diff --git a/404.html b/404.html
index 8ae9282..4c58910 100644
--- a/404.html
+++ b/404.html
@@ -18,8 +18,7 @@
 <link href="deps/Source_Code_Pro-0.4.8/font.css" rel="stylesheet">
 <!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous">
 <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous">
-<!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="pkgdown.js"></script><link href="extra.css" rel="stylesheet">
-<meta property="og:title" content="Page not found (404)">
+<!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="pkgdown.js"></script><meta property="og:title" content="Page not found (404)">
 <meta property="og:image" content="/logo.png">
 <!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
 <script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
@@ -30,7 +29,7 @@
     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
     
 
-    <nav class="navbar fixed-top navbar-light navbar-expand-lg bg-#0092ac"><div class="container">
+    <nav class="navbar fixed-top navbar-light navbar-expand-lg bg-light"><div class="container">
     
     <a class="navbar-brand me-2" href="index.html">HybridExpress</a>
 
diff --git a/CODE_OF_CONDUCT.html b/CODE_OF_CONDUCT.html
index c85c441..f24f7fc 100644
--- a/CODE_OF_CONDUCT.html
+++ b/CODE_OF_CONDUCT.html
@@ -1,12 +1,12 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>NA • HybridExpress</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="apple-touch-icon-60x60.png"><script src="deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="deps/Atkinson_Hyperlegible-0.4.8/font.css" rel="stylesheet"><link href="deps/Source_Code_Pro-0.4.8/font.css" rel="stylesheet"><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="pkgdown.js"></script><link href="extra.css" rel="stylesheet"><meta property="og:title" content="NA"><meta property="og:image" content="/logo.png"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>NA • HybridExpress</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="apple-touch-icon-60x60.png"><script src="deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="deps/Atkinson_Hyperlegible-0.4.8/font.css" rel="stylesheet"><link href="deps/Source_Code_Pro-0.4.8/font.css" rel="stylesheet"><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="pkgdown.js"></script><meta property="og:title" content="NA"><meta property="og:image" content="/logo.png"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
 <script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
 <script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
 <![endif]--></head><body>
     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
     
 
-    <nav class="navbar fixed-top navbar-light navbar-expand-lg bg-#0092ac"><div class="container">
+    <nav class="navbar fixed-top navbar-light navbar-expand-lg bg-light"><div class="container">
     
     <a class="navbar-brand me-2" href="index.html">HybridExpress</a>
 
diff --git a/SUPPORT.html b/SUPPORT.html
index c0d234e..e598b0a 100644
--- a/SUPPORT.html
+++ b/SUPPORT.html
@@ -1,12 +1,12 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Getting help with HybridExpress • HybridExpress</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="apple-touch-icon-60x60.png"><script src="deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="deps/Atkinson_Hyperlegible-0.4.8/font.css" rel="stylesheet"><link href="deps/Source_Code_Pro-0.4.8/font.css" rel="stylesheet"><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="pkgdown.js"></script><link href="extra.css" rel="stylesheet"><meta property="og:title" content="Getting help with HybridExpress"><meta property="og:image" content="/logo.png"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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--- a/articles/HybridExpress.html
+++ b/articles/HybridExpress.html
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-    <nav class="navbar fixed-top navbar-light navbar-expand-lg bg-#0092ac"><div class="container">
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     <a class="navbar-brand me-2" href="../index.html">HybridExpress</a>
 
@@ -132,7 +131,7 @@ <h2 id="introduction">Introduction<a class="anchor" aria-label="anchor" href="#i
 <div class="section level2">
 <h2 id="installation">Installation<a class="anchor" aria-label="anchor" href="#installation"></a>
 </h2>
-<p><em><a href="https://bioconductor.org/packages/3.19/HybridExpress" class="external-link">HybridExpress</a></em>
+<p><em><a href="https://github.com/HybridExpress" class="external-link">HybridExpress</a></em>
 can be installed from Bioconductor with the following code:</p>
 <div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="kw">if</span><span class="op">(</span><span class="op">!</span><span class="fu"><a href="https://rdrr.io/r/base/ns-load.html" class="external-link">requireNamespace</a></span><span class="op">(</span><span class="st">'BiocManager'</span>, quietly <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span><span class="op">)</span></span>
@@ -330,6 +329,101 @@ <h2 id="adding-midparent-expression-values">Adding midparent expression values<a
 object.</p>
 </div>
 <div class="section level2">
+<h2 id="normalizing-count-data">Normalizing count data<a class="anchor" aria-label="anchor" href="#normalizing-count-data"></a>
+</h2>
+<p>To normalize count data, the function <code><a href="../reference/add_size_factors.html">add_size_factors()</a></code>
+calculates size factors (used by <em><a href="https://bioconductor.org/packages/3.19/DESeq2" class="external-link">DESeq2</a></em> for
+normalization) and adds them as an extra column in the colData slot of
+your <code>SummarizedExperiment</code> object. Such size factors are
+calculated using one of two methods:</p>
+<ol style="list-style-type: decimal">
+<li><p>By library size (default) using the ‘median of ratios’ method
+implemented in <em><a href="https://bioconductor.org/packages/3.19/DESeq2" class="external-link">DESeq2</a></em>.</p></li>
+<li><p>By cell size/biomass using spike-in controls (if available). If
+spike-in controls are present in the count matrix, you can use them for
+normalization by setting <code>spikein = TRUE</code> and specifying the
+pattern used to indicate rows that contain spike-ins (usually they start
+with <em>ERCC</em>)<a class="footnote-ref" tabindex="0" data-bs-toggle="popover" data-bs-content='&lt;p&gt;&lt;strong&gt;Note:&lt;/strong&gt; if you use spike-in
+normalization, the function &lt;code&gt;&lt;a href="../reference/add_size_factors.html"&gt;add_size_factors()&lt;/a&gt;&lt;/code&gt;
+automatically removes rows containing spike-in counts from the
+&lt;code&gt;SummarizedExperiment&lt;/code&gt; object after normalization. However,
+if you have counts for spike-in controls in your count matrix, but do
+not want to use spike-in normalization, you should remove them before
+creating the &lt;code&gt;SummarizedExperiment&lt;/code&gt; object.&lt;/p&gt;'><sup>1</sup></a>. Normalization with spike-in controls is
+particularly useful if the amount of mRNA per cell is not equal between
+generations (due to, for instance, different ploidy levels, which in
+turn can lead to different cell sizes and/or biomass).</p></li>
+</ol>
+<p>In our example data set, spike-in controls are available in the last
+rows of the count matrix. Let’s take a look at them.</p>
+<div class="sourceCode" id="cb5"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="co"># Show last rows of the count matrix</span></span>
+<span><span class="fu"><a href="https://rdrr.io/pkg/SummarizedExperiment/man/SummarizedExperiment-class.html" class="external-link">assay</a></span><span class="op">(</span><span class="va">se</span><span class="op">)</span> <span class="op">|&gt;</span> </span>
+<span>    <span class="fu"><a href="https://rdrr.io/r/utils/head.html" class="external-link">tail</a></span><span class="op">(</span><span class="op">)</span></span>
+<span><span class="co">#&gt;              S1   S2   S3   S4   S5   S6   S7   S8   S9  S10  S11  S12  S13</span></span>
+<span><span class="co">#&gt; ERCC-00163   74   75   55   77   51   84  132  127   93  108   79  102   66</span></span>
+<span><span class="co">#&gt; ERCC-00164    0    4    0    1    4    1    6    4    1    4    3    2    5</span></span>
+<span><span class="co">#&gt; ERCC-00165  147  139   87  165  118  179  236  246  139  218  176  145  118</span></span>
+<span><span class="co">#&gt; ERCC-00168    2    5    2    2    2    6    5    1    1    5    0    3    4</span></span>
+<span><span class="co">#&gt; ERCC-00170   97   95   73  101   70  118  186  148  110  167  110  103   72</span></span>
+<span><span class="co">#&gt; ERCC-00171 4644 4959 3554 5357 4170 5946 8207 7915 4992 7843 6455 5884 4537</span></span>
+<span><span class="co">#&gt;             S14  S15  S16  S17  S18 midparent1 midparent2 midparent3 midparent4</span></span>
+<span><span class="co">#&gt; ERCC-00163   67   62   80   88   47         67         96        101         90</span></span>
+<span><span class="co">#&gt; ERCC-00164    4    3    5    4    2          2          2          4          2</span></span>
+<span><span class="co">#&gt; ERCC-00165  143  115  185  192  136        132        198        192        155</span></span>
+<span><span class="co">#&gt; ERCC-00168    6    2   11    5    2          1          6          3          2</span></span>
+<span><span class="co">#&gt; ERCC-00170   99   64  121  103   84         92        142        122        102</span></span>
+<span><span class="co">#&gt; ERCC-00171 5665 4598 6168 5771 4747       5004       6894       6437       5620</span></span>
+<span><span class="co">#&gt;            midparent5 midparent6</span></span>
+<span><span class="co">#&gt; ERCC-00163         92         84</span></span>
+<span><span class="co">#&gt; ERCC-00164          5          0</span></span>
+<span><span class="co">#&gt; ERCC-00165        177        143</span></span>
+<span><span class="co">#&gt; ERCC-00168          4          2</span></span>
+<span><span class="co">#&gt; ERCC-00170        128        104</span></span>
+<span><span class="co">#&gt; ERCC-00171       6188       4818</span></span></code></pre></div>
+<p>As you can see, rows with spike-in counts start with “ERCC”. Let’s
+add size factors to our <code>SummarizedExperiment</code> object using
+spike-in controls.</p>
+<div class="sourceCode" id="cb6"><pre class="downlit sourceCode r">
+<code class="sourceCode R"><span><span class="co"># Take a look at the original colData</span></span>
+<span><span class="fu"><a href="https://rdrr.io/pkg/SummarizedExperiment/man/SummarizedExperiment-class.html" class="external-link">colData</a></span><span class="op">(</span><span class="va">se</span><span class="op">)</span></span>
+<span><span class="co">#&gt; DataFrame with 24 rows and 2 columns</span></span>
+<span><span class="co">#&gt;                 Ploidy Generation</span></span>
+<span><span class="co">#&gt;            &lt;character&gt;   &lt;factor&gt;</span></span>
+<span><span class="co">#&gt; S1             diploid         P1</span></span>
+<span><span class="co">#&gt; S2             diploid         P1</span></span>
+<span><span class="co">#&gt; S3             diploid         P1</span></span>
+<span><span class="co">#&gt; S4             diploid         P1</span></span>
+<span><span class="co">#&gt; S5             diploid         P1</span></span>
+<span><span class="co">#&gt; ...                ...        ...</span></span>
+<span><span class="co">#&gt; midparent2          NA  midparent</span></span>
+<span><span class="co">#&gt; midparent3          NA  midparent</span></span>
+<span><span class="co">#&gt; midparent4          NA  midparent</span></span>
+<span><span class="co">#&gt; midparent5          NA  midparent</span></span>
+<span><span class="co">#&gt; midparent6          NA  midparent</span></span>
+<span></span>
+<span><span class="co"># Add size factors estimated from spike-in controls</span></span>
+<span><span class="va">se</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/add_size_factors.html">add_size_factors</a></span><span class="op">(</span><span class="va">se</span>, spikein <span class="op">=</span> <span class="cn">TRUE</span>, spikein_pattern <span class="op">=</span> <span class="st">"ERCC"</span><span class="op">)</span></span>
+<span><span class="co">#&gt; converting counts to integer mode</span></span>
+<span></span>
+<span><span class="co"># Take a look at the new colData</span></span>
+<span><span class="fu"><a href="https://rdrr.io/pkg/SummarizedExperiment/man/SummarizedExperiment-class.html" class="external-link">colData</a></span><span class="op">(</span><span class="va">se</span><span class="op">)</span></span>
+<span><span class="co">#&gt; DataFrame with 24 rows and 3 columns</span></span>
+<span><span class="co">#&gt;                 Ploidy Generation sizeFactor</span></span>
+<span><span class="co">#&gt;            &lt;character&gt;   &lt;factor&gt;  &lt;numeric&gt;</span></span>
+<span><span class="co">#&gt; S1             diploid         P1   0.856262</span></span>
+<span><span class="co">#&gt; S2             diploid         P1   0.938417</span></span>
+<span><span class="co">#&gt; S3             diploid         P1   0.648653</span></span>
+<span><span class="co">#&gt; S4             diploid         P1   0.969369</span></span>
+<span><span class="co">#&gt; S5             diploid         P1   0.718761</span></span>
+<span><span class="co">#&gt; ...                ...        ...        ...</span></span>
+<span><span class="co">#&gt; midparent2          NA  midparent   1.278078</span></span>
+<span><span class="co">#&gt; midparent3          NA  midparent   1.201072</span></span>
+<span><span class="co">#&gt; midparent4          NA  midparent   1.000892</span></span>
+<span><span class="co">#&gt; midparent5          NA  midparent   1.186773</span></span>
+<span><span class="co">#&gt; midparent6          NA  midparent   0.871889</span></span></code></pre></div>
+</div>
+<div class="section level2">
 <h2 id="exploratory-data-analyses">Exploratory data analyses<a class="anchor" aria-label="anchor" href="#exploratory-data-analyses"></a>
 </h2>
 <p>Next, you can perform exploratory analyses of sample clustering to
@@ -345,15 +439,14 @@ <h2 id="exploratory-data-analyses">Exploratory data analyses<a class="anchor" ar
 clustered pairwise sample correlations.</li>
 </ol>
 <p>Let’s start with the PCA plot:</p>
-<div class="sourceCode" id="cb5"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb7"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co"># For colData rows with missing values (midparent samples), add "midparent"</span></span>
 <span><span class="va">se</span><span class="op">$</span><span class="va">Ploidy</span><span class="op">[</span><span class="fu"><a href="https://rdrr.io/r/base/NA.html" class="external-link">is.na</a></span><span class="op">(</span><span class="va">se</span><span class="op">$</span><span class="va">Ploidy</span><span class="op">)</span><span class="op">]</span> <span class="op">&lt;-</span> <span class="st">"midparent"</span></span>
 <span><span class="va">se</span><span class="op">$</span><span class="va">Generation</span><span class="op">[</span><span class="fu"><a href="https://rdrr.io/r/base/NA.html" class="external-link">is.na</a></span><span class="op">(</span><span class="va">se</span><span class="op">$</span><span class="va">Generation</span><span class="op">)</span><span class="op">]</span> <span class="op">&lt;-</span> <span class="st">"midparent"</span></span>
 <span></span>
 <span><span class="co"># Create PCA plot</span></span>
-<span><span class="fu"><a href="../reference/pca_plot.html">pca_plot</a></span><span class="op">(</span><span class="va">se</span>, color_by <span class="op">=</span> <span class="st">"Generation"</span>, shape_by <span class="op">=</span> <span class="st">"Ploidy"</span>, add_mean <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span>
-<span><span class="co">#&gt; converting counts to integer mode</span></span></code></pre></div>
-<p><img src="HybridExpress_files/figure-html/unnamed-chunk-3-1.png" width="700"></p>
+<span><span class="fu"><a href="../reference/pca_plot.html">pca_plot</a></span><span class="op">(</span><span class="va">se</span>, color_by <span class="op">=</span> <span class="st">"Generation"</span>, shape_by <span class="op">=</span> <span class="st">"Ploidy"</span>, add_mean <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></code></pre></div>
+<p><img src="HybridExpress_files/figure-html/unnamed-chunk-5-1.png" width="700"></p>
 <p>In the plot above, each data point corresponds to a sample, and
 colors and shapes are mapped to levels of the variables specified in the
 arguments <code>color_by</code> and <code>shape_by</code>, respectively.
@@ -361,11 +454,10 @@ <h2 id="exploratory-data-analyses">Exploratory data analyses<a class="anchor" ar
 shape indicating the mean PC coordinates based on the variable in
 <code>color_by</code> (here, <em>“Generation”</em>).</p>
 <p>Now, let’s plot the heatmap of sample correlations:</p>
-<div class="sourceCode" id="cb6"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb8"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co"># Plot a heatmap of sample correlations</span></span>
-<span><span class="fu"><a href="../reference/plot_samplecor.html">plot_samplecor</a></span><span class="op">(</span><span class="va">se</span><span class="op">)</span></span>
-<span><span class="co">#&gt; converting counts to integer mode</span></span></code></pre></div>
-<p><img src="HybridExpress_files/figure-html/unnamed-chunk-4-1.png" width="700"></p>
+<span><span class="fu"><a href="../reference/plot_samplecor.html">plot_samplecor</a></span><span class="op">(</span><span class="va">se</span>, coldata_cols <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"Generation"</span>, <span class="st">"Ploidy"</span><span class="op">)</span><span class="op">)</span></span></code></pre></div>
+<p><img src="HybridExpress_files/figure-html/unnamed-chunk-6-1.png" width="700"></p>
 <p>We can see that samples group well together both in the PCA plot and
 in the correlation heatmap.</p>
 <p>Of note, both <code><a href="../reference/pca_plot.html">pca_plot()</a></code> and
@@ -399,52 +491,13 @@ <h2 id="identifying-differentially-expressed-genes-between-hybrids-and-their-par
 <code>F1_vs_midparent</code>: hybrid (numerator) vs midparent
 (denominator).</li>
 </ol>
-<p>By default, count data are normalized by library size using the
-standard normalization process in <em><a href="https://bioconductor.org/packages/3.19/DESeq2" class="external-link">DESeq2</a></em>.
-However, if spike-in standards are included in the count matrix, you can
-use them for normalization by setting <code>spikein_norm = TRUE</code>
-and specifying the pattern used to indicate rows that contain spike-ins
-(usually they start with <em>ERCC</em>)<a class="footnote-ref" tabindex="0" data-bs-toggle="popover" data-bs-content='&lt;p&gt;&lt;strong&gt;Note:&lt;/strong&gt; if you use spike-in
-normalization, the function &lt;code&gt;&lt;a href="../reference/get_deg_list.html"&gt;get_deg_list()&lt;/a&gt;&lt;/code&gt; automatically
-removes rows containing spike-in counts from the
-&lt;code&gt;SummarizedExperiment&lt;/code&gt; object after normalization. However,
-if you have counts for spike-in standards in your count matrix, but do
-not want to use spike-in normalization, you should remove them before
-creating the &lt;code&gt;SummarizedExperiment&lt;/code&gt; object.&lt;/p&gt;'><sup>1</sup></a>. In our example data
-set, spike-in standards are available in the last rows of the count
-matrix.</p>
-<div class="sourceCode" id="cb7"><pre class="downlit sourceCode r">
-<code class="sourceCode R"><span><span class="co"># Show last rows of the count matrix</span></span>
-<span><span class="fu"><a href="https://rdrr.io/pkg/SummarizedExperiment/man/SummarizedExperiment-class.html" class="external-link">assay</a></span><span class="op">(</span><span class="va">se</span><span class="op">)</span> <span class="op">|&gt;</span> </span>
-<span>    <span class="fu"><a href="https://rdrr.io/r/utils/head.html" class="external-link">tail</a></span><span class="op">(</span><span class="op">)</span></span>
-<span><span class="co">#&gt;              S1   S2   S3   S4   S5   S6   S7   S8   S9  S10  S11  S12  S13</span></span>
-<span><span class="co">#&gt; ERCC-00163   74   75   55   77   51   84  132  127   93  108   79  102   66</span></span>
-<span><span class="co">#&gt; ERCC-00164    0    4    0    1    4    1    6    4    1    4    3    2    5</span></span>
-<span><span class="co">#&gt; ERCC-00165  147  139   87  165  118  179  236  246  139  218  176  145  118</span></span>
-<span><span class="co">#&gt; ERCC-00168    2    5    2    2    2    6    5    1    1    5    0    3    4</span></span>
-<span><span class="co">#&gt; ERCC-00170   97   95   73  101   70  118  186  148  110  167  110  103   72</span></span>
-<span><span class="co">#&gt; ERCC-00171 4644 4959 3554 5357 4170 5946 8207 7915 4992 7843 6455 5884 4537</span></span>
-<span><span class="co">#&gt;             S14  S15  S16  S17  S18 midparent1 midparent2 midparent3 midparent4</span></span>
-<span><span class="co">#&gt; ERCC-00163   67   62   80   88   47         67         96        101         90</span></span>
-<span><span class="co">#&gt; ERCC-00164    4    3    5    4    2          2          2          4          2</span></span>
-<span><span class="co">#&gt; ERCC-00165  143  115  185  192  136        132        198        192        155</span></span>
-<span><span class="co">#&gt; ERCC-00168    6    2   11    5    2          1          6          3          2</span></span>
-<span><span class="co">#&gt; ERCC-00170   99   64  121  103   84         92        142        122        102</span></span>
-<span><span class="co">#&gt; ERCC-00171 5665 4598 6168 5771 4747       5004       6894       6437       5620</span></span>
-<span><span class="co">#&gt;            midparent5 midparent6</span></span>
-<span><span class="co">#&gt; ERCC-00163         92         84</span></span>
-<span><span class="co">#&gt; ERCC-00164          5          0</span></span>
-<span><span class="co">#&gt; ERCC-00165        177        143</span></span>
-<span><span class="co">#&gt; ERCC-00168          4          2</span></span>
-<span><span class="co">#&gt; ERCC-00170        128        104</span></span>
-<span><span class="co">#&gt; ERCC-00171       6188       4818</span></span></code></pre></div>
-<p>Now, we will use <code><a href="../reference/get_deg_list.html">get_deg_list()</a></code> to get differentially
-expressed genes (DEGs) for each contrast while using spike-in
-normalization.</p>
-<div class="sourceCode" id="cb8"><pre class="downlit sourceCode r">
+<p>The size factors estimated with <code><a href="../reference/add_size_factors.html">add_size_factors()</a></code> are
+used for normalization before differential expression testing. Let’s use
+<code><a href="../reference/get_deg_list.html">get_deg_list()</a></code> to get differentially expressed genes (DEGs)
+for each contrast.</p>
+<div class="sourceCode" id="cb9"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co"># Get a list of differentially expressed genes for each contrast</span></span>
-<span><span class="va">deg_list</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/get_deg_list.html">get_deg_list</a></span><span class="op">(</span><span class="va">se</span>, spikein_norm <span class="op">=</span> <span class="cn">TRUE</span>, spikein_pattern <span class="op">=</span> <span class="st">"ERCC"</span><span class="op">)</span></span>
-<span><span class="co">#&gt; converting counts to integer mode</span></span>
+<span><span class="va">deg_list</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/get_deg_list.html">get_deg_list</a></span><span class="op">(</span><span class="va">se</span><span class="op">)</span></span>
 <span><span class="co">#&gt; using pre-existing size factors</span></span>
 <span><span class="co">#&gt; estimating dispersions</span></span>
 <span><span class="co">#&gt; gene-wise dispersion estimates</span></span>
@@ -481,7 +534,7 @@ <h2 id="identifying-differentially-expressed-genes-between-hybrids-and-their-par
 <p>To summarize the frequencies of up- and down-regulated genes per
 contrast in a single data frame, use the function
 <code><a href="../reference/get_deg_counts.html">get_deg_counts()</a></code>.</p>
-<div class="sourceCode" id="cb9"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb10"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co"># Get a data frame with DEG frequencies for each contrast</span></span>
 <span><span class="va">deg_counts</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/get_deg_counts.html">get_deg_counts</a></span><span class="op">(</span><span class="va">deg_list</span><span class="op">)</span></span>
 <span></span>
@@ -497,7 +550,7 @@ <h2 id="identifying-differentially-expressed-genes-between-hybrids-and-their-par
 relative to the total number of genes in the count matrix. The total
 number of genes in the count matrix is stored in the <code>ngenes</code>
 attribute of the list returned by <code><a href="../reference/get_deg_list.html">get_deg_list()</a></code>:</p>
-<div class="sourceCode" id="cb10"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb11"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/attributes.html" class="external-link">attributes</a></span><span class="op">(</span><span class="va">deg_list</span><span class="op">)</span><span class="op">$</span><span class="va">ngenes</span></span>
 <span><span class="co">#&gt; [1] 12966</span></span></code></pre></div>
 <p>However, since the count matrix usually does not include all genes in
@@ -508,12 +561,12 @@ <h2 id="identifying-differentially-expressed-genes-between-hybrids-and-their-par
 genes in the genome as the reference, we need to update the
 <code>ngenes</code> attribute of the DEG list with the appropriate
 number as follows:</p>
-<div class="sourceCode" id="cb11"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb12"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co"># Total number of genes in the C. reinhardtii genome (v6.1): 16883</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/base/attributes.html" class="external-link">attributes</a></span><span class="op">(</span><span class="va">deg_list</span><span class="op">)</span><span class="op">$</span><span class="va">ngenes</span> <span class="op">&lt;-</span> <span class="fl">16883</span></span></code></pre></div>
 <p>Then, we can run <code><a href="../reference/get_deg_counts.html">get_deg_counts()</a></code> again to get the
 percentages relative to the total number of genes in the genome.</p>
-<div class="sourceCode" id="cb12"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb13"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="va">deg_counts</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/get_deg_counts.html">get_deg_counts</a></span><span class="op">(</span><span class="va">deg_list</span><span class="op">)</span></span>
 <span><span class="va">deg_counts</span></span>
 <span><span class="co">#&gt;          contrast   up down total perc_up perc_down perc_total</span></span>
@@ -525,10 +578,10 @@ <h2 id="identifying-differentially-expressed-genes-between-hybrids-and-their-par
 figure using the plot <code><a href="../reference/plot_expression_triangle.html">plot_expression_triangle()</a></code>, which
 shows the ‘experimental trio’ (i.e., hybrid and its progenitors) as a
 triangle, with the frequencies of DEGs indicated.</p>
-<div class="sourceCode" id="cb13"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb14"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co"># Plot expression triangle</span></span>
 <span><span class="fu"><a href="../reference/plot_expression_triangle.html">plot_expression_triangle</a></span><span class="op">(</span><span class="va">deg_counts</span><span class="op">)</span></span></code></pre></div>
-<p><img src="HybridExpress_files/figure-html/unnamed-chunk-11-1.png" width="700"></p>
+<p><img src="HybridExpress_files/figure-html/unnamed-chunk-12-1.png" width="700"></p>
 <p>This figure is commonly used in publications, and it was inspired by
 <span class="citation">Rapp, Udall, and Wendel (2009)</span>. For each
 edge (line), numbers in the middle (in bold) indicate the frequency of
@@ -539,13 +592,13 @@ <h2 id="identifying-differentially-expressed-genes-between-hybrids-and-their-par
 and 4005 are up-regulated in P1.</p>
 <p>For a custom figure, you can also specify your own color palette and
 labels for the boxes. For example:</p>
-<div class="sourceCode" id="cb14"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb15"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co"># Create vectors (length 4) of colors and box labels</span></span>
 <span><span class="va">pal</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"springgreen4"</span>, <span class="st">"darkorange3"</span>, <span class="st">"mediumpurple4"</span>, <span class="st">"mediumpurple3"</span><span class="op">)</span></span>
 <span><span class="va">labels</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"Parent 1\n(2n)"</span>, <span class="st">"Parent 2\n(n)"</span>, <span class="st">"Progeny\n(3n)"</span>, <span class="st">"Midparent"</span><span class="op">)</span></span>
 <span></span>
 <span><span class="fu"><a href="../reference/plot_expression_triangle.html">plot_expression_triangle</a></span><span class="op">(</span><span class="va">deg_counts</span>, palette <span class="op">=</span> <span class="va">pal</span>, box_labels <span class="op">=</span> <span class="va">labels</span><span class="op">)</span></span></code></pre></div>
-<p><img src="HybridExpress_files/figure-html/unnamed-chunk-12-1.png" width="700"></p>
+<p><img src="HybridExpress_files/figure-html/unnamed-chunk-13-1.png" width="700"></p>
 </div>
 <div class="section level2">
 <h2 id="expression-based-gene-classification">Expression-based gene classification<a class="anchor" aria-label="anchor" href="#expression-based-gene-classification"></a>
@@ -577,7 +630,7 @@ <h2 id="expression-based-gene-classification">Expression-based gene classificati
 (ELD_P2):</strong> gene expression in the hybrid is the same as in
 parent 2, but different from parent 1.</li>
 </ol>
-<div class="sourceCode" id="cb15"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb16"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co"># Classify genes in expression partitions</span></span>
 <span><span class="va">exp_partitions</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/expression_partitioning.html">expression_partitioning</a></span><span class="op">(</span><span class="va">deg_list</span><span class="op">)</span></span>
 <span></span>
@@ -605,28 +658,28 @@ <h2 id="expression-based-gene-classification">Expression-based gene classificati
 <p>To visualize the expression partitions as a scatter plot of
 expression divergences, you can use the function
 <code><a href="../reference/plot_expression_partitions.html">plot_expression_partitions()</a></code>.</p>
-<div class="sourceCode" id="cb16"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb17"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co"># Plot partitions as a scatter plot of divergences</span></span>
 <span><span class="fu"><a href="../reference/plot_expression_partitions.html">plot_expression_partitions</a></span><span class="op">(</span><span class="va">exp_partitions</span>, group_by <span class="op">=</span> <span class="st">"Category"</span><span class="op">)</span></span></code></pre></div>
-<p><img src="HybridExpress_files/figure-html/unnamed-chunk-14-1.png" width="768"></p>
+<p><img src="HybridExpress_files/figure-html/unnamed-chunk-15-1.png" width="768"></p>
 <p>By default, genes are grouped by <code>Category</code>. However, you
 can also group genes by <code>Class</code> as follows:</p>
-<div class="sourceCode" id="cb17"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb18"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co"># Group by `Class`</span></span>
 <span><span class="fu"><a href="../reference/plot_expression_partitions.html">plot_expression_partitions</a></span><span class="op">(</span><span class="va">exp_partitions</span>, group_by <span class="op">=</span> <span class="st">"Class"</span><span class="op">)</span></span></code></pre></div>
-<p><img src="HybridExpress_files/figure-html/unnamed-chunk-15-1.png" width="768"></p>
+<p><img src="HybridExpress_files/figure-html/unnamed-chunk-16-1.png" width="768"></p>
 <p>You can also visualize the frequencies of genes in each partition
 with the function <code><a href="../reference/plot_partition_frequencies.html">plot_partition_frequencies()</a></code>.</p>
-<div class="sourceCode" id="cb18"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb19"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co"># Visualize frequency of genes in each partition</span></span>
 <span><span class="co">## By `Category` (default)</span></span>
 <span><span class="fu"><a href="../reference/plot_partition_frequencies.html">plot_partition_frequencies</a></span><span class="op">(</span><span class="va">exp_partitions</span><span class="op">)</span></span></code></pre></div>
-<p><img src="HybridExpress_files/figure-html/unnamed-chunk-16-1.png" width="700"></p>
-<div class="sourceCode" id="cb19"><pre class="downlit sourceCode r">
+<p><img src="HybridExpress_files/figure-html/unnamed-chunk-17-1.png" width="700"></p>
