslib::card(plotly::ggplotly(p), full_screen = T)"
    ],
-   "id": "9f0af5e2-79f7-4c9d-9e10-895acd4d7e18"
+   "id": "26564221-af92-4eae-b4ed-b1d5b378ddf1"
   },
   {
    "cell_type": "markdown",
@@ -722,7 +722,7 @@
    "source": [
     "Distinct phenotypes identified on the peaks:"
    ],
-   "id": "ab3e9f45-7da4-4cf5-9d0d-e154941c4c29"
+   "id": "e2ed3c04-af0c-43d0-ba17-147ebb9f9cbf"
   },
   {
    "cell_type": "code",
@@ -740,7 +740,7 @@
    "source": [
     "length(phset)"
    ],
-   "id": "2f9b30e4-d70c-41bf-a498-4cb491e7a113"
+   "id": "3e92f3f5-745c-4515-85c8-1f21efdffe00"
   },
   {
    "cell_type": "markdown",
@@ -748,7 +748,7 @@
    "source": [
     "Now, how to do the inference of these phenotype on peaks of these b cells? We can use permutation on the genomic positions to test if the number of phenotypes found is due to chance or not."
    ],
-   "id": "a0691927-5a9e-4e75-9731-a6ebb7783b3b"
+   "id": "ad49be3e-83f3-4833-964e-7b510fb9e842"
   },
   {
    "cell_type": "code",
@@ -1020,7 +1020,7 @@
    "source": [
     "library(ph525x)"
    ],
-   "id": "b4461ad3-15e6-4dee-b03d-4fca1c03b307"
+   "id": "daab8d2c-845f-4547-ab08-d88952a97fe1"
   },
   {
    "cell_type": "markdown",
@@ -1674,7 +1674,7 @@
      "image/png": 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WuKwYCgFNUPoH2Xic5vrb1v0V7f4D9EPismIoBDTBZskyelkhoE1LXFYMhYAm2CxZRi8r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\nOaAPvpmerQf04rh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diff --git a/notebooks/TCGA-preview.html b/notebooks/TCGA-preview.html
index 9914234..2f30a91 100644
--- a/notebooks/TCGA-preview.html
+++ b/notebooks/TCGA-preview.html
@@ -156,6 +156,9 @@
 <script src="../site_libs/bootstrap/bootstrap.min.js"></script>
 <link href="../site_libs/bootstrap/bootstrap-icons.css" rel="stylesheet">
 <link href="../site_libs/bootstrap/bootstrap.min.css" rel="stylesheet" id="quarto-bootstrap" data-mode="light">
+<script src="../site_libs/quarto-contrib/glightbox/glightbox.min.js"></script>
+<link href="../site_libs/quarto-contrib/glightbox/glightbox.min.css" rel="stylesheet">
+<link href="../site_libs/quarto-contrib/glightbox/lightbox.css" rel="stylesheet">
      <script async="" src="https://hypothes.is/embed.js"></script>
 <script>
   window.document.addEventListener("DOMContentLoaded", function (_event) {
@@ -1174,6 +1177,41 @@ <h2 class="anchored" data-anchor-id="analyzing-mutation-data">Analyzing Mutation
 </div>
 </div></div>
 <p>We can visualize the most common mutated genes genes in rectum adenocarcinoma</p>
+<div class="cell-container"><div class="cell-decorator"><pre>In [18]:</pre></div><div id="cell-fig-genesmut" class="cell">
+<div class="sourceCode cell-code" id="cb152"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb152-1"><a href="#cb152-1" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb152-2"><a href="#cb152-2" aria-hidden="true" tabindex="-1"></a>p <span class="ot">&lt;-</span> var_class_df <span class="sc">|&gt;</span> </span>
+<span id="cb152-3"><a href="#cb152-3" aria-hidden="true" tabindex="-1"></a>  <span class="fu">as_tibble</span>() <span class="sc">|&gt;</span> </span>
+<span id="cb152-4"><a href="#cb152-4" aria-hidden="true" tabindex="-1"></a>  <span class="fu">group_by</span>(symbol,variant_class) <span class="sc">|&gt;</span> </span>
+<span id="cb152-5"><a href="#cb152-5" aria-hidden="true" tabindex="-1"></a>  <span class="fu">summarise</span>(<span class="at">n =</span> <span class="fu">n</span>()) <span class="sc">|&gt;</span> </span>
+<span id="cb152-6"><a href="#cb152-6" aria-hidden="true" tabindex="-1"></a>  <span class="fu">arrange</span>(<span class="fu">desc</span>(n)) <span class="sc">|&gt;</span> </span>
+<span id="cb152-7"><a href="#cb152-7" aria-hidden="true" tabindex="-1"></a>  <span class="fu">ungroup</span>() <span class="sc">|&gt;</span> </span>
+<span id="cb152-8"><a href="#cb152-8" aria-hidden="true" tabindex="-1"></a>  <span class="fu">slice_max</span>(<span class="at">order_by =</span> n, <span class="at">n =</span> <span class="dv">20</span>) <span class="sc">|&gt;</span> </span>
+<span id="cb152-9"><a href="#cb152-9" aria-hidden="true" tabindex="-1"></a>  <span class="fu">ungroup</span>() <span class="sc">|&gt;</span> </span>
+<span id="cb152-10"><a href="#cb152-10" aria-hidden="true" tabindex="-1"></a>  <span class="fu">mutate</span>(<span class="at">symbol =</span> <span class="fu">fct_reorder</span>(symbol,n)) <span class="sc">|&gt;</span> </span>
+<span id="cb152-11"><a href="#cb152-11" aria-hidden="true" tabindex="-1"></a>  <span class="fu">ggplot</span>(<span class="fu">aes</span>(n, symbol,<span class="at">fill=</span>variant_class)) <span class="sc">+</span></span>
