From 5ce6aefd8db1d29930243c016b3154458c42c776 Mon Sep 17 00:00:00 2001
From: Quarto GHA Workflow Runner <quarto-github-actions-publish@example.com>
Date: Thu, 7 Mar 2024 02:07:48 +0000
Subject: [PATCH] Built site for gh-pages

---
 .nojekyll          |   2 +-
 index-preview.html |   2 +-
 index.embed.ipynb  | 128 ++++++++++++++++++++++-----------------------
 index.html         |   2 +-
 index.out.ipynb    | 128 ++++++++++++++++++++++-----------------------
 5 files changed, 131 insertions(+), 131 deletions(-)

diff --git a/.nojekyll b/.nojekyll
index 4f650a6..14bf4df 100644
--- a/.nojekyll
+++ b/.nojekyll
@@ -1 +1 @@
-741ce508
\ No newline at end of file
+e73c8a7b
\ No newline at end of file
diff --git a/index-preview.html b/index-preview.html
index 8f743bd..b73d7c2 100644
--- a/index-preview.html
+++ b/index-preview.html
@@ -2117,7 +2117,7 @@ <h2 class="unnumbered anchored" data-anchor-id="references">References</h2>
     }
   }
 });
-</script>  </div> <!-- /content -->  <script>var lightboxQuarto = GLightbox({"loop":false,"descPosition":"bottom","closeEffect":"zoom","selector":".lightbox","openEffect":"zoom"});
+</script>  </div> <!-- /content -->  <script>var lightboxQuarto = GLightbox({"selector":".lightbox","openEffect":"zoom","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 9bcb0e3..860f5fe 100644
--- a/index.embed.ipynb
+++ b/index.embed.ipynb
@@ -23,7 +23,7 @@
     "\n",
     "We are gonna use the airway dataset from the `airway` package. Part of the analysis will be: - Differential gene expression using `DESeq2`. - Gene set analysis with fgsea from Cluster profiler. - Network analysis with networkx."
    ],
-   "id": "2ec2c0a1-a00e-4186-b353-9b565bc3f9a3"
+   "id": "e9cf850e-57d4-48ba-811f-e8d47206d6c0"
   },
   {
    "cell_type": "code",
@@ -753,7 +753,7 @@
    "source": [
     "library(airway)"
    ],
-   "id": "e4d5d352-c99f-477a-badb-fc10124c03cd"
+   "id": "f0974ec3-1aa5-4fd9-8426-2973b0e1429d"
   },
   {
    "cell_type": "markdown",
@@ -763,7 +763,7 @@
     "\n",
     "For all the analysis the @airway dataset will be used. Also, the @DESeq2 package will be used for the differential gene expression step."
    ],
-   "id": "766e7b49-36fd-41fd-90f4-47841de07e12"
+   "id": "3261b768-45e1-4d0b-9329-34e8615c2a84"
   },
   {
    "cell_type": "code",
@@ -775,7 +775,7 @@
     "\n",
     "dds <- DESeqDataSet(se = airway, design = ~ cell + dex)"
    ],
-   "id": "1f3dadae-331b-4b84-b0ef-cfba499f5220"
+   "id": "d81e56ce-47a6-4c75-89e5-b157d7c08ee9"
   },
   {
    "cell_type": "markdown",
@@ -783,7 +783,7 @@
    "source": [
     "### Pre-filtering"
    ],
-   "id": "e2bcc601-33b8-4cd6-8794-5281a7af4572"
+   "id": "64306cef-d936-43fa-bab4-5b6795139f0d"
   },
   {
    "cell_type": "code",
@@ -795,7 +795,7 @@
     "\n",
     "dds <- dds[keep,]"
    ],
-   "id": "1bc20f5b-8a39-4a0e-afa3-fcfc1a3bc343"
+   "id": "a3e2ed5d-2dbd-48a9-99d2-f97b8dcdd2d9"
   },
   {
    "cell_type": "markdown",
@@ -803,7 +803,7 @@
    "source": [
     "#### Re-leveling Factors"
    ],
-   "id": "b6c6a1ad-f7db-41aa-966e-fccc1b30fac0"
+   "id": "66ab6354-fbc8-4e6c-9acf-e19782914859"
   },
   {
    "cell_type": "code",
@@ -815,7 +815,7 @@
     "dds$dex <- relevel(dds$dex, ref = \"untrt\")\n",
     "dds$dex <- droplevels(dds$dex)"
    ],
-   "id": "429b1a62-c32e-48c4-8faa-ee5567ee2491"
+   "id": "ebdc0e03-aee5-4ed8-b75b-86e2ed836201"
   },
   {
    "cell_type": "markdown",
@@ -825,7 +825,7 @@
     "\n",
     "We can see how the variance is stabilized with the rlog transformation."
    ],
-   "id": "7329ded4-f56f-460b-bee8-acdd9fb9e485"
+   "id": "83b8e27d-9db3-43aa-80ac-87ca5804cae4"
   },
   {
    "cell_type": "code",
@@ -878,7 +878,7 @@
     "\n",
     "bslib::card(full_screen = T, bslib::card_title(\"Stabilized Variance\"), plotly::ggplotly(gghex))"
    ],
-   "id": "e80d1524-ee91-4444-ba95-36b5f71f114f"
+   "id": "608c4fe1-cab2-4cd2-9fef-cf62cc398d4a"
   },
   {
    "cell_type": "markdown",
@@ -886,7 +886,7 @@
    "source": [
     "Also, is important to check if appears some structure in the sample to sample distances plot."
    ],
-   "id": "4e5cc201-ecb0-4401-a3c9-512f20b0f48e"
+   "id": "a5b9e985-3d8b-475a-b593-9b87398179c4"
   },
   {
    "cell_type": "code",
@@ -929,7 +929,7 @@
     "\n",
     "bslib::card(full_screen = T, bslib::card_title(\"Sample-Sample Distance\"), ggheat)"
    ],
-   "id": "066b64d5-bf83-462a-bbc4-3149a2389450"
+   "id": "5dc3f428-6716-4ade-8a84-f93b2735cec5"
   },
   {
    "cell_type": "markdown",
@@ -939,7 +939,7 @@
     "\n",
     "Let’s calculate differential expressed genes using shrinked log2 fold changes with threshold of 1 and a FDR of 0.05."