+<div class="sourceCode" id="cb20"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span></span>
 <span><span class="co">## By `Class`</span></span>
 <span><span class="fu"><a href="../reference/plot_partition_frequencies.html">plot_partition_frequencies</a></span><span class="op">(</span><span class="va">exp_partitions</span>, group_by <span class="op">=</span> <span class="st">"Class"</span><span class="op">)</span></span></code></pre></div>
-<p><img src="HybridExpress_files/figure-html/unnamed-chunk-16-2.png" width="700"></p>
+<p><img src="HybridExpress_files/figure-html/unnamed-chunk-17-2.png" width="700"></p>
 </div>
 <div class="section level2">
 <h2 id="overrepresentation-analysis-of-functional-terms">Overrepresentation analysis of functional terms<a class="anchor" aria-label="anchor" href="#overrepresentation-analysis-of-functional-terms"></a>
@@ -640,7 +693,7 @@ <h2 id="overrepresentation-analysis-of-functional-terms">Overrepresentation anal
 reinhardtii</em> genes. This data set illustrates how the annotation
 data frame must be shaped: gene ID in the first column, and functional
 annotations in other columns.</p>
-<div class="sourceCode" id="cb20"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb21"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co"># Load example functional annotation (GO terms)</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/utils/data.html" class="external-link">data</a></span><span class="op">(</span><span class="va">go_chlamy</span><span class="op">)</span></span>
 <span></span>
@@ -654,7 +707,7 @@ <h2 id="overrepresentation-analysis-of-functional-terms">Overrepresentation anal
 <span><span class="co">#&gt; 6 Cre01.g000150                      transmembrane transport</span></span></code></pre></div>
 <p>To demonstrate the usage of <code><a href="../reference/ora.html">ora()</a></code>, let’s check if we can
 find enrichment for any GO term among genes assigned to class “ADD”.</p>
-<div class="sourceCode" id="cb21"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb22"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co"># Get a vector of genes in class "ADD"</span></span>
 <span><span class="va">genes_add</span> <span class="op">&lt;-</span> <span class="va">exp_partitions</span><span class="op">$</span><span class="va">Gene</span><span class="op">[</span><span class="va">exp_partitions</span><span class="op">$</span><span class="va">Class</span> <span class="op">==</span> <span class="st">"ADD"</span><span class="op">]</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/utils/head.html" class="external-link">head</a></span><span class="op">(</span><span class="va">genes_add</span><span class="op">)</span></span>
@@ -670,19 +723,19 @@ <h2 id="overrepresentation-analysis-of-functional-terms">Overrepresentation anal
 <span><span class="co"># Inspect results</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/utils/head.html" class="external-link">head</a></span><span class="op">(</span><span class="va">ora_add</span><span class="op">)</span></span>
 <span><span class="co">#&gt;                                                   term genes all         pval</span></span>
-<span><span class="co">#&gt; 20                      aminoacyl-tRNA ligase activity    14  33 3.517366e-05</span></span>
-<span><span class="co">#&gt; 84                                           cytoplasm    43 151 6.083978e-07</span></span>
-<span><span class="co">#&gt; 97                                     DNA replication    17  37 1.331552e-06</span></span>
-<span><span class="co">#&gt; 114                             endopeptidase activity     6  10 7.118363e-04</span></span>
-<span><span class="co">#&gt; 115                              endoplasmic reticulum     9  17 1.128774e-04</span></span>
-<span><span class="co">#&gt; 118 eukaryotic translation initiation factor 3 complex     7   9 2.044325e-05</span></span>
+<span><span class="co">#&gt; 20                      aminoacyl-tRNA ligase activity    14  33 3.811521e-05</span></span>
+<span><span class="co">#&gt; 84                                           cytoplasm    43 151 7.379762e-07</span></span>
+<span><span class="co">#&gt; 97                                     DNA replication    17  37 1.471956e-06</span></span>
+<span><span class="co">#&gt; 114                             endopeptidase activity     6  10 7.400151e-04</span></span>
+<span><span class="co">#&gt; 115                              endoplasmic reticulum     9  17 1.193854e-04</span></span>
+<span><span class="co">#&gt; 118 eukaryotic translation initiation factor 3 complex     7   9 2.144087e-05</span></span>
 <span><span class="co">#&gt;             padj category</span></span>
-<span><span class="co">#&gt; 20  1.629713e-03       GO</span></span>
-<span><span class="co">#&gt; 84  6.342547e-05       GO</span></span>
-<span><span class="co">#&gt; 97  1.110515e-04       GO</span></span>
-<span><span class="co">#&gt; 114 2.120255e-02       GO</span></span>
-<span><span class="co">#&gt; 115 4.706990e-03       GO</span></span>
-<span><span class="co">#&gt; 118 1.217834e-03       GO</span></span></code></pre></div>
+<span><span class="co">#&gt; 20  1.766005e-03       GO</span></span>
+<span><span class="co">#&gt; 84  7.693402e-05       GO</span></span>
+<span><span class="co">#&gt; 97  1.227611e-04       GO</span></span>
+<span><span class="co">#&gt; 114 2.204188e-02       GO</span></span>
+<span><span class="co">#&gt; 115 4.978369e-03       GO</span></span>
+<span><span class="co">#&gt; 118 1.277263e-03       GO</span></span></code></pre></div>
 <div class="section level3">
 <h3 id="example-1-overrepresentation-analyses-for-all-expression-based-classes">Example 1: overrepresentation analyses for all expression-based
 classes<a class="anchor" aria-label="anchor" href="#example-1-overrepresentation-analyses-for-all-expression-based-classes"></a>
@@ -699,7 +752,7 @@ <h3 id="example-1-overrepresentation-analyses-for-all-expression-based-classes">
 <p>Below you can find an example on how to do it using
 <code><a href="https://rdrr.io/r/base/lapply.html" class="external-link">lapply()</a></code>. Results for each class will be stored in elements
 of a list object.</p>
-<div class="sourceCode" id="cb22"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb23"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co"># Create a list of genes in each class</span></span>
 <span><span class="va">genes_by_class</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/split.html" class="external-link">split</a></span><span class="op">(</span><span class="va">exp_partitions</span><span class="op">$</span><span class="va">Gene</span>, <span class="va">exp_partitions</span><span class="op">$</span><span class="va">Class</span><span class="op">)</span></span>
 <span></span>
@@ -720,9 +773,9 @@ <h3 id="example-1-overrepresentation-analyses-for-all-expression-based-classes">
 <span><span class="va">ora_classes</span></span>
 <span><span class="co">#&gt; $UP</span></span>
 <span><span class="co">#&gt;                                    term genes all         pval         padj</span></span>
-<span><span class="co">#&gt; 29              ATP hydrolysis activity    21 119 3.074727e-04 0.0427387078</span></span>
-<span><span class="co">#&gt; 109                      dynein complex     8  13 1.172717e-06 0.0004890231</span></span>
-<span><span class="co">#&gt; 127 flavin adenine dinucleotide binding    11  42 2.687071e-04 0.0427387078</span></span>
+<span><span class="co">#&gt; 29              ATP hydrolysis activity    21 119 3.380614e-04 0.0469905310</span></span>
+<span><span class="co">#&gt; 109                      dynein complex     8  13 1.237752e-06 0.0005161427</span></span>
+<span><span class="co">#&gt; 127 flavin adenine dinucleotide binding    11  42 2.857201e-04 0.0469905310</span></span>
 <span><span class="co">#&gt;     category</span></span>
 <span><span class="co">#&gt; 29        GO</span></span>
 <span><span class="co">#&gt; 109       GO</span></span>
@@ -730,11 +783,11 @@ <h3 id="example-1-overrepresentation-analyses-for-all-expression-based-classes">
 <span><span class="co">#&gt; </span></span>
 <span><span class="co">#&gt; $DOWN</span></span>
 <span><span class="co">#&gt;                                  term genes all         pval         padj</span></span>
-<span><span class="co">#&gt; 242           oxidoreductase activity    21 274 2.668754e-04 2.225741e-02</span></span>
-<span><span class="co">#&gt; 271                    photosynthesis    13  58 3.367873e-08 7.022015e-06</span></span>
-<span><span class="co">#&gt; 272  photosynthesis, light harvesting    18  24 1.444312e-22 6.022782e-20</span></span>
-<span><span class="co">#&gt; 276                     photosystem I     5  15 8.360388e-05 8.715704e-03</span></span>
-<span><span class="co">#&gt; 376 tetrapyrrole biosynthetic process     5   8 1.885876e-06 2.621368e-04</span></span>
+<span><span class="co">#&gt; 242           oxidoreductase activity    21 274 2.931239e-04 2.444654e-02</span></span>
+<span><span class="co">#&gt; 271                    photosynthesis    13  58 3.656759e-08 7.624343e-06</span></span>
+<span><span class="co">#&gt; 272  photosynthesis, light harvesting    18  24 1.638332e-22 6.831844e-20</span></span>
+<span><span class="co">#&gt; 276                     photosystem I     5  15 8.644389e-05 9.011776e-03</span></span>
+<span><span class="co">#&gt; 376 tetrapyrrole biosynthetic process     5   8 1.952599e-06 2.714112e-04</span></span>
 <span><span class="co">#&gt;     category</span></span>
 <span><span class="co">#&gt; 242       GO</span></span>
 <span><span class="co">#&gt; 271       GO</span></span>
@@ -762,29 +815,29 @@ <h3 id="example-1-overrepresentation-analyses-for-all-expression-based-classes">
 <span><span class="co">#&gt; 410                                                              unfolded protein binding</span></span>
 <span><span class="co">#&gt; 414                                                            vesicle-mediated transport</span></span>
 <span><span class="co">#&gt;     genes all         pval         padj category</span></span>
-<span><span class="co">#&gt; 20     14  33 3.517366e-05 1.629713e-03       GO</span></span>
-<span><span class="co">#&gt; 84     43 151 6.083978e-07 6.342547e-05       GO</span></span>
-<span><span class="co">#&gt; 97     17  37 1.331552e-06 1.110515e-04       GO</span></span>
-<span><span class="co">#&gt; 114     6  10 7.118363e-04 2.120255e-02       GO</span></span>
-<span><span class="co">#&gt; 115     9  17 1.128774e-04 4.706990e-03       GO</span></span>
-<span><span class="co">#&gt; 118     7   9 2.044325e-05 1.217834e-03       GO</span></span>
-<span><span class="co">#&gt; 165    25  73 3.907713e-06 2.715861e-04       GO</span></span>
-<span><span class="co">#&gt; 227     6   9 3.209008e-04 1.115130e-02       GO</span></span>
-<span><span class="co">#&gt; 250    10  27 1.647170e-03 4.292937e-02       GO</span></span>
-<span><span class="co">#&gt; 284     4   4 3.114510e-04 1.115130e-02       GO</span></span>
-<span><span class="co">#&gt; 287     9  15 2.987692e-05 1.557334e-03       GO</span></span>
-<span><span class="co">#&gt; 293    29  81 2.288079e-07 3.180430e-05       GO</span></span>
-<span><span class="co">#&gt; 294     6  10 7.118363e-04 2.120255e-02       GO</span></span>
-<span><span class="co">#&gt; 304    10  10 1.686356e-09 7.032106e-07       GO</span></span>
-<span><span class="co">#&gt; 390     9  21 8.277887e-04 2.301252e-02       GO</span></span>
-<span><span class="co">#&gt; 410    19  38 5.773438e-08 1.203762e-05       GO</span></span>
-<span><span class="co">#&gt; 414    13  41 1.832917e-03 4.496037e-02       GO</span></span>
+<span><span class="co">#&gt; 20     14  33 3.811521e-05 1.766005e-03       GO</span></span>
+<span><span class="co">#&gt; 84     43 151 7.379762e-07 7.693402e-05       GO</span></span>
+<span><span class="co">#&gt; 97     17  37 1.471956e-06 1.227611e-04       GO</span></span>
+<span><span class="co">#&gt; 114     6  10 7.400151e-04 2.204188e-02       GO</span></span>
+<span><span class="co">#&gt; 115     9  17 1.193854e-04 4.978369e-03       GO</span></span>
+<span><span class="co">#&gt; 118     7   9 2.144087e-05 1.277263e-03       GO</span></span>
+<span><span class="co">#&gt; 165    25  73 4.442015e-06 3.087201e-04       GO</span></span>
+<span><span class="co">#&gt; 227     6   9 3.339022e-04 1.160310e-02       GO</span></span>
+<span><span class="co">#&gt; 250    10  27 1.739601e-03 4.533836e-02       GO</span></span>
+<span><span class="co">#&gt; 284     4   4 3.203862e-04 1.160310e-02       GO</span></span>
+<span><span class="co">#&gt; 287     9  15 3.165895e-05 1.650223e-03       GO</span></span>
+<span><span class="co">#&gt; 293    29  81 2.663812e-07 3.702698e-05       GO</span></span>
+<span><span class="co">#&gt; 294     6  10 7.400151e-04 2.204188e-02       GO</span></span>
+<span><span class="co">#&gt; 304    10  10 1.809949e-09 7.547489e-07       GO</span></span>
+<span><span class="co">#&gt; 390     9  21 8.722652e-04 2.424897e-02       GO</span></span>
+<span><span class="co">#&gt; 410    19  38 6.478683e-08 1.350805e-05       GO</span></span>
+<span><span class="co">#&gt; 414    13  41 1.955735e-03 4.797302e-02       GO</span></span>
 <span><span class="co">#&gt; </span></span>
 <span><span class="co">#&gt; $ELD_P1</span></span>
-<span><span class="co">#&gt;                term genes all         pval         padj category</span></span>
-<span><span class="co">#&gt; 38          binding    88 208 1.818832e-06 0.0007584527       GO</span></span>
-<span><span class="co">#&gt; 239         nucleus    77 193 8.891683e-05 0.0185391599       GO</span></span>
-<span><span class="co">#&gt; 345 rRNA processing    15  24 3.183647e-04 0.0442526890       GO</span></span>
+<span><span class="co">#&gt;                term genes all         pval        padj category</span></span>
+<span><span class="co">#&gt; 38          binding    88 208 2.494608e-06 0.001040251       GO</span></span>
+<span><span class="co">#&gt; 239         nucleus    77 193 1.141172e-04 0.023793431       GO</span></span>
+<span><span class="co">#&gt; 345 rRNA processing    15  24 3.464517e-04 0.048156786       GO</span></span>
 <span><span class="co">#&gt; </span></span>
 <span><span class="co">#&gt; $ELD_P2</span></span>
 <span><span class="co">#&gt;                                                                term genes all</span></span>
@@ -795,12 +848,12 @@ <h3 id="example-1-overrepresentation-analyses-for-all-expression-based-classes">
 <span><span class="co">#&gt; 278                                                  photosystem II    17  32</span></span>
 <span><span class="co">#&gt; 320 proton-transporting two-sector ATPase complex, catalytic domain     6   8</span></span>
 <span><span class="co">#&gt;             pval         padj category</span></span>
-<span><span class="co">#&gt; 28  2.888758e-04 2.007687e-02       GO</span></span>
-<span><span class="co">#&gt; 170 3.997247e-05 4.167130e-03       GO</span></span>
-<span><span class="co">#&gt; 242 4.722014e-06 6.563600e-04       GO</span></span>
-<span><span class="co">#&gt; 271 1.031130e-07 4.299811e-05       GO</span></span>
-<span><span class="co">#&gt; 278 8.669007e-07 1.807488e-04       GO</span></span>
-<span><span class="co">#&gt; 320 2.888758e-04 2.007687e-02       GO</span></span></code></pre></div>
+<span><span class="co">#&gt; 28  3.007465e-04 2.090188e-02       GO</span></span>
+<span><span class="co">#&gt; 170 4.577693e-05 4.772245e-03       GO</span></span>
+<span><span class="co">#&gt; 242 6.069186e-06 8.436168e-04       GO</span></span>
+<span><span class="co">#&gt; 271 1.191954e-07 4.970448e-05       GO</span></span>
+<span><span class="co">#&gt; 278 9.602908e-07 2.002206e-04       GO</span></span>
+<span><span class="co">#&gt; 320 3.007465e-04 2.090188e-02       GO</span></span></code></pre></div>
 <p>To do the same for each expression-based category (not class), you’d
 need to replace <code>Class</code> with <code>Category</code> in the
 <code><a href="https://rdrr.io/r/base/split.html" class="external-link">split()</a></code> function (see example above).</p>
@@ -813,7 +866,7 @@ <h3 id="example-2-overrepresentation-analyses-for-differentially-expressed-genes
 expression class, you can also loop through each contrast in the list
 returned by <code><a href="../reference/get_deg_list.html">get_deg_list()</a></code>, and perform ORA for up- and
 down-regulated genes. Below you can find an example:</p>
-<div class="sourceCode" id="cb23"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb24"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co"># Get up-regulated genes for each contrast</span></span>
 <span><span class="va">up_genes</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/lapply.html" class="external-link">lapply</a></span><span class="op">(</span><span class="va">deg_list</span>, <span class="kw">function</span><span class="op">(</span><span class="va">x</span><span class="op">)</span> <span class="fu"><a href="https://rdrr.io/r/base/colnames.html" class="external-link">rownames</a></span><span class="op">(</span><span class="va">x</span><span class="op">[</span><span class="va">x</span><span class="op">$</span><span class="va">log2FoldChange</span> <span class="op">&gt;</span> <span class="fl">0</span>, <span class="op">]</span><span class="op">)</span><span class="op">)</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/base/names.html" class="external-link">names</a></span><span class="op">(</span><span class="va">up_genes</span><span class="op">)</span></span>
@@ -836,51 +889,51 @@ <h3 id="example-2-overrepresentation-analyses-for-differentially-expressed-genes
 <span><span class="co">#&gt; </span></span>
 <span><span class="co">#&gt; $F1_vs_P1</span></span>
 <span><span class="co">#&gt;               term genes all         pval        padj category</span></span>
-<span><span class="co">#&gt; 109 dynein complex     8  13 1.944536e-05 0.008108715       GO</span></span>
+<span><span class="co">#&gt; 109 dynein complex     8  13 2.049704e-05 0.008547264       GO</span></span>
 <span><span class="co">#&gt; </span></span>
 <span><span class="co">#&gt; $F1_vs_P2</span></span>
 <span><span class="co">#&gt;                                              term genes all         pval</span></span>
-<span><span class="co">#&gt; 38                                        binding   138 208 7.344020e-07</span></span>
-<span><span class="co">#&gt; 96                                     DNA repair    56  83 7.655167e-04</span></span>
-<span><span class="co">#&gt; 144                                   GTP binding    72 111 8.525066e-04</span></span>
-<span><span class="co">#&gt; 169                                 ion transport    33  45 1.048368e-03</span></span>
-<span><span class="co">#&gt; 209                     microtubule-based process    10  10 9.123368e-04</span></span>
-<span><span class="co">#&gt; 239                                       nucleus   125 193 1.471530e-05</span></span>
-<span><span class="co">#&gt; 303                        protein polymerization    10  10 9.123368e-04</span></span>
-<span><span class="co">#&gt; 304                             protein refolding    10  10 9.123368e-04</span></span>
-<span><span class="co">#&gt; 307 protein serine/threonine phosphatase activity    20  24 6.876258e-04</span></span>
-<span><span class="co">#&gt; 338                                   RNA binding   101 157 1.388743e-04</span></span>
-<span><span class="co">#&gt; 384                  transcription, DNA-templated    28  36 5.139935e-04</span></span>
-<span><span class="co">#&gt;             padj category</span></span>
-<span><span class="co">#&gt; 38  0.0003062456       GO</span></span>
-<span><span class="co">#&gt; 96  0.0380444450       GO</span></span>
-<span><span class="co">#&gt; 144 0.0380444450       GO</span></span>
-<span><span class="co">#&gt; 169 0.0397426775       GO</span></span>
-<span><span class="co">#&gt; 209 0.0380444450       GO</span></span>
-<span><span class="co">#&gt; 239 0.0030681409       GO</span></span>
-<span><span class="co">#&gt; 303 0.0380444450       GO</span></span>
-<span><span class="co">#&gt; 304 0.0380444450       GO</span></span>
-<span><span class="co">#&gt; 307 0.0380444450       GO</span></span>
-<span><span class="co">#&gt; 338 0.0193035210       GO</span></span>
-<span><span class="co">#&gt; 384 0.0380444450       GO</span></span>
+<span><span class="co">#&gt; 38                                        binding   138 208 1.214715e-06</span></span>
+<span><span class="co">#&gt; 96                                     DNA repair    56  83 9.520576e-04</span></span>
+<span><span class="co">#&gt; 144                                   GTP binding    72 111 1.095054e-03</span></span>
+<span><span class="co">#&gt; 169                                 ion transport    33  45 1.225270e-03</span></span>
+<span><span class="co">#&gt; 209                     microtubule-based process    10  10 9.792020e-04</span></span>
+<span><span class="co">#&gt; 239                                       nucleus   125 193 2.252487e-05</span></span>
+<span><span class="co">#&gt; 303                        protein polymerization    10  10 9.792020e-04</span></span>
+<span><span class="co">#&gt; 304                             protein refolding    10  10 9.792020e-04</span></span>
+<span><span class="co">#&gt; 307 protein serine/threonine phosphatase activity    20  24 7.724253e-04</span></span>
+<span><span class="co">#&gt; 338                                   RNA binding   101 157 1.948736e-04</span></span>
+<span><span class="co">#&gt; 384                  transcription, DNA-templated    28  36 5.951883e-04</span></span>
+<span><span class="co">#&gt;            padj category</span></span>
+<span><span class="co">#&gt; 38  0.000506536       GO</span></span>
+<span><span class="co">#&gt; 96  0.045369693       GO</span></span>
+<span><span class="co">#&gt; 144 0.045663753       GO</span></span>
+<span><span class="co">#&gt; 169 0.046448881       GO</span></span>
+<span><span class="co">#&gt; 209 0.045369693       GO</span></span>
+<span><span class="co">#&gt; 239 0.004696435       GO</span></span>
+<span><span class="co">#&gt; 303 0.045369693       GO</span></span>
+<span><span class="co">#&gt; 304 0.045369693       GO</span></span>
+<span><span class="co">#&gt; 307 0.045369693       GO</span></span>
+<span><span class="co">#&gt; 338 0.027087430       GO</span></span>
+<span><span class="co">#&gt; 384 0.045369693       GO</span></span>
 <span><span class="co">#&gt; </span></span>
 <span><span class="co">#&gt; $F1_vs_midparent</span></span>
 <span><span class="co">#&gt;                                              term genes all         pval</span></span>
-<span><span class="co">#&gt; 80         cyclic nucleotide biosynthetic process    38 108 1.704386e-04</span></span>
-<span><span class="co">#&gt; 109                                dynein complex     9  13 1.689230e-04</span></span>
-<span><span class="co">#&gt; 166             intracellular signal transduction    38 110 2.618073e-04</span></span>
-<span><span class="co">#&gt; 168                          ion channel activity    20  48 5.098878e-04</span></span>
-<span><span class="co">#&gt; 169                                 ion transport    21  45 5.169533e-05</span></span>
-<span><span class="co">#&gt; 290                     protein dephosphorylation    21  44 3.395214e-05</span></span>
-<span><span class="co">#&gt; 307 protein serine/threonine phosphatase activity    15  24 6.787504e-06</span></span>
+<span><span class="co">#&gt; 80         cyclic nucleotide biosynthetic process    38 108 1.983329e-04</span></span>
+<span><span class="co">#&gt; 109                                dynein complex     9  13 1.789139e-04</span></span>
+<span><span class="co">#&gt; 166             intracellular signal transduction    38 110 3.036659e-04</span></span>
+<span><span class="co">#&gt; 168                          ion channel activity    20  48 5.611617e-04</span></span>
+<span><span class="co">#&gt; 169                                 ion transport    21  45 5.765412e-05</span></span>
+<span><span class="co">#&gt; 290                     protein dephosphorylation    21  44 3.792684e-05</span></span>
+<span><span class="co">#&gt; 307 protein serine/threonine phosphatase activity    15  24 7.437911e-06</span></span>
 <span><span class="co">#&gt;            padj category</span></span>
-<span><span class="co">#&gt; 80  0.014214579       GO</span></span>
-<span><span class="co">#&gt; 109 0.014214579       GO</span></span>
-<span><span class="co">#&gt; 166 0.018195605       GO</span></span>
-<span><span class="co">#&gt; 168 0.030374745       GO</span></span>
-<span><span class="co">#&gt; 169 0.007185651       GO</span></span>
-<span><span class="co">#&gt; 290 0.007079020       GO</span></span>
-<span><span class="co">#&gt; 307 0.002830389       GO</span></span></code></pre></div>
+<span><span class="co">#&gt; 80  0.016540962       GO</span></span>
+<span><span class="co">#&gt; 109 0.016540962       GO</span></span>
+<span><span class="co">#&gt; 166 0.021104781       GO</span></span>
+<span><span class="co">#&gt; 168 0.033429202       GO</span></span>
+<span><span class="co">#&gt; 169 0.008013923       GO</span></span>
+<span><span class="co">#&gt; 290 0.007907747       GO</span></span>
+<span><span class="co">#&gt; 307 0.003101609       GO</span></span></code></pre></div>
 <p>Likewise, for down-regulated genes, you need to replace the
 <code>&gt;</code> symbol with a <code>&lt;</code> symbol in the
 anonymous function to subset rows.</p>
@@ -916,7 +969,7 @@ <h2 class="unnumbered" id="faq">FAQ<a class="anchor" aria-label="anchor" href="#
 <p>To demonstrate how to create a <code>SummarizedExperiment</code>
 object, we will extract the <em>assay</em> and <em>colData</em> from the
 example object <code>se_chlamy</code> that comes with this package.</p>
-<div class="sourceCode" id="cb24"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb25"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co"># Get count matrix</span></span>
 <span><span class="va">count_matrix</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/SummarizedExperiment/man/SummarizedExperiment-class.html" class="external-link">assay</a></span><span class="op">(</span><span class="va">se_chlamy</span><span class="op">)</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/utils/head.html" class="external-link">head</a></span><span class="op">(</span><span class="va">count_matrix</span><span class="op">)</span></span>
@@ -949,7 +1002,7 @@ <h2 class="unnumbered" id="faq">FAQ<a class="anchor" aria-label="anchor" href="#
 <span><span class="co">#&gt; S6     diploid         P1</span></span></code></pre></div>
 <p>Once you have these two objects, you can create a
 <code>SummarizedExperiment</code> object with:</p>
-<div class="sourceCode" id="cb25"><pre class="downlit sourceCode r">
+<div class="sourceCode" id="cb26"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="co"># Create a SummarizedExperiment object</span></span>
 <span><span class="va">new_se</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/SummarizedExperiment/man/RangedSummarizedExperiment-class.html" class="external-link">SummarizedExperiment</a></span><span class="op">(</span></span>
 <span>    assays <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>counts <span class="op">=</span> <span class="va">count_matrix</span><span class="op">)</span>,</span>
@@ -975,7 +1028,7 @@ <h2 class="unnumbered" id="session-information">Session information<a class="anc
 <p>This document was created under the following conditions:</p>
 <pre><code><span><span class="co">#&gt; <span style="color: #00BBBB; font-weight: bold;">─ Session info ───────────────────────────────────────────────────────────────</span></span></span>
 <span><span class="co">#&gt;  <span style="color: #555555; font-style: italic;">setting </span> <span style="color: #555555; font-style: italic;">value</span></span></span>
-<span><span class="co">#&gt;  version  R Under development (unstable) (2023-11-22 r85609)</span></span>
+<span><span class="co">#&gt;  version  R Under development (unstable) (2024-01-23 r85822)</span></span>
 <span><span class="co">#&gt;  os       Ubuntu 22.04.3 LTS</span></span>
 <span><span class="co">#&gt;  system   x86_64, linux-gnu</span></span>
 <span><span class="co">#&gt;  ui       X11</span></span>
@@ -983,7 +1036,7 @@ <h2 class="unnumbered" id="session-information">Session information<a class="anc
 <span><span class="co">#&gt;  collate  en_US.UTF-8</span></span>
 <span><span class="co">#&gt;  ctype    en_US.UTF-8</span></span>
 <span><span class="co">#&gt;  tz       UTC</span></span>
-<span><span class="co">#&gt;  date     2023-12-12</span></span>
+<span><span class="co">#&gt;  date     2024-02-02</span></span>
 <span><span class="co">#&gt;  pandoc   3.1.1 @ /usr/local/bin/ (via rmarkdown)</span></span>
 <span><span class="co">#&gt; </span></span>
 <span><span class="co">#&gt; <span style="color: #00BBBB; font-weight: bold;">─ Packages ───────────────────────────────────────────────────────────────────</span></span></span>
@@ -999,17 +1052,17 @@ <h2 class="unnumbered" id="session-information">Session information<a class="anc
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+<span><span class="co">#&gt;  desc                   1.4.3     <span style="color: #555555;">2023-12-10</span> <span style="color: #555555;">[2]</span> <span style="color: #555555;">CRAN (R 4.4.0)</span></span></span>
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+<span><span class="co">#&gt;  digest                 0.6.34    <span style="color: #555555;">2024-01-11</span> <span style="color: #555555;">[2]</span> <span style="color: #555555;">CRAN (R 4.4.0)</span></span></span>
 <span><span class="co">#&gt;  doParallel             1.0.17    <span style="color: #555555;">2022-02-07</span> <span style="color: #555555;">[1]</span> <span style="color: #555555;">CRAN (R 4.4.0)</span></span></span>
 <span><span class="co">#&gt;  dplyr                  1.1.4     <span style="color: #555555;">2023-11-17</span> <span style="color: #555555;">[1]</span> <span style="color: #555555;">CRAN (R 4.4.0)</span></span></span>
 <span><span class="co">#&gt;  evaluate               0.23      <span style="color: #555555;">2023-11-01</span> <span style="color: #555555;">[2]</span> <span style="color: #555555;">CRAN (R 4.4.0)</span></span></span>
@@ -1019,18 +1072,18 @@ <h2 class="unnumbered" id="session-information">Session information<a class="anc
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-<span><span class="co">#&gt;  GenomeInfoDbData       1.2.11    <span style="color: #555555;">2023-12-11</span> <span style="color: #555555;">[1]</span> <span style="color: #555555;">Bioconductor</span></span></span>
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+<span><span class="co">#&gt;  GenomeInfoDb         * 1.39.5    <span style="color: #555555;">2024-01-01</span> <span style="color: #555555;">[1]</span> <span style="color: #555555;">Bioconductor 3.19 (R 4.4.0)</span></span></span>
+<span><span class="co">#&gt;  GenomeInfoDbData       1.2.11    <span style="color: #555555;">2024-02-02</span> <span style="color: #555555;">[1]</span> <span style="color: #555555;">Bioconductor</span></span></span>
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+<span><span class="co">#&gt;  HybridExpress        * 0.99.0    <span style="color: #555555;">2024-02-02</span> <span style="color: #555555;">[1]</span> <span style="color: #555555;">Bioconductor</span></span></span>
+<span><span class="co">#&gt;  IRanges              * 2.37.1    <span style="color: #555555;">2024-01-19</span> <span style="color: #555555;">[1]</span> <span style="color: #555555;">Bioconductor 3.19 (R 4.4.0)</span></span></span>
 <span><span class="co">#&gt;  iterators              1.0.14    <span style="color: #555555;">2022-02-05</span> <span style="color: #555555;">[1]</span> <span style="color: #555555;">CRAN (R 4.4.0)</span></span></span>
 <span><span class="co">#&gt;  jquerylib              0.1.4     <span style="color: #555555;">2021-04-26</span> <span style="color: #555555;">[2]</span> <span style="color: #555555;">CRAN (R 4.4.0)</span></span></span>
 <span><span class="co">#&gt;  jsonlite               1.8.8     <span style="color: #555555;">2023-12-04</span> <span style="color: #555555;">[2]</span> <span style="color: #555555;">CRAN (R 4.4.0)</span></span></span>
@@ -1040,45 +1093,45 @@ <h2 class="unnumbered" id="session-information">Session information<a class="anc
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+<span><span class="co">#&gt;  Matrix                 1.6-5     <span style="color: #555555;">2024-01-11</span> <span style="color: #555555;">[3]</span> <span style="color: #555555;">CRAN (R 4.4.0)</span></span></span>
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+<span><span class="co">#&gt;  patchwork              1.2.0     <span style="color: #555555;">2024-01-08</span> <span style="color: #555555;">[1]</span> <span style="color: #555555;">CRAN (R 4.4.0)</span></span></span>
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diff --git a/authors.html b/authors.html
index 2bbd24d..85ca0ac 100644
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+++ b/authors.html
@@ -1,12 +1,12 @@
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 <![endif]--></head><body>
     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
     