+<span id="cb152-12"><a href="#cb152-12" aria-hidden="true" tabindex="-1"></a>  <span class="fu">geom_col</span>() <span class="sc">+</span></span>
+<span id="cb152-13"><a href="#cb152-13" aria-hidden="true" tabindex="-1"></a>  <span class="fu">facet_wrap</span>(<span class="sc">~</span>variant_class) <span class="sc">+</span></span>
+<span id="cb152-14"><a href="#cb152-14" aria-hidden="true" tabindex="-1"></a>  paletteer<span class="sc">::</span><span class="fu">scale_fill_paletteer_d</span>(<span class="st">"awtools::a_palette"</span>) <span class="sc">+</span></span>
+<span id="cb152-15"><a href="#cb152-15" aria-hidden="true" tabindex="-1"></a>  <span class="fu">labs</span>(<span class="at">x =</span> <span class="st">"Samples with specific mutation"</span>, <span class="at">y=</span><span class="st">"Gene Symbol"</span>, <span class="at">fill=</span><span class="st">"Variant Class"</span>,</span>
+<span id="cb152-16"><a href="#cb152-16" aria-hidden="true" tabindex="-1"></a>       <span class="at">title=</span><span class="st">"Top 20 Samples with READ"</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="cell-output cell-output-stderr">
+<pre><code>`summarise()` has grouped output by 'symbol'. You can override using the
+`.groups` argument.</code></pre>
+</div>
+<div class="sourceCode cell-code" id="cb154"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb154-1"><a href="#cb154-1" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb154-2"><a href="#cb154-2" aria-hidden="true" tabindex="-1"></a>path <span class="ot">&lt;-</span> <span class="st">"images/"</span></span>
+<span id="cb154-3"><a href="#cb154-3" aria-hidden="true" tabindex="-1"></a></span>
+<span id="cb154-4"><a href="#cb154-4" aria-hidden="true" tabindex="-1"></a><span class="fu">ggsave</span>(<span class="fu">paste0</span>(path,<span class="st">"samples_with_mut.jpeg"</span>), <span class="at">device =</span> <span class="st">"jpeg"</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="cell-output cell-output-stderr">
+<pre><code>Saving 7 x 5 in image</code></pre>
+</div>
+</div></div>
+<div class="quarto-figure quarto-figure-center">
+<figure class="figure">
+<p><a href="images/samples_with_mut.jpeg" class="lightbox" data-gallery="quarto-lightbox-gallery-1" title="Samples with specific mutation per gene"><img src="images/samples_with_mut.jpeg" class="img-fluid figure-img" alt="Samples with specific mutation per gene"></a></p>
+<figcaption>Samples with specific mutation per gene</figcaption>
+</figure>
+</div>
 </section>
      </main>
 <!-- /main column -->  <script id="quarto-html-after-body" type="application/javascript">
@@ -1595,6 +1633,30 @@ <h2 class="anchored" data-anchor-id="analyzing-mutation-data">Analyzing Mutation
     }
   }
 });
-</script>  </div> <!-- /content --> 
+</script>  </div> <!-- /content -->  <script>var lightboxQuarto = GLightbox({"loop":false,"descPosition":"bottom","selector":".lightbox","closeEffect":"zoom","openEffect":"zoom"});
+window.onload = () => {
+  lightboxQuarto.on('slide_before_load', (data) => {
+    const { slideIndex, slideNode, slideConfig, player, trigger } = data;
+    const href = trigger.getAttribute('href');
+    if (href !== null) {
+      const imgEl = window.document.querySelector(`a[href="${href}"] img`);
+      if (imgEl !== null) {
+        const srcAttr = imgEl.getAttribute("src");
+        if (srcAttr && srcAttr.startsWith("data:")) {
+          slideConfig.href = srcAttr;
+        }
+      }
+    } 
+  });
+
+  lightboxQuarto.on('slide_after_load', (data) => {
+    const { slideIndex, slideNode, slideConfig, player, trigger } = data;
+    if (window.Quarto?.typesetMath) {
+      window.Quarto.typesetMath(slideNode);
+    }
+  });
+
+};
+          </script> 
   
 </body></html>
\ No newline at end of file
diff --git a/notebooks/TCGA.embed.ipynb b/notebooks/TCGA.embed.ipynb
index 2cc6495..95b0bfa 100644
--- a/notebooks/TCGA.embed.ipynb
+++ b/notebooks/TCGA.embed.ipynb
@@ -8,7 +8,7 @@
     "\n",
     "Analysis of Mutation Data from The Cancer Genome Atlas (TCGA)"
    ],
-   "id": "574aa135-2d60-40a4-8441-cb21a6ad25a4"
+   "id": "64e15aee-7ada-4c70-bff9-2d6a422e9347"
   },
   {
    "cell_type": "code",
@@ -92,7 +92,7 @@
     "#| include: false\n",
     "library(knitr)"
    ],
-   "id": "46c80529-9dea-4bde-b5d0-5a7a96e1baec"
+   "id": "8f428370-1dc9-45bf-8479-60cd3a29afe0"
   },
   {
    "cell_type": "markdown",
@@ -106,7 +106,7 @@
     "\n",
     "First, let’s load the packages needed."
