From b5c4183274df5f92363ca8c952769afe1b86929a Mon Sep 17 00:00:00 2001
From: Quarto GHA Workflow Runner <quarto-github-actions-publish@example.com>
Date: Tue, 30 Apr 2024 23:02:14 +0000
Subject: [PATCH] Built site for gh-pages

---
 .nojekyll                                     |   2 +-
 index-preview.html                            |   2 +-
 index.embed.ipynb                             |  44 ++---
 index.out.ipynb                               |  44 ++---
 notebooks/TCGA-preview.html                   |  20 +-
 notebooks/TCGA.embed.ipynb                    | 176 ++++++++++++------
 notebooks/TCGA.out.ipynb                      | 176 ++++++++++++------
 notebooks/TCGA.qmd                            |  20 +-
 .../number_mut_genes_per_tumor_stage.jpeg     | Bin 280357 -> 278777 bytes
 9 files changed, 307 insertions(+), 177 deletions(-)

diff --git a/.nojekyll b/.nojekyll
index 70bd55f..8298ed0 100644
--- a/.nojekyll
+++ b/.nojekyll
@@ -1 +1 @@
-f317a5e4
\ No newline at end of file
+ec56c8ab
\ No newline at end of file
diff --git a/index-preview.html b/index-preview.html
index 6985e68..1b2d2f1 100644
--- a/index-preview.html
+++ b/index-preview.html
@@ -1298,7 +1298,7 @@ <h2 class="unnumbered anchored" data-anchor-id="references">References</h2>
     }
   }
 });
-</script>  </div> <!-- /content -->  <script>var lightboxQuarto = GLightbox({"loop":false,"selector":".lightbox","closeEffect":"zoom","openEffect":"zoom","descPosition":"bottom"});
+</script>  </div> <!-- /content -->  <script>var lightboxQuarto = GLightbox({"openEffect":"zoom","selector":".lightbox","loop":false,"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 922275d..1d1f165 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": "b4b8eac6-fe07-4393-af3c-34487f4031fa"
+   "id": "1dde25b9-078b-476e-b0d2-fc780e20b497"
   },
   {
    "cell_type": "code",
@@ -286,7 +286,7 @@
    "source": [
     "library(tidyverse)"
    ],
-   "id": "69d92163-e302-4348-a220-5ceda2dcb105"
+   "id": "b260ab66-c086-495e-a4d3-590df558587a"
   },
   {
    "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": "3e2951f4-ec50-4312-a4c7-148d44beb16f"
+   "id": "3c718fb1-17fb-410c-b1b4-a52b6d68f5a4"
   },
   {
    "cell_type": "code",
@@ -328,7 +328,7 @@
     "\n",
     "gg = gwcat |> as_GRanges()"
    ],
-   "id": "c4089353-f16f-44a2-adc7-9fdb21de25c9"
+   "id": "be4979c0-0e8f-4a60-b608-e1ef875587f1"
   },
   {
    "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": "31aaebaf-f7cf-4d8e-ad4c-bcc5f2cfafa5"
+   "id": "09c6ae75-ff84-491f-92b6-c924b79fa111"
   },
   {
    "cell_type": "code",
@@ -373,7 +373,7 @@
    "source": [
     "ggbio::autoplot(gg, layout=\"karyogram\")"
    ],
-   "id": "7397edc6-b245-41f1-a511-3f058cb2cdae"
+   "id": "c6969933-2d93-4ed2-bfb2-907dc269c1a4"
   },
   {
    "cell_type": "markdown",
@@ -381,7 +381,7 @@
    "source": [
     "We can see the peak data as a `GRanges` object:"
    ],
-   "id": "9e08ff18-4235-4602-bf43-3deee26ffa63"
+   "id": "1146a264-92d5-4c12-9dbd-bfb1460ae742"
   },
   {
    "cell_type": "code",
@@ -429,7 +429,7 @@
     "\n",
     "GM12878"
    ],
-   "id": "03078248-d873-4d8e-a55f-3ee79a2ea47e"
+   "id": "3ba97276-91c9-4781-a771-acb8a239dfdd"
   },
   {
    "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": "6543721c-743f-472f-97c1-a1998f2a41fd"
+   "id": "cf651e9f-9a6d-44fb-b13a-b6688eea6748"
   },
   {
    "cell_type": "code",
@@ -555,7 +555,7 @@
    "source": [
     "library(AnnotationHub)"
    ],
-   "id": "9f2a09c2-325f-4208-b531-bb96d61995fb"
+   "id": "bd9538b6-f977-4270-8aa7-3e445a3a9b79"
   },
   {
    "cell_type": "code",
@@ -574,7 +574,7 @@
     "seqlevelsStyle(GM12878) <- \"UCSC\"\n",
     "seqlevelsStyle(gg) <- \"UCSC\""
    ],
-   "id": "d67c54bf-9073-4d84-9a83-71948fc8943a"
+   "id": "c6dd9dbf-094b-4c1f-a02b-7e8bb6d75b94"
   },
   {
    "cell_type": "markdown",
@@ -584,7 +584,7 @@
     "\n",
     "We can see the duplications with the `reduce` function from `IRanges` package:"
    ],
-   "id": "ce65f74c-0385-48b7-bdf3-802733dbe090"
+   "id": "d4be86bc-0fd2-47d9-b792-cf1c2bb3cd4d"
   },
   {
    "cell_type": "code",
@@ -603,7 +603,7 @@
     "# duplicated loci\n",
     "length(gg) - length(reduce(gg))"
    ],
-   "id": "5b69414b-28eb-4952-8281-24efb9f64040"
+   "id": "67347415-d15d-4521-ba9b-084617243fc4"
   },
   {
    "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": "f77a6b4c-23ac-4315-9aad-cc4d42549164"
+   "id": "aa5ad83a-5d1c-4e28-8f1e-e8441eb33e25"
   },
   {
    "cell_type": "code",
@@ -646,7 +646,7 @@
     "fo = findOverlaps(GM12878, reduce(gg))\n",
     "fo"
    ],
-   "id": "0617ce03-ee9b-465a-9787-7394b9774e44"
+   "id": "ff9024a9-f5db-4acb-bf9c-1e41c4568f27"
   },
   {
    "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": "fef5d949-9e49-4469-9619-dc354a36d3e8"
+   "id": "12beecb2-5ab9-4c8f-865e-2811575db5e6"
   },
   {
    "cell_type": "code",
@@ -705,7 +705,7 @@
     "\n",
     "htmltools::tagList(list(plotly::ggplotly(p)))"
    ],
-   "id": "c822db21-9033-4c6a-8137-61fdc3e8ee9d"
+   "id": "81c02982-536f-43e4-8e18-2b2020a66cf7"
   },
   {
    "cell_type": "markdown",
@@ -713,7 +713,7 @@
    "source": [
     "Distinct phenotypes identified on the peaks:"
    ],
-   "id": "0bbf067a-c088-4be6-8828-62c5d8e0465a"
+   "id": "4d4b0f9d-77ce-47c1-807d-cb390638008b"
   },
   {
    "cell_type": "code",
@@ -731,7 +731,7 @@
    "source": [
     "length(phset)"
    ],
-   "id": "e12820ff-a29b-44ed-a6c1-a48d868cae6d"
+   "id": "c9c177fa-449e-4eb7-90f8-3730521a951f"
   },
   {
    "cell_type": "markdown",
@@ -739,7 +739,7 @@
    "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": "824687a1-80c8-4e2a-afe9-e00f96247554"
+   "id": "804c21fc-c50a-4ac9-b237-2b26d3777bb0"
   },
   {
    "cell_type": "code",
@@ -1011,7 +1011,7 @@
    "source": [
     "library(ph525x)"
    ],
-   "id": "fab7c5c4-190b-4cf2-8d53-2c58e5f8ffee"
+   "id": "c34348cd-2bf9-4603-8af6-9b61e69075b1"
   },
   {
    "cell_type": "markdown",
@@ -1025,7 +1025,7 @@
     "\n",
     "## References"
    ],
-   "id": "a340632e-c94c-4d24-995b-fdaafc074a7c"
+   "id": "598bfe0a-ce1e-44a5-a9b5-9e48b89753a5"
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  "nbformat": 4,
diff --git a/index.out.ipynb b/index.out.ipynb
index 5991fbb..534b941 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": "25b758af-bc6c-424c-9bc1-12387b7f0c19"
+   "id": "e40caab4-533c-4c38-a3b1-9042842718b6"
   },
   {
    "cell_type": "code",
@@ -286,7 +286,7 @@
    "source": [
     "library(tidyverse)"
    ],
-   "id": "e0362b40-43b6-4114-8202-94cea87e949f"
+   "id": "d8b1989b-bf8b-4b2d-a9ba-bf0d77973218"
   },
   {
    "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": "7437e86d-1bd5-4b25-b044-281edf62ffa7"
+   "id": "96112d63-adc5-42c8-ab39-b1b2facb7bda"
   },
   {
    "cell_type": "code",
@@ -328,7 +328,7 @@
     "\n",
     "gg = gwcat |> as_GRanges()"
    ],
-   "id": "a8dfe28a-98cc-44f0-b629-334b1ca54c56"
+   "id": "2d5f695f-aa2a-44e2-94da-c3b135a341cb"
   },
   {
    "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": "aab97718-50ef-48ab-a8ed-f13f1c6a8381"
+   "id": "659c0ee6-c0f0-49d5-a414-2150e7b03661"
   },
   {
    "cell_type": "code",
@@ -373,7 +373,7 @@
    "source": [
     "ggbio::autoplot(gg, layout=\"karyogram\")"
    ],
-   "id": "255ff189-88d9-45a2-9d73-665b76411bac"
+   "id": "ad553f69-8d20-481b-9e6d-84bc7fa8f5e3"
   },
   {
    "cell_type": "markdown",
@@ -381,7 +381,7 @@
    "source": [
     "We can see the peak data as a `GRanges` object:"
    ],
-   "id": "e2c41255-f6ef-4939-b86b-80f7b0972603"
+   "id": "b5c98908-c607-4478-ad15-27b579b19708"
   },
   {
    "cell_type": "code",
@@ -429,7 +429,7 @@
     "\n",
     "GM12878"
    ],
-   "id": "7134e4d1-3d2e-4ea7-acc4-82f032468258"
+   "id": "b88d3fd4-35ba-42f5-b292-ef8e4f5dfbd3"
   },
   {
    "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": "8ecaca52-37d8-4f82-823c-857ad714a57a"
+   "id": "d22147b7-1149-4ada-86c3-692c1aabbb0c"
   },
   {
    "cell_type": "code",
@@ -555,7 +555,7 @@
    "source": [
     "library(AnnotationHub)"
    ],
-   "id": "97650698-283a-4b94-b623-2fdd562a2fc8"
+   "id": "41625148-1c15-4c7c-9dca-2bbd00572f3d"
   },
   {
    "cell_type": "code",
@@ -574,7 +574,7 @@
     "seqlevelsStyle(GM12878) <- \"UCSC\"\n",
     "seqlevelsStyle(gg) <- \"UCSC\""
    ],
-   "id": "149128e3-1fee-4125-a2db-fd7ff54d59da"
+   "id": "8d9834b1-401e-4ede-9a41-a9e68d3b9edf"
   },
   {
    "cell_type": "markdown",
@@ -584,7 +584,7 @@
     "\n",
     "We can see the duplications with the `reduce` function from `IRanges` package:"
    ],
-   "id": "b1dc0786-8ce2-497d-ac10-31a0be58cf6e"
+   "id": "0b03fd5b-bc00-439d-b326-e24fb8b51f00"
   },
   {
    "cell_type": "code",
@@ -603,7 +603,7 @@
     "# duplicated loci\n",
     "length(gg) - length(reduce(gg))"
    ],
-   "id": "f8b51399-a397-4abc-8f94-5227e20824ad"
+   "id": "b77136f9-88f5-40be-b574-e1e1d3bdc458"
   },
   {
    "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": "b30e1c06-1ffd-495b-b221-1d90741e677f"
+   "id": "8266548a-1c68-4948-938e-9a0f05ad5494"
   },
   {
    "cell_type": "code",
@@ -646,7 +646,7 @@
     "fo = findOverlaps(GM12878, reduce(gg))\n",
     "fo"
    ],
-   "id": "253a3e0e-c3cf-4f9f-a4b4-5b3c8d7afa57"
+   "id": "9b693089-86bf-439f-85cc-12363ed05a19"
   },
   {
    "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": "cf8c6b9b-96c7-45a3-8357-06d239b2ebe9"
+   "id": "0af414ea-b8dd-427e-8e89-4c6dacf45b7e"
   },
   {
    "cell_type": "code",
@@ -705,7 +705,7 @@
     "\n",
     "htmltools::tagList(list(plotly::ggplotly(p)))"
    ],
-   "id": "e3b96e33-ddef-4ab3-ba29-e0683aefbfa4"
+   "id": "b22456b1-5715-49cd-9797-65d0d53c0fc8"
   },
   {
    "cell_type": "markdown",
@@ -713,7 +713,7 @@
    "source": [
     "Distinct phenotypes identified on the peaks:"
    ],
-   "id": "9607b936-0996-43b6-8794-c5fe6ce733bd"
+   "id": "ea9a7c8e-3069-49a0-bff0-00573a4c63e4"
   },
   {
    "cell_type": "code",
@@ -731,7 +731,7 @@
    "source": [
     "length(phset)"
    ],
-   "id": "f25d763e-1736-41d2-bda3-6b8f6b91f2a4"
+   "id": "b7bf41e6-0540-460f-bb96-7eac4a27c356"
   },
   {
    "cell_type": "markdown",
@@ -739,7 +739,7 @@
    "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": "cbea976b-6e2b-4276-8037-152c3e2b5c85"
+   "id": "6274832d-711d-4347-ad29-8032bb96768f"
   },
   {
    "cell_type": "code",
@@ -1011,7 +1011,7 @@
    "source": [
     "library(ph525x)"
    ],
-   "id": "9c0830c4-1d72-46ea-a041-80ba03aa8a8a"
+   "id": "ab5653b9-f8fe-49ee-a8b0-c25af64ec4b7"
   },
   {
    "cell_type": "markdown",
@@ -1025,7 +1025,7 @@
     "\n",
     "## References"
    ],
-   "id": "b8f4b30f-4519-4755-969f-d02711da1509"
+   "id": "135c9888-6c27-4fca-a386-d64695f52f86"
   }
  ],
  "nbformat": 4,
diff --git a/notebooks/TCGA-preview.html b/notebooks/TCGA-preview.html
index eb8b30e..0f7d058 100644
--- a/notebooks/TCGA-preview.html
+++ b/notebooks/TCGA-preview.html
@@ -213,10 +213,7 @@ <h2 id="toc-title">Table of contents</h2>
   <li><a href="#review-the-clinical-metadata" id="toc-review-the-clinical-metadata" class="nav-link" data-scroll-target="#review-the-clinical-metadata">Review the Clinical Metadata</a></li>
   <li><a href="#analyzing-mutation-data" id="toc-analyzing-mutation-data" class="nav-link" data-scroll-target="#analyzing-mutation-data">Analyzing Mutation Data</a></li>
   <li><a href="#linking-expression-and-tumor-stage" id="toc-linking-expression-and-tumor-stage" class="nav-link" data-scroll-target="#linking-expression-and-tumor-stage">Linking expression and tumor stage</a></li>
-  <li><a href="#linking-methylation-and-expression" id="toc-linking-methylation-and-expression" class="nav-link" data-scroll-target="#linking-methylation-and-expression">Linking methylation and expression</a>
-  <ul class="collapse">
-  <li><a href="#conclusion" id="toc-conclusion" class="nav-link" data-scroll-target="#conclusion">Conclusion</a></li>
-  </ul></li>
+  <li><a href="#linking-methylation-and-expression" id="toc-linking-methylation-and-expression" class="nav-link" data-scroll-target="#linking-methylation-and-expression">Linking methylation and expression</a></li>
   </ul>
 </nav>
 </div>
@@ -1307,6 +1304,7 @@ <h2 class="anchored" data-anchor-id="analyzing-mutation-data">Analyzing Mutation
 </section>
 <section id="linking-expression-and-tumor-stage" class="level1">
 <h1>Linking expression and tumor stage</h1>
+<div class=""><p>We also can link the expression data (RNA-Seq) with the tumor stage data from the clinical metadata. </p><div class="no-row-height"><span class="margin-aside">Check the TCGA RNA-Seq protocol online</span></div></div>
 <div class="cell-container"><div class="cell-decorator"><pre>In [22]:</pre></div><div class="cell">
 <div class="sourceCode cell-code" id="cb164"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb164-1"><a href="#cb164-1" aria-hidden="true" tabindex="-1"></a>rnaseq <span class="ot">=</span> readData[[<span class="dv">2</span>]]</span>
 <span id="cb164-2"><a href="#cb164-2" aria-hidden="true" tabindex="-1"></a></span>
@@ -1323,6 +1321,7 @@ <h1>Linking expression and tumor stage</h1>
 colData names(0):</code></pre>
 </div>
 </div></div>
+<p>As before, we need to shorten the sample IDs so they can match to the clinical data.</p>
 <div class="cell-container"><div class="cell-decorator"><pre>In [23]:</pre></div><div class="cell">
