diff --git a/core/core.html b/core/core.html
index b44c689..c4ad157 100644
--- a/core/core.html
+++ b/core/core.html
@@ -273,7 +273,7 @@ <h1 class="title">Core: Supporting Information</h1>
     <div>
     <div class="quarto-title-meta-heading">Published</div>
     <div class="quarto-title-meta-contents">
-      <p class="date">13 September, 2024</p>
+      <p class="date">17 September, 2024</p>
     </div>
   </div>
   
diff --git a/core/week-1-old/overview.html b/core/week-1-old/overview.html
index e25aac1..ab768c7 100644
--- a/core/week-1-old/overview.html
+++ b/core/week-1-old/overview.html
@@ -178,7 +178,7 @@ <h1 class="title">Overview</h1>
     <div>
     <div class="quarto-title-meta-heading">Published</div>
     <div class="quarto-title-meta-contents">
-      <p class="date">13 September, 2024</p>
+      <p class="date">17 September, 2024</p>
     </div>
   </div>
   
diff --git a/core/week-1-old/study_after_workshop.html b/core/week-1-old/study_after_workshop.html
index aadb7a3..5a3aba7 100644
--- a/core/week-1-old/study_after_workshop.html
+++ b/core/week-1-old/study_after_workshop.html
@@ -178,7 +178,7 @@ <h1 class="title">Independent Study to consolidate this week</h1>
     <div>
     <div class="quarto-title-meta-heading">Published</div>
     <div class="quarto-title-meta-contents">
-      <p class="date">13 September, 2024</p>
+      <p class="date">17 September, 2024</p>
     </div>
   </div>
   
diff --git a/core/week-1-old/study_before_workshop.html b/core/week-1-old/study_before_workshop.html
index a229a65..8e75a10 100644
--- a/core/week-1-old/study_before_workshop.html
+++ b/core/week-1-old/study_before_workshop.html
@@ -171,7 +171,7 @@ <h1 class="title">Independent Study to prepare for workshop</h1>
     <div>
     <div class="quarto-title-meta-heading">Published</div>
     <div class="quarto-title-meta-contents">
-      <p class="date">13 September, 2024</p>
+      <p class="date">17 September, 2024</p>
     </div>
   </div>
   
diff --git a/core/week-1-old/workshop.html b/core/week-1-old/workshop.html
index 0d7f02e..b082000 100644
--- a/core/week-1-old/workshop.html
+++ b/core/week-1-old/workshop.html
@@ -232,7 +232,7 @@ <h1 class="title">Workshop</h1>
     <div>
     <div class="quarto-title-meta-heading">Published</div>
     <div class="quarto-title-meta-contents">
-      <p class="date">13 September, 2024</p>
+      <p class="date">17 September, 2024</p>
     </div>
   </div>
   
diff --git a/core/week-2-old/overview.html b/core/week-2-old/overview.html
index b6f9429..681cd68 100644
--- a/core/week-2-old/overview.html
+++ b/core/week-2-old/overview.html
@@ -169,7 +169,7 @@ <h1 class="title">Overview</h1>
     <div>
     <div class="quarto-title-meta-heading">Published</div>
     <div class="quarto-title-meta-contents">
-      <p class="date">13 September, 2024</p>
+      <p class="date">17 September, 2024</p>
     </div>
   </div>
   
diff --git a/core/week-2-old/study_after_workshop.html b/core/week-2-old/study_after_workshop.html
index 7b720ef..d5b5050 100644
--- a/core/week-2-old/study_after_workshop.html
+++ b/core/week-2-old/study_after_workshop.html
@@ -169,7 +169,7 @@ <h1 class="title">Independent Study to consolidate this week</h1>
     <div>
     <div class="quarto-title-meta-heading">Published</div>
     <div class="quarto-title-meta-contents">
-      <p class="date">13 September, 2024</p>
+      <p class="date">17 September, 2024</p>
     </div>
   </div>
   
diff --git a/core/week-2-old/study_before_workshop.html b/core/week-2-old/study_before_workshop.html
index 5920783..7ab3581 100644
--- a/core/week-2-old/study_before_workshop.html
+++ b/core/week-2-old/study_before_workshop.html
@@ -448,7 +448,7 @@
 </div>
 </div>
 
-  <p class="date">13 September, 2024</p>
+  <p class="date">17 September, 2024</p>
 </section><section id="overview" class="slide level2"><h2>Overview</h2>
 <ul>
 <li>RStudio Projects revisited
diff --git a/core/week-2-old/workshop.html b/core/week-2-old/workshop.html
index fce3096..8ed35b3 100644
--- a/core/week-2-old/workshop.html
+++ b/core/week-2-old/workshop.html
@@ -250,7 +250,7 @@ <h1 class="title">Workshop</h1>
     <div>
     <div class="quarto-title-meta-heading">Published</div>
     <div class="quarto-title-meta-contents">
-      <p class="date">13 September, 2024</p>
+      <p class="date">17 September, 2024</p>
     </div>
   </div>
   
@@ -338,16 +338,16 @@ <h1 class="title">Workshop</h1>
 <div class="sourceCode cell-code" id="cb9"><pre class="sourceCode bash code-with-copy"><code class="sourceCode bash"><span id="cb9-1"><a href="#cb9-1" aria-hidden="true" tabindex="-1"></a><span class="fu">ls</span> <span class="at">-l</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-stdout">
 <pre><code>total 128
-drwxr-xr-x 2 runner docker  4096 Sep 13 11:06 data
-drwxr-xr-x 2 runner docker  4096 Sep 13 11:06 images
--rw-r--r-- 1 runner docker  1597 Sep 13 11:06 overview.qmd
--rw-r--r-- 1 runner docker   184 Sep 13 11:06 study_after_workshop.qmd
--rw-r--r-- 1 runner docker  4807 Sep 13 11:06 study_before_workshop.ipynb
--rw-r--r-- 1 runner docker 13029 Sep 13 11:06 study_before_workshop.qmd
--rw-r--r-- 1 runner docker 58063 Sep 13 11:06 workshop.html
--rw-r--r-- 1 runner docker  8550 Sep 13 11:06 workshop.qmd
--rw-r--r-- 1 runner docker  8577 Sep 13 11:29 workshop.rmarkdown
-drwxr-xr-x 3 runner docker  4096 Sep 13 11:06 workshop_files</code></pre>
+drwxr-xr-x 2 runner docker  4096 Sep 17 16:24 data
+drwxr-xr-x 2 runner docker  4096 Sep 17 16:24 images
+-rw-r--r-- 1 runner docker  1597 Sep 17 16:24 overview.qmd
+-rw-r--r-- 1 runner docker   184 Sep 17 16:24 study_after_workshop.qmd
+-rw-r--r-- 1 runner docker  4807 Sep 17 16:24 study_before_workshop.ipynb
+-rw-r--r-- 1 runner docker 13029 Sep 17 16:24 study_before_workshop.qmd
+-rw-r--r-- 1 runner docker 58063 Sep 17 16:24 workshop.html
+-rw-r--r-- 1 runner docker  8550 Sep 17 16:24 workshop.qmd
+-rw-r--r-- 1 runner docker  8577 Sep 17 16:25 workshop.rmarkdown
+drwxr-xr-x 3 runner docker  4096 Sep 17 16:24 workshop_files</code></pre>
 </div>
 </div>
 <p>You can use more than one option at once. The <code>-h</code> option stands for “human readable” and makes the file sizes easier to understand for humans:</p>
@@ -355,16 +355,16 @@ <h1 class="title">Workshop</h1>
 <div class="sourceCode cell-code" id="cb11"><pre class="sourceCode bash code-with-copy"><code class="sourceCode bash"><span id="cb11-1"><a href="#cb11-1" aria-hidden="true" tabindex="-1"></a><span class="fu">ls</span> <span class="at">-hl</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-stdout">
 <pre><code>total 128K
-drwxr-xr-x 2 runner docker 4.0K Sep 13 11:06 data
-drwxr-xr-x 2 runner docker 4.0K Sep 13 11:06 images
--rw-r--r-- 1 runner docker 1.6K Sep 13 11:06 overview.qmd
--rw-r--r-- 1 runner docker  184 Sep 13 11:06 study_after_workshop.qmd
--rw-r--r-- 1 runner docker 4.7K Sep 13 11:06 study_before_workshop.ipynb
--rw-r--r-- 1 runner docker  13K Sep 13 11:06 study_before_workshop.qmd
--rw-r--r-- 1 runner docker  57K Sep 13 11:06 workshop.html
--rw-r--r-- 1 runner docker 8.4K Sep 13 11:06 workshop.qmd
--rw-r--r-- 1 runner docker 8.4K Sep 13 11:29 workshop.rmarkdown
-drwxr-xr-x 3 runner docker 4.0K Sep 13 11:06 workshop_files</code></pre>
+drwxr-xr-x 2 runner docker 4.0K Sep 17 16:24 data
+drwxr-xr-x 2 runner docker 4.0K Sep 17 16:24 images
+-rw-r--r-- 1 runner docker 1.6K Sep 17 16:24 overview.qmd
+-rw-r--r-- 1 runner docker  184 Sep 17 16:24 study_after_workshop.qmd
+-rw-r--r-- 1 runner docker 4.7K Sep 17 16:24 study_before_workshop.ipynb
+-rw-r--r-- 1 runner docker  13K Sep 17 16:24 study_before_workshop.qmd
+-rw-r--r-- 1 runner docker  57K Sep 17 16:24 workshop.html
+-rw-r--r-- 1 runner docker 8.4K Sep 17 16:24 workshop.qmd
+-rw-r--r-- 1 runner docker 8.4K Sep 17 16:25 workshop.rmarkdown
+drwxr-xr-x 3 runner docker 4.0K Sep 17 16:24 workshop_files</code></pre>
 </div>
 </div>
 <p>The <code>-a</code> option stands for “all” and shows us all the files, including hidden files.</p>
@@ -372,18 +372,18 @@ <h1 class="title">Workshop</h1>
 <div class="sourceCode cell-code" id="cb13"><pre class="sourceCode bash code-with-copy"><code class="sourceCode bash"><span id="cb13-1"><a href="#cb13-1" aria-hidden="true" tabindex="-1"></a><span class="fu">ls</span> <span class="at">-alh</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-stdout">
 <pre><code>total 136K
-drwxr-xr-x 5 runner docker 4.0K Sep 13 11:29 .
-drwxr-xr-x 7 runner docker 4.0K Sep 13 11:06 ..
-drwxr-xr-x 2 runner docker 4.0K Sep 13 11:06 data
-drwxr-xr-x 2 runner docker 4.0K Sep 13 11:06 images
--rw-r--r-- 1 runner docker 1.6K Sep 13 11:06 overview.qmd
--rw-r--r-- 1 runner docker  184 Sep 13 11:06 study_after_workshop.qmd
--rw-r--r-- 1 runner docker 4.7K Sep 13 11:06 study_before_workshop.ipynb
--rw-r--r-- 1 runner docker  13K Sep 13 11:06 study_before_workshop.qmd
--rw-r--r-- 1 runner docker  57K Sep 13 11:06 workshop.html
--rw-r--r-- 1 runner docker 8.4K Sep 13 11:06 workshop.qmd
--rw-r--r-- 1 runner docker 8.4K Sep 13 11:29 workshop.rmarkdown
-drwxr-xr-x 3 runner docker 4.0K Sep 13 11:06 workshop_files</code></pre>
+drwxr-xr-x 5 runner docker 4.0K Sep 17 16:25 .
+drwxr-xr-x 7 runner docker 4.0K Sep 17 16:24 ..
+drwxr-xr-x 2 runner docker 4.0K Sep 17 16:24 data
+drwxr-xr-x 2 runner docker 4.0K Sep 17 16:24 images
+-rw-r--r-- 1 runner docker 1.6K Sep 17 16:24 overview.qmd
+-rw-r--r-- 1 runner docker  184 Sep 17 16:24 study_after_workshop.qmd
+-rw-r--r-- 1 runner docker 4.7K Sep 17 16:24 study_before_workshop.ipynb
+-rw-r--r-- 1 runner docker  13K Sep 17 16:24 study_before_workshop.qmd
+-rw-r--r-- 1 runner docker  57K Sep 17 16:24 workshop.html
+-rw-r--r-- 1 runner docker 8.4K Sep 17 16:24 workshop.qmd
+-rw-r--r-- 1 runner docker 8.4K Sep 17 16:25 workshop.rmarkdown
+drwxr-xr-x 3 runner docker 4.0K Sep 17 16:24 workshop_files</code></pre>
 </div>
 </div>
 <p>You can move about with the <code>cd</code> command, which stands for “change directory”. You can use it to move into a directory by specifying the path to the directory:</p>
diff --git a/core/week-2/overview.html b/core/week-2/overview.html
index 70f4062..ededf3c 100644
--- a/core/week-2/overview.html
+++ b/core/week-2/overview.html
@@ -271,7 +271,7 @@ <h1 class="title">Overview</h1>
     <div>
     <div class="quarto-title-meta-heading">Published</div>
     <div class="quarto-title-meta-contents">
-      <p class="date">13 September, 2024</p>
+      <p class="date">17 September, 2024</p>
     </div>
   </div>
   
diff --git a/core/week-2/study_after_workshop.html b/core/week-2/study_after_workshop.html
index 8efa741..325a409 100644
--- a/core/week-2/study_after_workshop.html
+++ b/core/week-2/study_after_workshop.html
@@ -264,7 +264,7 @@ <h1 class="title">Independent Study to consolidate this week</h1>
     <div>
     <div class="quarto-title-meta-heading">Published</div>
     <div class="quarto-title-meta-contents">
-      <p class="date">13 September, 2024</p>
+      <p class="date">17 September, 2024</p>
     </div>
   </div>
   
diff --git a/core/week-2/study_before_workshop.html b/core/week-2/study_before_workshop.html
index a4c2f96..8f24a2b 100644
--- a/core/week-2/study_before_workshop.html
+++ b/core/week-2/study_before_workshop.html
@@ -264,7 +264,7 @@ <h1 class="title">Independent Study to prepare for workshop</h1>
     <div>
     <div class="quarto-title-meta-heading">Published</div>
     <div class="quarto-title-meta-contents">
-      <p class="date">13 September, 2024</p>
+      <p class="date">17 September, 2024</p>
     </div>
   </div>
   
diff --git a/core/week-2/workshop.html b/core/week-2/workshop.html
index 053e70b..6f26c7b 100644
--- a/core/week-2/workshop.html
+++ b/core/week-2/workshop.html
@@ -448,7 +448,7 @@
 </div>
 </div>
 