    ],
-   "id": "523b3ed6-fa85-43ad-afb4-f3713375452f"
+   "id": "e690fb6f-8e3b-4910-93e0-385f1d12d39f"
   },
   {
    "cell_type": "code",
@@ -474,7 +474,7 @@
    "source": [
     "library(curatedTCGAData)"
    ],
-   "id": "0b32b15c-969f-4bda-afe7-1f66cc3f1240"
+   "id": "b0acd422-bbf8-4c8d-80dd-af235275a04a"
   },
   {
    "cell_type": "markdown",
@@ -486,7 +486,7 @@
     "\n",
     "In this specific case, we are gonna work with RNA-Seq data, mutation data and methylation data from Rectum Adenocarcinoma (READ). The clinical data is included by default."
    ],
-   "id": "4c8ee72f-4b83-44d1-8a21-2c33c8fbf632"
+   "id": "3d192219-ba5b-47e3-94a5-d8af47a2b6ec"
   },
   {
    "cell_type": "code",
@@ -872,7 +872,7 @@
     "                           c(\"RNASeq2GeneNorm\", \"Mutation\", \"Methylation_methyl450\"), \n",
     "                           dry.run = FALSE, version = \"2.1.1\")"
    ],
-   "id": "29429639-a37b-49de-9d2e-b7fd2d755543"
+   "id": "40125fbc-1cb1-4dbf-848f-4884c6f3b75d"
   },
   {
    "cell_type": "markdown",
@@ -882,7 +882,7 @@
     "\n",
     "We can see which patients have data for each assay. The assay column gives the experiment type, the primary column gives the unique patient ID and the colname gives the sample ID used as a identifier within a given experiment."
    ],
-   "id": "23c3ca92-1e6e-4589-b937-36f8e1eeef80"
+   "id": "c24c45df-809a-4d77-bdff-cfe011073b78"
   },
   {
    "cell_type": "code",
@@ -913,7 +913,7 @@
    "source": [
     "sampleMap(readData)"
    ],
-   "id": "116f6b70-f233-4620-8bd0-c6dceffc80df"
+   "id": "f264c4bd-0859-422a-8aab-bb7ea259afa9"
   },
   {
    "cell_type": "markdown",
@@ -921,7 +921,7 @@
    "source": [
     "Not all patients have data for all assays, and some of them can have multiple data entries for one or more experiment type. This may correspond to multiple biopsies or matched tumor and normal samples from an individual patient."
    ],
-   "id": "d8132180-0b52-4512-b5be-46d94a8343c4"
+   "id": "d85d1c06-feaa-444b-9b46-b0524e1cf599"
   },
   {
    "cell_type": "code",
@@ -945,7 +945,7 @@
     "  table() |> \n",
     "  table()"
    ],
-   "id": "4e12c0d0-2f8b-45f6-92c0-c59ebb294dbf"
+   "id": "0f8d19f7-1ea0-4795-8080-979118f5de87"
   },
   {
    "cell_type": "markdown",
@@ -953,7 +953,7 @@
    "source": [
     "We can see the metadata of the patients with `colData`. Note that there are more than 2000 columns of data per patient (not necessarily complete)."
    ],
-   "id": "9fac6925-bec3-4e7f-b0d6-09c486a61db2"
+   "id": "ad9aa829-5781-4674-b9e0-81ab8ff21bc7"
   },
   {
    "cell_type": "code",
@@ -983,7 +983,7 @@
     "  as_tibble()\n",
     "dim(clin)"
    ],
-   "id": "4bb6a39a-0853-4e23-b051-3af9b7c808fa"
+   "id": "2c518625-5be4-4ff4-8267-3980b5b3b3bb"
   },
   {
    "cell_type": "markdown",
@@ -991,7 +991,7 @@
    "source": [
     "As an example, for rectum adenocarcinoma, we can see the tumor stage."
    ],
-   "id": "ed50c735-247e-4b7f-920d-37ae8acabf27"
+   "id": "d72ceb63-3bb2-4957-9b57-d48a916f20a5"
   },
   {
    "cell_type": "code",
@@ -1013,7 +1013,7 @@
     "  pull(pathology_T_stage) |> \n",
     "  table()"
    ],
-   "id": "3515cd8d-ec67-48ee-8064-5a1841333ad9"
+   "id": "68394637-928c-4b51-b8f4-b545946cf40e"
   },
   {
    "cell_type": "markdown",
@@ -1021,7 +1021,7 @@
    "source": [
     "Stage T4 have subgroups. To simplify the analysis, let’s combine all T4 tumors."
    ],
-   "id": "d171e769-98d5-4012-81a6-0c4faa955761"
+   "id": "f3a920d3-a5cf-4f9e-bb4c-1643f04b90d5"
   },
   {
    "cell_type": "code",
@@ -1048,7 +1048,7 @@
     "clin$t_stage |> \n",
     "  table()"
    ],
-   "id": "9ba65abf-887d-4d5f-83f0-e7c7eef525fd"
+   "id": "4b15e3aa-55a8-4553-b3f2-8ad080c5f7ad"
   },
   {
    "cell_type": "markdown",
@@ -1056,7 +1056,7 @@
    "source": [
     "Also, we can see the vital status (alive=0, deceased=1)"
    ],
-   "id": "aaa14215-a737-45e0-b49e-cc9a35792401"
+   "id": "0530fbf2-e9b9-4ab8-8977-875a667e3f1b"
   },
   {
    "cell_type": "code",
@@ -1077,7 +1077,7 @@
     "clin$vital_status |> \n",
     "  table()"
    ],
-   "id": "7b6bebd3-87fa-4981-83ff-7195117929ad"
+   "id": "a8509bb5-8c5e-4f5c-8298-20dc6da16de1"
   },
   {
    "cell_type": "markdown",
@@ -1085,7 +1085,7 @@
    "source": [
     "Or combine tumor status and vital status."