    ],
-   "id": "f5306ae6-e81e-438f-8a4e-77e11796aea4"
+   "id": "ac8d79e0-5366-4be2-bc0d-8fae1f7ec1d6"
   },
   {
    "cell_type": "code",
@@ -1009,7 +1009,7 @@
    "source": [
     "dds <- DESeq(dds)"
    ],
-   "id": "721b4993-95cf-4d25-ab5f-502b3751444b"
+   "id": "25d1fcb4-98a7-4772-a36f-a8d25582c25c"
   },
   {
    "cell_type": "markdown",
@@ -1017,7 +1017,7 @@
    "source": [
     "We can see the MA plots as a general view of up and down regulated genes."
    ],
-   "id": "80fe38d7-fef2-4e29-a9b5-f9567cd65d7a"
+   "id": "6cb4c43e-f1e2-48ef-b078-969ff196f935"
   },
   {
    "cell_type": "code",
@@ -1057,7 +1057,7 @@
     "\n",
     "p"
    ],
-   "id": "295e6027-3868-45d6-87ae-0876164da9c9"
+   "id": "78065dae-23e6-49b2-95bf-bd3ec8207228"
   },
   {
    "cell_type": "markdown",
@@ -1067,7 +1067,7 @@
     "\n",
     "As a general view, we can select the top 20 expressed genes ordered by the number of counts."
    ],
-   "id": "2da8c48b-19bd-4bf8-9010-458a804b4710"
+   "id": "53c1481a-354c-4af8-894c-65f6bd8f96e0"
   },
   {
    "cell_type": "code",
@@ -1101,7 +1101,7 @@
     "\n",
     "bslib::card(df, full_screen = T)"
    ],
-   "id": "2a4cef32-a70b-43f9-9305-a4f2ee3abf24"
+   "id": "7d63b252-3e57-4861-b16a-7eeb5d78a6e7"
   },
   {
    "cell_type": "markdown",
@@ -1111,7 +1111,7 @@
     "\n",
     "For this part of the analysis, is important to use annotation databases, in this case we are gonna use the human h19 genome annotation from the @org.Hs.eg.db package."
    ],
-   "id": "ac91fe14-e08f-4388-81d2-2eeaa584ddf1"
+   "id": "5612e125-6782-4eeb-b4e7-a43adff65563"
   },
   {
    "cell_type": "code",
@@ -1151,7 +1151,7 @@
     "        columns = c(\"SYMBOL\", \"GENENAME\")\n",
     "      )"
    ],
-   "id": "a9a831ca-fc9f-406a-ba44-6b400c6025ea"
+   "id": "64643cd7-a9bd-4119-b28d-5d8c843485e4"
   },
   {
    "cell_type": "markdown",
@@ -1159,7 +1159,7 @@
    "source": [
     "### Construction of Ranks for `fgsea`"
    ],
-   "id": "c16e1d70-8889-48d2-9aa2-a97b3baeed92"
+   "id": "599cd5f2-56de-4bca-a21e-d99f9e6e6441"
   },
   {
    "cell_type": "code",
@@ -1179,7 +1179,7 @@
     "res.lfc_ranks <-\n",
     "      deframe(res.lfc_for_ranks)"
    ],
-   "id": "24d97e76-e605-4570-9039-5f7f92aec1e0"
+   "id": "590c7b48-674a-47c6-be61-c46e293c8cfe"
   },
   {
    "cell_type": "markdown",
@@ -1189,7 +1189,7 @@
     "\n",
     "For this part of the process, the @clusterProfiler package will be used to make the gene sets enrichment analysis. Is important to mention that for this analysis we are gonna analyze only the biological process ontology,"
    ],
-   "id": "38ded884-4ae9-4185-b35b-4ddb548d6c79"
+   "id": "52e9b26f-8d9e-4b2a-bf1d-f287693890bd"
   },
   {
    "cell_type": "code",
@@ -1261,7 +1261,7 @@
    "source": [
     "ranks_entrez<-bitr(names(res.lfc_ranks),fromType = \"SYMBOL\",toType = \"ENTREZID\",OrgDb = org.Hs.eg.db)"
    ],
-   "id": "c3acfea5-ff95-40b0-9443-0cfbbb8fbcdc"
+   "id": "5c0b36ac-74ba-4608-adc9-19a31a2b669c"
   },
   {
    "cell_type": "markdown",
@@ -1269,7 +1269,7 @@
    "source": [
     "#### Summary Heatmap of Genesets"
    ],
-   "id": "c1071d7e-95c3-4e62-8390-fb07ace85092"
+   "id": "dc4fb1e3-d428-4874-ae7e-982617be549f"
   },
   {
    "cell_type": "code",
@@ -1290,7 +1290,7 @@
     "\n",
     "p"
    ],
-   "id": "40023d6d-d699-4f98-a2c4-42056acbc401"
+   "id": "9db48919-c1ea-4207-b681-88dbaabebb00"
   },
   {
    "cell_type": "markdown",
@@ -1300,7 +1300,7 @@
     "\n",
     "`GeneTonic` package have a lot of interactive plots that we can use to dig into our data. An example is a network of Gene sets and genes from which we are gonna perform a network analysis."
    ],
-   "id": "abd77ac9-6b2e-4519-a5a4-713255c25583"
+   "id": "7a242292-6445-46c3-9ade-6fce97081cef"
   },
   {
    "cell_type": "code",
@@ -1347,7 +1347,7 @@
     "\n",
     "htmltools::tagList(list(p))"
    ],
-   "id": "4a44ba8b-c7e7-4df5-9b74-c3093c447711"
+   "id": "e48be37c-dee2-4036-abf9-221e3f51ea8b"
   },
   {
    "cell_type": "markdown",
@@ -1366,7 +1366,7 @@
     "\\end{pmatrix}\n",
     "$$"
    ],
-   "id": "41d28cf4-1ce3-4319-a91d-edfaceb0d789"
+   "id": "d367d186-5199-4389-b433-0093414f2fe8"
   },
   {
    "cell_type": "code",
@@ -1381,7 +1381,7 @@
     "\n",
     "adj_matrix_bipartite <- as.matrix(as_adjacency_matrix(ggs, sparse = FALSE)) |> as.data.frame()"
    ],
-   "id": "dab84093-cf89-4fae-a491-53d768d746db"
+   "id": "26b78ea9-dcaf-4325-bb48-574b68056efa"
   },
   {
    "cell_type": "code",
@@ -1402,7 +1402,7 @@
     "import numpy as np\n",
     "import networkx as nx"
    ],
-   "id": "edeebbc7-ee18-4196-8d91-85140bdf6909"
+   "id": "4af0e76a-69af-4ab7-b37a-f50930df8f65"
   },
   {
    "cell_type": "markdown",
@@ -1410,7 +1410,7 @@
    "source": [
     "Checking the degree distribution of the graph."