-  <p class="date">13 September, 2024</p>
+  <p class="date">17 September, 2024</p>
 </section><section><section id="introduction" class="title-slide slide level1 center"><h1>Introduction</h1>
 
 </section><section id="session-overview" class="slide level2"><h2>Session overview</h2>
@@ -878,7 +878,7 @@
 <li>Excuse Me, Do You Have a Moment to Talk About Version Control? <span class="citation" data-cites="bryan2018">(<a href="#/references" role="doc-biblioref" onclick="">Bryan 2018</a>)</span>
 </li>
 </ul>
-<p>Pages made with R <span class="citation" data-cites="R-core">(<a href="#/references" role="doc-biblioref" onclick="">R Core Team 2024</a>)</span> and Quarto <span class="citation" data-cites="allaire2022">(<a href="#/references" role="doc-biblioref" onclick=""><strong>allaire2022?</strong></a>)</span></p>
+<p>Pages made with R <span class="citation" data-cites="R-core">(<a href="#/references" role="doc-biblioref" onclick="">R Core Team 2024</a>)</span>, Quarto <span class="citation" data-cites="Allaire_Quarto_2024">(<a href="#/references" role="doc-biblioref" onclick="">Allaire et al. 2024</a>)</span>, <code>knitr</code> [<span class="citation" data-cites="knitr1">Xie (<a href="#/references" role="doc-biblioref" onclick="">2024</a>)</span>; knitr2; knitr3], <code>kableExtra</code> <span class="citation" data-cites="kableExtra">(<a href="#/references" role="doc-biblioref" onclick="">Zhu 2021</a>)</span></p>
 </section><section id="references" class="slide level2 smaller scrollable"><h2>References</h2>
 
 <div class="quarto-auto-generated-content">
@@ -887,6 +887,9 @@
 </div>
 </div>
 <div id="refs" class="references csl-bib-body hanging-indent" data-entry-spacing="0" role="list">
+<div id="ref-Allaire_Quarto_2024" class="csl-entry" role="listitem">
+Allaire, J. J., Charles Teague, Carlos Scheidegger, Yihui Xie, and Christophe Dervieux. 2024. <span>“<span>Quarto</span>.”</span> <a href="https://doi.org/10.5281/zenodo.5960048">https://doi.org/10.5281/zenodo.5960048</a>.
+</div>
 <div id="ref-baggerly2009" class="csl-entry" role="listitem">
 Baggerly, Keith A, and Kevin R Coombes. 2009. <span>“DERIVING CHEMOSENSITIVITY FROM CELL LINES: FORENSIC BIOINFORMATICS AND REPRODUCIBLE RESEARCH IN HIGH-THROUGHPUT BIOLOGY.”</span> <em>Ann. Appl. Stat.</em> 3 (4): 1309–34. <a href="https://doi.org/10.2307/27801549">https://doi.org/10.2307/27801549</a>.
 </div>
@@ -914,6 +917,12 @@
 <div id="ref-wilson2017" class="csl-entry" role="listitem">
 Wilson, Greg, Jennifer Bryan, Karen Cranston, Justin Kitzes, Lex Nederbragt, and Tracy K Teal. 2017. <span>“Good Enough Practices in Scientific Computing.”</span> <em>PLoS Comput. Biol.</em> 13 (6): e1005510. <a href="https://doi.org/10.1371/journal.pcbi.1005510">https://doi.org/10.1371/journal.pcbi.1005510</a>.
 </div>
+<div id="ref-knitr1" class="csl-entry" role="listitem">
+Xie, Yihui. 2024. <em>Knitr: A General-Purpose Package for Dynamic Report Generation in r</em>. <a href="https://yihui.org/knitr/">https://yihui.org/knitr/</a>.
+</div>
+<div id="ref-kableExtra" class="csl-entry" role="listitem">
+Zhu, Hao. 2021. <span>“kableExtra: Construct Complex Table with ’Kable’ and Pipe Syntax.”</span> <a href="https://CRAN.R-project.org/package=kableExtra">https://CRAN.R-project.org/package=kableExtra</a>.
+</div>
 </div>
 </section></section>
 </div>
diff --git a/core/week-6-old/overview.html b/core/week-6-old/overview.html
index ca8a6fa..0487866 100644
--- a/core/week-6-old/overview.html
+++ b/core/week-6-old/overview.html
@@ -177,7 +177,7 @@ <h1 class="title">Overview</h1>
     <div>
     <div class="quarto-title-meta-heading">Published</div>
     <div class="quarto-title-meta-contents">
-      <p class="date">13 September, 2024</p>
+      <p class="date">17 September, 2024</p>
     </div>
   </div>
   
diff --git a/core/week-6-old/study_after_workshop.html b/core/week-6-old/study_after_workshop.html
index 8ae592c..8404c42 100644
--- a/core/week-6-old/study_after_workshop.html
+++ b/core/week-6-old/study_after_workshop.html
@@ -171,7 +171,7 @@ <h1 class="title">Independent Study to consolidate this week</h1>
     <div>
     <div class="quarto-title-meta-heading">Published</div>
     <div class="quarto-title-meta-contents">
-      <p class="date">13 September, 2024</p>
+      <p class="date">17 September, 2024</p>
     </div>
   </div>
   
diff --git a/core/week-6-old/study_before_workshop.html b/core/week-6-old/study_before_workshop.html
index a83f379..3df9bf7 100644
--- a/core/week-6-old/study_before_workshop.html
+++ b/core/week-6-old/study_before_workshop.html
@@ -171,7 +171,7 @@ <h1 class="title">Independent Study to prepare for workshop</h1>
     <div>
     <div class="quarto-title-meta-heading">Published</div>
     <div class="quarto-title-meta-contents">
-      <p class="date">13 September, 2024</p>
+      <p class="date">17 September, 2024</p>
     </div>
   </div>
   
diff --git a/core/week-6-old/workshop.html b/core/week-6-old/workshop.html
index 673b91c..e7d4a70 100644
--- a/core/week-6-old/workshop.html
+++ b/core/week-6-old/workshop.html
@@ -207,7 +207,7 @@ <h1 class="title">Workshop</h1>
     <div>
     <div class="quarto-title-meta-heading">Published</div>
     <div class="quarto-title-meta-contents">
-      <p class="date">13 September, 2024</p>
+      <p class="date">17 September, 2024</p>
     </div>
   </div>
   
diff --git a/core/week-6/overview.html b/core/week-6/overview.html
index 71a90d8..ad5a54b 100644
--- a/core/week-6/overview.html
+++ b/core/week-6/overview.html
@@ -271,7 +271,7 @@ <h1 class="title">Overview</h1>
     <div>
     <div class="quarto-title-meta-heading">Published</div>
     <div class="quarto-title-meta-contents">
-      <p class="date">13 September, 2024</p>
+      <p class="date">17 September, 2024</p>
     </div>
   </div>
   
diff --git a/core/week-6/study_after_workshop.html b/core/week-6/study_after_workshop.html
index 1793690..d51ab79 100644
--- a/core/week-6/study_after_workshop.html
+++ b/core/week-6/study_after_workshop.html
@@ -271,7 +271,7 @@ <h1 class="title">Independent Study to consolidate this week</h1>
     <div>
     <div class="quarto-title-meta-heading">Published</div>
     <div class="quarto-title-meta-contents">
-      <p class="date">13 September, 2024</p>
+      <p class="date">17 September, 2024</p>
     </div>
   </div>
   
diff --git a/core/week-6/study_before_workshop.html b/core/week-6/study_before_workshop.html
index 315388a..a5b76fa 100644
--- a/core/week-6/study_before_workshop.html
+++ b/core/week-6/study_before_workshop.html
@@ -264,7 +264,7 @@ <h1 class="title">Independent Study to prepare for workshop</h1>
     <div>
     <div class="quarto-title-meta-heading">Published</div>
     <div class="quarto-title-meta-contents">
-      <p class="date">13 September, 2024</p>
+      <p class="date">17 September, 2024</p>
     </div>
   </div>
   
diff --git a/core/week-6/workshop.html b/core/week-6/workshop.html
index 7ff2ff0..f038582 100644
--- a/core/week-6/workshop.html
+++ b/core/week-6/workshop.html
@@ -325,7 +325,7 @@ <h1 class="title">Workshop</h1>
     <div>
     <div class="quarto-title-meta-heading">Published</div>
     <div class="quarto-title-meta-contents">
-      <p class="date">13 September, 2024</p>
+      <p class="date">17 September, 2024</p>
     </div>
   </div>
   
@@ -557,7 +557,7 @@ <h1>🥳 Well Done! 🎉</h1>
 <section id="independent-study-following-the-workshop" class="level1">
 <h1>Independent study following the workshop</h1>
 <p><a href="../../core/week-6/study_after_workshop.html">Consolidate</a></p>
-<p>Pages made with R <span class="citation" data-cites="R-core">(<a href="#ref-R-core" role="doc-biblioref">R Core Team 2024</a>)</span>, Quarto <span class="citation" data-cites="allaire2022">(<a href="#ref-allaire2022" role="doc-biblioref"><strong>allaire2022?</strong></a>)</span>, <code>knitr</code> <span class="citation" data-cites="knitr">(<a href="#ref-knitr" role="doc-biblioref"><strong>knitr?</strong></a>)</span>, <code>kableExtra</code> <span class="citation" data-cites="kableExtra">(<a href="#ref-kableExtra" role="doc-biblioref">Zhu 2021</a>)</span></p>
+<p>Pages made with R <span class="citation" data-cites="R-core">(<a href="#ref-R-core" role="doc-biblioref">R Core Team 2024</a>)</span>, Quarto <span class="citation" data-cites="Allaire_Quarto_2024">(<a href="#ref-Allaire_Quarto_2024" role="doc-biblioref">Allaire et al. 2024</a>)</span>, <code>knitr</code> [<span class="citation" data-cites="knitr1">Xie (<a href="#ref-knitr1" role="doc-biblioref">2024</a>)</span>; knitr2; knitr3], <code>kableExtra</code> <span class="citation" data-cites="kableExtra">(<a href="#ref-kableExtra" role="doc-biblioref">Zhu 2021</a>)</span></p>
 </section>
 <section id="references" class="level1">
 
@@ -567,6 +567,9 @@ <h1>Independent study following the workshop</h1>
 </section>
 