    ],
-   "id": "3d2b747c-f1eb-45b1-9907-c5b58fa8ae18"
+   "id": "858bc86e-1d00-4f72-8e3b-680a19403547"
   },
   {
    "cell_type": "code",
@@ -1108,7 +1108,7 @@
    "source": [
     "table(clin$t_stage, clin$vital_status)"
    ],
-   "id": "04e5551f-8d28-46d2-a76b-3b3f9c77d070"
+   "id": "6a67d397-188c-4fc3-929e-e38adc9b92d2"
   },
   {
    "cell_type": "markdown",
@@ -1118,7 +1118,7 @@
     "\n",
     "Let’s begin analyzing the mutation data. Below is the code to retrieve the mutation data."
    ],
-   "id": "2033ee9f-7fcb-4b04-bd6d-eaa0d7a03a3c"
+   "id": "4c485987-38c7-4f96-943d-a8abfd83e5a4"
   },
   {
    "cell_type": "code",
@@ -1144,7 +1144,7 @@
     "\n",
     "mut_data"
    ],
-   "id": "a296ef66-0e67-4562-9c96-7183778b6432"
+   "id": "7558b07a-cc77-4d1f-98b0-64636c77b5d7"
   },
   {
    "cell_type": "markdown",
@@ -1152,7 +1152,7 @@
    "source": [
     "From the inspection of the sample IDs we can see that the mutation colnames match to the **primary** column from he clinical data."
    ],
-   "id": "8f501866-ce26-439f-9c54-f5b68874290f"
+   "id": "8728d427-c7d6-4e86-aade-dd0136898aec"
   },
   {
    "cell_type": "code",
@@ -1181,7 +1181,7 @@
     "mut_sample_ids = colnames(mut_data)\n",
     "head(mut_sample_ids)"
    ],
-   "id": "1fd7ca43-6209-4519-82b8-b8db63ee5d80"
+   "id": "3e39f6f8-ad96-4f2e-962b-aed5af9d4dfc"
   },
   {
    "cell_type": "markdown",
@@ -1189,7 +1189,7 @@
    "source": [
     "We need to manipulate these by substracting 12 characters."
    ],
-   "id": "52066818-c5a2-450d-9a8f-50100cf07e2b"
+   "id": "a802dd56-bce4-4549-a768-a1b78259d2e3"
   },
   {
    "cell_type": "code",
@@ -1210,7 +1210,7 @@
     "\n",
     "all(mut_sample_ids %in% clin$patientID)"
    ],
-   "id": "f84ea1ef-32d9-4992-84bf-6077917b7fbd"
+   "id": "e066d0a1-3c22-4d93-9a91-657989eaa9eb"
   },
   {
    "cell_type": "markdown",
@@ -1218,7 +1218,7 @@
    "source": [
     "Is important to note that the data stored in `assay(mut_data)` is difficult to work with because is a sparse matrix that has a row for each `GRanges` with a mutation in at least one sample."
    ],
-   "id": "9b33fb78-4dcd-40ff-8c6c-e78835fc6317"
+   "id": "9a6771d9-aac3-4d3c-acf4-b093ed2fa1f1"
   },
   {
    "cell_type": "code",
@@ -1252,7 +1252,7 @@
    "source": [
     "assay(mut_data)[1:3,1:3]"
    ],
-   "id": "113cb8c4-eadc-4e0e-9267-e8a5f61b224b"
+   "id": "fc1859d3-597d-4ce0-bf0c-d7defa77909b"
   },
   {
    "cell_type": "markdown",
@@ -1260,7 +1260,7 @@
    "source": [
     "We can get more information if we look at the mutation information for each patient."
    ],
-   "id": "d4936f94-97a9-4ed4-a0b4-76d1acb7cdfa"
+   "id": "878c2ebf-eff1-4a18-b7ae-756a146c280a"
   },
   {
    "cell_type": "code",
@@ -1433,7 +1433,7 @@
     "\n",
     "mut_assay # GRangesList"
    ],
-   "id": "bf4855bc-4c23-450c-8cb9-ccf7e853dc1f"
+   "id": "9a925b3c-e349-4f16-bcee-38a126cbe363"
   },
   {
    "cell_type": "markdown",
@@ -1441,7 +1441,7 @@
    "source": [
     "Let’s inspect the data from the first patient. We can see from the metadata information the Hugo Symbol, mutation status and predicted effect of each mutation at variant classification."
    ],
-   "id": "6f861f57-00b8-43ca-b470-26a54a77ca75"
+   "id": "0ea64fd1-845a-4b46-a46a-33a3075127e5"
   },
   {
    "cell_type": "code",
@@ -1626,7 +1626,7 @@
    "source": [
     "mut_assay[[1]]"
    ],
-   "id": "5c70092e-f261-4065-9738-8555bd4bc7bc"
+   "id": "234af931-4832-4e4c-8f11-544a12b2ceda"
   },
   {
    "cell_type": "markdown",
@@ -1634,7 +1634,7 @@
    "source": [
     "Now, is kind of a trouble to inspect manually each patient. So, lets get all mutation information from Hugo symbol and Variant classification for all the patients."