    ],
-   "id": "0e1ec83d-2262-4f08-9898-e8df23311f46"
+   "id": "68dffd5d-ccc5-4f3d-92e4-94f688df2986"
   },
   {
    "cell_type": "code",
@@ -1436,7 +1436,7 @@
     "sns.rugplot(degree_sequence)\n",
     "plt.show()"
    ],
-   "id": "64a34dc5-3ac9-4b34-8d66-9c31656f30b7"
+   "id": "e43dc50b-9d37-425e-87f8-9f6da5464d05"
   },
   {
    "cell_type": "markdown",
@@ -1452,7 +1452,7 @@
     "\n",
     "Gene sets or genes identified as high authorities could be central to multiple biological pathways, making them potential biomarkers for diseases or targets for therapeutic intervention. Their importance in various processes makes them subjects of interest for further research and analysis."
    ],
-   "id": "a24a9f0e-71d7-4fcd-ae79-f3eab0d7eacb"
+   "id": "78f2abd6-3869-4209-b742-6a26f93b97fe"
   },
   {
    "cell_type": "code",
@@ -1465,7 +1465,7 @@
     "df_hubs = pd.DataFrame.from_dict(h, orient=\"index\").sort_values(by=[0], ascending=False)\n",
     "df_authorities = pd.DataFrame.from_dict(a, orient=\"index\").sort_values(by=[0], ascending=False)"
    ],
-   "id": "ea253503-580a-4c89-8f7b-85ca347789fc"
+   "id": "5f86b515-1655-470c-b1dd-631409f74e9f"
   },
   {
    "cell_type": "code",
@@ -1508,7 +1508,7 @@
     "    buttons = c('copy', 'csv', 'excel')\n",
     "  ))))"
    ],
-   "id": "370305f7-f7df-421d-88bc-7ec83f95bd11"
+   "id": "f9d9c708-2bf8-4d94-b203-f9782d877772"
   },
   {
    "cell_type": "markdown",
@@ -1516,7 +1516,7 @@
    "source": [
     "We can inspect the log fold change and p-values of some genes and gene sets with high scores."
    ],
-   "id": "0ca497ae-25fd-48ad-a3c5-b994f2119a1a"
+   "id": "ffa4b6fb-bec6-4895-8879-174414a06934"
   },
   {
    "cell_type": "code",
@@ -1578,7 +1578,7 @@
     "\n",
     "res[which(rownames(res)==query),]"
    ],
-   "id": "17350319-f6e6-4f93-be17-e9aa44fa5f50"
+   "id": "7a526c8d-2247-44c8-a5e7-776b43f6c26c"
   },
   {
    "cell_type": "markdown",
@@ -1586,7 +1586,7 @@
    "source": [
     "Also, we can see the network colored by these scores."
    ],
-   "id": "69396b38-3130-4bf5-a496-d67500326c3e"
+   "id": "109ac2b2-07d0-4065-beca-5d2ce187d66e"
   },
   {
    "cell_type": "code",
@@ -1630,7 +1630,7 @@
     "\n",
     "draw(G, pos, h, 'DiGraph HITS Hubs')"
    ],
-   "id": "0670f6eb-413c-47a2-b69c-e72bd1bae47c"
+   "id": "0d4eabda-e917-471a-a120-782ac2efd9b5"
   },
   {
    "cell_type": "markdown",
@@ -1638,7 +1638,7 @@
    "source": [
     "Detecting communities within the network can helps us understand better the underlying biological process."
    ],
-   "id": "09e3ada6-ef01-44c6-88b4-9c12ed792633"
+   "id": "9e70a8bb-7602-4afd-83c8-f643fe9612f5"
   },
   {
    "cell_type": "code",
@@ -1674,7 +1674,7 @@
     "ax = fig.add_subplot(111)\n",
     "ax.axis('off')"
    ],
-   "id": "61dd2312-61b1-4ed4-985d-2659f8e488e5"
+   "id": "14eb5b5c-a67c-4f63-9b2d-56051e1c79d1"
   },
   {
    "cell_type": "markdown",
@@ -1682,7 +1682,7 @@
    "source": [
     "We can see the clusters and their respective members:"
    ],
-   "id": "83233832-4716-477c-9150-fedaee35b279"
+   "id": "72318164-45ae-4970-a7ea-3d79f3cb445a"
   },
   {
    "cell_type": "code",
@@ -1732,7 +1732,7 @@
     "    group_by(Cluster) |> \n",
     "    summarise(n = n())"
    ],
-   "id": "75b4434b-77ec-48fe-8494-22e2a467b85b"
+   "id": "bafc75b5-7879-4b98-a0ec-bb2377388af5"
   },
   {
    "cell_type": "markdown",
@@ -1740,7 +1740,7 @@
    "source": [
     "Also, we can focus on the most connected component of the network"
    ],
-   "id": "34a40e72-a251-4af0-883a-2c34d478ba80"
+   "id": "99bd1aa4-e5a8-49a1-b067-61779613433f"
   },
   {
    "cell_type": "code",
@@ -1768,7 +1768,7 @@
     "\n",
     "print(f'The largest component have: {len(largest_component)}')"
    ],
-   "id": "3ec69ca2-4fe1-4209-a02b-bf0c7ae20ebf"
+   "id": "40bb657c-6eec-42f7-8437-648f41931b0b"
   },
   {
    "cell_type": "markdown",
@@ -1776,7 +1776,7 @@
    "source": [
     "If instead of analysis a bipartite network we want to analyze only gene sets or only genes we need to create two matrices: genesets_coupling matrix and gene_co_expression matrix."