 <div class="sourceCode cell-code" id="cb166"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb166-1"><a href="#cb166-1" aria-hidden="true" tabindex="-1"></a><span class="fu">colnames</span>(rnaseq) <span class="ot">=</span> <span class="fu">colnames</span>(rnaseq) <span class="sc">|&gt;</span> </span>
 <span id="cb166-2"><a href="#cb166-2" aria-hidden="true" tabindex="-1"></a>  stringr<span class="sc">::</span><span class="fu">str_sub</span>(<span class="dv">1</span>,<span class="dv">12</span>)</span>
@@ -1339,6 +1338,7 @@ <h1>Linking expression and tumor stage</h1>
 <span id="cb166-13"><a href="#cb166-13" aria-hidden="true" tabindex="-1"></a></span>
 <span id="cb166-14"><a href="#cb166-14" aria-hidden="true" tabindex="-1"></a>rnaseq <span class="ot">&lt;-</span> rnaseq[,<span class="sc">!</span>idx]</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div></div>
+<p>Then, we can use the <code>limma</code> package to proceed with the differential expression analysis. In this case, we’ll ise the tumor stage as a variable to explain the expression of the genes.</p>
 <div class="cell-container"><div class="cell-decorator"><pre>In [24]:</pre></div><div class="cell">
 <div class="sourceCode cell-code" id="cb167"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb167-1"><a href="#cb167-1" aria-hidden="true" tabindex="-1"></a><span class="fu">library</span>(limma)</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">
@@ -1389,6 +1389,7 @@ <h1>Linking expression and tumor stage</h1>
 C1orf223  0.06719153</code></pre>
 </div>
 </div></div>
+<p>Let’s visualize two of the most expressed genes.</p>
 <div class="cell-container"><div class="cell-decorator"><pre>In [25]:</pre></div><div class="cell">
 <div class="sourceCode cell-code" id="cb176"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb176-1"><a href="#cb176-1" aria-hidden="true" tabindex="-1"></a><span class="fu">par</span>(<span class="at">mfrow=</span><span class="fu">c</span>(<span class="dv">1</span>,<span class="dv">2</span>))</span>
 <span id="cb176-2"><a href="#cb176-2" aria-hidden="true" tabindex="-1"></a><span class="fu">boxplot</span>(<span class="fu">split</span>(<span class="fu">assay</span>(rnaseq)[<span class="st">"PAM"</span>,], rnaseq<span class="sc">$</span>t_stage), <span class="at">main=</span><span class="st">"PAM"</span>)    <span class="co"># higher expression in lower t_stage</span></span>
@@ -1404,6 +1405,8 @@ <h1>Linking expression and tumor stage</h1>
 </section>
 <section id="linking-methylation-and-expression" class="level1">
 <h1>Linking methylation and expression</h1>
+<p>Finally, we can use the methylation data with the expression data. This is important because methylated cytosines of the DNA change the expression of the genes.</p>
+<p>Some patients have methylation data for multiples tissue types. This information is encoded in the fourth component of the sample names. The code <code>01A</code> correspond to primary tumor samples and the code <code>11A</code> correspond to normal tissue. We’ll keep the primary tumor samples.</p>
 <div class="cell-container"><div class="cell-decorator"><pre>In [26]:</pre></div><div class="cell">
 <div class="sourceCode cell-code" id="cb177"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb177-1"><a href="#cb177-1" aria-hidden="true" tabindex="-1"></a>methyl <span class="ot">&lt;-</span> readData[[<span class="dv">3</span>]]</span>
 <span id="cb177-2"><a href="#cb177-2" aria-hidden="true" tabindex="-1"></a></span>
@@ -1430,10 +1433,12 @@ <h1>Linking methylation and expression</h1>
 colData names(0):</code></pre>
 </div>
 </div></div>
+<p>As before, let’s truncate the names of the sample to match the clinical data.</p>
 <div class="cell-container"><div class="cell-decorator"><pre>In [27]:</pre></div><div class="cell">
 <div class="sourceCode cell-code" id="cb179"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb179-1"><a href="#cb179-1" aria-hidden="true" tabindex="-1"></a><span class="fu">colnames</span>(methyl) <span class="ot">&lt;-</span> <span class="fu">colnames</span>(methyl) <span class="sc">|&gt;</span> </span>
 <span id="cb179-2"><a href="#cb179-2" aria-hidden="true" tabindex="-1"></a>  <span class="fu">str_sub</span>(<span class="at">start =</span> <span class="dv">1</span>,<span class="at">end =</span> <span class="dv">12</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div></div>
+<p>We can add the clinical data to the methyl object and count the number of patients with methylation and transcription data.</p>
 <div class="cell-container"><div class="cell-decorator"><pre>In [28]:</pre></div><div class="cell">
 <div class="sourceCode cell-code" id="cb180"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb180-1"><a href="#cb180-1" aria-hidden="true" tabindex="-1"></a><span class="fu">colData</span>(methyl) <span class="ot">&lt;-</span> <span class="fu">as</span>(clin[<span class="fu">colnames</span>(methyl),],<span class="st">"DataFrame"</span>)</span>
 <span id="cb180-2"><a href="#cb180-2" aria-hidden="true" tabindex="-1"></a></span>
@@ -1443,6 +1448,7 @@ <h1>Linking methylation and expression</h1>
 <pre><code>[1] 93</code></pre>
 </div>
 </div></div>
+<p>Let’s subset common sample names and check the methylation data as row ranges.</p>
 <div class="cell-container"><div class="cell-decorator"><pre>In [29]:</pre></div><div class="cell">
 <div class="sourceCode cell-code" id="cb182"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb182-1"><a href="#cb182-1" aria-hidden="true" tabindex="-1"></a>methyl_subset <span class="ot">=</span> methyl[,<span class="fu">which</span>(<span class="fu">colnames</span>(methyl) <span class="sc">%in%</span> <span class="fu">colnames</span>(rnaseq))]</span>
 <span id="cb182-2"><a href="#cb182-2" aria-hidden="true" tabindex="-1"></a>rnaseq_subset <span class="ot">=</span> rnaseq[,<span class="fu">which</span>(<span class="fu">colnames</span>(rnaseq) <span class="sc">%in%</span> <span class="fu">colnames</span>(methyl))]</span>
@@ -1466,6 +1472,7 @@ <h1>Linking methylation and expression</h1>
 rs9839873           NA          NA                  0</code></pre>
 </div>
 </div></div>
+<p>This function takes a gene symbol and returns a scatter plot showing the relationship between 3 different sites near that gene and gene expression.</p>
 <div class="cell-container"><div class="cell-decorator"><pre>In [30]:</pre></div><div class="cell">
 <div class="sourceCode cell-code" id="cb184"><pre class="sourceCode r code-with-copy"><code class="sourceCode r"><span id="cb184-1"><a href="#cb184-1" aria-hidden="true" tabindex="-1"></a></span>
 <span id="cb184-2"><a href="#cb184-2" aria-hidden="true" tabindex="-1"></a>me_rna_cor <span class="ot">=</span> <span class="cf">function</span>(sym, <span class="at">mpick =</span> <span class="dv">3</span>){</span>
@@ -1545,9 +1552,6 @@ <h1>Linking methylation and expression</h1>
 <pre><code>[1] 0.0410095 0.1457715 0.0856926</code></pre>
 </div>
 </div></div>
-<section id="conclusion" class="level2">
-<h2 class="anchored" data-anchor-id="conclusion">Conclusion</h2>
-</section>
 </section>
      </main>
 <!-- /main column -->  <script id="quarto-html-after-body" type="application/javascript">
@@ -1969,7 +1973,7 @@ <h2 class="anchored" data-anchor-id="conclusion">Conclusion</h2>
     }
   }
 });
-</script>  </div> <!-- /content -->  <script>var lightboxQuarto = GLightbox({"descPosition":"bottom","closeEffect":"zoom","selector":".lightbox","loop":false,"openEffect":"zoom"});
+</script>  </div> <!-- /content -->  <script>var lightboxQuarto = GLightbox({"openEffect":"zoom","selector":".lightbox","closeEffect":"zoom","loop":false,"descPosition":"bottom"});
 window.onload = () => {
   lightboxQuarto.on('slide_before_load', (data) => {
     const { slideIndex, slideNode, slideConfig, player, trigger } = data;
diff --git a/notebooks/TCGA.embed.ipynb b/notebooks/TCGA.embed.ipynb
index e3e726f..5742978 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": "b5af7009-9077-4900-b0ab-61870a59c783"
+   "id": "99cd65bf-ecb5-4a2d-9260-422340530743"
   },
   {
    "cell_type": "code",
@@ -92,7 +92,7 @@
     "#| include: false\n",
     "library(knitr)"
    ],
-   "id": "3d761167-5404-4fb6-9967-cfaedfb4d67e"
+   "id": "89bd5bf8-bb85-4aab-b8e2-b2cb7170cca6"
   },
   {
    "cell_type": "markdown",
@@ -106,7 +106,7 @@
     "\n",
     "First, let’s load the packages needed."
    ],
-   "id": "841fba9a-902a-4dd3-91c4-2ad25efc78cc"
+   "id": "6822a4dd-1882-497b-84f0-96e5157767b0"
   },
   {
    "cell_type": "code",
@@ -474,7 +474,7 @@
    "source": [
     "library(curatedTCGAData)"
    ],
-   "id": "82544f94-b79f-4dc1-9e48-28c7be5b4fd9"
+   "id": "e011ace9-4f8f-451d-abac-6205dc155090"
   },
   {
    "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": "d13b5aa2-2c2e-482a-b074-24a90921b454"
+   "id": "524f8625-2018-44a9-b6f0-377b2913b417"
   },
   {
    "cell_type": "code",
@@ -872,7 +872,7 @@
     "                           c(\"RNASeq2GeneNorm\", \"Mutation\", \"Methylation_methyl450\"), \n",
     "                           dry.run = FALSE, version = \"2.1.1\")"
    ],
-   "id": "53b0b81a-3bbd-4736-8bc5-a297b303daab"
+   "id": "106097aa-6d0f-442c-8389-52d076a5d9dc"
   },
   {
    "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": "4427d2cd-1640-47f9-a74d-fb1479308a70"
+   "id": "5c9847fc-18fd-4b93-bc2a-5e554bb9adde"
   },
   {
    "cell_type": "code",
@@ -913,7 +913,7 @@
    "source": [
     "sampleMap(readData)"
    ],
-   "id": "14de7b65-49e5-4627-9564-5e623160677f"
+   "id": "693c4dfa-dc2f-4e31-b285-a6c5cfb2f784"
   },
   {
    "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": "89fe768b-8d68-49ef-9aef-b4eaa8d4bfc8"
+   "id": "b23be0ec-82ad-46a3-b749-59ef4ef15c2a"
   },
   {
    "cell_type": "code",
@@ -945,7 +945,7 @@
     "  table() |> \n",
     "  table()"
    ],
-   "id": "be444442-e275-4ba9-9caf-8e1d45a12da7"
+   "id": "a20dc483-c810-44e3-a588-b99966e21fc9"
   },
   {
    "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": "0db3f8e3-a4a8-40d2-9605-9b12eb45ac71"
+   "id": "d405fc8f-4674-40ac-8234-db1d7db973cb"
   },
   {
    "cell_type": "code",
@@ -983,7 +983,7 @@
     "  as_tibble()\n",
     "dim(clin)"
    ],
-   "id": "a8cb3de5-d64e-402e-817c-0956a387c06a"
+   "id": "32445fdf-a517-4e0a-9521-1e57e5dd1370"
   },
   {
    "cell_type": "markdown",
@@ -991,7 +991,7 @@
    "source": [
     "As an example, for rectum adenocarcinoma, we can see the tumor stage."
    ],
-   "id": "449899bb-0569-479c-83a3-b52d104ee880"
+   "id": "e36c03f7-ae99-4f62-809f-fb1534c8fc6a"
   },
   {
    "cell_type": "code",
@@ -1013,7 +1013,7 @@
     "  pull(pathology_T_stage) |> \n",
     "  table()"
    ],
-   "id": "57d30f7a-a637-46b5-8296-672361d07828"
+   "id": "bc21b397-4665-4ce3-8f24-af3ebd64ab8a"
   },
   {
    "cell_type": "markdown",
@@ -1021,7 +1021,7 @@
    "source": [
     "Stage T4 have subgroups. To simplify the analysis, let’s combine all T4 tumors."
    ],
-   "id": "d8f7be6f-6bc6-442f-9952-84b78a31bc4c"
+   "id": "32508352-4e81-412a-826b-954052b13d99"
   },
   {
    "cell_type": "code",
@@ -1048,7 +1048,7 @@
     "clin$t_stage |> \n",
     "  table()"
    ],
-   "id": "951d07a7-959f-4dca-a689-f49b92caffd6"
+   "id": "4ff87b99-ea50-48ce-8afe-4d012ff40d74"
   },
   {
    "cell_type": "markdown",
@@ -1056,7 +1056,7 @@
    "source": [
     "Also, we can see the vital status (alive=0, deceased=1)"
    ],
-   "id": "2c106362-4a33-49dd-a9f9-9c86e4e6a243"
+   "id": "94f7ae53-2dd4-451e-9b3b-8a302f5ea629"
   },
   {
    "cell_type": "code",
@@ -1077,7 +1077,7 @@
     "clin$vital_status |> \n",
     "  table()"
    ],
-   "id": "783aa3e1-bf88-47e7-82c7-50878bc05f01"
+   "id": "ed931b42-0d6f-44d9-9635-30c616a96230"
   },
   {
    "cell_type": "markdown",
@@ -1085,7 +1085,7 @@
    "source": [
     "Or combine tumor status and vital status."
    ],
-   "id": "928ee3ee-f7e8-425b-bcb1-8957952711e6"
+   "id": "57902ad8-a88d-49fb-a796-8753f83e9a99"
   },
   {
    "cell_type": "code",
@@ -1108,7 +1108,7 @@
    "source": [
     "table(clin$t_stage, clin$vital_status)"
    ],
-   "id": "bb31116d-0567-4fab-981e-21ab9636feeb"
+   "id": "282fbba5-31d3-463f-833c-d1a52087b0e3"
   },
   {
    "cell_type": "markdown",
@@ -1118,7 +1118,7 @@
     "\n",
     "Let’s begin analyzing the mutation data. Below is the code to retrieve the mutation data."
    ],
-   "id": "73c306a3-e504-40e9-afea-0b5eeb8857f5"
+   "id": "2c558c74-373a-4e0d-8666-7d3e7ed6b015"
   },
   {
    "cell_type": "code",
@@ -1144,7 +1144,7 @@
     "\n",
     "mut_data"
    ],
-   "id": "002d58a5-8e82-4d18-aea0-81769c0444ae"
+   "id": "e46b16be-0c70-49fc-b07e-1453245d992c"
   },
   {
    "cell_type": "markdown",
@@ -1152,7 +1152,7 @@
    "source": [
     "From the inspection of the sample IDs we can see that the mutation colnames match to the **primary** column from he clinical data."
    ],
-   "id": "95cc6643-7ad9-4171-9d49-d0fe412c3699"
+   "id": "981fe77c-c9a5-4925-8201-1f2753746e21"
   },
   {
    "cell_type": "code",
@@ -1181,7 +1181,7 @@
     "mut_sample_ids = colnames(mut_data)\n",
     "head(mut_sample_ids)"
    ],
-   "id": "220345c6-7eef-4ecb-9d72-82796f0380bd"
+   "id": "4f31dbaf-15a6-46c0-a000-3d0134e10223"
   },
   {
    "cell_type": "markdown",
@@ -1189,7 +1189,7 @@
    "source": [
     "We need to manipulate these by substracting 12 characters."