 
-    <nav class="navbar fixed-top navbar-light navbar-expand-lg bg-#0092ac"><div class="container">
+    <nav class="navbar fixed-top navbar-light navbar-expand-lg bg-light"><div class="container">
     
     <a class="navbar-brand me-2" href="index.html">HybridExpress</a>
 
@@ -67,14 +67,14 @@ <h2>Authors</h2>
       <h2 id="citation">Citation</h2>
       <p><small class="dont-index">Source: <a href="https://github.com/almeidasilvaf/HybridExpress/blob/HEAD/DESCRIPTION" class="external-link"><code>DESCRIPTION</code></a></small></p>
 
-      <p>Almeida-Silva F, Prost-Boxoen L, Van de Peer Y (2023).
+      <p>Almeida-Silva F, Prost-Boxoen L, Van de Peer Y (2024).
 <em>HybridExpress: Comparative analysis of RNA-seq data for hybrids and their progenitors</em>.
 R package version 0.99.0, <a href="https://github.com/almeidasilvaf/HybridExpress" class="external-link">https://github.com/almeidasilvaf/HybridExpress</a>. 
 </p>
       <pre>@Manual{,
   title = {HybridExpress: Comparative analysis of RNA-seq data for hybrids and their progenitors},
   author = {Fabricio Almeida-Silva and Lucas Prost-Boxoen and Yves {Van de Peer}},
-  year = {2023},
+  year = {2024},
   note = {R package version 0.99.0},
   url = {https://github.com/almeidasilvaf/HybridExpress},
 }</pre>
diff --git a/deps/bootstrap-5.3.1/bootstrap.min.css b/deps/bootstrap-5.3.1/bootstrap.min.css
index d8095c3..307d6bc 100644
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10rem;--bs-dropdown-padding-x: 0;--bs-dropdown-padding-y: .5rem;--bs-dropdown-spacer: .125rem;--bs-dropdown-font-size:1rem;--bs-dropdown-color: var(--bs-body-color);--bs-dropdown-bg: var(--bs-body-bg);--bs-dropdown-border-color: var(--bs-border-color-translucent);--bs-dropdown-border-radius: var(--bs-border-radius);--bs-dropdown-border-width: var(--bs-border-width);--bs-dropdown-inner-border-radius: calc(var(--bs-border-radius) - var(--bs-border-width));--bs-dropdown-divider-bg: var(--bs-border-color-translucent);--bs-dropdown-divider-margin-y: .5rem;--bs-dropdown-box-shadow: 0 0.5rem 1rem rgba(0,0,0,0.15);--bs-dropdown-link-color: var(--bs-body-color);--bs-dropdown-link-hover-color: var(--bs-body-color);--bs-dropdown-link-hover-bg: var(--bs-tertiary-bg);--bs-dropdown-link-active-color: #fff;--bs-dropdown-link-active-bg: #18416A;--bs-dropdown-link-disabled-color: var(--bs-tertiary-color);--bs-dropdown-item-padding-x: 1rem;--bs-dropdown-item-padding-y: .25rem;--bs-dropdown-header-color: #6c757d;--bs-dropdown-header-padding-x: 1rem;--bs-dropdown-header-padding-y: .5rem;position:absolute;z-index:var(--bs-dropdown-zindex);display:none;min-width:var(--bs-dropdown-min-width);padding:var(--bs-dropdown-padding-y) var(--bs-dropdown-padding-x);margin:0;font-size:var(--bs-dropdown-font-size);color:var(--bs-dropdown-color);text-align:left;list-style:none;background-color:var(--bs-dropdown-bg);background-clip:padding-box;border:var(--bs-dropdown-border-width) solid var(--bs-dropdown-border-color);border-radius:var(--bs-dropdown-border-radius)}.dropdown-menu[data-bs-popper]{top:100%;left:0;margin-top:var(--bs-dropdown-spacer)}.dropdown-menu-start{--bs-position: start}.dropdown-menu-start[data-bs-popper]{right:auto;left:0}.dropdown-menu-end{--bs-position: end}.dropdown-menu-end[data-bs-popper]{right:0;left:auto}@media (min-width: 576px){.dropdown-menu-sm-start{--bs-position: start}.dropdown-menu-sm-start[data-bs-popper]{right:auto;left:0}.dropdown-menu-sm-end{--bs-position: end}.dropdown-menu-sm-end[data-bs-popper]{right:0;left:auto}}@media (min-width: 768px){.dropdown-menu-md-start{--bs-position: start}.dropdown-menu-md-start[data-bs-popper]{right:auto;left:0}.dropdown-menu-md-end{--bs-position: end}.dropdown-menu-md-end[data-bs-popper]{right:0;left:auto}}@media (min-width: 992px){.dropdown-menu-lg-start{--bs-position: start}.dropdown-menu-lg-start[data-bs-popper]{right:auto;left:0}.dropdown-menu-lg-end{--bs-position: end}.dropdown-menu-lg-end[data-bs-popper]{right:0;left:auto}}@media (min-width: 1200px){.dropdown-menu-xl-start{--bs-position: start}.dropdown-menu-xl-start[data-bs-popper]{right:auto;left:0}.dropdown-menu-xl-end{--bs-position: end}.dropdown-menu-xl-end[data-bs-popper]{right:0;left:auto}}@media (min-width: 1400px){.dropdown-menu-xxl-start{--bs-position: start}.dropdown-menu-xxl-start[data-bs-popper]{right:auto;left:0}.dropdown-menu-xxl-end{--bs-position: end}.dropdown-menu-xxl-end[data-bs-popper]{right:0;left:auto}}.dropup .dropdown-menu[data-bs-popper]{top:auto;bottom:100%;margin-top:0;margin-bottom:var(--bs-dropdown-spacer)}.dropup .dropdown-toggle::after{display:inline-block;margin-left:.255em;vertical-align:.255em;content:"";border-top:0;border-right:.3em solid transparent;border-bottom:.3em solid;border-left:.3em solid transparent}.dropup .dropdown-toggle:empty::after{margin-left:0}.dropend .dropdown-menu[data-bs-popper]{top:0;right:auto;left:100%;margin-top:0;margin-left:var(--bs-dropdown-spacer)}.dropend .dropdown-toggle::after{display:inline-block;margin-left:.255em;vertical-align:.255em;content:"";border-top:.3em solid transparent;border-right:0;border-bottom:.3em solid transparent;border-left:.3em solid}.dropend .dropdown-toggle:empty::after{margin-left:0}.dropend .dropdown-toggle::after{vertical-align:0}.dropstart .dropdown-menu[data-bs-popper]{top:0;right:100%;left:auto;margin-top:0;margin-right:var(--bs-dropdown-spacer)}.dropstart .dropdown-toggle::after{display:inline-block;margin-left:.255em;vertical-align:.255em;content:""}.dropstart .dropdown-toggle::after{display:none}.dropstart .dropdown-toggle::before{display:inline-block;margin-right:.255em;vertical-align:.255em;content:"";border-top:.3em solid transparent;border-right:.3em solid;border-bottom:.3em solid transparent}.dropstart .dropdown-toggle:empty::after{margin-left:0}.dropstart .dropdown-toggle::before{vertical-align:0}.dropdown-divider{height:0;margin:var(--bs-dropdown-divider-margin-y) 0;overflow:hidden;border-top:1px solid var(--bs-dropdown-divider-bg);opacity:1}.dropdown-item{display:block;width:100%;padding:var(--bs-dropdown-item-padding-y) var(--bs-dropdown-item-padding-x);clear:both;font-weight:400;color:var(--bs-dropdown-link-color);text-align:inherit;text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;white-space:nowrap;background-color:transparent;border:0;border-radius:var(--bs-dropdown-item-border-radius, 0)}.dropdown-item:hover,.dropdown-item:focus{color:var(--bs-dropdown-link-hover-color);background-color:var(--bs-dropdown-link-hover-bg)}.dropdown-item.active,.dropdown-item:active{color:var(--bs-dropdown-link-active-color);text-decoration:none;background-color:var(--bs-dropdown-link-active-bg)}.dropdown-item.disabled,.dropdown-item:disabled{color:var(--bs-dropdown-link-disabled-color);pointer-events:none;background-color:transparent}.dropdown-menu.show{display:block}.dropdown-header{display:block;padding:var(--bs-dropdown-header-padding-y) var(--bs-dropdown-header-padding-x);margin-bottom:0;font-size:.875rem;color:var(--bs-dropdown-header-color);white-space:nowrap}.dropdown-item-text{display:block;padding:var(--bs-dropdown-item-padding-y) var(--bs-dropdown-item-padding-x);color:var(--bs-dropdown-link-color)}.dropdown-menu-dark{--bs-dropdown-color: #dee2e6;--bs-dropdown-bg: #343a40;--bs-dropdown-border-color: var(--bs-border-color-translucent);--bs-dropdown-box-shadow: ;--bs-dropdown-link-color: #dee2e6;--bs-dropdown-link-hover-color: #fff;--bs-dropdown-divider-bg: var(--bs-border-color-translucent);--bs-dropdown-link-hover-bg: rgba(255,255,255,0.15);--bs-dropdown-link-active-color: #fff;--bs-dropdown-link-active-bg: #18416A;--bs-dropdown-link-disabled-color: #adb5bd;--bs-dropdown-header-color: #adb5bd}.btn-group,.btn-group-vertical{position:relative;display:inline-flex;vertical-align:middle}.btn-group>.btn,.btn-group-vertical>.btn{position:relative;flex:1 1 auto;-webkit-flex:1 1 auto}.btn-group>.btn-check:checked+.btn,.btn-group>.btn-check:focus+.btn,.btn-group>.btn:hover,.btn-group>.btn:focus,.btn-group>.btn:active,.btn-group>.btn.active,.btn-group-vertical>.btn-check:checked+.btn,.btn-group-vertical>.btn-check:focus+.btn,.btn-group-vertical>.btn:hover,.btn-group-vertical>.btn:focus,.btn-group-vertical>.btn:active,.btn-group-vertical>.btn.active{z-index:1}.btn-toolbar{display:flex;display:-webkit-flex;flex-wrap:wrap;-webkit-flex-wrap:wrap;justify-content:flex-start;-webkit-justify-content:flex-start}.btn-toolbar .input-group{width:auto}.btn-group{border-radius:var(--bs-border-radius)}.btn-group>:not(.btn-check:first-child)+.btn,.btn-group>.btn-group:not(:first-child){margin-left:calc(var(--bs-border-width) * -1)}.btn-group>.btn:not(:last-child):not(.dropdown-toggle),.btn-group>.btn.dropdown-toggle-split:first-child,.btn-group>.btn-group:not(:last-child)>.btn{border-top-right-radius:0;border-bottom-right-radius:0}.btn-group>.btn:nth-child(n + 3),.btn-group>:not(.btn-check)+.btn,.btn-group>.btn-group:not(:first-child)>.btn{border-top-left-radius:0;border-bottom-left-radius:0}.dropdown-toggle-split{padding-right:.5625rem;padding-left:.5625rem}.dropdown-toggle-split::after,.dropup .dropdown-toggle-split::after,.dropend .dropdown-toggle-split::after{margin-left:0}.dropstart .dropdown-toggle-split::before{margin-right:0}.btn-sm+.dropdown-toggle-split,.btn-group-sm>.btn+.dropdown-toggle-split{padding-right:.375rem;padding-left:.375rem}.btn-lg+.dropdown-toggle-split,.btn-group-lg>.btn+.dropdown-toggle-split{padding-right:.75rem;padding-left:.75rem}.btn-group-vertical{flex-direction:column;-webkit-flex-direction:column;align-items:flex-start;-webkit-align-items:flex-start;justify-content:center;-webkit-justify-content:center}.btn-group-vertical>.btn,.btn-group-vertical>.btn-group{width:100%}.btn-group-vertical>.btn:not(:first-child),.btn-group-vertical>.btn-group:not(:first-child){margin-top:calc(var(--bs-border-width) * -1)}.btn-group-vertical>.btn:not(:last-child):not(.dropdown-toggle),.btn-group-vertical>.btn-group:not(:last-child)>.btn{border-bottom-right-radius:0;border-bottom-left-radius:0}.btn-group-vertical>.btn~.btn,.btn-group-vertical>.btn-group:not(:first-child)>.btn{border-top-left-radius:0;border-top-right-radius:0}.nav{--bs-nav-link-padding-x: 1rem;--bs-nav-link-padding-y: .5rem;--bs-nav-link-font-weight: ;--bs-nav-link-color: var(--bs-link-color);--bs-nav-link-hover-color: var(--bs-link-hover-color);--bs-nav-link-disabled-color: var(--bs-secondary-color);display:flex;display:-webkit-flex;flex-wrap:wrap;-webkit-flex-wrap:wrap;padding-left:0;margin-bottom:0;list-style:none}.nav-link{display:block;padding:var(--bs-nav-link-padding-y) var(--bs-nav-link-padding-x);font-size:var(--bs-nav-link-font-size);font-weight:var(--bs-nav-link-font-weight);color:var(--bs-nav-link-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background:none;border:0;transition:color 0.15s ease-in-out,background-color 0.15s ease-in-out,border-color 0.15s ease-in-out}@media (prefers-reduced-motion: reduce){.nav-link{transition:none}}.nav-link:hover,.nav-link:focus{color:var(--bs-nav-link-hover-color)}.nav-link:focus-visible{outline:0;box-shadow:0 0 0 .25rem rgba(24,65,106,0.25)}.nav-link.disabled,.nav-link:disabled{color:var(--bs-nav-link-disabled-color);pointer-events:none;cursor:default}.nav-tabs{--bs-nav-tabs-border-width: var(--bs-border-width);--bs-nav-tabs-border-color: var(--bs-border-color);--bs-nav-tabs-border-radius: var(--bs-border-radius);--bs-nav-tabs-link-hover-border-color: var(--bs-secondary-bg) var(--bs-secondary-bg) var(--bs-border-color);--bs-nav-tabs-link-active-color: var(--bs-emphasis-color);--bs-nav-tabs-link-active-bg: var(--bs-body-bg);--bs-nav-tabs-link-active-border-color: var(--bs-border-color) var(--bs-border-color) var(--bs-body-bg);border-bottom:var(--bs-nav-tabs-border-width) solid var(--bs-nav-tabs-border-color)}.nav-tabs .nav-link{margin-bottom:calc(-1 * var(--bs-nav-tabs-border-width));border:var(--bs-nav-tabs-border-width) solid transparent;border-top-left-radius:var(--bs-nav-tabs-border-radius);border-top-right-radius:var(--bs-nav-tabs-border-radius)}.nav-tabs .nav-link:hover,.nav-tabs .nav-link:focus{isolation:isolate;border-color:var(--bs-nav-tabs-link-hover-border-color)}.nav-tabs .nav-link.active,.nav-tabs .nav-item.show .nav-link{color:var(--bs-nav-tabs-link-active-color);background-color:var(--bs-nav-tabs-link-active-bg);border-color:var(--bs-nav-tabs-link-active-border-color)}.nav-tabs .dropdown-menu{margin-top:calc(-1 * var(--bs-nav-tabs-border-width));border-top-left-radius:0;border-top-right-radius:0}.nav-pills{--bs-nav-pills-border-radius: var(--bs-border-radius);--bs-nav-pills-link-active-color: #fff;--bs-nav-pills-link-active-bg: #18416A}.nav-pills .nav-link{border-radius:var(--bs-nav-pills-border-radius)}.nav-pills .nav-link.active,.nav-pills .show>.nav-link{color:var(--bs-nav-pills-link-active-color);background-color:var(--bs-nav-pills-link-active-bg)}.nav-underline{--bs-nav-underline-gap: 1rem;--bs-nav-underline-border-width: .125rem;--bs-nav-underline-link-active-color: var(--bs-emphasis-color);gap:var(--bs-nav-underline-gap)}.nav-underline .nav-link{padding-right:0;padding-left:0;border-bottom:var(--bs-nav-underline-border-width) solid transparent}.nav-underline .nav-link:hover,.nav-underline .nav-link:focus{border-bottom-color:currentcolor}.nav-underline .nav-link.active,.nav-underline 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var(--bs-navbar-hover-color);--bs-nav-link-disabled-color: var(--bs-navbar-disabled-color);display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;padding-left:0;margin-bottom:0;list-style:none}.navbar-nav .nav-link.active,.navbar-nav .nav-link.show{color:var(--bs-navbar-active-color)}.navbar-nav .dropdown-menu{position:static}.navbar-text{padding-top:.5rem;padding-bottom:.5rem;color:var(--bs-navbar-color)}.navbar-text a,.navbar-text a:hover,.navbar-text a:focus{color:var(--bs-navbar-active-color)}.navbar-collapse{flex-basis:100%;-webkit-flex-basis:100%;flex-grow:1;-webkit-flex-grow:1;align-items:center;-webkit-align-items:center}.navbar-toggler{padding:var(--bs-navbar-toggler-padding-y) var(--bs-navbar-toggler-padding-x);font-size:var(--bs-navbar-toggler-font-size);line-height:1;color:var(--bs-navbar-color);background-color:transparent;border:var(--bs-border-width) solid 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992px){.navbar-expand-lg{flex-wrap:nowrap;-webkit-flex-wrap:nowrap;justify-content:flex-start;-webkit-justify-content:flex-start}.navbar-expand-lg .navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand-lg .navbar-nav .dropdown-menu{position:absolute}.navbar-expand-lg .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand-lg .navbar-nav-scroll{overflow:visible}.navbar-expand-lg .navbar-collapse{display:flex !important;display:-webkit-flex !important;flex-basis:auto;-webkit-flex-basis:auto}.navbar-expand-lg .navbar-toggler{display:none}.navbar-expand-lg .offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:transparent !important;border:0 !important;transform:none !important;transition:none}.navbar-expand-lg .offcanvas .offcanvas-header{display:none}.navbar-expand-lg .offcanvas 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!important;transform:none !important;transition:none}.navbar-expand-xl .offcanvas .offcanvas-header{display:none}.navbar-expand-xl .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}}@media (min-width: 1400px){.navbar-expand-xxl{flex-wrap:nowrap;-webkit-flex-wrap:nowrap;justify-content:flex-start;-webkit-justify-content:flex-start}.navbar-expand-xxl .navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand-xxl .navbar-nav .dropdown-menu{position:absolute}.navbar-expand-xxl .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand-xxl .navbar-nav-scroll{overflow:visible}.navbar-expand-xxl .navbar-collapse{display:flex !important;display:-webkit-flex !important;flex-basis:auto;-webkit-flex-basis:auto}.navbar-expand-xxl .navbar-toggler{display:none}.navbar-expand-xxl .offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:transparent !important;border:0 !important;transform:none !important;transition:none}.navbar-expand-xxl .offcanvas .offcanvas-header{display:none}.navbar-expand-xxl .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}}.navbar-expand{flex-wrap:nowrap;-webkit-flex-wrap:nowrap;justify-content:flex-start;-webkit-justify-content:flex-start}.navbar-expand .navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand .navbar-nav .dropdown-menu{position:absolute}.navbar-expand .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand .navbar-nav-scroll{overflow:visible}.navbar-expand .navbar-collapse{display:flex !important;display:-webkit-flex !important;flex-basis:auto;-webkit-flex-basis:auto}.navbar-expand .navbar-toggler{display:none}.navbar-expand .offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:transparent !important;border:0 !important;transform:none !important;transition:none}.navbar-expand .offcanvas .offcanvas-header{display:none}.navbar-expand .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}.navbar-dark,.navbar[data-bs-theme="dark"]{--bs-navbar-color: rgba(0,0,0,0.55);--bs-navbar-hover-color: rgba(0,0,0,0.75);--bs-navbar-disabled-color: rgba(0,0,0,0.25);--bs-navbar-active-color: #000;--bs-navbar-brand-color: #000;--bs-navbar-brand-hover-color: #000;--bs-navbar-toggler-border-color: rgba(0,0,0,0.1);--bs-navbar-toggler-icon-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 30 30'%3e%3cpath stroke='rgba%280,0,0,0.75%29' stroke-linecap='round' stroke-miterlimit='10' stroke-width='2' d='M4 7h22M4 15h22M4 23h22'/%3e%3c/svg%3e")}[data-bs-theme="dark"] .navbar-toggler-icon{--bs-navbar-toggler-icon-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 30 30'%3e%3cpath stroke='rgba%280,0,0,0.75%29' stroke-linecap='round' stroke-miterlimit='10' stroke-width='2' d='M4 7h22M4 15h22M4 23h22'/%3e%3c/svg%3e")}.card{--bs-card-spacer-y: 1rem;--bs-card-spacer-x: 1rem;--bs-card-title-spacer-y: .5rem;--bs-card-title-color: ;--bs-card-subtitle-color: ;--bs-card-border-width: var(--bs-border-width);--bs-card-border-color: var(--bs-border-color-translucent);--bs-card-border-radius: var(--bs-border-radius);--bs-card-box-shadow: ;--bs-card-inner-border-radius: calc(var(--bs-border-radius) - (var(--bs-border-width)));--bs-card-cap-padding-y: .5rem;--bs-card-cap-padding-x: 1rem;--bs-card-cap-bg: rgba(var(--bs-body-color-rgb), 0.03);--bs-card-cap-color: ;--bs-card-height: ;--bs-card-color: ;--bs-card-bg: var(--bs-body-bg);--bs-card-img-overlay-padding: 1rem;--bs-card-group-margin: .75rem;position:relative;display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;min-width:0;height:var(--bs-card-height);color:var(--bs-body-color);word-wrap:break-word;background-color:var(--bs-card-bg);background-clip:border-box;border:var(--bs-card-border-width) solid 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url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%230a1a2a'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-focus-border-color: #8ca0b5;--bs-accordion-btn-focus-box-shadow: 0 0 0 .25rem rgba(24,65,106,0.25);--bs-accordion-body-padding-x: 1.25rem;--bs-accordion-body-padding-y: 1rem;--bs-accordion-active-color: var(--bs-primary-text-emphasis);--bs-accordion-active-bg: var(--bs-primary-bg-subtle)}.accordion-button{position:relative;display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;width:100%;padding:var(--bs-accordion-btn-padding-y) var(--bs-accordion-btn-padding-x);font-size:1rem;color:var(--bs-accordion-btn-color);text-align:left;background-color:var(--bs-accordion-btn-bg);border:0;border-radius:0;overflow-anchor:none;transition:var(--bs-accordion-transition)}@media (prefers-reduced-motion: reduce){.accordion-button{transition:none}}.accordion-button:not(.collapsed){color:var(--bs-accordion-active-color);background-color:var(--bs-accordion-active-bg);box-shadow:inset 0 calc(-1 * var(--bs-accordion-border-width)) 0 var(--bs-accordion-border-color)}.accordion-button:not(.collapsed)::after{background-image:var(--bs-accordion-btn-active-icon);transform:var(--bs-accordion-btn-icon-transform)}.accordion-button::after{flex-shrink:0;-webkit-flex-shrink:0;width:var(--bs-accordion-btn-icon-width);height:var(--bs-accordion-btn-icon-width);margin-left:auto;content:"";background-image:var(--bs-accordion-btn-icon);background-repeat:no-repeat;background-size:var(--bs-accordion-btn-icon-width);transition:var(--bs-accordion-btn-icon-transition)}@media (prefers-reduced-motion: reduce){.accordion-button::after{transition:none}}.accordion-button:hover{z-index:2}.accordion-button:focus{z-index:3;border-color:var(--bs-accordion-btn-focus-border-color);outline:0;box-shadow:var(--bs-accordion-btn-focus-box-shadow)}.accordion-header{margin-bottom:0}.accordion-item{color:var(--bs-accordion-color);background-color:var(--bs-accordion-bg);border:var(--bs-accordion-border-width) solid var(--bs-accordion-border-color)}.accordion-item:first-of-type{border-top-left-radius:var(--bs-accordion-border-radius);border-top-right-radius:var(--bs-accordion-border-radius)}.accordion-item:first-of-type .accordion-button{border-top-left-radius:var(--bs-accordion-inner-border-radius);border-top-right-radius:var(--bs-accordion-inner-border-radius)}.accordion-item:not(:first-of-type){border-top:0}.accordion-item:last-of-type{border-bottom-right-radius:var(--bs-accordion-border-radius);border-bottom-left-radius:var(--bs-accordion-border-radius)}.accordion-item:last-of-type .accordion-button.collapsed{border-bottom-right-radius:var(--bs-accordion-inner-border-radius);border-bottom-left-radius:var(--bs-accordion-inner-border-radius)}.accordion-item:last-of-type .accordion-collapse{border-bottom-right-radius:var(--bs-accordion-border-radius);border-bottom-left-radius:var(--bs-accordion-border-radius)}.accordion-body{padding:var(--bs-accordion-body-padding-y) var(--bs-accordion-body-padding-x)}.accordion-flush .accordion-collapse{border-width:0}.accordion-flush .accordion-item{border-right:0;border-left:0;border-radius:0}.accordion-flush .accordion-item:first-child{border-top:0}.accordion-flush .accordion-item:last-child{border-bottom:0}.accordion-flush .accordion-item .accordion-button,.accordion-flush .accordion-item .accordion-button.collapsed{border-radius:0}[data-bs-theme="dark"] .accordion-button::after{--bs-accordion-btn-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23748da6'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-active-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23748da6'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e")}.breadcrumb{--bs-breadcrumb-padding-x: 0;--bs-breadcrumb-padding-y: 0;--bs-breadcrumb-margin-bottom: 1rem;--bs-breadcrumb-bg: ;--bs-breadcrumb-border-radius: ;--bs-breadcrumb-divider-color: var(--bs-secondary-color);--bs-breadcrumb-item-padding-x: .5rem;--bs-breadcrumb-item-active-color: var(--bs-secondary-color);display:flex;display:-webkit-flex;flex-wrap:wrap;-webkit-flex-wrap:wrap;padding:var(--bs-breadcrumb-padding-y) var(--bs-breadcrumb-padding-x);margin-bottom:var(--bs-breadcrumb-margin-bottom);font-size:var(--bs-breadcrumb-font-size);list-style:none;background-color:var(--bs-breadcrumb-bg);border-radius:var(--bs-breadcrumb-border-radius)}.breadcrumb-item+.breadcrumb-item{padding-left:var(--bs-breadcrumb-item-padding-x)}.breadcrumb-item+.breadcrumb-item::before{float:left;padding-right:var(--bs-breadcrumb-item-padding-x);color:var(--bs-breadcrumb-divider-color);content:var(--bs-breadcrumb-divider, "/") /* rtl: var(--bs-breadcrumb-divider, "/") */}.breadcrumb-item.active{color:var(--bs-breadcrumb-item-active-color)}.pagination{--bs-pagination-padding-x: .75rem;--bs-pagination-padding-y: .375rem;--bs-pagination-font-size:1rem;--bs-pagination-color: var(--bs-link-color);--bs-pagination-bg: var(--bs-body-bg);--bs-pagination-border-width: var(--bs-border-width);--bs-pagination-border-color: var(--bs-border-color);--bs-pagination-border-radius: var(--bs-border-radius);--bs-pagination-hover-color: var(--bs-link-hover-color);--bs-pagination-hover-bg: var(--bs-tertiary-bg);--bs-pagination-hover-border-color: var(--bs-border-color);--bs-pagination-focus-color: var(--bs-link-hover-color);--bs-pagination-focus-bg: var(--bs-secondary-bg);--bs-pagination-focus-box-shadow: 0 0 0 .25rem rgba(24,65,106,0.25);--bs-pagination-active-color: #fff;--bs-pagination-active-bg: #18416A;--bs-pagination-active-border-color: #18416A;--bs-pagination-disabled-color: var(--bs-secondary-color);--bs-pagination-disabled-bg: var(--bs-secondary-bg);--bs-pagination-disabled-border-color: var(--bs-border-color);display:flex;display:-webkit-flex;padding-left:0;list-style:none}.page-link{position:relative;display:block;padding:var(--bs-pagination-padding-y) var(--bs-pagination-padding-x);font-size:var(--bs-pagination-font-size);color:var(--bs-pagination-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-pagination-bg);border:var(--bs-pagination-border-width) solid var(--bs-pagination-border-color);transition:color 0.15s ease-in-out,background-color 0.15s ease-in-out,border-color 0.15s ease-in-out,box-shadow 0.15s ease-in-out}@media (prefers-reduced-motion: reduce){.page-link{transition:none}}.page-link:hover{z-index:2;color:var(--bs-pagination-hover-color);background-color:var(--bs-pagination-hover-bg);border-color:var(--bs-pagination-hover-border-color)}.page-link:focus{z-index:3;color:var(--bs-pagination-focus-color);background-color:var(--bs-pagination-focus-bg);outline:0;box-shadow:var(--bs-pagination-focus-box-shadow)}.page-link.active,.active>.page-link{z-index:3;color:var(--bs-pagination-active-color);background-color:var(--bs-pagination-active-bg);border-color:var(--bs-pagination-active-border-color)}.page-link.disabled,.disabled>.page-link{color:var(--bs-pagination-disabled-color);pointer-events:none;background-color:var(--bs-pagination-disabled-bg);border-color:var(--bs-pagination-disabled-border-color)}.page-item:not(:first-child) .page-link{margin-left:calc(var(--bs-border-width) * -1)}.page-item:first-child .page-link{border-top-left-radius:var(--bs-pagination-border-radius);border-bottom-left-radius:var(--bs-pagination-border-radius)}.page-item:last-child .page-link{border-top-right-radius:var(--bs-pagination-border-radius);border-bottom-right-radius:var(--bs-pagination-border-radius)}.pagination-lg{--bs-pagination-padding-x: 1.5rem;--bs-pagination-padding-y: .75rem;--bs-pagination-font-size:1.25rem;--bs-pagination-border-radius: var(--bs-border-radius-lg)}.pagination-sm{--bs-pagination-padding-x: .5rem;--bs-pagination-padding-y: .25rem;--bs-pagination-font-size:.875rem;--bs-pagination-border-radius: var(--bs-border-radius-sm)}.badge{--bs-badge-padding-x: .65em;--bs-badge-padding-y: .35em;--bs-badge-font-size:.75em;--bs-badge-font-weight: 700;--bs-badge-color: #fff;--bs-badge-border-radius: var(--bs-border-radius);display:inline-block;padding:var(--bs-badge-padding-y) var(--bs-badge-padding-x);font-size:var(--bs-badge-font-size);font-weight:var(--bs-badge-font-weight);line-height:1;color:var(--bs-badge-color);text-align:center;white-space:nowrap;vertical-align:baseline;border-radius:var(--bs-badge-border-radius)}.badge:empty{display:none}.btn .badge{position:relative;top:-1px}.alert{--bs-alert-bg: transparent;--bs-alert-padding-x: 1rem;--bs-alert-padding-y: 1rem;--bs-alert-margin-bottom: 1rem;--bs-alert-color: inherit;--bs-alert-border-color: transparent;--bs-alert-border: var(--bs-border-width) solid var(--bs-alert-border-color);--bs-alert-border-radius: var(--bs-border-radius);--bs-alert-link-color: inherit;position:relative;padding:var(--bs-alert-padding-y) var(--bs-alert-padding-x);margin-bottom:var(--bs-alert-margin-bottom);color:var(--bs-alert-color);background-color:var(--bs-alert-bg);border:var(--bs-alert-border);border-radius:var(--bs-alert-border-radius)}.alert-heading{color:inherit}.alert-link{font-weight:700;color:var(--bs-alert-link-color)}.alert-dismissible{padding-right:3rem}.alert-dismissible .btn-close{position:absolute;top:0;right:0;z-index:2;padding:1.25rem 1rem}.alert-default{--bs-alert-color: var(--bs-default-text-emphasis);--bs-alert-bg: var(--bs-default-bg-subtle);--bs-alert-border-color: var(--bs-default-border-subtle);--bs-alert-link-color: var(--bs-default-text-emphasis)}.alert-primary{--bs-alert-color: var(--bs-primary-text-emphasis);--bs-alert-bg: var(--bs-primary-bg-subtle);--bs-alert-border-color: var(--bs-primary-border-subtle);--bs-alert-link-color: var(--bs-primary-text-emphasis)}.alert-secondary{--bs-alert-color: var(--bs-secondary-text-emphasis);--bs-alert-bg: var(--bs-secondary-bg-subtle);--bs-alert-border-color: var(--bs-secondary-border-subtle);--bs-alert-link-color: var(--bs-secondary-text-emphasis)}.alert-success{--bs-alert-color: var(--bs-success-text-emphasis);--bs-alert-bg: var(--bs-success-bg-subtle);--bs-alert-border-color: var(--bs-success-border-subtle);--bs-alert-link-color: var(--bs-success-text-emphasis)}.alert-info{--bs-alert-color: var(--bs-info-text-emphasis);--bs-alert-bg: var(--bs-info-bg-subtle);--bs-alert-border-color: var(--bs-info-border-subtle);--bs-alert-link-color: var(--bs-info-text-emphasis)}.alert-warning{--bs-alert-color: var(--bs-warning-text-emphasis);--bs-alert-bg: var(--bs-warning-bg-subtle);--bs-alert-border-color: var(--bs-warning-border-subtle);--bs-alert-link-color: var(--bs-warning-text-emphasis)}.alert-danger{--bs-alert-color: var(--bs-danger-text-emphasis);--bs-alert-bg: var(--bs-danger-bg-subtle);--bs-alert-border-color: var(--bs-danger-border-subtle);--bs-alert-link-color: var(--bs-danger-text-emphasis)}.alert-light{--bs-alert-color: var(--bs-light-text-emphasis);--bs-alert-bg: var(--bs-light-bg-subtle);--bs-alert-border-color: var(--bs-light-border-subtle);--bs-alert-link-color: var(--bs-light-text-emphasis)}.alert-dark{--bs-alert-color: var(--bs-dark-text-emphasis);--bs-alert-bg: var(--bs-dark-bg-subtle);--bs-alert-border-color: var(--bs-dark-border-subtle);--bs-alert-link-color: var(--bs-dark-text-emphasis)}@keyframes progress-bar-stripes{0%{background-position-x:1rem}}.progress,.progress-stacked{--bs-progress-height: 1rem;--bs-progress-font-size:.75rem;--bs-progress-bg: var(--bs-secondary-bg);--bs-progress-border-radius: var(--bs-border-radius);--bs-progress-box-shadow: var(--bs-box-shadow-inset);--bs-progress-bar-color: #fff;--bs-progress-bar-bg: #18416A;--bs-progress-bar-transition: width 0.6s 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var(--bs-list-group-border-color)}.list-group-item:first-child{border-top-left-radius:inherit;border-top-right-radius:inherit}.list-group-item:last-child{border-bottom-right-radius:inherit;border-bottom-left-radius:inherit}.list-group-item.disabled,.list-group-item:disabled{color:var(--bs-list-group-disabled-color);pointer-events:none;background-color:var(--bs-list-group-disabled-bg)}.list-group-item.active{z-index:2;color:var(--bs-list-group-active-color);background-color:var(--bs-list-group-active-bg);border-color:var(--bs-list-group-active-border-color)}.list-group-item+.list-group-item{border-top-width:0}.list-group-item+.list-group-item.active{margin-top:calc(-1 * 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576px){.list-group-horizontal-sm{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-sm>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-sm>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-sm>.list-group-item.active{margin-top:0}.list-group-horizontal-sm>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-sm>.list-group-item+.list-group-item.active{margin-left:calc(-1 * var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media (min-width: 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var(--bs-list-group-border-width)}.list-group-flush>.list-group-item:last-child{border-bottom-width:0}.list-group-item-default{--bs-list-group-color: var(--bs-default-text-emphasis);--bs-list-group-bg: var(--bs-default-bg-subtle);--bs-list-group-border-color: var(--bs-default-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-default-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-default-border-subtle);--bs-list-group-active-color: var(--bs-default-bg-subtle);--bs-list-group-active-bg: var(--bs-default-text-emphasis);--bs-list-group-active-border-color: var(--bs-default-text-emphasis)}.list-group-item-primary{--bs-list-group-color: var(--bs-primary-text-emphasis);--bs-list-group-bg: var(--bs-primary-bg-subtle);--bs-list-group-border-color: var(--bs-primary-border-subtle);--bs-list-group-action-hover-color: 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var(--bs-secondary-text-emphasis);--bs-list-group-active-border-color: var(--bs-secondary-text-emphasis)}.list-group-item-success{--bs-list-group-color: var(--bs-success-text-emphasis);--bs-list-group-bg: var(--bs-success-bg-subtle);--bs-list-group-border-color: var(--bs-success-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-success-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-success-border-subtle);--bs-list-group-active-color: var(--bs-success-bg-subtle);--bs-list-group-active-bg: var(--bs-success-text-emphasis);--bs-list-group-active-border-color: var(--bs-success-text-emphasis)}.list-group-item-info{--bs-list-group-color: var(--bs-info-text-emphasis);--bs-list-group-bg: var(--bs-info-bg-subtle);--bs-list-group-border-color: var(--bs-info-border-subtle);--bs-list-group-action-hover-color: 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var(--bs-warning-text-emphasis)}.list-group-item-danger{--bs-list-group-color: var(--bs-danger-text-emphasis);--bs-list-group-bg: var(--bs-danger-bg-subtle);--bs-list-group-border-color: var(--bs-danger-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-danger-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-danger-border-subtle);--bs-list-group-active-color: var(--bs-danger-bg-subtle);--bs-list-group-active-bg: var(--bs-danger-text-emphasis);--bs-list-group-active-border-color: var(--bs-danger-text-emphasis)}.list-group-item-light{--bs-list-group-color: var(--bs-light-text-emphasis);--bs-list-group-bg: var(--bs-light-bg-subtle);--bs-list-group-border-color: var(--bs-light-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-light-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-light-border-subtle);--bs-list-group-active-color: var(--bs-light-bg-subtle);--bs-list-group-active-bg: var(--bs-light-text-emphasis);--bs-list-group-active-border-color: var(--bs-light-text-emphasis)}.list-group-item-dark{--bs-list-group-color: var(--bs-dark-text-emphasis);--bs-list-group-bg: var(--bs-dark-bg-subtle);--bs-list-group-border-color: var(--bs-dark-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-dark-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-dark-border-subtle);--bs-list-group-active-color: var(--bs-dark-bg-subtle);--bs-list-group-active-bg: var(--bs-dark-text-emphasis);--bs-list-group-active-border-color: var(--bs-dark-text-emphasis)}.btn-close{--bs-btn-close-color: #000;--bs-btn-close-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23000'%3e%3cpath d='M.293.293a1 1 0 0 1 1.414 0L8 6.586 14.293.293a1 1 0 1 1 1.414 1.414L9.414 8l6.293 6.293a1 1 0 0 1-1.414 1.414L8 9.414l-6.293 6.293a1 1 0 0 1-1.414-1.414L6.586 8 .293 1.707a1 1 0 0 1 0-1.414z'/%3e%3c/svg%3e");--bs-btn-close-opacity: .5;--bs-btn-close-hover-opacity: .75;--bs-btn-close-focus-shadow: 0 0 0 .25rem rgba(24,65,106,0.25);--bs-btn-close-focus-opacity: 1;--bs-btn-close-disabled-opacity: .25;--bs-btn-close-white-filter: invert(1) grayscale(100%) brightness(200%);box-sizing:content-box;width:1em;height:1em;padding:.25em .25em;color:var(--bs-btn-close-color);background:transparent var(--bs-btn-close-bg) center/1em auto no-repeat;border:0;border-radius:.375rem;opacity:var(--bs-btn-close-opacity)}.btn-close:hover{color:var(--bs-btn-close-color);text-decoration:none;opacity:var(--bs-btn-close-hover-opacity)}.btn-close:focus{outline:0;box-shadow:var(--bs-btn-close-focus-shadow);opacity:var(--bs-btn-close-focus-opacity)}.btn-close:disabled,.btn-close.disabled{pointer-events:none;user-select:none;-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;-o-user-select:none;opacity:var(--bs-btn-close-disabled-opacity)}.btn-close-white{filter:var(--bs-btn-close-white-filter)}[data-bs-theme="dark"] .btn-close{filter:var(--bs-btn-close-white-filter)}.toast{--bs-toast-zindex: 1090;--bs-toast-padding-x: .75rem;--bs-toast-padding-y: .5rem;--bs-toast-spacing: 1.5rem;--bs-toast-max-width: 350px;--bs-toast-font-size:.875rem;--bs-toast-color: ;--bs-toast-bg: rgba(var(--bs-body-bg-rgb), 0.85);--bs-toast-border-width: var(--bs-border-width);--bs-toast-border-color: var(--bs-border-color-translucent);--bs-toast-border-radius: var(--bs-border-radius);--bs-toast-box-shadow: var(--bs-box-shadow);--bs-toast-header-color: var(--bs-secondary-color);--bs-toast-header-bg: rgba(var(--bs-body-bg-rgb), 0.85);--bs-toast-header-border-color: var(--bs-border-color-translucent);width:var(--bs-toast-max-width);max-width:100%;font-size:var(--bs-toast-font-size);color:var(--bs-toast-color);pointer-events:auto;background-color:var(--bs-toast-bg);background-clip:padding-box;border:var(--bs-toast-border-width) solid var(--bs-toast-border-color);box-shadow:var(--bs-toast-box-shadow);border-radius:var(--bs-toast-border-radius)}.toast.showing{opacity:0}.toast:not(.show){display:none}.toast-container{--bs-toast-zindex: 1090;position:absolute;z-index:var(--bs-toast-zindex);width:max-content;width:-webkit-max-content;width:-moz-max-content;width:-ms-max-content;width:-o-max-content;max-width:100%;pointer-events:none}.toast-container>:not(:last-child){margin-bottom:var(--bs-toast-spacing)}.toast-header{display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;padding:var(--bs-toast-padding-y) var(--bs-toast-padding-x);color:var(--bs-toast-header-color);background-color:var(--bs-toast-header-bg);background-clip:padding-box;border-bottom:var(--bs-toast-border-width) solid var(--bs-toast-header-border-color);border-top-left-radius:calc(var(--bs-toast-border-radius) - var(--bs-toast-border-width));border-top-right-radius:calc(var(--bs-toast-border-radius) - var(--bs-toast-border-width))}.toast-header .btn-close{margin-right:calc(-.5 * 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reduce){.btn{transition:none}}.btn:hover{color:var(--bs-btn-hover-color);background-color:var(--bs-btn-hover-bg);border-color:var(--bs-btn-hover-border-color)}.btn-check+.btn:hover{color:var(--bs-btn-color);background-color:var(--bs-btn-bg);border-color:var(--bs-btn-border-color)}.btn:focus-visible{color:var(--bs-btn-hover-color);background-color:var(--bs-btn-hover-bg);border-color:var(--bs-btn-hover-border-color);outline:0;box-shadow:var(--bs-btn-focus-box-shadow)}.btn-check:focus-visible+.btn{border-color:var(--bs-btn-hover-border-color);outline:0;box-shadow:var(--bs-btn-focus-box-shadow)}.btn-check:checked+.btn,:not(.btn-check)+.btn:active,.btn:first-child:active,.btn.active,.btn.show{color:var(--bs-btn-active-color);background-color:var(--bs-btn-active-bg);border-color:var(--bs-btn-active-border-color)}.btn-check:checked+.btn:focus-visible,:not(.btn-check)+.btn:active:focus-visible,.btn:first-child:active:focus-visible,.btn.active:focus-visible,.btn.show:focus-visible{box-shadow:var(--bs-btn-focus-box-shadow)}.btn:disabled,.btn.disabled,fieldset:disabled 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#fff;--bs-btn-disabled-bg: #198754;--bs-btn-disabled-border-color: #198754}.btn-info{--bs-btn-color: #000;--bs-btn-bg: #0dcaf0;--bs-btn-border-color: #0dcaf0;--bs-btn-hover-color: #000;--bs-btn-hover-bg: #31d2f2;--bs-btn-hover-border-color: #25cff2;--bs-btn-focus-shadow-rgb: 11,172,204;--bs-btn-active-color: #000;--bs-btn-active-bg: #3dd5f3;--bs-btn-active-border-color: #25cff2;--bs-btn-active-shadow: inset 0 3px 5px rgba(0,0,0,0.125);--bs-btn-disabled-color: #000;--bs-btn-disabled-bg: #0dcaf0;--bs-btn-disabled-border-color: #0dcaf0}.btn-warning{--bs-btn-color: #000;--bs-btn-bg: #ffc107;--bs-btn-border-color: #ffc107;--bs-btn-hover-color: #000;--bs-btn-hover-bg: #ffca2c;--bs-btn-hover-border-color: #ffc720;--bs-btn-focus-shadow-rgb: 217,164,6;--bs-btn-active-color: #000;--bs-btn-active-bg: #ffcd39;--bs-btn-active-border-color: #ffc720;--bs-btn-active-shadow: inset 0 3px 5px rgba(0,0,0,0.125);--bs-btn-disabled-color: #000;--bs-btn-disabled-bg: #ffc107;--bs-btn-disabled-border-color: 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#18416A;--bs-btn-hover-color: #fff;--bs-btn-hover-bg: #18416A;--bs-btn-hover-border-color: #18416A;--bs-btn-focus-shadow-rgb: 24,65,106;--bs-btn-active-color: #fff;--bs-btn-active-bg: #18416A;--bs-btn-active-border-color: #18416A;--bs-btn-active-shadow: inset 0 3px 5px rgba(0,0,0,0.125);--bs-btn-disabled-color: #18416A;--bs-btn-disabled-bg: transparent;--bs-btn-disabled-border-color: #18416A;--bs-btn-bg: transparent;--bs-gradient: none}.btn-outline-secondary{--bs-btn-color: #6c757d;--bs-btn-border-color: #6c757d;--bs-btn-hover-color: #fff;--bs-btn-hover-bg: #6c757d;--bs-btn-hover-border-color: #6c757d;--bs-btn-focus-shadow-rgb: 108,117,125;--bs-btn-active-color: #fff;--bs-btn-active-bg: #6c757d;--bs-btn-active-border-color: #6c757d;--bs-btn-active-shadow: inset 0 3px 5px rgba(0,0,0,0.125);--bs-btn-disabled-color: #6c757d;--bs-btn-disabled-bg: transparent;--bs-btn-disabled-border-color: #6c757d;--bs-btn-bg: transparent;--bs-gradient: none}.btn-outline-success{--bs-btn-color: #198754;--bs-btn-border-color: #198754;--bs-btn-hover-color: #fff;--bs-btn-hover-bg: #198754;--bs-btn-hover-border-color: #198754;--bs-btn-focus-shadow-rgb: 25,135,84;--bs-btn-active-color: #fff;--bs-btn-active-bg: #198754;--bs-btn-active-border-color: #198754;--bs-btn-active-shadow: inset 0 3px 5px rgba(0,0,0,0.125);--bs-btn-disabled-color: #198754;--bs-btn-disabled-bg: transparent;--bs-btn-disabled-border-color: #198754;--bs-btn-bg: transparent;--bs-gradient: none}.btn-outline-info{--bs-btn-color: #0dcaf0;--bs-btn-border-color: #0dcaf0;--bs-btn-hover-color: #000;--bs-btn-hover-bg: #0dcaf0;--bs-btn-hover-border-color: #0dcaf0;--bs-btn-focus-shadow-rgb: 13,202,240;--bs-btn-active-color: #000;--bs-btn-active-bg: #0dcaf0;--bs-btn-active-border-color: #0dcaf0;--bs-btn-active-shadow: inset 0 3px 5px rgba(0,0,0,0.125);--bs-btn-disabled-color: #0dcaf0;--bs-btn-disabled-bg: transparent;--bs-btn-disabled-border-color: #0dcaf0;--bs-btn-bg: transparent;--bs-gradient: none}.btn-outline-warning{--bs-btn-color: #ffc107;--bs-btn-border-color: #ffc107;--bs-btn-hover-color: #000;--bs-btn-hover-bg: #ffc107;--bs-btn-hover-border-color: #ffc107;--bs-btn-focus-shadow-rgb: 255,193,7;--bs-btn-active-color: #000;--bs-btn-active-bg: #ffc107;--bs-btn-active-border-color: #ffc107;--bs-btn-active-shadow: inset 0 3px 5px rgba(0,0,0,0.125);--bs-btn-disabled-color: #ffc107;--bs-btn-disabled-bg: transparent;--bs-btn-disabled-border-color: #ffc107;--bs-btn-bg: transparent;--bs-gradient: none}.btn-outline-danger{--bs-btn-color: #dc3545;--bs-btn-border-color: #dc3545;--bs-btn-hover-color: #fff;--bs-btn-hover-bg: #dc3545;--bs-btn-hover-border-color: #dc3545;--bs-btn-focus-shadow-rgb: 220,53,69;--bs-btn-active-color: #fff;--bs-btn-active-bg: #dc3545;--bs-btn-active-border-color: #dc3545;--bs-btn-active-shadow: inset 0 3px 5px rgba(0,0,0,0.125);--bs-btn-disabled-color: #dc3545;--bs-btn-disabled-bg: transparent;--bs-btn-disabled-border-color: #dc3545;--bs-btn-bg: transparent;--bs-gradient: none}.btn-outline-light{--bs-btn-color: #f8f9fa;--bs-btn-border-color: #f8f9fa;--bs-btn-hover-color: #000;--bs-btn-hover-bg: #f8f9fa;--bs-btn-hover-border-color: #f8f9fa;--bs-btn-focus-shadow-rgb: 248,249,250;--bs-btn-active-color: #000;--bs-btn-active-bg: #f8f9fa;--bs-btn-active-border-color: #f8f9fa;--bs-btn-active-shadow: inset 0 3px 5px rgba(0,0,0,0.125);--bs-btn-disabled-color: #f8f9fa;--bs-btn-disabled-bg: transparent;--bs-btn-disabled-border-color: #f8f9fa;--bs-btn-bg: transparent;--bs-gradient: none}.btn-outline-dark{--bs-btn-color: #212529;--bs-btn-border-color: #212529;--bs-btn-hover-color: #fff;--bs-btn-hover-bg: #212529;--bs-btn-hover-border-color: #212529;--bs-btn-focus-shadow-rgb: 33,37,41;--bs-btn-active-color: #fff;--bs-btn-active-bg: #212529;--bs-btn-active-border-color: #212529;--bs-btn-active-shadow: inset 0 3px 5px rgba(0,0,0,0.125);--bs-btn-disabled-color: #212529;--bs-btn-disabled-bg: transparent;--bs-btn-disabled-border-color: #212529;--bs-btn-bg: transparent;--bs-gradient: none}.btn-link{--bs-btn-font-weight: 400;--bs-btn-color: var(--bs-link-color);--bs-btn-bg: transparent;--bs-btn-border-color: transparent;--bs-btn-hover-color: var(--bs-link-hover-color);--bs-btn-hover-border-color: transparent;--bs-btn-active-color: var(--bs-link-hover-color);--bs-btn-active-border-color: transparent;--bs-btn-disabled-color: #6c757d;--bs-btn-disabled-border-color: transparent;--bs-btn-box-shadow: 0 0 0 #000;--bs-btn-focus-shadow-rgb: 