    ],
-   "id": "a799fb4e-a783-4d49-82ba-eb4263187f26"
+   "id": "3a4ee726-fa8c-46c5-8d66-0146fc171a83"
   },
   {
    "cell_type": "code",
@@ -1801,7 +1801,7 @@
     "\n",
     "ontology_coupling <- ontology_coupling |> as.data.frame()"
    ],
-   "id": "580bee03-2d7f-4326-b1f3-a3cc22fbbee6"
+   "id": "a721ef21-4bc2-4559-8335-8182f84360f9"
   },
   {
    "cell_type": "markdown",
@@ -1817,7 +1817,7 @@
     "S = G*G^T\n",
     "$$"
    ],
-   "id": "4710a892-87b7-41b3-b66b-e45927b5075b"
+   "id": "9291dc81-714f-4aa5-a066-a396db70a0b0"
   },
   {
    "cell_type": "code",
@@ -1830,7 +1830,7 @@
     "G = nx.from_pandas_adjacency(graph, create_using=nx.Graph)\n",
     "G.remove_edges_from(nx.selfloop_edges(G))"
    ],
-   "id": "fd9d5483-0f28-4690-9309-b6fb92ca3c24"
+   "id": "28c620b3-0523-4fcd-9692-ab68db58cf5c"
   },
   {
    "cell_type": "code",
@@ -1858,7 +1858,7 @@
     "\n",
     "pos = nx.spring_layout(G)"
    ],
-   "id": "eae36df7-d24f-4367-9a17-ce198d0c3b69"
+   "id": "68f2d8db-209d-4982-8b3b-740c8d76bad7"
   },
   {
    "cell_type": "code",
@@ -1890,7 +1890,7 @@
    "source": [
     "draw(G, pos, nx.get_node_attributes(G,name=\"degree_centrality\"),\"Degree Centrality\")"
    ],
-   "id": "0b41da6c-8f4b-4ed9-8aec-e2b7614536cb"
+   "id": "f50f228d-f5bf-40ca-8e93-1389d2fad1b2"
   },
   {
    "cell_type": "code",
@@ -1919,7 +1919,7 @@
     "    buttons = c('copy', 'csv', 'excel')\n",
     "  ))))"
    ],
-   "id": "b5332f22-f029-4065-a370-619adf55793a"
+   "id": "b70ff6f5-e4f7-45e8-9481-207886df31cc"
   },
   {
    "cell_type": "markdown",
@@ -1927,7 +1927,7 @@
    "source": [
     "We can sort the table by eigenvector centrality to see which gene sets are the most important in the network"
    ],
-   "id": "e2301e30-5808-47fd-bec6-bd2dffcade47"
+   "id": "7c84612f-38fa-416a-a7ff-6f92a2a24073"
   },
   {
    "cell_type": "code",
@@ -1975,7 +1975,7 @@
     "\n",
     "termOntology(qterms)"
    ],
-   "id": "8b89585e-6834-4a3f-8857-498d45b14e1a"
+   "id": "8bc31048-bc86-4e5d-affa-799756f9bacf"
   },
   {
    "cell_type": "markdown",
@@ -1991,7 +1991,7 @@
     "C = G^T*G\n",
     "$$"
    ],
-   "id": "1b5a6b93-e214-4c71-b3e9-c22887ff209f"
+   "id": "225cc7ba-45c8-4c9c-bf53-f7eb7f89d495"
   },
   {
    "cell_type": "code",
@@ -2003,7 +2003,7 @@
     "\n",
     "genes_coapprearence <- genes_coapprearence |> as.data.frame()"
    ],
-   "id": "56fe232f-3de3-4760-914b-0393d48ec73d"
+   "id": "e55f9d56-f70a-44f5-ae75-9e8840ab9a75"
   },
   {
    "cell_type": "code",
@@ -2016,7 +2016,7 @@
     "G = nx.from_pandas_adjacency(graph, create_using=nx.Graph)\n",
     "G.remove_edges_from(nx.selfloop_edges(G))"
    ],
-   "id": "09a9cfc8-1abd-44bc-88ab-d92ecf2cc2be"
+   "id": "0060d197-6001-4083-a561-cd484a591f28"
   },
   {
    "cell_type": "code",
@@ -2044,7 +2044,7 @@
     "\n",
     "pos = nx.spring_layout(G)"
    ],
-   "id": "0b4ffb5a-e0bd-4f14-89c2-8fdee8fe2cbd"
+   "id": "4b62ee29-24f2-48db-8b2e-4143e8f1b9b9"
   },
   {
    "cell_type": "code",
@@ -2076,7 +2076,7 @@
    "source": [
     "draw(G, pos, nx.get_node_attributes(G,name=\"degree_centrality\"),\"Degree Centrality\")"
    ],
-   "id": "df726414-92de-46ae-891b-4a6b56320b81"
+   "id": "902c9c3e-287c-43c4-b7e9-db198a75216a"
   },
   {
    "cell_type": "code",
@@ -2105,7 +2105,7 @@
     "    buttons = c('copy', 'csv', 'excel')\n",
     "  ))))"
    ],
-   "id": "cb48e588-fb31-4490-8649-06b3841a611d"
+   "id": "97120c8a-ea74-4570-a25a-6908bc348e8b"
   },
   {
    "cell_type": "markdown",
@@ -2113,7 +2113,7 @@
    "source": [
     "We can sort the table by betweenness centrality to look for genes that serve as bridges or connectors within the network, facilitating the flow of information."