 <div id="quarto-appendix" class="default"><section class="quarto-appendix-contents" role="doc-bibliography" id="quarto-bibliography"><h2 class="anchored quarto-appendix-heading">References</h2><div id="refs" class="references csl-bib-body hanging-indent" data-entry-spacing="0" role="list">
+<div id="ref-Allaire_Quarto_2024" class="csl-entry" role="listitem">
+Allaire, J. J., Charles Teague, Carlos Scheidegger, Yihui Xie, and Christophe Dervieux. 2024. <span>“<span>Quarto</span>.”</span> <a href="https://doi.org/10.5281/zenodo.5960048">https://doi.org/10.5281/zenodo.5960048</a>.
+</div>
 <div id="ref-baggerly2009" class="csl-entry" role="listitem">
 Baggerly, Keith A, and Kevin R Coombes. 2009. <span>“DERIVING CHEMOSENSITIVITY FROM CELL LINES: FORENSIC BIOINFORMATICS AND REPRODUCIBLE RESEARCH IN HIGH-THROUGHPUT BIOLOGY.”</span> <em>Ann. Appl. Stat.</em> 3 (4): 1309–34. <a href="https://doi.org/10.2307/27801549">https://doi.org/10.2307/27801549</a>.
 </div>
@@ -588,6 +591,9 @@ <h1>Independent study following the workshop</h1>
 <div id="ref-sessioninfo" class="csl-entry" role="listitem">
 Wickham, Hadley, Winston Chang, Robert Flight, Kirill Müller, and Jim Hester. 2021. <em>Sessioninfo: R Session Information</em>. <a href="https://github.com/r-lib/sessioninfo#readme">https://github.com/r-lib/sessioninfo#readme</a>.
 </div>
+<div id="ref-knitr1" class="csl-entry" role="listitem">
+Xie, Yihui. 2024. <em>Knitr: A General-Purpose Package for Dynamic Report Generation in r</em>. <a href="https://yihui.org/knitr/">https://yihui.org/knitr/</a>.
+</div>
 <div id="ref-kableExtra" class="csl-entry" role="listitem">
 Zhu, Hao. 2021. <span>“kableExtra: Construct Complex Table with ’Kable’ and Pipe Syntax.”</span> <a href="https://CRAN.R-project.org/package=kableExtra">https://CRAN.R-project.org/package=kableExtra</a>.
 </div>
diff --git a/images/images.html b/images/images.html
index 5783deb..e4e37e7 100644
--- a/images/images.html
+++ b/images/images.html
@@ -218,7 +218,7 @@ <h1 class="title">Image Data Analysis for Group Project</h1>
     <div>
     <div class="quarto-title-meta-heading">Published</div>
     <div class="quarto-title-meta-contents">
-      <p class="date">13 September, 2024</p>
+      <p class="date">17 September, 2024</p>
     </div>
   </div>
   
diff --git a/index.html b/index.html
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+++ b/index.html
@@ -21,7 +21,27 @@
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+/* CSS for citations */
+div.csl-bib-body { }
+div.csl-entry {
+  clear: both;
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+.hanging-indent div.csl-entry {
+  margin-left:2em;
+  text-indent:-2em;
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+  float:left;
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+div.csl-indent {
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 <script src="site_libs/quarto-nav/quarto-nav.js"></script>
@@ -181,7 +201,7 @@ <h1 class="title">Data Analysis for the Group Research Project</h1>
     <div>
     <div class="quarto-title-meta-heading">Published</div>
     <div class="quarto-title-meta-contents">
-      <p class="date">13 September, 2024</p>
+      <p class="date">17 September, 2024</p>
     </div>
   </div>
   
@@ -306,12 +326,28 @@ <h2 class="anchored" data-anchor-id="what-is-this-site-for">What is this site fo
 <section id="please-cite-as" class="level2">
 <h2 class="anchored" data-anchor-id="please-cite-as">Please cite as</h2>
 <p>Rand E (2024). Data Analysis for Group Project. https://3mmarand.github.io/BIO00088H-data/.</p>
+<p>Pages made with R <span class="citation" data-cites="R-core">(<a href="#ref-R-core" role="doc-biblioref">R Core Team 2024</a>)</span>, Quarto <span class="citation" data-cites="Allaire_Quarto_2024">(<a href="#ref-Allaire_Quarto_2024" role="doc-biblioref">Allaire et al. 2024</a>)</span>, <code>knitr</code> [<span class="citation" data-cites="knitr1">Xie (<a href="#ref-knitr1" role="doc-biblioref">2024</a>)</span>; knitr2; knitr3], <code>kableExtra</code> <span class="citation" data-cites="kableExtra">(<a href="#ref-kableExtra" role="doc-biblioref">Zhu 2021</a>)</span></p>
+<p>References</p>
+
 
