From 29a2ff0dc3267764d31d0e4b7cd7b3abd8614d90 Mon Sep 17 00:00:00 2001
From: Quarto GHA Workflow Runner <quarto-github-actions-publish@example.com>
Date: Wed, 1 May 2024 01:51:46 +0000
Subject: [PATCH] Built site for gh-pages

---
 .nojekyll                     |   2 +-
 index-preview.html            |   2 +-
 index.embed.ipynb             |  44 +--
 index.html                    |   4 +-
 index.out.ipynb               |  44 +--
 notebooks/ENCODE-preview.html | 598 ++++++++++++++++++++++++++++++++++
 notebooks/ENCODE.embed.ipynb  |  17 +
 notebooks/ENCODE.out.ipynb    |  17 +
 notebooks/ENCODE.qmd          |   6 +
 notebooks/TCGA-preview.html   |   2 +-
 notebooks/TCGA.embed.ipynb    | 114 +++----
 notebooks/TCGA.out.ipynb      | 114 +++----
 12 files changed, 801 insertions(+), 163 deletions(-)
 create mode 100644 notebooks/ENCODE-preview.html
 create mode 100644 notebooks/ENCODE.embed.ipynb
 create mode 100644 notebooks/ENCODE.out.ipynb
 create mode 100644 notebooks/ENCODE.qmd

diff --git a/.nojekyll b/.nojekyll
index 1c96645..dbed9d5 100644
--- a/.nojekyll
+++ b/.nojekyll
@@ -1 +1 @@
-fb00be48
\ No newline at end of file
+2fb62505
\ No newline at end of file
diff --git a/index-preview.html b/index-preview.html
index 22f8ca4..d555ca3 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,"closeEffect":"zoom","selector":".lightbox","descPosition":"bottom","openEffect":"zoom"});
+</script>  </div> <!-- /content -->  <script>var lightboxQuarto = GLightbox({"openEffect":"zoom","closeEffect":"zoom","descPosition":"bottom","selector":".lightbox","loop":false});
 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 3881b3a..b3bd7f8 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": "7de79265-d6c3-45ff-b7af-499c61d43da2"
+   "id": "23d795dd-c8a5-4cc7-9993-83268793bbfc"
   },
   {
    "cell_type": "code",
@@ -286,7 +286,7 @@
    "source": [
     "library(tidyverse)"
    ],
-   "id": "fad55962-5f06-4258-bc5a-c082389e7aec"
+   "id": "e3f539b4-1a89-4fb7-844b-7dc5e656d807"
   },
   {
    "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": "5a7bf4a0-503a-4739-8a0a-8663afbac833"
+   "id": "24bed4d1-0de8-4078-9e0c-fcf8ba287541"
   },
   {
    "cell_type": "code",
@@ -328,7 +328,7 @@
     "\n",
     "gg = gwcat |> as_GRanges()"
    ],
-   "id": "ea7211b2-43aa-48c5-92e3-4fb545505f45"
+   "id": "1bc277a5-8bad-4ed7-876e-8d0c36c66c21"
   },
   {
    "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": "6a39016d-dc91-4e45-abcf-389813ed252b"
+   "id": "2234023b-2f3e-4b65-bd0a-73f54d92fadf"
   },
   {
    "cell_type": "code",
@@ -373,7 +373,7 @@
    "source": [
     "ggbio::autoplot(gg, layout=\"karyogram\")"
    ],
-   "id": "66471194-2059-4f1b-8e69-2d8595839798"
+   "id": "6827dda9-8dd1-4d65-a7a1-ba45e15957df"
   },
   {
    "cell_type": "markdown",
@@ -381,7 +381,7 @@
    "source": [
     "We can see the peak data as a `GRanges` object:"
    ],
-   "id": "d864736e-b9fe-4076-830b-edbbd3e01889"
+   "id": "5f030a23-ad3d-4249-a1a5-5e069e5c785f"
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   {
    "cell_type": "code",
@@ -429,7 +429,7 @@
     "\n",
     "GM12878"
    ],
-   "id": "3dcf546f-e4eb-4e73-ae1a-f15ef32d7eb0"
+   "id": "b0325143-38be-43ba-bd1b-9f199a6bcdb2"
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   {
    "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": "9ca8dd77-2d96-4aa4-871c-fc3b595f7cf1"
+   "id": "dbc47a23-225f-4fc8-9363-69f2b7f9063a"
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   {
    "cell_type": "code",
@@ -555,7 +555,7 @@
    "source": [
     "library(AnnotationHub)"
    ],
-   "id": "8cc21c95-2293-4166-9cf7-d0ada567b16e"
+   "id": "d0950b26-fa4e-4493-80bd-792deb7298f2"
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   {
    "cell_type": "code",
@@ -574,7 +574,7 @@
     "seqlevelsStyle(GM12878) <- \"UCSC\"\n",
     "seqlevelsStyle(gg) <- \"UCSC\""
    ],
-   "id": "e8b4a5d9-3450-416f-a767-4e6e81172127"
+   "id": "b3676dcf-43f2-41ea-8e6b-76d28a1ce6da"
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   {
    "cell_type": "markdown",
@@ -584,7 +584,7 @@
     "\n",
     "We can see the duplications with the `reduce` function from `IRanges` package:"
    ],
-   "id": "ee2130a3-3d40-43ca-970d-c1de65f9d197"
+   "id": "ac59ceec-d1a7-4c11-a131-f4ff9ca4a509"
   },
   {
    "cell_type": "code",
@@ -603,7 +603,7 @@
     "# duplicated loci\n",
     "length(gg) - length(reduce(gg))"
    ],
-   "id": "11c8cbab-a227-422c-b933-4a31f60b71bd"
+   "id": "a8d73100-9efa-434f-baa1-3f54ec19d2be"
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   {
    "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": "bd644ecb-56ad-49e1-aab3-300f41102790"
+   "id": "8892842e-8d00-4510-b893-4a52c4944f63"
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   {
    "cell_type": "code",
@@ -646,7 +646,7 @@
     "fo = findOverlaps(GM12878, reduce(gg))\n",
     "fo"
    ],
-   "id": "fe5b50d2-c02c-41c6-95e6-7083782787e7"
+   "id": "00d9812e-145d-4500-8220-5e8fb904c7dc"
   },
   {
    "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": "f174273e-91ef-4a28-808d-98ead0638bd6"
+   "id": "eada5c05-ae5a-4096-b9da-b319249ea6c1"
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   {
    "cell_type": "code",
@@ -705,7 +705,7 @@
     "\n",
     "htmltools::tagList(list(plotly::ggplotly(p)))"
    ],
-   "id": "9953452d-eb43-499d-acac-b04c2481c97e"
+   "id": "5209b359-1f3b-4e4a-8aca-57cc44448772"
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   {
    "cell_type": "markdown",
@@ -713,7 +713,7 @@
    "source": [
     "Distinct phenotypes identified on the peaks:"
    ],
-   "id": "78a4813e-c0bd-424f-ac5d-21b9c38a0fd8"
+   "id": "6c5566f2-f135-4db6-a53a-b37e4acf3182"
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@@ -731,7 +731,7 @@
    "source": [
     "length(phset)"
    ],
-   "id": "589f7898-1fe3-43be-ae2f-9ac180dbff12"
+   "id": "6f62f254-007d-4c91-9fb9-2406c1fdeb21"
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   {
    "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": "a77ca086-0407-473e-89d7-cbcd809afb6e"
+   "id": "7fafdfea-6df1-46cb-9ca4-916931061cc4"
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@@ -1011,7 +1011,7 @@
    "source": [
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    "cell_type": "markdown",
@@ -1025,7 +1025,7 @@
     "\n",
     "## References"
    ],
-   "id": "533ee32d-d806-40c2-ac21-b7ec9fc4411c"
+   "id": "b0b248d5-122f-4135-82fc-8abc0947d14c"
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diff --git a/index.html b/index.html
index 048f405..4b32067 100644
--- a/index.html
+++ b/index.html
@@ -151,7 +151,7 @@ <h2 id="toc-title">Table of contents</h2>
   <li><a href="#conclusion" id="toc-conclusion" class="nav-link" data-scroll-target="#conclusion"><span class="header-section-number">4</span> Conclusion</a></li>
   <li><a href="#references" id="toc-references" class="nav-link" data-scroll-target="#references">References</a></li>
   </ul>