    ],
-   "id": "08b26242-00d6-4f79-a442-ff9c55f8a735"
+   "id": "66892786-6db3-4306-a06d-53e0351441db"
   },
   {
    "cell_type": "code",
@@ -2141,7 +2141,7 @@
     "\n",
     "res[which(rownames(res)==query),]"
    ],
-   "id": "7512dcc8-0491-48f7-a0be-02a82b6fb5cd"
+   "id": "76c6cbe0-0205-4b3a-93cf-cdd15cbe3973"
   },
   {
    "cell_type": "markdown",
@@ -2155,7 +2155,7 @@
     "\n",
     "## References"
    ],
-   "id": "de895512-1380-4141-9fcf-67451abf61f0"
+   "id": "4ad32892-f186-437a-9909-01b12781b2bb"
   }
  ],
  "nbformat": 4,
diff --git a/index.html b/index.html
index dda2012..805505c 100644
--- a/index.html
+++ b/index.html
@@ -1516,7 +1516,7 @@ <h2 class="unnumbered anchored" data-anchor-id="references">References</h2>
 });
 </script>
 </div> <!-- /content -->
-<script>var lightboxQuarto = GLightbox({"selector":".lightbox","loop":false,"descPosition":"bottom","openEffect":"zoom","closeEffect":"zoom"});
+<script>var lightboxQuarto = GLightbox({"closeEffect":"zoom","descPosition":"bottom","selector":".lightbox","openEffect":"zoom","loop":false});
 window.onload = () => {
   lightboxQuarto.on('slide_before_load', (data) => {
     const { slideIndex, slideNode, slideConfig, player, trigger } = data;
diff --git a/index.out.ipynb b/index.out.ipynb
index 7b4b962..423799c 100644
--- a/index.out.ipynb
+++ b/index.out.ipynb
@@ -23,7 +23,7 @@
     "\n",
     "We are gonna use the airway dataset from the `airway` package. Part of the analysis will be: - Differential gene expression using `DESeq2`. - Gene set analysis with fgsea from Cluster profiler. - Network analysis with networkx."
    ],
-   "id": "e0608c10-3b0a-45b8-9472-d50bfd751fbb"
+   "id": "c2178d59-5672-4891-8e71-ab2b5fc51d61"
   },
   {
    "cell_type": "code",
@@ -753,7 +753,7 @@
    "source": [
     "library(airway)"
    ],
-   "id": "0cd02db3-5964-4cc0-9f3a-5a97bbc281cb"
+   "id": "56a55d29-2081-414e-914b-0f068989d49c"
   },
   {
    "cell_type": "markdown",
@@ -763,7 +763,7 @@
     "\n",
     "For all the analysis the Himes et al. ([2014](#ref-airway)) dataset will be used. Also, the Love, Huber, and Anders ([2014](#ref-DESeq2)) package will be used for the differential gene expression step."
    ],
-   "id": "2ed4d39c-ad44-4f51-a239-acb6411f4dc9"
+   "id": "fe61c39f-f5c0-4954-b0d7-f49643ef7192"
   },
   {
    "cell_type": "code",
@@ -775,7 +775,7 @@
     "\n",
     "dds <- DESeqDataSet(se = airway, design = ~ cell + dex)"
    ],
-   "id": "e61bafa1-b2f3-4c42-9887-456b93688e85"
+   "id": "00f7016a-1e3e-4ea7-8023-4e4551668d89"
   },
   {
    "cell_type": "markdown",
@@ -783,7 +783,7 @@
    "source": [
     "### 2.2 Pre-filtering"
    ],
-   "id": "5ffe73e6-9da6-43eb-800d-fe3b3bc3c5e2"
+   "id": "e956780d-f827-4ea0-8d92-301ace612cd4"
   },
   {
    "cell_type": "code",
@@ -795,7 +795,7 @@
     "\n",
     "dds <- dds[keep,]"
    ],
-   "id": "82205d9c-b6cf-4cb4-90a0-e44561aff4f0"
+   "id": "a5c22546-4f1c-4b38-82de-aa26df3a6989"
   },
   {
    "cell_type": "markdown",
@@ -803,7 +803,7 @@
    "source": [
     "#### 2.2.1 Re-leveling Factors"
    ],
-   "id": "e9bc4b8a-3bde-4664-ac9d-cf320ec4a386"
+   "id": "cc3efa0e-9b77-433a-8385-54e3d025b48b"
   },
   {
    "cell_type": "code",
@@ -815,7 +815,7 @@
     "dds$dex <- relevel(dds$dex, ref = \"untrt\")\n",
     "dds$dex <- droplevels(dds$dex)"
    ],
-   "id": "86f9092e-8601-4eba-82df-5e51e09a203f"
+   "id": "4af0844d-dd6b-4669-b61f-b8947fb55603"
   },
   {
    "cell_type": "markdown",
@@ -825,7 +825,7 @@
     "\n",
     "We can see how the variance is stabilized with the rlog transformation."
    ],
-   "id": "c6355243-4596-4512-aa1c-5b216bd71409"
+   "id": "2ebcb6ae-0c50-4740-9c25-81e440aa5440"
   },
   {
    "cell_type": "code",
@@ -878,7 +878,7 @@
     "\n",
     "bslib::card(full_screen = T, bslib::card_title(\"Stabilized Variance\"), plotly::ggplotly(gghex))"
    ],
-   "id": "a0f109d1-53b6-4afe-a6f9-15755da31f52"
+   "id": "6571db6d-fec7-4656-95e1-cd6752e52435"
   },
   {
    "cell_type": "markdown",
@@ -886,7 +886,7 @@
    "source": [
     "Also, is important to check if appears some structure in the sample to sample distances plot."
    ],
-   "id": "195df368-88b2-48a8-8046-c22091b2641b"
+   "id": "a332b8aa-0b47-40b9-840f-26c452bbc0ae"
   },
   {
    "cell_type": "code",
@@ -929,7 +929,7 @@
     "\n",
     "bslib::card(full_screen = T, bslib::card_title(\"Sample-Sample Distance\"), ggheat)"
    ],
-   "id": "40b1e550-d479-49cc-81c4-54ee3735033d"
+   "id": "b641d1a3-4189-42c3-a2b7-98c52f97eff9"
   },
   {
    "cell_type": "markdown",
@@ -939,7 +939,7 @@
     "\n",
     "Let’s calculate differential expressed genes using shrinked log2 fold changes with threshold of 1 and a FDR of 0.05."
    ],
-   "id": "66069747-b0b2-4f64-bca1-a19494801107"
+   "id": "f926a6cd-1f5b-496d-8701-955471d26894"
   },
   {
    "cell_type": "code",
@@ -1009,7 +1009,7 @@
    "source": [
     "dds <- DESeq(dds)"
    ],
-   "id": "e8f9a75d-a374-4cb2-a3b0-f9251d023944"
+   "id": "3c929bfd-d01b-469f-8ecc-c9e81f4a9625"
   },
   {
    "cell_type": "markdown",
@@ -1017,7 +1017,7 @@
    "source": [
     "We can see the MA plots as a general view of up and down regulated genes."