59,94,128;text-decoration:underline;-webkit-text-decoration:underline;-moz-text-decoration:underline;-ms-text-decoration:underline;-o-text-decoration:underline}.btn-link:focus-visible{color:var(--bs-btn-color)}.btn-link:hover{color:var(--bs-btn-hover-color)}.btn-lg,.btn-group-lg>.btn{--bs-btn-padding-y: .5rem;--bs-btn-padding-x: 1rem;--bs-btn-font-size:1.25rem;--bs-btn-border-radius: var(--bs-border-radius-lg)}.btn-sm,.btn-group-sm>.btn{--bs-btn-padding-y: .25rem;--bs-btn-padding-x: .5rem;--bs-btn-font-size:.875rem;--bs-btn-border-radius: var(--bs-border-radius-sm)}.fade{transition:opacity 0.15s linear}@media (prefers-reduced-motion: reduce){.fade{transition:none}}.fade:not(.show){opacity:0}.collapse:not(.show){display:none}.collapsing{height:0;overflow:hidden;transition:height 0.35s ease}@media (prefers-reduced-motion: reduce){.collapsing{transition:none}}.collapsing.collapse-horizontal{width:0;height:auto;transition:width 0.35s ease}@media (prefers-reduced-motion: reduce){.collapsing.collapse-horizontal{transition:none}}.dropup,.dropend,.dropdown,.dropstart,.dropup-center,.dropdown-center{position:relative}.dropdown-toggle{white-space:nowrap}.dropdown-toggle::after{display:inline-block;margin-left:.255em;vertical-align:.255em;content:"";border-top:.3em solid;border-right:.3em solid transparent;border-bottom:0;border-left:.3em solid transparent}.dropdown-toggle:empty::after{margin-left:0}.dropdown-menu{--bs-dropdown-zindex: 1000;--bs-dropdown-min-width: 10rem;--bs-dropdown-padding-x: 0;--bs-dropdown-padding-y: .5rem;--bs-dropdown-spacer: .125rem;--bs-dropdown-font-size:1rem;--bs-dropdown-color: var(--bs-body-color);--bs-dropdown-bg: var(--bs-body-bg);--bs-dropdown-border-color: var(--bs-border-color-translucent);--bs-dropdown-border-radius: var(--bs-border-radius);--bs-dropdown-border-width: var(--bs-border-width);--bs-dropdown-inner-border-radius: calc(var(--bs-border-radius) - var(--bs-border-width));--bs-dropdown-divider-bg: var(--bs-border-color-translucent);--bs-dropdown-divider-margin-y: .5rem;--bs-dropdown-box-shadow: 0 0.5rem 1rem rgba(0,0,0,0.15);--bs-dropdown-link-color: var(--bs-body-color);--bs-dropdown-link-hover-color: var(--bs-body-color);--bs-dropdown-link-hover-bg: var(--bs-tertiary-bg);--bs-dropdown-link-active-color: #fff;--bs-dropdown-link-active-bg: #18416A;--bs-dropdown-link-disabled-color: var(--bs-tertiary-color);--bs-dropdown-item-padding-x: 1rem;--bs-dropdown-item-padding-y: .25rem;--bs-dropdown-header-color: #6c757d;--bs-dropdown-header-padding-x: 1rem;--bs-dropdown-header-padding-y: .5rem;position:absolute;z-index:var(--bs-dropdown-zindex);display:none;min-width:var(--bs-dropdown-min-width);padding:var(--bs-dropdown-padding-y) var(--bs-dropdown-padding-x);margin:0;font-size:var(--bs-dropdown-font-size);color:var(--bs-dropdown-color);text-align:left;list-style:none;background-color:var(--bs-dropdown-bg);background-clip:padding-box;border:var(--bs-dropdown-border-width) solid var(--bs-dropdown-border-color);border-radius:var(--bs-dropdown-border-radius)}.dropdown-menu[data-bs-popper]{top:100%;left:0;margin-top:var(--bs-dropdown-spacer)}.dropdown-menu-start{--bs-position: start}.dropdown-menu-start[data-bs-popper]{right:auto;left:0}.dropdown-menu-end{--bs-position: end}.dropdown-menu-end[data-bs-popper]{right:0;left:auto}@media (min-width: 576px){.dropdown-menu-sm-start{--bs-position: start}.dropdown-menu-sm-start[data-bs-popper]{right:auto;left:0}.dropdown-menu-sm-end{--bs-position: end}.dropdown-menu-sm-end[data-bs-popper]{right:0;left:auto}}@media (min-width: 768px){.dropdown-menu-md-start{--bs-position: start}.dropdown-menu-md-start[data-bs-popper]{right:auto;left:0}.dropdown-menu-md-end{--bs-position: end}.dropdown-menu-md-end[data-bs-popper]{right:0;left:auto}}@media (min-width: 992px){.dropdown-menu-lg-start{--bs-position: start}.dropdown-menu-lg-start[data-bs-popper]{right:auto;left:0}.dropdown-menu-lg-end{--bs-position: end}.dropdown-menu-lg-end[data-bs-popper]{right:0;left:auto}}@media (min-width: 1200px){.dropdown-menu-xl-start{--bs-position: start}.dropdown-menu-xl-start[data-bs-popper]{right:auto;left:0}.dropdown-menu-xl-end{--bs-position: end}.dropdown-menu-xl-end[data-bs-popper]{right:0;left:auto}}@media (min-width: 1400px){.dropdown-menu-xxl-start{--bs-position: start}.dropdown-menu-xxl-start[data-bs-popper]{right:auto;left:0}.dropdown-menu-xxl-end{--bs-position: end}.dropdown-menu-xxl-end[data-bs-popper]{right:0;left:auto}}.dropup 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var(--bs-dropdown-header-padding-x);margin-bottom:0;font-size:.875rem;color:var(--bs-dropdown-header-color);white-space:nowrap}.dropdown-item-text{display:block;padding:var(--bs-dropdown-item-padding-y) var(--bs-dropdown-item-padding-x);color:var(--bs-dropdown-link-color)}.dropdown-menu-dark{--bs-dropdown-color: #dee2e6;--bs-dropdown-bg: #343a40;--bs-dropdown-border-color: var(--bs-border-color-translucent);--bs-dropdown-box-shadow: ;--bs-dropdown-link-color: #dee2e6;--bs-dropdown-link-hover-color: #fff;--bs-dropdown-divider-bg: var(--bs-border-color-translucent);--bs-dropdown-link-hover-bg: rgba(255,255,255,0.15);--bs-dropdown-link-active-color: #fff;--bs-dropdown-link-active-bg: #18416A;--bs-dropdown-link-disabled-color: #adb5bd;--bs-dropdown-header-color: #adb5bd}.btn-group,.btn-group-vertical{position:relative;display:inline-flex;vertical-align:middle}.btn-group>.btn,.btn-group-vertical>.btn{position:relative;flex:1 1 auto;-webkit-flex:1 1 auto}.btn-group>.btn-check:checked+.btn,.btn-group>.btn-check:focus+.btn,.btn-group>.btn:hover,.btn-group>.btn:focus,.btn-group>.btn:active,.btn-group>.btn.active,.btn-group-vertical>.btn-check:checked+.btn,.btn-group-vertical>.btn-check:focus+.btn,.btn-group-vertical>.btn:hover,.btn-group-vertical>.btn:focus,.btn-group-vertical>.btn:active,.btn-group-vertical>.btn.active{z-index:1}.btn-toolbar{display:flex;display:-webkit-flex;flex-wrap:wrap;-webkit-flex-wrap:wrap;justify-content:flex-start;-webkit-justify-content:flex-start}.btn-toolbar .input-group{width:auto}.btn-group{border-radius:var(--bs-border-radius)}.btn-group>:not(.btn-check:first-child)+.btn,.btn-group>.btn-group:not(:first-child){margin-left:calc(var(--bs-border-width) * -1)}.btn-group>.btn:not(:last-child):not(.dropdown-toggle),.btn-group>.btn.dropdown-toggle-split:first-child,.btn-group>.btn-group:not(:last-child)>.btn{border-top-right-radius:0;border-bottom-right-radius:0}.btn-group>.btn:nth-child(n + 3),.btn-group>:not(.btn-check)+.btn,.btn-group>.btn-group:not(:first-child)>.btn{border-top-left-radius:0;border-bottom-left-radius:0}.dropdown-toggle-split{padding-right:.5625rem;padding-left:.5625rem}.dropdown-toggle-split::after,.dropup .dropdown-toggle-split::after,.dropend .dropdown-toggle-split::after{margin-left:0}.dropstart .dropdown-toggle-split::before{margin-right:0}.btn-sm+.dropdown-toggle-split,.btn-group-sm>.btn+.dropdown-toggle-split{padding-right:.375rem;padding-left:.375rem}.btn-lg+.dropdown-toggle-split,.btn-group-lg>.btn+.dropdown-toggle-split{padding-right:.75rem;padding-left:.75rem}.btn-group-vertical{flex-direction:column;-webkit-flex-direction:column;align-items:flex-start;-webkit-align-items:flex-start;justify-content:center;-webkit-justify-content:center}.btn-group-vertical>.btn,.btn-group-vertical>.btn-group{width:100%}.btn-group-vertical>.btn:not(:first-child),.btn-group-vertical>.btn-group:not(:first-child){margin-top:calc(var(--bs-border-width) * -1)}.btn-group-vertical>.btn:not(:last-child):not(.dropdown-toggle),.btn-group-vertical>.btn-group:not(:last-child)>.btn{border-bottom-right-radius:0;border-bottom-left-radius:0}.btn-group-vertical>.btn~.btn,.btn-group-vertical>.btn-group:not(:first-child)>.btn{border-top-left-radius:0;border-top-right-radius:0}.nav{--bs-nav-link-padding-x: 1rem;--bs-nav-link-padding-y: .5rem;--bs-nav-link-font-weight: ;--bs-nav-link-color: var(--bs-link-color);--bs-nav-link-hover-color: var(--bs-link-hover-color);--bs-nav-link-disabled-color: var(--bs-secondary-color);display:flex;display:-webkit-flex;flex-wrap:wrap;-webkit-flex-wrap:wrap;padding-left:0;margin-bottom:0;list-style:none}.nav-link{display:block;padding:var(--bs-nav-link-padding-y) var(--bs-nav-link-padding-x);font-size:var(--bs-nav-link-font-size);font-weight:var(--bs-nav-link-font-weight);color:var(--bs-nav-link-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background:none;border:0;transition:color 0.15s ease-in-out,background-color 0.15s ease-in-out,border-color 0.15s ease-in-out}@media (prefers-reduced-motion: reduce){.nav-link{transition:none}}.nav-link:hover,.nav-link:focus{color:var(--bs-nav-link-hover-color)}.nav-link:focus-visible{outline:0;box-shadow:0 0 0 .25rem rgba(24,65,106,0.25)}.nav-link.disabled,.nav-link:disabled{color:var(--bs-nav-link-disabled-color);pointer-events:none;cursor:default}.nav-tabs{--bs-nav-tabs-border-width: var(--bs-border-width);--bs-nav-tabs-border-color: var(--bs-border-color);--bs-nav-tabs-border-radius: var(--bs-border-radius);--bs-nav-tabs-link-hover-border-color: var(--bs-secondary-bg) var(--bs-secondary-bg) var(--bs-border-color);--bs-nav-tabs-link-active-color: var(--bs-emphasis-color);--bs-nav-tabs-link-active-bg: var(--bs-body-bg);--bs-nav-tabs-link-active-border-color: var(--bs-border-color) var(--bs-border-color) var(--bs-body-bg);border-bottom:var(--bs-nav-tabs-border-width) solid var(--bs-nav-tabs-border-color)}.nav-tabs .nav-link{margin-bottom:calc(-1 * var(--bs-nav-tabs-border-width));border:var(--bs-nav-tabs-border-width) solid transparent;border-top-left-radius:var(--bs-nav-tabs-border-radius);border-top-right-radius:var(--bs-nav-tabs-border-radius)}.nav-tabs .nav-link:hover,.nav-tabs .nav-link:focus{isolation:isolate;border-color:var(--bs-nav-tabs-link-hover-border-color)}.nav-tabs .nav-link.active,.nav-tabs .nav-item.show .nav-link{color:var(--bs-nav-tabs-link-active-color);background-color:var(--bs-nav-tabs-link-active-bg);border-color:var(--bs-nav-tabs-link-active-border-color)}.nav-tabs .dropdown-menu{margin-top:calc(-1 * 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992px){.navbar-expand-lg{flex-wrap:nowrap;-webkit-flex-wrap:nowrap;justify-content:flex-start;-webkit-justify-content:flex-start}.navbar-expand-lg .navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand-lg .navbar-nav .dropdown-menu{position:absolute}.navbar-expand-lg .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand-lg .navbar-nav-scroll{overflow:visible}.navbar-expand-lg .navbar-collapse{display:flex !important;display:-webkit-flex !important;flex-basis:auto;-webkit-flex-basis:auto}.navbar-expand-lg .navbar-toggler{display:none}.navbar-expand-lg .offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:transparent !important;border:0 !important;transform:none !important;transition:none}.navbar-expand-lg .offcanvas .offcanvas-header{display:none}.navbar-expand-lg .offcanvas 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.offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:transparent !important;border:0 !important;transform:none !important;transition:none}.navbar-expand-xxl .offcanvas .offcanvas-header{display:none}.navbar-expand-xxl .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}}.navbar-expand{flex-wrap:nowrap;-webkit-flex-wrap:nowrap;justify-content:flex-start;-webkit-justify-content:flex-start}.navbar-expand .navbar-nav{flex-direction:row;-webkit-flex-direction:row}.navbar-expand .navbar-nav .dropdown-menu{position:absolute}.navbar-expand .navbar-nav .nav-link{padding-right:var(--bs-navbar-nav-link-padding-x);padding-left:var(--bs-navbar-nav-link-padding-x)}.navbar-expand .navbar-nav-scroll{overflow:visible}.navbar-expand .navbar-collapse{display:flex !important;display:-webkit-flex !important;flex-basis:auto;-webkit-flex-basis:auto}.navbar-expand .navbar-toggler{display:none}.navbar-expand .offcanvas{position:static;z-index:auto;flex-grow:1;-webkit-flex-grow:1;width:auto !important;height:auto !important;visibility:visible !important;background-color:transparent !important;border:0 !important;transform:none !important;transition:none}.navbar-expand .offcanvas .offcanvas-header{display:none}.navbar-expand .offcanvas .offcanvas-body{display:flex;display:-webkit-flex;flex-grow:0;-webkit-flex-grow:0;padding:0;overflow-y:visible}.navbar-dark,.navbar[data-bs-theme="dark"]{--bs-navbar-color: rgba(var(--bs-emphasis-color-rgb), 0.55);--bs-navbar-hover-color: rgba(var(--bs-emphasis-color-rgb), 0.75);--bs-navbar-disabled-color: rgba(var(--bs-emphasis-color-rgb), 0.25);--bs-navbar-active-color: rgba(var(--bs-emphasis-color-rgb), 1);--bs-navbar-brand-color: rgba(var(--bs-emphasis-color-rgb), 1);--bs-navbar-brand-hover-color: rgba(var(--bs-emphasis-color-rgb), 1);--bs-navbar-toggler-border-color: rgba(var(--bs-emphasis-color-rgb), 0.1);--bs-navbar-toggler-icon-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 30 30'%3e%3cpath stroke='rgba%28255,255,255,0.75%29' stroke-linecap='round' stroke-miterlimit='10' stroke-width='2' d='M4 7h22M4 15h22M4 23h22'/%3e%3c/svg%3e")}[data-bs-theme="dark"] .navbar-toggler-icon{--bs-navbar-toggler-icon-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 30 30'%3e%3cpath stroke='rgba%28255,255,255,0.75%29' stroke-linecap='round' stroke-miterlimit='10' stroke-width='2' d='M4 7h22M4 15h22M4 23h22'/%3e%3c/svg%3e")}.card{--bs-card-spacer-y: 1rem;--bs-card-spacer-x: 1rem;--bs-card-title-spacer-y: .5rem;--bs-card-title-color: ;--bs-card-subtitle-color: ;--bs-card-border-width: var(--bs-border-width);--bs-card-border-color: var(--bs-border-color-translucent);--bs-card-border-radius: var(--bs-border-radius);--bs-card-box-shadow: ;--bs-card-inner-border-radius: calc(var(--bs-border-radius) - (var(--bs-border-width)));--bs-card-cap-padding-y: .5rem;--bs-card-cap-padding-x: 1rem;--bs-card-cap-bg: rgba(var(--bs-body-color-rgb), 0.03);--bs-card-cap-color: ;--bs-card-height: ;--bs-card-color: ;--bs-card-bg: var(--bs-body-bg);--bs-card-img-overlay-padding: 1rem;--bs-card-group-margin: .75rem;position:relative;display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;min-width:0;height:var(--bs-card-height);color:var(--bs-body-color);word-wrap:break-word;background-color:var(--bs-card-bg);background-clip:border-box;border:var(--bs-card-border-width) solid var(--bs-card-border-color);border-radius:var(--bs-card-border-radius)}.card>hr{margin-right:0;margin-left:0}.card>.list-group{border-top:inherit;border-bottom:inherit}.card>.list-group:first-child{border-top-width:0;border-top-left-radius:var(--bs-card-inner-border-radius);border-top-right-radius:var(--bs-card-inner-border-radius)}.card>.list-group:last-child{border-bottom-width:0;border-bottom-right-radius:var(--bs-card-inner-border-radius);border-bottom-left-radius:var(--bs-card-inner-border-radius)}.card>.card-header+.list-group,.card>.list-group+.card-footer{border-top:0}.card-body{flex:1 1 auto;-webkit-flex:1 1 auto;padding:var(--bs-card-spacer-y) var(--bs-card-spacer-x);color:var(--bs-card-color)}.card-title{margin-bottom:var(--bs-card-title-spacer-y);color:var(--bs-card-title-color)}.card-subtitle{margin-top:calc(-.5 * 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url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%230a1a2a'%3e%3cpath fill-rule='evenodd' d='M1.646 4.646a.5.5 0 0 1 .708 0L8 10.293l5.646-5.647a.5.5 0 0 1 .708.708l-6 6a.5.5 0 0 1-.708 0l-6-6a.5.5 0 0 1 0-.708z'/%3e%3c/svg%3e");--bs-accordion-btn-focus-border-color: #8ca0b5;--bs-accordion-btn-focus-box-shadow: 0 0 0 .25rem rgba(24,65,106,0.25);--bs-accordion-body-padding-x: 1.25rem;--bs-accordion-body-padding-y: 1rem;--bs-accordion-active-color: var(--bs-primary-text-emphasis);--bs-accordion-active-bg: var(--bs-primary-bg-subtle)}.accordion-button{position:relative;display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;width:100%;padding:var(--bs-accordion-btn-padding-y) var(--bs-accordion-btn-padding-x);font-size:1rem;color:var(--bs-accordion-btn-color);text-align:left;background-color:var(--bs-accordion-btn-bg);border:0;border-radius:0;overflow-anchor:none;transition:var(--bs-accordion-transition)}@media 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.accordion-button.collapsed{border-bottom-right-radius:var(--bs-accordion-inner-border-radius);border-bottom-left-radius:var(--bs-accordion-inner-border-radius)}.accordion-item:last-of-type .accordion-collapse{border-bottom-right-radius:var(--bs-accordion-border-radius);border-bottom-left-radius:var(--bs-accordion-border-radius)}.accordion-body{padding:var(--bs-accordion-body-padding-y) var(--bs-accordion-body-padding-x)}.accordion-flush .accordion-collapse{border-width:0}.accordion-flush .accordion-item{border-right:0;border-left:0;border-radius:0}.accordion-flush .accordion-item:first-child{border-top:0}.accordion-flush .accordion-item:last-child{border-bottom:0}.accordion-flush .accordion-item .accordion-button,.accordion-flush .accordion-item .accordion-button.collapsed{border-radius:0}[data-bs-theme="dark"] .accordion-button::after{--bs-accordion-btn-icon: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23748da6'%3e%3cpath 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var(--bs-breadcrumb-padding-x);margin-bottom:var(--bs-breadcrumb-margin-bottom);font-size:var(--bs-breadcrumb-font-size);list-style:none;background-color:var(--bs-breadcrumb-bg);border-radius:var(--bs-breadcrumb-border-radius)}.breadcrumb-item+.breadcrumb-item{padding-left:var(--bs-breadcrumb-item-padding-x)}.breadcrumb-item+.breadcrumb-item::before{float:left;padding-right:var(--bs-breadcrumb-item-padding-x);color:var(--bs-breadcrumb-divider-color);content:var(--bs-breadcrumb-divider, "/") /* rtl: var(--bs-breadcrumb-divider, "/") */}.breadcrumb-item.active{color:var(--bs-breadcrumb-item-active-color)}.pagination{--bs-pagination-padding-x: .75rem;--bs-pagination-padding-y: .375rem;--bs-pagination-font-size:1rem;--bs-pagination-color: var(--bs-link-color);--bs-pagination-bg: var(--bs-body-bg);--bs-pagination-border-width: var(--bs-border-width);--bs-pagination-border-color: var(--bs-border-color);--bs-pagination-border-radius: var(--bs-border-radius);--bs-pagination-hover-color: var(--bs-link-hover-color);--bs-pagination-hover-bg: var(--bs-tertiary-bg);--bs-pagination-hover-border-color: var(--bs-border-color);--bs-pagination-focus-color: var(--bs-link-hover-color);--bs-pagination-focus-bg: var(--bs-secondary-bg);--bs-pagination-focus-box-shadow: 0 0 0 .25rem rgba(24,65,106,0.25);--bs-pagination-active-color: #fff;--bs-pagination-active-bg: #18416A;--bs-pagination-active-border-color: #18416A;--bs-pagination-disabled-color: var(--bs-secondary-color);--bs-pagination-disabled-bg: var(--bs-secondary-bg);--bs-pagination-disabled-border-color: var(--bs-border-color);display:flex;display:-webkit-flex;padding-left:0;list-style:none}.page-link{position:relative;display:block;padding:var(--bs-pagination-padding-y) var(--bs-pagination-padding-x);font-size:var(--bs-pagination-font-size);color:var(--bs-pagination-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-pagination-bg);border:var(--bs-pagination-border-width) solid var(--bs-pagination-border-color);transition:color 0.15s ease-in-out,background-color 0.15s ease-in-out,border-color 0.15s ease-in-out,box-shadow 0.15s ease-in-out}@media (prefers-reduced-motion: reduce){.page-link{transition:none}}.page-link:hover{z-index:2;color:var(--bs-pagination-hover-color);background-color:var(--bs-pagination-hover-bg);border-color:var(--bs-pagination-hover-border-color)}.page-link:focus{z-index:3;color:var(--bs-pagination-focus-color);background-color:var(--bs-pagination-focus-bg);outline:0;box-shadow:var(--bs-pagination-focus-box-shadow)}.page-link.active,.active>.page-link{z-index:3;color:var(--bs-pagination-active-color);background-color:var(--bs-pagination-active-bg);border-color:var(--bs-pagination-active-border-color)}.page-link.disabled,.disabled>.page-link{color:var(--bs-pagination-disabled-color);pointer-events:none;background-color:var(--bs-pagination-disabled-bg);border-color:var(--bs-pagination-disabled-border-color)}.page-item:not(:first-child) .page-link{margin-left:calc(var(--bs-border-width) * -1)}.page-item:first-child .page-link{border-top-left-radius:var(--bs-pagination-border-radius);border-bottom-left-radius:var(--bs-pagination-border-radius)}.page-item:last-child .page-link{border-top-right-radius:var(--bs-pagination-border-radius);border-bottom-right-radius:var(--bs-pagination-border-radius)}.pagination-lg{--bs-pagination-padding-x: 1.5rem;--bs-pagination-padding-y: .75rem;--bs-pagination-font-size:1.25rem;--bs-pagination-border-radius: var(--bs-border-radius-lg)}.pagination-sm{--bs-pagination-padding-x: .5rem;--bs-pagination-padding-y: .25rem;--bs-pagination-font-size:.875rem;--bs-pagination-border-radius: var(--bs-border-radius-sm)}.badge{--bs-badge-padding-x: .65em;--bs-badge-padding-y: .35em;--bs-badge-font-size:.75em;--bs-badge-font-weight: 700;--bs-badge-color: #fff;--bs-badge-border-radius: var(--bs-border-radius);display:inline-block;padding:var(--bs-badge-padding-y) var(--bs-badge-padding-x);font-size:var(--bs-badge-font-size);font-weight:var(--bs-badge-font-weight);line-height:1;color:var(--bs-badge-color);text-align:center;white-space:nowrap;vertical-align:baseline;border-radius:var(--bs-badge-border-radius)}.badge:empty{display:none}.btn .badge{position:relative;top:-1px}.alert{--bs-alert-bg: transparent;--bs-alert-padding-x: 1rem;--bs-alert-padding-y: 1rem;--bs-alert-margin-bottom: 1rem;--bs-alert-color: inherit;--bs-alert-border-color: transparent;--bs-alert-border: var(--bs-border-width) solid var(--bs-alert-border-color);--bs-alert-border-radius: var(--bs-border-radius);--bs-alert-link-color: inherit;position:relative;padding:var(--bs-alert-padding-y) var(--bs-alert-padding-x);margin-bottom:var(--bs-alert-margin-bottom);color:var(--bs-alert-color);background-color:var(--bs-alert-bg);border:var(--bs-alert-border);border-radius:var(--bs-alert-border-radius)}.alert-heading{color:inherit}.alert-link{font-weight:700;color:var(--bs-alert-link-color)}.alert-dismissible{padding-right:3rem}.alert-dismissible .btn-close{position:absolute;top:0;right:0;z-index:2;padding:1.25rem 1rem}.alert-default{--bs-alert-color: var(--bs-default-text-emphasis);--bs-alert-bg: var(--bs-default-bg-subtle);--bs-alert-border-color: var(--bs-default-border-subtle);--bs-alert-link-color: var(--bs-default-text-emphasis)}.alert-primary{--bs-alert-color: var(--bs-primary-text-emphasis);--bs-alert-bg: var(--bs-primary-bg-subtle);--bs-alert-border-color: var(--bs-primary-border-subtle);--bs-alert-link-color: var(--bs-primary-text-emphasis)}.alert-secondary{--bs-alert-color: var(--bs-secondary-text-emphasis);--bs-alert-bg: var(--bs-secondary-bg-subtle);--bs-alert-border-color: var(--bs-secondary-border-subtle);--bs-alert-link-color: var(--bs-secondary-text-emphasis)}.alert-success{--bs-alert-color: var(--bs-success-text-emphasis);--bs-alert-bg: var(--bs-success-bg-subtle);--bs-alert-border-color: var(--bs-success-border-subtle);--bs-alert-link-color: var(--bs-success-text-emphasis)}.alert-info{--bs-alert-color: var(--bs-info-text-emphasis);--bs-alert-bg: var(--bs-info-bg-subtle);--bs-alert-border-color: var(--bs-info-border-subtle);--bs-alert-link-color: var(--bs-info-text-emphasis)}.alert-warning{--bs-alert-color: var(--bs-warning-text-emphasis);--bs-alert-bg: var(--bs-warning-bg-subtle);--bs-alert-border-color: var(--bs-warning-border-subtle);--bs-alert-link-color: var(--bs-warning-text-emphasis)}.alert-danger{--bs-alert-color: var(--bs-danger-text-emphasis);--bs-alert-bg: var(--bs-danger-bg-subtle);--bs-alert-border-color: var(--bs-danger-border-subtle);--bs-alert-link-color: var(--bs-danger-text-emphasis)}.alert-light{--bs-alert-color: var(--bs-light-text-emphasis);--bs-alert-bg: var(--bs-light-bg-subtle);--bs-alert-border-color: var(--bs-light-border-subtle);--bs-alert-link-color: var(--bs-light-text-emphasis)}.alert-dark{--bs-alert-color: var(--bs-dark-text-emphasis);--bs-alert-bg: var(--bs-dark-bg-subtle);--bs-alert-border-color: var(--bs-dark-border-subtle);--bs-alert-link-color: var(--bs-dark-text-emphasis)}@keyframes progress-bar-stripes{0%{background-position-x:1rem}}.progress,.progress-stacked{--bs-progress-height: 1rem;--bs-progress-font-size:.75rem;--bs-progress-bg: var(--bs-secondary-bg);--bs-progress-border-radius: var(--bs-border-radius);--bs-progress-box-shadow: var(--bs-box-shadow-inset);--bs-progress-bar-color: #fff;--bs-progress-bar-bg: #18416A;--bs-progress-bar-transition: width 0.6s ease;display:flex;display:-webkit-flex;height:var(--bs-progress-height);overflow:hidden;font-size:var(--bs-progress-font-size);background-color:var(--bs-progress-bg);border-radius:var(--bs-progress-border-radius)}.progress-bar{display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;justify-content:center;-webkit-justify-content:center;overflow:hidden;color:var(--bs-progress-bar-color);text-align:center;white-space:nowrap;background-color:var(--bs-progress-bar-bg);transition:var(--bs-progress-bar-transition)}@media (prefers-reduced-motion: reduce){.progress-bar{transition:none}}.progress-bar-striped{background-image:linear-gradient(45deg, rgba(255,255,255,0.15) 25%, transparent 25%, transparent 50%, rgba(255,255,255,0.15) 50%, rgba(255,255,255,0.15) 75%, transparent 75%, transparent);background-size:var(--bs-progress-height) var(--bs-progress-height)}.progress-stacked>.progress{overflow:visible}.progress-stacked>.progress>.progress-bar{width:100%}.progress-bar-animated{animation:1s linear infinite progress-bar-stripes}@media (prefers-reduced-motion: reduce){.progress-bar-animated{animation:none}}.list-group{--bs-list-group-color: var(--bs-body-color);--bs-list-group-bg: var(--bs-body-bg);--bs-list-group-border-color: var(--bs-border-color);--bs-list-group-border-width: var(--bs-border-width);--bs-list-group-border-radius: var(--bs-border-radius);--bs-list-group-item-padding-x: 1rem;--bs-list-group-item-padding-y: .5rem;--bs-list-group-action-color: var(--bs-secondary-color);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-tertiary-bg);--bs-list-group-action-active-color: var(--bs-body-color);--bs-list-group-action-active-bg: var(--bs-secondary-bg);--bs-list-group-disabled-color: var(--bs-secondary-color);--bs-list-group-disabled-bg: var(--bs-body-bg);--bs-list-group-active-color: #fff;--bs-list-group-active-bg: #18416A;--bs-list-group-active-border-color: #18416A;display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;padding-left:0;margin-bottom:0;border-radius:var(--bs-list-group-border-radius)}.list-group-numbered{list-style-type:none;counter-reset:section}.list-group-numbered>.list-group-item::before{content:counters(section, ".") ". ";counter-increment:section}.list-group-item-action{width:100%;color:var(--bs-list-group-action-color);text-align:inherit}.list-group-item-action:hover,.list-group-item-action:focus{z-index:1;color:var(--bs-list-group-action-hover-color);text-decoration:none;background-color:var(--bs-list-group-action-hover-bg)}.list-group-item-action:active{color:var(--bs-list-group-action-active-color);background-color:var(--bs-list-group-action-active-bg)}.list-group-item{position:relative;display:block;padding:var(--bs-list-group-item-padding-y) var(--bs-list-group-item-padding-x);color:var(--bs-list-group-color);text-decoration:none;-webkit-text-decoration:none;-moz-text-decoration:none;-ms-text-decoration:none;-o-text-decoration:none;background-color:var(--bs-list-group-bg);border:var(--bs-list-group-border-width) solid var(--bs-list-group-border-color)}.list-group-item:first-child{border-top-left-radius:inherit;border-top-right-radius:inherit}.list-group-item:last-child{border-bottom-right-radius:inherit;border-bottom-left-radius:inherit}.list-group-item.disabled,.list-group-item:disabled{color:var(--bs-list-group-disabled-color);pointer-events:none;background-color:var(--bs-list-group-disabled-bg)}.list-group-item.active{z-index:2;color:var(--bs-list-group-active-color);background-color:var(--bs-list-group-active-bg);border-color:var(--bs-list-group-active-border-color)}.list-group-item+.list-group-item{border-top-width:0}.list-group-item+.list-group-item.active{margin-top:calc(-1 * var(--bs-list-group-border-width));border-top-width:var(--bs-list-group-border-width)}.list-group-horizontal{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal>.list-group-item.active{margin-top:0}.list-group-horizontal>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal>.list-group-item+.list-group-item.active{margin-left:calc(-1 * var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}@media (min-width: 576px){.list-group-horizontal-sm{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-sm>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-sm>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-sm>.list-group-item.active{margin-top:0}.list-group-horizontal-sm>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-sm>.list-group-item+.list-group-item.active{margin-left:calc(-1 * var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media (min-width: 768px){.list-group-horizontal-md{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-md>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-md>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-md>.list-group-item.active{margin-top:0}.list-group-horizontal-md>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-md>.list-group-item+.list-group-item.active{margin-left:calc(-1 * var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media (min-width: 992px){.list-group-horizontal-lg{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-lg>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-lg>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-lg>.list-group-item.active{margin-top:0}.list-group-horizontal-lg>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-lg>.list-group-item+.list-group-item.active{margin-left:calc(-1 * var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media (min-width: 1200px){.list-group-horizontal-xl{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-xl>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-xl>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-xl>.list-group-item.active{margin-top:0}.list-group-horizontal-xl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xl>.list-group-item+.list-group-item.active{margin-left:calc(-1 * var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}@media (min-width: 1400px){.list-group-horizontal-xxl{flex-direction:row;-webkit-flex-direction:row}.list-group-horizontal-xxl>.list-group-item:first-child:not(:last-child){border-bottom-left-radius:var(--bs-list-group-border-radius);border-top-right-radius:0}.list-group-horizontal-xxl>.list-group-item:last-child:not(:first-child){border-top-right-radius:var(--bs-list-group-border-radius);border-bottom-left-radius:0}.list-group-horizontal-xxl>.list-group-item.active{margin-top:0}.list-group-horizontal-xxl>.list-group-item+.list-group-item{border-top-width:var(--bs-list-group-border-width);border-left-width:0}.list-group-horizontal-xxl>.list-group-item+.list-group-item.active{margin-left:calc(-1 * var(--bs-list-group-border-width));border-left-width:var(--bs-list-group-border-width)}}.list-group-flush{border-radius:0}.list-group-flush>.list-group-item{border-width:0 0 var(--bs-list-group-border-width)}.list-group-flush>.list-group-item:last-child{border-bottom-width:0}.list-group-item-default{--bs-list-group-color: var(--bs-default-text-emphasis);--bs-list-group-bg: var(--bs-default-bg-subtle);--bs-list-group-border-color: var(--bs-default-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-default-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-default-border-subtle);--bs-list-group-active-color: var(--bs-default-bg-subtle);--bs-list-group-active-bg: var(--bs-default-text-emphasis);--bs-list-group-active-border-color: var(--bs-default-text-emphasis)}.list-group-item-primary{--bs-list-group-color: var(--bs-primary-text-emphasis);--bs-list-group-bg: var(--bs-primary-bg-subtle);--bs-list-group-border-color: var(--bs-primary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-primary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-primary-border-subtle);--bs-list-group-active-color: var(--bs-primary-bg-subtle);--bs-list-group-active-bg: var(--bs-primary-text-emphasis);--bs-list-group-active-border-color: var(--bs-primary-text-emphasis)}.list-group-item-secondary{--bs-list-group-color: var(--bs-secondary-text-emphasis);--bs-list-group-bg: var(--bs-secondary-bg-subtle);--bs-list-group-border-color: var(--bs-secondary-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-secondary-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-secondary-border-subtle);--bs-list-group-active-color: var(--bs-secondary-bg-subtle);--bs-list-group-active-bg: var(--bs-secondary-text-emphasis);--bs-list-group-active-border-color: var(--bs-secondary-text-emphasis)}.list-group-item-success{--bs-list-group-color: var(--bs-success-text-emphasis);--bs-list-group-bg: var(--bs-success-bg-subtle);--bs-list-group-border-color: var(--bs-success-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-success-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-success-border-subtle);--bs-list-group-active-color: var(--bs-success-bg-subtle);--bs-list-group-active-bg: var(--bs-success-text-emphasis);--bs-list-group-active-border-color: var(--bs-success-text-emphasis)}.list-group-item-info{--bs-list-group-color: var(--bs-info-text-emphasis);--bs-list-group-bg: var(--bs-info-bg-subtle);--bs-list-group-border-color: var(--bs-info-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-info-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-info-border-subtle);--bs-list-group-active-color: var(--bs-info-bg-subtle);--bs-list-group-active-bg: var(--bs-info-text-emphasis);--bs-list-group-active-border-color: var(--bs-info-text-emphasis)}.list-group-item-warning{--bs-list-group-color: var(--bs-warning-text-emphasis);--bs-list-group-bg: var(--bs-warning-bg-subtle);--bs-list-group-border-color: var(--bs-warning-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-warning-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-warning-border-subtle);--bs-list-group-active-color: var(--bs-warning-bg-subtle);--bs-list-group-active-bg: var(--bs-warning-text-emphasis);--bs-list-group-active-border-color: var(--bs-warning-text-emphasis)}.list-group-item-danger{--bs-list-group-color: var(--bs-danger-text-emphasis);--bs-list-group-bg: var(--bs-danger-bg-subtle);--bs-list-group-border-color: var(--bs-danger-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-danger-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-danger-border-subtle);--bs-list-group-active-color: var(--bs-danger-bg-subtle);--bs-list-group-active-bg: var(--bs-danger-text-emphasis);--bs-list-group-active-border-color: var(--bs-danger-text-emphasis)}.list-group-item-light{--bs-list-group-color: var(--bs-light-text-emphasis);--bs-list-group-bg: var(--bs-light-bg-subtle);--bs-list-group-border-color: var(--bs-light-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-light-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-light-border-subtle);--bs-list-group-active-color: var(--bs-light-bg-subtle);--bs-list-group-active-bg: var(--bs-light-text-emphasis);--bs-list-group-active-border-color: var(--bs-light-text-emphasis)}.list-group-item-dark{--bs-list-group-color: var(--bs-dark-text-emphasis);--bs-list-group-bg: var(--bs-dark-bg-subtle);--bs-list-group-border-color: var(--bs-dark-border-subtle);--bs-list-group-action-hover-color: var(--bs-emphasis-color);--bs-list-group-action-hover-bg: var(--bs-dark-border-subtle);--bs-list-group-action-active-color: var(--bs-emphasis-color);--bs-list-group-action-active-bg: var(--bs-dark-border-subtle);--bs-list-group-active-color: var(--bs-dark-bg-subtle);--bs-list-group-active-bg: var(--bs-dark-text-emphasis);--bs-list-group-active-border-color: var(--bs-dark-text-emphasis)}.btn-close{--bs-btn-close-color: #000;--bs-btn-close-bg: url("data:image/svg+xml,%3csvg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 16 16' fill='%23000'%3e%3cpath d='M.293.293a1 1 0 0 1 1.414 0L8 6.586 14.293.293a1 1 0 1 1 1.414 1.414L9.414 8l6.293 6.293a1 1 0 0 1-1.414 1.414L8 9.414l-6.293 6.293a1 1 0 0 1-1.414-1.414L6.586 8 .293 1.707a1 1 0 0 1 0-1.414z'/%3e%3c/svg%3e");--bs-btn-close-opacity: .5;--bs-btn-close-hover-opacity: .75;--bs-btn-close-focus-shadow: 0 0 0 .25rem rgba(24,65,106,0.25);--bs-btn-close-focus-opacity: 1;--bs-btn-close-disabled-opacity: .25;--bs-btn-close-white-filter: invert(1) grayscale(100%) brightness(200%);box-sizing:content-box;width:1em;height:1em;padding:.25em .25em;color:var(--bs-btn-close-color);background:transparent var(--bs-btn-close-bg) center/1em auto no-repeat;border:0;border-radius:.375rem;opacity:var(--bs-btn-close-opacity)}.btn-close:hover{color:var(--bs-btn-close-color);text-decoration:none;opacity:var(--bs-btn-close-hover-opacity)}.btn-close:focus{outline:0;box-shadow:var(--bs-btn-close-focus-shadow);opacity:var(--bs-btn-close-focus-opacity)}.btn-close:disabled,.btn-close.disabled{pointer-events:none;user-select:none;-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;-o-user-select:none;opacity:var(--bs-btn-close-disabled-opacity)}.btn-close-white{filter:var(--bs-btn-close-white-filter)}[data-bs-theme="dark"] .btn-close{filter:var(--bs-btn-close-white-filter)}.toast{--bs-toast-zindex: 1090;--bs-toast-padding-x: .75rem;--bs-toast-padding-y: .5rem;--bs-toast-spacing: 1.5rem;--bs-toast-max-width: 350px;--bs-toast-font-size:.875rem;--bs-toast-color: ;--bs-toast-bg: rgba(var(--bs-body-bg-rgb), 0.85);--bs-toast-border-width: var(--bs-border-width);--bs-toast-border-color: var(--bs-border-color-translucent);--bs-toast-border-radius: var(--bs-border-radius);--bs-toast-box-shadow: var(--bs-box-shadow);--bs-toast-header-color: var(--bs-secondary-color);--bs-toast-header-bg: rgba(var(--bs-body-bg-rgb), 0.85);--bs-toast-header-border-color: var(--bs-border-color-translucent);width:var(--bs-toast-max-width);max-width:100%;font-size:var(--bs-toast-font-size);color:var(--bs-toast-color);pointer-events:auto;background-color:var(--bs-toast-bg);background-clip:padding-box;border:var(--bs-toast-border-width) solid var(--bs-toast-border-color);box-shadow:var(--bs-toast-box-shadow);border-radius:var(--bs-toast-border-radius)}.toast.showing{opacity:0}.toast:not(.show){display:none}.toast-container{--bs-toast-zindex: 1090;position:absolute;z-index:var(--bs-toast-zindex);width:max-content;width:-webkit-max-content;width:-moz-max-content;width:-ms-max-content;width:-o-max-content;max-width:100%;pointer-events:none}.toast-container>:not(:last-child){margin-bottom:var(--bs-toast-spacing)}.toast-header{display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;padding:var(--bs-toast-padding-y) var(--bs-toast-padding-x);color:var(--bs-toast-header-color);background-color:var(--bs-toast-header-bg);background-clip:padding-box;border-bottom:var(--bs-toast-border-width) solid var(--bs-toast-header-border-color);border-top-left-radius:calc(var(--bs-toast-border-radius) - var(--bs-toast-border-width));border-top-right-radius:calc(var(--bs-toast-border-radius) - var(--bs-toast-border-width))}.toast-header .btn-close{margin-right:calc(-.5 * var(--bs-toast-padding-x));margin-left:var(--bs-toast-padding-x)}.toast-body{padding:var(--bs-toast-padding-x);word-wrap:break-word}.modal{--bs-modal-zindex: 1055;--bs-modal-width: 500px;--bs-modal-padding: 1rem;--bs-modal-margin: .5rem;--bs-modal-color: ;--bs-modal-bg: var(--bs-body-bg);--bs-modal-border-color: var(--bs-border-color-translucent);--bs-modal-border-width: var(--bs-border-width);--bs-modal-border-radius: var(--bs-border-radius-lg);--bs-modal-box-shadow: 0 0.125rem 0.25rem rgba(0,0,0,0.075);--bs-modal-inner-border-radius: calc(var(--bs-border-radius-lg) - (var(--bs-border-width)));--bs-modal-header-padding-x: 1rem;--bs-modal-header-padding-y: 1rem;--bs-modal-header-padding: 1rem 1rem;--bs-modal-header-border-color: var(--bs-border-color);--bs-modal-header-border-width: var(--bs-border-width);--bs-modal-title-line-height: 1.5;--bs-modal-footer-gap: .5rem;--bs-modal-footer-bg: ;--bs-modal-footer-border-color: var(--bs-border-color);--bs-modal-footer-border-width: var(--bs-border-width);position:fixed;top:0;left:0;z-index:var(--bs-modal-zindex);display:none;width:100%;height:100%;overflow-x:hidden;overflow-y:auto;outline:0}.modal-dialog{position:relative;width:auto;margin:var(--bs-modal-margin);pointer-events:none}.modal.fade .modal-dialog{transition:transform 0.3s ease-out;transform:translate(0, -50px)}@media (prefers-reduced-motion: reduce){.modal.fade .modal-dialog{transition:none}}.modal.show .modal-dialog{transform:none}.modal.modal-static .modal-dialog{transform:scale(1.02)}.modal-dialog-scrollable{height:calc(100% - var(--bs-modal-margin) * 2)}.modal-dialog-scrollable .modal-content{max-height:100%;overflow:hidden}.modal-dialog-scrollable .modal-body{overflow-y:auto}.modal-dialog-centered{display:flex;display:-webkit-flex;align-items:center;-webkit-align-items:center;min-height:calc(100% - var(--bs-modal-margin) * 2)}.modal-content{position:relative;display:flex;display:-webkit-flex;flex-direction:column;-webkit-flex-direction:column;width:100%;color:var(--bs-modal-color);pointer-events:auto;background-color:var(--bs-modal-bg);background-clip:padding-box;border:var(--bs-modal-border-width) solid var(--bs-modal-border-color);border-radius:var(--bs-modal-border-radius);outline:0}.modal-backdrop{--bs-backdrop-zindex: 1050;--bs-backdrop-bg: #000;--bs-backdrop-opacity: .5;position:fixed;top:0;left:0;z-index:var(--bs-backdrop-zindex);width:100vw;height:100vh;background-color:var(--bs-backdrop-bg)}.modal-backdrop.fade{opacity:0}.modal-backdrop.show{opacity:var(--bs-backdrop-opacity)}.modal-header{display:flex;display:-webkit-flex;flex-shrink:0;-webkit-flex-shrink:0;align-items:center;-webkit-align-items:center;justify-content:space-between;-webkit-justify-content:space-between;padding:var(--bs-modal-header-padding);border-bottom:var(--bs-modal-header-border-width) solid 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var(--bs-modal-footer-border-color);border-bottom-right-radius:var(--bs-modal-inner-border-radius);border-bottom-left-radius:var(--bs-modal-inner-border-radius)}.modal-footer>*{margin:calc(var(--bs-modal-footer-gap) * .5)}@media (min-width: 576px){.modal{--bs-modal-margin: 1.75rem;--bs-modal-box-shadow: 0 0.5rem 1rem rgba(0,0,0,0.15)}.modal-dialog{max-width:var(--bs-modal-width);margin-right:auto;margin-left:auto}.modal-sm{--bs-modal-width: 300px}}@media (min-width: 992px){.modal-lg,.modal-xl{--bs-modal-width: 800px}}@media (min-width: 1200px){.modal-xl{--bs-modal-width: 1140px}}.modal-fullscreen{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen .modal-content{height:100%;border:0;border-radius:0}.modal-fullscreen .modal-header,.modal-fullscreen .modal-footer{border-radius:0}.modal-fullscreen .modal-body{overflow-y:auto}@media (max-width: 575.98px){.modal-fullscreen-sm-down{width:100vw;max-width:none;height:100%;margin:0}.modal-fullscreen-sm-down 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#dee2e6;padding-bottom:0.5rem;margin-bottom:0.5rem;margin-top:1.5rem}dd{margin-left:1.5rem}summary{margin-bottom:0.5rem}details{margin-bottom:1rem}.html-widget{margin-bottom:1rem}a.anchor{display:none;margin-left:5px;width:Min(0.9em, 20px);height:Min(0.9em, 20px);background-image:url(../../link.svg);background-repeat:no-repeat;background-size:Min(0.9em, 20px) Min(0.9em, 20px);background-position:center center}h2:hover .anchor,.h2:hover .anchor,h3:hover .anchor,.h3:hover .anchor,h4:hover .anchor,.h4:hover .anchor,h5:hover .anchor,.h5:hover .anchor,h6:hover .anchor,.h6:hover .anchor{display:inline-block}.orcid{color:#A6CE39;margin-right:4px}.fab{font-family:"Font Awesome 5 Brands" !important}img.logo{float:right;width:100px;margin-left:30px}.template-home img.logo{width:120px}@media (max-width: 575.98px){img.logo{width:80px}}@media (min-width: 576px){.page-header{min-height:88px}.template-home .page-header{min-height:104px}}.line-block{margin-bottom:1rem}.template-reference-index 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--- a/index.html
+++ b/index.html
@@ -27,8 +27,7 @@
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-<meta property="og:title" content="Comparative analysis of RNA-seq data for hybrids and their progenitors">
+<!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="pkgdown.js"></script><meta property="og:title" content="Comparative analysis of RNA-seq data for hybrids and their progenitors">
 <meta property="og:description" content="HybridExpress can be used to perform comparative transcriptomics
     analysis of hybrids (or allopolyploids) relative to their
     progenitor species. The package features functions to perform exploratory
@@ -48,7 +47,7 @@
     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
     