-<div class="quarto-alternate-notebooks"><h2>Notebooks</h2><ul><li><a href="index-preview.html"><i class="bi bi-journal-code"></i>Article Notebook</a></li><li><a href="notebooks/TCGA-preview.html"><i class="bi bi-journal-code"></i>Integrative Analysis with TCGA Data</a></li></ul></div></nav>
+<div class="quarto-alternate-notebooks"><h2>Notebooks</h2><ul><li><a href="index-preview.html"><i class="bi bi-journal-code"></i>Article Notebook</a></li><li><a href="notebooks/ENCODE-preview.html"><i class="bi bi-journal-code"></i>ENCODE Data Analysis</a></li><li><a href="notebooks/TCGA-preview.html"><i class="bi bi-journal-code"></i>Integrative Analysis with TCGA Data</a></li></ul></div></nav>
 </div>
 <div id="quarto-margin-sidebar" class="sidebar margin-sidebar zindex-bottom">
 </div>
@@ -733,7 +733,7 @@ <h2 class="unnumbered anchored" data-anchor-id="references">References</h2>
 });
 </script>
 </div> <!-- /content -->
-<script>var lightboxQuarto = GLightbox({"selector":".lightbox","descPosition":"bottom","loop":false,"openEffect":"zoom","closeEffect":"zoom"});
+<script>var lightboxQuarto = GLightbox({"selector":".lightbox","openEffect":"zoom","descPosition":"bottom","loop":false,"closeEffect":"zoom"});
 window.onload = () => {
   lightboxQuarto.on('slide_before_load', (data) => {
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diff --git a/index.out.ipynb b/index.out.ipynb
index 5857765..fe6d81a 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": "c19dcf71-57ec-4023-bc30-90e12cf8c211"
+   "id": "b67793a2-1c71-4cb4-8a61-1450f9443238"
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@@ -286,7 +286,7 @@
    "source": [
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-   "id": "ceef6c0e-5f16-44da-b1dd-1c19d5bfa05a"
+   "id": "d41b3697-99c9-4a3f-b48f-f19f544fe32a"
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@@ -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": "2399e518-c4d5-419a-9ff5-9efe264f383f"
+   "id": "4c8a0821-1e43-44bb-b668-0f93dc76c2cb"
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@@ -328,7 +328,7 @@
     "\n",
     "gg = gwcat |> as_GRanges()"
    ],
-   "id": "a1d0660d-55b7-4845-8066-061f10d1a407"
+   "id": "475dbb8e-7b57-4d93-9920-041adc922bd4"
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    "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": "8732d82b-b863-4021-bbb9-30b634298d1a"
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   {
    "cell_type": "code",
@@ -373,7 +373,7 @@
    "source": [
     "ggbio::autoplot(gg, layout=\"karyogram\")"
    ],
-   "id": "04238b29-d65d-4bc8-9bd4-243794ebb78d"
+   "id": "a35a9fcf-911a-40dc-9e27-45686ac36891"
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   {
    "cell_type": "markdown",
@@ -381,7 +381,7 @@
    "source": [
     "We can see the peak data as a `GRanges` object:"
    ],
-   "id": "981ca131-4779-4e3b-b8fc-c53c0e789e6f"
+   "id": "b9253ab2-135b-44a7-bf1c-b38a469451a0"
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   {
    "cell_type": "code",
@@ -429,7 +429,7 @@
     "\n",
     "GM12878"
    ],
-   "id": "dea8817e-144c-4b14-9550-235df0044dc9"
+   "id": "c097bea1-80e7-4ef8-ad9b-a82e8755723b"
   },
   {
    "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": "22d6d02f-36ea-43d0-b2d2-5d2d6a64fe10"
+   "id": "aeaf4025-0c56-4fdc-818a-35dbb3e4b6c7"
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@@ -555,7 +555,7 @@
    "source": [
     "library(AnnotationHub)"
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-   "id": "19663251-b087-481e-a447-727389c6c982"
+   "id": "67bc658f-107a-4ff0-9f09-d460b7350d85"
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@@ -574,7 +574,7 @@
     "seqlevelsStyle(GM12878) <- \"UCSC\"\n",
     "seqlevelsStyle(gg) <- \"UCSC\""
    ],
-   "id": "4ada482d-8cc9-447e-b0aa-0c737a9343f2"
+   "id": "7e47f83a-3373-4840-a53e-c11cf73ff1be"
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    "cell_type": "markdown",
@@ -584,7 +584,7 @@
     "\n",
     "We can see the duplications with the `reduce` function from `IRanges` package:"
    ],
-   "id": "670f4c2e-c975-42a7-acb4-b5cc9256d639"
+   "id": "365fb0a7-b03f-47e4-a998-3028b494f806"
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@@ -603,7 +603,7 @@
     "# duplicated loci\n",
     "length(gg) - length(reduce(gg))"
    ],
-   "id": "96f7c8fe-2467-4e18-8658-0eee7021017c"
+   "id": "4a014fec-b44b-4215-a9ce-75a3aa766704"
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    "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": "c5c6a251-139a-4f8d-8dc3-0b6b8034fd63"
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    "cell_type": "code",
@@ -646,7 +646,7 @@
     "fo = findOverlaps(GM12878, reduce(gg))\n",
     "fo"
    ],
-   "id": "b98f88bd-03c3-4183-b81b-3dbd1061b63d"
+   "id": "1514203b-3a6e-48b1-917a-442810141aea"
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    "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": "3c316b44-a190-4740-9dd5-ca76c16dda55"
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@@ -705,7 +705,7 @@
     "\n",
     "htmltools::tagList(list(plotly::ggplotly(p)))"
    ],
-   "id": "8d80c21f-8a07-4ea1-a2f5-fed4bad35234"
+   "id": "fe10ec2e-f1cc-4711-a3e5-ba0b52c9880e"
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   {
    "cell_type": "markdown",
@@ -713,7 +713,7 @@
    "source": [
     "Distinct phenotypes identified on the peaks:"
    ],
-   "id": "4d0ba8ce-058e-406c-ab6f-2f178cd6b697"
+   "id": "d7693a41-4357-4841-9783-9e00ac3e272b"
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@@ -731,7 +731,7 @@
    "source": [
     "length(phset)"
    ],
-   "id": "bf78f810-b013-45eb-a74e-d7a0ad3d1035"
+   "id": "f374f20e-8d63-4ef9-9f70-d48ba1c3fb1e"
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   {
    "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": "2816bc5a-6257-400d-8871-4c687f6aec0f"
+   "id": "568285f0-9945-4a91-8de5-7e0924fbe45f"
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@@ -1011,7 +1011,7 @@
    "source": [
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+   "id": "5ee0f214-9bb7-4941-b7d8-e498de3fc891"
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    "cell_type": "markdown",
@@ -1025,7 +1025,7 @@
     "\n",
     "## References"
    ],
-   "id": "e8504b32-bcf8-4f84-a361-37edcfafc2f5"
+   "id": "63a0639c-941a-4d87-ab0a-45e0552ebb6b"
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diff --git a/notebooks/ENCODE-preview.html b/notebooks/ENCODE-preview.html
new file mode 100644
index 0000000..3786b1b
--- /dev/null
+++ b/notebooks/ENCODE-preview.html
@@ -0,0 +1,598 @@
+<!DOCTYPE html>
+<html xmlns="http://www.w3.org/1999/xhtml" lang="en" xml:lang="en"><head>
+    <meta charset="utf-8">
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+    <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=yes">
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+        margin: 0 0.8em 0.2em -1em; /* quarto-specific, see https://github.com/quarto-dev/quarto-cli/issues/4556 */ 