    ],
-   "id": "2487b79c-fbee-4224-b70e-ddc73515a780"
+   "id": "1fc51002-37e0-4e50-88b7-703df4d9338e"
   },
   {
    "cell_type": "code",
@@ -1057,7 +1057,7 @@
     "\n",
     "p"
    ],
-   "id": "1cda5dd0-bf91-4993-b02e-28d919dc92d8"
+   "id": "8749ed6a-dbbd-4f76-8db5-59e4b0a60680"
   },
   {
    "cell_type": "markdown",
@@ -1067,7 +1067,7 @@
     "\n",
     "As a general view, we can select the top 20 expressed genes ordered by the number of counts."
    ],
-   "id": "5168477c-d820-4d62-a8e1-f1b4206dc1da"
+   "id": "29fd8979-cc38-4ab2-91c5-fe7bc4a94cc8"
   },
   {
    "cell_type": "code",
@@ -1101,7 +1101,7 @@
     "\n",
     "bslib::card(df, full_screen = T)"
    ],
-   "id": "d09690fa-a13d-4779-959a-13b3d1b67eef"
+   "id": "e0d955f3-3543-4cf9-910d-a0cc71c66ef5"
   },
   {
    "cell_type": "markdown",
@@ -1111,7 +1111,7 @@
     "\n",
     "For this part of the analysis, is important to use annotation databases, in this case we are gonna use the human h19 genome annotation from the Carlson ([2023](#ref-org.Hs.eg.db)) package."
    ],
-   "id": "231c9940-1043-4f96-9eaf-b65a6d0f63a7"
+   "id": "23416e00-18db-4534-815b-6338e42f9d9e"
   },
   {
    "cell_type": "code",
@@ -1151,7 +1151,7 @@
     "        columns = c(\"SYMBOL\", \"GENENAME\")\n",
     "      )"
    ],
-   "id": "004ed07d-535f-440b-aeda-3cbaa14e1e9a"
+   "id": "546b7efa-9312-4729-86be-fe8e93c73bc9"
   },
   {
    "cell_type": "markdown",
@@ -1159,7 +1159,7 @@
    "source": [
     "### 2.6 Construction of Ranks for `fgsea`"
    ],
-   "id": "8ea56c12-cf7c-4339-9897-09cf491831fb"
+   "id": "26129604-05b3-4a5a-8549-ca01babadb58"
   },
   {
    "cell_type": "code",
@@ -1179,7 +1179,7 @@
     "res.lfc_ranks <-\n",
     "      deframe(res.lfc_for_ranks)"
    ],
-   "id": "7ab2c455-f0f0-4050-b1a7-075bade21899"
+   "id": "c6dc043f-d85c-41d1-b82c-ef7a51a5a957"
   },
   {
    "cell_type": "markdown",
@@ -1189,7 +1189,7 @@
     "\n",
     "For this part of the process, the Wu et al. ([2021](#ref-clusterProfiler)) package will be used to make the gene sets enrichment analysis. Is important to mention that for this analysis we are gonna analyze only the biological process ontology,"
    ],
-   "id": "bdb4182b-0165-42f2-8196-10ab244f04c5"
+   "id": "a834838d-7432-46d6-bffb-8a6f5d9aa13f"
   },
   {
    "cell_type": "code",
@@ -1261,7 +1261,7 @@
    "source": [
     "ranks_entrez<-bitr(names(res.lfc_ranks),fromType = \"SYMBOL\",toType = \"ENTREZID\",OrgDb = org.Hs.eg.db)"
    ],
-   "id": "5cc340b4-e761-4529-aef0-f5eee55e4f90"
+   "id": "09a192ec-fba0-4c74-991e-c0cb9fce5d8c"
   },
   {
    "cell_type": "markdown",
@@ -1269,7 +1269,7 @@
    "source": [
     "#### 2.7.1 Summary Heatmap of Genesets"
    ],
-   "id": "56a1cad9-95aa-44f1-9596-c53de2286735"
+   "id": "389d7642-d12f-460b-bf30-ab22006c050a"
   },
   {
    "cell_type": "code",
@@ -1290,7 +1290,7 @@
     "\n",
     "p"
    ],
-   "id": "732cd9db-df0e-455c-86c2-efaabb0b4bfd"
+   "id": "9b4202ab-564b-4cda-91cb-6bfeef2bf98e"
   },
   {
    "cell_type": "markdown",
@@ -1300,7 +1300,7 @@
     "\n",
     "`GeneTonic` package have a lot of interactive plots that we can use to dig into our data. An example is a network of Gene sets and genes from which we are gonna perform a network analysis."
    ],
-   "id": "b13169ec-1e53-4091-9deb-7fd376b2c975"
+   "id": "c837f920-52e7-45a7-ac84-64e0f7931817"
   },
   {
    "cell_type": "code",
@@ -1347,7 +1347,7 @@
     "\n",
     "htmltools::tagList(list(p))"
    ],
-   "id": "e17f0bc3-bcf6-4625-ba5d-c7a70e662a77"
+   "id": "47ba6c20-7af8-4d6e-8210-b2ddf9737d50"
   },
   {
    "cell_type": "markdown",
@@ -1366,7 +1366,7 @@
     "\\end{pmatrix}\n",
     "$$"
    ],
-   "id": "0d546948-916c-42f8-9521-c32b471d1453"
+   "id": "14461050-9664-435c-9108-353ecf3716ca"
   },
   {
    "cell_type": "code",
@@ -1381,7 +1381,7 @@
     "\n",
     "adj_matrix_bipartite <- as.matrix(as_adjacency_matrix(ggs, sparse = FALSE)) |> as.data.frame()"
    ],
-   "id": "cbccf791-fdf4-4fbc-bb3b-32f46d520bbf"
+   "id": "b11c14dd-7b71-4351-b135-bbfaac076c44"
   },
   {
    "cell_type": "code",
@@ -1402,7 +1402,7 @@
     "import numpy as np\n",
     "import networkx as nx"
    ],
-   "id": "c9b77582-36ad-4df4-a097-acaa976b4bfa"
+   "id": "5122e298-6c54-4917-bdb5-dd8a566789ab"
   },
   {
    "cell_type": "markdown",
@@ -1410,7 +1410,7 @@
    "source": [
     "Checking the degree distribution of the graph."