 
-    <nav class="navbar fixed-top navbar-light navbar-expand-lg bg-#0092ac"><div class="container">
+    <nav class="navbar fixed-top navbar-light navbar-expand-lg bg-light"><div class="container">
     
     <a class="navbar-brand me-2" href="index.html">HybridExpress</a>
 
@@ -159,7 +158,6 @@ <h2 id="development-tools">Development tools<a class="anchor" aria-label="anchor
 <li>Continuous code testing is possible thanks to <a href="https://www.tidyverse.org/blog/2020/04/usethis-1-6-0/" class="external-link">GitHub actions</a> through <em><a href="https://CRAN.R-project.org/package=usethis" class="external-link">usethis</a></em>, <em><a href="https://CRAN.R-project.org/package=remotes" class="external-link">remotes</a></em>, and <em><a href="https://CRAN.R-project.org/package=rcmdcheck" class="external-link">rcmdcheck</a></em> customized to use <a href="https://www.bioconductor.org/help/docker/" class="external-link">Bioconductor’s docker containers</a> and <em><a href="https://bioconductor.org/packages/3.19/BiocCheck" class="external-link">BiocCheck</a></em>.</li>
 <li>Code coverage assessment is possible thanks to <a href="https://codecov.io/gh" class="external-link">codecov</a> and <em><a href="https://CRAN.R-project.org/package=covr" class="external-link">covr</a></em>.</li>
 <li>The <a href="http://almeidasilvaf.github.io/HybridExpress" class="external-link">documentation website</a> is automatically updated thanks to <em><a href="https://CRAN.R-project.org/package=pkgdown" class="external-link">pkgdown</a></em>.</li>
-<li>The code is styled automatically thanks to <em><a href="https://CRAN.R-project.org/package=styler" class="external-link">styler</a></em>.</li>
 <li>The documentation is formatted thanks to <em><a href="https://CRAN.R-project.org/package=devtools" class="external-link">devtools</a></em> and <em><a href="https://CRAN.R-project.org/package=roxygen2" class="external-link">roxygen2</a></em>.</li>
 </ul>
 <p>This package was developed using <em><a href="https://bioconductor.org/packages/3.19/biocthis" class="external-link">biocthis</a></em>.</p>
diff --git a/news/index.html b/news/index.html
index 7810e42..7acf37c 100644
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+++ b/news/index.html
@@ -1,12 +1,12 @@
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 <![endif]--></head><body>
     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
     
 
-    <nav class="navbar fixed-top navbar-light navbar-expand-lg bg-#0092ac"><div class="container">
+    <nav class="navbar fixed-top navbar-light navbar-expand-lg bg-light"><div class="container">
     
     <a class="navbar-brand me-2" href="../index.html">HybridExpress</a>
 
diff --git a/pkgdown.yml b/pkgdown.yml
index 05d2203..1454153 100644
--- a/pkgdown.yml
+++ b/pkgdown.yml
@@ -3,5 +3,5 @@ pkgdown: 2.0.7
 pkgdown_sha: ~
 articles:
   HybridExpress: HybridExpress.html
-last_built: 2023-12-12T10:49Z
+last_built: 2024-02-02T10:07Z
 
diff --git a/reference/add_midparent_expression.html b/reference/add_midparent_expression.html
index 1ca8eb4..aab3a8e 100644
--- a/reference/add_midparent_expression.html
+++ b/reference/add_midparent_expression.html
@@ -1,12 +1,12 @@
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diff --git a/reference/add_size_factors.html b/reference/add_size_factors.html
new file mode 100644
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+++ b/reference/add_size_factors.html
@@ -0,0 +1,114 @@
+<!DOCTYPE html>
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+  <main id="main" class="col-md-9"><div class="page-header">
+      <img src="../logo.png" class="logo" alt=""><h1>Add size factors to normalize count data by library size or by biomass</h1>
+      <small class="dont-index">Source: <a href="https://github.com/almeidasilvaf/HybridExpress/blob/HEAD/R/01_data_processing.R" class="external-link"><code>R/01_data_processing.R</code></a></small>
+      <div class="d-none name"><code>add_size_factors.Rd</code></div>
+    </div>
+
+    <div class="ref-description section level2">
+    <p>Add size factors to normalize count data by library size or by biomass</p>
+    </div>
+
+    <div class="section level2">
+    <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">add_size_factors</span><span class="op">(</span><span class="va">se</span>, spikein <span class="op">=</span> <span class="cn">FALSE</span>, spikein_pattern <span class="op">=</span> <span class="st">"ERCC"</span><span class="op">)</span></span></code></pre></div>
+    </div>
+
+    <div class="section level2">
+    <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
+    <dl><dt>se</dt>
+<dd><p>A <code>SummarizedExperiment</code> object with a count matrix and sample
+metadata.</p></dd>
+
+
+<dt>spikein</dt>
+<dd><p>Logical indicating whether or not to normalize data
+using spike-ins. If FALSE, data will be normalized by library size.
+Default: FALSE.</p></dd>
+
+
+<dt>spikein_pattern</dt>
+<dd><p>Character with the pattern (regex) to use
+to identify spike-in features in the count matrix. Only valid
+if <code>spikein_norm = TRUE</code>.</p></dd>
+
+</dl></div>
+    <div class="section level2">
+    <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
+    
+
+<p>A <code>SummarizedExperiment</code> object as in <strong>se</strong>, but with an extra
+column in the colData slot named "sizeFactor". This column contains size
+factors that will be used by DESeq2 when performing differential expression
+analyses.</p>
+    </div>
+
+    <div class="section level2">
+    <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
+    <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/utils/data.html" class="external-link">data</a></span><span class="op">(</span><span class="va">se_chlamy</span><span class="op">)</span></span></span>
+<span class="r-in"><span><span class="va">se_norm</span> <span class="op">&lt;-</span> <span class="fu">add_size_factors</span><span class="op">(</span><span class="va">se_chlamy</span><span class="op">)</span></span></span>
+</code></pre></div>
+    </div>
+  </main><aside class="col-md-3"><nav id="toc"><h2>On this page</h2>
+    </nav></aside></div>
+
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+    <footer><div class="pkgdown-footer-left">
+  <p></p><p>Developed by Fabricio Almeida-Silva, Lucas Prost-Boxoen, Yves Van de Peer.</p>
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+  <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.7.</p>
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diff --git a/reference/deg_counts.html b/reference/deg_counts.html
index 97efdf4..15a444c 100644
--- a/reference/deg_counts.html
+++ b/reference/deg_counts.html
@@ -1,6 +1,6 @@
 <!DOCTYPE html>
 <!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="This object was obtained with get_deg_counts() using the example
-data set deg_list."><title>Data frame with frequencies (absolute and relative) of DEGs per contrast — deg_counts • HybridExpress</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/Atkinson_Hyperlegible-0.4.8/font.css" rel="stylesheet"><link href="../deps/Source_Code_Pro-0.4.8/font.css" rel="stylesheet"><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Data frame with frequencies (absolute and relative) of DEGs per contrast — deg_counts"><meta property="og:description" content="This object was obtained with get_deg_counts() using the example
+data set deg_list."><title>Data frame with frequencies (absolute and relative) of DEGs per contrast — deg_counts • HybridExpress</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/Atkinson_Hyperlegible-0.4.8/font.css" rel="stylesheet"><link href="../deps/Source_Code_Pro-0.4.8/font.css" rel="stylesheet"><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Data frame with frequencies (absolute and relative) of DEGs per contrast — deg_counts"><meta property="og:description" content="This object was obtained with get_deg_counts() using the example
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@@ -8,7 +8,7 @@
     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
     
 
-    <nav class="navbar fixed-top navbar-light navbar-expand-lg bg-#0092ac"><div class="container">
+    <nav class="navbar fixed-top navbar-light navbar-expand-lg bg-light"><div class="container">
     
     <a class="navbar-brand me-2" href="../index.html">HybridExpress</a>
 
diff --git a/reference/deg_list.html b/reference/deg_list.html
index 4c2b934..5cdf2cd 100644
--- a/reference/deg_list.html
+++ b/reference/deg_list.html
@@ -1,6 +1,6 @@
 <!DOCTYPE html>
 <!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="This object was obtained with get_deg_list() using the example
-data set se_chlamy."><title>List of differentially expressed genes for all contrasts — deg_list • HybridExpress</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/Atkinson_Hyperlegible-0.4.8/font.css" rel="stylesheet"><link href="../deps/Source_Code_Pro-0.4.8/font.css" rel="stylesheet"><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="List of differentially expressed genes for all contrasts — deg_list"><meta property="og:description" content="This object was obtained with get_deg_list() using the example
+data set se_chlamy."><title>List of differentially expressed genes for all contrasts — deg_list • HybridExpress</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/Atkinson_Hyperlegible-0.4.8/font.css" rel="stylesheet"><link href="../deps/Source_Code_Pro-0.4.8/font.css" rel="stylesheet"><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="List of differentially expressed genes for all contrasts — deg_list"><meta property="og:description" content="This object was obtained with get_deg_list() using the example
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@@ -8,7 +8,7 @@
     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
     
 
-    <nav class="navbar fixed-top navbar-light navbar-expand-lg bg-#0092ac"><div class="container">
+    <nav class="navbar fixed-top navbar-light navbar-expand-lg bg-light"><div class="container">
     
     <a class="navbar-brand me-2" href="../index.html">HybridExpress</a>
 
diff --git a/reference/expression_partitioning.html b/reference/expression_partitioning.html
index 648f952..717f75a 100644
--- a/reference/expression_partitioning.html
+++ b/reference/expression_partitioning.html
@@ -1,12 +1,12 @@
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diff --git a/reference/get_deg_counts.html b/reference/get_deg_counts.html
index 2f2ef9a..37ffe89 100644
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diff --git a/reference/get_deg_list.html b/reference/get_deg_list.html
index 7440b4d..2a4b94a 100644
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+++ b/reference/get_deg_list.html
@@ -1,12 +1,12 @@
 <!DOCTYPE html>
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+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Get a table of differential expression expression statistics with DESeq2"><title>Get a table of differential expression expression statistics with DESeq2 — get_deg_list • HybridExpress</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/Atkinson_Hyperlegible-0.4.8/font.css" rel="stylesheet"><link href="../deps/Source_Code_Pro-0.4.8/font.css" rel="stylesheet"><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Get a table of differential expression expression statistics with DESeq2 — get_deg_list"><meta property="og:description" content="Get a table of differential expression expression statistics with DESeq2"><meta property="og:image" content="/logo.png"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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 <![endif]--></head><body>
     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
     
 
-    <nav class="navbar fixed-top navbar-light navbar-expand-lg bg-#0092ac"><div class="container">
+    <nav class="navbar fixed-top navbar-light navbar-expand-lg bg-light"><div class="container">
     
     <a class="navbar-brand me-2" href="../index.html">HybridExpress</a>
 
@@ -61,8 +61,6 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
 <span>  parent2 <span class="op">=</span> <span class="st">"P2"</span>,</span>
 <span>  offspring <span class="op">=</span> <span class="st">"F1"</span>,</span>
 <span>  midparent <span class="op">=</span> <span class="st">"midparent"</span>,</span>
-<span>  spikein_norm <span class="op">=</span> <span class="cn">FALSE</span>,</span>
-<span>  spikein_pattern <span class="op">=</span> <span class="st">"ERCC"</span>,</span>
 <span>  lfcThreshold <span class="op">=</span> <span class="fl">0</span>,</span>
 <span>  alpha <span class="op">=</span> <span class="fl">0.01</span>,</span>
 <span>  <span class="va">...</span></span>
@@ -104,17 +102,6 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 "midparent", as returned by <code><a href="add_midparent_expression.html">add_midparent_expression()</a></code>.</p></dd>
 
 
-<dt>spikein_norm</dt>
-<dd><p>Logical indicating whether or not to normalize data
-using spike-ins. Default: FALSE.</p></dd>
-
-
-<dt>spikein_pattern</dt>
-<dd><p>Character with the pattern (regex) to use
-to identify spike-in features in the count matrix. Only valid
-if <code>spikein_norm = TRUE</code>.</p></dd>
-
-
 <dt>lfcThreshold</dt>
 <dd><p>Numeric indicating the log2 fold-change threshold
 to use to consider differentially expressed genes. Default: 0.</p></dd>
@@ -179,8 +166,9 @@ <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="fu"><a href="https://rdrr.io/r/utils/data.html" class="external-link">data</a></span><span class="op">(</span><span class="va">se_chlamy</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">se</span> <span class="op">&lt;-</span> <span class="fu"><a href="add_midparent_expression.html">add_midparent_expression</a></span><span class="op">(</span><span class="va">se_chlamy</span><span class="op">)</span></span></span>
-<span class="r-in"><span><span class="va">deg_list</span> <span class="op">&lt;-</span> <span class="fu">get_deg_list</span><span class="op">(</span><span class="va">se</span>, spikein_norm <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
+<span class="r-in"><span><span class="va">se</span> <span class="op">&lt;-</span> <span class="fu"><a href="add_size_factors.html">add_size_factors</a></span><span class="op">(</span><span class="va">se</span>, spikein <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> converting counts to integer mode</span>
+<span class="r-in"><span><span class="va">deg_list</span> <span class="op">&lt;-</span> <span class="fu">get_deg_list</span><span class="op">(</span><span class="va">se</span><span class="op">)</span></span></span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> using pre-existing size factors</span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> estimating dispersions</span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span> gene-wise dispersion estimates</span>
diff --git a/reference/go_chlamy.html b/reference/go_chlamy.html
index 7042ccb..afb86b7 100644
--- a/reference/go_chlamy.html
+++ b/reference/go_chlamy.html
@@ -1,6 +1,6 @@
 <!DOCTYPE html>
 <!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Data were obtained from Phytozome and processed so that each row contains
-only one GO term (long format)."><title>Data frame with GO terms annotated to each gene of Chlamydomonas reinhardtii — go_chlamy • HybridExpress</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/Atkinson_Hyperlegible-0.4.8/font.css" rel="stylesheet"><link href="../deps/Source_Code_Pro-0.4.8/font.css" rel="stylesheet"><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Data frame with GO terms annotated to each gene of Chlamydomonas reinhardtii — go_chlamy"><meta property="og:description" content="Data were obtained from Phytozome and processed so that each row contains
+only one GO term (long format)."><title>Data frame with GO terms annotated to each gene of Chlamydomonas reinhardtii — go_chlamy • HybridExpress</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/Atkinson_Hyperlegible-0.4.8/font.css" rel="stylesheet"><link href="../deps/Source_Code_Pro-0.4.8/font.css" rel="stylesheet"><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Data frame with GO terms annotated to each gene of Chlamydomonas reinhardtii — go_chlamy"><meta property="og:description" content="Data were obtained from Phytozome and processed so that each row contains
 only one GO term (long format)."><meta property="og:image" content="/logo.png"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
 <script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
 <script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
@@ -8,7 +8,7 @@
     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
     
 
-    <nav class="navbar fixed-top navbar-light navbar-expand-lg bg-#0092ac"><div class="container">
+    <nav class="navbar fixed-top navbar-light navbar-expand-lg bg-light"><div class="container">
     
     <a class="navbar-brand me-2" href="../index.html">HybridExpress</a>
 
diff --git a/reference/index.html b/reference/index.html
index b519012..cd54f71 100644
--- a/reference/index.html
+++ b/reference/index.html
@@ -1,12 +1,12 @@
 <!DOCTYPE html>
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+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><title>Function reference • HybridExpress</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/Atkinson_Hyperlegible-0.4.8/font.css" rel="stylesheet"><link href="../deps/Source_Code_Pro-0.4.8/font.css" rel="stylesheet"><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Function reference"><meta property="og:image" content="/logo.png"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
     
 
-    <nav class="navbar fixed-top navbar-light navbar-expand-lg bg-#0092ac"><div class="container">
+    <nav class="navbar fixed-top navbar-light navbar-expand-lg bg-light"><div class="container">
     
     <a class="navbar-brand me-2" href="../index.html">HybridExpress</a>
 
@@ -64,6 +64,11 @@ <h2 id="all-functions">All functions<a class="anchor" aria-label="anchor" href="
         <dd>Add midparent expression to <code>SummarizedExperiment</code> object</dd>
       </dl><dl><dt>
           
+          <code><a href="add_size_factors.html">add_size_factors()</a></code> 
+        </dt>
+        <dd>Add size factors to normalize count data by library size or by biomass</dd>
+      </dl><dl><dt>
+          
           <code><a href="deg_counts.html">deg_counts</a></code> 
         </dt>
         <dd>Data frame with frequencies (absolute and relative) of DEGs per contrast</dd>
diff --git a/reference/ora.html b/reference/ora.html
index 60945ab..28d8e4d 100644
--- a/reference/ora.html
+++ b/reference/ora.html
@@ -1,12 +1,12 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Perform overrepresentation analysis for a set of genes"><title>Perform overrepresentation analysis for a set of genes — ora • HybridExpress</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/Atkinson_Hyperlegible-0.4.8/font.css" rel="stylesheet"><link href="../deps/Source_Code_Pro-0.4.8/font.css" rel="stylesheet"><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Perform overrepresentation analysis for a set of genes — ora"><meta property="og:description" content="Perform overrepresentation analysis for a set of genes"><meta property="og:image" content="/logo.png"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Perform overrepresentation analysis for a set of genes"><title>Perform overrepresentation analysis for a set of genes — ora • HybridExpress</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/Atkinson_Hyperlegible-0.4.8/font.css" rel="stylesheet"><link href="../deps/Source_Code_Pro-0.4.8/font.css" rel="stylesheet"><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Perform overrepresentation analysis for a set of genes — ora"><meta property="og:description" content="Perform overrepresentation analysis for a set of genes"><meta property="og:image" content="/logo.png"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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-    <nav class="navbar fixed-top navbar-light navbar-expand-lg bg-#0092ac"><div class="container">
+    <nav class="navbar fixed-top navbar-light navbar-expand-lg bg-light"><div class="container">
     
     <a class="navbar-brand me-2" href="../index.html">HybridExpress</a>
 
diff --git a/reference/pca_plot-1.png b/reference/pca_plot-1.png
index 22438ac..c057f5d 100644
Binary files a/reference/pca_plot-1.png and b/reference/pca_plot-1.png differ
diff --git a/reference/pca_plot.html b/reference/pca_plot.html
index b8dcc35..7d0e180 100644
--- a/reference/pca_plot.html
+++ b/reference/pca_plot.html
@@ -1,12 +1,12 @@
 <!DOCTYPE html>
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diff --git a/reference/plot_expression_partitions.html b/reference/plot_expression_partitions.html
index fd1a878..4ddf84d 100644
--- a/reference/plot_expression_partitions.html
+++ b/reference/plot_expression_partitions.html
@@ -1,12 +1,12 @@
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diff --git a/reference/plot_expression_triangle.html b/reference/plot_expression_triangle.html
index 105f8ab..6f501e9 100644
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+++ b/reference/plot_expression_triangle.html
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diff --git a/reference/plot_partition_frequencies.html b/reference/plot_partition_frequencies.html
index b2345e4..b7ce068 100644
--- a/reference/plot_partition_frequencies.html
+++ b/reference/plot_partition_frequencies.html
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diff --git a/reference/plot_samplecor-1.png b/reference/plot_samplecor-1.png
index ac05152..12c0589 100644
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diff --git a/reference/plot_samplecor.html b/reference/plot_samplecor.html
index c8904dd..e9bb880 100644
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+++ b/reference/plot_samplecor.html
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diff --git a/reference/se_chlamy.html b/reference/se_chlamy.html
index f9da36d..5ed17ae 100644
--- a/reference/se_chlamy.html
+++ b/reference/se_chlamy.html
@@ -1,6 +1,6 @@
 <!DOCTYPE html>
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-levels were crossed to generate an allopolyploid (F1)."><title>Expression data (in counts) for 3 Chlamydomonas lines (P1, P2, and F1) — se_chlamy • HybridExpress</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/Atkinson_Hyperlegible-0.4.8/font.css" rel="stylesheet"><link href="../deps/Source_Code_Pro-0.4.8/font.css" rel="stylesheet"><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><link href="../extra.css" rel="stylesheet"><meta property="og:title" content="Expression data (in counts) for 3 Chlamydomonas lines (P1, P2, and F1) — se_chlamy"><meta property="og:description" content="Two lines (referred to as parent 1 and parent 2) with different ploidy
+levels were crossed to generate an allopolyploid (F1)."><title>Expression data (in counts) for 3 Chlamydomonas lines (P1, P2, and F1) — se_chlamy • HybridExpress</title><!-- favicons --><link rel="icon" type="image/png" sizes="16x16" href="../favicon-16x16.png"><link rel="icon" type="image/png" sizes="32x32" href="../favicon-32x32.png"><link rel="apple-touch-icon" type="image/png" sizes="180x180" href="../apple-touch-icon.png"><link rel="apple-touch-icon" type="image/png" sizes="120x120" href="../apple-touch-icon-120x120.png"><link rel="apple-touch-icon" type="image/png" sizes="76x76" href="../apple-touch-icon-76x76.png"><link rel="apple-touch-icon" type="image/png" sizes="60x60" href="../apple-touch-icon-60x60.png"><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><link href="../deps/Atkinson_Hyperlegible-0.4.8/font.css" rel="stylesheet"><link href="../deps/Source_Code_Pro-0.4.8/font.css" rel="stylesheet"><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Expression data (in counts) for 3 Chlamydomonas lines (P1, P2, and F1) — se_chlamy"><meta property="og:description" content="Two lines (referred to as parent 1 and parent 2) with different ploidy
 levels were crossed to generate an allopolyploid (F1)."><meta property="og:image" content="/logo.png"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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@@ -8,7 +8,7 @@
     <a href="#main" class="visually-hidden-focusable">Skip to contents</a>
     
 
-    <nav class="navbar fixed-top navbar-light navbar-expand-lg bg-#0092ac"><div class="container">
+    <nav class="navbar fixed-top navbar-light navbar-expand-lg bg-light"><div class="container">
     