    ],
-   "id": "415c66a4-57d1-44e2-a987-39e2d2f14ce9"
+   "id": "4d0f32f0-50c6-4dd0-b415-b4ae7ffd52b5"
   },
   {
    "cell_type": "code",
@@ -1210,7 +1210,7 @@
     "\n",
     "all(mut_sample_ids %in% clin$patientID)"
    ],
-   "id": "a4f906a8-35ce-4e73-93c4-cf1a68d4e5c5"
+   "id": "520d2983-4e78-4260-95e3-644f2456b388"
   },
   {
    "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": "8e3e1b61-d585-44e9-b203-e80ff0f9eca9"
+   "id": "d131a43b-173b-40c3-a13e-3311a428ab14"
   },
   {
    "cell_type": "code",
@@ -1252,7 +1252,7 @@
    "source": [
     "assay(mut_data)[1:3,1:3]"
    ],
-   "id": "1b3b586f-c280-4cde-b65c-7a2fe5320c57"
+   "id": "8d11b0fd-e1ca-458a-a053-790e1a882525"
   },
   {
    "cell_type": "markdown",
@@ -1260,7 +1260,7 @@
    "source": [
     "We can get more information if we look at the mutation information for each patient."
    ],
-   "id": "ebb2102b-15f3-4239-9bdc-b368dbdbc758"
+   "id": "919ef2c1-3bd6-4f12-a142-0b0cf43a790f"
   },
   {
    "cell_type": "code",
@@ -1433,7 +1433,7 @@
     "\n",
     "mut_assay # GRangesList"
    ],
-   "id": "6d69018d-c550-4663-aec5-b47edd4d5d25"
+   "id": "87b2285f-bc9d-4c14-8f46-e53ddc0f0451"
   },
   {
    "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": "addb5af1-a8d7-4ca6-b375-cedf3c32c6f2"
+   "id": "28c52d86-3ec8-48b9-9a31-2e11f877b08d"
   },
   {
    "cell_type": "code",
@@ -1626,7 +1626,7 @@
    "source": [
     "mut_assay[[1]]"
    ],
-   "id": "96ea1d3d-35d2-4902-aa5d-f92e29dca446"
+   "id": "05f6f302-2684-408e-99d1-cefdf371f2d0"
   },
   {
    "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": "548cedf3-e703-47c8-bfd2-b6bc8905c14a"
+   "id": "8ef2f089-5e0a-4ca7-9ffe-e53eb726f60e"
   },
   {
    "cell_type": "code",
@@ -1675,7 +1675,7 @@
     "\n",
     "head(var_class_df)"
    ],
-   "id": "a36ee049-86b0-4a39-9e9e-8a1ece7090f8"
+   "id": "9c100ce6-ca2a-49e5-ab1f-ace5fe582cd1"
   },
   {
    "cell_type": "markdown",
@@ -1683,7 +1683,7 @@
    "source": [
     "We can visualize the most common mutated genes genes in rectum adenocarcinoma"
    ],
-   "id": "bc4e0348-bb60-480c-89f4-52fea863192a"
+   "id": "4e6a8885-7f56-4a62-a0fc-eb6176e2fc00"
   },
   {
    "cell_type": "code",
@@ -1744,7 +1744,7 @@
      "image/jpeg": 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KoXztoyVYDjcBk5HUA9xz6F8Hvi7d+Jr4eHPELK+olC1t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\ngjjBXfyOvy14p8APER0Xxtd+HriVTDqKlUKsGXz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j3SLljBeWkZkhT+JZX/cN+TeVXzh8\nOvFcvhTwD490i5YwXlpGZIU/iWV/3Dfk3lV6P8AtEWz+GrXcqBjqdxJIcjqi/uwD+Ksf+BV6P8At\nEWz+GrXcqBjqdxJIcjqi/uwD+Ksf+BV6P8AtEWz+GrXcqBjqdxJIcjqi/uwD+Ksf+BV578N9aX4b\n+MPG2k3R/d21tM8Yc4MrwMdgH+8rE1578N9aX4b+MPG2k3R/d21tM8Yc4MrwMdgH+8rE1578N9aX\n4b+MPG2k3R/d21tM8Yc4MrwMdgH+8rE11H7OmjyNYa14lusvPdzi3R3HzEL8znPfJYfitdR+zpo8\njWGteJbrLz3c4t0dx8xC/M5z3yWH4rXUfs6aPI1hrXiW6y893OLdHcfMQvzOc98lh+K11nxz/wCS\nTar/ANdIP/Rq11nxz/5JNqv/AF0g/wDRq11nxz/5JNqv/XSD/wBGrUXwI/5JTYf9d5//AEM1F8CP\n+SU2H/Xef/0M1F8CP+SU2H/Xef8A9DNedfFL/k4Pw9/v2P8A6ONedfFL/k4Pw9/v2P8A6ONedfFL\n/k4Pw9/v2P8A6ONfR9fR9fR9fOEn/J14/wCvof8ApNXzhJ/ydeP+vof+k1fOEn/J14/6+h/6TV9H\n19H19H14B+0108L/APb3/wC0a8A/aa6eF/8At7/9o14B+0108L/9vf8A7Rr2bwj/AMiVoP8A2Drf\n/wBFrXs3hH/kStB/7B1v/wCi1r2bwj/yJWg/9g63/wDRa1s1s1s1na//AMi3qn/XpL/6Aaztf/5F\nvVP+vSX/ANANZ2v/APIt6p/16S/+gGvBf2af+Qxr/wD17xf+hNXgv7NP/IY1/wD694v/AEJq8F/Z\np/5DGv8A/XvF/wChNX0XX0XX0XXh3x5+Ha3tk3i/S4QLq3UC/RF5kjHAk+q9/wDZ/wB2vDvjz8O1\nvbJvF+lwgXVuoF+iLzJGOBJ9V7/7P+7Xh3x5+Ha3tk3i/S4QLq3UC/RF5kjHAk+q9/8AZ/3a2vgn\n8RH8V6K+japOX1fT0GHc/NcQ9Ax9WBwCfcHqTW18E/iI/ivRX0bVJy+r6egw7n5riHoGPqwOAT7g\n9Sa2vgn8RH8V6K+japOX1fT0GHc/NcQ9Ax9WBwCfcHqTXo+vf8i7qf8A16S/+gGvR9e/5F3U/wDr\n0l/9ANej69/yLup/9ekv/oBrwP8AZq/5Dev/APXtF/6Ea8D/AGav+Q3r/wD17Rf+hGvA/wBmr/kN\n6/8A9e0X/oRr6Mr6Mr6Moooooorwb9o/xL5dppfhmF/mlP2y4AP8IyqD3BO8/wDARXg37R/iXy7T\nS/DML/NKftlwAf4RlUHuCd5/4CK8G/aP8S+XaaX4Zhf5pT9suAD/AAjKoPcE7z/wEVNof7O2i3Wg\n2FxquoapDfywJJPFE0YWNyMlRlCeM469qm0P9nbRbrQbC41XUNUhv5YEkniiaMLG5GSoyhPGcde1\nTaH+ztot1oNhcarqGqQ38sCSTxRNGFjcjJUZQnjOOvatD/hm/wAK/wDQV1n/AL+Rf/G60P8Ahm/w\nr/0FdZ/7+Rf/AButD/hm/wAK/wDQV1n/AL+Rf/G68s+I3gx/hV4v0m60i5nkgIW5tprjBYSxt8yn\nAAOPkPT+KvLPiN4Mf4VeL9JutIuZ5ICFubaa4wWEsbfMpwADj5D0/iryz4jeDH+FXi/SbrSLmeSA\nhbm2muMFhLG3zKcAA4+Q9P4q+ptC1e31/QbHV7U/ubyBZlGclcjlT7g5B9xX1NoWr2+v6DY6van9\nzeQLMozkrkcqfcHIPuK+ptC1e31/QbHV7U/ubyBZlGclcjlT7g5B9xXzlpv/ACdG/wD2FJv/AEBq\n+ctN/wCTo3/7Ck3/AKA1fOWm/wDJ0b/9hSb/ANAavp2vp2vp2iiivmH40643jX4i2HhzR8XH2Jvs\niFTkNcSMN4z6DCg+hU18w/GnXG8a/EWw8OaPi4+xN9kQqchriRhvGfQYUH0KmvmH40643jX4i2Hh\nzR8XH2JvsiFTkNcSMN4z6DCg+hU19IaNpkWjaHYaXCSYrO3jgUnqQqhc/pX0ho2mRaNodhpcJJis\n7eOBSepCqFz+lfSGjaZFo2h2GlwkmKzt44FJ6kKoXP6Veq9V6iiiiiiiiiiiiiiiiiiiiiiiiiii\niiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiii\niiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiii\niiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiii\niiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiii\niiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiii\niiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiii\niiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiis7xBFJN4b1SKJGkke0lVEUZLE\nocADuazvEEUk3hvVIokaSR7SVURRksShwAO5rO8QRSTeG9UiiRpJHtJVRFGSxKHAA7mvGP2e/D+t\naLqOutqukX9gssMIjN1bPEHILZxuAzXjH7Pfh/WtF1HXW1XSL+wWWGERm6tniDkFs43AZrxj9nvw\n/rWi6jrrarpF/YLLDCIzdWzxByC2cbgM17uyq6FHUMrDBBGQRXu7KroUdQysMEEZBFe7squhR1DK\nwwQRkEV8+6/8JfFXgzxM3iD4eTs8JJItldRJEp5KENxInTAPPTgkZr591/4S+KvBniZvEHw8nZ4S\nSRbK6iSJTyUIbiROmAeenBIzXz7r/wAJfFXgzxM3iD4eTs8JJItldRJEp5KENxInTAPPTgkZqR/H\nnxq1BG0+Dwn9lnYbRcrp8iEH1DSMU/TFSP48+NWoI2nweE/ss7DaLldPkQg+oaRin6YqR/Hnxq1B\nG0+Dwn9lnYbRcrp8iEH1DSMU/TFa/wANfg9fabrg8U+MJ1uNV3mWK3L+YUkP/LSRujMDnGMgHnOe\nmv8ADX4PX2m64PFPjCdbjVd5lity/mFJD/y0kbozA5xjIB5znpr/AA1+D19puuDxT4wnW41XeZYr\ncv5hSQ/8tJG6MwOcYyAec56ehfEX/km/iT/sHTf+gGvQviL/AMk38Sf9g6b/ANANehfEX/km/iT/\nALB03/oBr5x+G/hXxXcaTceK/Bt6E1OxuTbvakgedGVVjy3ynk/dPpkHIFfOPw38K+K7jSbjxX4N\nvQmp2Nybd7UkDzoyqseW+U8n7p9Mg5Ar5x+G/hXxXcaTceK/Bt6E1OxuTbvakgedGVVjy3ynk/dP\npkHIFdld+L/jXr8Mmk2/hltPlcbGuYbR4TjviSRyo+o59CK7K78X/GvX4ZNJt/DLafK42Ncw2jwn\nHfEkjlR9Rz6EV2V34v8AjXr8Mmk2/hltPlcbGuYbR4TjviSRyo+o59CK7P4UfCn/AIQhZdV1WWO4\n1u4TYdhykCE5Kgnqx4yfwHcns/hR8Kf+EIWXVdVljuNbuE2HYcpAhOSoJ6seMn8B3J7P4UfCn/hC\nFl1XVZY7jW7hNh2HKQITkqCerHjJ/AdyfT69Pr0+iiivBvj9B4o1zU9N0fStF1O802CL7TI9rayS\nI0rFlAJUEZVR/wCPmvBvj9B4o1zU9N0fStF1O802CL7TI9raySI0rFlAJUEZVR/4+a8G+P0HijXN\nT03R9K0XU7zTYIvtMj2trJIjSsWUAlQRlVH/AI+a5fw14k+KXhLRItJ0nwZdR20ZLZbRpyzsTyzH\nuf6ADtXL+GvEnxS8JaJFpOk+DLqO2jJbLaNOWdieWY9z/QAdq5fw14k+KXhLRItJ0nwZdR20ZLZb\nRpyzsTyzHuf6ADtWv/wsn4y/9Clc/wDgmnrX/wCFk/GX/oUrn/wTT1r/APCyfjL/ANClc/8Agmnr\nvPhf4p8deINUvofFmjS2FvFCGhd7GSDc27BGW68dq7z4X+KfHXiDVL6HxZo0thbxQhoXexkg3Nuw\nRluvHau8+F/inx14g1S+h8WaNLYW8UIaF3sZINzbsEZbrx2rU+KHw6h+IGhxxxSpb6paEtazP905\n6o2P4Tgc9QQD6g6nxQ+HUPxA0OOOKVLfVLQlrWZ/unPVGx/CcDnqCAfUHU+KHw6h+IGhxxxSpb6p\naEtazP8AdOeqNj+E4HPUEA+oPl+ma78ZfAtlHop8OvqcEPyQO9s9zsUdAHiYcem7p04xivL9M134\ny+BbKPRT4dfU4Ifkgd7Z7nYo6APEw49N3TpxjFeX6Zrvxl8C2Ueinw6+pwQ/JA72z3OxR0AeJhx6\nbunTjGKk0r4beM/iJ4pi1zx/utbCMjFuxCsyZz5aIv3F9ScH6nmpNK+G3jP4ieKYtc8f7rWwjIxb\nsQrMmc+WiL9xfUnB+p5qTSvht4z+InimLXPH+61sIyMW7EKzJnPloi/cX1Jwfqea+hI40ijWONVR\nEAVVUYAA6AV9CRxpFGscaqiIAqqowAB0Ar6EjjSKNY41VEQBVVRgADoBTqdTqKKK8k+KPwZj8X3b\n61ok0VrqzACaOXIjuMcA5H3WxxnBBwOnWvJPij8GY/F92+taJNFa6swAmjlyI7jHAOR91scZwQcD\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\nru5sfBF7cWlxLbzK0e2SJyjDLqDgjmorwm8+EjS3R8+RtHEjNL8xL+VncSe+ec+tRXhN58JGluj5\n8jaOJGaX5iX8rO4k98859aivCbz4SNLdHz5G0cSM0vzEv5WdxJ755z615v8ADqz0DUNRGna7pqyT\nzjzbOZ3dQ+MgrgEA9DjjsR6CvN/h1Z6BqGojTtd01ZJ5x5tnM7uofGQVwCAehxx2I9BXm/w6s9A1\nDURp2u6ask8482zmd3UPjIK4BAPQ447Eegr262srXTrP7PZW8VvCoJCRIFA/AV7dbWVrp1n9nsre\nK3hUEhIkCgfgK9utrK106z+z2VvFbwqCQkSBQPwFcB8JLeO702+1u53z6nJctA9zLIzOyBYzg5Pr\n+NcB8JLeO702+1u53z6nJctA9zLIzOyBYzg5Pr+NcB8JLeO702+1u53z6nJctA9zLIzOyBYzg5Pr\n+Nd9qd/Fpel3V/P/AKq3iaRh64GcfU9K77U7+LS9Lur+f/VW8TSMPXAzj6npXfanfxaXpd1fz/6q\n3iaRh64GcfU9K8c0sXOpato3/CXSz3uma35jwxSzuqwyhiF2gHjqAAOMOPSvHNLFzqWraN/wl0s9\n7pmt+Y8MUs7qsMoYhdoB46gADjDj0rxzSxc6lq2jf8JdLPe6ZrfmPDFLO6rDKGIXaAeOoAA4w49K\n9qiiSCFIUzsRQq5YscDjqeT+Ne1RRJBCkKZ2IoVcsWOBx1PJ/GvaookghSFM7EUKuWLHA46nk/jX\nk9p4d0nWPi7rthe2SPapb+asSExgN+65+Uj+8fzrye08O6TrHxd12wvbJHtUt/NWJCYwG/dc/KR/\neP515PaeHdJ1j4u67YXtkj2qW/mrEhMYDfuuflI/vH866jWLzw98O9NLWGnwJezjZBDGP3kp9267\nQa6jWLzw98O9NLWGnwJezjZBDGP3kp9267Qa6jWLzw98O9NLWGnwJezjZBDGP3kp9267Qaf4O0fX\nd76z4h1G7a6uMtHY+cwihB9UzjPt2+vR/g7R9d3vrPiHUbtrq4y0dj5zCKEH1TOM+3b69H+DtH13\ne+s+IdRu2urjLR2PnMIoQfVM4z7dvr053xlZR+KvGqeHNNs7OK5jjE17ftEC6jAwPfgr+JA4wa53\nxlZR+KvGqeHNNs7OK5jjE17ftEC6jAwPfgr+JA4wa53xlZR+KvGqeHNNs7OK5jjE17ftEC6jAwPf\ngr+JA4waq3fgLUfBFsdf0fUILyW0HmSRT2q/dH3ipJOOPTBxnmqt34C1HwRbHX9H1CC8ltB5kkU9\nqv3R94qSTjj0wcZ5qrd+AtR8EWx1/R9QgvJbQeZJFPar90feKkk449MHGea9L0DV4te0Kz1OJdi3\nCbiuc7WBww/AgivS9A1eLXtCs9TiXYtwm4rnO1gcMPwIIr0vQNXi17QrPU4l2LcJuK5ztYHDD8CC\nK0a0a0aKKKKKKKKKKKKq3nVPxqredU/Gqt51T8asjoKsjoKsjoKWlpaKKKKKKKKKKKKKKKKKKa/3\nG+hpr/cb6Gmv9xvoabB/qU+lNg/1KfSmwf6lPpUlSVJRRRRRRRRRRRRRRRRRRRRRVS3/AOPl/wAf\n51Ut/wDj5f8AH+dVLf8A4+