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+<link href="../site_libs/bootstrap/bootstrap.min.css" rel="stylesheet" id="quarto-bootstrap" data-mode="light">
+     <script async="" src="https://hypothes.is/embed.js"></script>
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+      
+      <a onclick="window.quartoBackToArticle(); return false;" class="btn btn-primary quarto-back-link" href=""><i class="bi bi-caret-left"></i> Back to Article</a>
+      <h6><i class="bi bi-journal-code"></i> ENCODE Data Analysis</h6>
+
+            <a href="../notebooks/ENCODE.qmd" class="btn btn-primary quarto-download-embed" download="ENCODE.qmd">Download Source</a>
+          </div>
+
+     <header id="title-block-header" class="quarto-title-block default toc-left page-columns page-full">
+  <div class="quarto-title-banner page-columns page-full">
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+      <h1 class="title">ENCODE Data Analysis</h1>
+          </div>
+
+    
+    <div class="quarto-title-meta-container">
+      <div class="quarto-title-meta-column-start">
+        
+        <div class="quarto-title-meta">
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+<div id="quarto-margin-sidebar" class="sidebar margin-sidebar zindex-bottom">
+</div>
+<main class="content quarto-banner-title-block" id="quarto-document-content">      
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+     <p>Hola</p>
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+        let height = null;
+        let parent = null;
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+      throttle(() => {
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+    );
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+              timer = throttle = false;
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+    const citeInfo = findCites(ref);
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+        citeInfo.cites.forEach(function(cite) {
+          var citeDiv = window.document.createElement('div');
+          citeDiv.classList.add('hanging-indent');
+          citeDiv.classList.add('csl-entry');
+          var biblioDiv = window.document.getElementById('ref-' + cite);
+          if (biblioDiv) {
+            citeDiv.innerHTML = biblioDiv.innerHTML;
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+          popup.appendChild(citeDiv);
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+        return popup.innerHTML;
+      });
+    }
+  }
+});
+</script>  </div> <!-- /content --> 
+  
+</body></html>
\ No newline at end of file
diff --git a/notebooks/ENCODE.embed.ipynb b/notebooks/ENCODE.embed.ipynb
new file mode 100644
index 0000000..2068d07
--- /dev/null
+++ b/notebooks/ENCODE.embed.ipynb
@@ -0,0 +1,17 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# ENCODE Data Analysis\n",
+    "\n",
+    "Hola"
+   ],
+   "id": "cc5b67b8-8354-40de-aaae-6132aa396af1"
+  }
+ ],
+ "nbformat": 4,
+ "nbformat_minor": 5,
+ "metadata": {}
+}
diff --git a/notebooks/ENCODE.out.ipynb b/notebooks/ENCODE.out.ipynb
new file mode 100644
index 0000000..6ac0648
--- /dev/null
+++ b/notebooks/ENCODE.out.ipynb
@@ -0,0 +1,17 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# ENCODE Data Analysis\n",
+    "\n",
+    "Hola"
+   ],
+   "id": "8665b5e6-13ee-40e3-a6e6-3a444de43a1d"
+  }
+ ],
+ "nbformat": 4,
+ "nbformat_minor": 5,
+ "metadata": {}
+}
diff --git a/notebooks/ENCODE.qmd b/notebooks/ENCODE.qmd
new file mode 100644
index 0000000..a07b3d7
--- /dev/null
+++ b/notebooks/ENCODE.qmd
@@ -0,0 +1,6 @@
+---
+title: "ENCODE Data Analysis"
+editor: visual
+---
+
+Hola
\ No newline at end of file
diff --git a/notebooks/TCGA-preview.html b/notebooks/TCGA-preview.html
index 6d7d3d4..8687b4e 100644
--- a/notebooks/TCGA-preview.html
+++ b/notebooks/TCGA-preview.html
@@ -1973,7 +1973,7 @@ <h1>Linking methylation and expression</h1>
     }
   }
 });
-</script>  </div> <!-- /content -->  <script>var lightboxQuarto = GLightbox({"selector":".lightbox","loop":false,"descPosition":"bottom","openEffect":"zoom","closeEffect":"zoom"});
+</script>  </div> <!-- /content -->  <script>var lightboxQuarto = GLightbox({"descPosition":"bottom","selector":".lightbox","openEffect":"zoom","loop":false,"closeEffect":"zoom"});
 window.onload = () => {
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diff --git a/notebooks/TCGA.embed.ipynb b/notebooks/TCGA.embed.ipynb
index 12d4822..211083b 100644
--- a/notebooks/TCGA.embed.ipynb
+++ b/notebooks/TCGA.embed.ipynb
@@ -8,7 +8,7 @@
     "\n",
     "Analysis of Mutation, Transcription and Methylation Data from The Cancer Genome Atlas (TCGA)"
    ],
-   "id": "47c07354-2969-4344-902a-fe784a7f423c"
+   "id": "a6e270c7-718e-4edd-b195-48bca2dc4e9d"
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     "#| include: false\n",
     "library(knitr)"
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@@ -106,7 +106,7 @@
     "\n",
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   },
   {
    "cell_type": "code",
@@ -474,7 +474,7 @@
    "source": [
     "library(curatedTCGAData)"
    ],
-   "id": "f160c708-5cde-4e91-80df-c68017228cbd"
+   "id": "7fcdaa8c-eb90-416a-b448-edfae811efb1"
   },
   {
    "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": "3ce5cf96-2b8a-4971-bb9e-314d0dd98a18"
+   "id": "a5e23844-caba-4544-bd45-5aba1a4c511f"
   },
   {
    "cell_type": "code",
@@ -872,7 +872,7 @@
     "                           c(\"RNASeq2GeneNorm\", \"Mutation\", \"Methylation_methyl450\"), \n",
     "                           dry.run = FALSE, version = \"2.1.1\")"
    ],
-   "id": "7a00a906-cd2a-439d-8716-842a25226c1e"
+   "id": "1b552f7d-8b86-4147-9b4b-7dec6b8b7deb"
   },
   {
    "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": "5b78816e-beb4-4e61-8117-278e0841448a"
+   "id": "9a94359c-2a7d-4e07-9b58-a0d71ce979e0"
   },
   {
    "cell_type": "code",
@@ -913,7 +913,7 @@
    "source": [
     "sampleMap(readData)"
    ],
-   "id": "b24f7b5e-5012-4de1-a2d6-db3758ae3ad7"
+   "id": "b7c48ced-1335-4548-8907-cbdab25e6612"
   },
   {
    "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": "7030be50-315d-4006-bf96-8f2b8c8b061f"
+   "id": "84b044b8-3e38-4f54-b8c4-1cf87f7a714e"
   },
   {
    "cell_type": "code",
@@ -945,7 +945,7 @@
     "  table() |> \n",
     "  table()"
    ],
-   "id": "02ff3e2e-ff6c-431b-a442-605257451b51"
+   "id": "ce1c8644-d41e-491a-8e9e-943841ca56be"
   },
   {
    "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": "ce434580-aef5-4211-81f7-354066becd6f"
+   "id": "a6d800e1-4def-45bc-b7d1-5b247df09959"
   },
   {
    "cell_type": "code",
@@ -983,7 +983,7 @@
     "  as_tibble()\n",
     "dim(clin)"
    ],
-   "id": "b8f1c6ba-6991-40e2-aacb-984b8f020287"
+   "id": "50bd6219-29d2-4907-836c-2d7b2cd56bb3"
   },
   {
    "cell_type": "markdown",
@@ -991,7 +991,7 @@
    "source": [
     "As an example, for rectum adenocarcinoma, we can see the tumor stage."