     <a class="navbar-brand me-2" href="../index.html">HybridExpress</a>
 
diff --git a/search.json b/search.json
index b449a2c..5906c67 100644
--- a/search.json
+++ b/search.json
@@ -1 +1 @@
-[{"path":"/SUPPORT.html","id":null,"dir":"","previous_headings":"","what":"Getting help with HybridExpress","title":"Getting help with HybridExpress","text":"Thank using HybridExpress! filing issue, things know make process smooth possible parties.","code":""},{"path":"/SUPPORT.html","id":"make-a-reprex","dir":"","previous_headings":"","what":"Make a reprex","title":"Getting help with HybridExpress","text":"Start making minimally reproducible example, also known ‘reprex’. may use reprex R package create one, though necessary help. make R-question-asking endeavors easier. Learning use takes 5 10 minutes. tips make minimally reproducible example, see StackOverflow link.","code":""},{"path":"/SUPPORT.html","id":"where-to-post-it","dir":"","previous_headings":"","what":"Where to post it?","title":"Getting help with HybridExpress","text":"Bioconductor help web page gives overview places may help answer question. Bioconductor software related questions, bug reports feature requests, addressed appropriate Bioconductor/HybridExpress GitHub repository. Follow bug report feature request templates GitHub. package GitHub repository, see next bullet point. Bioconductor software usage questions addressed Bioconductor Support Website. Make sure use appropriate package tag, otherwise package authors get notification. General R questions can posed StackOverflow RStudio Community website especially pertain tidyverse RStudio GUI related products.","code":""},{"path":"/SUPPORT.html","id":"issues-or-feature-requests","dir":"","previous_headings":"","what":"Issues or Feature Requests","title":"Getting help with HybridExpress","text":"opening new issue feature request, sure search issues pull requests ensure one already exist implemented development version. Note. can remove :open search term issues page search open closed issues. See link learn modifying search.","code":""},{"path":"/SUPPORT.html","id":"what-happens-next","dir":"","previous_headings":"","what":"What happens next?","title":"Getting help with HybridExpress","text":"Bioconductor maintainers limited resources strive responsive possible. Please forget tag appropriate maintainer issue GitHub username (e.g., @username). order make easy possible Bioconductor core developers remediate issue. Provide accurate, brief, reproducible report outlined issue templates. Thank trusting Bioconductor.","code":""},{"path":"/articles/HybridExpress.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Comparative transcriptomic analysis of hybrids and their progenitors","text":"formation hybrids fusion distinct genomes subsequent genome duplication cases allopolyploidy represents significant evolutionary event complex effects cellular biology, particularly gene expression. impact genome mergings duplications transcription remain incompletely understood. bridge gap, introduce HybridExpress, comprehensive package designed facilitate comparative transcriptomic analysis hybrids progenitor species. HybridExpress tailored RNA-Seq data derived ‘experimental trio’: hybrid organism two parental species. package offers suite intuitive functions enabling researchers perform differential expression analysis ease, generate principal component analysis (PCA) plots visualizing gene expression trends, categorize genes 12 distinct expression pattern groups (Rapp, Udall, Wendel (2009)), conduct -depth functional analyses. Acknowledging potential variability cell transcriptome size across species ploidy levels, HybridExpress incorporates features rigorous normalization count data. Specifically, allows integration spike-standards directly normalization process, ensuring accurate transcriptome size adjustments standards present RNA-Seq count data (see full methodology Coate (2023)). offering capabilities, HybridExpress provides robust toolset unraveling intricate effects genome doubling merging hybrid gene expression, paving way novel insights cellular biology hybrid organisms.","code":""},{"path":"/articles/HybridExpress.html","id":"installation","dir":"Articles","previous_headings":"","what":"Installation","title":"Comparative transcriptomic analysis of hybrids and their progenitors","text":"HybridExpress can installed Bioconductor following code:","code":"if(!requireNamespace('BiocManager', quietly = TRUE))   install.packages('BiocManager')  BiocManager::install(\"HybridExpress\") # Load package after installation library(HybridExpress)  set.seed(123) # for reproducibility"},{"path":"/articles/HybridExpress.html","id":"data-description","dir":"Articles","previous_headings":"","what":"Data description","title":"Comparative transcriptomic analysis of hybrids and their progenitors","text":"vignette, use example data set comprises (unpublished) gene expression data (counts) Chlamydomonas reinhardtii. lab, crossed diploid line C. reinhardtii (hereafter “P1”) haploid line (hereafter “P2”), thus generating triploid line merging two parental genomes. count matrix sample metadata stored SummarizedExperiment objects, standard Bioconductor data structure required HybridExpress. instructions create SummarizedExperiment object, check FAQ section vignette. Let’s load example data take quick look : can see, count matrix contains 13058 genes 18 samples, 6 replicates parent 1 (P1, diploid), 6 replicates parent 2 (P2, haploid), 6 replicates progeny (F1, triploid).","code":"library(SummarizedExperiment)  # Load data data(se_chlamy)  # Inspect the `SummarizedExperiment` object se_chlamy #> class: SummarizedExperiment  #> dim: 13058 18  #> metadata(0): #> assays(1): counts #> rownames(13058): Cre01.g000050 Cre01.g000150 ... ERCC-00170 ERCC-00171 #> rowData names(0): #> colnames(18): S1 S2 ... S17 S18 #> colData names(2): Ploidy Generation  ## Take a look at the colData and count matrix colData(se_chlamy) #> DataFrame with 18 rows and 2 columns #>          Ploidy Generation #>     <character>   <factor> #> S1      diploid         P1 #> S2      diploid         P1 #> S3      diploid         P1 #> S4      diploid         P1 #> S5      diploid         P1 #> ...         ...        ... #> S14    triploid         F1 #> S15    triploid         F1 #> S16    triploid         F1 #> S17    triploid         F1 #> S18    triploid         F1 assay(se_chlamy) |> head() #>                 S1  S2   S3   S4  S5   S6   S7   S8   S9 S10 S11  S12  S13  S14 #> Cre01.g000050   31  21   26   33  19   48   17    6   13   6  10    7   31   22 #> Cre01.g000150   50  29   35   99  11   58   51   46   41  33  28   14   53   25 #> Cre01.g000200   36  26   24   29  17   36   14   15   12   8  14    7   25   24 #> Cre01.g000250  440 272  394  332 283  585  272  274  255 160 225  235  405  391 #> Cre01.g000300 1242 839 1216 1251 811 1785 1341 1306 1122 877 844 1082 1704 1739 #> Cre01.g000350  412 264  294  336 252  478  233  221  195 155 190  201  299  272 #>                S15  S16  S17  S18 #> Cre01.g000050   29   22   21   16 #> Cre01.g000150   37   17   24   21 #> Cre01.g000200   24   26   18   21 #> Cre01.g000250  358  339  340  332 #> Cre01.g000300 1524 1720 1517 1243 #> Cre01.g000350  276  324  246  275  table(se_chlamy$Ploidy, se_chlamy$Generation) #>            #>            F1 P1 P2 #>   diploid   0  6  0 #>   haploid   0  0  6 #>   triploid  6  0  0"},{"path":"/articles/HybridExpress.html","id":"adding-midparent-expression-values","dir":"Articles","previous_headings":"","what":"Adding midparent expression values","title":"Comparative transcriptomic analysis of hybrids and their progenitors","text":"First , ’d want add count matrix silico samples contain expression values midparent. can done function add_midparent_expression(), takes random sample pair (one sample parent, sampling without replacement) calculates midparent expression value one three ways: Mean (default): get mean expression two samples. Sum: get sum two samples. Weighted mean: get weighted mean two samples multiplying expression value parent weight. Typically, can used two parents different ploidy levels, weights correspond ploidy level parent. function, besides specifying method obtain midparent expression values (.e., “mean”, “sum”, “weightedmean”), users must also specify name column colData contains information generations (default: “Generation”), well levels corresponding parent (default: “P1” “P2” parents 1 2, respectively). proceed analyses midparent expression values obtained mean counts, stored se object.","code":"# Add midparent expression using the mean of the counts se <- add_midparent_expression(se_chlamy) head(assay(se)) #>                 S1  S2   S3   S4  S5   S6   S7   S8   S9 S10 S11  S12  S13  S14 #> Cre01.g000050   31  21   26   33  19   48   17    6   13   6  10    7   31   22 #> Cre01.g000150   50  29   35   99  11   58   51   46   41  33  28   14   53   25 #> Cre01.g000200   36  26   24   29  17   36   14   15   12   8  14    7   25   24 #> Cre01.g000250  440 272  394  332 283  585  272  274  255 160 225  235  405  391 #> Cre01.g000300 1242 839 1216 1251 811 1785 1341 1306 1122 877 844 1082 1704 1739 #> Cre01.g000350  412 264  294  336 252  478  233  221  195 155 190  201  299  272 #>                S15  S16  S17  S18 midparent1 midparent2 midparent3 midparent4 #> Cre01.g000050   29   22   21   16         18         27         14         20 #> Cre01.g000150   37   17   24   21         32         46         38         56 #> Cre01.g000200   24   26   18   21         19         22         20         18 #> Cre01.g000250  358  339  340  332        310        372        273        284 #> Cre01.g000300 1524 1720 1517 1243       1030       1331       1072       1166 #> Cre01.g000350  276  324  246  275        242        316        242        268 #>               midparent5 midparent6 #> Cre01.g000050         18         22 #> Cre01.g000150         31         46 #> Cre01.g000200         16         24 #> Cre01.g000250        278        348 #> Cre01.g000300       1076       1182 #> Cre01.g000350        242        304  # Alternative 1: using the sum of the counts add_midparent_expression(se_chlamy, method = \"sum\") |>     assay() |>      head() #>                 S1  S2   S3   S4  S5   S6   S7   S8   S9 S10 S11  S12  S13  S14 #> Cre01.g000050   31  21   26   33  19   48   17    6   13   6  10    7   31   22 #> Cre01.g000150   50  29   35   99  11   58   51   46   41  33  28   14   53   25 #> Cre01.g000200   36  26   24   29  17   36   14   15   12   8  14    7   25   24 #> Cre01.g000250  440 272  394  332 283  585  272  274  255 160 225  235  405  391 #> Cre01.g000300 1242 839 1216 1251 811 1785 1341 1306 1122 877 844 1082 1704 1739 #> Cre01.g000350  412 264  294  336 252  478  233  221  195 155 190  201  299  272 #>                S15  S16  S17  S18 midparent1 midparent2 midparent3 midparent4 #> Cre01.g000050   29   22   21   16         43         26         58         46 #> Cre01.g000150   37   17   24   21         86         25         86        140 #> Cre01.g000200   24   26   18   21         38         24         50         41 #> Cre01.g000250  358  339  340  332        666        518        810        587 #> Cre01.g000300 1524 1720 1517 1243       2557       1893       2629       2373 #> Cre01.g000350  276  324  246  275        527        453        668        531 #>               midparent5 midparent6 #> Cre01.g000050         37         27 #> Cre01.g000150         96         62 #> Cre01.g000200         51         34 #> Cre01.g000250        714        432 #> Cre01.g000300       2548       1716 #> Cre01.g000350        633        419  # Alternative 2: using the weighted mean of the counts (weights = ploidy) w <- c(2, 1) # P1 = diploid; P2 = haploid add_midparent_expression(se_chlamy, method = \"weightedmean\", weights = w) |>     assay() |>      head() #>                 S1  S2   S3   S4  S5   S6   S7   S8   S9 S10 S11  S12  S13  S14 #> Cre01.g000050   31  21   26   33  19   48   17    6   13   6  10    7   31   22 #> Cre01.g000150   50  29   35   99  11   58   51   46   41  33  28   14   53   25 #> Cre01.g000200   36  26   24   29  17   36   14   15   12   8  14    7   25   24 #> Cre01.g000250  440 272  394  332 283  585  272  274  255 160 225  235  405  391 #> Cre01.g000300 1242 839 1216 1251 811 1785 1341 1306 1122 877 844 1082 1704 1739 #> Cre01.g000350  412 264  294  336 252  478  233  221  195 155 190  201  299  272 #>                S15  S16  S17  S18 midparent1 midparent2 midparent3 midparent4 #> Cre01.g000050   29   22   21   16         25         29         39         22 #> Cre01.g000150   37   17   24   21         27         30         29         16 #> Cre01.g000200   24   26   18   21         27         32         33         21 #> Cre01.g000250  358  339  340  332        181        232        270        210 #> Cre01.g000300 1524 1720 1517 1243       1453       1576       1753       1532 #> Cre01.g000350  276  324  246  275        166        202        223        165 #>               midparent5 midparent6 #> Cre01.g000050         26         17 #> Cre01.g000150         48         15 #> Cre01.g000200         29         17 #> Cre01.g000250        202        148 #> Cre01.g000300       1705       1125 #> Cre01.g000350        186        136"},{"path":"/articles/HybridExpress.html","id":"exploratory-data-analyses","dir":"Articles","previous_headings":"","what":"Exploratory data analyses","title":"Comparative transcriptomic analysis of hybrids and their progenitors","text":"Next, can perform exploratory analyses sample clustering verify samples group expected. HybridExpress, can performed using two functions: pca_plot(): creates principal component analysis (PCA) plots, colors shapes (optional) mapped levels colData variables; plot_samplecor(): plots heatmap hierarchically clustered pairwise sample correlations. Let’s start PCA plot:  plot , data point corresponds sample, colors shapes mapped levels variables specified arguments color_by shape_by, respectively. Besides, specifying add_mean = TRUE, added diamond shape indicating mean PC coordinates based variable color_by (, “Generation”). Now, let’s plot heatmap sample correlations:  can see samples group well together PCA plot correlation heatmap. note, pca_plot() plot_samplecor() use top 500 genes highest variances create plot. genes low variances (.e., genes vary much across samples) uninformative typically add noise. can change number (use less genes) ntop argument functions.","code":"# For colData rows with missing values (midparent samples), add \"midparent\" se$Ploidy[is.na(se$Ploidy)] <- \"midparent\" se$Generation[is.na(se$Generation)] <- \"midparent\"  # Create PCA plot pca_plot(se, color_by = \"Generation\", shape_by = \"Ploidy\", add_mean = TRUE) #> converting counts to integer mode # Plot a heatmap of sample correlations plot_samplecor(se) #> converting counts to integer mode"},{"path":"/articles/HybridExpress.html","id":"identifying-differentially-expressed-genes-between-hybrids-and-their-parents","dir":"Articles","previous_headings":"","what":"Identifying differentially expressed genes between hybrids and their parents","title":"Comparative transcriptomic analysis of hybrids and their progenitors","text":"compare gene expression levels hybrids progenitor species, can use function get_deg_list(). function performs differential expression analyses using DESeq2 returns list data frames gene-wise test statistics following contrasts: P2_vs_P1: parent 2 (numerator) versus parent 1 (denominator). F1_vs_P1: hybrid (numerator) versus parent 1 (denominator). F1_vs_P2: hybrid (numerator) versus parent 2 (denominator). F1_vs_midparent: hybrid (numerator) vs midparent (denominator). default, count data normalized library size using standard normalization process DESeq2. However, spike-standards included count matrix, can use normalization setting spikein_norm = TRUE specifying pattern used indicate rows contain spike-ins (usually start ERCC)1. example data set, spike-standards available last rows count matrix. Now, use get_deg_list() get differentially expressed genes (DEGs) contrast using spike-normalization. summarize frequencies - -regulated genes per contrast single data frame, use function get_deg_counts(). important note columns perc_up, perc_down, perc_total show percentages -regulated, -regulated, differentially expressed genes relative total number genes count matrix. total number genes count matrix stored ngenes attribute list returned get_deg_list(): However, since count matrix usually include genes genome (e.g., lowly expressed genes genes low variance usually filtered ), percentages perc_up, perc_down, perc_total relative total number genes genome. use total number genes genome reference, need update ngenes attribute DEG list appropriate number follows: , can run get_deg_counts() get percentages relative total number genes genome. Finally, can summarize everything single publication-ready figure using plot plot_expression_triangle(), shows ‘experimental trio’ (.e., hybrid progenitors) triangle, frequencies DEGs indicated.  figure commonly used publications, inspired Rapp, Udall, Wendel (2009). edge (line), numbers middle (bold) indicate frequency DEGs, numbers ends (close boxes) indicate frequency -regulated genes generation. instance, figure shows , contrast F1 P1, 5476 DEGs (32.4% genome), 1471 -regulated F1, 4005 -regulated P1. custom figure, can also specify color palette labels boxes. example:","code":"# Show last rows of the count matrix assay(se) |>      tail() #>              S1   S2   S3   S4   S5   S6   S7   S8   S9  S10  S11  S12  S13 #> ERCC-00163   74   75   55   77   51   84  132  127   93  108   79  102   66 #> ERCC-00164    0    4    0    1    4    1    6    4    1    4    3    2    5 #> ERCC-00165  147  139   87  165  118  179  236  246  139  218  176  145  118 #> ERCC-00168    2    5    2    2    2    6    5    1    1    5    0    3    4 #> ERCC-00170   97   95   73  101   70  118  186  148  110  167  110  103   72 #> ERCC-00171 4644 4959 3554 5357 4170 5946 8207 7915 4992 7843 6455 5884 4537 #>             S14  S15  S16  S17  S18 midparent1 midparent2 midparent3 midparent4 #> ERCC-00163   67   62   80   88   47         67         96        101         90 #> ERCC-00164    4    3    5    4    2          2          2          4          2 #> ERCC-00165  143  115  185  192  136        132        198        192        155 #> ERCC-00168    6    2   11    5    2          1          6          3          2 #> ERCC-00170   99   64  121  103   84         92        142        122        102 #> ERCC-00171 5665 4598 6168 5771 4747       5004       6894       6437       5620 #>            midparent5 midparent6 #> ERCC-00163         92         84 #> ERCC-00164          5          0 #> ERCC-00165        177        143 #> ERCC-00168          4          2 #> ERCC-00170        128        104 #> ERCC-00171       6188       4818 # Get a list of differentially expressed genes for each contrast deg_list <- get_deg_list(se, spikein_norm = TRUE, spikein_pattern = \"ERCC\") #> converting counts to integer mode #> using pre-existing size factors #> estimating dispersions #> gene-wise dispersion estimates #> mean-dispersion relationship #> final dispersion estimates #> fitting model and testing  # Inspecting the output ## Getting contrast names names(deg_list) #> [1] \"P2_vs_P1\"        \"F1_vs_P1\"        \"F1_vs_P2\"        \"F1_vs_midparent\"  ## Accessing gene-wise test statistics for contrast `F1_vs_P1` head(deg_list$F1_vs_P1) #>                baseMean log2FoldChange     lfcSE      stat       pvalue #> Cre01.g000450  39.59273     -1.1632346 0.2165407 -5.371898 7.791211e-08 #> Cre01.g000750 103.10296      0.8807307 0.1842856  4.779162 1.760270e-06 #> Cre01.g000900 471.44084      0.6807925 0.1980730  3.437079 5.880239e-04 #> Cre01.g001000  10.34574     -6.6304915 0.9631384 -6.884256 5.809030e-12 #> Cre01.g001100 453.87151     -0.6457115 0.1506903 -4.285025 1.827187e-05 #> Cre01.g001200 159.03643      0.7231060 0.1946090  3.715685 2.026536e-04 #>                       padj #> Cre01.g000450 4.653531e-07 #> Cre01.g000750 8.329159e-06 #> Cre01.g000900 1.707442e-03 #> Cre01.g001000 6.659069e-11 #> Cre01.g001100 7.200448e-05 #> Cre01.g001200 6.453951e-04  ## Counting the number of DEGs per contrast sapply(deg_list, nrow) #>        P2_vs_P1        F1_vs_P1        F1_vs_P2 F1_vs_midparent  #>            8698            5476            7350            4348 # Get a data frame with DEG frequencies for each contrast deg_counts <- get_deg_counts(deg_list)  deg_counts #>          contrast   up down total perc_up perc_down perc_total #> 1        P2_vs_P1  828 7870  8698     6.4      60.7       67.1 #> 2        F1_vs_P1 1471 4005  5476    11.3      30.9       42.2 #> 3        F1_vs_P2 6487  863  7350    50.0       6.7       56.7 #> 4 F1_vs_midparent 2620 1728  4348    20.2      13.3       33.5 attributes(deg_list)$ngenes #> [1] 12966 # Total number of genes in the C. reinhardtii genome (v6.1): 16883 attributes(deg_list)$ngenes <- 16883 deg_counts <- get_deg_counts(deg_list) deg_counts #>          contrast   up down total perc_up perc_down perc_total #> 1        P2_vs_P1  828 7870  8698     4.9      46.6       51.5 #> 2        F1_vs_P1 1471 4005  5476     8.7      23.7       32.4 #> 3        F1_vs_P2 6487  863  7350    38.4       5.1       43.5 #> 4 F1_vs_midparent 2620 1728  4348    15.5      10.2       25.8 # Plot expression triangle plot_expression_triangle(deg_counts) # Create vectors (length 4) of colors and box labels pal <- c(\"springgreen4\", \"darkorange3\", \"mediumpurple4\", \"mediumpurple3\") labels <- c(\"Parent 1\\n(2n)\", \"Parent 2\\n(n)\", \"Progeny\\n(3n)\", \"Midparent\")  plot_expression_triangle(deg_counts, palette = pal, box_labels = labels)"},{"path":"/articles/HybridExpress.html","id":"expression-based-gene-classification","dir":"Articles","previous_headings":"","what":"Expression-based gene classification","title":"Comparative transcriptomic analysis of hybrids and their progenitors","text":"identifying DEGs different contrasts, ’d typically want classify genes expression partitions based expression patterns. can performed function expression_partitioning(), classifies genes one 12 categories Rapp, Udall, Wendel (2009), 5 major classes summarize 12 categories. five classes : Transgressive -regulation (): gene -regulated hybrid compared parents. Transgressive -regulation (): gene -regulated hybrid compared parents. Additivity (ADD): gene expression hybrid mean parents (additive effect). Expression-level dominance toward parent 1 (ELD_P1): gene expression hybrid parent 1, different parent 2. Expression-level dominance toward parent 2 (ELD_P2): gene expression hybrid parent 2, different parent 1. visualize expression partitions scatter plot expression divergences, can use function plot_expression_partitions().  default, genes grouped Category. However, can also group genes Class follows:  can also visualize frequencies genes partition function plot_partition_frequencies().","code":"# Classify genes in expression partitions exp_partitions <- expression_partitioning(deg_list)  # Inspect the output head(exp_partitions) #>            Gene Category Class lFC_F1_vs_P1 lFC_F1_vs_P2 #> 1 Cre01.g003650        1   ADD    0.7838125   -1.0484464 #> 2 Cre01.g005150        1   ADD    1.0473362   -0.5041601 #> 3 Cre01.g008600        1   ADD    5.0518384   -1.4840829 #> 4 Cre01.g013500        1   ADD    2.1099265   -1.5329846 #> 5 Cre01.g034850        1   ADD    1.5838851   -0.7611868 #> 6 Cre01.g800005        1   ADD    1.4928449   -0.9315119  # Count number of genes per category table(exp_partitions$Category) #>  #>    1    2    3    4    5    6    7    8    9   10   11   12  #>   70  262   66 3283   77  287  214  651  280  147 1760 1666  # Count number of genes per class table(exp_partitions$Class) #>  #>     UP   DOWN    ADD ELD_P1 ELD_P2  #>   1015    427   1736   3563   2022 # Plot partitions as a scatter plot of divergences plot_expression_partitions(exp_partitions, group_by = \"Category\") # Group by `Class` plot_expression_partitions(exp_partitions, group_by = \"Class\") # Visualize frequency of genes in each partition ## By `Category` (default) plot_partition_frequencies(exp_partitions) ## By `Class` plot_partition_frequencies(exp_partitions, group_by = \"Class\")"},{"path":"/articles/HybridExpress.html","id":"overrepresentation-analysis-of-functional-terms","dir":"Articles","previous_headings":"","what":"Overrepresentation analysis of functional terms","title":"Comparative transcriptomic analysis of hybrids and their progenitors","text":"Lastly, ’d want explore whether gene sets interest (e.g., -regulated genes contrast) enriched particular GO term, pathway, protein domain, etc. , use function ora(), performs overrepresentation analysis gene set given data frame functional annotation gene. , use example data set GO annotation C. reinhardtii genes. data set illustrates annotation data frame must shaped: gene ID first column, functional annotations columns. demonstrate usage ora(), let’s check can find enrichment GO term among genes assigned class “ADD”.","code":"# Load example functional annotation (GO terms) data(go_chlamy)  head(go_chlamy) #>            gene                                           GO #> 1 Cre01.g000050                                         <NA> #> 2 Cre01.g000100                                         <NA> #> 3 Cre01.g000150                                     membrane #> 4 Cre01.g000150                          metal ion transport #> 5 Cre01.g000150 metal ion transmembrane transporter activity #> 6 Cre01.g000150                      transmembrane transport # Get a vector of genes in class \"ADD\" genes_add <- exp_partitions$Gene[exp_partitions$Class == \"ADD\"] head(genes_add) #> [1] \"Cre01.g003650\" \"Cre01.g005150\" \"Cre01.g008600\" \"Cre01.g013500\" #> [5] \"Cre01.g034850\" \"Cre01.g800005\"  # Get background genes - genes in the count matrix bg <- rownames(se)  # Perform overrepresentation analysis ora_add <- ora(genes_add, go_chlamy, background = bg)  # Inspect results head(ora_add) #>                                                   term genes all         pval #> 20                      aminoacyl-tRNA ligase activity    14  33 3.517366e-05 #> 84                                           cytoplasm    43 151 6.083978e-07 #> 97                                     DNA replication    17  37 1.331552e-06 #> 114                             endopeptidase activity     6  10 7.118363e-04 #> 115                              endoplasmic reticulum     9  17 1.128774e-04 #> 118 eukaryotic translation initiation factor 3 complex     7   9 2.044325e-05 #>             padj category #> 20  1.629713e-03       GO #> 84  6.342547e-05       GO #> 97  1.110515e-04       GO #> 114 2.120255e-02       GO #> 115 4.706990e-03       GO #> 118 1.217834e-03       GO"},{"path":"/articles/HybridExpress.html","id":"example-1-overrepresentation-analyses-for-all-expression-based-classes","dir":"Articles","previous_headings":"Overrepresentation analysis of functional terms","what":"Example 1: overrepresentation analyses for all expression-based classes","title":"Comparative transcriptomic analysis of hybrids and their progenitors","text":"example , performed overrepresentation analysis genes associated class “ADD”. wanted classes? case, run ora() multiple times looping class. details, following: class, create vector genes associated ; Run ora() get data frame ORA results. can find example using lapply(). Results class stored elements list object. expression-based category (class), ’d need replace Class Category split() function (see example ).","code":"# Create a list of genes in each class genes_by_class <- split(exp_partitions$Gene, exp_partitions$Class)  names(genes_by_class) #> [1] \"UP\"     \"DOWN\"   \"ADD\"    \"ELD_P1\" \"ELD_P2\" head(genes_by_class$UP) #> [1] \"Cre01.g029250\" \"Cre01.g034380\" \"Cre01.g036700\" \"Cre01.g049400\" #> [5] \"Cre02.g085500\" \"Cre02.g087150\"  # Iterate through each class and perform ORA ora_classes <- lapply(     genes_by_class, # list through which we will iterate     ora, # function we will apply to each element of the list     annotation = go_chlamy, background = bg # additional arguments to function )  # Inspect output ora_classes #> $UP #>                                    term genes all         pval         padj #> 29              ATP hydrolysis activity    21 119 3.074727e-04 0.0427387078 #> 109                      dynein complex     8  13 1.172717e-06 0.0004890231 #> 127 flavin adenine dinucleotide binding    11  42 2.687071e-04 0.0427387078 #>     category #> 29        GO #> 109       GO #> 127       GO #>  #> $DOWN #>                                  term genes all         pval         padj #> 242           oxidoreductase activity    21 274 2.668754e-04 2.225741e-02 #> 271                    photosynthesis    13  58 3.367873e-08 7.022015e-06 #> 272  photosynthesis, light harvesting    18  24 1.444312e-22 6.022782e-20 #> 276                     photosystem I     5  15 8.360388e-05 8.715704e-03 #> 376 tetrapyrrole biosynthetic process     5   8 1.885876e-06 2.621368e-04 #>     category #> 242       GO #> 271       GO #> 272       GO #> 276       GO #> 376       GO #>  #> $ADD #>                                                                                      term #> 20                                                         aminoacyl-tRNA ligase activity #> 84                                                                              cytoplasm #> 97                                                                        DNA replication #> 114                                                                endopeptidase activity #> 115                                                                 endoplasmic reticulum #> 118                                    eukaryotic translation initiation factor 3 complex #> 165                                                       intracellular protein transport #> 227                                                                          nuclear pore #> 250 oxidoreductase activity, acting on the CH-OH group of donors, NAD or NADP as acceptor #> 284                                                                     prefoldin complex #> 287                                                               proteasome core complex #> 293                                                                       protein folding #> 294                                                                 protein glycosylation #> 304                                                                     protein refolding #> 390                                                translation initiation factor activity #> 410                                                              unfolded protein binding #> 414                                                            vesicle-mediated transport #>     genes all         pval         padj category #> 20     14  33 3.517366e-05 1.629713e-03       GO #> 84     43 151 6.083978e-07 6.342547e-05       GO #> 97     17  37 1.331552e-06 1.110515e-04       GO #> 114     6  10 7.118363e-04 2.120255e-02       GO #> 115     9  17 1.128774e-04 4.706990e-03       GO #> 118     7   9 2.044325e-05 1.217834e-03       GO #> 165    25  73 3.907713e-06 2.715861e-04       GO #> 227     6   9 3.209008e-04 1.115130e-02       GO #> 250    10  27 1.647170e-03 4.292937e-02       GO #> 284     4   4 3.114510e-04 1.115130e-02       GO #> 287     9  15 2.987692e-05 1.557334e-03       GO #> 293    29  81 2.288079e-07 3.180430e-05       GO #> 294     6  10 7.118363e-04 2.120255e-02       GO #> 304    10  10 1.686356e-09 7.032106e-07       GO #> 390     9  21 8.277887e-04 2.301252e-02       GO #> 410    19  38 5.773438e-08 1.203762e-05       GO #> 414    13  41 1.832917e-03 4.496037e-02       GO #>  #> $ELD_P1 #>                term genes all         pval         padj category #> 38          binding    88 208 1.818832e-06 0.0007584527       GO #> 239         nucleus    77 193 8.891683e-05 0.0185391599       GO #> 345 rRNA processing    15  24 3.183647e-04 0.0442526890       GO #>  #> $ELD_P2 #>                                                                term genes all #> 28                             aspartic-type endopeptidase activity     6   8 #> 170                                                iron ion binding    30  93 #> 242                                         oxidoreductase activity    71 274 #> 271                                                  photosynthesis    26  58 #> 278                                                  photosystem II    17  32 #> 320 proton-transporting two-sector ATPase complex, catalytic domain     6   8 #>             pval         padj category #> 28  2.888758e-04 2.007687e-02       GO #> 170 3.997247e-05 4.167130e-03       GO #> 242 4.722014e-06 6.563600e-04       GO #> 271 1.031130e-07 4.299811e-05       GO #> 278 8.669007e-07 1.807488e-04       GO #> 320 2.888758e-04 2.007687e-02       GO"},{"path":"/articles/HybridExpress.html","id":"example-2-overrepresentation-analyses-for-differentially-expressed-genes","dir":"Articles","previous_headings":"Overrepresentation analysis of functional terms","what":"Example 2: overrepresentation analyses for differentially expressed genes","title":"Comparative transcriptomic analysis of hybrids and their progenitors","text":"can use lapply() loop expression class, can also loop contrast list returned get_deg_list(), perform ORA - -regulated genes. can find example: Likewise, -regulated genes, need replace > symbol < symbol anonymous function subset rows.","code":"# Get up-regulated genes for each contrast up_genes <- lapply(deg_list, function(x) rownames(x[x$log2FoldChange > 0, ])) names(up_genes) #> [1] \"P2_vs_P1\"        \"F1_vs_P1\"        \"F1_vs_P2\"        \"F1_vs_midparent\" head(up_genes$F1_vs_P1) #> [1] \"Cre01.g000750\" \"Cre01.g000900\" \"Cre01.g001200\" \"Cre01.g002750\" #> [5] \"Cre01.g003524\" \"Cre01.g003650\"  # Perform ORA ora_up <- lapply(     up_genes,     ora,     annotation = go_chlamy, background = bg )  ora_up #> $P2_vs_P1 #> [1] term     genes    all      pval     padj     category #> <0 rows> (or 0-length row.names) #>  #> $F1_vs_P1 #>               term genes all         pval        padj category #> 109 dynein complex     8  13 1.944536e-05 0.008108715       GO #>  #> $F1_vs_P2 #>                                              term genes all         pval #> 38                                        binding   138 208 7.344020e-07 #> 96                                     DNA repair    56  83 7.655167e-04 #> 144                                   GTP binding    72 111 8.525066e-04 #> 169                                 ion transport    33  45 1.048368e-03 #> 209                     microtubule-based process    10  10 9.123368e-04 #> 239                                       nucleus   125 193 1.471530e-05 #> 303                        protein polymerization    10  10 9.123368e-04 #> 304                             protein refolding    10  10 9.123368e-04 #> 307 protein serine/threonine phosphatase activity    20  24 6.876258e-04 #> 338                                   RNA binding   101 157 1.388743e-04 #> 384                  transcription, DNA-templated    28  36 5.139935e-04 #>             padj category #> 38  0.0003062456       GO #> 96  0.0380444450       GO #> 144 0.0380444450       GO #> 169 0.0397426775       GO #> 209 0.0380444450       GO #> 239 0.0030681409       GO #> 303 0.0380444450       GO #> 304 0.0380444450       GO #> 307 0.0380444450       GO #> 338 0.0193035210       GO #> 384 0.0380444450       GO #>  #> $F1_vs_midparent #>                                              term genes all         pval #> 80         cyclic nucleotide biosynthetic process    38 108 1.704386e-04 #> 109                                dynein complex     9  13 1.689230e-04 #> 166             intracellular signal transduction    38 110 2.618073e-04 #> 168                          ion channel activity    20  48 5.098878e-04 #> 169                                 ion transport    21  45 5.169533e-05 #> 290                     protein dephosphorylation    21  44 3.395214e-05 #> 307 protein serine/threonine phosphatase activity    15  24 6.787504e-06 #>            padj category #> 80  0.014214579       GO #> 109 0.014214579       GO #> 166 0.018195605       GO #> 168 0.030374745       GO #> 169 0.007185651       GO #> 290 0.007079020       GO #> 307 0.002830389       GO"},{"path":"/articles/HybridExpress.html","id":"faq","dir":"Articles","previous_headings":"","what":"FAQ","title":"Comparative transcriptomic analysis of hybrids and their progenitors","text":"create SummarizedExperiment object? SummarizedExperiment data structure (S4 class) can used store, single object, following elements: assay: quantitative matrix features rows samples columns. context HybridExpress, gene expression matrix (counts) genes rows samples columns. colData: data frame sample metadata samples rows variables describe samples (e.g., tissue, treatment, covariates) columns. rowData: data frame gene metadata genes rows variables describe genes (e.g., chromosome name, alternative IDs, functional information, etc) columns. package, must assay containing count matrix colData slot sample metadata. rowData can present, required. demonstrate create SummarizedExperiment object, extract assay colData example object se_chlamy comes package. two objects, can create SummarizedExperiment object : details data structure, read vignette SummarizedExperiment package.","code":"# Get count matrix count_matrix <- assay(se_chlamy) head(count_matrix) #>                 S1  S2   S3   S4  S5   S6   S7   S8   S9 S10 S11  S12  S13  S14 #> Cre01.g000050   31  21   26   33  19   48   17    6   13   6  10    7   31   22 #> Cre01.g000150   50  29   35   99  11   58   51   46   41  33  28   14   53   25 #> Cre01.g000200   36  26   24   29  17   36   14   15   12   8  14    7   25   24 #> Cre01.g000250  440 272  394  332 283  585  272  274  255 160 225  235  405  391 #> Cre01.g000300 1242 839 1216 1251 811 1785 1341 1306 1122 877 844 1082 1704 1739 #> Cre01.g000350  412 264  294  336 252  478  233  221  195 155 190  201  299  272 #>                S15  S16  S17  S18 #> Cre01.g000050   29   22   21   16 #> Cre01.g000150   37   17   24   21 #> Cre01.g000200   24   26   18   21 #> Cre01.g000250  358  339  340  332 #> Cre01.g000300 1524 1720 1517 1243 #> Cre01.g000350  276  324  246  275  # Get colData (data frame of sample metadata) coldata <- colData(se_chlamy) head(coldata) #> DataFrame with 6 rows and 2 columns #>         Ploidy Generation #>    <character>   <factor> #> S1     diploid         P1 #> S2     diploid         P1 #> S3     diploid         P1 #> S4     diploid         P1 #> S5     diploid         P1 #> S6     diploid         P1 # Create a SummarizedExperiment object new_se <- SummarizedExperiment(     assays = list(counts = count_matrix),     colData = coldata )  new_se #> class: SummarizedExperiment  #> dim: 13058 18  #> metadata(0): #> assays(1): counts #> rownames(13058): Cre01.g000050 Cre01.g000150 ... ERCC-00170 ERCC-00171 #> rowData names(0): #> colnames(18): S1 S2 ... S17 S18 #> colData names(2): Ploidy Generation"},{"path":"/articles/HybridExpress.html","id":"session-information","dir":"Articles","previous_headings":"","what":"Session information","title":"Comparative transcriptomic analysis of hybrids and their progenitors","text":"document created following conditions:","code":"#> ─ Session info ─────────────────────────────────────────────────────────────── #>  setting  value #>  version  R Under development (unstable) (2023-11-22 r85609) #>  os       Ubuntu 22.04.3 LTS #>  system   x86_64, linux-gnu #>  ui       X11 #>  language en #>  collate  en_US.UTF-8 #>  ctype    en_US.UTF-8 #>  tz       UTC #>  date     2023-12-12 #>  pandoc   3.1.1 @ /usr/local/bin/ (via rmarkdown) #>  #> ─ Packages ─────────────────────────────────────────────────────────────────── #>  package              * version   date (UTC) lib source #>  abind                  1.4-5     2016-07-21 [1] CRAN (R 4.4.0) #>  Biobase              * 2.63.0    2023-10-24 [1] Bioconductor 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Author, maintainer. Lucas Prost-Boxoen. Author. Yves Van de Peer. Author.","code":""},{"path":"/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Almeida-Silva F, Prost-Boxoen L, Van de Peer Y (2023). HybridExpress: Comparative analysis RNA-seq data hybrids progenitors. R package version 0.99.0, https://github.com/almeidasilvaf/HybridExpress.","code":"@Manual{,   title = {HybridExpress: Comparative analysis of RNA-seq data for hybrids and their progenitors},   author = {Fabricio Almeida-Silva and Lucas Prost-Boxoen and Yves {Van de Peer}},   year = {2023},   note = {R package version 0.99.0},   url = {https://github.com/almeidasilvaf/HybridExpress}, }"},{"path":"/index.html","id":"hybridexpress-","dir":"","previous_headings":"","what":"Comparative analysis of RNA-seq data for hybrids and their progenitors","title":"Comparative analysis of RNA-seq data for hybrids and their progenitors","text":"goal HybridExpress perform comparative transcriptomic analyses hybrids relative progenitor species (.k.. experimental trios). package features: Calculation midparent expression values, silico samples obtained mean, sum, weighted mean random sample pairs parent; Exploratory analyses sample grouping PCA plots heatmaps hierarchically-clustered pairwise sample correlations; Identification differentially expressed genes hybrids progenitor species, hybrids midparent values, two parents. spike-standards available, HybridExpress uses normalize count data transcriptome size; Classification genes expression-based categories classes based Rapp et al. (2009). 12 expression categories proposed Rapp et al. (2009) grouped 5 major classes (transgressive -regulation, transgressive -regulation, additivity, expression-level dominance toward parent 1, expression-level dominance toward parent 2); Functional analyses identification overrepresented functional terms gene sets interest.","code":""},{"path":"/index.html","id":"installation-instructions","dir":"","previous_headings":"","what":"Installation instructions","title":"Comparative analysis of RNA-seq data for hybrids and their progenitors","text":"Get latest stable R release CRAN. install HybridExpress Bioconductor using following code: development version GitHub :","code":"if (!requireNamespace(\"BiocManager\", quietly = TRUE)) {     install.packages(\"BiocManager\") }  BiocManager::install(\"HybridExpress\") BiocManager::install(\"almeidasilvaf/HybridExpress\")"},{"path":"/index.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Comparative analysis of RNA-seq data for hybrids and their progenitors","text":"citation output using citation('HybridExpress') R. Please run check updates cite HybridExpress. Please note HybridExpress made possible thanks many R bioinformatics software authors, cited either vignettes /paper(s) describing package.","code":"print(citation('HybridExpress'), bibtex = TRUE) #> To cite package 'HybridExpress' in publications use: #>  #>   Almeida-Silva F, Prost-Boxoen L, Van de Peer Y (2023). #>   _HybridExpress: Comparative analysis of RNA-seq data for hybrids and #>   their progenitors_. R package version 0.99.0, #>   <https://github.com/almeidasilvaf/HybridExpress>. #>  #> A BibTeX entry for LaTeX users is #>  #>   @Manual{, #>     title = {HybridExpress: Comparative analysis of RNA-seq data for hybrids and their progenitors}, #>     author = {Fabricio Almeida-Silva and Lucas Prost-Boxoen and Yves {Van de Peer}}, #>     year = {2023}, #>     note = {R package version 0.99.0}, #>     url = {https://github.com/almeidasilvaf/HybridExpress}, #>   }"},{"path":"/index.html","id":"code-of-conduct","dir":"","previous_headings":"","what":"Code of Conduct","title":"Comparative analysis of RNA-seq data for hybrids and their progenitors","text":"Please note HybridExpress project released Contributor Code Conduct. contributing project, agree abide terms.","code":""},{"path":"/index.html","id":"development-tools","dir":"","previous_headings":"","what":"Development tools","title":"Comparative analysis of RNA-seq data for hybrids and their progenitors","text":"Continuous code testing possible thanks GitHub actions usethis, remotes, rcmdcheck customized use Bioconductor’s docker containers BiocCheck. Code coverage assessment possible thanks codecov covr. documentation website automatically updated thanks pkgdown. code styled automatically thanks styler. documentation formatted thanks devtools roxygen2. package developed using biocthis.","code":""},{"path":"/reference/add_midparent_expression.html","id":null,"dir":"Reference","previous_headings":"","what":"Add midparent expression to SummarizedExperiment object — add_midparent_expression","title":"Add midparent expression to SummarizedExperiment object — add_midparent_expression","text":"Add midparent expression SummarizedExperiment object","code":""},{"path":"/reference/add_midparent_expression.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add midparent expression to SummarizedExperiment object — add_midparent_expression","text":"","code":"add_midparent_expression(   se,   coldata_column = \"Generation\",   parent1 = \"P1\",   parent2 = \"P2\",   method = \"mean\",   weights = c(1, 1) )"},{"path":"/reference/add_midparent_expression.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add midparent expression to SummarizedExperiment object — add_midparent_expression","text":"se SummarizedExperiment object count matrix sample metadata. coldata_column Character indicating name column colData(se) information generation stored. Default: \"Generation\". parent1 Character indicating level variable coldata_column represents parent 1. Default: \"P1\". parent2 Character indicating level variable coldata_column represents parent 2. Default: \"P2\". method Character indicating method use create midparent values. One 'mean' (default), 'sum', 'weightedmean'. weights Numeric vector length 2 indicating weights give parents 1 2 (respectively) method == \"weightedmean\". Setting method == \"weightedmean\" used sometimes parents different ploidy levels. cases, ploidy levels parents 1 2 can passed vector. Default: c(1, 2).","code":""},{"path":"/reference/add_midparent_expression.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Add midparent expression to SummarizedExperiment object — add_midparent_expression","text":"SummarizedExperiment object.","code":""},{"path":"/reference/add_midparent_expression.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Add midparent expression to SummarizedExperiment object — add_midparent_expression","text":"","code":"data(se_chlamy) new_se <- add_midparent_expression(se_chlamy)"},{"path":"/reference/deg_counts.html","id":null,"dir":"Reference","previous_headings":"","what":"Data frame with frequencies (absolute and relative) of DEGs per contrast — deg_counts","title":"Data frame with frequencies (absolute and relative) of DEGs per contrast — deg_counts","text":"object obtained get_deg_counts() using example data set deg_list.","code":""},{"path":"/reference/deg_counts.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Data frame with frequencies (absolute and relative) of DEGs per contrast — deg_counts","text":"","code":"data(deg_counts)"},{"path":"/reference/deg_counts.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Data frame with frequencies (absolute and relative) of DEGs per contrast — deg_counts","text":"data frame frequencies (absolute relative) - -regulated genes contrast. Relative frequencies calculated relative total number genes count matrix used differential expression analysis.","code":""},{"path":"/reference/deg_counts.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Data frame with frequencies (absolute and relative) of DEGs per contrast — deg_counts","text":"","code":"data(deg_counts)"},{"path":"/reference/deg_list.html","id":null,"dir":"Reference","previous_headings":"","what":"List of differentially expressed genes for all contrasts — deg_list","title":"List of differentially expressed genes for all contrasts — deg_list","text":"object obtained get_deg_list() using example data set se_chlamy.","code":""},{"path":"/reference/deg_list.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"List of differentially expressed genes for all contrasts — deg_list","text":"","code":"data(deg_list)"},{"path":"/reference/deg_list.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"List of differentially expressed genes for all contrasts — deg_list","text":"list data frames gene-wise test statistics differentially expressed genes contrast. Contrasts \"P2_vs_P1\", \"F1_vs_P1\", \"F1_vs_P2\", \"F1_vs_midparent\", ID 'vs' represents numerator, ID 'vs' represents denominator.","code":""},{"path":"/reference/deg_list.