X/AB/nVurdW6KKKKKKKKKKKKKKKKKKKKKil/10X1NRS/66L6mopf8A\nXRfU1LUtS0UUUUUUUUUUUUUUUUUUUUVDdf6k/Wobr/Un61Ddf6k/Wlt/9Qv4/wA6W3/1C/j/ADpb\nf/UL+P8AOpalqWiiiiiiiiiiiiiiiiiiiiiooOsv++aig6y/75qKDrL/AL5qWpaloooooooooooo\noooooooooqpJ/wAfi/UVUk/4/F+oqpJ/x+L9RVurdW6KKKKKKKKKKKKKKKKKKKKKiuf9Q34VFc/6\nhvwqK5/1DfhUvape1S9qKKKKKKKKKKKKKKKKKKKKKbJ/q2+hpsn+rb6Gmyf6tvoags/uv9ags/uv\n9ags/uv9as1ZqzRRRRRRRRRRRRRRRRRRRRRVRP8Aj9P1NVE/4/T9TVRP+P0/U1bq3Vuiiiiiiiii\niiiiiiiiiiiioZ/+Wf8AvioZ/wDln/vioZ/+Wf8Avipqmqaiiiiiiiiiiiiiiiiiiiiio7j/AFDV\nHcf6hqjuP9Q1Mtf9T+NMtf8AU/jTLX/U/jU9T1PRRRRRRRRRRRRRRRRRRRRRUMX+vm/CoYv9fN+F\nQxf6+b8KmqapqKKKKKKKKKKKKKKKKKKKKKqXP/Hwn0H86qXP/Hwn0H86qXP/AB8J9B/OrdW6t0UU\nUUUUUUUUUUUUUUUUUUUyb/Uv9KZN/qX+lMm/1L/Slj/1a/QUsf8Aq1+gpY/9Wv0FOp1Ooooooooo\nooooooooooo7UdqO1VLP7z/Sqln95/pVSz+8/wBKt1bq3RRRRRRRRRRRRRRRRRRRRRUTf8fSf7pq\nJv8Aj6T/AHTUTf8AH0n+6alqWpaKKKKKKKKKKKKKKKKKKKKKr3f+rX61Xu/9Wv1qvd/6tfrUsX+p\nT6CpYv8AUp9BUsX+pT6Cn0+n0UUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUU\nUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUU\nUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUU\nUUUUUUUUUUUUUUUUUUUUUUUUUUUUUUVieLtZ/sHwvfX6tiVY9kP/AF0bhfyJz+FYni7Wf7B8L31+\nrYlWPZD/ANdG4X8ic/hWJ4u1n+wfC99fq2JVj2Q/9dG4X8ic/hWN4U8KxR/DkaXdLtk1CFpZyRyG\nccHnuBt/EVjeFPCsUfw5Gl3S7ZNQhaWckchnHB57gbfxFY3hTwrFH8ORpd0u2TUIWlnJHIZxwee4\nG38RUPww1OWTRbnRLvi80qZoWUnJ25OPyIYfQCofhhqcsmi3OiXfF5pUzQspOTtycfkQw+gFQ/DD\nU5ZNFudEu+LzSpmhZScnbk4/Ihh9AKrfGT/kUrT/AK/k/wDRclVvjJ/yKVp/1/J/6Lkqt8ZP+RSt\nP+v5P/RclJH8QfECxqB4C1QgADOZOf8AyFSR/EHxAsageAtUIAAzmTn/AMhUkfxB8QLGoHgLVCAA\nM5k5/wDIVYXjLxhrGreGLmyu/CV/p8LlC1zMX2rhgRnMYHOMde9YXjLxhrGreGLmyu/CV/p8LlC1\nzMX2rhgRnMYHOMde9YXjLxhrGreGLmyu/CV/p8LlC1zMX2rhgRnMYHOMde9dk/8AyR8f9gMf+ia7\nJ/8Akj4/7AY/9E12T/8AJHx/2Ax/6JrkrLwxJrfwq0i+sMrq1gZZbd04ZgJnJX68ZHuPc1yVl4Yk\n1v4VaRfWGV1awMstu6cMwEzkr9eMj3Hua5Ky8MSa38KtIvrDK6tYGWW3dOGYCZyV+vGR7j3Ndx4P\n8Tx+KPDouSVW8iHl3MY/hfHUD0PUfiO1dx4P8Tx+KPDouSVW8iHl3MY/hfHUD0PUfiO1dx4P8Tx+\nKPDouSVW8iHl3MY/hfHUD0PUfiO1YHwc/wCRRu/+v9//AEXHWB8HP+RRu/8Ar/f/ANFx1gfBz/kU\nbv8A6/3/APRcdTfEy6lu7fTfDVo3+kapcKG4ziNSOT7Zwf8AgJqb4mXUt3b6b4atG/0jVLhQ3GcR\nqRyfbOD/AMBNTfEy6lu7fTfDVo3+kapcKG4ziNSOT7Zwf+AmpvH/AIfR/A6CxUpJpASWAqeVRBg8\n+y8/8BFTeP8Aw+j+B0FipSTSAksBU8qiDB59l5/4CKm8f+H0fwOgsVKSaQElgKnlUQYPPsvP/ARX\nReG9YTXvDtlqS43TRjzAOzjhh+YNdF4b1hNe8O2WpLjdNGPMA7OOGH5g10XhvWE17w7ZakuN00Y8\nwDs44YfmDXmV34hj8NfFTxDeNC880luIYIUHLyHyiB9ODXmV34hj8NfFTxDeNC880luIYIUHLyHy\niB9ODXmV34hj8NfFTxDeNC880luIYIUHLyHyiB9ODU3hB2l+IdwfF0Lrrjor2glxsTjOFHQHHT6H\nvU3hB2l+IdwfF0Lrrjor2glxsTjOFHQHHT6HvU3hB2l+IdwfF0Lrrjor2glxsTjOFHQHHT6HvXrl\neuV65UC2dql212ttCty67WmEYDkccFuuOB+VQLZ2qXbXa20K3LrtaYRgORxwW644H5VAtnapdtdr\nbQrcuu1phGA5HHBbrjgflXG/EDxFttW8M6WpudX1AeUYk58tG6k+hIz9ByeOvG/EDxFttW8M6Wpu\ndX1AeUYk58tG6k+hIz9ByeOvG/EDxFttW8M6WpudX1AeUYk58tG6k+hIz9ByeOvSeG9HGgeHbLTN\nwZoI8Ow6FiSzEe2Sa6Tw3o40Dw7ZaZuDNBHh2HQsSWYj2yTXSeG9HGgeHbLTNwZoI8Ow6FiSzEe2\nSa1a1a1aKKKqtdkMRsHB9aqtdkMRsHB9aqtdkMRsHB9aT7Y39wfnSfbG/uD86T7Y39wfnR9sb+4P\nzo+2N/cH50fbG/uD86ilmMuMjGKilmMuMjGKilmMuMjGKlF2QPuD86lF2QPuD86lF2QPuD86Ptjf\n3B+dH2xv7g/Oj7Y39wfnR9sb+4Pzo+2N/cH50fbG/uD86Ptjf3B+dH2xv7g/Oj7Y39wfnR9sb+4P\nzo+2N/cH50fbG/uD86Ptjf3B+dH2xv7g/Oj7Y39wfnR9sb+4Pzo+2N/cH50fbG/uD86Ptjf3B+dH\n2xv7g/Oj7Y39wfnSG7JUjYOfekN2SpGwc+9IbslSNg596RLkogXaDj3pEuSiBdoOPekS5KIF2g49\n6d9sb+4Pzp32xv7g/OnfbG/uD86Ptjf3B+dH2xv7g/Oj7Y39wfnR9sb+4Pzo+2N/cH50fbG/uD86\nPtjf3B+dH2xv7g/Oj7Y39wfnR9sb+4Pzo+2N/cH50fbG/uD86Ptjf3B+dH2xv7g/Oj7Y39wfnR9s\nb+4Pzo+2N/cH50fbG/uD86Ptjf3B+dH2xv7g/Oj7Y39wfnUUcpSQvjOaijlKSF8ZzUUcpSQvjOal\n+2N/cH51L9sb+4PzqX7Y39wfnR9sb+4Pzo+2N/cH50fbG/uD86Ptjf3B+dH2xv7g/Oj7Y39wfnR9\nsb+4Pzo+2N/cH50fbG/uD86Ptjf3B+dH2xv7g/Oj7Y39wfnR9sb+4Pzo+2N/cH50fbG/uD86Ptjf\n3B+dH2xv7g/Oj7Y39wfnR9sb+4Pzo+2N/cH50fbG/uD86a1yWZW2j5aa1yWZW2j5aa1yWZW2j5ad\n9sb+4Pzp32xv7g/OnfbG/uD86Ptjf3B+dH2xv7g/Oj7Y39wfnR9sb+4Pzo+2N/cH50fbG/uD86Pt\njf3B+dH2xv7g/Oj7Y39wfnR9sb+4Pzo+2N/cH50fbG/uD86Ptjf3B+dH2xv7g/Oj7Y39wfnR9sb+\n4Pzo+2N/cH50fbG/uD86Ptjf3B+dH2xv7g/Oj7Y39wfnTJLgyJtKgUyS4MibSoFMkuDIm0qBSx3J\njQLtBxSx3JjQLtBxSx3JjQLtBxTvtjf3B+dO+2N/cH5077Y39wfnR9sb+4Pzo+2N/cH50fbG/uD8\n6Ptjf3B+dH2xv7g/Oj7Y39wfnR9sb+4Pzo+2N/cH50fbG/uD86Ptjf3B+dH2xv7g/Oj7Y39wfnR9\nsb+4Pzo+2N/cH50fbG/uD86Ptjf3B+dH2xv7g/Oj7Y39wfnR9sb+4Pzo+2N/cH50fbG/uD86alyU\nLfKDuOaalyULfKDuOaalyULfKDuOad9sb+4Pzp32xv7g/OnfbG/uD86Ptjf3B+dH2xv7g/Oj7Y39\nwfnR9sb+4Pzo+2N/cH50fbG/uD86Ptjf3B+dH2xv7g/Oj7Y39wfnR9sb+4Pzo+2N/cH50fbG/uD8\n6Ptjf3B+dH2xv7g/Oj7Y39wfnR9sb+4Pzo+2N/cH50fbG/uD86Ptjf3B+dH2xv7g/Oj7Y39wfnUT\nSlphJjkdqiaUtMJMcjtUTSlphJjkdql+2N/cH51L9sb+4PzqX7Y39wfnR9sb+4Pzo+2N/cH50fbG\n/uD86Ptjf3B+dH2xv7g/Oj7Y39wfnR9sb+4Pzo+2N/cH50fbG/uD86Ptjf3B+dH2xv7g/Oj7Y39w\nfnR9sb+4Pzo+2N/cH50fbG/uD86Ptjf3B+dH2xv7g/Oj7Y39wfnR9sb+4Pzo+2N/cH50fbG/uD86\nbJcmRCpUDNNkuTIhUqBmmyXJkQqVAzTvth/uD86d9sP9wfnTvth/uD86Ptjf3B+dH2xv7g/Oj7Y3\n9wfnR9sb+4Pzo+2N/cH50fbG/uD86Ptjf3B+dH2xv7g/Oj7Y39wfnR9sb+4Pzo+2N/cH50fbG/uD\n86Ptjf3B+dH2xv7g/Oj7Y39wfnR9sb+4Pzo+2N/cH50fbG/uD86Ptjf3B+dH2xv7g/Oj7Y39wfnS\nNdllI2jkY60jXZZSNo5GOtI12WUjaORjrTIpjECAM5pkUxiBAGc0yKYxAgDOak+2N/cH51J9sb+4\nPzqT7Y39wfnSrdksBsHJ9aVbslgNg5PrSrdksBsHJ9atVaq1RRRTZH2RlsZxTZH2RlsZxTZH2Rls\nZxVb7Y39wfnVb7Y39wfnVb7Y39wfnR9sb+4Pzo+2N/cH50fbG/uD86Ptjf3B+dH2xv7g/Oj7Y39w\nfnUQlImMmOfSohKRMZMc+lRCUiYyY59Kl+2N/cH51L9sb+4PzqX7Y39wfnR9sb+4Pzo+2N/cH50f\nbG/uD86Ptjf3B+dH2xv7g/Oj7Y39wfnR9sb+4Pzo+2N/cH50fbG/uD86Ptjf3B+dH2xv7g/Oj7Y3\n9wfnR9sb+4Pzo+2N/cH50fbG/uD86Ptjf3B+dH2xv7g/Oj7Y39wfnR9sb+4Pzo+2N/cH50fbG/uD\n86Y9wX2/KBg5pj3Bfb8oGDmmPcF9vygYOaf9sb+4Pzp/2xv7g/On/bG/uD86Ptjf3B+dH2xv7g/O\nj7Y39wfnR9sb+4Pzo+2N/cH50fbG/uD86Ptjf3B+dH2xv7g/Oj7Y39wfnR9sb+4Pzo+2N/cH50fb\nG/uD86Ptjf3B+dH2xv7g/Oj7Y39wfnR9sb+4Pzo+2N/cH50fbG/uD86Ptjf3B+dH2xv7g/Oj7Y39\nwfnTZLkuhXaBmmyXJdCu0DNNkuS6FdoGaSK4MSbQoNJFcGJNoUGkiuDEm0KDT/tjf3B+dP8Atjf3\nB+dP+2N/cH50fbG/uD86Ptjf3B+dH2xv7g/Oj7Y39wfnR9sb+4Pzo+2N/cH50fbG/uD86Ptjf3B+\ndH2xv7g/Oj7Y39wfnR9sb+4Pzo+2N/cH50fbG/uD86Ptjf3B+dH2xv7g/Oj7Y39wfnR9sb+4Pzo+\n2N/cH50fbG/uD86Ptjf3B+dH2xv7g/OmLcFXdto+amLcFXdto+amLcFXdto+an/bG/uD86f9sb+4\nPzp/2xv7g/Oj7Y39wfnR9sb+4Pzo+2N/cH50fbG/uD86Ptjf3B+dH2xv7g/Oj7Y39wfnR9sb+4Pz\no+2N/cH50fbG/uD86Ptjf3B+dH2xv7g/Oj7Y39wfnR9sb+4Pzo+2N/cH50fbG/uD86Ptjf3B+dH2\nxv7g/Oj7Y39wfnR9sb+4Pzo+2N/cH51FJKZHDEYxUUkpkcMRjFRSSmRwxGMVL9sb+4PzqX7Y39wf\nnUv2xv7g/Oj7Y39wfnR9sb+4Pzo+2N/cH50fbG/uD86Ptjf3B+dH2xv7g/Oj7Y39wfnR9sb+4Pzo\n+2N/cH50fbG/uD86Ptjf3B+dH2xv7g/Oj7Y39wfnR9sb+4Pzo+2N/cH50fbG/uD86Ptjf3B+dH2x\nv7g/Oj7Y39wfnR9sb+4Pzo+2N/cH5017ouhXaBn3pr3RdCu0DPvTXui6FdoGfelW7KqBtHAx1pVu\nyqgbRwMdaVbsqoG0cDHWl+2N/cH50v2xv7g/Ol+2N/cH50fbG/uD86Ptjf3B+dH2xv7g/Oj7Y39w\nfnR9sb+4Pzo+2N/cH50fbG/uD86Ptjf3B+dH2xv7g/Oj7Y39wfnR9sb+4Pzo+2N/cH50fbG/uD86\nPtjf3B+dH2xv7g/Oj7Y39wfnR9sb+4Pzo+2N/cH50fbD/cH50fbD/cH50fbD/cH51FFKYiSBnNRR\nSmIkgZzUUUpiJIGc1L9sb+4PzqX7Y39wfnUv2xv7g/Oj7Y39wfnR9sb+4Pzo+2N/cH50fbG/uD86\nPtjf3B+dH2xv7g/Oj7Y39wfnR9sb+4Pzo+2N/cH50fbG/uD86Ptjf3B+dH2xv7g/Oj7Y39wfnR9s\nb+4Pzo+2N/cH50fbG/uD86Ptjf3B+dH2xv7g/Oj7Y39wfnR9sb+4Pzo+2N/cH5003JMgfaOBjFNN\nyTIH2jgYxTTckyB9o4GMU77Y39wfnTvtjf3B+dO+2N/cH50fbG/uD86Ptjf3B+dH2xv7g/Oj7Y39\nwfnR9sb+4Pzo+2N/cH50fbG/uD86Ptjf3B+dH2xv7g/Oj7Y39wfnR9sb+4Pzo+2N/cH50fbG/uD8\n6Ptjf3B+dH2xv7g/Oj7Y39wfnR9sb+4Pzo+2N/cH50fbG/uD86Ptjf3B+dH2xv7g/Oo5ZzKoBUDB\nzUcs5lUAqBg5qOWcyqAVAwc09boqgXaOBjrT1uiqBdo4GOtPW6KoF2jgY60v2xv7g/Ol+2N/cH50\nv2xv7g/OpIZzK5UqBxmpIZzK5UqBxmpIZzK5UqBxmp6nqeiiiiiiiiiiiiiiiiiiiiiiiiiiiiii\niiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiii\niiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiii\niiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiivOPHf/ABUfi7RPCaMwhLfabor2GD37\nHaG/76FeceO/+Kj8XaJ4TRmEJb7TdFewwe/Y7Q3/AH0K848d/wDFR+LtE8JozCEt9puivYYPfsdo\nb/voVc/4VJ4a9b3/AL/D/Crn/CpPDXre/wDf4f4Vc/4VJ4a9b3/v8P8ACsIadB8O/iLpn2ZpP7L1\nKLyGMrZIYnB59jsOfQmsIadB8O/iLpn2ZpP7L1KLyGMrZIYnB59jsOfQmsIadB8O/iLpn2ZpP7L1\nKLyGMrZIYnB59jsOfQmtX4yf8ilaf9fyf+i5K1fjJ/yKVp/1/J/6LkrV+Mn/ACKVp/1/J/6Lkr0C\nH/UR/wC6P5V6BD/qI/8AdH8q9Ah/1Ef+6P5VyXxQ/wCRAv8A/ei/9GLXJfFD/kQL/wD3ov8A0Ytc\nl8UP+RAv/wDei/8ARi0r/wDJHx/2Ax/6JpX/AOSPj/sBj/0TSv8A8kfH/YDH/omn/DP/AJJ7pf8A\n21/9GvT/AIZ/8k90v/tr/wCjXp/wz/5J7pf/AG1/9GvXN+JIJvAfixfEdjGzaVfkx3sKdFY85A9/\nvD3yOAa5vxJBN4D8WL4jsY2bSr8mO9hTorHnIHv94e+RwDXN+JIJvAfixfEdjGzaVfkx3sKdFY85\nA9/vD3yOAau/Bz/kUbv/AK/3/wDRcdXfg5/yKN3/ANf7/wDouOrvwc/5FG7/AOv9/wD0XHWZFpFt\n8RPHus3F48v9m6eq20PlttJIJ6HnIyHP4isyLSLb4ie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hmeGC7kjlmK8gmSQxqSP9kBj+Na/wAcr251zxn4Y8DwzPDBdyRyzFeQTJIY1JH+yAx/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\nk/2j9g8m7W583yPNzhHXbjcv9/Oc9qq/E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     }
    },
-   "id": "d29b0595-b87a-48c5-a151-141e6d1450f1"
+   "id": "165b5c4f-d534-4d86-b7cf-95318984387c"
   },
   {
    "cell_type": "code",
@@ -1781,7 +1781,7 @@
     "\n",
     "head(clin_and_mutation) "
    ],
-   "id": "547036c0-d806-4cdf-9769-29bdf57c0e23"
+   "id": "1868309c-1d99-4767-b319-4ea7adba34ed"
   },
   {
    "cell_type": "markdown",
@@ -1789,7 +1789,7 @@
    "source": [
     "We can count the number of genes with mutations per patient and then make a `left_join` to the clinical data to plot the mutated genes per tumor stage."