    ],
-   "id": "5c6bf792-f31c-4d40-a9b5-a2687b9d9c33"
+   "id": "a09cf4bb-4906-41e3-998c-dc37d52ad8f3"
   },
   {
    "cell_type": "code",
@@ -1675,7 +1675,7 @@
     "\n",
     "head(var_class_df)"
    ],
-   "id": "ff466972-3bae-4825-85c8-dcf3f96a9670"
+   "id": "0905057b-a894-47fe-99be-ccbca3811950"
   },
   {
    "cell_type": "markdown",
@@ -1683,7 +1683,67 @@
    "source": [
     "We can visualize the most common mutated genes genes in rectum adenocarcinoma"
    ],
-   "id": "0553f3cd-4e84-401a-8c19-de7179ee1835"
+   "id": "24bdad6d-b945-4634-b983-7282c7d1d586"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "`summarise()` has grouped output by 'symbol'. You can override using the\n",
+      "`.groups` argument."
+     ]
+    },
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "Saving 7 x 5 in image"
+     ]
+    }
+   ],
+   "source": [
+    "#| label: fig-genesmut\n",
+    "#| message: false\n",
+    "#| warning: false\n",
+    "\n",
+    "p <- var_class_df |> \n",
+    "  as_tibble() |> \n",
+    "  group_by(symbol,variant_class) |> \n",
+    "  summarise(n = n()) |> \n",
+    "  arrange(desc(n)) |> \n",
+    "  ungroup() |> \n",
+    "  slice_max(order_by = n, n = 20) |> \n",
+    "  ungroup() |> \n",
+    "  mutate(symbol = fct_reorder(symbol,n)) |> \n",
+    "  ggplot(aes(n, symbol,fill=variant_class)) +\n",
+    "  geom_col() +\n",
+    "  facet_wrap(~variant_class) +\n",
+    "  paletteer::scale_fill_paletteer_d(\"awtools::a_palette\") +\n",
+    "  labs(x = \"Samples with specific mutation\", y=\"Gene Symbol\", fill=\"Variant Class\",\n",
+    "       title=\"Top 20 Samples with READ\")"
+   ],
+   "id": "cell-fig-genesmut"
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<figure>\n",
+    "<img src=\"attachment:images/samples_with_mut.jpeg\" alt=\"Samples with specific mutation per gene\" />\n",
+    "<figcaption aria-hidden=\"true\">Samples with specific mutation per gene</figcaption>\n",
+    "</figure>"
+   ],
+   "attachments": {
+    "images/samples_with_mut.jpeg": {
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KoXztoyVYDjcBk5HUA9xz6F8Hvi7d+Jr4eHPELK+olC1t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\ngjjBXfyOvy14p8APER0Xxtd+HriVTDqKlUKsGXz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j3SLljBeWkZkhT+JZX/cN+TeVXzh8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\np1ryT4o/BmPxfdvrWiTRWurMAJo5ciO4xwDkfdbHGcEHA6da4i01D44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sXw/4L8UoMLBNJbXLYz+7aR/6F/zFcNZj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CW3nmZlAU/eGVBI5Cnp1UVwXxT8BXXjjR7EaXcQW2q2FwJbeeZmUBT94ZUEjkKe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\nzc+K/s3jqz8NfYt32mEy/aPNxtwrn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9FFR6/4g0/w3prXt/LtXoka8vI3ooqP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\nrwj8Yf8AhKfE9no39hfZftO/999r37dqM3TYM/dx171s+FPi3/wk/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OktqXhLxvpWjQ61Z3TbJtRto/3qED5RI4555HPIYYzzWV8LB4R0ltS8JeN9K0aHWr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\nU3yo/wC4v5U3yo/7i/lTfKj/ALi/lR5Uf9x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PIi/u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\nU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InKMMuoOCOa5j4jXdzY+CL24tLiW3mVo9skTlGGXUHBHNcx8R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\n8RPHus3F48v9m6eq20PlttJIJ6HnIyHP4isyLSLb4ie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hmeGC7kjlmK8gmSQxqSP9kBj+Na/wAcr251zxn4Y8DwzPDBdyRyzFeQTJIY1JH+yAx/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\n"
+    }
+   },
+   "id": "71803f22-7a18-4d2c-a605-e1fab7827635"
   }
  ],
  "nbformat": 4,
diff --git a/notebooks/TCGA.out.ipynb b/notebooks/TCGA.out.ipynb
index 47bff5d..0b4bfd8 100644
--- a/notebooks/TCGA.out.ipynb
+++ b/notebooks/TCGA.out.ipynb
@@ -8,7 +8,7 @@
     "\n",
     "Analysis of Mutation Data from The Cancer Genome Atlas (TCGA)"
    ],
-   "id": "42771dfe-c052-4bd9-a636-7b9b453c95ed"
+   "id": "39a13194-1315-46ea-a1ad-794b8905ced6"
   },
   {
    "cell_type": "code",
@@ -91,7 +91,7 @@
    "source": [
     "library(knitr)\n"
    ],
-   "id": "d8e9a57f-d03d-4d85-9e29-a3f55a6b7121"
+   "id": "ef3dbcea-d22c-4a99-84c0-e17d83e304c7"
   },
   {
    "cell_type": "markdown",
@@ -105,7 +105,7 @@
     "\n",
     "First, let’s load the packages needed."
    ],
-   "id": "f2c381be-cee9-4730-ae08-5851cf3c45c2"
+   "id": "1ba16af4-4b54-4abc-b4ae-fbac73f5e4ea"
   },
   {
    "cell_type": "code",
@@ -473,7 +473,7 @@
    "source": [
     "library(curatedTCGAData)\n"
    ],
-   "id": "9ae82b5a-4a75-4e99-a4f8-c65157b97887"
+   "id": "fe078c40-9d9b-48a3-8301-027bcd18a023"
   },
   {
    "cell_type": "markdown",
@@ -485,7 +485,7 @@
     "\n",
     "In this specific case, we are gonna work with RNA-Seq data, mutation data and methylation data from Rectum Adenocarcinoma (READ). The clinical data is included by default."