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"List of differentially expressed genes for all contrasts — deg_list","text":"","code":"data(deg_list)"},{"path":"/reference/expression_partitioning.html","id":null,"dir":"Reference","previous_headings":"","what":"Partition genes in groups based on their expression patterns — expression_partitioning","title":"Partition genes in groups based on their expression patterns — expression_partitioning","text":"Partition genes groups based expression patterns","code":""},{"path":"/reference/expression_partitioning.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Partition genes in groups based on their expression patterns — expression_partitioning","text":"","code":"expression_partitioning(deg_list)"},{"path":"/reference/expression_partitioning.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Partition genes in groups based on their expression patterns — expression_partitioning","text":"deg_list list data frames gene-wise test statistics differentially expressed genes returned get_deg_list().","code":""},{"path":"/reference/expression_partitioning.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Partition genes in groups based on their expression patterns — expression_partitioning","text":"data following variables: Gene Character, gene ID. Category Factor, expression group. Category names numbers 1 12. Class Factor, expression group class. One \"\" (transgressive -regulation), \"\" (transgressive -regulation), \"ADD\" (additivity), \"ELD_P1\" (expression-level dominance toward parent 1), \"ELD_P2\" (expression-level dominance toward parent 2).","code":""},{"path":"/reference/expression_partitioning.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Partition genes in groups based on their expression patterns — expression_partitioning","text":"","code":"data(deg_list) exp_partitions <- expression_partitioning(deg_list)"},{"path":"/reference/get_deg_counts.html","id":null,"dir":"Reference","previous_headings":"","what":"Get a count table of differentially expressed genes per contrast — get_deg_counts","title":"Get a count table of differentially expressed genes per contrast — get_deg_counts","text":"Get count table differentially expressed genes per contrast","code":""},{"path":"/reference/get_deg_counts.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get a count table of differentially expressed genes per contrast — get_deg_counts","text":"","code":"get_deg_counts(deg_list)"},{"path":"/reference/get_deg_counts.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get a count table of differentially expressed genes per contrast — get_deg_counts","text":"deg_list list data frames gene-wise test statistics differentially expressed genes returned get_deg_list().","code":""},{"path":"/reference/get_deg_counts.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get a count table of differentially expressed genes per contrast — get_deg_counts","text":"data frame following variables: contrast Character, contrast name. Numeric, number -regulated genes. Numeric, number -regulated genes. total Numeric, total number differentially expressed genes. perc_up Numeric, percentage -regulated genes. perc_down Numeric, percentage -regulated genes. perc_total Numeric, percentage diffferentially expressed genes.","code":""},{"path":"/reference/get_deg_counts.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get a count table of differentially expressed genes per contrast — get_deg_counts","text":"","code":"data(deg_list) deg_counts <- get_deg_counts(deg_list)"},{"path":"/reference/get_deg_list.html","id":null,"dir":"Reference","previous_headings":"","what":"Get a table of differential expression expression statistics with DESeq2 — get_deg_list","title":"Get a table of differential expression expression statistics with DESeq2 — get_deg_list","text":"Get table differential expression expression statistics DESeq2","code":""},{"path":"/reference/get_deg_list.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get a table of differential expression expression statistics with DESeq2 — get_deg_list","text":"","code":"get_deg_list(   se,   coldata_column = \"Generation\",   parent1 = \"P1\",   parent2 = \"P2\",   offspring = \"F1\",   midparent = \"midparent\",   spikein_norm = FALSE,   spikein_pattern = \"ERCC\",   lfcThreshold = 0,   alpha = 0.01,   ... )"},{"path":"/reference/get_deg_list.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get a table of differential expression expression statistics with DESeq2 — get_deg_list","text":"se SummarizedExperiment object count matrix sample metadata. coldata_column Character indicating name column colData(se) information generation stored. Default: \"Generation\". parent1 Character indicating level variable coldata_column represents parent 1. Default: \"P1\". parent2 Character indicating level variable coldata_column represents parent 2. Default: \"P2\". offspring Character indicating level variable coldata_column represents offspring (hybrid allopolyploid). Default: \"F1\" midparent Character indicating level variable coldata_column represents midparent value. Default: \"midparent\", returned add_midparent_expression(). spikein_norm Logical indicating whether normalize data using spike-ins. Default: FALSE. spikein_pattern Character pattern (regex) use identify spike-features count matrix. valid spikein_norm = TRUE. lfcThreshold Numeric indicating log2 fold-change threshold use consider differentially expressed genes. Default: 0. alpha Numeric indicating adjusted P-value threshold use consider differentially expressed genes. Default: 0.01. ... Additional arguments passed DESeq2::results().","code":""},{"path":"/reference/get_deg_list.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get a table of differential expression expression statistics with DESeq2 — get_deg_list","text":"list data frames DESeq2's gene-wise tests statistics contrast. data frame contains columns output DESeq2::results(). Contrasts (list names) : P2_vs_P1 Parent 2 (numerator) versus parent 1 (denominator). F1_vs_P1 Offspring (numerator) versus parent 1 (denominator). F1_vs_P2 Offspring (numerator) versus parent 2 (denominator). F1_vs_midparent Offspring (numerator) versus midparent (denominator). data frame gene-wise test statistics list element contains following variables: baseMean Numeric, base mean. log2FoldChange Numeric, log2-transformed fold changes. lfcSE Numeric, standard error log2-transformed fold changes. stat Numeric, observed test statistic. pvalue Numeric, p-value. padj Numeric, P-value adjusted multiple testing. list contains two additional attributes named ngenes (numeric, total number genes), plotdata, 3-column data frame variables \"gene\" (character, gene ID), \"lFC_F1_vs_P1\" (numeric, log2 fold change F1 P1), \"lFC_F1_vs_P2\" (numeric, log2 fold change F1 P2).","code":""},{"path":"/reference/get_deg_list.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get a table of differential expression expression statistics with DESeq2 — get_deg_list","text":"","code":"data(se_chlamy) se <- add_midparent_expression(se_chlamy) deg_list <- get_deg_list(se, spikein_norm = TRUE) #> converting counts to integer mode #> using pre-existing size factors #> estimating dispersions #> gene-wise dispersion estimates #> mean-dispersion relationship #> final dispersion estimates #> fitting model and testing"},{"path":"/reference/go_chlamy.html","id":null,"dir":"Reference","previous_headings":"","what":"Data frame with GO terms annotated to each gene of Chlamydomonas reinhardtii — go_chlamy","title":"Data frame with GO terms annotated to each gene of Chlamydomonas reinhardtii — go_chlamy","text":"Data obtained Phytozome processed row contains one GO term (long format).","code":""},{"path":"/reference/go_chlamy.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Data frame with GO terms annotated to each gene of Chlamydomonas reinhardtii — go_chlamy","text":"","code":"data(go_chlamy)"},{"path":"/reference/go_chlamy.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Data frame with GO terms annotated to each gene of Chlamydomonas reinhardtii — go_chlamy","text":"2-column data frame columns gene (character, gene ID), GO (character, name GO term.)","code":""},{"path":"/reference/go_chlamy.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Data frame with GO terms annotated to each gene of Chlamydomonas reinhardtii — go_chlamy","text":"","code":"data(go_chlamy)"},{"path":"/reference/ora.html","id":null,"dir":"Reference","previous_headings":"","what":"Perform overrepresentation analysis for a set of genes — ora","title":"Perform overrepresentation analysis for a set of genes — ora","text":"Perform overrepresentation analysis set genes","code":""},{"path":"/reference/ora.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Perform overrepresentation analysis for a set of genes — ora","text":"","code":"ora(   genes,   annotation,   column = NULL,   background,   correction = \"BH\",   alpha = 0.05,   min_setsize = 5,   max_setsize = 500,   bp_param = BiocParallel::SerialParam() )"},{"path":"/reference/ora.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Perform overrepresentation analysis for a set of genes — ora","text":"genes Character vector containing genes overrepresentation analysis. annotation Annotation data frame genes first column functional annotation columns. data frame can exported Biomart similar databases. column Column columns annotation used enrichment. character numeric values column indices can used. users want supply one column, input character numeric vector. Default: columns annotation. background Character vector genes used background overrepresentation analysis. correction Multiple testing correction method. One \"holm\", \"hochberg\", \"hommel\", \"bonferroni\", \"BH\", \"\", \"fdr\" \"none\". Default \"BH\". alpha Numeric indicating adjusted P-value threshold significance. Default: 0.05. min_setsize Numeric indicating minimum gene set size considered. Gene sets correspond levels variable annotation). Default: 5. max_setsize Numeric indicating maximum gene set size considered. Gene sets correspond levels variable annotation). Default: 500. bp_param BiocParallel back-end used. Default: BiocParallel::SerialParam()","code":""},{"path":"/reference/ora.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Perform overrepresentation analysis for a set of genes — ora","text":"data frame overrepresentation results following variables: term Character, functional term ID/name. genes Numeric, intersection length input genes genes particular functional term. Numeric, number genes particular functional term. pval Numeric, P-value hypergeometric test. padj Numeric, P-value adjusted multiple comparisons using method specified parameter adj. category Character, name grouping variable (.e., column name annotation).","code":""},{"path":"/reference/ora.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Perform overrepresentation analysis for a set of genes — ora","text":"","code":"data(se_chlamy) data(go_chlamy) data(deg_list)  # Perform ORA for up-regulated genes in contrast F1_vs_P1 up_genes <- deg_list$F1_vs_P1 up_genes <- rownames(up_genes[up_genes$log2FoldChange > 0, ])  background <- rownames(se_chlamy)  ora(up_genes, go_chlamy, background = background) #>               term genes all         pval       padj category #> 133 dynein complex     8  13 1.924882e-05 0.00802676       GO"},{"path":"/reference/pca_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"Perform a principal component analysis (PCA) and plot PCs — pca_plot","title":"Perform a principal component analysis (PCA) and plot PCs — pca_plot","text":"Perform principal component analysis (PCA) plot PCs","code":""},{"path":"/reference/pca_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Perform a principal component analysis (PCA) and plot PCs — pca_plot","text":"","code":"pca_plot(   se,   PCs = c(1, 2),   ntop = 500,   color_by = NULL,   shape_by = NULL,   add_mean = FALSE,   palette = NULL )"},{"path":"/reference/pca_plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Perform a principal component analysis (PCA) and plot PCs — pca_plot","text":"se SummarizedExperiment object count matrix sample metadata. PCs Numeric vector indicating principal components show x-axis y-axis, respectively. Default: c(1,2). ntop Numeric indicating number top genes highest variances use PCA. Default: 500. color_by Character name column colData(se) use group samples color. Default: NULL. shape_by Character name column colData(se) use group samples shape. Default: NULL. add_mean Logical indicating whether add diamond symbol mean value level variable indicated color_by. Default: FALSE palette Character vector colors use level variable indicated color_by. NULL, default color palette used.","code":""},{"path":"/reference/pca_plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Perform a principal component analysis (PCA) and plot PCs — pca_plot","text":"ggplot object PCA plot showing 2 principal components axis along % variance explained.","code":""},{"path":"/reference/pca_plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Perform a principal component analysis (PCA) and plot PCs — pca_plot","text":"","code":"data(se_chlamy) se <- add_midparent_expression(se_chlamy) se$Ploidy[is.na(se$Ploidy)] <- \"midparent\" se$Generation[is.na(se$Generation)] <- \"midparent\" pca_plot(se, color_by = \"Generation\", shape_by = \"Ploidy\", add_mean = TRUE) #> converting counts to integer mode"},{"path":"/reference/plot_expression_partitions.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot expression partitions — plot_expression_partitions","title":"Plot expression partitions — plot_expression_partitions","text":"Plot expression partitions","code":""},{"path":"/reference/plot_expression_partitions.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot expression partitions — plot_expression_partitions","text":"","code":"plot_expression_partitions(   partition_table,   group_by = \"Category\",   palette = NULL,   labels = c(\"P1\", \"F1\", \"P2\") )"},{"path":"/reference/plot_expression_partitions.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot expression partitions — plot_expression_partitions","text":"partition_table data frame genes per expression partition returned expression_partitioning(). group_by Character indicating name variable partition_table use group genes. One \"Category\" \"Class\". Default: \"Category\". palette Character vector color names used level variable specified group_by. group_by = \"Category\", must vector length 12. group_by = \"Class\", must vector length 5. NULL, default color palette used. labels character vector length 3 indicating labels given parent 1, offspring, parent 2. Default: c(\"P1\", \"F1\", \"P2\").","code":""},{"path":"/reference/plot_expression_partitions.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot expression partitions — plot_expression_partitions","text":"ggplot object plot showing genes expression partition.","code":""},{"path":"/reference/plot_expression_partitions.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot expression partitions — plot_expression_partitions","text":"","code":"data(deg_list) partition_table <- expression_partitioning(deg_list) plot_expression_partitions(partition_table)"},{"path":"/reference/plot_expression_triangle.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot a triangle of comparisons of DEG sets among generations — plot_expression_triangle","title":"Plot a triangle of comparisons of DEG sets among generations — plot_expression_triangle","text":"Plot triangle comparisons DEG sets among generations","code":""},{"path":"/reference/plot_expression_triangle.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot a triangle of comparisons of DEG sets among generations — plot_expression_triangle","text":"","code":"plot_expression_triangle(deg_counts, palette = NULL, box_labels = NULL)"},{"path":"/reference/plot_expression_triangle.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot a triangle of comparisons of DEG sets among generations — plot_expression_triangle","text":"deg_counts Data frame number differentially expressed genes per contrast returned get_deg_counts. palette Character vector length 4 indicating colors boxes P1, P2, F1, midparent, respectively. NULL, default color palette used. box_labels Character vector length 4 indicating labels boxes P1, P2, F1, midparent, respectively. Default: NULL, lead labels \"P1\", \"P2\", \"F1\", \"Midparent\", respectively.","code":""},{"path":"/reference/plot_expression_triangle.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot a triangle of comparisons of DEG sets among generations — plot_expression_triangle","text":"ggplot object expression triangle.","code":""},{"path":"/reference/plot_expression_triangle.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Plot a triangle of comparisons of DEG sets among generations — plot_expression_triangle","text":"expression triangle plot shows number differentially expressed genes (DEGs) contrast. Numbers center lines (bold) indicate total number DEGs, numbers near boxes indicate number -regulated genes generation triangle.","code":""},{"path":"/reference/plot_expression_triangle.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot a triangle of comparisons of DEG sets among generations — plot_expression_triangle","text":"","code":"data(deg_counts) plot_expression_triangle(deg_counts)"},{"path":"/reference/plot_partition_frequencies.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot a barplot of gene frequencies per expression partition — plot_partition_frequencies","title":"Plot a barplot of gene frequencies per expression partition — plot_partition_frequencies","text":"Plot barplot gene frequencies per expression partition","code":""},{"path":"/reference/plot_partition_frequencies.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot a barplot of gene frequencies per expression partition — plot_partition_frequencies","text":"","code":"plot_partition_frequencies(   partition_table,   group_by = \"Category\",   palette = NULL,   labels = c(\"P1\", \"F1\", \"P2\") )"},{"path":"/reference/plot_partition_frequencies.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot a barplot of gene frequencies per expression partition — plot_partition_frequencies","text":"partition_table data frame genes per expression partition returned expression_partitioning(). group_by Character indicating name variable partition_table use group genes. One \"Category\" \"Class\". Default: \"Category\". palette Character vector color names used level variable specified group_by. group_by = \"Category\", must vector length 12. group_by = \"Class\", must vector length 5. NULL, default color palette used. labels character vector length 3 indicating labels given parent 1, offspring, parent 2. Default: c(\"P1\", \"F1\", \"P2\").","code":""},{"path":"/reference/plot_partition_frequencies.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot a barplot of gene frequencies per expression partition — plot_partition_frequencies","text":"ggplot object barplot showing gene frequencies per partition next explanatory line plots depicting partition.","code":""},{"path":"/reference/plot_partition_frequencies.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot a barplot of gene frequencies per expression partition — plot_partition_frequencies","text":"","code":"data(deg_list) partition_table <- expression_partitioning(deg_list) plot_partition_frequencies(partition_table)"},{"path":"/reference/plot_samplecor.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot a heatmap of pairwise sample correlations with hierarchical clustering — plot_samplecor","title":"Plot a heatmap of pairwise sample correlations with hierarchical clustering — plot_samplecor","text":"Plot heatmap pairwise sample correlations hierarchical clustering","code":""},{"path":"/reference/plot_samplecor.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot a heatmap of pairwise sample correlations with hierarchical clustering — plot_samplecor","text":"","code":"plot_samplecor(   se,   coldata_cols = NULL,   rowdata_cols = NULL,   ntop = 500,   cor_method = \"pearson\",   palette = \"Blues\",   ... )"},{"path":"/reference/plot_samplecor.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot a heatmap of pairwise sample correlations with hierarchical clustering — plot_samplecor","text":"se SummarizedExperiment object count matrix sample metadata colData slot. rowData slot available, can also used clustering rows. coldata_cols vector (either numeric character) indicating columns extracted colData(se). rowdata_cols vector (either numeric character) indicating columns extracted rowData(se). ntop Numeric indicating number top genes highest variances use PCA. Default: 500. cor_method Character indicating correlation method use. One \"pearson\" \"spearman\". Default: \"pearson\". palette Character indicating name color palette RColorBrewer package use. Default: \"Blues\". ... Additional arguments passed ComplexHeatmap::pheatmap(). arguments can used control heatmap aesthetics, show/hide row column names, change font size, activate/deactivate hierarchical clustering, etc. complete list options, see ?ComplexHeatmap::pheatmap().","code":""},{"path":"/reference/plot_samplecor.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot a heatmap of pairwise sample correlations with hierarchical clustering — plot_samplecor","text":"heatmap hierarchically clustered pairwise sample correlations.","code":""},{"path":"/reference/plot_samplecor.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot a heatmap of pairwise sample correlations with hierarchical clustering — plot_samplecor","text":"","code":"data(se_chlamy) se <- add_midparent_expression(se_chlamy) se$Ploidy[is.na(se$Ploidy)] <- \"midparent\" se$Generation[is.na(se$Generation)] <- \"midparent\" plot_samplecor(se, ntop = 500) #> converting counts to integer mode"},{"path":"/reference/se_chlamy.html","id":null,"dir":"Reference","previous_headings":"","what":"Expression data (in counts) for 3 Chlamydomonas lines (P1, P2, and F1) — se_chlamy","title":"Expression data (in counts) for 3 Chlamydomonas lines (P1, P2, and F1) — se_chlamy","text":"Two lines (referred parent 1 parent 2) different ploidy levels crossed generate allopolyploid (F1).","code":""},{"path":"/reference/se_chlamy.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Expression data (in counts) for 3 Chlamydomonas lines (P1, P2, and F1) — se_chlamy","text":"","code":"data(se_chlamy)"},{"path":"/reference/se_chlamy.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Expression data (in counts) for 3 Chlamydomonas lines (P1, P2, and F1) — se_chlamy","text":"SummarizedExperiment object assay (count) colData.","code":""},{"path":"/reference/se_chlamy.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Expression data (in counts) for 3 Chlamydomonas lines (P1, P2, and F1) — se_chlamy","text":"","code":"data(se_chlamy)"},{"path":"/news/index.html","id":"hybridexpress-0990","dir":"Changelog","previous_headings":"","what":"HybridExpress 0.99.0","title":"HybridExpress 0.99.0","text":"NEW FEATURES Added NEWS.md file track changes package.","code":""}]
+[{"path":"/SUPPORT.html","id":null,"dir":"","previous_headings":"","what":"Getting help with HybridExpress","title":"Getting help with HybridExpress","text":"Thank using HybridExpress! filing issue, things know make process smooth possible parties.","code":""},{"path":"/SUPPORT.html","id":"make-a-reprex","dir":"","previous_headings":"","what":"Make a reprex","title":"Getting help with HybridExpress","text":"Start making minimally reproducible example, also known ‘reprex’. may use reprex R package create one, though necessary help. make R-question-asking endeavors easier. Learning use takes 5 10 minutes. tips make minimally reproducible example, see StackOverflow link.","code":""},{"path":"/SUPPORT.html","id":"where-to-post-it","dir":"","previous_headings":"","what":"Where to post it?","title":"Getting help with HybridExpress","text":"Bioconductor help web page gives overview places may help answer question. Bioconductor software related questions, bug reports feature requests, addressed appropriate Bioconductor/HybridExpress GitHub repository. Follow bug report feature request templates GitHub. package GitHub repository, see next bullet point. Bioconductor software usage questions addressed Bioconductor Support Website. Make sure use appropriate package tag, otherwise package authors get notification. General R questions can posed StackOverflow RStudio Community website especially pertain tidyverse RStudio GUI related products.","code":""},{"path":"/SUPPORT.html","id":"issues-or-feature-requests","dir":"","previous_headings":"","what":"Issues or Feature Requests","title":"Getting help with HybridExpress","text":"opening new issue feature request, sure search issues pull requests ensure one already exist implemented development version. Note. can remove :open search term issues page search open closed issues. See link learn modifying search.","code":""},{"path":"/SUPPORT.html","id":"what-happens-next","dir":"","previous_headings":"","what":"What happens next?","title":"Getting help with HybridExpress","text":"Bioconductor maintainers limited resources strive responsive possible. Please forget tag appropriate maintainer issue GitHub username (e.g., @username). order make easy possible Bioconductor core developers remediate issue. Provide accurate, brief, reproducible report outlined issue templates. Thank trusting Bioconductor.","code":""},{"path":"/articles/HybridExpress.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Comparative transcriptomic analysis of hybrids and their progenitors","text":"formation hybrids fusion distinct genomes subsequent genome duplication cases allopolyploidy represents significant evolutionary event complex effects cellular biology, particularly gene expression. impact genome mergings duplications transcription remain incompletely understood. bridge gap, introduce HybridExpress, comprehensive package designed facilitate comparative transcriptomic analysis hybrids progenitor species. HybridExpress tailored RNA-Seq data derived ‘experimental trio’: hybrid organism two parental species. package offers suite intuitive functions enabling researchers perform differential expression analysis ease, generate principal component analysis (PCA) plots visualizing gene expression trends, categorize genes 12 distinct expression pattern groups (Rapp, Udall, Wendel (2009)), conduct -depth functional analyses. Acknowledging potential variability cell transcriptome size across species ploidy levels, HybridExpress incorporates features rigorous normalization count data. Specifically, allows integration spike-standards directly normalization process, ensuring accurate transcriptome size adjustments standards present RNA-Seq count data (see full methodology Coate (2023)). offering capabilities, HybridExpress provides robust toolset unraveling intricate effects genome doubling merging hybrid gene expression, paving way novel insights cellular biology hybrid organisms.","code":""},{"path":"/articles/HybridExpress.html","id":"installation","dir":"Articles","previous_headings":"","what":"Installation","title":"Comparative transcriptomic analysis of hybrids and their progenitors","text":"HybridExpress can installed Bioconductor following code:","code":"if(!requireNamespace('BiocManager', quietly = TRUE))   install.packages('BiocManager')  BiocManager::install(\"HybridExpress\") # Load package after installation library(HybridExpress)  set.seed(123) # for reproducibility"},{"path":"/articles/HybridExpress.html","id":"data-description","dir":"Articles","previous_headings":"","what":"Data description","title":"Comparative transcriptomic analysis of hybrids and their progenitors","text":"vignette, use example data set comprises (unpublished) gene expression data (counts) Chlamydomonas reinhardtii. lab, crossed diploid line C. reinhardtii (hereafter “P1”) haploid line (hereafter “P2”), thus generating triploid line merging two parental genomes. count matrix sample metadata stored SummarizedExperiment objects, standard Bioconductor data structure required HybridExpress. instructions create SummarizedExperiment object, check FAQ section vignette. Let’s load example data take quick look : can see, count matrix contains 13058 genes 18 samples, 6 replicates parent 1 (P1, diploid), 6 replicates parent 2 (P2, haploid), 6 replicates progeny (F1, triploid).","code":"library(SummarizedExperiment)  # Load data data(se_chlamy)  # Inspect the `SummarizedExperiment` object se_chlamy #> class: SummarizedExperiment  #> dim: 13058 18  #> metadata(0): #> assays(1): counts #> rownames(13058): Cre01.g000050 Cre01.g000150 ... ERCC-00170 ERCC-00171 #> rowData names(0): #> colnames(18): S1 S2 ... S17 S18 #> colData names(2): Ploidy Generation  ## Take a look at the colData and count matrix colData(se_chlamy) #> DataFrame with 18 rows and 2 columns #>          Ploidy Generation #>     <character>   <factor> #> S1      diploid         P1 #> S2      diploid         P1 #> S3      diploid         P1 #> S4      diploid         P1 #> S5      diploid         P1 #> ...         ...        ... #> S14    triploid         F1 #> S15    triploid         F1 #> S16    triploid         F1 #> S17    triploid         F1 #> S18    triploid         F1 assay(se_chlamy) |> head() #>                 S1  S2   S3   S4  S5   S6   S7   S8   S9 S10 S11  S12  S13  S14 #> Cre01.g000050   31  21   26   33  19   48   17    6   13   6  10    7   31   22 #> Cre01.g000150   50  29   35   99  11   58   51   46   41  33  28   14   53   25 #> Cre01.g000200   36  26   24   29  17   36   14   15   12   8  14    7   25   24 #> Cre01.g000250  440 272  394  332 283  585  272  274  255 160 225  235  405  391 #> Cre01.g000300 1242 839 1216 1251 811 1785 1341 1306 1122 877 844 1082 1704 1739 #> Cre01.g000350  412 264  294  336 252  478  233  221  195 155 190  201  299  272 #>                S15  S16  S17  S18 #> Cre01.g000050   29   22   21   16 #> Cre01.g000150   37   17   24   21 #> Cre01.g000200   24   26   18   21 #> Cre01.g000250  358  339  340  332 #> Cre01.g000300 1524 1720 1517 1243 #> Cre01.g000350  276  324  246  275  table(se_chlamy$Ploidy, se_chlamy$Generation) #>            #>            F1 P1 P2 #>   diploid   0  6  0 #>   haploid   0  0  6 #>   triploid  6  0  0"},{"path":"/articles/HybridExpress.html","id":"adding-midparent-expression-values","dir":"Articles","previous_headings":"","what":"Adding midparent expression values","title":"Comparative transcriptomic analysis of hybrids and their progenitors","text":"First , ’d want add count matrix silico samples contain expression values midparent. can done function add_midparent_expression(), takes random sample pair (one sample parent, sampling without replacement) calculates midparent expression value one three ways: Mean (default): get mean expression two samples. Sum: get sum two samples. Weighted mean: get weighted mean two samples multiplying expression value parent weight. Typically, can used two parents different ploidy levels, weights correspond ploidy level parent. function, besides specifying method obtain midparent expression values (.e., “mean”, “sum”, “weightedmean”), users must also specify name column colData contains information generations (default: “Generation”), well levels corresponding parent (default: “P1” “P2” parents 1 2, respectively). proceed analyses midparent expression values obtained mean counts, stored se object.","code":"# Add midparent expression using the mean of the counts se <- add_midparent_expression(se_chlamy) head(assay(se)) #>                 S1  S2   S3   S4  S5   S6   S7   S8   S9 S10 S11  S12  S13  S14 #> Cre01.g000050   31  21   26   33  19   48   17    6   13   6  10    7   31   22 #> Cre01.g000150   50  29   35   99  11   58   51   46   41  33  28   14   53   25 #> Cre01.g000200   36  26   24   29  17   36   14   15   12   8  14    7   25   24 #> Cre01.g000250  440 272  394  332 283  585  272  274  255 160 225  235  405  391 #> Cre01.g000300 1242 839 1216 1251 811 1785 1341 1306 1122 877 844 1082 1704 1739 #> Cre01.g000350  412 264  294  336 252  478  233  221  195 155 190  201  299  272 #>                S15  S16  S17  S18 midparent1 midparent2 midparent3 midparent4 #> Cre01.g000050   29   22   21   16         18         27         14         20 #> Cre01.g000150   37   17   24   21         32         46         38         56 #> Cre01.g000200   24   26   18   21         19         22         20         18 #> Cre01.g000250  358  339  340  332        310        372        273        284 #> Cre01.g000300 1524 1720 1517 1243       1030       1331       1072       1166 #> Cre01.g000350  276  324  246  275        242        316        242        268 #>               midparent5 midparent6 #> Cre01.g000050         18         22 #> Cre01.g000150         31         46 #> Cre01.g000200         16         24 #> Cre01.g000250        278        348 #> Cre01.g000300       1076       1182 #> Cre01.g000350        242        304  # Alternative 1: using the sum of the counts add_midparent_expression(se_chlamy, method = \"sum\") |>     assay() |>      head() #>                 S1  S2   S3   S4  S5   S6   S7   S8   S9 S10 S11  S12  S13  S14 #> Cre01.g000050   31  21   26   33  19   48   17    6   13   6  10    7   31   22 #> Cre01.g000150   50  29   35   99  11   58   51   46   41  33  28   14   53   25 #> Cre01.g000200   36  26   24   29  17   36   14   15   12   8  14    7   25   24 #> Cre01.g000250  440 272  394  332 283  585  272  274  255 160 225  235  405  391 #> Cre01.g000300 1242 839 1216 1251 811 1785 1341 1306 1122 877 844 1082 1704 1739 #> Cre01.g000350  412 264  294  336 252  478  233  221  195 155 190  201  299  272 #>                S15  S16  S17  S18 midparent1 midparent2 midparent3 midparent4 #> Cre01.g000050   29   22   21   16         43         26         58         46 #> Cre01.g000150   37   17   24   21         86         25         86        140 #> Cre01.g000200   24   26   18   21         38         24         50         41 #> Cre01.g000250  358  339  340  332        666        518        810        587 #> Cre01.g000300 1524 1720 1517 1243       2557       1893       2629       2373 #> Cre01.g000350  276  324  246  275        527        453        668        531 #>               midparent5 midparent6 #> Cre01.g000050         37         27 #> Cre01.g000150         96         62 #> Cre01.g000200         51         34 #> Cre01.g000250        714        432 #> Cre01.g000300       2548       1716 #> Cre01.g000350        633        419  # Alternative 2: using the weighted mean of the counts (weights = ploidy) w <- c(2, 1) # P1 = diploid; P2 = haploid add_midparent_expression(se_chlamy, method = \"weightedmean\", weights = w) |>     assay() |>      head() #>                 S1  S2   S3   S4  S5   S6   S7   S8   S9 S10 S11  S12  S13  S14 #> Cre01.g000050   31  21   26   33  19   48   17    6   13   6  10    7   31   22 #> Cre01.g000150   50  29   35   99  11   58   51   46   41  33  28   14   53   25 #> Cre01.g000200   36  26   24   29  17   36   14   15   12   8  14    7   25   24 #> Cre01.g000250  440 272  394  332 283  585  272  274  255 160 225  235  405  391 #> Cre01.g000300 1242 839 1216 1251 811 1785 1341 1306 1122 877 844 1082 1704 1739 #> Cre01.g000350  412 264  294  336 252  478  233  221  195 155 190  201  299  272 #>                S15  S16  S17  S18 midparent1 midparent2 midparent3 midparent4 #> Cre01.g000050   29   22   21   16         25         29         39         22 #> Cre01.g000150   37   17   24   21         27         30         29         16 #> Cre01.g000200   24   26   18   21         27         32         33         21 #> Cre01.g000250  358  339  340  332        181        232        270        210 #> Cre01.g000300 1524 1720 1517 1243       1453       1576       1753       1532 #> Cre01.g000350  276  324  246  275        166        202        223        165 #>               midparent5 midparent6 #> Cre01.g000050         26         17 #> Cre01.g000150         48         15 #> Cre01.g000200         29         17 #> Cre01.g000250        202        148 #> Cre01.g000300       1705       1125 #> Cre01.g000350        186        136"},{"path":"/articles/HybridExpress.html","id":"normalizing-count-data","dir":"Articles","previous_headings":"","what":"Normalizing count data","title":"Comparative transcriptomic analysis of hybrids and their progenitors","text":"normalize count data, function add_size_factors() calculates size factors (used DESeq2 normalization) adds extra column colData slot SummarizedExperiment object. size factors calculated using one two methods: library size (default) using ‘median ratios’ method implemented DESeq2. cell size/biomass using spike-controls (available). spike-controls present count matrix, can use normalization setting spikein = TRUE specifying pattern used indicate rows contain spike-ins (usually start ERCC)1. Normalization spike-controls particularly useful amount mRNA per cell equal generations (due , instance, different ploidy levels, turn can lead different cell sizes /biomass). example data set, spike-controls available last rows count matrix. Let’s take look . can see, rows spike-counts start “ERCC”. Let’s add size factors SummarizedExperiment object using spike-controls.","code":"# Show last rows of the count matrix assay(se) |>      tail() #>              S1   S2   S3   S4   S5   S6   S7   S8   S9  S10  S11  S12  S13 #> ERCC-00163   74   75   55   77   51   84  132  127   93  108   79  102   66 #> ERCC-00164    0    4    0    1    4    1    6    4    1    4    3    2    5 #> ERCC-00165  147  139   87  165  118  179  236  246  139  218  176  145  118 #> ERCC-00168    2    5    2    2    2    6    5    1    1    5    0    3    4 #> ERCC-00170   97   95   73  101   70  118  186  148  110  167  110  103   72 #> ERCC-00171 4644 4959 3554 5357 4170 5946 8207 7915 4992 7843 6455 5884 4537 #>             S14  S15  S16  S17  S18 midparent1 midparent2 midparent3 midparent4 #> ERCC-00163   67   62   80   88   47         67         96        101         90 #> ERCC-00164    4    3    5    4    2          2          2          4          2 #> ERCC-00165  143  115  185  192  136        132        198        192        155 #> ERCC-00168    6    2   11    5    2          1          6          3          2 #> ERCC-00170   99   64  121  103   84         92        142        122        102 #> ERCC-00171 5665 4598 6168 5771 4747       5004       6894       6437       5620 #>            midparent5 midparent6 #> ERCC-00163         92         84 #> ERCC-00164          5          0 #> ERCC-00165        177        143 #> ERCC-00168          4          2 #> ERCC-00170        128        104 #> ERCC-00171       6188       4818 # Take a look at the original colData colData(se) #> DataFrame with 24 rows and 2 columns #>                 Ploidy Generation #>            <character>   <factor> #> S1             diploid         P1 #> S2             diploid         P1 #> S3             diploid         P1 #> S4             diploid         P1 #> S5             diploid         P1 #> ...                ...        ... #> midparent2          NA  midparent #> midparent3          NA  midparent #> midparent4          NA  midparent #> midparent5          NA  midparent #> midparent6          NA  midparent  # Add size factors estimated from spike-in controls se <- add_size_factors(se, spikein = TRUE, spikein_pattern = \"ERCC\") #> converting counts to integer mode  # Take a look at the new colData colData(se) #> DataFrame with 24 rows and 3 columns #>                 Ploidy Generation sizeFactor #>            <character>   <factor>  <numeric> #> S1             diploid         P1   0.856262 #> S2             diploid         P1   0.938417 #> S3             diploid         P1   0.648653 #> S4             diploid         P1   0.969369 #> S5             diploid         P1   0.718761 #> ...                ...        ...        ... #> midparent2          NA  midparent   1.278078 #> midparent3          NA  midparent   1.201072 #> midparent4          NA  midparent   1.000892 #> midparent5          NA  midparent   1.186773 #> midparent6          NA  midparent   0.871889"},{"path":"/articles/HybridExpress.html","id":"exploratory-data-analyses","dir":"Articles","previous_headings":"","what":"Exploratory data analyses","title":"Comparative transcriptomic analysis of hybrids and their progenitors","text":"Next, can perform exploratory analyses sample clustering verify samples group expected. HybridExpress, can performed using two functions: pca_plot(): creates principal component analysis (PCA) plots, colors shapes (optional) mapped levels colData variables; plot_samplecor(): plots heatmap hierarchically clustered pairwise sample correlations. Let’s start PCA plot:  plot , data point corresponds sample, colors shapes mapped levels variables specified arguments color_by shape_by, respectively. Besides, specifying add_mean = TRUE, added diamond shape indicating mean PC coordinates based variable color_by (, “Generation”). Now, let’s plot heatmap sample correlations:  can see samples group well together PCA plot correlation heatmap. note, pca_plot() plot_samplecor() use top 500 genes highest variances create plot. genes low variances (.e., genes vary much across samples) uninformative typically add noise. can change number (use less genes) ntop argument functions.","code":"# For colData rows with missing values (midparent samples), add \"midparent\" se$Ploidy[is.na(se$Ploidy)] <- \"midparent\" se$Generation[is.na(se$Generation)] <- \"midparent\"  # Create PCA plot pca_plot(se, color_by = \"Generation\", shape_by = \"Ploidy\", add_mean = TRUE) # Plot a heatmap of sample correlations plot_samplecor(se, coldata_cols = c(\"Generation\", \"Ploidy\"))"},{"path":"/articles/HybridExpress.html","id":"identifying-differentially-expressed-genes-between-hybrids-and-their-parents","dir":"Articles","previous_headings":"","what":"Identifying differentially expressed genes between hybrids and their parents","title":"Comparative transcriptomic analysis of hybrids and their progenitors","text":"compare gene expression levels hybrids progenitor species, can use function get_deg_list(). function performs differential expression analyses using DESeq2 returns list data frames gene-wise test statistics following contrasts: P2_vs_P1: parent 2 (numerator) versus parent 1 (denominator). F1_vs_P1: hybrid (numerator) versus parent 1 (denominator). F1_vs_P2: hybrid (numerator) versus parent 2 (denominator). F1_vs_midparent: hybrid (numerator) vs midparent (denominator). size factors estimated add_size_factors() used normalization differential expression testing. Let’s use get_deg_list() get differentially expressed genes (DEGs) contrast. summarize frequencies - -regulated genes per contrast single data frame, use function get_deg_counts(). important note columns perc_up, perc_down, perc_total show percentages -regulated, -regulated, differentially expressed genes relative total number genes count matrix. total number genes count matrix stored ngenes attribute list returned get_deg_list(): However, since count matrix usually include genes genome (e.g., lowly expressed genes genes low variance usually filtered ), percentages perc_up, perc_down, perc_total relative total number genes genome. use total number genes genome reference, need update ngenes attribute DEG list appropriate number follows: , can run get_deg_counts() get percentages relative total number genes genome. Finally, can summarize everything single publication-ready figure using plot plot_expression_triangle(), shows ‘experimental trio’ (.e., hybrid progenitors) triangle, frequencies DEGs indicated.  figure commonly used publications, inspired Rapp, Udall, Wendel (2009). edge (line), numbers middle (bold) indicate frequency DEGs, numbers ends (close boxes) indicate frequency -regulated genes generation. instance, figure shows , contrast F1 P1, 5476 DEGs (32.4% genome), 1471 -regulated F1, 4005 -regulated P1. custom figure, can also specify color palette labels boxes. example:","code":"# Get a list of differentially expressed genes for each contrast deg_list <- get_deg_list(se) #> using pre-existing size factors #> estimating dispersions #> gene-wise dispersion estimates #> mean-dispersion relationship #> final dispersion estimates #> fitting model and testing  # Inspecting the output ## Getting contrast names names(deg_list) #> [1] \"P2_vs_P1\"        \"F1_vs_P1\"        \"F1_vs_P2\"        \"F1_vs_midparent\"  ## Accessing gene-wise test statistics for contrast `F1_vs_P1` head(deg_list$F1_vs_P1) #>                baseMean log2FoldChange     lfcSE      stat       pvalue #> Cre01.g000450  39.59273     -1.1632346 0.2165407 -5.371898 7.791211e-08 #> Cre01.g000750 103.10296      0.8807307 0.1842856  4.779162 1.760270e-06 #> Cre01.g000900 471.44084      0.6807925 0.1980730  3.437079 5.880239e-04 #> Cre01.g001000  10.34574     -6.6304915 0.9631384 -6.884256 5.809030e-12 #> Cre01.g001100 453.87151     -0.6457115 0.1506903 -4.285025 1.827187e-05 #> Cre01.g001200 159.03643      0.7231060 0.1946090  3.715685 2.026536e-04 #>                       padj #> Cre01.g000450 4.653531e-07 #> Cre01.g000750 8.329159e-06 #> Cre01.g000900 1.707442e-03 #> Cre01.g001000 6.659069e-11 #> Cre01.g001100 7.200448e-05 #> Cre01.g001200 6.453951e-04  ## Counting the number of DEGs per contrast sapply(deg_list, nrow) #>        P2_vs_P1        F1_vs_P1        F1_vs_P2 F1_vs_midparent  #>            8698            5476            7350            4348 # Get a data frame with DEG frequencies for each contrast deg_counts <- get_deg_counts(deg_list)  deg_counts #>          contrast   up down total perc_up perc_down perc_total #> 1        P2_vs_P1  828 7870  8698     6.4      60.7       67.1 #> 2        F1_vs_P1 1471 4005  5476    11.3      30.9       42.2 #> 3        F1_vs_P2 6487  863  7350    50.0       6.7       56.7 #> 4 F1_vs_midparent 2620 1728  4348    20.2      13.3       33.5 attributes(deg_list)$ngenes #> [1] 12966 # Total number of genes in the C. reinhardtii genome (v6.1): 16883 attributes(deg_list)$ngenes <- 16883 deg_counts <- get_deg_counts(deg_list) deg_counts #>          contrast   up down total perc_up perc_down perc_total #> 1        P2_vs_P1  828 7870  8698     4.9      46.6       51.5 #> 2        F1_vs_P1 1471 4005  5476     8.7      23.7       32.4 #> 3        F1_vs_P2 6487  863  7350    38.4       5.1       43.5 #> 4 F1_vs_midparent 2620 1728  4348    15.5      10.2       25.8 # Plot expression triangle plot_expression_triangle(deg_counts) # Create vectors (length 4) of colors and box labels pal <- c(\"springgreen4\", \"darkorange3\", \"mediumpurple4\", \"mediumpurple3\") labels <- c(\"Parent 1\\n(2n)\", \"Parent 2\\n(n)\", \"Progeny\\n(3n)\", \"Midparent\")  plot_expression_triangle(deg_counts, palette = pal, box_labels = labels)"},{"path":"/articles/HybridExpress.html","id":"expression-based-gene-classification","dir":"Articles","previous_headings":"","what":"Expression-based gene classification","title":"Comparative transcriptomic analysis of hybrids and their progenitors","text":"identifying DEGs different contrasts, ’d typically want classify genes expression partitions based expression patterns. can performed function expression_partitioning(), classifies genes one 12 categories Rapp, Udall, Wendel (2009), 5 major classes summarize 12 categories. five classes : Transgressive -regulation (): gene -regulated hybrid compared parents. Transgressive -regulation (): gene -regulated hybrid compared parents. Additivity (ADD): gene expression hybrid mean parents (additive effect). Expression-level dominance toward parent 1 (ELD_P1): gene expression hybrid parent 1, different parent 2. Expression-level dominance toward parent 2 (ELD_P2): gene expression hybrid parent 2, different parent 1. visualize expression partitions scatter plot expression divergences, can use function plot_expression_partitions().  