    ],
-   "id": "5427ae26-1f43-41d3-8e96-264d97663903"
+   "id": "2d61f631-53a8-4fd7-b2e2-cc8f36d3bec9"
   },
   {
    "cell_type": "code",
@@ -1853,10 +1853,10 @@
    ],
    "attachments": {
     "images/number_mut_genes_per_tumor_stage.jpeg": {
-     "image/jpeg": 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KKKKKKKKKKKKKKK\nKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKK\nKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKK\nKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKK\nKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKK\nKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKK\nKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKK\nKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKK\nKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKK\nKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKK\nKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKK\nKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKK\nKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKK\nKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKK\nKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKK\nKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKK\nKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKK\nKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKK\nKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKK\nKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKK/9k=\n"
     }
    },
-   "id": "25fcb81a-8e6c-4049-a44f-217969746cba"
+   "id": "a4f36d2d-25d1-4c02-a23c-63bb1ed161a6"
   },
   {
    "cell_type": "code",
@@ -1904,14 +1904,16 @@
     "<figcaption aria-hidden=\"true\">Number of mutations on gene TP53 per tumor stage</figcaption>\n",
     "</figure>\n",
     "\n",
-    "# Linking expression and tumor stage"
+    "# Linking expression and tumor stage\n",
+    "\n",
+    "We also can link the expression data (RNA-Seq) with the tumor stage data from the clinical metadata. <span class=\"column-margin margin-aside\">Check the TCGA RNA-Seq protocol online</span>"
    ],
    "attachments": {
     "images/number_mut_tp53_per_tumor_stage.jpeg": {
      "image/jpeg": 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mOvmb4ggN+0npoIBBvdPBB78x1W8e6DffCP4j2niLQk2adPKZbdQPkU/8tIG9\niCcex45XNVvHug33wj+I9p4i0JNmnTymW3UD5FP/AC0gb2IJx7Hjlc1W8e6DffCP4j2niLQk2adP\nKZbdQPkU/wDLSBvYgnHseOVzX0d4c1+y8T6BZ6xp77re5j3AHqh6FT7g5B+lfR3hzX7LxPoFnrGn\nvut7mPcAeqHoVPuDkH6V9HeHNfsvE+gWesae+63uY9wB6oehU+4OQfpXj37S3/IK8Pf9d5v/AEFa\n8e/aW/5BXh7/AK7zf+grXj37S3/IK8Pf9d5v/QVr1PwB/wAk68Nf9gu2/wDRa16n4A/5J14a/wCw\nXbf+i1r1PwB/yTrw1/2C7b/0WtebR+PvEzftBHwudS/4k32hk+zeRH08ktjdt3deetebR+PvEzft\nBHwudS/4k32hk+zeRH08ktjdt3deetebR+PvEzftBHwudS/4k32hk+zeRH08ktjdt3deete117XX\ntdePfHPxx4i8HSaENB1D7ILoT+d+5jk3bfL2/fU4+8enrXj3xz8ceIvB0mhDQdQ+yC6E/nfuY5N2\n3y9v31OPvHp61498c/HHiLwdJoQ0HUPsguhP537mOTdt8vb99Tj7x6eteq6TPJc6NY3EzbpZbeN3\nbGMkqCTxXqukzyXOjWNxM26WW3jd2xjJKgk8V6rpM8lzo1jcTNullt43dsYySoJPFeC6d8a9U0Xx\nl4nj8Q3zXmn2fnx2NmkMaF5RMFRd4UHAXdkkngHqcV4Lp3xr1TRfGXiePxDfNeafZ+fHY2aQxoXl\nEwVF3hQcBd2SSeAepxXgunfGvVNF8ZeJ4/EN815p9n58djZpDGheUTBUXeFBwF3ZJJ4B6nFSXfiv\n436pAdWsNFexscF1gitYyxX/AHZMuePQDPapLvxX8b9UgOrWGivY2OC6wRWsZYr/ALsmXPHoBntU\nl34r+N+qQHVrDRXsbHBdYIrWMsV/3ZMuePQDPatLwV8fYLiwv4/F0cdtd2kRljkt1I+0YIHlhCeH\nyR3x16YrS8FfH2C4sL+PxdHHbXdpEZY5LdSPtGCB5YQnh8kd8demK0vBXx9guLC/j8XRx213aRGW\nOS3Uj7RggeWEJ4fJHfHXpiqNx42+MXiiE6n4c0FtP0s/NCFhjZ3TqDmXl/qqgVRuPG3xi8UQnU/D\nmgtp+ln5oQsMbO6dQcy8v9VUCqNx42+MXiiE6n4c0FtP0s/NCFhjZ3TqDmXl/qqgVo/DT4yapq/i\nZfDHiu0jhvZGaOKdYzEwlXOUkQ9CcEcY54xzkaPw0+Mmqav4mXwx4rtI4b2RmjinWMxMJVzlJEPQ\nnBHGOeMc5Gj8NPjJqmr+Jl8MeK7SOG9kZo4p1jMTCVc5SRD0JwRxjnjHOR6N8R/+SbeI/wDsHzf+\ngmvRviP/AMk28R/9g+b/ANBNejfEf/km3iP/ALB83/oJr56+G/xHufCPhW60bRdLfUdev77dBFsL\nIq7FGSF5Y5B4GO5J9fnr4b/Ee58I+FbrRtF0t9R16/vt0EWwsirsUZIXljkHgY7kn1+evhv8R7nw\nj4VutG0XS31HXr++3QRbCyKuxRkheWOQeBjuSfXqb3xJ8dNDhk1fUNOR7JBveHyIJFRfcRnzAB3J\nPFdTe+JPjpocMmr6hpyPZIN7w+RBIqL7iM+YAO5J4rqb3xJ8dNDhk1fUNOR7JBveHyIJFRfcRnzA\nB3JPFeh/DL4o2fxAtZbeSBbPV7ZA81uGyrr03oeuM4yD0yOT1r0P4ZfFGz+IFrLbyQLZ6vbIHmtw\n2Vdem9D1xnGQemRyeteh/DL4o2fxAtZbeSBbPV7ZA81uGyrr03oeuM4yD0yOT1rrfEGv6d4Y0S51\nfVJvKtYFycDLMeyqO5J4Fdb4g1/TvDGiXOr6pN5VrAuTgZZj2VR3JPArrfEGv6d4Y0S51fVJvKtY\nFycDLMeyqO5J4FeHj4p/Ejx3ezx+CNEW3tIzgSeWrsP96SQiME9cY/PrXh4+KfxI8d3s8fgjRFt7\nSM4Enlq7D/ekkIjBPXGPz614ePin8SPHd7PH4I0Rbe0jOBJ5auw/3pJCIwT1xj8+tJN8SPip4Dlh\nl8Y6LHdWMjbWkZEX8BJFlAfYg9KSb4kfFTwHLDL4x0WO6sZG2tIyIv4CSLKA+xB6Uk3xI+KngOWG\nXxjosd1YyNtaRkRfwEkWUB9iD0r2vwv4n0zxfoMGr6VKXgl4ZWGHicdUYdiP8CMgg17X4X8T6Z4v\n0GDV9KlLwS8MrDDxOOqMOxH+BGQQa9r8L+J9M8X6DBq+lSl4JeGVhh4nHVGHYj/AjIINea/G7x14\ni8Hahoceiaj9kiuUlaceRHJu2lcfeU46npXmvxu8deIvB2oaHHomo/ZIrlJWnHkRybtpXH3lOOp6\nV5r8bvHXiLwdqGhx6JqP2SK5SVpx5Ecm7aVx95TjqelZd147+J/jW5nvfAeleRocMpSKZ0iDTgdS\nTKcH6KOOmc1l3Xjv4n+Nbme98B6V5GhwylIpnSINOB1JMpwfoo46ZzWXdeO/if41uZ73wHpXkaHD\nKUimdIg04HUkynB+ijjpnNeqeBrvxHfeFbe58VW8Vvqjs+6KOMoVUHA3DJ5OM8cYI4r1TwNd+I77\nwrb3Piq3it9Udn3RRxlCqg4G4ZPJxnjjBHFeqeBrvxHfeFbe58VW8Vvqjs+6KOMoVUHA3DJ5OM8c\nYI4ro66OujrI8TeJdN8JaFPq+qzGO3i4CqMvIx6Io7k//XOACayPE3iXTfCWhT6vqsxjt4uAqjLy\nMeiKO5P/ANc4AJrI8TeJdN8JaFPq+qzGO3i4CqMvIx6Io7k//XOACa8Sj+KPxM8d3U6+C9Djt7SM\n4EgRXK+zSSEJnvjH514lH8UfiZ47up18F6HHb2kZwJAiuV9mkkITPfGPzrxKP4o/Ezx3dTr4L0OO\n3tIzgSBFcr7NJIQme+MfnST/ABK+KngOaB/GGjR3VnI2C7xoufYSRZQN7EHp0pJ/iV8VPAc0D+MN\nGjurORsF3jRc+wkiygb2IPTpST/Er4qeA5oH8YaNHdWcjYLvGi59hJFlA3sQenSvafCfivTPGWgx\natpUhaJjskjfh4nHVWHY8j6gg17T4T8V6Z4y0GLVtKkLRMdkkb8PE46qw7HkfUEGvafCfivTPGWg\nxatpUhaJjskjfh4nHVWHY8j6gg189/GGH4hpZs3ie4ik0I6i32JEEOQcPszsAb7mev8AOvnv4ww/\nENLNm8T3EUmhHUW+xIghyDh9mdgDfcz1/nXz38YYfiGlmzeJ7iKTQjqLfYkQQ5Bw+zOwBvuZ6/zr\nofhzB8WTY+HHtruAeGN8RMZFvu+zbxuHK7vu575rofhzB8WTY+HHtruAeGN8RMZFvu+zbxuHK7vu\n575rofhzB8WTY+HHtruAeGN8RMZFvu+zbxuHK7vu575r3+vf69/ooor5i8cf8nOWX/YS07+UVfMX\njj/k5yy/7CWnfyir5i8cf8nOWX/YS07+UVX7Af8ACvP2kpLc/u7DVJSi8cFJ/mUD0AlwPopq/YD/\nAIV5+0lJbn93YapKUXjgpP8AMoHoBLgfRTV+wH/CvP2kpLc/u7DVJSi8cFJ/mUD0AlwPopr6Or6O\nr6OrxL9oPVZ7mHQvCNj891qNwJWjVsE87I1PsWY/ileJftB6rPcw6F4RsfnutRuBK0atgnnZGp9i\nzH8UrxL9oPVZ7mHQvCNj891qNwJWjVsE87I1PsWY/ilcN8T9Kh0L4l+F9Kt+YrOxsoVP97bIwyfc\n9fxrhvifpUOhfEvwvpVvzFZ2NlCp/vbZGGT7nr+NcN8T9Kh0L4l+F9Kt+YrOxsoVP97bIwyfc9fx\nr6fvpHh0+5ljOHSJmU46EA4r6fvpHh0+5ljOHSJmU46EA4r6fvpHh0+5ljOHSJmU46EA4ryL4HeO\nfEfjG61tNe1H7WtskJiHkRx7SxfP3FGeg615F8DvHPiPxjda2mvaj9rW2SExDyI49pYvn7ijPQda\n8i+B3jnxH4xutbTXtR+1rbJCYh5Ece0sXz9xRnoOteyV7JXsleJah4/8TQftAp4Xj1LboxuYYzbe\nRGflaFWI3bd3Uk9a8S1Dx/4mg/aBTwvHqW3Rjcwxm28iM/K0KsRu27upJ614lqHj/wATQftAp4Xj\n1LboxuYYzbeRGflaFWI3bd3Uk9a634oxeO5LOzPgyeKKFY5/t4cRfMuF243g9t/T/Cut+KMXjuSz\nsz4MniihWOf7eHEXzLhduN4Pbf0/wrrfijF47ks7M+DJ4ooVjn+3hxF8y4XbjeD239P8K8D+FkXj\n2WbVP+EGuIoXCxfa/MERyPm2f6wH/a6V4H8LIvHss2qf8INcRQuFi+1+YIjkfNs/1gP+10rwP4WR\nePZZtU/4Qa4ihcLF9r8wRHI+bZ/rAf8Aa6V9NnWn8M+BIdW8Uzhbi0s42vnUL8020BgoGBkscADA\n5r6bOtP4Z8CQ6t4pnC3FpZxtfOoX5ptoDBQMDJY4AGBzX02dafwz4Eh1bxTOFuLSzja+dQvzTbQG\nCgYGSxwAMDmvIIvin8SPHN1cL4J0CKGziOPNZVdl/wB53ITPfGPzryCL4p/EjxzdXC+CdAihs4jj\nzWVXZf8AedyEz3xj868gi+KfxI8c3VwvgnQIobOI481lV2X/AHnchM98Y/Oq2oeLPjd4RtjqOtWS\nT2acuXt4ZEUerGEgqPckVW1DxZ8bvCNsdR1qySezTly9vDIij1YwkFR7kiq2oeLPjd4RtjqOtWST\n2acuXt4ZEUerGEgqPckV6j8M/iLb/EHRppjbi11C0ZUuYA25ec7WU+hweDyMHr1PqPwz+Itv8QdG\nmmNuLXULRlS5gDbl5ztZT6HB4PIwevU+o/DP4i2/xB0aaY24tdQtGVLmANuXnO1lPocHg8jB69T5\nx8c4vHYj1WSSeI+DjJB5ceItwbavtv8A9Znv+lecfHOLx2I9VkkniPg4yQeXHiLcG2r7b/8AWZ7/\nAKV5x8c4vHYj1WSSeI+DjJB5ceItwbavtv8A9Znv+lUvhRB8Tm0rRH0m6hXwr9r/AHkbCDcY/N/e\n9V3/AN7vn0ql8KIPic2laI+k3UK+Fftf7yNhBuMfm/veq7/73fPpVL4UQfE5tK0R9JuoV8K/a/3k\nbCDcY/N/e9V3/wB7vn0rqPjl458R+DrnRE0HUfsi3KTGYeRHJuKlMffU46npXUfHLxz4j8HXOiJo\nOo/ZFuUmMw8iOTcVKY++px1PSuo+OXjnxH4OudETQdR+yLcpMZh5Ecm4qUx99Tjqeleu2MjzafbS\nyHLvErMfUkDNeu2MjzafbSyHLvErMfUkDNeu2MjzafbSyHLvErMfUkDNeNeMPjjcjXToHgjTl1K8\nEhiNwyNIrsOojReWx/eJxweCOa8a8YfHG5GunQPBGnLqV4JDEbhkaRXYdRGi8tj+8Tjg8Ec1414w\n+ONyNdOgeCNOXUrwSGI3DI0iuw6iNF5bH94nHB4I5rMk1n496cjajcacJrdRuMAht5OP92M+Z/Ws\nyTWfj3pyNqNxpwmt1G4wCG3k4/3Yz5n9azJNZ+PenI2o3GnCa3UbjAIbeTj/AHYz5n9a6/4afGG1\n8a3H9kanbpYa0FJVFJ8ufAydmeQRydpzwM5POOv