    ],
-   "id": "00b88b9f-5f7c-45af-9888-6d8249671a4b"
+   "id": "a4bd6560-6d87-4758-b515-261ddf9b346b"
   },
   {
    "cell_type": "code",
@@ -868,7 +868,7 @@
     "                           c(\"RNASeq2GeneNorm\", \"Mutation\", \"Methylation_methyl450\"), \n",
     "                           dry.run = FALSE, version = \"2.1.1\")\n"
    ],
-   "id": "0bbd9f55-7577-4ec6-b799-0555124f7ef9"
+   "id": "7b03f01f-cddb-415a-9049-b194772cb3f4"
   },
   {
    "cell_type": "markdown",
@@ -878,7 +878,7 @@
     "\n",
     "We can see which patients have data for each assay. The assay column gives the experiment type, the primary column gives the unique patient ID and the colname gives the sample ID used as a identifier within a given experiment."
    ],
-   "id": "79881828-915e-4ef5-9a8a-3a6b5294e170"
+   "id": "97b47252-569f-4890-85ce-b2b38f895d8f"
   },
   {
    "cell_type": "code",
@@ -909,7 +909,7 @@
    "source": [
     "sampleMap(readData)\n"
    ],
-   "id": "d4526c3d-732b-4d09-b617-3400159b2510"
+   "id": "56d52743-ef3f-4003-bc6c-b5da271e2805"
   },
   {
    "cell_type": "markdown",
@@ -917,7 +917,7 @@
    "source": [
     "Not all patients have data for all assays, and some of them can have multiple data entries for one or more experiment type. This may correspond to multiple biopsies or matched tumor and normal samples from an individual patient."
    ],
-   "id": "7b930329-a029-4268-93a4-d1c21ab8a145"
+   "id": "dbf9ffe7-984c-4fce-94a9-68b988ee0404"
   },
   {
    "cell_type": "code",
@@ -941,7 +941,7 @@
     "  table() |> \n",
     "  table()\n"
    ],
-   "id": "8ea4a808-51fb-4322-a11d-28a77f817950"
+   "id": "664fda09-332f-4787-94a9-a2d68a3e0531"
   },
   {
    "cell_type": "markdown",
@@ -949,7 +949,7 @@
    "source": [
     "We can see the metadata of the patients with `colData`. Note that there are more than 2000 columns of data per patient (not necessarily complete)."
    ],
-   "id": "9932e4c0-164a-4b13-8c2b-6f1f50583e70"
+   "id": "8e426b44-107b-411b-9975-feef6f9a3bf3"
   },
   {
    "cell_type": "code",
@@ -979,7 +979,7 @@
     "  as_tibble()\n",
     "dim(clin)\n"
    ],
-   "id": "c4deeccc-44e6-4588-9507-812cf915ad79"
+   "id": "80442c0a-c83c-4bbd-877f-b561fd6a3c55"
   },
   {
    "cell_type": "markdown",
@@ -987,7 +987,7 @@
    "source": [
     "As an example, for rectum adenocarcinoma, we can see the tumor stage."
    ],
-   "id": "45aa879e-bf5a-441a-ba89-1190a7e8bdc8"
+   "id": "561f961f-0e2e-4156-bb97-552eecbfe6c9"
   },
   {
    "cell_type": "code",
@@ -1009,7 +1009,7 @@
     "  pull(pathology_T_stage) |> \n",
     "  table()\n"
    ],
-   "id": "08cca382-01d6-454d-807d-f6728e005bd0"
+   "id": "dbcfc2d8-fcbd-4ab4-a48d-dd6d27952779"
   },
   {
    "cell_type": "markdown",
@@ -1017,7 +1017,7 @@
    "source": [
     "Stage T4 have subgroups. To simplify the analysis, let’s combine all T4 tumors."
    ],
-   "id": "0a9b6f9e-c611-4be7-94cb-7705873ee4fb"
+   "id": "9541b885-36f5-46b2-aa45-06d72628b677"
   },
   {
    "cell_type": "code",
@@ -1044,7 +1044,7 @@
     "clin$t_stage |> \n",
     "  table()\n"
    ],
-   "id": "5dbad1e3-be3e-43e9-ad86-f56b4aa070f6"
+   "id": "481add08-b5cb-458a-b5b4-78584b05cbab"
   },
   {
    "cell_type": "markdown",
@@ -1052,7 +1052,7 @@
    "source": [
     "Also, we can see the vital status (alive=0, deceased=1)"
    ],
-   "id": "6b933122-9f80-4e32-aec5-11791c9aadc1"
+   "id": "f15cb995-e612-4070-96b4-1af63aa432cc"
   },
   {
    "cell_type": "code",
@@ -1073,7 +1073,7 @@
     "clin$vital_status |> \n",
     "  table()\n"
    ],
-   "id": "7d1507a3-8f65-4a93-aaaa-712a14b343fa"
+   "id": "cacd46dc-c694-4e45-abeb-8526e7d33d2f"
   },
   {
    "cell_type": "markdown",
@@ -1081,7 +1081,7 @@
    "source": [
     "Or combine tumor status and vital status."