    ],
-   "id": "c3a8e816-03e4-48d7-bd3f-86cf631f7e58"
+   "id": "20f2f3c1-3292-44b3-9e99-9e9ea63ecd3c"
   },
   {
    "cell_type": "code",
@@ -1013,7 +1013,7 @@
     "  pull(pathology_T_stage) |> \n",
     "  table()"
    ],
-   "id": "c4895c01-ea41-437a-adc7-6c14c4259d86"
+   "id": "ed54cde8-fe05-42f0-b251-53094818a2f9"
   },
   {
    "cell_type": "markdown",
@@ -1021,7 +1021,7 @@
    "source": [
     "Stage T4 have subgroups. To simplify the analysis, let’s combine all T4 tumors."
    ],
-   "id": "69961da9-3606-4d8c-a8d8-3ed66593941a"
+   "id": "600f9128-3b7f-4f57-b34c-8a1c3f2725ff"
   },
   {
    "cell_type": "code",
@@ -1048,7 +1048,7 @@
     "clin$t_stage |> \n",
     "  table()"
    ],
-   "id": "0a4a96af-e131-478f-ac37-061764bde2d8"
+   "id": "acf1f38a-2125-48ce-a0f6-538ba54fb34f"
   },
   {
    "cell_type": "markdown",
@@ -1056,7 +1056,7 @@
    "source": [
     "Also, we can see the vital status (alive=0, deceased=1)"
    ],
-   "id": "e6951139-888c-4c3d-bd31-c14d143a428b"
+   "id": "ae8957df-e36c-456a-8312-c38fca18a5eb"
   },
   {
    "cell_type": "code",
@@ -1077,7 +1077,7 @@
     "clin$vital_status |> \n",
     "  table()"
    ],
-   "id": "c89db924-5f84-4c37-8a67-8e34272bec66"
+   "id": "6c1fe061-f88d-492f-8cac-c65b40ba8d01"
   },
   {
    "cell_type": "markdown",
@@ -1085,7 +1085,7 @@
    "source": [
     "Or combine tumor status and vital status."
    ],
-   "id": "b7e23fc4-c8ba-4c93-b39d-c6f6412dcddc"
+   "id": "0564010f-67e8-4111-a2b1-82a5c0dec73a"
   },
   {
    "cell_type": "code",
@@ -1108,7 +1108,7 @@
    "source": [
     "table(clin$t_stage, clin$vital_status)"
    ],
-   "id": "aae64ebc-1843-4447-87be-0b39e270fba7"
+   "id": "f8bd1c71-263e-4fd7-a2c6-2eed5c68cad0"
   },
   {
    "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": "a69c7585-5c36-437a-ae52-0232061b6eca"
+   "id": "efafccf3-88cd-411a-8795-8f5de465f903"
   },
   {
    "cell_type": "code",
@@ -1144,7 +1144,7 @@
     "\n",
     "mut_data"
    ],
-   "id": "23354246-d784-4d63-80bb-ddaacf3853a8"
+   "id": "b33a5ef3-a986-4050-856d-a568ecfe36a8"
   },
   {
    "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": "608f8e44-cd95-4e14-9b91-947599a22ef2"
+   "id": "d52f61ed-5763-4eed-bb6f-1b0c2f77a40e"
   },
   {
    "cell_type": "code",
@@ -1181,7 +1181,7 @@
     "mut_sample_ids = colnames(mut_data)\n",
     "head(mut_sample_ids)"
    ],
-   "id": "b7c27c8f-d0c6-4a7d-a03d-df92f4034887"
+   "id": "92847118-6786-4e1f-b05e-14a8c2266c36"
   },
   {
    "cell_type": "markdown",
@@ -1189,7 +1189,7 @@
    "source": [
     "We need to manipulate these by substracting 12 characters."
    ],
-   "id": "34bdfdab-ac43-415e-9e45-f0987df59198"
+   "id": "9c6dfe80-6802-4c1c-afc1-49b1d5539235"
   },
   {
    "cell_type": "code",
@@ -1210,7 +1210,7 @@
     "\n",
     "all(mut_sample_ids %in% clin$patientID)"
    ],
-   "id": "1e9b2941-da0f-4799-ac3b-871c9c09598f"
+   "id": "ba01e9d8-5976-446a-b361-feed2d2ea59f"
   },
   {
    "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": "064162bf-4c7a-4960-bce2-3a915557ed92"
+   "id": "320b36a2-1032-4e3c-8abc-715d4999ef92"
   },
   {
    "cell_type": "code",
@@ -1252,7 +1252,7 @@
    "source": [
     "assay(mut_data)[1:3,1:3]"
    ],
-   "id": "7b421ae1-bd90-4bd1-9870-12ba374b7420"
+   "id": "d507ce9a-5ab0-4196-b0ba-460ee0f3b291"
   },
   {
    "cell_type": "markdown",
@@ -1260,7 +1260,7 @@
    "source": [
     "We can get more information if we look at the mutation information for each patient."