+GnxhtfGtx/ZGp26WGtBSVRSfLnwMnZnkEcna\nc8DOTzjr/hp8YbXxrcf2RqdulhrQUlUUny58DJ2Z5BHJ2nPAzk847PxpqN1pPgnW9RsZfKurayll\nik2htrBSQcEEH8a7PxpqN1pPgnW9RsZfKurayllik2htrBSQcEEH8a7PxpqN1pPgnW9RsZfKuray\nllik2htrBSQcEEH8a8U8O/HLW18JfZ5Yn13xVdXrx20KwBQkQRMErGBu+YtgDk4OSMDPinh345a2\nvhL7PLE+u+Krq9eO2hWAKEiCJglYwN3zFsAcnByRgZ8U8O/HLW18JfZ5Yn13xVdXrx20KwBQkQRM\nErGBu+YtgDk4OSMDJqPir446VC+r3mnmOxQb3iW0idUXuSFy4A7knijUfFXxx0qF9XvNPMdig3vE\ntpE6ovckLlwB3JPFGo+KvjjpUL6veaeY7FBveJbSJ1Re5IXLgDuSeK7/AOFXxUTx/FcWV7bR2ur2\nqCR0iJ8uZM4LKDyMEgEHPUcnPHf/AAq+KieP4riyvbaO11e1QSOkRPlzJnBZQeRgkAg56jk547/4\nVfFRPH8VxZXttHa6vaoJHSIny5kzgsoPIwSAQc9Ryc8bPxE+IFl8P9DS8mi+03lwxS1tg23eR1JP\nZRkZ+oHetn4ifECy+H+hpeTRfaby4Ypa2wbbvI6knsoyM/UDvWz8RPiBZfD/AENLyaL7TeXDFLW2\nDbd5HUk9lGRn6gd68ug8S/HLxPbRanpGmxWlnKN0YSKBA69iBMSxB9e9eXQeJfjl4ntotT0jTYrS\nzlG6MJFAgdexAmJYg+vevLoPEvxy8T20Wp6RpsVpZyjdGEigQOvYgTEsQfXvUum/GjxT4X8Qx6P8\nQdIWJGxunjj2SIpON+ASsi/7uOh6kYqXTfjR4p8L+IY9H+IOkLEjY3Txx7JEUnG/AJWRf93HQ9SM\nVLpvxo8U+F/EMej/ABB0hYkbG6eOPZIik434BKyL/u46HqRivekdZEV0YMjDKsDkEete9I6yIrow\nZGGVYHII9a96R1kRXRgyMMqwOQR606nU6iiivE/GHxxuRrp0DwRpy6leCQxG4ZGkV2HURovLY/vE\n44PBHNeJ+MPjjcjXToHgjTl1K8EhiNwyNIrsOojReWx/eJxweCOa8T8YfHG5GunQPBGnLqV4JDEb\nhkaRXYdRGi8tj+8Tjg8Ec1mSaz8e9ORtRuNOE1uo3GAQ28nH+7GfM/rWZJrPx705G1G404TW6jcY\nBDbycf7sZ8z+tZkms/HvTkbUbjThNbqNxgENvJx/uxnzP611/wANPjDa+Nbj+yNTt0sNaCkqik+X\nPgZOzPII5O054GcnnHX/AA0+MNr41uP7I1O3Sw1oKSqKT5c+Bk7M8gjk7TngZyecdf8ADT4w2vjW\n4/sjU7dLDWgpKopPlz4GTszyCOTtOeBnJ5x2fjTUbrSfBOt6jYy+VdW1lLLFJtDbWCkg4IIP412f\njTUbrSfBOt6jYy+VdW1lLLFJtDbWCkg4IIP412fjTUbrSfBOt6jYy+VdW1lLLFJtDbWCkg4IIP41\n4p4d+OWtr4S+zyxPrviq6vXjtoVgChIgiYJWMDd8xbAHJwckYGfFPDvxy1tfCX2eWJ9d8VXV68dt\nCsAUJEETBKxgbvmLYA5ODkjAz4p4d+OWtr4S+zyxPrviq6vXjtoVgChIgiYJWMDd8xbAHJwckYGT\nUfFXxx0qF9XvNPMdig3vEtpE6ovckLlwB3JPFGo+KvjjpUL6veaeY7FBveJbSJ1Re5IXLgDuSeKN\nR8VfHHSoX1e808x2KDe8S2kTqi9yQuXAHck8V3/wq+KieP4riyvbaO11e1QSOkRPlzJnBZQeRgkA\ng56jk547/wCFXxUTx/FcWV7bR2ur2qCR0iJ8uZM4LKDyMEgEHPUcnPHf/Cr4qJ4/iuLK9to7XV7V\nBI6RE+XMmcFlB5GCQCDnqOTnj0mvSa9JoooryT9oTW307wFDp0T7X1K6VHHcxoNx/wDHgn515J+0\nJrb6d4Ch06J9r6ldKjjuY0G4/wDjwT868k/aE1t9O8BQ6dE+19SulRx3MaDcf/Hgn51e+BXh6LR/\nhzbXpiC3Wpu1xKxHJUEqgz6YGf8AgRq98CvD0Wj/AA5tr0xBbrU3a4lYjkqCVQZ9MDP/AAI1e+BX\nh6LR/hzbXpiC3Wpu1xKxHJUEqgz6YGf+BGvTa9Nr02iiiuS1X4beGda8WR+JL+0kkv4xHt2zMi7k\nOVchcEt0HJxhRxXJar8NvDOteLI/El/aSSX8Yj27ZmRdyHKuQuCW6Dk4wo4rktV+G3hnWvFkfiS/\ntJJL+MR7dszIu5DlXIXBLdBycYUcV1busaM7sFRRlmY4AHqa6t3WNGd2CooyzMcAD1NdW7rGjO7B\nUUZZmOAB6mvC/EHxu1nWdeOh/D7ShePkqLl4jI0mOrKvAVR/eb8hXhfiD43azrOvHQ/h9pQvHyVF\ny8RkaTHVlXgKo/vN+QrwvxB8btZ1nXjofw+0oXj5Ki5eIyNJjqyrwFUf3m/IVTuPEnxz8N276nqm\nmpdWcY3OjQwSBV7kiEhgB69qp3HiT45+G7d9T1TTUurOMbnRoYJAq9yRCQwA9e1U7jxJ8c/Ddu+p\n6ppqXVnGNzo0MEgVe5IhIYAevavSfhx8TdO+IFjIqRfZNUtwDPaM275em9D3X9QeD1BPpPw4+Jun\nfECxkVIvsmqW4BntGbd8vTeh7r+oPB6gn0n4cfE3TviBYyKkX2TVLcAz2jNu+XpvQ91/UHg9QTJ8\nV9e1Lw18Pb/VNIufs97E8QSTy1fAaRQeGBHQntUnxX17UvDXw9v9U0i5+z3sTxBJPLV8BpFB4YEd\nCe1SfFfXtS8NfD2/1TSLn7PexPEEk8tXwGkUHhgR0J7V5pp3xr8Q3nhjSNL0exbXPFdwkj3UnkfL\nEPNYL8iAAnbtOeAOCc5OPNNO+NfiG88MaRpej2La54ruEke6k8j5Yh5rBfkQAE7dpzwBwTnJx5pp\n3xr8Q3nhjSNL0exbXPFdwkj3UnkfLEPNYL8iAAnbtOeAOCc5OKWp+NfjT4VT+1NZsgbHI3CS1ieJ\nM9Nxj+ZR25IqlqfjX40+FU/tTWbIGxyNwktYniTPTcY/mUduSKpan41+NPhVP7U1myBscjcJLWJ4\nkz03GP5lHbkivWPhr8Qrb4g6HLciAW1/asEurcNuAJHysp/unB68ggjnGT6x8NfiFbfEHQ5bkQC2\nv7Vgl1bhtwBI+VlP904PXkEEc4yfWPhr8Qrb4g6HLciAW1/asEurcNuAJHysp/unB68ggjnGT0Xi\nDX9O8MaJc6vqk3lWsC5OBlmPZVHck8Cui8Qa/p3hjRLnV9Um8q1gXJwMsx7Ko7kngV0XiDX9O8Ma\nJc6vqk3lWsC5OBlmPZVHck8CvDx8U/iR47vZ4/BGiLb2kZwJPLV2H+9JIRGCeuMfn1rw8fFP4keO\n72ePwRoi29pGcCTy1dh/vSSERgnrjH59a8PHxT+JHju9nj8EaItvaRnAk8tXYf70khEYJ64x+fWk\nm+JHxU8Bywy+MdFjurGRtrSMiL+AkiygPsQelJN8SPip4Dlhl8Y6LHdWMjbWkZEX8BJFlAfYg9KS\nb4kfFTwHLDL4x0WO6sZG2tIyIv4CSLKA+xB6V7X4X8T6Z4v0GDV9KlLwS8MrDDxOOqMOxH+BGQQa\n9r8L+J9M8X6DBq+lSl4JeGVhh4nHVGHYj/AjIINe1+F/E+meL9Bg1fSpS8EvDKww8TjqjDsR/gRk\nEGtmtmtmiiiiiivDP2lv+QLoH/XxL/6CteGftLf8gXQP+viX/wBBWvDP2lv+QLoH/XxL/wCgrXpX\nw1/5Jp4c/wCvCL+VelfDX/kmnhz/AK8Iv5V6V8Nf+SaeHP8Arwi/lXU11NdTXzX+0n/yNWjf9eJ/\n9DNfNf7Sf/I1aN/14n/0M181/tJ/8jVo3/Xif/QzX0Xp3/IMtP8Arin/AKCK+i9O/wCQZaf9cU/9\nBFfRenf8gy0/64p/6CKs1ZqzXzl+0pqNpPrOhafE6tdWsM0kwH8IcptB/wC+GOPcetfOX7Smo2k+\ns6Fp8Tq11awzSTAfwhym0H/vhjj3HrX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HmlaFZATXFtAEKIefNlbh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     }
    },
-   "id": "7977dbb8-0387-4794-9f4b-bd57a5aa9645"
+   "id": "da530c51-d59f-4736-a3dd-bcaa4a84be50"
   },
   {
    "cell_type": "code",
@@ -1939,7 +1941,15 @@
     "\n",
     "rnaseq"
    ],
-   "id": "0fbc5409-9c97-41a0-957a-8d583f6a44ee"
+   "id": "1109b82a-7cae-4d50-a43b-b535db743359"
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As before, we need to shorten the sample IDs so they can match to the clinical data."
+   ],
+   "id": "6f66b61d-e71b-4b81-b6ec-5c9c3cce6579"
   },
   {
    "cell_type": "code",
@@ -1962,7 +1972,15 @@
     "\n",
     "rnaseq <- rnaseq[,!idx]"
    ],
-   "id": "9d071cd6-e79d-4b95-a1e1-52c396100e83"
+   "id": "55c76eb8-035d-4661-97e1-c7aafeec76d2"
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Then, we can use the `limma` package to proceed with the differential expression analysis. In this case, we’ll ise the tumor stage as a variable to explain the expression of the genes."
+   ],
+   "id": "df38b193-f7c2-444f-9e2d-6c986d401da3"
   },
   {
    "cell_type": "code",
@@ -2039,7 +2057,15 @@
    "source": [
     "library(limma)"
    ],
-   "id": "5df511cf-a642-42f1-a17b-85f08b8d3d21"
+   "id": "e6c1ed1c-91d2-4c32-a63f-3967417735d1"
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let’s visualize two of the most expressed genes."
+   ],
+   "id": "a017905a-458e-4fcf-a1a0-d15133e52a30"
   },
   {
    "cell_type": "code",
@@ -2059,15 +2085,19 @@
     "boxplot(split(assay(rnaseq)[\"PAM\",], rnaseq$t_stage), main=\"PAM\")    # higher expression in lower t_stage\n",
     "boxplot(split(assay(rnaseq)[\"PAIP2\",], rnaseq$t_stage), main=\"PAIP2\")"
    ],
-   "id": "a5cddb6d-591c-40cc-b392-57bba046ec14"
+   "id": "cf5490d8-5910-4d5b-8e9b-8510ea537cd5"
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Linking methylation and expression"
+    "# Linking methylation and expression\n",
+    "\n",
+    "Finally, we can use the methylation data with the expression data. This is important because methylated cytosines of the DNA change the expression of the genes.\n",
+    "\n",
+    "Some patients have methylation data for multiples tissue types. This information is encoded in the fourth component of the sample names. The code `01A` correspond to primary tumor samples and the code `11A` correspond to normal tissue. We’ll keep the primary tumor samples."
    ],
-   "id": "f6a4af75-d376-465c-9b9e-31a992f6b60f"
+   "id": "ca34e9c7-0379-47dd-af33-66265ceb5987"
   },
   {
    "cell_type": "code",
@@ -2105,7 +2135,15 @@
     "\n",
     "methyl"
    ],
-   "id": "7a39de75-20c4-4754-b9af-c0c103768ebb"
+   "id": "833a02a6-830e-43c0-8253-7b403b4f87ef"
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As before, let’s truncate the names of the sample to match the clinical data."
+   ],
+   "id": "e1ee2bff-40d1-41f0-91e2-6dde3508ca16"
   },
   {
    "cell_type": "code",
@@ -2116,7 +2154,15 @@
     "colnames(methyl) <- colnames(methyl) |> \n",
     "  str_sub(start = 1,end = 12)"
    ],
-   "id": "6c485c73-3856-480f-a3ed-6c398eba1551"
+   "id": "1773020c-37a1-457f-bf56-35a659d007b4"
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can add the clinical data to the methyl object and count the number of patients with methylation and transcription data."
+   ],
+   "id": "bd210bd9-01a0-4b05-9167-de057a43d730"
   },
   {
    "cell_type": "code",
@@ -2137,7 +2183,15 @@
     "\n",
     "intersect(colnames(methyl), colnames(rnaseq)) |> length()"
    ],
-   "id": "db877121-b0ab-421e-b2cc-0a43a482c301"
+   "id": "9d1b212e-9d88-4782-ade0-6ab94478f51a"
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let’s subset common sample names and check the methylation data as row ranges."