    ],
-   "id": "94921643-6a62-4793-9b87-28c7d21560ec"
+   "id": "049ff171-0fba-4d4f-9a4b-c36fa8100448"
   },
   {
    "cell_type": "code",
@@ -1104,7 +1104,7 @@
    "source": [
     "table(clin$t_stage, clin$vital_status)\n"
    ],
-   "id": "55dac323-eef4-412f-837c-4de9c97d6894"
+   "id": "9adaf372-c300-454f-9858-2899f622ce88"
   },
   {
    "cell_type": "markdown",
@@ -1114,7 +1114,7 @@
     "\n",
     "Let’s begin analyzing the mutation data. Below is the code to retrieve the mutation data."
    ],
-   "id": "f2f42ae5-7957-4756-84ac-6c614b4a7d1f"
+   "id": "4ed64078-dfde-43cd-a0c8-acfad64945f3"
   },
   {
    "cell_type": "code",
@@ -1140,7 +1140,7 @@
     "\n",
     "mut_data\n"
    ],
-   "id": "7b5368bb-999f-468f-b65c-5b84dc9e5e56"
+   "id": "834f9c10-7405-47d0-90a2-3536ef6a8499"
   },
   {
    "cell_type": "markdown",
@@ -1148,7 +1148,7 @@
    "source": [
     "From the inspection of the sample IDs we can see that the mutation colnames match to the **primary** column from he clinical data."
    ],
-   "id": "4460c898-6a85-46be-8bd5-990a91562bee"
+   "id": "af06fd36-cb0d-4676-b4dd-c60c939e16a5"
   },
   {
    "cell_type": "code",
@@ -1177,7 +1177,7 @@
     "mut_sample_ids = colnames(mut_data)\n",
     "head(mut_sample_ids)\n"
    ],
-   "id": "cea8120c-1aac-472a-8725-f21d35c04947"
+   "id": "4e58f8cc-7b53-4d54-8b7f-2d7343d8642f"
   },
   {
    "cell_type": "markdown",
@@ -1185,7 +1185,7 @@
    "source": [
     "We need to manipulate these by substracting 12 characters."
    ],
-   "id": "77321fa7-8bc6-4045-b4e5-abf717d2046e"
+   "id": "9fe52475-c16f-41c6-bb1a-9f9d2be36147"
   },
   {
    "cell_type": "code",
@@ -1206,7 +1206,7 @@
     "\n",
     "all(mut_sample_ids %in% clin$patientID)\n"
    ],
-   "id": "b5dd5b79-8058-40e6-9094-264e29496231"
+   "id": "28f3bfd3-7332-464f-8c96-ef79ba2e6d40"
   },
   {
    "cell_type": "markdown",
@@ -1214,7 +1214,7 @@
    "source": [
     "Is important to note that the data stored in `assay(mut_data)` is difficult to work with because is a sparse matrix that has a row for each `GRanges` with a mutation in at least one sample."
    ],
-   "id": "3378a39a-c6a0-485f-bd33-4600dd4cf6e7"
+   "id": "5bde339d-e644-461c-8e07-7268579dab9a"
   },
   {
    "cell_type": "code",
@@ -1248,7 +1248,7 @@
    "source": [
     "assay(mut_data)[1:3,1:3]\n"
    ],
-   "id": "ed38698a-f8be-46d3-9b55-e4dcf077f3f8"
+   "id": "a5562175-84c9-4c81-ba30-cd0527a834a2"
   },
   {
    "cell_type": "markdown",
@@ -1256,7 +1256,7 @@
    "source": [
     "We can get more information if we look at the mutation information for each patient."
    ],
-   "id": "16df7221-f124-425d-a203-ece411635d8f"
+   "id": "107c6ec1-863f-434c-a48d-8e946914f544"
   },
   {
    "cell_type": "code",
@@ -1429,7 +1429,7 @@
     "\n",
     "mut_assay # GRangesList\n"
    ],
-   "id": "fceb8ed1-e738-45c9-a8ae-d6f050e25c34"
+   "id": "54cf936a-db5c-4770-a04b-f46206aa3a3a"
   },
   {
    "cell_type": "markdown",
@@ -1437,7 +1437,7 @@
    "source": [
     "Let’s inspect the data from the first patient. We can see from the metadata information the Hugo Symbol, mutation status and predicted effect of each mutation at variant classification."
    ],
-   "id": "3bfda83d-0403-48b7-857c-74f0a1c03c32"
+   "id": "d9f91662-4fd1-4aef-ad56-df52186bd14b"
   },
   {
    "cell_type": "code",
@@ -1622,7 +1622,7 @@
    "source": [
     "mut_assay[[1]]\n"
    ],
-   "id": "d632ef3e-b9b5-49a6-93ef-502c13f9f5db"
+   "id": "db5e2bd2-4d98-41a6-a3b0-ddbd9bf1be4b"
   },
   {
    "cell_type": "markdown",
@@ -1630,7 +1630,7 @@
    "source": [
     "Now, is kind of a trouble to inspect manually each patient. So, lets get all mutation information from Hugo symbol and Variant classification for all the patients."
    ],
-   "id": "42d23513-aed5-4d00-80a6-11ed0891b535"
+   "id": "a0e878a0-e71d-4231-baf9-1cf1e89cb150"
   },
   {
    "cell_type": "code",
@@ -1671,7 +1671,7 @@
     "\n",
     "head(var_class_df)\n"
    ],
-   "id": "c2c9a441-cf3f-462e-ad6f-3634034f22be"
+   "id": "49261cfc-0083-4b19-8ff0-57530e526da3"
   },
   {
    "cell_type": "markdown",
@@ -1679,7 +1679,64 @@
    "source": [
     "We can visualize the most common mutated genes genes in rectum adenocarcinoma"
    ],
-   "id": "11c28f9a-38d2-42f3-a3f3-e4936b01777e"
+   "id": "c8793d2c-ce93-4851-a96f-839f718a2f5a"
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "`summarise()` has grouped output by 'symbol'. You can override using the\n",
+      "`.groups` argument."