    ],
-   "id": "50dfd32e-a418-49fa-99ce-9f00721e5595"
+   "id": "7ed53014-98d0-490d-864c-8dcfa036ea30"
   },
   {
    "cell_type": "code",
@@ -1433,7 +1433,7 @@
     "\n",
     "mut_assay # GRangesList"
    ],
-   "id": "b5889def-2a9e-49c2-a25f-baf8d30709a5"
+   "id": "3307533c-c587-417e-8d20-b7dffe2ca20b"
   },
   {
    "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": "c42d820e-e963-4e9d-83c2-16b268d6be0d"
+   "id": "95de6159-b759-4efe-a460-b1ca31a6cc10"
   },
   {
    "cell_type": "code",
@@ -1626,7 +1626,7 @@
    "source": [
     "mut_assay[[1]]"
    ],
-   "id": "67f05be3-f299-4b0d-800b-490d6e7244e7"
+   "id": "908a8557-aa2c-456c-ac19-38aa1350d46f"
   },
   {
    "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": "db7d764d-0b4a-4b12-b5a2-05b4047c2d1e"
+   "id": "c6b17901-5a45-4609-9b58-dde165888ade"
   },
   {
    "cell_type": "code",
@@ -1675,7 +1675,7 @@
     "\n",
     "head(var_class_df)"
    ],
-   "id": "2a222384-9752-4ad6-9461-1fbc38a38fd3"
+   "id": "1a4f9eba-4fac-4cb3-a4c3-f085bb150fa5"
   },
   {
    "cell_type": "markdown",
@@ -1683,7 +1683,7 @@
    "source": [
     "We can visualize the most common mutated genes genes in rectum adenocarcinoma"
    ],
-   "id": "664b4ff7-ac16-4371-ab18-a947796e11aa"
+   "id": "454a4dd6-93d6-477b-9372-3cad4dca8422"
   },
   {
    "cell_type": "code",
@@ -1744,7 +1744,7 @@
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KoXztoyVYDjcBk5HUA9xz6F8Hvi7d+Jr4eHPELK+olC1t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\ngjjBXfyOvy14p8APER0Xxtd+HriVTDqKlUKsGXz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j3SLljBeWkZkhT+JZX/cN+TeVXzh8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\np1ryT4o/BmPxfdvrWiTRWurMAJo5ciO4xwDkfdbHGcEHA6da4i01D44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sXw/4L8UoMLBNJbXLYz+7aR/6F/zFcNZj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CW3nmZlAU/eGVBI5Cnp1UVwXxT8BXXjjR7EaXcQW2q2FwJbeeZmUBT94ZUEjkKe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\nzc+K/s3jqz8NfYt32mEy/aPNxtwrn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9FFR6/4g0/w3prXt/LtXoka8vI3ooqP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\nrwj8Yf8AhKfE9no39hfZftO/999r37dqM3TYM/dx171s+FPi3/wk/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OktqXhLxvpWjQ61Z3TbJtRto/3qED5RI4555HPIYYzzWV8LB4R0ltS8JeN9K0aHWr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\nU3yo/wC4v5U3yo/7i/lTfKj/ALi/lR5Uf9x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PIi/u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\nU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InKMMuoOCOa5j4jXdzY+CL24tLiW3mVo9skTlGGXUHBHNcx8R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\n8RPHus3F48v9m6eq20PlttJIJ6HnIyHP4isyLSLb4ie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hmeGC7kjlmK8gmSQxqSP9kBj+Na/wAcr251zxn4Y8DwzPDBdyRyzFeQTJIY1JH+yAx/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\nk/2j9g8m7W583yPNzhHXbjcv9/Oc9qq/E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     }
    },
-   "id": "769a243b-7a5d-4902-b3d3-4ec587fc731f"
+   "id": "527ed4b8-64e4-4012-889e-4a245dca64bb"
   },
   {
    "cell_type": "code",
@@ -1781,7 +1781,7 @@
     "\n",
     "head(clin_and_mutation) "
    ],
-   "id": "e343830a-39c5-40fd-8dfe-1feed29d54dc"
+   "id": "eb6fedbd-696a-46db-83c1-586d3f2bfc24"
   },
   {
    "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": "97078a89-00a1-4d8b-90d3-a58d6e872f5d"
+   "id": "f14c765f-0d0d-4aa9-8794-822ae2151aba"
   },
   {
    "cell_type": "code",
@@ -1856,7 +1856,7 @@
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     }
    },
-   "id": "9aab1c59-050b-440e-8929-d43c038488a4"
+   "id": "a928a8db-004a-4b6b-9ecc-3c0d56193355"
   },
   {
    "cell_type": "code",
@@ -1913,7 +1913,7 @@
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mOvmb4ggN+0npoIBBvdPBB78x1W8e6DffCP4j2niLQk2adPKZbdQPkU/8tIG9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\ns6Fp8Tq11awzSTAfwhym0H/vhjj3HrX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HmlaFZATXFtAEKIefNlbh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     }
    },
-   "id": "2edac292-7185-4820-800e-a5ae8d285654"
+   "id": "64d5e3db-5d87-45b0-b111-9695b07e41eb"
   },
   {
    "cell_type": "code",
@@ -1941,7 +1941,7 @@
     "\n",
     "rnaseq"
    ],
-   "id": "79e50068-c820-44d6-bc3c-a4f5cbaa27ac"
+   "id": "73239178-8f0b-4b78-843e-c18ba955b0d3"
   },
   {
    "cell_type": "markdown",
@@ -1949,7 +1949,7 @@
    "source": [
     "As before, we need to shorten the sample IDs so they can match to the clinical data."
    ],
-   "id": "09b764a7-f769-4252-a94e-b1fcaaae44fb"
+   "id": "476f31a9-a761-49c8-8227-0e47e7b28ff9"
   },
   {
    "cell_type": "code",
@@ -1972,7 +1972,7 @@
     "\n",
     "rnaseq <- rnaseq[,!idx]"
    ],
-   "id": "18ef0852-1c8b-4fba-903f-bfdd039fe9b1"
+   "id": "1d75cfaf-09b4-4278-b700-891982ff45fd"
   },
   {
    "cell_type": "markdown",
@@ -1980,7 +1980,7 @@
    "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": "b217de52-6b4c-46c8-9c82-2d3afdf5c735"
+   "id": "b7a72c7d-79d9-4916-aac2-193351b6d57a"
   },
   {
    "cell_type": "code",
@@ -2057,7 +2057,7 @@
    "source": [
     "library(limma)"
    ],
-   "id": "a2adaae9-d1f1-47b6-9e1e-fd026872c99f"
+   "id": "e8fea281-904e-44d3-95cc-9a2afb27c5ca"
   },
   {
    "cell_type": "markdown",
@@ -2065,7 +2065,7 @@
    "source": [
     "Let’s visualize two of the most expressed genes."