 
 </section>
 </section>
 
-</main> <!-- /main -->
+<div id="quarto-appendix" class="default"><section class="quarto-appendix-contents" role="doc-bibliography" id="quarto-bibliography"><h2 class="anchored quarto-appendix-heading">References</h2><div id="refs" class="references csl-bib-body hanging-indent" data-entry-spacing="0" role="list">
+<div id="ref-Allaire_Quarto_2024" class="csl-entry" role="listitem">
+Allaire, J. J., Charles Teague, Carlos Scheidegger, Yihui Xie, and Christophe Dervieux. 2024. <span>“<span>Quarto</span>.”</span> <a href="https://doi.org/10.5281/zenodo.5960048">https://doi.org/10.5281/zenodo.5960048</a>.
+</div>
+<div id="ref-R-core" class="csl-entry" role="listitem">
+R Core Team. 2024. <em>R: A Language and Environment for Statistical Computing</em>. Vienna, Austria: R Foundation for Statistical Computing. <a href="https://www.R-project.org/">https://www.R-project.org/</a>.
+</div>
+<div id="ref-knitr1" class="csl-entry" role="listitem">
+Xie, Yihui. 2024. <em>Knitr: A General-Purpose Package for Dynamic Report Generation in r</em>. <a href="https://yihui.org/knitr/">https://yihui.org/knitr/</a>.
+</div>
+<div id="ref-kableExtra" class="csl-entry" role="listitem">
+Zhu, Hao. 2021. <span>“kableExtra: Construct Complex Table with ’Kable’ and Pipe Syntax.”</span> <a href="https://CRAN.R-project.org/package=kableExtra">https://CRAN.R-project.org/package=kableExtra</a>.
+</div>
+</div></section></div></main> <!-- /main -->
 <script id="quarto-html-after-body" type="application/javascript">
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diff --git a/search.json b/search.json
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--- a/search.json
+++ b/search.json
@@ -4,7 +4,7 @@
     "href": "index.html",
     "title": "Data Analysis for the Group Research Project",
     "section": "",
-    "text": "You are either\n\nan integrated masters student doing BIO00088H Group Research Project or\nan MSc Bioinformatics student doing BIO00070M Research, Professional and Team Skills\n\nIntegrated masters students doing 88H will be doing one of these projects:\nThe project types are:\n\n\n\n\n\n\n\n\nTitle\nDirector\nData analysis strand\n\n\n\n\nIdentifying transcriptional targets of FGF signalling in Xenopus embryos.\nBetsy Pownall\nTranscriptomics, Emma Rand\n\n\nInvestigating the differentiation of stem cells in healthy bone marrow\nJillian Barlow\nTranscriptomics, Emma Rand\n\n\nInvestigating  pathways involved in the Nickel detoxification in Willow\nLiz Rylott\nTranscriptomics, Emma Rand\n\n\nInvestigating differential RNA expression through the Leishmania lifecycle\nPegine Walrad\nTranscriptomics, Emma Rand\n\n\nIdentifying novel proteins regulating synaptophagy\nRichard Maguire\nImage analysis, Richard Bingham\n\n\nDefining pathological cascades in dopaminergic neurons in a Parkinson’s model\nSean Sweeney\nImage analysis, Richard Bingham\n\n\nDiscovery proteins for biotech applications: new classes of antibody mimetics\nMichael Plevin\nStructure Analysis, Jon Agirre\n\n\n\nData Analysis compromises five workshops covering computational skills needed in your project. MSc Bioinformatics students do the Core workshops and the transcriptomics workshops as part of BIO00070M. The data analysis workshops are:\n\n\n\n\n\n\n\nWeek\nData Strand\n\n\n\n\n2\nCore 1 Supporting Information - reproducibility, project-oriented workflow, naming things, cool code, handy shortcuts\n\n\n3\nStrand specific 1\n\n\n4\nStrand specific 2\n\n\n5\nStrand specific 3\n\n\n6\nCore 2 Supporting Information - documenting with a README, curating code, non-coded processes\n\n\n\n\n\n\n\n\n\nStudents who successfully complete this module will be able to\n\nuse appropriate computational techniques to reproducibly process, analyse and visualise data and generate scientific reports based on project work.\n\n\n\n\nAll material is on the VLE so why is this site useful? This site collects everything together in a searchable way. The search icon is on the top right.\n\n\n\nRand E (2024). Data Analysis for Group Project. https://3mmarand.github.io/BIO00088H-data/."
+    "text": "You are either\n\nan integrated masters student doing BIO00088H Group Research Project or\nan MSc Bioinformatics student doing BIO00070M Research, Professional and Team Skills\n\nIntegrated masters students doing 88H will be doing one of these projects:\nThe project types are:\n\n\n\n\n\n\n\n\nTitle\nDirector\nData analysis strand\n\n\n\n\nIdentifying transcriptional targets of FGF signalling in Xenopus embryos.\nBetsy Pownall\nTranscriptomics, Emma Rand\n\n\nInvestigating the differentiation of stem cells in healthy bone marrow\nJillian Barlow\nTranscriptomics, Emma Rand\n\n\nInvestigating  pathways involved in the Nickel detoxification in Willow\nLiz Rylott\nTranscriptomics, Emma Rand\n\n\nInvestigating differential RNA expression through the Leishmania lifecycle\nPegine Walrad\nTranscriptomics, Emma Rand\n\n\nIdentifying novel proteins regulating synaptophagy\nRichard Maguire\nImage analysis, Richard Bingham\n\n\nDefining pathological cascades in dopaminergic neurons in a Parkinson’s model\nSean Sweeney\nImage analysis, Richard Bingham\n\n\nDiscovery proteins for biotech applications: new classes of antibody mimetics\nMichael Plevin\nStructure Analysis, Jon Agirre\n\n\n\nData Analysis compromises five workshops covering computational skills needed in your project. MSc Bioinformatics students do the Core workshops and the transcriptomics workshops as part of BIO00070M. The data analysis workshops are:\n\n\n\n\n\n\n\nWeek\nData Strand\n\n\n\n\n2\nCore 1 Supporting Information - reproducibility, project-oriented workflow, naming things, cool code, handy shortcuts\n\n\n3\nStrand specific 1\n\n\n4\nStrand specific 2\n\n\n5\nStrand specific 3\n\n\n6\nCore 2 Supporting Information - documenting with a README, curating code, non-coded processes\n\n\n\n\n\n\n\n\n\nStudents who successfully complete this module will be able to\n\nuse appropriate computational techniques to reproducibly process, analyse and visualise data and generate scientific reports based on project work.\n\n\n\n\nAll material is on the VLE so why is this site useful? This site collects everything together in a searchable way. The search icon is on the top right.\n\n\n\nRand E (2024). Data Analysis for Group Project. https://3mmarand.github.io/BIO00088H-data/.\nPages made with R (R Core Team 2024), Quarto (Allaire et al. 2024), knitr [Xie (2024); knitr2; knitr3], kableExtra (Zhu 2021)\nReferences"
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     "section": "",
-    "text": "Rand E (2024). Data Analysis for Group Project. https://3mmarand.github.io/BIO00088H-data/."
+    "text": "Rand E (2024). Data Analysis for Group Project. https://3mmarand.github.io/BIO00088H-data/.\nPages made with R (R Core Team 2024), Quarto (Allaire et al. 2024), knitr [Xie (2024); knitr2; knitr3], kableExtra (Zhu 2021)\nReferences"
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@@ -172,7 +172,7 @@
     "href": "transcriptomics/week-3/study_before_workshop.html#experimental-design-3",
     "title": "Independent Study to prepare for workshop",
     "section": "🐸 Experimental design",
-    "text": "🐸 Experimental design\n\nSchematic of frog development experiment\n\n3 fertilisations. These are the replicates, .5, .6, A\ntwo siblings from each fertilisation one control, one FGF treated. The treatments are paired\nsequenced at three time points. S14, S20, S30\n3 x 2 x 3 = 18 groups",
+    "text": "🐸 Experimental design\n\nSchematic of frog development experiment\n\n3 fertilisations. These are the replicates, 1, 2, 3\ntwo siblings from each fertilisation one control, one FGF treated. The treatments are paired\nsequenced at three time points. S14, S20, S30\n3 x 2 x 3 = 18 groups",
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@@ -292,7 +292,7 @@
     "href": "transcriptomics/week-3/study_before_workshop.html#raw-sequence-data-2",
     "title": "Independent Study to prepare for workshop",
     "section": "Raw Sequence data",
-    "text": "Raw Sequence data\n\n\nThe RNA-seq data are from an Illumina machine 150-300bp; Metagenomic data are often Nanopore 10,000 - 30000bp\nReads are in FASTQ files\nFASTQ files contain the sequence of each read and a quality score for each base",
+    "text": "Raw Sequence data\n\n\nThe RNA-seq data are from an Illumina machine 150-300bp\nReads are in FASTQ files\nFASTQ files contain the sequence of each read and a quality score for each base",
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     "href": "transcriptomics/week-3/study_before_workshop.html#general-steps",
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     "section": "General steps",
-    "text": "General steps\n\n\nReads are filtered and trimmed on the basis of the quality score\nThey are then aligned/pseudo-aligned to a reference genome/transcriptome or, in metagenomics, assembled de novo.\nReads are then counted to quantify the expression or number of genomes in metagenomics\nCounts are normalised to account for differences in sequencing depth and gene/transcript/genome length before statistical analysis",
+    "text": "General steps\n\n\nReads are filtered and trimmed on the basis of the quality score\nThey are then aligned/pseudo-aligned to a reference genome/transcriptome\nReads are then counted to quantify the expression\nCounts will need to be normalised to account for differences in sequencing depth and gene/transcript/ length before, or as part of, statistical analysis.",
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     "section": "Explore",
-    "text": "Explore\nThe first task is to get an overview. We want to know\n\nare there any missing values? If so, how many and how are they distributed?\nhow may zeros are there and how are they distributed\ndoes it look as tough all the samples/cells were equally “successful”? Can we spot any problematic anomalies?\nwhat is the distribution of values?\n\nIf our data collection has gone well we would hope to see approximately the same average expression in each sample or cell of the same type. That is replicates should be similar. We would also expect to see that the average expression of genes varies. We might have genes which are zero in every cell/sample. We will want to to filter those out.\nWe get this overview by looking at:\n\nThe distribution of values across the whole dataset\nThe distribution of values across the sample/cells (i.e., averaged across genes). This allows us to see variation between samples/cells:\nThe distribution of values across the genes (i.e., averaged across samples/cells). This allows us to see variation between genes.\n\nDistribution of values across the whole dataset\nIn all data sets, the values are spread over multiple columns so in order to plot the distribution as a whole, we will need to first use pivot_longer() to put the data in ‘tidy’ format (Wickham2014-nl?) by stacking the columns. We could save a copy of the stacked data and then plot it, but here, I have just piped the stacked data straight into ggplot().\n🐸 Frogs\n🎬 Pivot the counts (stack the columns) so all the counts are in a single column (count) and pipe into ggplot() to create a histogram:\n\ns30 |&gt;\n  pivot_longer(cols = -xenbase_gene_id,\n               names_to = \"sample\",\n               values_to = \"count\") |&gt;\n  ggplot(aes(x = count)) +\n  geom_histogram()\n\n\n\n\n\n\n\nThis data is very skewed - there are so many low values that we can’t see the tiny bars for the higher values. Logging the counts is a way to make the distribution more visible.\n🎬 Repeat the plot on log of the counts.\n\ns30 |&gt;\n  pivot_longer(cols = -xenbase_gene_id,\n               names_to = \"sample\",\n               values_to = \"count\") |&gt;\n  ggplot(aes(x = log10(count))) +\n  geom_histogram()\n\n\n\n\n\n\n\nI’ve used base 10 only because it easy to convert to the original scale (1 is 10, 2 is 100, 3 is 1000 etc). The warning about rows being removed is expected - these are the counts of 0 since you can’t log a value of 0. The peak at zero suggests quite a few counts of 1. We would expect we would expect the distribution of counts to be roughly log normal because this is expression of all the genes in the genome1. That small peak near the low end suggests that these lower counts might be anomalies.\nThe excess number of low counts indicates we might want to create a cut off for quality control. The removal of low counts is a common processing step in ’omic data. We will revisit this after we have considered the distribution of counts across samples and genes.