    ],
-   "id": "a534324c-89c9-4fc8-bb7a-934e1067592c"
+   "id": "887f80d0-e02a-46fa-b91a-8a3ef1de527a"
   },
   {
    "cell_type": "code",
@@ -1436,7 +1436,7 @@
     "sns.rugplot(degree_sequence)\n",
     "plt.show()"
    ],
-   "id": "c57cf089-fcc0-4db4-bc1f-f5ed0459c4cd"
+   "id": "9eb7f647-9e14-4e9d-928f-45ec4dc49110"
   },
   {
    "cell_type": "markdown",
@@ -1452,7 +1452,7 @@
     "\n",
     "Gene sets or genes identified as high authorities could be central to multiple biological pathways, making them potential biomarkers for diseases or targets for therapeutic intervention. Their importance in various processes makes them subjects of interest for further research and analysis."
    ],
-   "id": "fd3f4214-7dee-450c-b4de-19db50f36e63"
+   "id": "d3ca870e-cb7d-418a-9264-bb65434b06c5"
   },
   {
    "cell_type": "code",
@@ -1465,7 +1465,7 @@
     "df_hubs = pd.DataFrame.from_dict(h, orient=\"index\").sort_values(by=[0], ascending=False)\n",
     "df_authorities = pd.DataFrame.from_dict(a, orient=\"index\").sort_values(by=[0], ascending=False)"
    ],
-   "id": "b2f74b8b-2582-48c3-bbc9-13fb042377eb"
+   "id": "a05e28de-e796-4d4a-81e4-040626aaccbc"
   },
   {
    "cell_type": "code",
@@ -1508,7 +1508,7 @@
     "    buttons = c('copy', 'csv', 'excel')\n",
     "  ))))"
    ],
-   "id": "73747e2f-0c95-4cd7-8771-54015e3ce364"
+   "id": "cf21354c-f68c-4561-9730-439af5d09827"
   },
   {
    "cell_type": "markdown",
@@ -1516,7 +1516,7 @@
    "source": [
     "We can inspect the log fold change and p-values of some genes and gene sets with high scores."
    ],
-   "id": "f2c145dc-7d12-4d0a-b52a-bdd1672a3391"
+   "id": "4e11229a-3144-4722-bb31-70ff1273a020"
   },
   {
    "cell_type": "code",
@@ -1578,7 +1578,7 @@
     "\n",
     "res[which(rownames(res)==query),]"
    ],
-   "id": "dda2d067-ae0c-4b0a-8f00-8e178bbe228b"
+   "id": "307b0753-b0ee-4307-b727-c10998c12af8"
   },
   {
    "cell_type": "markdown",
@@ -1586,7 +1586,7 @@
    "source": [
     "Also, we can see the network colored by these scores."
    ],
-   "id": "5a38c25d-b022-4467-ad8b-aae149aca5a9"
+   "id": "9b381a69-6a47-4dc3-b72a-74e728bdd55e"
   },
   {
    "cell_type": "code",
@@ -1630,7 +1630,7 @@
     "\n",
     "draw(G, pos, h, 'DiGraph HITS Hubs')"
    ],
-   "id": "598ce39c-61ee-4d49-81a7-909ab7198b5e"
+   "id": "d1b5740e-fac2-4bfd-8c9e-5063088dde6a"
   },
   {
    "cell_type": "markdown",
@@ -1638,7 +1638,7 @@
    "source": [
     "Detecting communities within the network can helps us understand better the underlying biological process."
    ],
-   "id": "0fd65e21-8797-41b5-a7a5-a4caeb121c1f"
+   "id": "e31512db-8d7c-4d46-8dab-ad54dd3cffa2"
   },
   {
    "cell_type": "code",
@@ -1674,7 +1674,7 @@
     "ax = fig.add_subplot(111)\n",
     "ax.axis('off')"
    ],
-   "id": "ea7da7f6-7e44-457f-a4aa-7fb522c96ab3"
+   "id": "6000390f-d94c-4da5-ac71-df0297c03d1f"
   },
   {
    "cell_type": "markdown",
@@ -1682,7 +1682,7 @@
    "source": [
     "We can see the clusters and their respective members:"
    ],
-   "id": "89d87164-f2d7-4948-9576-292d756f822a"
+   "id": "372a593f-c33a-4676-86cd-af82a86514b9"
   },
   {
    "cell_type": "code",
@@ -1732,7 +1732,7 @@
     "    group_by(Cluster) |> \n",
     "    summarise(n = n())"
    ],
-   "id": "b8fb1a19-55c3-4f99-9964-f5407868bc87"
+   "id": "5c5eb8a3-35b8-46e3-b936-02dc4d3e7d50"
   },
   {
    "cell_type": "markdown",
@@ -1740,7 +1740,7 @@
    "source": [
     "Also, we can focus on the most connected component of the network"
    ],
-   "id": "cdb93dc8-8b4c-45bb-9175-0c0053cc23a2"
+   "id": "c7a18b8a-2408-43ee-a529-faefe809d2db"
   },
   {
    "cell_type": "code",
@@ -1768,7 +1768,7 @@
     "\n",
     "print(f'The largest component have: {len(largest_component)}')"
    ],
-   "id": "e86d4d29-56fd-4a6f-ae46-ec1cf5a52c52"
+   "id": "28f25e0b-42ae-4db6-a0cd-3aada7e73940"
   },
   {
    "cell_type": "markdown",
@@ -1776,7 +1776,7 @@
    "source": [
     "If instead of analysis a bipartite network we want to analyze only gene sets or only genes we need to create two matrices: genesets_coupling matrix and gene_co_expression matrix."