+     ]
+    },
+    {
+     "output_type": "stream",
+     "name": "stderr",
+     "text": [
+      "Saving 7 x 5 in image"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "p <- var_class_df |> \n",
+    "  as_tibble() |> \n",
+    "  group_by(symbol,variant_class) |> \n",
+    "  summarise(n = n()) |> \n",
+    "  arrange(desc(n)) |> \n",
+    "  ungroup() |> \n",
+    "  slice_max(order_by = n, n = 20) |> \n",
+    "  ungroup() |> \n",
+    "  mutate(symbol = fct_reorder(symbol,n)) |> \n",
+    "  ggplot(aes(n, symbol,fill=variant_class)) +\n",
+    "  geom_col() +\n",
+    "  facet_wrap(~variant_class) +\n",
+    "  paletteer::scale_fill_paletteer_d(\"awtools::a_palette\") +\n",
+    "  labs(x = \"Samples with specific mutation\", y=\"Gene Symbol\", fill=\"Variant Class\",\n",
+    "       title=\"Top 20 Samples with READ\")\n"
+   ],
+   "id": "cell-fig-genesmut"
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<figure>\n",
+    "<img src=\"attachment:images/samples_with_mut.jpeg\" alt=\"Samples with specific mutation per gene\" />\n",
+    "<figcaption aria-hidden=\"true\">Samples with specific mutation per gene</figcaption>\n",
+    "</figure>"
+   ],
+   "attachments": {
+    "images/samples_with_mut.jpeg": {
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KoXztoyVYDjcBk5HUA9xz6F8Hvi7d+Jr4eHPELK+olC1t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\ngjjBXfyOvy14p8APER0Xxtd+HriVTDqKlUKsGXz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j3SLljBeWkZkhT+JZX/cN+TeVXzh8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\np1ryT4o/BmPxfdvrWiTRWurMAJo5ciO4xwDkfdbHGcEHA6da4i01D44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sXw/4L8UoMLBNJbXLYz+7aR/6F/zFcNZj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CW3nmZlAU/eGVBI5Cnp1UVwXxT8BXXjjR7EaXcQW2q2FwJbeeZmUBT94ZUEjkKe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\nzc+K/s3jqz8NfYt32mEy/aPNxtwrn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9FFR6/4g0/w3prXt/LtXoka8vI3ooqP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\nrwj8Yf8AhKfE9no39hfZftO/999r37dqM3TYM/dx171s+FPi3/wk/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OktqXhLxvpWjQ61Z3TbJtRto/3qED5RI4555HPIYYzzWV8LB4R0ltS8JeN9K0aHWr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\nU3yo/wC4v5U3yo/7i/lTfKj/ALi/lR5Uf9x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PIi/u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\nU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InKMMuoOCOa5j4jXdzY+CL24tLiW3mVo9skTlGGXUHBHNcx8R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\n8RPHus3F48v9m6eq20PlttJIJ6HnIyHP4isyLSLb4ie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hmeGC7kjlmK8gmSQxqSP9kBj+Na/wAcr251zxn4Y8DwzPDBdyRyzFeQTJIY1JH+yAx/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\nk/2j9g8m7W583yPNzhHXbjcv9/Oc9qq/E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+    }
+   },
+   "id": "24952f10-37fd-4234-a4e6-0b3b99cb0fc5"
   }
  ],
  "nbformat": 4,
diff --git a/notebooks/TCGA.qmd b/notebooks/TCGA.qmd
index d2c2726..6a123d9 100644
--- a/notebooks/TCGA.qmd
+++ b/notebooks/TCGA.qmd
@@ -36,7 +36,6 @@ library(MultiAssayExperiment)
 
 ```
 
-
 ## Download the Data
 
 To download the data we need to use `curatedTCGAData`function. The first argument is a four letter disease (cancer) code (A complete list of disease codes used by the TCGA project are available on the [NCI Genomic Data Commons website](https://gdc.cancer.gov/resources-tcga-users/tcga-code-tables/tcga-study-abbreviations)), the second argument is a vector of data types we want to download. We need to specify `dry.run = FALSE` to download the data.
@@ -220,5 +219,32 @@ head(var_class_df)
 
 We can visualize the most common mutated genes genes in rectum adenocarcinoma
 
+```{r}
+#| label: fig-genesmut
+#| message: false
+#| warning: false
+
+p <- var_class_df |> 
+  as_tibble() |> 
+  group_by(symbol,variant_class) |> 
+  summarise(n = n()) |> 
+  arrange(desc(n)) |> 
+  ungroup() |> 
+  slice_max(order_by = n, n = 20) |> 
+  ungroup() |> 
+  mutate(symbol = fct_reorder(symbol,n)) |> 
+  ggplot(aes(n, symbol,fill=variant_class)) +
+  geom_col() +
+  facet_wrap(~variant_class) +
+  paletteer::scale_fill_paletteer_d("awtools::a_palette") +
+  labs(x = "Samples with specific mutation", y="Gene Symbol", fill="Variant Class",
+       title="Top 20 Samples with READ")
+
+path <- "images/"
+
+ggsave(paste0(path,"samples_with_mut.jpeg"), device = "jpeg")
 
 
+```
+
+![Samples with specific mutation per gene](images/samples_with_mut.jpeg)
diff --git a/notebooks/TCGA_files/figure-html/fig-genesmut-1.png b/notebooks/TCGA_files/figure-html/fig-genesmut-1.png
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