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@@ -1 +1 @@
-0843835f
+f317a5e4

index-preview.html

@@ -1298,7 +1298,7 @@ <h2 class="unnumbered anchored" data-anchor-id="references">References</h2>
     }
   }
 });
-</script>  </div> <!-- /content -->  <script>var lightboxQuarto = GLightbox({"closeEffect":"zoom","loop":false,"selector":".lightbox","descPosition":"bottom","openEffect":"zoom"});
+</script>  </div> <!-- /content -->  <script>var lightboxQuarto = GLightbox({"loop":false,"selector":".lightbox","closeEffect":"zoom","openEffect":"zoom","descPosition":"bottom"});
 window.onload = () => {
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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": "1d022e36-c85a-4b72-aefe-1ad360f19f48"
+   "id": "b4b8eac6-fe07-4393-af3c-34487f4031fa"
   },
   {
    "cell_type": "code",
@@ -286,15 +286,15 @@
    "source": [
     "library(tidyverse)"
    ],
-   "id": "061499f5-c3fa-4d77-ac94-3d0f6b43e983"
+   "id": "69d92163-e302-4348-a220-5ceda2dcb105"
   },
   {
    "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": "a023c75a-fbd4-46d2-b388-4206777664ad"
+   "id": "3e2951f4-ec50-4312-a4c7-148d44beb16f"
   },
   {
    "cell_type": "code",
@@ -328,15 +328,15 @@
     "\n",
     "gg = gwcat |> as_GRanges()"
    ],
-   "id": "4d720c43-a619-48b8-8c46-0518a17a475f"
+   "id": "c4089353-f16f-44a2-adc7-9fdb21de25c9"
   },
   {
    "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": "93cfb002-23bb-4412-9329-01c3c991c4f1"
+   "id": "31aaebaf-f7cf-4d8e-ad4c-bcc5f2cfafa5"
   },
   {
    "cell_type": "code",
@@ -373,15 +373,15 @@
    "source": [
     "ggbio::autoplot(gg, layout=\"karyogram\")"
    ],
-   "id": "8611cede-3880-4969-876c-bbb1f13c5d22"
+   "id": "7397edc6-b245-41f1-a511-3f058cb2cdae"
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "We can see the peak data as a `GRanges` object:"
    ],
-   "id": "082a260f-9edf-4ea9-a516-e00e4bf506e8"
+   "id": "9e08ff18-4235-4602-bf43-3deee26ffa63"
   },
   {
    "cell_type": "code",
@@ -429,15 +429,15 @@
     "\n",
     "GM12878"
    ],
-   "id": "fbac45bd-2c5b-4927-8c03-785782eb06b1"
+   "id": "03078248-d873-4d8e-a55f-3ee79a2ea47e"
   },
   {
    "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": "c96f3eb0-ee64-49e0-8a5f-43267633ab43"
+   "id": "6543721c-743f-472f-97c1-a1998f2a41fd"
   },
   {
    "cell_type": "code",
@@ -555,7 +555,7 @@
    "source": [
     "library(AnnotationHub)"
    ],
-   "id": "fd568436-b423-4302-a284-9fd43632c43f"
+   "id": "9f2a09c2-325f-4208-b531-bb96d61995fb"
   },
   {
    "cell_type": "code",
@@ -574,7 +574,7 @@
     "seqlevelsStyle(GM12878) <- \"UCSC\"\n",
     "seqlevelsStyle(gg) <- \"UCSC\""
    ],
-   "id": "d4b91118-4e4c-4e6e-a9cf-1898b98346e8"
+   "id": "d67c54bf-9073-4d84-9a83-71948fc8943a"
   },
   {
    "cell_type": "markdown",
@@ -584,7 +584,7 @@
     "\n",
     "We can see the duplications with the `reduce` function from `IRanges` package:"
    ],
-   "id": "1a444f0d-2599-418b-83ac-d36c6aaba25b"
+   "id": "ce65f74c-0385-48b7-bdf3-802733dbe090"
   },
   {
    "cell_type": "code",
@@ -603,15 +603,15 @@
     "# duplicated loci\n",
     "length(gg) - length(reduce(gg))"
    ],
-   "id": "25c6e372-6517-4a69-8c58-f34fc5f2dfe8"
+   "id": "5b69414b-28eb-4952-8281-24efb9f64040"
   },
   {
    "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": "7e1725d3-0843-49bb-881f-b2ad0cd1f863"
+   "id": "f77a6b4c-23ac-4315-9aad-cc4d42549164"
   },
   {
    "cell_type": "code",
@@ -646,15 +646,15 @@
     "fo = findOverlaps(GM12878, reduce(gg))\n",
     "fo"
    ],
-   "id": "d53b0571-0b15-4221-ab7c-ff8f98e5aac3"
+   "id": "0617ce03-ee9b-465a-9787-7394b9774e44"
   },
   {
    "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": "c89648f7-057d-45e2-bccc-3613116a6e38"
+   "id": "fef5d949-9e49-4469-9619-dc354a36d3e8"
   },
   {
    "cell_type": "code",
@@ -705,15 +705,15 @@
     "\n",
     "htmltools::tagList(list(plotly::ggplotly(p)))"
    ],
-   "id": "e432a1d2-06b6-4f0d-bd71-81a5c2e6f6cb"
+   "id": "c822db21-9033-4c6a-8137-61fdc3e8ee9d"
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "Distinct phenotypes identified on the peaks:"
    ],
-   "id": "9f579115-dbda-46da-ba0b-984829a8d731"
+   "id": "0bbf067a-c088-4be6-8828-62c5d8e0465a"
   },
   {
    "cell_type": "code",
@@ -731,15 +731,15 @@
    "source": [
     "length(phset)"
    ],
-   "id": "fcccceec-16ff-4361-b946-59d9e86e47ee"
+   "id": "e12820ff-a29b-44ed-a6c1-a48d868cae6d"
   },
   {
    "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": "2bd21fec-f361-49df-a877-83f8fec58bd9"
+   "id": "824687a1-80c8-4e2a-afe9-e00f96247554"
   },
   {
    "cell_type": "code",
@@ -1011,7 +1011,7 @@
    "source": [
     "library(ph525x)"
    ],
-   "id": "0be7d023-d032-4716-84f0-a6eaa9a2c1cd"
+   "id": "fab7c5c4-190b-4cf2-8d53-2c58e5f8ffee"
   },
   {
    "cell_type": "markdown",
@@ -1025,7 +1025,7 @@
     "\n",
     "## References"
    ],
-   "id": "4ed71c55-32fb-4212-9c9c-39c749f201ea"
+   "id": "a340632e-c94c-4d24-995b-fdaafc074a7c"
   }
  ],
  "nbformat": 4,

index.html

@@ -733,7 +733,7 @@ <h2 class="unnumbered anchored" data-anchor-id="references">References</h2>
 });
 </script>
 </div> <!-- /content -->
-<script>var lightboxQuarto = GLightbox({"closeEffect":"zoom","descPosition":"bottom","loop":false,"openEffect":"zoom","selector":".lightbox"});
+<script>var lightboxQuarto = GLightbox({"closeEffect":"zoom","selector":".lightbox","openEffect":"zoom","descPosition":"bottom","loop":false});
 window.onload = () => {
   lightboxQuarto.on('slide_before_load', (data) => {
     const { slideIndex, slideNode, slideConfig, player, trigger } = data;