    ],
-   "id": "3d5bd803-3821-4e68-8132-6c512a44f71c"
+   "id": "d3923e3d-14b7-44cc-9ae0-f93fa170702c"
   },
   {
    "cell_type": "code",
@@ -1801,7 +1801,7 @@
     "\n",
     "ontology_coupling <- ontology_coupling |> as.data.frame()"
    ],
-   "id": "194a5e2e-02b1-436a-8088-bcccd47aa906"
+   "id": "9b7109a3-535d-4d52-95f2-4054a8745b59"
   },
   {
    "cell_type": "markdown",
@@ -1817,7 +1817,7 @@
     "S = G*G^T\n",
     "$$"
    ],
-   "id": "6947b8a4-3009-447b-9259-1a7b7c9e662e"
+   "id": "885ac82c-1529-43ae-b1cd-c3f3b2d93058"
   },
   {
    "cell_type": "code",
@@ -1830,7 +1830,7 @@
     "G = nx.from_pandas_adjacency(graph, create_using=nx.Graph)\n",
     "G.remove_edges_from(nx.selfloop_edges(G))"
    ],
-   "id": "b12a6979-d58c-4280-b007-29da1b871735"
+   "id": "49b3280f-d6e6-4f10-976b-a318557b0f29"
   },
   {
    "cell_type": "code",
@@ -1858,7 +1858,7 @@
     "\n",
     "pos = nx.spring_layout(G)"
    ],
-   "id": "21ca4484-f1f8-47fb-91c6-846b9d18c5c3"
+   "id": "4ba52bf9-19d1-4e54-811b-5844d95169aa"
   },
   {
    "cell_type": "code",
@@ -1890,7 +1890,7 @@
    "source": [
     "draw(G, pos, nx.get_node_attributes(G,name=\"degree_centrality\"),\"Degree Centrality\")"
    ],
-   "id": "cf32a326-5ba5-446c-a5a7-06054ee6c018"
+   "id": "790ae0b6-76e4-4afa-8fb7-3b89cb35ff10"
   },
   {
    "cell_type": "code",
@@ -1919,7 +1919,7 @@
     "    buttons = c('copy', 'csv', 'excel')\n",
     "  ))))"
    ],
-   "id": "142ee52a-53d9-4219-849c-30b571f01eb9"
+   "id": "d7a03461-c78d-41b6-b82a-f84adeaa5508"
   },
   {
    "cell_type": "markdown",
@@ -1927,7 +1927,7 @@
    "source": [
     "We can sort the table by eigenvector centrality to see which gene sets are the most important in the network"
    ],
-   "id": "b848c6e4-963b-41d8-bd0a-402916774e89"
+   "id": "cab07665-7346-4c7d-b6b1-c8f4eb7de7cf"
   },
   {
    "cell_type": "code",
@@ -1975,7 +1975,7 @@
     "\n",
     "termOntology(qterms)"
    ],
-   "id": "0704f37f-6926-464f-8ab2-33d8187182fc"
+   "id": "1abf3f72-3b9e-4f0c-82ad-dacc272daad0"
   },
   {
    "cell_type": "markdown",
@@ -1991,7 +1991,7 @@
     "C = G^T*G\n",
     "$$"
    ],
-   "id": "9c4e995c-e68f-42d1-a125-93a20977ff6a"
+   "id": "fab57e1f-fbf0-4350-a839-591594b5d3e2"
   },
   {
    "cell_type": "code",
@@ -2003,7 +2003,7 @@
     "\n",
     "genes_coapprearence <- genes_coapprearence |> as.data.frame()"
    ],
-   "id": "31d127f3-866d-46bb-80fc-50e279e9ae64"
+   "id": "4a078dcc-5856-447a-b247-cf051f21072e"
   },
   {
    "cell_type": "code",
@@ -2016,7 +2016,7 @@
     "G = nx.from_pandas_adjacency(graph, create_using=nx.Graph)\n",
     "G.remove_edges_from(nx.selfloop_edges(G))"
    ],
-   "id": "7a8f7cd9-1e3f-4708-99de-d45b192bc52e"
+   "id": "59358d6f-48be-461c-af06-0ffc4e296b23"
   },
   {
    "cell_type": "code",
@@ -2044,7 +2044,7 @@
     "\n",
     "pos = nx.spring_layout(G)"
    ],
-   "id": "b70f5650-8db0-4e48-b20f-4c0951b07b55"
+   "id": "972d867a-7249-4a94-ae76-bb96dd7707e0"
   },
   {
    "cell_type": "code",
@@ -2076,7 +2076,7 @@
    "source": [
     "draw(G, pos, nx.get_node_attributes(G,name=\"degree_centrality\"),\"Degree Centrality\")"
    ],
-   "id": "180d396f-c123-449c-9f10-ef7b24bbac70"
+   "id": "245fddae-c8cd-45c3-8fc1-017d8da16190"
   },
   {
    "cell_type": "code",
@@ -2105,7 +2105,7 @@
     "    buttons = c('copy', 'csv', 'excel')\n",
     "  ))))"
    ],
-   "id": "3fb15979-5d7c-4da0-b1fe-8c5369f62607"
+   "id": "0160d927-bba6-426b-8b4e-de3239039999"
   },
   {
    "cell_type": "markdown",
@@ -2113,7 +2113,7 @@
    "source": [
     "We can sort the table by betweenness centrality to look for genes that serve as bridges or connectors within the network, facilitating the flow of information."
    ],
-   "id": "85a04912-8b2a-4c75-9b93-c024af6cfba0"
+   "id": "b4f438c1-d1d7-4bfc-ac93-86b6529a2287"
   },
   {
    "cell_type": "code",
@@ -2141,7 +2141,7 @@
     "\n",
     "res[which(rownames(res)==query),]"
    ],
-   "id": "451d4fc8-47c1-43bf-a71a-0a775afc1284"
+   "id": "16aedb16-ec19-4c02-af45-eb809bcd18dc"
   },
   {
    "cell_type": "markdown",
@@ -2165,7 +2165,7 @@
     "\n",
     "Wu, Tianzhi, Erqiang Hu, Shuangbin Xu, Meijun Chen, Pingfan Guo, Zehan Dai, Tingze Feng, et al. 2021. “clusterProfiler 4.0: A Universal Enrichment Tool for Interpreting Omics Data” 2: 100141. <https://doi.org/10.1016/j.xinn.2021.100141>."
    ],
-   "id": "02140e0a-c236-4950-994e-224fcb2a9418"
+   "id": "d8e1feb8-2a8e-48b8-8eed-e1da7e52b09b"
   }
  ],
  "nbformat": 4,