+   ],
+   "id": "f4624d30-6b21-4901-a07e-e7b06e4fd672"
   },
   {
    "cell_type": "code",
@@ -2172,7 +2226,15 @@
     "methyl_genes = rowData(methyl_subset)\n",
     "methyl_genes"
    ],
-   "id": "8f774b6b-0944-4828-a03b-cc191c03ba38"
+   "id": "eac7008a-ec42-48a2-9463-57ced987211c"
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This function takes a gene symbol and returns a scatter plot showing the relationship between 3 different sites near that gene and gene expression."
+   ],
+   "id": "d64c3732-7286-4938-a9a8-389f31521365"
   },
   {
    "cell_type": "code",
@@ -2292,15 +2354,7 @@
     "\n",
     "me_rna_cor(\"TAC1\", mpick=3)"
    ],
-   "id": "5fed20fc-82b3-4084-bc5f-8bc3b6929611"
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Conclusion"
-   ],
-   "id": "aa55f378-8647-40f0-b597-28c3794aa345"
+   "id": "928bbdef-2b19-4dfb-ab6e-3bbe47a0c9b0"
   }
  ],
  "nbformat": 4,
diff --git a/notebooks/TCGA.out.ipynb b/notebooks/TCGA.out.ipynb
index 585373e..29039c3 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": "6eacdb4f-ab5b-4be5-b800-70a66baaef19"
+   "id": "0cb65cfe-e7fe-43a4-889e-d13eafae17c0"
   },
   {
    "cell_type": "code",
@@ -91,7 +91,7 @@
    "source": [
     "library(knitr)\n"
    ],
-   "id": "09a582cf-7745-42cb-9f93-de5e4b310be1"
+   "id": "7126376c-1f69-4adc-8067-913257d1be6b"
   },
   {
    "cell_type": "markdown",
@@ -105,7 +105,7 @@
     "\n",
     "First, let’s load the packages needed."
    ],
-   "id": "e3d9a8b2-2346-4068-a721-b6265285aa2a"
+   "id": "6b4a7c24-a43f-4550-a404-8d491898b38a"
   },
   {
    "cell_type": "code",
@@ -473,7 +473,7 @@
    "source": [
     "library(curatedTCGAData)\n"
    ],
-   "id": "62513d41-f8b1-4381-aded-f5a94d0ba404"
+   "id": "7f22fa71-84e1-4f72-bb6d-8d4f0a3dd786"
   },
   {
    "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": "d6a111ac-d7eb-40ae-99b3-6e5b0f618fc5"
+   "id": "80dbaeca-2254-49b1-b437-82549e169735"
   },
   {
    "cell_type": "code",
@@ -868,7 +868,7 @@
     "                           c(\"RNASeq2GeneNorm\", \"Mutation\", \"Methylation_methyl450\"), \n",
     "                           dry.run = FALSE, version = \"2.1.1\")\n"
    ],
-   "id": "f1fcf266-4921-4a3f-a773-e6d9667d3d70"
+   "id": "e84adb0e-5f7e-4954-bdaf-208082593e27"
   },
   {
    "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": "5738c07b-78a5-4937-ae7d-0397423dfa84"
+   "id": "f01d159f-bf09-44ef-a5b5-e856b5fecabc"
   },
   {
    "cell_type": "code",
@@ -909,7 +909,7 @@
    "source": [
     "sampleMap(readData)\n"
    ],
-   "id": "f5b18795-6dd5-4dd0-a9d8-e0f243b062f5"
+   "id": "bac796a0-6fb3-4a4f-827d-2af3894b566f"
   },
   {
    "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": "827b83aa-a14e-45f0-9e74-0b228c2e49a2"
+   "id": "a556806b-2684-40e4-92cf-164f80b41690"
   },
   {
    "cell_type": "code",
@@ -941,7 +941,7 @@
     "  table() |> \n",
     "  table()\n"
    ],
-   "id": "ef3efd6a-17f7-423a-bdf2-a61db2ee5533"
+   "id": "a381a72e-eab6-4a16-aca6-d4df3bd329cb"
   },
   {
    "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": "8b08f7b3-fe4d-42bc-9e4f-8453309ef536"
+   "id": "220f0006-7ed7-4f0d-bc36-d3e8c5be2480"
   },
   {
    "cell_type": "code",
@@ -979,7 +979,7 @@
     "  as_tibble()\n",
     "dim(clin)\n"
    ],
-   "id": "838cc584-f64d-4947-94f0-b033fa7b54ba"
+   "id": "b68c3adf-dcdf-4509-a2f7-21d5aaf416a7"
   },
   {
    "cell_type": "markdown",
@@ -987,7 +987,7 @@
    "source": [
     "As an example, for rectum adenocarcinoma, we can see the tumor stage."
    ],
-   "id": "9cc08554-1beb-4627-a958-4bb3028ef56e"
+   "id": "80517bcb-42fb-48d0-a266-aac87208c936"
   },
   {
    "cell_type": "code",
@@ -1009,7 +1009,7 @@
     "  pull(pathology_T_stage) |> \n",
     "  table()\n"
    ],
-   "id": "4e36c4bf-1a19-48d0-aa8b-2e16ab5b9361"
+   "id": "fc66e1dc-3c44-427d-9591-6f723f2fb8e4"
   },
   {
    "cell_type": "markdown",
@@ -1017,7 +1017,7 @@
    "source": [
     "Stage T4 have subgroups. To simplify the analysis, let’s combine all T4 tumors."
    ],
-   "id": "730bb145-df4c-416e-ab93-73f0ff9b1686"
+   "id": "70adf48f-fdc8-41b7-9ca7-492cdf4170ef"
   },
   {
    "cell_type": "code",
@@ -1044,7 +1044,7 @@
     "clin$t_stage |> \n",
     "  table()\n"
    ],
-   "id": "6ae9f105-e330-4361-838a-10f01d77588a"
+   "id": "e9fff9ec-7180-4067-a6b7-b872d6ecbc38"
   },
   {
    "cell_type": "markdown",
@@ -1052,7 +1052,7 @@
    "source": [
     "Also, we can see the vital status (alive=0, deceased=1)"
    ],
-   "id": "983d50ba-9f09-429f-aaa1-aa83c14f1257"
+   "id": "e3d65e1c-ae1b-48bd-a4a1-98da860e07b5"
   },
   {
    "cell_type": "code",
@@ -1073,7 +1073,7 @@
     "clin$vital_status |> \n",
     "  table()\n"
    ],
-   "id": "6bc9d2cd-78a5-46e9-b6ff-8bfd3e0b9e42"
+   "id": "e598a760-4b17-471f-b09a-642b305e9ae5"
   },
   {
    "cell_type": "markdown",
@@ -1081,7 +1081,7 @@
    "source": [
     "Or combine tumor status and vital status."
    ],
-   "id": "7b42c806-3e73-45f7-97cd-1fe28446d3e4"
+   "id": "0ecb87f0-1103-4187-9e85-1c8860961ee1"
   },
   {
    "cell_type": "code",
@@ -1104,7 +1104,7 @@
    "source": [
     "table(clin$t_stage, clin$vital_status)\n"
    ],
-   "id": "7f904465-3327-4fd9-a008-8cf1e863a92a"
+   "id": "4537180b-7ffe-49fb-aa2a-5496606437c9"
   },
   {
    "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": "6d4af501-78d7-4909-9915-5443222e6ad6"
+   "id": "ab98d4cb-62da-4898-929f-0c28286b7e2e"
   },
   {
    "cell_type": "code",
@@ -1140,7 +1140,7 @@
     "\n",
     "mut_data\n"
    ],
-   "id": "097f6db2-cc40-460b-a16f-b4d4db2a7282"
+   "id": "7cc12b85-69be-4c3f-92f0-1a79e65ce105"
   },
   {
    "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": "daa34695-7785-49a1-bd48-e0ae5b59e8c5"
+   "id": "e7ac4227-dd18-48bd-9a3f-cb2ba434caf9"
   },
   {
    "cell_type": "code",
@@ -1177,7 +1177,7 @@
     "mut_sample_ids = colnames(mut_data)\n",
     "head(mut_sample_ids)\n"
    ],
-   "id": "7a1bad57-0c86-4c88-8b4e-5eb8a38ba807"
+   "id": "b4b3b400-62ba-410b-b7bb-ef7c7d9ce5db"
   },
   {
    "cell_type": "markdown",
@@ -1185,7 +1185,7 @@
    "source": [
     "We need to manipulate these by substracting 12 characters."
    ],
-   "id": "a94f9bde-6cc7-4d10-a7b3-4b4b740dbbb0"
+   "id": "bb7d463f-95cc-4b6e-a4d2-17e692f26ef6"
   },
   {
    "cell_type": "code",
@@ -1206,7 +1206,7 @@
     "\n",
     "all(mut_sample_ids %in% clin$patientID)\n"
    ],
-   "id": "b09c6c29-6fa0-423a-8ef2-cb0893be1ac3"
+   "id": "4f803dd8-33ba-4310-8dbd-189c9bc9112c"
   },
   {
    "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": "e0c600a4-6e0e-44b2-a5cc-459f2ad59235"
+   "id": "813790b8-f0d6-447b-a3ac-68d0baeeb60e"
   },
   {
    "cell_type": "code",
@@ -1248,7 +1248,7 @@
    "source": [
     "assay(mut_data)[1:3,1:3]\n"
    ],
-   "id": "2b6c6b4c-b5a3-481c-8a62-e41580fcab4d"
+   "id": "7e5cf08c-f9a8-4285-a47e-4fd2edf3c051"
   },
   {
    "cell_type": "markdown",
@@ -1256,7 +1256,7 @@
    "source": [
     "We can get more information if we look at the mutation information for each patient."
    ],
-   "id": "61b2a676-b742-44cb-91e3-8aa1b1eddee2"
+   "id": "1eb8ac21-6a12-48ea-b4eb-73e62b0cbec9"
   },
   {
    "cell_type": "code",
@@ -1429,7 +1429,7 @@
     "\n",
     "mut_assay # GRangesList\n"
    ],
-   "id": "a7926dd4-10af-4406-806e-4a58567c565e"
+   "id": "9b0a5a72-f118-4806-8d5c-355eb8f18920"
   },
   {
    "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": "bce883ed-72f9-429b-8ec1-3cb945c64e41"
+   "id": "63a8100e-4725-4447-8a83-b0f67cd20466"
   },
   {
    "cell_type": "code",
@@ -1622,7 +1622,7 @@
    "source": [
     "mut_assay[[1]]\n"
    ],
-   "id": "3297b02c-10db-4a6c-89ea-78653d7374f3"
+   "id": "bd67ea2e-16c3-4692-a1d0-2373c3d6cb37"
   },
   {
    "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": "25ba81dc-1543-45ee-bb3a-bcefc94936f4"
+   "id": "8ebc0f9f-e943-4ef5-a1cb-c6f0d0d5d58a"
   },
   {
    "cell_type": "code",
@@ -1671,7 +1671,7 @@
     "\n",
     "head(var_class_df)\n"
    ],
-   "id": "ded762ce-1091-4a25-b163-f913af2a58d6"
+   "id": "498a3862-2286-415a-91b5-428e657fb7b2"
   },
   {
    "cell_type": "markdown",
@@ -1679,7 +1679,7 @@
    "source": [
     "We can visualize the most common mutated genes genes in rectum adenocarcinoma"
    ],
-   "id": "9780cc31-71b1-4708-91e8-60c2c560cadd"
+   "id": "2d1d57fe-16c8-4f24-b7d2-efd279cb386b"
   },
   {
    "cell_type": "code",
@@ -1737,7 +1737,7 @@
      "image/jpeg": 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KoXztoyVYDjcBk5HUA9xz6F8Hvi7d+Jr4eHPELK+olC1t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\ngjjBXfyOvy14p8APER0Xxtd+HriVTDqKlUKsGXz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j3SLljBeWkZkhT+JZX/cN+TeVXzh8\nOvFcvhTwD490i5YwXlpGZIU/iWV/3Dfk3lV6P8AtEWz+GrXcqBjqdxJIcjqi/uwD+Ksf+BV6P8At\nEWz+GrXcqBjqdxJIcjqi/uwD+Ksf+BV6P8AtEWz+GrXcqBjqdxJIcjqi/uwD+Ksf+BV578N9aX4b\n+MPG2k3R/d21tM8Yc4MrwMdgH+8rE1578N9aX4b+MPG2k3R/d21tM8Yc4MrwMdgH+8rE1578N9aX\n4b+MPG2k3R/d21tM8Yc4MrwMdgH+8rE11H7OmjyNYa14lusvPdzi3R3HzEL8znPfJYfitdR+zpo8\njWGteJbrLz3c4t0dx8xC/M5z3yWH4rXUfs6aPI1hrXiW6y893OLdHcfMQvzOc98lh+K11nxz/wCS\nTar/ANdIP/Rq11nxz/5JNqv/AF0g/wDRq11nxz/5JNqv/XSD/wBGrUXwI/5JTYf9d5//AEM1F8CP\n+SU2H/Xef/0M1F8CP+SU2H/Xef8A9DNedfFL/k4Pw9/v2P8A6ONedfFL/k4Pw9/v2P8A6ONedfFL\n/k4Pw9/v2P8A6ONfR9fR9fR9fOEn/J14/wCvof8ApNXzhJ/ydeP+vof+k1fOEn/J14/6+h/6TV9H\n19H19H14B+0108L/APb3/wC0a8A/aa6eF/8At7/9o14B+0108L/9vf8A7Rr2bwj/AMiVoP8A2Drf\n/wBFrXs3hH/kStB/7B1v/wCi1r2bwj/yJWg/9g63/wDRa1s1s1s1na//AMi3qn/XpL/6Aaztf/5F\nvVP+vSX/ANANZ2v/APIt6p/16S/+gGvBf2af+Qxr/wD17xf+hNXgv7NP/IY1/wD694v/AEJq8F/Z\np/5DGv8A/XvF/wChNX0XX0XX0XXh3x5+Ha3tk3i/S4QLq3UC/RF5kjHAk+q9/wDZ/wB2vDvjz8O1\nvbJvF+lwgXVuoF+iLzJGOBJ9V7/7P+7Xh3x5+Ha3tk3i/S4QLq3UC/RF5kjHAk+q9/8AZ/3a2vgn\n8RH8V6K+japOX1fT0GHc/NcQ9Ax9WBwCfcHqTW18E/iI/ivRX0bVJy+r6egw7n5riHoGPqwOAT7g\n9Sa2vgn8RH8V6K+japOX1fT0GHc/NcQ9Ax9WBwCfcHqTXo+vf8i7qf8A16S/+gGvR9e/5F3U/wDr\n0l/9ANej69/yLup/9ekv/oBrwP8AZq/5Dev/APXtF/6Ea8D/AGav+Q3r/wD17Rf+hGvA/wBmr/kN\n6/8A9e0X/oRr6Mr6Mr6Moooooorwb9o/xL5dppfhmF/mlP2y4AP8IyqD3BO8/wDARXg37R/iXy7T\nS/DML/NKftlwAf4RlUHuCd5/4CK8G/aP8S+XaaX4Zhf5pT9suAD/AAjKoPcE7z/wEVNof7O2i3Wg\n2FxquoapDfywJJPFE0YWNyMlRlCeM469qm0P9nbRbrQbC41XUNUhv5YEkniiaMLG5GSoyhPGcde1\nTaH+ztot1oNhcarqGqQ38sCSTxRNGFjcjJUZQnjOOvatD/hm/wAK/wDQV1n/AL+Rf/G60P8Ahm/w\nr/0FdZ/7+Rf/AButD/hm/wAK/wDQV1n/AL+Rf/G68s+I3gx/hV4v0m60i5nkgIW5tprjBYSxt8yn\nAAOPkPT+KvLPiN4Mf4VeL9JutIuZ5ICFubaa4wWEsbfMpwADj5D0/iryz4jeDH+FXi/SbrSLmeSA\nhbm2muMFhLG3zKcAA4+Q9P4q+ptC1e31/QbHV7U/ubyBZlGclcjlT7g5B9xX1NoWr2+v6DY6van9\nzeQLMozkrkcqfcHIPuK+ptC1e31/QbHV7U/ubyBZlGclcjlT7g5B9xXzlpv/ACdG/wD2FJv/AEBq\n+ctN/wCTo3/7Ck3/AKA1fOWm/wDJ0b/9hSb/ANAavp2vp2vp2iiivmH40643jX4i2HhzR8XH2Jvs\niFTkNcSMN4z6DCg+hU18w/GnXG8a/EWw8OaPi4+xN9kQqchriRhvGfQYUH0KmvmH40643jX4i2Hh\nzR8XH2JvsiFTkNcSMN4z6DCg+hU19IaNpkWjaHYaXCSYrO3jgUnqQqhc/pX0ho2mRaNodhpcJJis\n7eOBSepCqFz+lfSGjaZFo2h2GlwkmKzt44FJ6kKoXP6Veq9V6iiiiiiiiiiiiiiiiiiiiiiiiiii\niiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiii\niiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiii\niiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiii\niiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiii\niiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiii\niiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiii\niiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiiis7xBFJN4b1SKJGkke0lVEUZLE\nocADuazvEEUk3hvVIokaSR7SVURRksShwAO5rO8QRSTeG9UiiRpJHtJVRFGSxKHAA7mvGP2e/D+t\naLqOutqukX9gssMIjN1bPEHILZxuAzXjH7Pfh/WtF1HXW1XSL+wWWGERm6tniDkFs43AZrxj9nvw\n/rWi6jrrarpF/YLLDCIzdWzxByC2cbgM17uyq6FHUMrDBBGQRXu7KroUdQysMEEZBFe7squhR1DK\nwwQRkEV8+6/8JfFXgzxM3iD4eTs8JJItldRJEp5KENxInTAPPTgkZr591/4S+KvBniZvEHw8nZ4S\nSRbK6iSJTyUIbiROmAeenBIzXz7r/wAJfFXgzxM3iD4eTs8JJItldRJEp5KENxInTAPPTgkZqR/H\nnxq1BG0+Dwn9lnYbRcrp8iEH1DSMU/TFSP48+NWoI2nweE/ss7DaLldPkQg+oaRin6YqR/Hnxq1B\nG0+Dwn9lnYbRcrp8iEH1DSMU/TFa/wANfg9fabrg8U+MJ1uNV3mWK3L+YUkP/LSRujMDnGMgHnOe\nmv8ADX4PX2m64PFPjCdbjVd5lity/mFJD/y0kbozA5xjIB5znpr/AA1+D19puuDxT4wnW41XeZYr\ncv5hSQ/8tJG6MwOcYyAec56ehfEX/km/iT/sHTf+gGvQviL/AMk38Sf9g6b/ANANehfEX/km/iT/\nALB03/oBr5x+G/hXxXcaTceK/Bt6E1OxuTbvakgedGVVjy3ynk/dPpkHIFfOPw38K+K7jSbjxX4N\nvQmp2Nybd7UkDzoyqseW+U8n7p9Mg5Ar5x+G/hXxXcaTceK/Bt6E1OxuTbvakgedGVVjy3ynk/dP\npkHIFdld+L/jXr8Mmk2/hltPlcbGuYbR4TjviSRyo+o59CK7K78X/GvX4ZNJt/DLafK42Ncw2jwn\nHfEkjlR9Rz6EV2V34v8AjXr8Mmk2/hltPlcbGuYbR4TjviSRyo+o59CK7P4UfCn/AIQhZdV1WWO4\n1u4TYdhykCE5Kgnqx4yfwHcns/hR8Kf+EIWXVdVljuNbuE2HYcpAhOSoJ6seMn8B3J7P4UfCn/hC\nFl1XVZY7jW7hNh2HKQITkqCerHjJ/AdyfT69Pr0+iiivBvj9B4o1zU9N0fStF1O802CL7TI9rayS\nI0rFlAJUEZVR/wCPmvBvj9B4o1zU9N0fStF1O802CL7TI9raySI0rFlAJUEZVR/4+a8G+P0HijXN\nT03R9K0XU7zTYIvtMj2trJIjSsWUAlQRlVH/AI+a5fw14k+KXhLRItJ0nwZdR20ZLZbRpyzsTyzH\nuf6ADtXL+GvEnxS8JaJFpOk+DLqO2jJbLaNOWdieWY9z/QAdq5fw14k+KXhLRItJ0nwZdR20ZLZb\nRpyzsTyzHuf6ADtWv/wsn4y/9Clc/wDgmnrX/wCFk/GX/oUrn/wTT1r/APCyfjL/ANClc/8Agmnr\nvPhf4p8deINUvofFmjS2FvFCGhd7GSDc27BGW68dq7z4X+KfHXiDVL6HxZo0thbxQhoXexkg3Nuw\nRluvHau8+F/inx14g1S+h8WaNLYW8UIaF3sZINzbsEZbrx2rU+KHw6h+IGhxxxSpb6paEtazP905\n6o2P4Tgc9QQD6g6nxQ+HUPxA0OOOKVLfVLQlrWZ/unPVGx/CcDnqCAfUHU+KHw6h+IGhxxxSpb6p\naEtazP8AdOeqNj+E4HPUEA+oPl+ma78ZfAtlHop8OvqcEPyQO9s9zsUdAHiYcem7p04xivL9M134\ny+BbKPRT4dfU4Ifkgd7Z7nYo6APEw49N3TpxjFeX6Zrvxl8C2Ueinw6+pwQ/JA72z3OxR0AeJhx6\nbunTjGKk0r4beM/iJ4pi1zx/utbCMjFuxCsyZz5aIv3F9ScH6nmpNK+G3jP4ieKYtc8f7rWwjIxb\nsQrMmc+WiL9xfUnB+p5qTSvht4z+InimLXPH+61sIyMW7EKzJnPloi/cX1Jwfqea+hI40ijWONVR\nEAVVUYAA6AV9CRxpFGscaqiIAqqowAB0Ar6EjjSKNY41VEQBVVRgADoBTqdTqKKK8k+KPwZj8X3b\n61ok0VrqzACaOXIjuMcA5H3WxxnBBwOnWvJPij8GY/F92+taJNFa6swAmjlyI7jHAOR91scZwQcD\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": "a9669daa-85c9-4cf4-8b9a-cfc9a5af751a"
