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@@ -1 +1 @@
-2fb62505
+d4b90054

index-preview.html

@@ -1298,7 +1298,7 @@ <h2 class="unnumbered anchored" data-anchor-id="references">References</h2>
     }
   }
 });
-</script>  </div> <!-- /content -->  <script>var lightboxQuarto = GLightbox({"openEffect":"zoom","closeEffect":"zoom","descPosition":"bottom","selector":".lightbox","loop":false});
+</script>  </div> <!-- /content -->  <script>var lightboxQuarto = GLightbox({"closeEffect":"zoom","selector":".lightbox","loop":false,"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": "23d795dd-c8a5-4cc7-9993-83268793bbfc"
+   "id": "4002332c-8e9b-415c-9c14-e0438cabee24"
   },
   {
    "cell_type": "code",
@@ -286,15 +286,15 @@
    "source": [
     "library(tidyverse)"
    ],
-   "id": "e3f539b4-1a89-4fb7-844b-7dc5e656d807"
+   "id": "efa053cc-a57f-4248-81d8-9c3f0e3754ee"
   },
   {
    "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": "24bed4d1-0de8-4078-9e0c-fcf8ba287541"
+   "id": "24671321-69e4-47c8-b38f-f2fc46278c6a"
   },
   {
    "cell_type": "code",
@@ -328,15 +328,15 @@
     "\n",
     "gg = gwcat |> as_GRanges()"
    ],
-   "id": "1bc277a5-8bad-4ed7-876e-8d0c36c66c21"
+   "id": "2c9aaf78-3793-4c92-97eb-99a7e19e5f2f"
   },
   {
    "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": "2234023b-2f3e-4b65-bd0a-73f54d92fadf"
+   "id": "764430bd-2aac-4ad0-ae68-2b9ca8dc8bda"
   },
   {
    "cell_type": "code",
@@ -373,15 +373,15 @@
    "source": [
     "ggbio::autoplot(gg, layout=\"karyogram\")"
    ],
-   "id": "6827dda9-8dd1-4d65-a7a1-ba45e15957df"
+   "id": "c28a698c-579f-48de-af47-60cd343bbe2b"
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "We can see the peak data as a `GRanges` object:"
    ],
-   "id": "5f030a23-ad3d-4249-a1a5-5e069e5c785f"
+   "id": "d8fde223-9ddf-48c3-b0b5-b967f41e6b2f"
   },
   {
    "cell_type": "code",
@@ -429,15 +429,15 @@
     "\n",
     "GM12878"
    ],
-   "id": "b0325143-38be-43ba-bd1b-9f199a6bcdb2"
+   "id": "7618d240-1e58-4796-aae4-6fe4a5ebb3be"
   },
   {
    "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": "dbc47a23-225f-4fc8-9363-69f2b7f9063a"
+   "id": "76ce70ed-124d-4307-8faa-119a56fed9a5"
   },
   {
    "cell_type": "code",
@@ -555,7 +555,7 @@
    "source": [
     "library(AnnotationHub)"
    ],
-   "id": "d0950b26-fa4e-4493-80bd-792deb7298f2"
+   "id": "2ed6aa27-31ff-4b12-9ada-ce69f3309413"
   },
   {
    "cell_type": "code",
@@ -574,7 +574,7 @@
     "seqlevelsStyle(GM12878) <- \"UCSC\"\n",
     "seqlevelsStyle(gg) <- \"UCSC\""
    ],
-   "id": "b3676dcf-43f2-41ea-8e6b-76d28a1ce6da"
+   "id": "b6e2b244-6c4d-41de-b113-4eef4a3105c0"
   },
   {
    "cell_type": "markdown",
@@ -584,7 +584,7 @@
     "\n",
     "We can see the duplications with the `reduce` function from `IRanges` package:"
    ],
-   "id": "ac59ceec-d1a7-4c11-a131-f4ff9ca4a509"
+   "id": "8164f7bf-ad2a-4352-90e4-6aed16c9c31a"
   },
   {
    "cell_type": "code",
@@ -603,15 +603,15 @@
     "# duplicated loci\n",
     "length(gg) - length(reduce(gg))"
    ],
-   "id": "a8d73100-9efa-434f-baa1-3f54ec19d2be"
+   "id": "1d0300ef-d68b-41e5-b9ce-cdad4dd6d3c0"
   },
   {
    "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": "8892842e-8d00-4510-b893-4a52c4944f63"
+   "id": "1be4613c-98af-4ca9-afb4-2039b9a9fadb"
   },
   {
    "cell_type": "code",
@@ -646,15 +646,15 @@
     "fo = findOverlaps(GM12878, reduce(gg))\n",
     "fo"
    ],
-   "id": "00d9812e-145d-4500-8220-5e8fb904c7dc"
+   "id": "63c7b9ad-f830-435f-9e07-0de953ccc9fb"
   },
   {
    "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": "eada5c05-ae5a-4096-b9da-b319249ea6c1"
+   "id": "2abe9bc7-d6a8-4eb6-bf07-0094f781aa10"
   },
   {
    "cell_type": "code",
@@ -705,15 +705,15 @@
     "\n",
     "htmltools::tagList(list(plotly::ggplotly(p)))"
    ],
-   "id": "5209b359-1f3b-4e4a-8aca-57cc44448772"
+   "id": "7ec48cb3-b38c-4def-8fda-577b7e5ddd91"
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "Distinct phenotypes identified on the peaks:"
    ],
-   "id": "6c5566f2-f135-4db6-a53a-b37e4acf3182"
+   "id": "c9f89aba-10c8-40ac-a73e-e41f9f2dd371"
   },
   {
    "cell_type": "code",
@@ -731,15 +731,15 @@
    "source": [
     "length(phset)"
    ],
-   "id": "6f62f254-007d-4c91-9fb9-2406c1fdeb21"
+   "id": "2f28fe62-318f-4cc5-9536-ef1f50799e26"
   },
   {
    "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": "7fafdfea-6df1-46cb-9ca4-916931061cc4"
+   "id": "004c9396-2153-4ad3-a1f4-e8f669e00b26"
   },
   {
    "cell_type": "code",
@@ -1011,7 +1011,7 @@
    "source": [
     "library(ph525x)"
    ],
-   "id": "996da57e-2dc0-4909-8bf3-76cefbad92d8"
+   "id": "74d5e826-17b9-4091-978a-aae34d5c3fc2"
   },
   {
    "cell_type": "markdown",
@@ -1025,7 +1025,7 @@
     "\n",
     "## References"
    ],
-   "id": "b0b248d5-122f-4135-82fc-8abc0947d14c"
+   "id": "25b467ec-1735-45b5-a083-ed7ac34571ec"
   }
  ],
  "nbformat": 4,

index.html

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