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@@ -1 +1 @@
-fb00be48
+2fb62505

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});
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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",
@@ -286,15 +286,15 @@
    "source": [
     "library(tidyverse)"
    ],
-   "id": "fad55962-5f06-4258-bc5a-c082389e7aec"
+   "id": "e3f539b4-1a89-4fb7-844b-7dc5e656d807"
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "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",
@@ -328,15 +328,15 @@
     "\n",
     "gg = gwcat |> as_GRanges()"
    ],
-   "id": "ea7211b2-43aa-48c5-92e3-4fb545505f45"
+   "id": "1bc277a5-8bad-4ed7-876e-8d0c36c66c21"
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "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",
@@ -373,15 +373,15 @@
    "source": [
     "ggbio::autoplot(gg, layout=\"karyogram\")"
    ],
-   "id": "66471194-2059-4f1b-8e69-2d8595839798"
+   "id": "6827dda9-8dd1-4d65-a7a1-ba45e15957df"
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "We can see the peak data as a `GRanges` object:"
    ],
-   "id": "d864736e-b9fe-4076-830b-edbbd3e01889"
+   "id": "5f030a23-ad3d-4249-a1a5-5e069e5c785f"
   },
   {
    "cell_type": "code",
@@ -429,15 +429,15 @@
     "\n",
     "GM12878"
    ],
-   "id": "3dcf546f-e4eb-4e73-ae1a-f15ef32d7eb0"
+   "id": "b0325143-38be-43ba-bd1b-9f199a6bcdb2"
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "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"
   },
   {
    "cell_type": "code",
@@ -555,7 +555,7 @@
    "source": [
     "library(AnnotationHub)"
    ],
-   "id": "8cc21c95-2293-4166-9cf7-d0ada567b16e"
+   "id": "d0950b26-fa4e-4493-80bd-792deb7298f2"
   },
   {
    "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"
   },
   {
    "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",
@@ -603,15 +603,15 @@
     "# duplicated loci\n",
     "length(gg) - length(reduce(gg))"
    ],
-   "id": "11c8cbab-a227-422c-b933-4a31f60b71bd"
+   "id": "a8d73100-9efa-434f-baa1-3f54ec19d2be"
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "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"
   },
   {
    "cell_type": "code",
@@ -646,15 +646,15 @@
     "fo = findOverlaps(GM12878, reduce(gg))\n",
     "fo"
    ],
-   "id": "fe5b50d2-c02c-41c6-95e6-7083782787e7"
+   "id": "00d9812e-145d-4500-8220-5e8fb904c7dc"
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "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"
   },
   {
    "cell_type": "code",
@@ -705,15 +705,15 @@
     "\n",
     "htmltools::tagList(list(plotly::ggplotly(p)))"
    ],
-   "id": "9953452d-eb43-499d-acac-b04c2481c97e"
+   "id": "5209b359-1f3b-4e4a-8aca-57cc44448772"
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "Distinct phenotypes identified on the peaks:"
    ],
-   "id": "78a4813e-c0bd-424f-ac5d-21b9c38a0fd8"
+   "id": "6c5566f2-f135-4db6-a53a-b37e4acf3182"
   },
   {
    "cell_type": "code",
@@ -731,15 +731,15 @@
    "source": [
     "length(phset)"
    ],
-   "id": "589f7898-1fe3-43be-ae2f-9ac180dbff12"
+   "id": "6f62f254-007d-4c91-9fb9-2406c1fdeb21"
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "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"
   },
   {
    "cell_type": "code",
@@ -1011,7 +1011,7 @@
    "source": [
     "library(ph525x)"
    ],
-   "id": "af92e685-571b-4885-9327-adb03cf1d9bb"
+   "id": "996da57e-2dc0-4909-8bf3-76cefbad92d8"
   },
   {
    "cell_type": "markdown",
@@ -1025,7 +1025,7 @@
     "\n",
     "## References"
    ],
-   "id": "533ee32d-d806-40c2-ac21-b7ec9fc4411c"
+   "id": "b0b248d5-122f-4135-82fc-8abc0947d14c"
   }
  ],
  "nbformat": 4,

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) => {
     const { slideIndex, slideNode, slideConfig, player, trigger } = data;