+   "id": "3a862de9-c281-4dd7-b2c1-bba0881cd5fe"
   },
   {
    "cell_type": "code",
@@ -1774,7 +1774,7 @@
     "\n",
     "head(clin_and_mutation) \n"
    ],
-   "id": "5dc59371-c8df-4127-a5a5-d7d105ce8086"
+   "id": "8365ac37-e11c-4f96-9846-f3506abf005a"
   },
   {
    "cell_type": "markdown",
@@ -1782,7 +1782,7 @@
    "source": [
     "We can count the number of genes with mutations per patient and then make a `left_join` to the clinical data to plot the mutated genes per tumor stage."
    ],
-   "id": "5fce452b-1e87-4b1c-9bf0-72853f36d85d"
+   "id": "c5a3326f-8e1e-4562-a4ec-f69c5fab434b"
   },
   {
    "cell_type": "code",
@@ -1845,10 +1845,10 @@
    ],
    "attachments": {
     "images/number_mut_genes_per_tumor_stage.jpeg": {
-     "image/jpeg": 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KKKKKKKKKKKKKKK\nKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKK\nKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKK\nKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKK\nKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKK\nKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKK\nKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKK\nKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKK\nKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKK\nKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKK\nKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKK\nKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKK\nKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKK\nKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKK\nKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKK\nKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKK\nKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKK\nKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKK\nKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKK\nKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKKK/9k=\n"
     }
    },
-   "id": "15aa06a5-03ad-4750-aae2-9a630fd62b8b"
+   "id": "cd9ef1f5-e537-4bba-b64b-49fed70ba79f"
   },
   {
    "cell_type": "code",
@@ -1895,14 +1895,16 @@
     "<figcaption aria-hidden=\"true\">Number of mutations on gene TP53 per tumor stage</figcaption>\n",
     "</figure>\n",
     "\n",
-    "# Linking expression and tumor stage"
+    "# Linking expression and tumor stage\n",
+    "\n",
+    "We also can link the expression data (RNA-Seq) with the tumor stage data from the clinical metadata. <span class=\"column-margin margin-aside\">Check the TCGA RNA-Seq protocol online</span>"
    ],
    "attachments": {
     "images/number_mut_tp53_per_tumor_stage.jpeg": {
      "image/jpeg": 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mOvmb4ggN+0npoIBBvdPBB78x1W8e6DffCP4j2niLQk2adPKZbdQPkU/8tIG9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\ns6Fp8Tq11awzSTAfwhym0H/vhjj3HrX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HmlaFZATXFtAEKIefNlbh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     }
    },
-   "id": "9e6a2db5-8850-4e23-b9cd-9d4ee40c3d51"
+   "id": "1e1b24d4-4116-4a8d-b365-5a6f55bcc318"
   },
   {
    "cell_type": "code",
@@ -1930,7 +1932,15 @@
     "\n",
     "rnaseq\n"
    ],
-   "id": "7410a0a7-3aaa-47e7-bc4a-a1fe64b4e210"
+   "id": "d95e6bfe-30a2-4f93-8587-7d1aed8fea37"
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As before, we need to shorten the sample IDs so they can match to the clinical data."
+   ],
+   "id": "a40d9b52-c0c6-4a83-a740-aea23427db31"
   },
   {
    "cell_type": "code",
@@ -1953,7 +1963,15 @@
     "\n",
     "rnaseq <- rnaseq[,!idx]\n"
    ],
-   "id": "083d3ae0-0d28-42dc-ab1c-8d5b8d10455c"
+   "id": "ed45c9ae-8cbe-4562-9cc5-4d747c5eb220"
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Then, we can use the `limma` package to proceed with the differential expression analysis. In this case, we’ll ise the tumor stage as a variable to explain the expression of the genes."
+   ],
+   "id": "24ff93c0-c129-4066-9316-77e088ac7a0b"
   },
   {
    "cell_type": "code",
@@ -2030,7 +2048,15 @@
    "source": [
     "library(limma)\n"
    ],
-   "id": "b2d0e228-a932-42fb-80e0-1a06e76bb223"
+   "id": "e2ed1fa7-9476-4e11-9c2a-b57caae63253"
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let’s visualize two of the most expressed genes."
+   ],
+   "id": "2161aee4-a871-48cb-ade2-45e5691064b5"
   },
   {
    "cell_type": "code",
@@ -2050,15 +2076,19 @@
     "boxplot(split(assay(rnaseq)[\"PAM\",], rnaseq$t_stage), main=\"PAM\")    # higher expression in lower t_stage\n",
     "boxplot(split(assay(rnaseq)[\"PAIP2\",], rnaseq$t_stage), main=\"PAIP2\")\n"
    ],
-   "id": "ab3c7aa6-8fe5-47b9-8522-60bd46a2c92d"
+   "id": "cafe3f26-2013-499c-acfe-afc0628f1d1b"
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Linking methylation and expression"
+    "# Linking methylation and expression\n",
+    "\n",
+    "Finally, we can use the methylation data with the expression data. This is important because methylated cytosines of the DNA change the expression of the genes.\n",
+    "\n",
+    "Some patients have methylation data for multiples tissue types. This information is encoded in the fourth component of the sample names. The code `01A` correspond to primary tumor samples and the code `11A` correspond to normal tissue. We’ll keep the primary tumor samples."
    ],
-   "id": "a51685f9-9b2a-43ac-9474-f831756093c2"
+   "id": "eb6bdedb-748c-4f69-9f5b-e28a61c6f737"
   },
   {
    "cell_type": "code",
@@ -2096,7 +2126,15 @@
     "\n",
     "methyl\n"
    ],
-   "id": "22c4b6cc-bcd3-40c1-b892-38c0e953631f"
+   "id": "1590b9a1-12d2-4e13-b9af-743d37c3f6f0"
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As before, let’s truncate the names of the sample to match the clinical data."
+   ],
+   "id": "209c04e8-93fd-47b6-aa3d-4d04ad44ad2f"
   },
   {
    "cell_type": "code",
@@ -2107,7 +2145,15 @@
     "colnames(methyl) <- colnames(methyl) |> \n",
     "  str_sub(start = 1,end = 12)\n"
    ],
-   "id": "7028384b-8e7a-4620-86ba-572a639aac4b"
+   "id": "e01d3eb2-8de6-44d6-9424-771ff9b47760"
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can add the clinical data to the methyl object and count the number of patients with methylation and transcription data."
+   ],
+   "id": "61784a96-cb6b-4335-873e-8322a338c1d2"
   },
   {
    "cell_type": "code",
@@ -2128,7 +2174,15 @@
     "\n",
     "intersect(colnames(methyl), colnames(rnaseq)) |> length()\n"
    ],
-   "id": "51235740-1d0b-48a3-b6b2-f085f65fb744"
+   "id": "c9067bcb-b37a-40e7-9da8-a7b2030b7fbe"
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let’s subset common sample names and check the methylation data as row ranges."
+   ],
+   "id": "732af783-52b5-4d37-856a-efed284a4cbe"
   },
   {
    "cell_type": "code",
@@ -2163,7 +2217,15 @@
     "methyl_genes = rowData(methyl_subset)\n",
     "methyl_genes\n"
    ],
-   "id": "c925d69c-1c71-4979-94a8-e249e30ff26b"
+   "id": "4718f8e3-f8e9-402a-8ea1-41ca3d93584b"
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This function takes a gene symbol and returns a scatter plot showing the relationship between 3 different sites near that gene and gene expression."
+   ],
+   "id": "58de15e1-6ba1-45a3-8f96-e5fc75c25c8e"
   },
   {
    "cell_type": "code",
@@ -2282,15 +2344,7 @@
     "\n",
     "me_rna_cor(\"TAC1\", mpick=3)\n"
    ],
-   "id": "082ba28d-666e-4c36-8020-e106f1b5f0d9"
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Conclusion"
-   ],
-   "id": "a4e2a797-18bb-4216-aa5a-a7a5ffabcf72"
+   "id": "8ce9f538-b938-4fbb-a497-d0c825640c7a"
   }
  ],
  "nbformat": 4,
diff --git a/notebooks/TCGA.qmd b/notebooks/TCGA.qmd
index cf7c00d..687ba9a 100644
--- a/notebooks/TCGA.qmd
+++ b/notebooks/TCGA.qmd
@@ -319,12 +319,15 @@ ggsave(paste0(path,"number_mut_tp53_per_tumor_stage.jpeg"), device = "jpeg")
 
 # Linking expression and tumor stage
 
+We also can link the expression data (RNA-Seq) with the tumor stage data from the clinical metadata. [Check the TCGA RNA-Seq protocol online]{.aside}
+
 ```{r}
 rnaseq = readData[[2]]
 
 rnaseq
 
 ```
+As before, we need to shorten the sample IDs so they can match to the clinical data.
 
 ```{r}
 
@@ -345,6 +348,8 @@ rnaseq <- rnaseq[,!idx]
 
 ```
 
+Then, we can use the `limma` package to proceed with the differential expression analysis. In this case, we'll ise the tumor stage as a variable to explain the expression of the genes. 
+
 ```{r}
 
 library(limma)
@@ -356,6 +361,8 @@ topTable(ef1) |>
 
 ```
 
+Let's visualize two of the most expressed genes.
+
 ```{r}
 par(mfrow=c(1,2))
 boxplot(split(assay(rnaseq)["PAM",], rnaseq$t_stage), main="PAM")    # higher expression in lower t_stage
@@ -364,6 +371,10 @@ boxplot(split(assay(rnaseq)["PAIP2",], rnaseq$t_stage), main="PAIP2")
 
 # Linking methylation and expression
 
+Finally, we can use the methylation data with the expression data. This is important because methylated cytosines of the DNA change the expression of the genes.
+
+Some patients have methylation data for multiples tissue types. This information is encoded in the fourth component of the sample names. The code `01A` correspond to primary tumor samples and the code `11A` correspond to normal tissue. We'll keep the primary tumor samples.
+
 ```{r}
 
 methyl <- readData[[3]]
@@ -381,11 +392,15 @@ methyl = methyl[,idx]
 methyl
 ```
 
+As before, let's truncate the names of the sample to match the clinical data.
+
 ```{r}
 colnames(methyl) <- colnames(methyl) |> 
   str_sub(start = 1,end = 12)
 ```
 
+We can add the clinical data to the methyl object and count the number of patients with methylation and transcription data.
+
 ```{r}
 colData(methyl) <- as(clin[colnames(methyl),],"DataFrame")
 
@@ -394,6 +409,8 @@ intersect(colnames(methyl), colnames(rnaseq)) |> length()
 
 ```
 
+Let's subset common sample names and check the methylation data as row ranges.
+
 ```{r}
 
 methyl_subset = methyl[,which(colnames(methyl) %in% colnames(rnaseq))]
@@ -404,6 +421,8 @@ methyl_genes
 
 ```
 
+This function takes a gene symbol and returns a scatter plot showing the relationship between 3 different sites near that gene and gene expression.
+
 ```{r}
 
 #| label: figplot
@@ -444,4 +463,3 @@ me_rna_cor("TAC1", mpick=3)
 
 ```
 
-## Conclusion
diff --git a/notebooks/images/number_mut_genes_per_tumor_stage.jpeg b/notebooks/images/number_mut_genes_per_tumor_stage.jpeg
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