    ],
-   "id": "bc464324-239a-44c2-8f3a-15c23063b547"
+   "id": "daaa0fda-12db-4faa-92ae-63750c56f8f4"
   },
   {
    "cell_type": "code",
@@ -2085,7 +2085,7 @@
     "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": "a4c37a9c-3d23-4bc2-aea8-3ccc90315f47"
+   "id": "d9fc91bd-3e5e-4a59-b47d-a28b5155b3f0"
   },
   {
    "cell_type": "markdown",
@@ -2097,7 +2097,7 @@
     "\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": "f19f69a8-e7b3-435f-9191-3b3c65b96106"
+   "id": "4a982a4f-a561-4ac9-8868-43240069e5df"
   },
   {
    "cell_type": "code",
@@ -2135,7 +2135,7 @@
     "\n",
     "methyl"
    ],
-   "id": "d7f585ca-4bba-485a-aef1-1a38ef107812"
+   "id": "6cd2fa70-eb0b-49ef-b68c-a6d27e86e5e9"
   },
   {
    "cell_type": "markdown",
@@ -2143,7 +2143,7 @@
    "source": [
     "As before, let’s truncate the names of the sample to match the clinical data."
    ],
-   "id": "93ef9f7a-9d0b-4c93-aaaa-04044a238dfa"
+   "id": "e5a8f7a0-98d4-466c-a657-2807f39fce2e"
   },
   {
    "cell_type": "code",
@@ -2154,7 +2154,7 @@
     "colnames(methyl) <- colnames(methyl) |> \n",
     "  str_sub(start = 1,end = 12)"
    ],
-   "id": "4ee234ab-0e49-42b9-bb90-64c8980263d3"
+   "id": "48d478c1-26e6-45c6-9123-59643e110052"
   },
   {
    "cell_type": "markdown",
@@ -2162,7 +2162,7 @@
    "source": [
     "We can add the clinical data to the methyl object and count the number of patients with methylation and transcription data."
    ],
-   "id": "8361c54c-1447-4550-a96b-68cdcac5328c"
+   "id": "6491c19e-8e45-42b7-9bdb-2eb917c372e0"
   },
   {
    "cell_type": "code",
@@ -2183,7 +2183,7 @@
     "\n",
     "intersect(colnames(methyl), colnames(rnaseq)) |> length()"
    ],
-   "id": "f9f692f3-2f88-4280-9ba8-15220444f3c3"
+   "id": "1e615bf9-53ed-408b-a7a6-809eda93f2ea"
   },
   {
    "cell_type": "markdown",
@@ -2191,7 +2191,7 @@
    "source": [
     "Let’s subset common sample names and check the methylation data as row ranges."
    ],
-   "id": "93a7f4b8-0992-4f9f-b05d-08e38a44df4c"
+   "id": "bcca4929-2c04-4776-bd4d-aba400787c6f"
   },
   {
    "cell_type": "code",
@@ -2226,7 +2226,7 @@
     "methyl_genes = rowData(methyl_subset)\n",
     "methyl_genes"
    ],
-   "id": "bbcac8c7-162f-40ec-a6c8-82968188f18a"
+   "id": "1917a377-4832-47bc-a4ea-95546791d3d1"
   },
   {
    "cell_type": "markdown",
@@ -2234,7 +2234,7 @@
    "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": "75139bd7-9b65-4ab6-903a-16389044445d"
+   "id": "da30c2b6-58a0-425e-b788-daec51a5e7c1"
   },
   {
    "cell_type": "code",
@@ -2354,7 +2354,7 @@
     "\n",
     "me_rna_cor(\"TAC1\", mpick=3)"
    ],
-   "id": "c54b9e89-39b9-4797-ae76-ddfe53b7442a"
+   "id": "8ca23da4-fc4d-4736-adc4-6207e9b226d2"
   }
  ],
  "nbformat": 4,
diff --git a/notebooks/TCGA.out.ipynb b/notebooks/TCGA.out.ipynb
index 726c114..4aeee1c 100644
--- a/notebooks/TCGA.out.ipynb
+++ b/notebooks/TCGA.out.ipynb
@@ -8,7 +8,7 @@
     "\n",
     "Analysis of Mutation, Transcription and Methylation Data from The Cancer Genome Atlas (TCGA)"
    ],
-   "id": "a843f15a-1519-4291-857f-f0d793a401bd"
+   "id": "e1127567-2be5-43d6-a6b1-94db1b25b423"
   },
   {
    "cell_type": "code",
@@ -91,7 +91,7 @@
    "source": [
     "library(knitr)\n"
    ],
-   "id": "b20c7d27-69de-4317-a8af-8c143581215d"
+   "id": "1bf2e488-617c-46bb-a677-1af7cc6b1751"
   },
   {
    "cell_type": "markdown",
@@ -105,7 +105,7 @@
     "\n",
     "First, let’s load the packages needed."
    ],
-   "id": "fa05c8cd-599c-4cf6-93ec-3731cb52bc25"
+   "id": "cf832a74-5aae-45dc-933b-06785b532c60"
   },
   {
    "cell_type": "code",
@@ -473,7 +473,7 @@
    "source": [
     "library(curatedTCGAData)\n"
    ],
-   "id": "c168ae2b-0029-4184-b50f-bfd23985aa52"
+   "id": "4d573295-e9d4-425a-a707-c45b558fcdd0"
   },
   {
    "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": "63616b99-55b3-4155-aaea-c5699b78cfd5"
+   "id": "3e682c88-84a5-4999-8888-ff19e8cc4ff2"
   },
   {
    "cell_type": "code",
@@ -868,7 +868,7 @@
     "                           c(\"RNASeq2GeneNorm\", \"Mutation\", \"Methylation_methyl450\"), \n",
     "                           dry.run = FALSE, version = \"2.1.1\")\n"
    ],
-   "id": "0fd73e70-f955-4277-8fb5-4f35d002aad5"
+   "id": "e828a0a5-4f31-4794-9313-b665ed9c3c4e"
   },
   {
    "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": "188a7590-61f8-48b8-b263-401d60efb355"
+   "id": "4728899f-2903-411b-96fb-5f200b96c665"
   },
   {
    "cell_type": "code",
@@ -909,7 +909,7 @@
    "source": [
     "sampleMap(readData)\n"
    ],
-   "id": "45df4cd9-8b6a-49bc-8dc2-f14a867d0b3b"
+   "id": "3417b5a8-7ac6-4b2f-ba4a-95c802a735f3"
   },
   {
    "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": "f3b9efbe-3824-45e8-b44e-c9bc6c50373d"
+   "id": "4e4370dc-99f2-4548-a707-11a14caecf12"
   },
   {
    "cell_type": "code",
@@ -941,7 +941,7 @@
     "  table() |> \n",
     "  table()\n"
    ],
-   "id": "39dde439-e47a-41d1-802e-36f95d03179d"
+   "id": "3b2bc2b4-cd93-4722-8b76-fc5ea88c9c06"
   },
   {
    "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": "a1a3910d-4a31-4134-a714-0cf3e83b645f"
+   "id": "78d14c31-72e7-43da-9356-ecee2cd16e96"
   },
   {
    "cell_type": "code",
@@ -979,7 +979,7 @@
     "  as_tibble()\n",
     "dim(clin)\n"
    ],
-   "id": "e3ea24df-9033-4033-b338-5faa4d9ced35"
+   "id": "c96bae58-b3eb-497c-82f5-3b5c9e632a8f"
   },
   {
    "cell_type": "markdown",
@@ -987,7 +987,7 @@
    "source": [
     "As an example, for rectum adenocarcinoma, we can see the tumor stage."