default, genes grouped Category. However, can also group genes Class follows:  can also visualize frequencies genes partition function plot_partition_frequencies().","code":"# Classify genes in expression partitions exp_partitions <- expression_partitioning(deg_list)  # Inspect the output head(exp_partitions) #>            Gene Category Class lFC_F1_vs_P1 lFC_F1_vs_P2 #> 1 Cre01.g003650        1   ADD    0.7838125   -1.0484464 #> 2 Cre01.g005150        1   ADD    1.0473362   -0.5041601 #> 3 Cre01.g008600        1   ADD    5.0518384   -1.4840829 #> 4 Cre01.g013500        1   ADD    2.1099265   -1.5329846 #> 5 Cre01.g034850        1   ADD    1.5838851   -0.7611868 #> 6 Cre01.g800005        1   ADD    1.4928449   -0.9315119  # Count number of genes per category table(exp_partitions$Category) #>  #>    1    2    3    4    5    6    7    8    9   10   11   12  #>   70  262   66 3283   77  287  214  651  280  147 1760 1666  # Count number of genes per class table(exp_partitions$Class) #>  #>     UP   DOWN    ADD ELD_P1 ELD_P2  #>   1015    427   1736   3563   2022 # Plot partitions as a scatter plot of divergences plot_expression_partitions(exp_partitions, group_by = \"Category\") # Group by `Class` plot_expression_partitions(exp_partitions, group_by = \"Class\") # Visualize frequency of genes in each partition ## By `Category` (default) plot_partition_frequencies(exp_partitions) ## By `Class` plot_partition_frequencies(exp_partitions, group_by = \"Class\")"},{"path":"/articles/HybridExpress.html","id":"overrepresentation-analysis-of-functional-terms","dir":"Articles","previous_headings":"","what":"Overrepresentation analysis of functional terms","title":"Comparative transcriptomic analysis of hybrids and their progenitors","text":"Lastly, ’d want explore whether gene sets interest (e.g., -regulated genes contrast) enriched particular GO term, pathway, protein domain, etc. , use function ora(), performs overrepresentation analysis gene set given data frame functional annotation gene. , use example data set GO annotation C. reinhardtii genes. data set illustrates annotation data frame must shaped: gene ID first column, functional annotations columns. demonstrate usage ora(), let’s check can find enrichment GO term among genes assigned class “ADD”.","code":"# Load example functional annotation (GO terms) data(go_chlamy)  head(go_chlamy) #>            gene                                           GO #> 1 Cre01.g000050                                         <NA> #> 2 Cre01.g000100                                         <NA> #> 3 Cre01.g000150                                     membrane #> 4 Cre01.g000150                          metal ion transport #> 5 Cre01.g000150 metal ion transmembrane transporter activity #> 6 Cre01.g000150                      transmembrane transport # Get a vector of genes in class \"ADD\" genes_add <- exp_partitions$Gene[exp_partitions$Class == \"ADD\"] head(genes_add) #> [1] \"Cre01.g003650\" \"Cre01.g005150\" \"Cre01.g008600\" \"Cre01.g013500\" #> [5] \"Cre01.g034850\" \"Cre01.g800005\"  # Get background genes - genes in the count matrix bg <- rownames(se)  # Perform overrepresentation analysis ora_add <- ora(genes_add, go_chlamy, background = bg)  # Inspect results head(ora_add) #>                                                   term genes all         pval #> 20                      aminoacyl-tRNA ligase activity    14  33 3.811521e-05 #> 84                                           cytoplasm    43 151 7.379762e-07 #> 97                                     DNA replication    17  37 1.471956e-06 #> 114                             endopeptidase activity     6  10 7.400151e-04 #> 115                              endoplasmic reticulum     9  17 1.193854e-04 #> 118 eukaryotic translation initiation factor 3 complex     7   9 2.144087e-05 #>             padj category #> 20  1.766005e-03       GO #> 84  7.693402e-05       GO #> 97  1.227611e-04       GO #> 114 2.204188e-02       GO #> 115 4.978369e-03       GO #> 118 1.277263e-03       GO"},{"path":"/articles/HybridExpress.html","id":"example-1-overrepresentation-analyses-for-all-expression-based-classes","dir":"Articles","previous_headings":"Overrepresentation analysis of functional terms","what":"Example 1: overrepresentation analyses for all expression-based classes","title":"Comparative transcriptomic analysis of hybrids and their progenitors","text":"example , performed overrepresentation analysis genes associated class “ADD”. wanted classes? case, run ora() multiple times looping class. details, following: class, create vector genes associated ; Run ora() get data frame ORA results. can find example using lapply(). Results class stored elements list object. expression-based category (class), ’d need replace Class Category split() function (see example ).","code":"# Create a list of genes in each class genes_by_class <- split(exp_partitions$Gene, exp_partitions$Class)  names(genes_by_class) #> [1] \"UP\"     \"DOWN\"   \"ADD\"    \"ELD_P1\" \"ELD_P2\" head(genes_by_class$UP) #> [1] \"Cre01.g029250\" \"Cre01.g034380\" \"Cre01.g036700\" \"Cre01.g049400\" #> [5] \"Cre02.g085500\" \"Cre02.g087150\"  # Iterate through each class and perform ORA ora_classes <- lapply(     genes_by_class, # list through which we will iterate     ora, # function we will apply to each element of the list     annotation = go_chlamy, background = bg # additional arguments to function )  # Inspect output ora_classes #> $UP #>                                    term genes all         pval         padj #> 29              ATP hydrolysis activity    21 119 3.380614e-04 0.0469905310 #> 109                      dynein complex     8  13 1.237752e-06 0.0005161427 #> 127 flavin adenine dinucleotide binding    11  42 2.857201e-04 0.0469905310 #>     category #> 29        GO #> 109       GO #> 127       GO #>  #> $DOWN #>                                  term genes all         pval         padj #> 242           oxidoreductase activity    21 274 2.931239e-04 2.444654e-02 #> 271                    photosynthesis    13  58 3.656759e-08 7.624343e-06 #> 272  photosynthesis, light harvesting    18  24 1.638332e-22 6.831844e-20 #> 276                     photosystem I     5  15 8.644389e-05 9.011776e-03 #> 376 tetrapyrrole biosynthetic process     5   8 1.952599e-06 2.714112e-04 #>     category #> 242       GO #> 271       GO #> 272       GO #> 276       GO #> 376       GO #>  #> $ADD #>                                                                                      term #> 20                                                         aminoacyl-tRNA ligase activity #> 84                                                                              cytoplasm #> 97                                                                        DNA replication #> 114                                                                endopeptidase activity #> 115                                                                 endoplasmic reticulum #> 118                                    eukaryotic translation initiation factor 3 complex #> 165                                                       intracellular protein transport #> 227                                                                          nuclear pore #> 250 oxidoreductase activity, acting on the CH-OH group of donors, NAD or NADP as acceptor #> 284                                                                     prefoldin complex #> 287                                                               proteasome core complex #> 293                                                                       protein folding #> 294                                                                 protein glycosylation #> 304                                                                     protein refolding #> 390                                                translation initiation factor activity #> 410                                                              unfolded protein binding #> 414                                                            vesicle-mediated transport #>     genes all         pval         padj category #> 20     14  33 3.811521e-05 1.766005e-03       GO #> 84     43 151 7.379762e-07 7.693402e-05       GO #> 97     17  37 1.471956e-06 1.227611e-04       GO #> 114     6  10 7.400151e-04 2.204188e-02       GO #> 115     9  17 1.193854e-04 4.978369e-03       GO #> 118     7   9 2.144087e-05 1.277263e-03       GO #> 165    25  73 4.442015e-06 3.087201e-04       GO #> 227     6   9 3.339022e-04 1.160310e-02       GO #> 250    10  27 1.739601e-03 4.533836e-02       GO #> 284     4   4 3.203862e-04 1.160310e-02       GO #> 287     9  15 3.165895e-05 1.650223e-03       GO #> 293    29  81 2.663812e-07 3.702698e-05       GO #> 294     6  10 7.400151e-04 2.204188e-02       GO #> 304    10  10 1.809949e-09 7.547489e-07       GO #> 390     9  21 8.722652e-04 2.424897e-02       GO #> 410    19  38 6.478683e-08 1.350805e-05       GO #> 414    13  41 1.955735e-03 4.797302e-02       GO #>  #> $ELD_P1 #>                term genes all         pval        padj category #> 38          binding    88 208 2.494608e-06 0.001040251       GO #> 239         nucleus    77 193 1.141172e-04 0.023793431       GO #> 345 rRNA processing    15  24 3.464517e-04 0.048156786       GO #>  #> $ELD_P2 #>                                                                term genes all #> 28                             aspartic-type endopeptidase activity     6   8 #> 170                                                iron ion binding    30  93 #> 242                                         oxidoreductase activity    71 274 #> 271                                                  photosynthesis    26  58 #> 278                                                  photosystem II    17  32 #> 320 proton-transporting two-sector ATPase complex, catalytic domain     6   8 #>             pval         padj category #> 28  3.007465e-04 2.090188e-02       GO #> 170 4.577693e-05 4.772245e-03       GO #> 242 6.069186e-06 8.436168e-04       GO #> 271 1.191954e-07 4.970448e-05       GO #> 278 9.602908e-07 2.002206e-04       GO #> 320 3.007465e-04 2.090188e-02       GO"},{"path":"/articles/HybridExpress.html","id":"example-2-overrepresentation-analyses-for-differentially-expressed-genes","dir":"Articles","previous_headings":"Overrepresentation analysis of functional terms","what":"Example 2: overrepresentation analyses for differentially expressed genes","title":"Comparative transcriptomic analysis of hybrids and their progenitors","text":"can use lapply() loop expression class, can also loop contrast list returned get_deg_list(), perform ORA - -regulated genes. can find example: Likewise, -regulated genes, need replace > symbol < symbol anonymous function subset rows.","code":"# Get up-regulated genes for each contrast up_genes <- lapply(deg_list, function(x) rownames(x[x$log2FoldChange > 0, ])) names(up_genes) #> [1] \"P2_vs_P1\"        \"F1_vs_P1\"        \"F1_vs_P2\"        \"F1_vs_midparent\" head(up_genes$F1_vs_P1) #> [1] \"Cre01.g000750\" \"Cre01.g000900\" \"Cre01.g001200\" \"Cre01.g002750\" #> [5] \"Cre01.g003524\" \"Cre01.g003650\"  # Perform ORA ora_up <- lapply(     up_genes,     ora,     annotation = go_chlamy, background = bg )  ora_up #> $P2_vs_P1 #> [1] term     genes    all      pval     padj     category #> <0 rows> (or 0-length row.names) #>  #> $F1_vs_P1 #>               term genes all         pval        padj category #> 109 dynein complex     8  13 2.049704e-05 0.008547264       GO #>  #> $F1_vs_P2 #>                                              term genes all         pval #> 38                                        binding   138 208 1.214715e-06 #> 96                                     DNA repair    56  83 9.520576e-04 #> 144                                   GTP binding    72 111 1.095054e-03 #> 169                                 ion transport    33  45 1.225270e-03 #> 209                     microtubule-based process    10  10 9.792020e-04 #> 239                                       nucleus   125 193 2.252487e-05 #> 303                        protein polymerization    10  10 9.792020e-04 #> 304                             protein refolding    10  10 9.792020e-04 #> 307 protein serine/threonine phosphatase activity    20  24 7.724253e-04 #> 338                                   RNA binding   101 157 1.948736e-04 #> 384                  transcription, DNA-templated    28  36 5.951883e-04 #>            padj category #> 38  0.000506536       GO #> 96  0.045369693       GO #> 144 0.045663753       GO #> 169 0.046448881       GO #> 209 0.045369693       GO #> 239 0.004696435       GO #> 303 0.045369693       GO #> 304 0.045369693       GO #> 307 0.045369693       GO #> 338 0.027087430       GO #> 384 0.045369693       GO #>  #> $F1_vs_midparent #>                                              term genes all         pval #> 80         cyclic nucleotide biosynthetic process    38 108 1.983329e-04 #> 109                                dynein complex     9  13 1.789139e-04 #> 166             intracellular signal transduction    38 110 3.036659e-04 #> 168                          ion channel activity    20  48 5.611617e-04 #> 169                                 ion transport    21  45 5.765412e-05 #> 290                     protein dephosphorylation    21  44 3.792684e-05 #> 307 protein serine/threonine phosphatase activity    15  24 7.437911e-06 #>            padj category #> 80  0.016540962       GO #> 109 0.016540962       GO #> 166 0.021104781       GO #> 168 0.033429202       GO #> 169 0.008013923       GO #> 290 0.007907747       GO #> 307 0.003101609       GO"},{"path":"/articles/HybridExpress.html","id":"faq","dir":"Articles","previous_headings":"","what":"FAQ","title":"Comparative transcriptomic analysis of hybrids and their progenitors","text":"create SummarizedExperiment object? SummarizedExperiment data structure (S4 class) can used store, single object, following elements: assay: quantitative matrix features rows samples columns. context HybridExpress, gene expression matrix (counts) genes rows samples columns. colData: data frame sample metadata samples rows variables describe samples (e.g., tissue, treatment, covariates) columns. rowData: data frame gene metadata genes rows variables describe genes (e.g., chromosome name, alternative IDs, functional information, etc) columns. package, must assay containing count matrix colData slot sample metadata. rowData can present, required. demonstrate create SummarizedExperiment object, extract assay colData example object se_chlamy comes package. two objects, can create SummarizedExperiment object : details data structure, read vignette SummarizedExperiment package.","code":"# Get count matrix count_matrix <- assay(se_chlamy) head(count_matrix) #>                 S1  S2   S3   S4  S5   S6   S7   S8   S9 S10 S11  S12  S13  S14 #> Cre01.g000050   31  21   26   33  19   48   17    6   13   6  10    7   31   22 #> Cre01.g000150   50  29   35   99  11   58   51   46   41  33  28   14   53   25 #> Cre01.g000200   36  26   24   29  17   36   14   15   12   8  14    7   25   24 #> Cre01.g000250  440 272  394  332 283  585  272  274  255 160 225  235  405  391 #> Cre01.g000300 1242 839 1216 1251 811 1785 1341 1306 1122 877 844 1082 1704 1739 #> Cre01.g000350  412 264  294  336 252  478  233  221  195 155 190  201  299  272 #>                S15  S16  S17  S18 #> Cre01.g000050   29   22   21   16 #> Cre01.g000150   37   17   24   21 #> Cre01.g000200   24   26   18   21 #> Cre01.g000250  358  339  340  332 #> Cre01.g000300 1524 1720 1517 1243 #> Cre01.g000350  276  324  246  275  # Get colData (data frame of sample metadata) coldata <- colData(se_chlamy) head(coldata) #> DataFrame with 6 rows and 2 columns #>         Ploidy Generation #>    <character>   <factor> #> S1     diploid         P1 #> S2     diploid         P1 #> S3     diploid         P1 #> S4     diploid         P1 #> S5     diploid         P1 #> S6     diploid         P1 # Create a SummarizedExperiment object new_se <- SummarizedExperiment(     assays = list(counts = count_matrix),     colData = coldata )  new_se #> class: SummarizedExperiment  #> dim: 13058 18  #> metadata(0): #> assays(1): counts #> rownames(13058): Cre01.g000050 Cre01.g000150 ... ERCC-00170 ERCC-00171 #> rowData names(0): #> colnames(18): S1 S2 ... 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yaml                   2.3.8     2023-12-11 [2] CRAN (R 4.4.0) #>  zlibbioc               1.49.0    2023-10-24 [1] Bioconductor #>  #>  [1] /__w/_temp/Library #>  [2] /usr/local/lib/R/site-library #>  [3] /usr/local/lib/R/library #>  #> ──────────────────────────────────────────────────────────────────────────────"},{"path":[]},{"path":"/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Fabricio Almeida-Silva. Author, maintainer. Lucas Prost-Boxoen. Author. Yves Van de Peer. Author.","code":""},{"path":"/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Almeida-Silva F, Prost-Boxoen L, Van de Peer Y (2024). HybridExpress: Comparative analysis RNA-seq data hybrids progenitors. R package version 0.99.0, https://github.com/almeidasilvaf/HybridExpress.","code":"@Manual{,   title = {HybridExpress: Comparative analysis of RNA-seq data for hybrids and their progenitors},   author = {Fabricio Almeida-Silva and Lucas Prost-Boxoen and Yves {Van de Peer}},   year = {2024},   note = {R package version 0.99.0},   url = {https://github.com/almeidasilvaf/HybridExpress}, }"},{"path":"/index.html","id":"hybridexpress-","dir":"","previous_headings":"","what":"Comparative analysis of RNA-seq data for hybrids and their progenitors","title":"Comparative analysis of RNA-seq data for hybrids and their progenitors","text":"goal HybridExpress perform comparative transcriptomic analyses hybrids relative progenitor species (.k.. experimental trios). package features: Calculation midparent expression values, silico samples obtained mean, sum, weighted mean random sample pairs parent; Exploratory analyses sample grouping PCA plots heatmaps hierarchically-clustered pairwise sample correlations; Identification differentially expressed genes hybrids progenitor species, hybrids midparent values, two parents. spike-standards available, HybridExpress uses normalize count data transcriptome size; Classification genes expression-based categories classes based Rapp et al. (2009). 12 expression categories proposed Rapp et al. (2009) grouped 5 major classes (transgressive -regulation, transgressive -regulation, additivity, expression-level dominance toward parent 1, expression-level dominance toward parent 2); Functional analyses identification overrepresented functional terms gene sets interest.","code":""},{"path":"/index.html","id":"installation-instructions","dir":"","previous_headings":"","what":"Installation instructions","title":"Comparative analysis of RNA-seq data for hybrids and their progenitors","text":"Get latest stable R release CRAN. install HybridExpress Bioconductor using following code: development version GitHub :","code":"if (!requireNamespace(\"BiocManager\", quietly = TRUE)) {     install.packages(\"BiocManager\") }  BiocManager::install(\"HybridExpress\") BiocManager::install(\"almeidasilvaf/HybridExpress\")"},{"path":"/index.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Comparative analysis of RNA-seq data for hybrids and their progenitors","text":"citation output using citation('HybridExpress') R. Please run check updates cite HybridExpress. Please note HybridExpress made possible thanks many R bioinformatics software authors, cited either vignettes /paper(s) describing package.","code":"print(citation('HybridExpress'), bibtex = TRUE) #> To cite package 'HybridExpress' in publications use: #>  #>   Almeida-Silva F, Prost-Boxoen L, Van de Peer Y (2023). #>   _HybridExpress: Comparative analysis of RNA-seq data for hybrids and #>   their progenitors_. R package version 0.99.0, #>   <https://github.com/almeidasilvaf/HybridExpress>. #>  #> A BibTeX entry for LaTeX users is #>  #>   @Manual{, #>     title = {HybridExpress: Comparative analysis of RNA-seq data for hybrids and their progenitors}, #>     author = {Fabricio Almeida-Silva and Lucas Prost-Boxoen and Yves {Van de Peer}}, #>     year = {2023}, #>     note = {R package version 0.99.0}, #>     url = {https://github.com/almeidasilvaf/HybridExpress}, #>   }"},{"path":"/index.html","id":"code-of-conduct","dir":"","previous_headings":"","what":"Code of Conduct","title":"Comparative analysis of RNA-seq data for hybrids and their progenitors","text":"Please note HybridExpress project released Contributor Code Conduct. contributing project, agree abide terms.","code":""},{"path":"/index.html","id":"development-tools","dir":"","previous_headings":"","what":"Development tools","title":"Comparative analysis of RNA-seq data for hybrids and their progenitors","text":"Continuous code testing possible thanks GitHub actions usethis, remotes, rcmdcheck customized use Bioconductor’s docker containers BiocCheck. Code coverage assessment possible thanks codecov covr. documentation website automatically updated thanks pkgdown. documentation formatted thanks devtools roxygen2. package developed using biocthis.","code":""},{"path":"/reference/add_midparent_expression.html","id":null,"dir":"Reference","previous_headings":"","what":"Add midparent expression to SummarizedExperiment object — add_midparent_expression","title":"Add midparent expression to SummarizedExperiment object — add_midparent_expression","text":"Add midparent expression SummarizedExperiment object","code":""},{"path":"/reference/add_midparent_expression.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add midparent expression to SummarizedExperiment object — add_midparent_expression","text":"","code":"add_midparent_expression(   se,   coldata_column = \"Generation\",   parent1 = \"P1\",   parent2 = \"P2\",   method = \"mean\",   weights = c(1, 1) )"},{"path":"/reference/add_midparent_expression.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add midparent expression to SummarizedExperiment object — add_midparent_expression","text":"se SummarizedExperiment object count matrix sample metadata. coldata_column Character indicating name column colData(se) information generation stored. Default: \"Generation\". parent1 Character indicating level variable coldata_column represents parent 1. Default: \"P1\". parent2 Character indicating level variable coldata_column represents parent 2. Default: \"P2\". method Character indicating method use create midparent values. One 'mean' (default), 'sum', 'weightedmean'. weights Numeric vector length 2 indicating weights give parents 1 2 (respectively) method == \"weightedmean\". Setting method == \"weightedmean\" used sometimes parents different ploidy levels. cases, ploidy levels parents 1 2 can passed vector. Default: c(1, 2).","code":""},{"path":"/reference/add_midparent_expression.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Add midparent expression to SummarizedExperiment object — add_midparent_expression","text":"SummarizedExperiment object.","code":""},{"path":"/reference/add_midparent_expression.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Add midparent expression to SummarizedExperiment object — add_midparent_expression","text":"","code":"data(se_chlamy) new_se <- add_midparent_expression(se_chlamy)"},{"path":"/reference/add_size_factors.html","id":null,"dir":"Reference","previous_headings":"","what":"Add size factors to normalize count data by library size or by biomass — add_size_factors","title":"Add size factors to normalize count data by library size or by biomass — add_size_factors","text":"Add size factors normalize count data library size biomass","code":""},{"path":"/reference/add_size_factors.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add size factors to normalize count data by library size or by biomass — add_size_factors","text":"","code":"add_size_factors(se, spikein = FALSE, spikein_pattern = \"ERCC\")"},{"path":"/reference/add_size_factors.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add size factors to normalize count data by library size or by biomass — add_size_factors","text":"se SummarizedExperiment object count matrix sample metadata. spikein Logical indicating whether normalize data using spike-ins. FALSE, data normalized library size. Default: FALSE. spikein_pattern Character pattern (regex) use identify spike-features count matrix. valid spikein_norm = TRUE.","code":""},{"path":"/reference/add_size_factors.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Add size factors to normalize count data by library size or by biomass — add_size_factors","text":"SummarizedExperiment object se, extra column colData slot named \"sizeFactor\". column contains size factors used DESeq2 performing differential expression analyses.","code":""},{"path":"/reference/add_size_factors.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Add size factors to normalize count data by library size or by biomass — add_size_factors","text":"","code":"data(se_chlamy) se_norm <- add_size_factors(se_chlamy)"},{"path":"/reference/deg_counts.html","id":null,"dir":"Reference","previous_headings":"","what":"Data frame with frequencies (absolute and relative) of DEGs per contrast — deg_counts","title":"Data frame with frequencies (absolute and relative) of DEGs per contrast — deg_counts","text":"object obtained get_deg_counts() using example data set deg_list.","code":""},{"path":"/reference/deg_counts.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Data frame with frequencies (absolute and relative) of DEGs per contrast — deg_counts","text":"","code":"data(deg_counts)"},{"path":"/reference/deg_counts.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Data frame with frequencies (absolute and relative) of DEGs per contrast — deg_counts","text":"data frame frequencies (absolute relative) - -regulated genes contrast. Relative frequencies calculated relative total number genes count matrix used differential expression analysis.","code":""},{"path":"/reference/deg_counts.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Data frame with frequencies (absolute and relative) of DEGs per contrast — deg_counts","text":"","code":"data(deg_counts)"},{"path":"/reference/deg_list.html","id":null,"dir":"Reference","previous_headings":"","what":"List of differentially expressed genes for all contrasts — deg_list","title":"List of differentially expressed genes for all contrasts — deg_list","text":"object obtained get_deg_list() using example data set se_chlamy.","code":""},{"path":"/reference/deg_list.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"List of differentially expressed genes for all contrasts — deg_list","text":"","code":"data(deg_list)"},{"path":"/reference/deg_list.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"List of differentially expressed genes for all contrasts — deg_list","text":"list data frames gene-wise test statistics differentially expressed genes contrast. Contrasts \"P2_vs_P1\", \"F1_vs_P1\", \"F1_vs_P2\", \"F1_vs_midparent\", ID 'vs' represents numerator, ID 'vs' represents denominator.","code":""},{"path":"/reference/deg_list.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"List of differentially expressed genes for all contrasts — deg_list","text":"","code":"data(deg_list)"},{"path":"/reference/expression_partitioning.html","id":null,"dir":"Reference","previous_headings":"","what":"Partition genes in groups based on their expression patterns — expression_partitioning","title":"Partition genes in groups based on their expression patterns — expression_partitioning","text":"Partition genes groups based expression patterns","code":""},{"path":"/reference/expression_partitioning.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Partition genes in groups based on their expression patterns — expression_partitioning","text":"","code":"expression_partitioning(deg_list)"},{"path":"/reference/expression_partitioning.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Partition genes in groups based on their expression patterns — expression_partitioning","text":"deg_list list data frames gene-wise test statistics differentially expressed genes returned get_deg_list().","code":""},{"path":"/reference/expression_partitioning.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Partition genes in groups based on their expression patterns — expression_partitioning","text":"data following variables: Gene Character, gene ID. Category Factor, expression group. Category names numbers 1 12. Class Factor, expression group class. One \"\" (transgressive -regulation), \"\" (transgressive -regulation), \"ADD\" (additivity), \"ELD_P1\" (expression-level dominance toward parent 1), \"ELD_P2\" (expression-level dominance toward parent 2).","code":""},{"path":"/reference/expression_partitioning.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Partition genes in groups based on their expression patterns — expression_partitioning","text":"","code":"data(deg_list) exp_partitions <- expression_partitioning(deg_list)"},{"path":"/reference/get_deg_counts.html","id":null,"dir":"Reference","previous_headings":"","what":"Get a count table of differentially expressed genes per contrast — get_deg_counts","title":"Get a count table of differentially expressed genes per contrast — get_deg_counts","text":"Get count table differentially expressed genes per contrast","code":""},{"path":"/reference/get_deg_counts.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get a count table of differentially expressed genes per contrast — get_deg_counts","text":"","code":"get_deg_counts(deg_list)"},{"path":"/reference/get_deg_counts.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get a count table of differentially expressed genes per contrast — get_deg_counts","text":"deg_list list data frames gene-wise test statistics differentially expressed genes returned get_deg_list().","code":""},{"path":"/reference/get_deg_counts.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get a count table of differentially expressed genes per contrast — get_deg_counts","text":"data frame following variables: contrast Character, contrast name. Numeric, number -regulated genes. Numeric, number -regulated genes. total Numeric, total number differentially expressed genes. perc_up Numeric, percentage -regulated genes. perc_down Numeric, percentage -regulated genes. perc_total Numeric, percentage diffferentially expressed genes.","code":""},{"path":"/reference/get_deg_counts.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get a count table of differentially expressed genes per contrast — get_deg_counts","text":"","code":"data(deg_list) deg_counts <- get_deg_counts(deg_list)"},{"path":"/reference/get_deg_list.html","id":null,"dir":"Reference","previous_headings":"","what":"Get a table of differential expression expression statistics with DESeq2 — get_deg_list","title":"Get a table of differential expression expression statistics with DESeq2 — get_deg_list","text":"Get table differential expression expression statistics DESeq2","code":""},{"path":"/reference/get_deg_list.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get a table of differential expression expression statistics with DESeq2 — get_deg_list","text":"","code":"get_deg_list(   se,   coldata_column = \"Generation\",   parent1 = \"P1\",   parent2 = \"P2\",   offspring = \"F1\",   midparent = \"midparent\",   lfcThreshold = 0,   alpha = 0.01,   ... )"},{"path":"/reference/get_deg_list.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get a table of differential expression expression statistics with DESeq2 — get_deg_list","text":"se SummarizedExperiment object count matrix sample metadata. coldata_column Character indicating name column colData(se) information generation stored. Default: \"Generation\". parent1 Character indicating level variable coldata_column represents parent 1. Default: \"P1\". parent2 Character indicating level variable coldata_column represents parent 2. Default: \"P2\". offspring Character indicating level variable coldata_column represents offspring (hybrid allopolyploid). Default: \"F1\" midparent Character indicating level variable coldata_column represents midparent value. Default: \"midparent\", returned add_midparent_expression(). lfcThreshold Numeric indicating log2 fold-change threshold use consider differentially expressed genes. Default: 0. alpha Numeric indicating adjusted P-value threshold use consider differentially expressed genes. Default: 0.01. ... Additional arguments passed DESeq2::results().","code":""},{"path":"/reference/get_deg_list.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get a table of differential expression expression statistics with DESeq2 — get_deg_list","text":"list data frames DESeq2's gene-wise tests statistics contrast. data frame contains columns output DESeq2::results(). Contrasts (list names) : P2_vs_P1 Parent 2 (numerator) versus parent 1 (denominator). F1_vs_P1 Offspring (numerator) versus parent 1 (denominator). F1_vs_P2 Offspring (numerator) versus parent 2 (denominator). F1_vs_midparent Offspring (numerator) versus midparent (denominator). data frame gene-wise test statistics list element contains following variables: baseMean Numeric, base mean. log2FoldChange Numeric, log2-transformed fold changes. lfcSE Numeric, standard error log2-transformed fold changes. stat Numeric, observed test statistic. pvalue Numeric, p-value. padj Numeric, P-value adjusted multiple testing. list contains two additional attributes named ngenes (numeric, total number genes), plotdata, 3-column data frame variables \"gene\" (character, gene ID), \"lFC_F1_vs_P1\" (numeric, log2 fold change F1 P1), \"lFC_F1_vs_P2\" (numeric, log2 fold change F1 P2).","code":""},{"path":"/reference/get_deg_list.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get a table of differential expression expression statistics with DESeq2 — get_deg_list","text":"","code":"data(se_chlamy) se <- add_midparent_expression(se_chlamy) se <- add_size_factors(se, spikein = TRUE) #> converting counts to integer mode deg_list <- get_deg_list(se) #> using pre-existing size factors #> estimating dispersions #> gene-wise dispersion estimates #> mean-dispersion relationship #> final dispersion estimates #> fitting model and testing"},{"path":"/reference/go_chlamy.html","id":null,"dir":"Reference","previous_headings":"","what":"Data frame with GO terms annotated to each gene of Chlamydomonas reinhardtii — go_chlamy","title":"Data frame with GO terms annotated to each gene of Chlamydomonas reinhardtii — go_chlamy","text":"Data obtained Phytozome processed row contains one GO term (long format).","code":""},{"path":"/reference/go_chlamy.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Data frame with GO terms annotated to each gene of Chlamydomonas reinhardtii — go_chlamy","text":"","code":"data(go_chlamy)"},{"path":"/reference/go_chlamy.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Data frame with GO terms annotated to each gene of Chlamydomonas reinhardtii — go_chlamy","text":"2-column data frame columns gene (character, gene ID), GO (character, name GO term.)","code":""},{"path":"/reference/go_chlamy.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Data frame with GO terms annotated to each gene of Chlamydomonas reinhardtii — go_chlamy","text":"","code":"data(go_chlamy)"},{"path":"/reference/ora.html","id":null,"dir":"Reference","previous_headings":"","what":"Perform overrepresentation analysis for a set of genes — ora","title":"Perform overrepresentation analysis for a set of genes — ora","text":"Perform overrepresentation analysis set genes","code":""},{"path":"/reference/ora.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Perform overrepresentation analysis for a set of genes — ora","text":"","code":"ora(   genes,   annotation,   column = NULL,   background,   correction = \"BH\",   alpha = 0.05,   min_setsize = 5,   max_setsize = 500,   bp_param = BiocParallel::SerialParam() )"},{"path":"/reference/ora.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Perform overrepresentation analysis for a set of genes — ora","text":"genes Character vector containing genes overrepresentation analysis. annotation Annotation data frame genes first column functional annotation columns. data frame can exported Biomart similar databases. column Column columns annotation used enrichment. character numeric values column indices can used. users want supply one column, input character numeric vector. Default: columns annotation. background Character vector genes used background overrepresentation analysis. correction Multiple testing correction method. One \"holm\", \"hochberg\", \"hommel\", \"bonferroni\", \"BH\", \"\", \"fdr\" \"none\". Default \"BH\". alpha Numeric indicating adjusted P-value threshold significance. Default: 0.05. min_setsize Numeric indicating minimum gene set size considered. Gene sets correspond levels variable annotation). Default: 5. max_setsize Numeric indicating maximum gene set size considered. Gene sets correspond levels variable annotation). Default: 500. bp_param BiocParallel back-end used. Default: BiocParallel::SerialParam()","code":""},{"path":"/reference/ora.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Perform overrepresentation analysis for a set of genes — ora","text":"data frame overrepresentation results following variables: term Character, functional term ID/name. genes Numeric, intersection length input genes genes particular functional term. Numeric, number genes particular functional term. pval Numeric, P-value hypergeometric test. padj Numeric, P-value adjusted multiple comparisons using method specified parameter adj. category Character, name grouping variable (.e., column name annotation).","code":""},{"path":"/reference/ora.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Perform overrepresentation analysis for a set of genes — ora","text":"","code":"data(se_chlamy) data(go_chlamy) data(deg_list)  # Perform ORA for up-regulated genes in contrast F1_vs_P1 up_genes <- deg_list$F1_vs_P1 up_genes <- rownames(up_genes[up_genes$log2FoldChange > 0, ])  background <- rownames(se_chlamy)  ora(up_genes, go_chlamy, background = background) #>               term genes all         pval       padj category #> 133 dynein complex     8  13 1.924882e-05 0.00802676       GO"},{"path":"/reference/pca_plot.html","id":null,"dir":"Reference","previous_headings":"","what":"Perform a principal component analysis (PCA) and plot PCs — pca_plot","title":"Perform a principal component analysis (PCA) and plot PCs — pca_plot","text":"Perform principal component analysis (PCA) plot PCs","code":""},{"path":"/reference/pca_plot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Perform a principal component analysis (PCA) and plot PCs — pca_plot","text":"","code":"pca_plot(   se,   PCs = c(1, 2),   ntop = 500,   color_by = NULL,   shape_by = NULL,   add_mean = FALSE,   palette = NULL )"},{"path":"/reference/pca_plot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Perform a principal component analysis (PCA) and plot PCs — pca_plot","text":"se SummarizedExperiment object count matrix sample metadata. PCs Numeric vector indicating principal components show x-axis y-axis, respectively. Default: c(1,2). ntop Numeric indicating number top genes highest variances use PCA. Default: 500. color_by Character name column colData(se) use group samples color. Default: NULL. shape_by Character name column colData(se) use group samples shape. Default: NULL. add_mean Logical indicating whether add diamond symbol mean value level variable indicated color_by. Default: FALSE palette Character vector colors use level variable indicated color_by. NULL, default color palette used.","code":""},{"path":"/reference/pca_plot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Perform a principal component analysis (PCA) and plot PCs — pca_plot","text":"ggplot object PCA plot showing 2 principal components axis along % variance explained.","code":""},{"path":"/reference/pca_plot.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Perform a principal component analysis (PCA) and plot PCs — pca_plot","text":"","code":"data(se_chlamy) se <- add_midparent_expression(se_chlamy) se$Ploidy[is.na(se$Ploidy)] <- \"midparent\" se$Generation[is.na(se$Generation)] <- \"midparent\" pca_plot(se, color_by = \"Generation\", shape_by = \"Ploidy\", add_mean = TRUE) #> converting counts to integer mode"},{"path":"/reference/plot_expression_partitions.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot expression partitions — plot_expression_partitions","title":"Plot expression partitions — plot_expression_partitions","text":"Plot expression partitions","code":""},{"path":"/reference/plot_expression_partitions.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot expression partitions — plot_expression_partitions","text":"","code":"plot_expression_partitions(   partition_table,   group_by = \"Category\",   palette = NULL,   labels = c(\"P1\", \"F1\", \"P2\") )"},{"path":"/reference/plot_expression_partitions.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot expression partitions — plot_expression_partitions","text":"partition_table data frame genes per expression partition returned expression_partitioning(). group_by Character indicating name variable partition_table use group genes. One \"Category\" \"Class\". Default: \"Category\". palette Character vector color names used level variable specified group_by. group_by = \"Category\", must vector length 12. group_by = \"Class\", must vector length 5. NULL, default color palette used. labels character vector length 3 indicating labels given parent 1, offspring, parent 2. Default: c(\"P1\", \"F1\", \"P2\").","code":""},{"path":"/reference/plot_expression_partitions.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot expression partitions — plot_expression_partitions","text":"ggplot object plot showing genes expression partition.","code":""},{"path":"/reference/plot_expression_partitions.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot expression partitions — plot_expression_partitions","text":"","code":"data(deg_list) partition_table <- expression_partitioning(deg_list) plot_expression_partitions(partition_table)"},{"path":"/reference/plot_expression_triangle.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot a triangle of comparisons of DEG sets among generations — plot_expression_triangle","title":"Plot a triangle of comparisons of DEG sets among generations — plot_expression_triangle","text":"Plot triangle comparisons DEG sets among generations","code":""},{"path":"/reference/plot_expression_triangle.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot a triangle of comparisons of DEG sets among generations — plot_expression_triangle","text":"","code":"plot_expression_triangle(deg_counts, palette = NULL, box_labels = NULL)"},{"path":"/reference/plot_expression_triangle.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot a triangle of comparisons of DEG sets among generations — plot_expression_triangle","text":"deg_counts Data frame number differentially expressed genes per contrast returned get_deg_counts. palette Character vector length 4 indicating colors boxes P1, P2, F1, midparent, respectively. NULL, default color palette used. box_labels Character vector length 4 indicating labels boxes P1, P2, F1, midparent, respectively. Default: NULL, lead labels \"P1\", \"P2\", \"F1\", \"Midparent\", respectively.","code":""},{"path":"/reference/plot_expression_triangle.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot a triangle of comparisons of DEG sets among generations — plot_expression_triangle","text":"ggplot object expression triangle.","code":""},{"path":"/reference/plot_expression_triangle.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Plot a triangle of comparisons of DEG sets among generations — plot_expression_triangle","text":"expression triangle plot shows number differentially expressed genes (DEGs) contrast. Numbers center lines (bold) indicate total number DEGs, numbers near boxes indicate number -regulated genes generation triangle.","code":""},{"path":"/reference/plot_expression_triangle.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot a triangle of comparisons of DEG sets among generations — plot_expression_triangle","text":"","code":"data(deg_counts) plot_expression_triangle(deg_counts)"},{"path":"/reference/plot_partition_frequencies.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot a barplot of gene frequencies per expression partition — plot_partition_frequencies","title":"Plot a barplot of gene frequencies per expression partition — plot_partition_frequencies","text":"Plot barplot gene frequencies per expression partition","code":""},{"path":"/reference/plot_partition_frequencies.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot a barplot of gene frequencies per expression partition — plot_partition_frequencies","text":"","code":"plot_partition_frequencies(   partition_table,   group_by = \"Category\",   palette = NULL,   labels = c(\"P1\", \"F1\", \"P2\") )"},{"path":"/reference/plot_partition_frequencies.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot a barplot of gene frequencies per expression partition — plot_partition_frequencies","text":"partition_table data frame genes per expression partition returned expression_partitioning(). group_by Character indicating name variable partition_table use group genes. One \"Category\" \"Class\". Default: \"Category\". palette Character vector color names used level variable specified group_by. group_by = \"Category\", must vector length 12. group_by = \"Class\", must vector length 5. NULL, default color palette used. labels character vector length 3 indicating labels given parent 1, offspring, parent 2. Default: c(\"P1\", \"F1\", \"P2\").","code":""},{"path":"/reference/plot_partition_frequencies.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot a barplot of gene frequencies per expression partition — plot_partition_frequencies","text":"ggplot object barplot showing gene frequencies per partition next explanatory line plots depicting partition.","code":""},{"path":"/reference/plot_partition_frequencies.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot a barplot of gene frequencies per expression partition — plot_partition_frequencies","text":"","code":"data(deg_list) partition_table <- expression_partitioning(deg_list) plot_partition_frequencies(partition_table)"},{"path":"/reference/plot_samplecor.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot a heatmap of pairwise sample correlations with hierarchical clustering — plot_samplecor","title":"Plot a heatmap of pairwise sample correlations with hierarchical clustering — plot_samplecor","text":"Plot heatmap pairwise sample correlations hierarchical clustering","code":""},{"path":"/reference/plot_samplecor.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot a heatmap of pairwise sample correlations with hierarchical clustering — plot_samplecor","text":"","code":"plot_samplecor(   se,   coldata_cols = NULL,   rowdata_cols = NULL,   ntop = 500,   cor_method = \"pearson\",   palette = \"Blues\",   ... )"},{"path":"/reference/plot_samplecor.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot a heatmap of pairwise sample correlations with hierarchical clustering — plot_samplecor","text":"se SummarizedExperiment object count matrix sample metadata colData slot. rowData slot available, can also used clustering rows. coldata_cols vector (either numeric character) indicating columns extracted colData(se). rowdata_cols vector (either numeric character) indicating columns extracted rowData(se). ntop Numeric indicating number top genes highest variances use PCA. Default: 500. cor_method Character indicating correlation method use. One \"pearson\" \"spearman\". Default: \"pearson\". palette Character indicating name color palette RColorBrewer package use. Default: \"Blues\". ... Additional arguments passed ComplexHeatmap::pheatmap(). arguments can used control heatmap aesthetics, show/hide row column names, change font size, activate/deactivate hierarchical clustering, etc. complete list options, see ?ComplexHeatmap::pheatmap().","code":""},{"path":"/reference/plot_samplecor.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot a heatmap of pairwise sample correlations with hierarchical clustering — plot_samplecor","text":"heatmap hierarchically clustered pairwise sample correlations.","code":""},{"path":"/reference/plot_samplecor.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot a heatmap of pairwise sample correlations with hierarchical clustering — plot_samplecor","text":"","code":"data(se_chlamy) se <- add_midparent_expression(se_chlamy) se$Ploidy[is.na(se$Ploidy)] <- \"midparent\" se$Generation[is.na(se$Generation)] <- \"midparent\" plot_samplecor(se, ntop = 500) #> converting counts to integer mode"},{"path":"/reference/se_chlamy.html","id":null,"dir":"Reference","previous_headings":"","what":"Expression data (in counts) for 3 Chlamydomonas lines (P1, P2, and F1) — se_chlamy","title":"Expression data (in counts) for 3 Chlamydomonas lines (P1, P2, and F1) — se_chlamy","text":"Two lines (referred parent 1 parent 2) different ploidy levels crossed generate allopolyploid (F1).","code":""},{"path":"/reference/se_chlamy.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Expression data (in counts) for 3 Chlamydomonas lines (P1, P2, and F1) — se_chlamy","text":"","code":"data(se_chlamy)"},{"path":"/reference/se_chlamy.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Expression data (in counts) for 3 Chlamydomonas lines (P1, P2, and F1) — se_chlamy","text":"SummarizedExperiment object assay (count) colData.","code":""},{"path":"/reference/se_chlamy.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Expression data (in counts) for 3 Chlamydomonas lines (P1, P2, and F1) — se_chlamy","text":"","code":"data(se_chlamy)"},{"path":"/news/index.html","id":"hybridexpress-0990","dir":"Changelog","previous_headings":"","what":"HybridExpress 0.99.0","title":"HybridExpress 0.99.0","text":"NEW FEATURES Added NEWS.md file track changes package.","code":""}]
diff --git a/sitemap.xml b/sitemap.xml
index 0bbee73..4091bee 100644
--- a/sitemap.xml
+++ b/sitemap.xml
@@ -27,6 +27,9 @@
   <url>
     <loc>/reference/add_midparent_expression.html</loc>
   </url>
+  <url>
+    <loc>/reference/add_size_factors.html</loc>
+  </url>
   <url>
     <loc>/reference/deg_counts.html</loc>
   </url>