    ],
-   "id": "bb139d88-1493-4423-9e90-a833e6fa6536"
+   "id": "5a011f20-4fd4-47a1-a6a9-1fa028e5b1dc"
   },
   {
    "cell_type": "code",
@@ -1009,7 +1009,7 @@
     "  pull(pathology_T_stage) |> \n",
     "  table()\n"
    ],
-   "id": "dcadb198-50bc-40c2-8e9e-cb5b3a9cebaf"
+   "id": "8bc916fd-0e73-4ac1-b519-76bcad9504ae"
   },
   {
    "cell_type": "markdown",
@@ -1017,7 +1017,7 @@
    "source": [
     "Stage T4 have subgroups. To simplify the analysis, let’s combine all T4 tumors."
    ],
-   "id": "789bff60-c0b7-4a34-af95-bad1bd3fb1fd"
+   "id": "91d4396c-e8e9-44fb-9c63-460cfbc25712"
   },
   {
    "cell_type": "code",
@@ -1044,7 +1044,7 @@
     "clin$t_stage |> \n",
     "  table()\n"
    ],
-   "id": "3798170c-01a1-4f65-955c-6735dcaef75d"
+   "id": "ffb4b95f-ad2c-41c2-8da7-0423d11474d8"
   },
   {
    "cell_type": "markdown",
@@ -1052,7 +1052,7 @@
    "source": [
     "Also, we can see the vital status (alive=0, deceased=1)"
    ],
-   "id": "2dc4d343-f50d-4d0f-b8e8-2af5ad54af06"
+   "id": "3f9fb51f-e19c-4d53-8b52-cde4d6b064fb"
   },
   {
    "cell_type": "code",
@@ -1073,7 +1073,7 @@
     "clin$vital_status |> \n",
     "  table()\n"
    ],
-   "id": "650ec3ce-6d16-4a13-8d94-8bc6ab853c63"
+   "id": "026d5f50-2f1a-415d-b71d-eb2bbf1c115f"
   },
   {
    "cell_type": "markdown",
@@ -1081,7 +1081,7 @@
    "source": [
     "Or combine tumor status and vital status."
    ],
-   "id": "73d3b4c0-1667-4143-98be-21fb0383f76c"
+   "id": "3fc072b6-89a9-4d14-be49-4872ca344945"
   },
   {
    "cell_type": "code",
@@ -1104,7 +1104,7 @@
    "source": [
     "table(clin$t_stage, clin$vital_status)\n"
    ],
-   "id": "e35c8487-f9ad-43ee-8241-542d89762a62"
+   "id": "406371f9-65d6-4911-bd8d-f895acf63ab7"
   },
   {
    "cell_type": "markdown",
@@ -1114,7 +1114,7 @@
     "\n",
     "Let’s begin analyzing the mutation data. Below is the code to retrieve the mutation data."
    ],
-   "id": "5d174d3f-0182-4b9a-8ce9-c69b056ab3ee"
+   "id": "3843084a-8ceb-4225-9835-858e3d068449"
   },
   {
    "cell_type": "code",
@@ -1140,7 +1140,7 @@
     "\n",
     "mut_data\n"
    ],
-   "id": "2b5584de-570d-4ebd-9ad0-2d5876a3d8ca"
+   "id": "81be16c8-ec38-445a-a73f-61dd5a2f329c"
   },
   {
    "cell_type": "markdown",
@@ -1148,7 +1148,7 @@
    "source": [
     "From the inspection of the sample IDs we can see that the mutation colnames match to the **primary** column from he clinical data."
    ],
-   "id": "7f9e342e-7759-4927-a550-cc2a6894d605"
+   "id": "f5a9a42f-d6b8-4278-a9cc-80584a147f88"
   },
   {
    "cell_type": "code",
@@ -1177,7 +1177,7 @@
     "mut_sample_ids = colnames(mut_data)\n",
     "head(mut_sample_ids)\n"
    ],
-   "id": "8429fc3a-7f1c-4c75-bce6-45d152f8ea04"
+   "id": "c2decf2d-2038-4f3f-84a7-6389920a9ffb"
   },
   {
    "cell_type": "markdown",
@@ -1185,7 +1185,7 @@
    "source": [
     "We need to manipulate these by substracting 12 characters."
    ],
-   "id": "004cbe3a-af23-42a2-b9a6-1b5138e352b8"
+   "id": "c9f17272-45f6-4446-b1de-e8214c171cbb"
   },
   {
    "cell_type": "code",
@@ -1206,7 +1206,7 @@
     "\n",
     "all(mut_sample_ids %in% clin$patientID)\n"
    ],
-   "id": "1731545c-3aae-4a2c-a71a-5309c34c2269"
+   "id": "4277ea73-995f-4193-a5dc-6cee715e5c60"
   },
   {
    "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": "fa6d74fc-d072-4440-be37-6db79261e69a"
+   "id": "68224c9d-5a20-44dc-b8ca-2b03cd1d90ba"
   },
   {
    "cell_type": "code",
@@ -1248,7 +1248,7 @@
    "source": [
     "assay(mut_data)[1:3,1:3]\n"
    ],
-   "id": "bd053e68-8c24-4e3a-abd5-cdf6a022ea25"
+   "id": "5dab20d7-4e15-48c0-bbf8-a71c84150ad3"
   },
   {
    "cell_type": "markdown",
@@ -1256,7 +1256,7 @@
    "source": [
     "We can get more information if we look at the mutation information for each patient."
    ],
-   "id": "c64b3ee5-b606-446f-b001-6f0cc473fe3d"
+   "id": "563b89f8-f38f-4368-9191-cef3f8d59eed"
   },
   {
    "cell_type": "code",
@@ -1429,7 +1429,7 @@
     "\n",
     "mut_assay # GRangesList\n"
    ],
-   "id": "ccb279bf-a8c7-4ffc-8860-7519079f3e3e"
+   "id": "523e4f7d-7aa2-45ce-989a-e514af268246"
   },
   {
    "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": "b8391f82-0ea6-4017-a619-40a4f43d8a25"
+   "id": "1cd17745-6f90-4c04-8cef-174a07790bce"
   },
   {
    "cell_type": "code",
@@ -1622,7 +1622,7 @@
    "source": [
     "mut_assay[[1]]\n"
    ],
-   "id": "f840b796-5f88-4833-8997-c934d0095949"
+   "id": "d8f4f793-7e95-4827-b828-3c4ffc529a3c"
   },
   {
    "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": "4f0cbdcf-ec0e-453d-8f4b-606891341e60"
+   "id": "f7cbe8bc-08f6-4240-b528-95dd8c4124a4"
   },
   {
    "cell_type": "code",
@@ -1671,7 +1671,7 @@
     "\n",
     "head(var_class_df)\n"
    ],
-   "id": "91de88d6-0ccd-4d19-8ac7-0a8965ea785e"
+   "id": "36ccbdb3-56ae-4fc4-83d1-8a1dac0617af"
   },
   {
    "cell_type": "markdown",
@@ -1679,7 +1679,7 @@
    "source": [
     "We can visualize the most common mutated genes genes in rectum adenocarcinoma"
    ],
-   "id": "8372d5b4-ae1d-48a3-841c-8a5e0f0479ad"
+   "id": "a7be1327-ec11-4365-8225-0a8d4f56b084"
   },
   {
    "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": "9d53d4bf-ee02-46ee-af45-340b008e7051"
+   "id": "47d9d454-947c-404f-b34c-19d3b6a70667"
   },
   {
    "cell_type": "code",
@@ -1774,7 +1774,7 @@
     "\n",
     "head(clin_and_mutation) \n"
    ],
-   "id": "9c2f1815-826a-4fb3-91e7-dba31c4188ed"
+   "id": "10c6dec1-2a18-449b-a82d-c6f77a61303a"
   },
   {
    "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": "46474ceb-cfaf-4432-aae0-4a9cd4eb60d3"
+   "id": "a532dde4-9ed4-48f6-be2a-fd81641dd3ed"
   },
   {
    "cell_type": "code",
@@ -1848,7 +1848,7 @@
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     }
    },
-   "id": "03b65940-1c55-4d4d-bfe5-4ea702c24d0a"
+   "id": "212c4608-413a-4477-ae7d-7f0478c38455"
   },
   {
    "cell_type": "code",
@@ -1904,7 +1904,7 @@
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mOvmb4ggN+0npoIBBvdPBB78x1W8e6DffCP4j2niLQk2adPKZbdQPkU/8tIG9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\ns6Fp8Tq11awzSTAfwhym0H/vhjj3HrX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HmlaFZATXFtAEKIefNlbh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     }
    },
-   "id": "18fa12ba-b214-4041-951e-f299812af83b"
+   "id": "e8acd865-9339-42a7-b6e9-9e7e205d75af"
   },
   {
    "cell_type": "code",
@@ -1932,7 +1932,7 @@
     "\n",
     "rnaseq\n"
    ],
-   "id": "700e26f0-3288-4231-a4c2-0b7b6b7c7452"
+   "id": "9c1ea050-49c3-460b-be53-bb6c1dc51c78"
   },
   {
    "cell_type": "markdown",
@@ -1940,7 +1940,7 @@
    "source": [
     "As before, we need to shorten the sample IDs so they can match to the clinical data."
    ],
-   "id": "9ba42e17-0947-4733-b38a-56ceebe409f3"
+   "id": "e3272715-92a7-488a-bf04-c791c8ac83fb"
   },
   {
    "cell_type": "code",
@@ -1963,7 +1963,7 @@
     "\n",
     "rnaseq <- rnaseq[,!idx]\n"
    ],
-   "id": "0fd1ca65-43d7-4651-b6ac-1b68ab55f314"
+   "id": "4d20596b-33d9-4e9e-b97f-976d69ca6558"
   },
   {
    "cell_type": "markdown",
@@ -1971,7 +1971,7 @@
    "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": "eaa5c741-4345-4f9a-923e-6baa53740a8c"
+   "id": "32eb89dd-2c67-4c22-81e8-b19dccae97f9"
   },
   {
    "cell_type": "code",
@@ -2048,7 +2048,7 @@
    "source": [
     "library(limma)\n"
    ],
-   "id": "3e617d37-da55-4ea6-991e-63fcf55e623e"
+   "id": "6485e1d1-128c-4a1e-b5d0-12e2db789987"
   },
   {
    "cell_type": "markdown",
@@ -2056,7 +2056,7 @@
    "source": [
     "Let’s visualize two of the most expressed genes."
    ],
-   "id": "5874185f-2242-42ec-8f42-726ccbe55bbc"
+   "id": "4d3cd24e-a66b-405a-ab70-3ac227d9f957"
   },
   {
    "cell_type": "code",
@@ -2076,7 +2076,7 @@
     "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": "6423761b-2544-4e56-8150-49cc87bbaa07"
+   "id": "3283aebc-6d47-40a6-9ca8-1fbd9e43cc81"
   },
   {
    "cell_type": "markdown",
@@ -2088,7 +2088,7 @@
     "\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": "dd3ff9d6-c22d-435b-935a-83b2ddb71f55"
+   "id": "b23ac326-74fa-4b02-8fd3-4a6f25706e0a"
   },
   {
    "cell_type": "code",
@@ -2126,7 +2126,7 @@
     "\n",
     "methyl\n"
    ],
-   "id": "c5e8cc91-ff9f-4824-9139-4259c1ee47cd"
+   "id": "62b6235b-b43a-464f-9a72-2bdea3092957"
   },
   {
    "cell_type": "markdown",
@@ -2134,7 +2134,7 @@
    "source": [
     "As before, let’s truncate the names of the sample to match the clinical data."
    ],
-   "id": "5513527a-7bd4-47b1-82ad-a059774da8d1"
+   "id": "5bf8cb63-998d-47f1-85f5-a7b3a8a1aec4"
   },
   {
    "cell_type": "code",
@@ -2145,7 +2145,7 @@
     "colnames(methyl) <- colnames(methyl) |> \n",
     "  str_sub(start = 1,end = 12)\n"
    ],
-   "id": "c584fc3b-1eb2-4f4b-bda6-ed4a1f615b7f"
+   "id": "ddaaeba5-4e00-4697-8309-05fbbbfbcb89"
   },
   {
    "cell_type": "markdown",
@@ -2153,7 +2153,7 @@
    "source": [
     "We can add the clinical data to the methyl object and count the number of patients with methylation and transcription data."
    ],
-   "id": "261241c8-a171-4a43-830b-c7296bfc16b2"
+   "id": "dab6672f-0039-4ac3-a709-d4ab34ae4806"
   },
   {
    "cell_type": "code",
@@ -2174,7 +2174,7 @@
     "\n",
     "intersect(colnames(methyl), colnames(rnaseq)) |> length()\n"
    ],
-   "id": "de61fba6-fc28-403a-8269-140f96e6170e"
+   "id": "8e9abce8-d5ba-49af-b376-9e1c0d013916"
   },
   {
    "cell_type": "markdown",
@@ -2182,7 +2182,7 @@
    "source": [
     "Let’s subset common sample names and check the methylation data as row ranges."
    ],
-   "id": "0c4f58b5-963f-4352-97de-4a9764b4c3d5"
+   "id": "d27a2be8-e07f-4ef8-8187-c57cdbb03c00"
   },
   {
    "cell_type": "code",
@@ -2217,7 +2217,7 @@
     "methyl_genes = rowData(methyl_subset)\n",
     "methyl_genes\n"
    ],
-   "id": "b0f7430b-5bf0-4773-aeda-1e22e63e6bfc"
+   "id": "ce7ffa09-47c0-42dc-af1d-55be3a26e9da"
   },
   {
    "cell_type": "markdown",
@@ -2225,7 +2225,7 @@
    "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": "e4191788-01c1-4ce1-92e5-1b738b4c676d"
+   "id": "adbf4edd-8e49-4277-a5cb-f8feea5982fe"
   },
   {
    "cell_type": "code",
@@ -2344,7 +2344,7 @@
     "\n",
     "me_rna_cor(\"TAC1\", mpick=3)\n"
    ],
-   "id": "5553d5bb-f50c-4e16-90f5-3201e773a048"
+   "id": "6dbb2162-b19f-426e-b723-6f6ad7861398"
   }
  ],
  "nbformat": 4,