\n🐭 Mice\n🎬 Pivot the expression values (stack the columns) so all the counts are in a single column (expr) and pipe into ggplot() to create a histogram:\n\nhspc |&gt;\n  pivot_longer(cols = -ensembl_gene_id,\n               names_to = \"cell\",\n               values_to = \"expr\") |&gt; \n  ggplot(aes(x = expr)) +\n  geom_histogram()\n\n\n\n\n\n\n\nThis is a very striking distribution. Is it what we are expecting? Again,the excess number of low values is almost certainly anomalous. They will be inaccurate measure and we will want to exclude expression values below (about) 1. We will revisit this after we have considered the distribution of expression across cells and genes.\nWhat about the bimodal appearance of the the ‘real’ values? If we had the whole genome we would not expect to see such a pattern - we’d expect to see a roughly normal distribution2. However, this is a subset of the genome and the nature of the subsetting has had an influence here. These are a subset of cell surface proteins that show a significant difference between at least two of twelve cell subtypes. That is, all of these genes are either high or low.\nDistribution of values across the sample/cells\n🐸 Frog samples\nSummary statistics including the the number of NAs can be seen using the summary(). It is most helpful which you have up to about 30 columns. There is nothing special about the number 30, it is just that text summaries of a larger number of columns are difficult to grasp.\n🎬 Get a quick overview of the columns:\n\n# examine all the columns quickly\n# works well with smaller numbers of column\nsummary(s30)\n\n xenbase_gene_id       S30_C_5            S30_C_6            S30_C_A        \n Length:11893       Min.   :     0.0   Min.   :     0.0   Min.   :     0.0  \n Class :character   1st Qu.:    14.0   1st Qu.:    14.0   1st Qu.:    23.0  \n Mode  :character   Median :    70.0   Median :    75.0   Median :   107.0  \n                    Mean   :   317.1   Mean   :   335.8   Mean   :   426.3  \n                    3rd Qu.:   205.0   3rd Qu.:   220.0   3rd Qu.:   301.0  \n                    Max.   :101746.0   Max.   :118708.0   Max.   :117945.0  \n    S30_F_5            S30_F_6            S30_F_A       \n Min.   :     0.0   Min.   :     0.0   Min.   :    0.0  \n 1st Qu.:    19.0   1st Qu.:    17.0   1st Qu.:   16.0  \n Median :    88.0   Median :    84.0   Median :   69.0  \n Mean   :   376.2   Mean   :   376.5   Mean   :  260.4  \n 3rd Qu.:   251.0   3rd Qu.:   246.0   3rd Qu.:  187.0  \n Max.   :117573.0   Max.   :130672.0   Max.   :61531.0  \n\n\nNotice that: - the minimum count is 0 and the maximums are very high in all the columns - the medians are quite a lot lower than the means so the data are skewed (hump to the left, tail to the right) - there must be quite a lot of zeros - the columns are roughly similar and it doesn’t look like there is an anomalous replicate.\nTo find out how may zeros there are in a column we can make use of the fact that TRUE evaluates to 1 and FALSE evaluates to 0. This means sum(S30_C_5 == 0) gives the number of 0 in the S30_C_5 column\n🎬 Find the number of zeros in all six columns:\n\ns30 |&gt;\n  summarise(sum(S30_C_5 == 0),\n            sum(S30_C_6 == 0),\n            sum(S30_C_A == 0),\n            sum(S30_F_5 == 0),\n            sum(S30_F_6 == 0),\n            sum(S30_F_A == 0))\n\n# A tibble: 1 × 6\n  `sum(S30_C_5 == 0)` `sum(S30_C_6 == 0)` `sum(S30_C_A == 0)`\n                &lt;int&gt;               &lt;int&gt;               &lt;int&gt;\n1                1340                1361                 998\n# ℹ 3 more variables: `sum(S30_F_5 == 0)` &lt;int&gt;, `sum(S30_F_6 == 0)` &lt;int&gt;,\n#   `sum(S30_F_A == 0)` &lt;int&gt;\n\n\nThere is a better way of doing this that saves you having to repeat so much code - especially useful if you have a lot more than 6 columns. We can use pivot_longer() to put the data in tidy format and then use the group_by() and summarise() approach we have used extensively before.\n🎬 Find the number of zeros in all columns:\n\ns30 |&gt;\n  pivot_longer(cols = -xenbase_gene_id,\n               names_to = \"sample\",\n               values_to = \"count\") |&gt;\n  group_by(sample) |&gt;\n  summarise(n_zero = sum(count == 0))\n\n# A tibble: 6 × 2\n  sample  n_zero\n  &lt;chr&gt;    &lt;int&gt;\n1 S30_C_5   1340\n2 S30_C_6   1361\n3 S30_C_A    998\n4 S30_F_5   1210\n5 S30_F_6   1199\n6 S30_F_A    963\n\n\nYou could expand to get all the summary information\n🎬 Summarise all the samples:\n\ns30 |&gt;\n  pivot_longer(cols = -xenbase_gene_id,\n               names_to = \"sample\",\n               values_to = \"count\") |&gt;\n  group_by(sample) |&gt;\n  summarise(min = min(count),\n            lowerq = quantile(count, 0.25),\n            mean = mean(count),\n            median = median(count),\n            upperq = quantile(count, 0.75),\n            max = max(count),\n            n_zero = sum(count == 0))\n\n# A tibble: 6 × 8\n  sample    min lowerq  mean median upperq    max n_zero\n  &lt;chr&gt;   &lt;dbl&gt;  &lt;dbl&gt; &lt;dbl&gt;  &lt;dbl&gt;  &lt;dbl&gt;  &lt;dbl&gt;  &lt;int&gt;\n1 S30_C_5     0     14  317.     70    205 101746   1340\n2 S30_C_6     0     14  336.     75    220 118708   1361\n3 S30_C_A     0     23  426.    107    301 117945    998\n4 S30_F_5     0     19  376.     88    251 117573   1210\n5 S30_F_6     0     17  376.     84    246 130672   1199\n6 S30_F_A     0     16  260.     69    187  61531    963\n\n\nThe mean count ranges from 260 to 426.\nOne advantage this has over using summary() is that the output is a dataframe. For results, this is useful, and makes it easier to:\n\nwrite to file\nuse in ggplot()\n\nformat in a Quarto report\n\n🎬 Save the summary as a dataframe, s30_summary_samp.\nWe can write to file using write_csv()\n🎬 Write s30_summary_samp to a file called “s30_summary_samp.csv”:\n\nwrite_csv(s30_summary_samp, \n          file = \"data-processed/s30_summary_samp.csv\")\n\nPlotting the distribution of values is perhaps the easiest way to understand the data. We could plot each column separately or we can pipe the tidy format of data into ggplot() and make use of facet_wrap()\n🎬 Pivot the data and pipe into ggplot:\n\ns30 |&gt;\n  pivot_longer(cols = -xenbase_gene_id,\n               names_to = \"sample\",\n               values_to = \"count\") |&gt;\n  ggplot(aes(count)) +\n  geom_density() +\n  facet_wrap(. ~ sample, nrow = 3)\n\n\n\n\n\n\n\nWe have many values (11893) so we are not limited to using geom_histogram(). geom_density() gives us a smooth distribution.\nWe have many low values and a few very high ones which makes it tricky to see the distributions. Logging the counts will make these clearer.\n🎬 Repeat the graph but taking the base 10 log of the counts:\n\ns30 |&gt;\n  pivot_longer(cols = -xenbase_gene_id,\n               names_to = \"sample\",\n               values_to = \"count\") |&gt;\n  ggplot(aes(log10(count))) +\n  geom_density() +\n  facet_wrap(. ~ sample, nrow = 3)\n\n\n\n\n\n\n\nThe key information to take from these plots is:\n\nthe distributions are roughly similar in width, height, location and overall shape so it doesn’t look as though we have any suspect samples\nthe peak at zero suggests quite a few counts of 1.\nsince we would expect the distribution of counts in each sample to be roughly log normal so that the small rise near the low end, even before the peak at zero, suggests that these lower counts might be anomalies.\n\nThe excess number of low counts indicates we might want to create a cut off for quality control. The removal of low counts is a common processing step in ’omic data. We will revisit this after we have considered the distribution of counts across genes (averaged over the samples).\n🐭 Mouse cells\nWe used the summary() function to get an overview of the columns in the frog data. Let’s try that here.\n🎬 Get a quick overview of the columns:\n\nsummary(hspc)\n\n ensembl_gene_id       HSPC_001         HSPC_002         HSPC_003      \n Length:280         Min.   : 0.000   Min.   : 0.000   Min.   : 0.0000  \n Class :character   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.0000  \n Mode  :character   Median : 0.000   Median : 0.000   Median : 0.9929  \n                    Mean   : 2.143   Mean   : 1.673   Mean   : 2.5964  \n                    3rd Qu.: 2.120   3rd Qu.: 2.239   3rd Qu.: 6.1559  \n                    Max.   :12.567   Max.   :11.976   Max.   :11.1138  \n    HSPC_004         HSPC_006         HSPC_008         HSPC_009    \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   :0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.:0.000  \n Median : 0.000   Median : 1.276   Median : 0.000   Median :0.000  \n Mean   : 1.851   Mean   : 2.338   Mean   : 2.375   Mean   :2.220  \n 3rd Qu.: 2.466   3rd Qu.: 3.536   3rd Qu.: 3.851   3rd Qu.:3.594  \n Max.   :11.133   Max.   :10.014   Max.   :11.574   Max.   :9.997  \n    HSPC_011         HSPC_012         HSPC_014         HSPC_015     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 0.000   Median : 1.750   Median : 0.000   Median : 0.000  \n Mean   : 2.285   Mean   : 2.431   Mean   : 2.295   Mean   : 2.515  \n 3rd Qu.: 3.193   3rd Qu.: 3.741   3rd Qu.: 3.150   3rd Qu.: 3.789  \n Max.   :11.260   Max.   :10.905   Max.   :11.051   Max.   :10.751  \n    HSPC_016          HSPC_017         HSPC_018         HSPC_020     \n Min.   : 0.0000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.0000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 0.9488   Median : 0.000   Median : 1.248   Median : 0.000  \n Mean   : 2.6115   Mean   : 2.146   Mean   : 2.710   Mean   : 2.509  \n 3rd Qu.: 5.9412   3rd Qu.: 2.357   3rd Qu.: 6.006   3rd Qu.: 4.470  \n Max.   :11.3082   Max.   :12.058   Max.   :11.894   Max.   :11.281  \n    HSPC_021         HSPC_022         HSPC_023         HSPC_024     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 0.000   Median : 0.000   Median : 0.000   Median : 0.000  \n Mean   : 2.170   Mean   : 2.287   Mean   : 2.314   Mean   : 2.195  \n 3rd Qu.: 2.996   3rd Qu.: 3.351   3rd Qu.: 2.749   3rd Qu.: 2.944  \n Max.   :10.709   Max.   :11.814   Max.   :12.113   Max.   :11.279  \n    HSPC_025         HSPC_026         HSPC_027         HSPC_028     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 1.572   Median : 1.385   Median : 0.000   Median : 0.000  \n Mean   : 2.710   Mean   : 2.721   Mean   : 2.458   Mean   : 1.906  \n 3rd Qu.: 5.735   3rd Qu.: 6.392   3rd Qu.: 5.496   3rd Qu.: 2.037  \n Max.   :11.309   Max.   :10.865   Max.   :11.266   Max.   :10.777  \n    HSPC_030         HSPC_031          HSPC_033         HSPC_034      \n Min.   : 0.000   Min.   : 0.0000   Min.   : 0.000   Min.   : 0.0000  \n 1st Qu.: 0.000   1st Qu.: 0.0000   1st Qu.: 0.000   1st Qu.: 0.0000  \n Median : 1.119   Median : 0.9026   Median : 0.000   Median : 0.7984  \n Mean   : 2.338   Mean   : 2.3049   Mean   : 1.938   Mean   : 2.3220  \n 3rd Qu.: 3.005   3rd Qu.: 2.9919   3rd Qu.: 2.434   3rd Qu.: 4.8324  \n Max.   :11.391   Max.   :11.1748   Max.   :10.808   Max.   :10.6707  \n    HSPC_035         HSPC_036          HSPC_037         HSPC_038     \n Min.   : 0.000   Min.   : 0.0000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.0000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 0.000   Median : 0.8879   Median : 1.517   Median : 0.000  \n Mean   : 1.810   Mean   : 2.6918   Mean   : 2.327   Mean   : 2.212  \n 3rd Qu.: 2.175   3rd Qu.: 5.9822   3rd Qu.: 3.079   3rd Qu.: 2.867  \n Max.   :11.221   Max.   :11.3018   Max.   :11.399   Max.   :12.275  \n    HSPC_040         HSPC_041         HSPC_042          HSPC_043     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.0000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.0000   1st Qu.: 0.000  \n Median : 0.000   Median : 0.000   Median : 0.8673   Median : 1.342  \n Mean   : 2.509   Mean   : 2.492   Mean   : 2.3673   Mean   : 2.420  \n 3rd Qu.: 3.995   3rd Qu.: 3.943   3rd Qu.: 3.8371   3rd Qu.: 3.731  \n Max.   :11.863   Max.   :11.016   Max.   :11.4852   Max.   :11.123  \n    HSPC_044         HSPC_045         HSPC_046          HSPC_047     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.0000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.0000   1st Qu.: 0.000  \n Median : 0.000   Median : 0.000   Median : 0.8452   Median : 2.195  \n Mean   : 2.382   Mean   : 2.277   Mean   : 1.9707   Mean   : 2.498  \n 3rd Qu.: 3.998   3rd Qu.: 2.843   3rd Qu.: 2.0656   3rd Qu.: 3.937  \n Max.   :10.782   Max.   :10.629   Max.   :11.0311   Max.   :10.180  \n    HSPC_048         HSPC_049         HSPC_050         HSPC_051      \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.0000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.0000  \n Median : 1.108   Median : 1.275   Median : 0.000   Median : 0.9757  \n Mean   : 2.289   Mean   : 2.453   Mean   : 2.673   Mean   : 2.2693  \n 3rd Qu.: 2.988   3rd Qu.: 3.819   3rd Qu.: 5.772   3rd Qu.: 3.1644  \n Max.   :10.335   Max.   :11.844   Max.   :11.301   Max.   :10.8692  \n    HSPC_052         HSPC_053         HSPC_054         HSPC_055     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 1.509   Median : 0.818   Median : 0.000   Median : 0.000  \n Mean   : 2.561   Mean   : 2.684   Mean   : 2.107   Mean   : 1.959  \n 3rd Qu.: 4.644   3rd Qu.: 5.937   3rd Qu.: 2.568   3rd Qu.: 2.573  \n Max.   :11.674   Max.   :11.624   Max.   :10.770   Max.   :11.105  \n    HSPC_056         HSPC_057         HSPC_058         HSPC_060     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 0.000   Median : 0.000   Median : 1.399   Median : 0.000  \n Mean   : 2.295   Mean   : 2.430   Mean   : 2.296   Mean   : 2.112  \n 3rd Qu.: 3.721   3rd Qu.: 3.806   3rd Qu.: 3.199   3rd Qu.: 2.201  \n Max.   :11.627   Max.   :10.575   Max.   :11.134   Max.   :10.631  \n    HSPC_061         HSPC_062         HSPC_063         HSPC_064     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 0.000   Median : 0.000   Median : 1.515   Median : 1.101  \n Mean   : 1.934   Mean   : 2.129   Mean   : 2.508   Mean   : 2.696  \n 3rd Qu.: 2.489   3rd Qu.: 2.875   3rd Qu.: 4.895   3rd Qu.: 6.412  \n Max.   :11.190   Max.   :10.433   Max.   :10.994   Max.   :10.873  \n    HSPC_065          HSPC_066         HSPC_067         HSPC_068     \n Min.   : 0.0000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.0000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 0.4852   Median : 0.000   Median : 1.441   Median : 0.000  \n Mean   : 2.2676   Mean   : 2.136   Mean   : 2.480   Mean   : 2.449  \n 3rd Qu.: 3.8217   3rd Qu.: 2.632   3rd Qu.: 3.548   3rd Qu.: 4.517  \n Max.   :10.9023   Max.   :11.608   Max.   :11.147   Max.   :10.901  \n    HSPC_069         HSPC_070          HSPC_071          HSPC_072     \n Min.   : 0.000   Min.   : 0.0000   Min.   : 0.0000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.0000   1st Qu.: 0.0000   1st Qu.: 0.000  \n Median : 0.000   Median : 0.8949   Median : 0.9272   Median : 1.121  \n Mean   : 2.406   Mean   : 2.5826   Mean   : 2.2844   Mean   : 2.545  \n 3rd Qu.: 4.705   3rd Qu.: 5.4749   3rd Qu.: 3.2531   3rd Qu.: 4.939  \n Max.   :11.258   Max.   :11.6715   Max.   :10.7886   Max.   :11.397  \n    HSPC_073         HSPC_074        HSPC_075         HSPC_076     \n Min.   : 0.000   Min.   : 0.00   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.00   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 0.000   Median : 0.00   Median : 1.674   Median : 0.000  \n Mean   : 2.491   Mean   : 2.46   Mean   : 2.413   Mean   : 2.289  \n 3rd Qu.: 4.134   3rd Qu.: 3.40   3rd Qu.: 3.013   3rd Qu.: 2.550  \n Max.   :11.844   Max.   :11.66   Max.   :11.976   Max.   :12.136  \n    HSPC_077          HSPC_078         HSPC_079         HSPC_080     \n Min.   : 0.0000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.0000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 0.6624   Median : 1.492   Median : 1.384   Median : 1.013  \n Mean   : 2.4336   Mean   : 2.637   Mean   : 2.432   Mean   : 2.881  \n 3rd Qu.: 5.4937   3rd Qu.: 5.472   3rd Qu.: 3.617   3rd Qu.: 7.220  \n Max.   :11.6020   Max.   :10.673   Max.   :11.199   Max.   :11.836  \n    HSPC_081          HSPC_082         HSPC_083         HSPC_084     \n Min.   : 0.0000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.0000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 0.7671   Median : 0.000   Median : 1.896   Median : 1.128  \n Mean   : 1.9227   Mean   : 2.474   Mean   : 2.864   Mean   : 2.289  \n 3rd Qu.: 1.6349   3rd Qu.: 3.488   3rd Qu.: 5.101   3rd Qu.: 2.792  \n Max.   :11.4681   Max.   :11.962   Max.   :10.865   Max.   :11.834  \n    HSPC_085         HSPC_087         HSPC_088         HSPC_089     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 0.000   Median : 0.000   Median : 0.000   Median : 0.000  \n Mean   : 2.157   Mean   : 2.314   Mean   : 2.202   Mean   : 2.329  \n 3rd Qu.: 3.010   3rd Qu.: 3.245   3rd Qu.: 2.092   3rd Qu.: 3.246  \n Max.   :10.809   Max.   :10.976   Max.   :11.362   Max.   :11.301  \n    HSPC_090         HSPC_094         HSPC_095         HSPC_096    \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   :0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.:0.000  \n Median : 0.000   Median : 0.000   Median : 2.055   Median :0.000  \n Mean   : 2.286   Mean   : 2.186   Mean   : 2.756   Mean   :2.348  \n 3rd Qu.: 4.174   3rd Qu.: 2.002   3rd Qu.: 4.370   3rd Qu.:4.482  \n Max.   :11.124   Max.   :11.694   Max.   :11.385   Max.   :9.601  \n    HSPC_098         HSPC_099         HSPC_100         HSPC_101     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 0.000   Median : 0.000   Median : 0.000   Median : 1.007  \n Mean   : 2.209   Mean   : 2.082   Mean   : 2.313   Mean   : 2.587  \n 3rd Qu.: 3.354   3rd Qu.: 2.505   3rd Qu.: 2.775   3rd Qu.: 5.334  \n Max.   :11.070   Max.   :10.200   Max.   :11.452   Max.   :11.456  \n    HSPC_102         HSPC_103         HSPC_104         HSPC_105     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 1.111   Median : 0.000   Median : 0.000   Median : 0.000  \n Mean   : 2.210   Mean   : 2.853   Mean   : 2.099   Mean   : 1.893  \n 3rd Qu.: 2.993   3rd Qu.: 6.123   3rd Qu.: 2.720   3rd Qu.: 2.129  \n Max.   :11.153   Max.   :11.328   Max.   :10.746   Max.   :10.721  \n    HSPC_106         HSPC_107         HSPC_108         HSPC_109     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 0.000   Median : 0.000   Median : 0.000   Median : 1.595  \n Mean   : 1.980   Mean   : 2.279   Mean   : 2.296   Mean   : 2.420  \n 3rd Qu.: 2.425   3rd Qu.: 3.396   3rd Qu.: 3.361   3rd Qu.: 4.006  \n Max.   :10.919   Max.   :10.982   Max.   :11.744   Max.   :10.463  \n    HSPC_110         HSPC_111         HSPC_114          HSPC_115     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.0000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.0000   1st Qu.: 0.000  \n Median : 0.000   Median : 0.000   Median : 0.9173   Median : 2.349  \n Mean   : 2.159   Mean   : 1.800   Mean   : 1.8376   Mean   : 2.943  \n 3rd Qu.: 2.667   3rd Qu.: 2.214   3rd Qu.: 1.8741   3rd Qu.: 6.223  \n Max.   :11.121   Max.   :11.109   Max.   :10.4645   Max.   :11.124  \n    HSPC_117         HSPC_118         HSPC_119         HSPC_120     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 0.000   Median : 0.000   Median : 0.000   Median : 1.187  \n Mean   : 1.919   Mean   : 1.855   Mean   : 2.289   Mean   : 2.041  \n 3rd Qu.: 2.306   3rd Qu.: 2.387   3rd Qu.: 3.292   3rd Qu.: 2.610  \n Max.   :14.579   Max.   :11.119   Max.   :12.534   Max.   :11.438  \n    HSPC_121         HSPC_122         HSPC_123         HSPC_125     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 0.000   Median : 0.000   Median : 0.000   Median : 0.000  \n Mean   : 2.803   Mean   : 2.072   Mean   : 2.200   Mean   : 2.116  \n 3rd Qu.: 5.798   3rd Qu.: 2.140   3rd Qu.: 3.215   3rd Qu.: 2.409  \n Max.   :11.320   Max.   :11.013   Max.   :11.163   Max.   :11.368  \n    HSPC_126          HSPC_127         HSPC_130         HSPC_131     \n Min.   : 0.0000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.0000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 0.9381   Median : 1.147   Median : 0.000   Median : 0.000  \n Mean   : 2.0014   Mean   : 2.287   Mean   : 2.551   Mean   : 2.240  \n 3rd Qu.: 2.2215   3rd Qu.: 3.051   3rd Qu.: 3.968   3rd Qu.: 3.773  \n Max.   :10.9622   Max.   :11.028   Max.   :10.585   Max.   :11.216  \n    HSPC_132          HSPC_133         HSPC_134         HSPC_135     \n Min.   : 0.0000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.0000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 0.4438   Median : 2.234   Median : 0.000   Median : 0.000  \n Mean   : 2.1659   Mean   : 2.582   Mean   : 2.335   Mean   : 2.402  \n 3rd Qu.: 1.8512   3rd Qu.: 4.591   3rd Qu.: 3.659   3rd Qu.: 4.134  \n Max.   :10.6431   Max.   :10.730   Max.   :11.995   Max.   :11.573  \n    HSPC_136         HSPC_138          HSPC_139         HSPC_140     \n Min.   : 0.000   Min.   : 0.0000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.0000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 0.000   Median : 0.7062   Median : 2.078   Median : 0.000  \n Mean   : 2.546   Mean   : 2.1054   Mean   : 2.876   Mean   : 2.220  \n 3rd Qu.: 5.219   3rd Qu.: 1.8181   3rd Qu.: 4.604   3rd Qu.: 3.716  \n Max.   :11.281   Max.   :11.1177   Max.   :11.013   Max.   :10.893  \n    HSPC_141         HSPC_142         HSPC_143         HSPC_144     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 0.000   Median : 0.000   Median : 0.000   Median : 1.075  \n Mean   : 2.385   Mean   : 2.232   Mean   : 2.592   Mean   : 2.004  \n 3rd Qu.: 4.149   3rd Qu.: 2.523   3rd Qu.: 4.248   3rd Qu.: 2.441  \n Max.   :11.099   Max.   :11.902   Max.   :12.932   Max.   :11.121  \n    HSPC_146         HSPC_148         HSPC_149         HSPC_151      \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.0000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.0000  \n Median : 0.000   Median : 0.000   Median : 0.000   Median : 0.9711  \n Mean   : 2.418   Mean   : 2.385   Mean   : 2.314   Mean   : 2.4375  \n 3rd Qu.: 4.430   3rd Qu.: 3.288   3rd Qu.: 3.139   3rd Qu.: 3.2523  \n Max.   :10.385   Max.   :12.823   Max.   :10.910   Max.   :11.7148  \n    HSPC_152         HSPC_153         HSPC_154         HSPC_155     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 0.000   Median : 0.000   Median : 0.000   Median : 0.000  \n Mean   : 2.247   Mean   : 2.415   Mean   : 2.476   Mean   : 2.468  \n 3rd Qu.: 3.293   3rd Qu.: 3.524   3rd Qu.: 4.653   3rd Qu.: 3.621  \n Max.   :12.463   Max.   :12.205   Max.   :11.437   Max.   :11.207  \n    HSPC_156          HSPC_157         HSPC_158         HSPC_159     \n Min.   : 0.0000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.0000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 0.5545   Median : 1.993   Median : 0.000   Median : 0.000  \n Mean   : 2.2297   Mean   : 2.493   Mean   : 2.119   Mean   : 2.461  \n 3rd Qu.: 2.0977   3rd Qu.: 3.692   3rd Qu.: 2.930   3rd Qu.: 3.340  \n Max.   :11.2431   Max.   :10.539   Max.   :11.336   Max.   :11.123  \n    HSPC_161         HSPC_162          HSPC_164         HSPC_165     \n Min.   : 0.000   Min.   : 0.0000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.0000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 1.701   Median : 0.7152   Median : 0.000   Median : 0.000  \n Mean   : 2.533   Mean   : 2.3473   Mean   : 2.161   Mean   : 2.084  \n 3rd Qu.: 3.616   3rd Qu.: 2.4973   3rd Qu.: 2.553   3rd Qu.: 3.020  \n Max.   :11.429   Max.   :11.0065   Max.   :11.865   Max.   :10.282  \n    HSPC_166         HSPC_168         HSPC_169         HSPC_170     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 0.000   Median : 1.002   Median : 1.158   Median : 0.000  \n Mean   : 2.177   Mean   : 2.390   Mean   : 2.038   Mean   : 2.401  \n 3rd Qu.: 3.296   3rd Qu.: 4.701   3rd Qu.: 2.232   3rd Qu.: 3.703  \n Max.   :11.427   Max.   :10.393   Max.   :10.447   Max.   :11.288  \n    HSPC_171         HSPC_172          HSPC_173         HSPC_174     \n Min.   : 0.000   Min.   : 0.0000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.0000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 1.525   Median : 0.7679   Median : 0.000   Median : 1.257  \n Mean   : 2.312   Mean   : 2.3115   Mean   : 2.288   Mean   : 2.444  \n 3rd Qu.: 2.729   3rd Qu.: 3.7889   3rd Qu.: 3.037   3rd Qu.: 4.996  \n Max.   :10.468   Max.   :11.1442   Max.   :11.074   Max.   :11.095  \n    HSPC_175         HSPC_176         HSPC_177         HSPC_178     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 1.496   Median : 2.024   Median : 1.971   Median : 1.003  \n Mean   : 2.613   Mean   : 2.593   Mean   : 2.421   Mean   : 2.277  \n 3rd Qu.: 4.845   3rd Qu.: 4.092   3rd Qu.: 3.665   3rd Qu.: 2.812  \n Max.   :11.235   Max.   :10.379   Max.   :10.864   Max.   :10.979  \n    HSPC_179         HSPC_180         HSPC_181         HSPC_182     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 0.836   Median : 1.544   Median : 2.030   Median : 0.000  \n Mean   : 2.205   Mean   : 2.556   Mean   : 2.890   Mean   : 2.363  \n 3rd Qu.: 2.300   3rd Qu.: 4.798   3rd Qu.: 4.846   3rd Qu.: 3.779  \n Max.   :11.244   Max.   :10.802   Max.   :10.945   Max.   :10.399  \n    HSPC_183         HSPC_185         HSPC_186         HSPC_187     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 1.020   Median : 0.000   Median : 1.606   Median : 0.000  \n Mean   : 2.242   Mean   : 2.708   Mean   : 2.053   Mean   : 2.360  \n 3rd Qu.: 2.842   3rd Qu.: 4.855   3rd Qu.: 2.834   3rd Qu.: 3.541  \n Max.   :10.530   Max.   :11.079   Max.   :11.016   Max.   :10.923  \n    HSPC_189         HSPC_190         HSPC_191         HSPC_192     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 0.000   Median : 0.000   Median : 0.000   Median : 1.412  \n Mean   : 2.120   Mean   : 2.417   Mean   : 2.175   Mean   : 2.192  \n 3rd Qu.: 2.652   3rd Qu.: 5.226   3rd Qu.: 2.574   3rd Qu.: 2.669  \n Max.   :11.300   Max.   :11.023   Max.   :11.454   Max.   :10.225  \n    HSPC_193         HSPC_195          HSPC_196         HSPC_198     \n Min.   :0.0000   Min.   : 0.0000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.:0.0000   1st Qu.: 0.0000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median :0.9691   Median : 0.9175   Median : 1.379   Median : 1.105  \n Mean   :2.5448   Mean   : 2.7307   Mean   : 2.327   Mean   : 2.155  \n 3rd Qu.:5.1191   3rd Qu.: 5.8899   3rd Qu.: 2.625   3rd Qu.: 2.756  \n Max.   :9.8728   Max.   :10.4757   Max.   :11.319   Max.   :11.405  \n    HSPC_199         HSPC_200         HSPC_202          HSPC_203     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.0000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.0000   1st Qu.: 0.000  \n Median : 1.069   Median : 1.572   Median : 0.8045   Median : 1.311  \n Mean   : 1.909   Mean   : 2.346   Mean   : 2.1384   Mean   : 2.058  \n 3rd Qu.: 2.431   3rd Qu.: 2.791   3rd Qu.: 2.0569   3rd Qu.: 2.792  \n Max.   :11.377   Max.   :11.334   Max.   :11.0516   Max.   :10.852  \n    HSPC_204         HSPC_205         HSPC_206         HSPC_207      \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.0000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.0000  \n Median : 1.342   Median : 1.997   Median : 1.076   Median : 0.9235  \n Mean   : 2.716   Mean   : 2.520   Mean   : 2.426   Mean   : 2.2974  \n 3rd Qu.: 5.611   3rd Qu.: 4.244   3rd Qu.: 4.057   3rd Qu.: 2.6736  \n Max.   :10.269   Max.   :10.817   Max.   :11.866   Max.   :11.4287  \n    HSPC_208         HSPC_210         HSPC_211         HSPC_212     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 2.263   Median : 1.021   Median : 1.351   Median : 0.000  \n Mean   : 2.893   Mean   : 2.315   Mean   : 2.425   Mean   : 2.336  \n 3rd Qu.: 5.014   3rd Qu.: 2.676   3rd Qu.: 3.820   3rd Qu.: 3.443  \n Max.   :11.375   Max.   :12.208   Max.   :11.360   Max.   :11.808  \n    HSPC_213         HSPC_214          HSPC_215         HSPC_216      \n Min.   : 0.000   Min.   : 0.0000   Min.   : 0.000   Min.   : 0.0000  \n 1st Qu.: 0.000   1st Qu.: 0.0000   1st Qu.: 0.000   1st Qu.: 0.0000  \n Median : 1.270   Median : 0.9195   Median : 1.653   Median : 0.8022  \n Mean   : 2.483   Mean   : 2.1976   Mean   : 2.563   Mean   : 2.6010  \n 3rd Qu.: 4.903   3rd Qu.: 2.7139   3rd Qu.: 4.344   3rd Qu.: 6.0076  \n Max.   :11.548   Max.   :10.6947   Max.   :10.933   Max.   :11.2119  \n    HSPC_218         HSPC_219         HSPC_220         HSPC_221     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 0.000   Median : 1.027   Median : 0.000   Median : 1.269  \n Mean   : 2.467   Mean   : 2.291   Mean   : 2.449   Mean   : 2.641  \n 3rd Qu.: 3.980   3rd Qu.: 2.853   3rd Qu.: 4.486   3rd Qu.: 3.617  \n Max.   :11.654   Max.   :10.801   Max.   :10.410   Max.   :11.651  \n    HSPC_222         HSPC_223         HSPC_224         HSPC_225     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 1.449   Median : 0.000   Median : 0.000   Median : 0.000  \n Mean   : 2.262   Mean   : 2.271   Mean   : 2.492   Mean   : 2.585  \n 3rd Qu.: 3.271   3rd Qu.: 3.727   3rd Qu.: 3.769   3rd Qu.: 5.253  \n Max.   :11.133   Max.   :12.000   Max.   :11.114   Max.   :11.671  \n    HSPC_227         HSPC_228         HSPC_229         HSPC_230     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 0.000   Median : 0.000   Median : 2.484   Median : 0.000  \n Mean   : 2.492   Mean   : 2.370   Mean   : 2.742   Mean   : 2.586  \n 3rd Qu.: 3.692   3rd Qu.: 4.488   3rd Qu.: 4.836   3rd Qu.: 5.188  \n Max.   :10.815   Max.   :10.165   Max.   :11.143   Max.   :10.734  \n    HSPC_231         HSPC_232         HSPC_233         HSPC_235     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 0.000   Median : 1.869   Median : 1.254   Median : 0.000  \n Mean   : 2.379   Mean   : 2.264   Mean   : 2.531   Mean   : 2.552  \n 3rd Qu.: 4.787   3rd Qu.: 3.163   3rd Qu.: 3.925   3rd Qu.: 4.389  \n Max.   :10.790   Max.   :12.098   Max.   :11.533   Max.   :11.765  \n    HSPC_236         HSPC_237         HSPC_239         HSPC_240     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 0.000   Median : 0.000   Median : 2.207   Median : 0.892  \n Mean   : 2.205   Mean   : 2.457   Mean   : 2.656   Mean   : 2.049  \n 3rd Qu.: 3.748   3rd Qu.: 3.488   3rd Qu.: 4.904   3rd Qu.: 2.617  \n Max.   :10.234   Max.   :10.630   Max.   :10.858   Max.   :10.528  \n    HSPC_243         HSPC_244          HSPC_245         HSPC_246     \n Min.   : 0.000   Min.   : 0.0000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.0000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 1.118   Median : 0.7872   Median : 1.459   Median : 1.629  \n Mean   : 2.311   Mean   : 2.6638   Mean   : 2.360   Mean   : 2.321  \n 3rd Qu.: 2.574   3rd Qu.: 6.2395   3rd Qu.: 3.000   3rd Qu.: 3.229  \n Max.   :11.069   Max.   :10.0730   Max.   :11.297   Max.   :11.237  \n    HSPC_247        HSPC_248          HSPC_249         HSPC_250     \n Min.   :0.000   Min.   : 0.0000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.:0.000   1st Qu.: 0.0000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median :0.000   Median : 0.8453   Median : 0.000   Median : 1.278  \n Mean   :2.537   Mean   : 2.3719   Mean   : 1.803   Mean   : 2.751  \n 3rd Qu.:4.687   3rd Qu.: 3.3090   3rd Qu.: 2.335   3rd Qu.: 6.330  \n Max.   :9.821   Max.   :10.8128   Max.   :10.568   Max.   :11.256  \n    HSPC_251          HSPC_253         HSPC_254         HSPC_255      \n Min.   : 0.0000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.0000  \n 1st Qu.: 0.0000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.0000  \n Median : 0.9714   Median : 1.265   Median : 0.000   Median : 0.9098  \n Mean   : 2.5626   Mean   : 2.492   Mean   : 2.177   Mean   : 2.1878  \n 3rd Qu.: 4.9167   3rd Qu.: 4.185   3rd Qu.: 3.437   3rd Qu.: 2.4313  \n Max.   :11.1252   Max.   :10.435   Max.   :10.422   Max.   :10.7952  \n    HSPC_256          HSPC_257         HSPC_258         HSPC_261      \n Min.   : 0.0000   Min.   : 0.000   Min.   :0.0000   Min.   : 0.0000  \n 1st Qu.: 0.0000   1st Qu.: 0.000   1st Qu.:0.0000   1st Qu.: 0.0000  \n Median : 0.8248   Median : 1.241   Median :0.8526   Median : 0.5387  \n Mean   : 2.1051   Mean   : 2.630   Mean   :2.0295   Mean   : 2.1419  \n 3rd Qu.: 2.3331   3rd Qu.: 5.646   3rd Qu.:3.0784   3rd Qu.: 1.9352  \n Max.   :13.0375   Max.   :11.499   Max.   :9.9116   Max.   :11.3247  \n    HSPC_263         HSPC_264         HSPC_265         HSPC_266     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 1.538   Median : 1.426   Median : 1.883   Median : 1.839  \n Mean   : 2.613   Mean   : 2.374   Mean   : 3.177   Mean   : 2.833  \n 3rd Qu.: 4.485   3rd Qu.: 3.238   3rd Qu.: 5.702   3rd Qu.: 5.801  \n Max.   :10.571   Max.   :11.136   Max.   :12.436   Max.   :10.338  \n    HSPC_267          HSPC_268          HSPC_269          HSPC_270      \n Min.   : 0.0000   Min.   : 0.0000   Min.   : 0.0000   Min.   : 0.0000  \n 1st Qu.: 0.0000   1st Qu.: 0.0000   1st Qu.: 0.0000   1st Qu.: 0.0000  \n Median : 0.9675   Median : 0.7787   Median : 0.8632   Median : 0.9637  \n Mean   : 2.4910   Mean   : 2.5342   Mean   : 2.4029   Mean   : 2.6899  \n 3rd Qu.: 3.5345   3rd Qu.: 4.9871   3rd Qu.: 4.3176   3rd Qu.: 5.7266  \n Max.   :10.0139   Max.   :10.7848   Max.   :11.2689   Max.   :11.1648  \n    HSPC_271         HSPC_274         HSPC_275          HSPC_276     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.0000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.0000   1st Qu.: 0.000  \n Median : 1.352   Median : 1.730   Median : 0.5252   Median : 1.156  \n Mean   : 2.493   Mean   : 2.382   Mean   : 2.5375   Mean   : 2.485  \n 3rd Qu.: 4.430   3rd Qu.: 3.360   3rd Qu.: 5.7329   3rd Qu.: 4.623  \n Max.   :11.636   Max.   :11.165   Max.   :11.6234   Max.   :11.562  \n    HSPC_278         HSPC_279         HSPC_280         HSPC_281     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 0.000   Median : 1.487   Median : 1.608   Median : 2.611  \n Mean   : 2.161   Mean   : 2.497   Mean   : 2.580   Mean   : 2.737  \n 3rd Qu.: 2.270   3rd Qu.: 3.813   3rd Qu.: 3.985   3rd Qu.: 4.731  \n Max.   :11.734   Max.   :10.900   Max.   :11.673   Max.   :10.076  \n    HSPC_282          HSPC_283         HSPC_285          HSPC_286     \n Min.   : 0.0000   Min.   : 0.000   Min.   : 0.0000   Min.   : 0.000  \n 1st Qu.: 0.0000   1st Qu.: 0.000   1st Qu.: 0.0000   1st Qu.: 0.000  \n Median : 0.7021   Median : 1.911   Median : 0.8658   Median : 1.178  \n Mean   : 2.4272   Mean   : 2.534   Mean   : 2.4868   Mean   : 2.293  \n 3rd Qu.: 4.1254   3rd Qu.: 3.888   3rd Qu.: 5.3804   3rd Qu.: 2.597  \n Max.   :11.1094   Max.   :10.258   Max.   :10.5533   Max.   :11.112  \n    HSPC_287         HSPC_288          HSPC_289         HSPC_290     \n Min.   : 0.000   Min.   : 0.0000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.0000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 1.049   Median : 0.8548   Median : 1.953   Median : 1.176  \n Mean   : 2.775   Mean   : 2.6412   Mean   : 2.925   Mean   : 2.304  \n 3rd Qu.: 5.476   3rd Qu.: 5.4204   3rd Qu.: 5.613   3rd Qu.: 3.445  \n Max.   :10.925   Max.   :11.0814   Max.   :10.199   Max.   :11.094  \n    HSPC_291         HSPC_292         HSPC_293         HSPC_294      \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.0000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.0000  \n Median : 1.176   Median : 1.320   Median : 1.077   Median : 0.9161  \n Mean   : 2.662   Mean   : 2.534   Mean   : 2.538   Mean   : 2.4365  \n 3rd Qu.: 5.690   3rd Qu.: 4.297   3rd Qu.: 3.458   3rd Qu.: 4.8204  \n Max.   :12.255   Max.   :11.090   Max.   :10.987   Max.   :10.6135  \n    HSPC_295        HSPC_296         HSPC_297         HSPC_298     \n Min.   :0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.:0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median :1.479   Median : 2.157   Median : 2.444   Median : 1.281  \n Mean   :2.849   Mean   : 2.977   Mean   : 3.062   Mean   : 2.277  \n 3rd Qu.:5.282   3rd Qu.: 5.006   3rd Qu.: 5.005   3rd Qu.: 2.749  \n Max.   :9.986   Max.   :10.830   Max.   :11.009   Max.   :10.636  \n    HSPC_299         HSPC_300         HSPC_301         HSPC_302     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 1.716   Median : 1.163   Median : 2.235   Median : 2.240  \n Mean   : 2.597   Mean   : 2.346   Mean   : 2.739   Mean   : 2.890  \n 3rd Qu.: 3.762   3rd Qu.: 2.876   3rd Qu.: 4.593   3rd Qu.: 4.945  \n Max.   :11.663   Max.   :11.690   Max.   :10.364   Max.   :10.498  \n    HSPC_303          HSPC_304          HSPC_305         HSPC_306     \n Min.   : 0.0000   Min.   : 0.0000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.0000   1st Qu.: 0.0000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 0.8348   Median : 0.9727   Median : 1.152   Median : 1.303  \n Mean   : 2.3400   Mean   : 2.3710   Mean   : 2.469   Mean   : 2.496  \n 3rd Qu.: 3.2942   3rd Qu.: 2.9942   3rd Qu.: 3.300   3rd Qu.: 3.015  \n Max.   :10.3022   Max.   :11.7185   Max.   :11.051   Max.   :11.211  \n    HSPC_307        HSPC_308         HSPC_309         HSPC_310     \n Min.   :0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.:0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median :1.976   Median : 1.634   Median : 1.804   Median : 1.743  \n Mean   :2.873   Mean   : 2.812   Mean   : 2.892   Mean   : 2.874  \n 3rd Qu.:5.396   3rd Qu.: 5.089   3rd Qu.: 5.165   3rd Qu.: 5.004  \n Max.   :9.921   Max.   :10.527   Max.   :10.476   Max.   :11.107  \n    HSPC_312         HSPC_313         HSPC_314         HSPC_315     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 1.420   Median : 1.592   Median : 1.635   Median : 2.262  \n Mean   : 2.645   Mean   : 2.637   Mean   : 2.564   Mean   : 2.628  \n 3rd Qu.: 4.925   3rd Qu.: 4.257   3rd Qu.: 4.297   3rd Qu.: 4.092  \n Max.   :11.367   Max.   :10.644   Max.   :10.882   Max.   :12.140  \n    HSPC_317         HSPC_318         HSPC_320         HSPC_321     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 2.335   Median : 1.728   Median : 2.340   Median : 1.835  \n Mean   : 2.648   Mean   : 2.637   Mean   : 3.064   Mean   : 2.742  \n 3rd Qu.: 4.103   3rd Qu.: 4.483   3rd Qu.: 5.325   3rd Qu.: 4.340  \n Max.   :10.933   Max.   :11.712   Max.   :11.589   Max.   :11.695  \n    HSPC_322          HSPC_323         HSPC_324         HSPC_325     \n Min.   : 0.0000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.0000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 0.9842   Median : 0.989   Median : 1.088   Median : 2.132  \n Mean   : 2.5948   Mean   : 2.905   Mean   : 2.655   Mean   : 3.091  \n 3rd Qu.: 3.4619   3rd Qu.: 5.629   3rd Qu.: 3.772   3rd Qu.: 5.191  \n Max.   :11.9594   Max.   :12.267   Max.   :11.310   Max.   :11.134  \n    HSPC_326         HSPC_327         HSPC_328         HSPC_329     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 1.781   Median : 1.085   Median : 1.936   Median : 1.954  \n Mean   : 3.021   Mean   : 2.838   Mean   : 2.582   Mean   : 3.034  \n 3rd Qu.: 5.582   3rd Qu.: 6.388   3rd Qu.: 4.048   3rd Qu.: 5.497  \n Max.   :11.268   Max.   :11.433   Max.   :11.908   Max.   :10.927  \n    HSPC_330         HSPC_331         HSPC_332         HSPC_333     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 1.870   Median : 2.953   Median : 1.644   Median : 1.320  \n Mean   : 2.791   Mean   : 3.058   Mean   : 2.768   Mean   : 2.428  \n 3rd Qu.: 4.409   3rd Qu.: 5.118   3rd Qu.: 5.141   3rd Qu.: 2.985  \n Max.   :11.561   Max.   :10.855   Max.   :10.420   Max.   :11.946  \n    HSPC_334         HSPC_335         HSPC_336         HSPC_337     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 1.931   Median : 1.541   Median : 2.761   Median : 0.000  \n Mean   : 2.894   Mean   : 2.746   Mean   : 3.051   Mean   : 2.415  \n 3rd Qu.: 4.160   3rd Qu.: 4.461   3rd Qu.: 4.408   3rd Qu.: 4.188  \n Max.   :11.592   Max.   :11.076   Max.   :11.246   Max.   :10.205  \n    HSPC_338         HSPC_339         HSPC_341          HSPC_342      \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.0000   Min.   : 0.0000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.0000   1st Qu.: 0.0000  \n Median : 0.000   Median : 0.000   Median : 0.9553   Median : 0.4452  \n Mean   : 2.205   Mean   : 2.325   Mean   : 2.0823   Mean   : 2.4572  \n 3rd Qu.: 2.449   3rd Qu.: 3.136   3rd Qu.: 2.0118   3rd Qu.: 4.9582  \n Max.   :12.052   Max.   :11.858   Max.   :11.3855   Max.   :11.8066  \n    HSPC_343         HSPC_344         HSPC_345         HSPC_346      \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.0000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.0000  \n Median : 0.000   Median : 0.000   Median : 0.000   Median : 0.5197  \n Mean   : 2.363   Mean   : 2.290   Mean   : 1.984   Mean   : 2.5126  \n 3rd Qu.: 4.285   3rd Qu.: 3.238   3rd Qu.: 2.561   3rd Qu.: 5.2033  \n Max.   :11.422   Max.   :11.877   Max.   :10.939   Max.   :11.1527  \n    HSPC_348         HSPC_349         HSPC_350        HSPC_351     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.00   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.00   1st Qu.: 0.000  \n Median : 1.113   Median : 0.000   Median : 0.00   Median : 0.000  \n Mean   : 2.232   Mean   : 1.949   Mean   : 2.11   Mean   : 2.259  \n 3rd Qu.: 2.875   3rd Qu.: 2.784   3rd Qu.: 3.07   3rd Qu.: 3.214  \n Max.   :11.161   Max.   :10.720   Max.   :11.15   Max.   :10.912  \n    HSPC_352         HSPC_353         HSPC_354         HSPC_356     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 0.000   Median : 0.000   Median : 0.000   Median : 0.000  \n Mean   : 2.333   Mean   : 2.162   Mean   : 2.427   Mean   : 2.135  \n 3rd Qu.: 3.197   3rd Qu.: 2.819   3rd Qu.: 3.808   3rd Qu.: 2.709  \n Max.   :12.275   Max.   :11.351   Max.   :11.190   Max.   :10.662  \n    HSPC_358         HSPC_359         HSPC_360         HSPC_361     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 0.000   Median : 0.000   Median : 0.000   Median : 0.000  \n Mean   : 2.278   Mean   : 2.012   Mean   : 2.381   Mean   : 2.137  \n 3rd Qu.: 3.608   3rd Qu.: 1.460   3rd Qu.: 3.044   3rd Qu.: 2.875  \n Max.   :10.924   Max.   :11.678   Max.   :11.203   Max.   :10.847  \n    HSPC_362         HSPC_363         HSPC_365         HSPC_367     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 0.000   Median : 0.000   Median : 0.000   Median : 0.000  \n Mean   : 1.783   Mean   : 1.987   Mean   : 2.937   Mean   : 2.449  \n 3rd Qu.: 1.594   3rd Qu.: 2.750   3rd Qu.: 5.572   3rd Qu.: 3.936  \n Max.   :11.889   Max.   :10.389   Max.   :12.427   Max.   :11.081  \n    HSPC_368         HSPC_370          HSPC_371          HSPC_372     \n Min.   : 0.000   Min.   : 0.0000   Min.   : 0.0000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.0000   1st Qu.: 0.0000   1st Qu.: 0.000  \n Median : 0.000   Median : 0.7971   Median : 0.7613   Median : 0.000  \n Mean   : 1.877   Mean   : 2.7681   Mean   : 2.4278   Mean   : 2.487  \n 3rd Qu.: 2.018   3rd Qu.: 6.5358   3rd Qu.: 4.9578   3rd Qu.: 4.226  \n Max.   :11.523   Max.   :11.9636   Max.   :11.4223   Max.   :11.700  \n    HSPC_373         HSPC_374        HSPC_376         HSPC_377     \n Min.   : 0.000   Min.   : 0.00   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.00   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 0.000   Median : 0.00   Median : 0.000   Median : 0.000  \n Mean   : 2.330   Mean   : 2.21   Mean   : 2.625   Mean   : 2.456  \n 3rd Qu.: 3.784   3rd Qu.: 2.44   3rd Qu.: 4.365   3rd Qu.: 4.875  \n Max.   :11.672   Max.   :12.04   Max.   :12.011   Max.   :11.282  \n    HSPC_380         HSPC_382          HSPC_383         HSPC_386     \n Min.   : 0.000   Min.   : 0.0000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.0000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 0.000   Median : 0.9728   Median : 1.753   Median : 0.000  \n Mean   : 2.291   Mean   : 2.3318   Mean   : 2.307   Mean   : 2.351  \n 3rd Qu.: 2.403   3rd Qu.: 2.7605   3rd Qu.: 3.113   3rd Qu.: 3.704  \n Max.   :11.415   Max.   :11.3370   Max.   :11.592   Max.   :11.079  \n    HSPC_387         HSPC_388          HSPC_389         HSPC_390     \n Min.   : 0.000   Min.   : 0.0000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.0000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 0.000   Median : 0.9037   Median : 0.000   Median : 0.000  \n Mean   : 2.255   Mean   : 2.4969   Mean   : 2.081   Mean   : 2.131  \n 3rd Qu.: 3.151   3rd Qu.: 5.3587   3rd Qu.: 2.723   3rd Qu.: 2.738  \n Max.   :11.700   Max.   :10.9923   Max.   :11.868   Max.   :10.913  \n    HSPC_391         HSPC_392         HSPC_393         HSPC_395     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 0.000   Median : 0.000   Median : 0.000   Median : 0.000  \n Mean   : 2.026   Mean   : 2.356   Mean   : 2.063   Mean   : 1.779  \n 3rd Qu.: 2.126   3rd Qu.: 3.781   3rd Qu.: 2.163   3rd Qu.: 1.924  \n Max.   :12.021   Max.   :11.370   Max.   :10.530   Max.   :12.219  \n    HSPC_396         HSPC_398         HSPC_399         HSPC_400     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 0.000   Median : 0.000   Median : 0.000   Median : 0.000  \n Mean   : 2.164   Mean   : 2.309   Mean   : 1.831   Mean   : 2.091  \n 3rd Qu.: 2.681   3rd Qu.: 3.994   3rd Qu.: 1.844   3rd Qu.: 2.781  \n Max.   :11.292   Max.   :11.431   Max.   :11.343   Max.   :10.863  \n    HSPC_402         HSPC_403        HSPC_404         HSPC_405      \n Min.   : 0.000   Min.   : 0.00   Min.   : 0.000   Min.   : 0.0000  \n 1st Qu.: 0.000   1st Qu.: 0.00   1st Qu.: 0.000   1st Qu.: 0.0000  \n Median : 0.000   Median : 0.00   Median : 0.000   Median : 0.5496  \n Mean   : 2.343   Mean   : 2.06   Mean   : 1.878   Mean   : 2.3660  \n 3rd Qu.: 4.552   3rd Qu.: 2.45   3rd Qu.: 1.644   3rd Qu.: 2.5449  \n Max.   :11.444   Max.   :12.00   Max.   :11.188   Max.   :12.2605  \n    HSPC_406         HSPC_407         HSPC_408          HSPC_409     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.0000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.0000   1st Qu.: 0.000  \n Median : 0.000   Median : 0.565   Median : 0.5775   Median : 0.000  \n Mean   : 2.169   Mean   : 2.611   Mean   : 1.9174   Mean   : 2.234  \n 3rd Qu.: 2.606   3rd Qu.: 6.000   3rd Qu.: 1.3086   3rd Qu.: 3.044  \n Max.   :10.866   Max.   :11.296   Max.   :12.8185   Max.   :11.595  \n    HSPC_410         HSPC_411          HSPC_412          HSPC_413     \n Min.   : 0.000   Min.   : 0.0000   Min.   : 0.0000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.0000   1st Qu.: 0.0000   1st Qu.: 0.000  \n Median : 0.000   Median : 0.9059   Median : 0.6614   Median : 0.000  \n Mean   : 2.308   Mean   : 3.1194   Mean   : 3.0437   Mean   : 2.433  \n 3rd Qu.: 4.022   3rd Qu.: 7.7574   3rd Qu.: 7.4695   3rd Qu.: 3.329  \n Max.   :11.620   Max.   :12.0858   Max.   :11.5582   Max.   :12.549  \n    HSPC_415         HSPC_416         HSPC_417          HSPC_418     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.0000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.0000   1st Qu.: 0.000  \n Median : 0.000   Median : 0.000   Median : 0.7222   Median : 0.000  \n Mean   : 2.904   Mean   : 2.228   Mean   : 2.4242   Mean   : 2.508  \n 3rd Qu.: 5.531   3rd Qu.: 3.111   3rd Qu.: 3.0795   3rd Qu.: 3.249  \n Max.   :12.359   Max.   :11.338   Max.   :12.0314   Max.   :11.857  \n    HSPC_419          HSPC_420         HSPC_421         HSPC_422     \n Min.   : 0.0000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.0000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 0.6924   Median : 0.000   Median : 0.000   Median : 0.000  \n Mean   : 2.6246   Mean   : 2.514   Mean   : 2.075   Mean   : 2.552  \n 3rd Qu.: 4.8156   3rd Qu.: 5.709   3rd Qu.: 3.682   3rd Qu.: 5.382  \n Max.   :12.0526   Max.   :11.270   Max.   :10.250   Max.   :11.691  \n    HSPC_423        HSPC_424         HSPC_425         HSPC_426     \n Min.   : 0.00   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.00   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 0.00   Median : 0.000   Median : 1.016   Median : 0.000  \n Mean   : 2.12   Mean   : 2.225   Mean   : 2.658   Mean   : 2.235  \n 3rd Qu.: 1.55   3rd Qu.: 2.471   3rd Qu.: 6.474   3rd Qu.: 3.134  \n Max.   :11.56   Max.   :11.734   Max.   :11.303   Max.   :10.888  \n    HSPC_427         HSPC_431         HSPC_432        HSPC_435     \n Min.   : 0.000   Min.   : 0.000   Min.   :0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.:0.000   1st Qu.: 0.000  \n Median : 0.000   Median : 1.102   Median :0.000   Median : 1.098  \n Mean   : 1.829   Mean   : 2.360   Mean   :2.169   Mean   : 2.060  \n 3rd Qu.: 2.980   3rd Qu.: 3.640   3rd Qu.:3.261   3rd Qu.: 2.744  \n Max.   :10.517   Max.   :10.533   Max.   :9.911   Max.   :10.677  \n    HSPC_436          HSPC_440         HSPC_441         HSPC_442     \n Min.   : 0.0000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.0000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 0.4719   Median : 1.385   Median : 1.084   Median : 0.595  \n Mean   : 2.3880   Mean   : 1.712   Mean   : 2.265   Mean   : 2.109  \n 3rd Qu.: 4.3738   3rd Qu.: 2.079   3rd Qu.: 2.828   3rd Qu.: 2.193  \n Max.   :11.2839   Max.   :11.065   Max.   :11.152   Max.   :11.560  \n    HSPC_443          HSPC_444         HSPC_446         HSPC_447     \n Min.   : 0.0000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.0000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 0.7734   Median : 1.374   Median : 0.000   Median : 1.113  \n Mean   : 2.5663   Mean   : 2.262   Mean   : 1.475   Mean   : 2.446  \n 3rd Qu.: 4.9423   3rd Qu.: 2.952   3rd Qu.: 1.683   3rd Qu.: 4.733  \n Max.   :10.9262   Max.   :10.705   Max.   :10.545   Max.   :10.303  \n    HSPC_448         HSPC_449         HSPC_450         HSPC_451     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 1.139   Median : 1.344   Median : 0.000   Median : 1.759  \n Mean   : 2.396   Mean   : 2.164   Mean   : 1.946   Mean   : 1.806  \n 3rd Qu.: 3.660   3rd Qu.: 2.490   3rd Qu.: 2.483   3rd Qu.: 2.528  \n Max.   :11.091   Max.   :11.324   Max.   :10.397   Max.   :10.395  \n    HSPC_453          HSPC_454          HSPC_455         HSPC_456      \n Min.   : 0.0000   Min.   : 0.0000   Min.   : 0.000   Min.   : 0.0000  \n 1st Qu.: 0.0000   1st Qu.: 0.0000   1st Qu.: 0.000   1st Qu.: 0.0000  \n Median : 0.9321   Median : 0.5303   Median : 0.000   Median : 0.6497  \n Mean   : 2.4906   Mean   : 2.4477   Mean   : 2.379   Mean   : 2.4263  \n 3rd Qu.: 4.9604   3rd Qu.: 4.8773   3rd Qu.: 3.016   3rd Qu.: 5.4740  \n Max.   :10.5263   Max.   :11.1628   Max.   :11.437   Max.   :10.9787  \n    HSPC_457         HSPC_459         HSPC_460         HSPC_461     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 0.000   Median : 0.000   Median : 0.000   Median : 1.313  \n Mean   : 2.060   Mean   : 2.403   Mean   : 1.712   Mean   : 1.875  \n 3rd Qu.: 2.937   3rd Qu.: 3.029   3rd Qu.: 1.598   3rd Qu.: 2.104  \n Max.   :11.746   Max.   :12.135   Max.   :12.526   Max.   :10.210  \n    HSPC_462         HSPC_463          HSPC_465         HSPC_466      \n Min.   : 0.000   Min.   : 0.0000   Min.   : 0.000   Min.   : 0.0000  \n 1st Qu.: 0.000   1st Qu.: 0.0000   1st Qu.: 0.000   1st Qu.: 0.0000  \n Median : 0.000   Median : 0.7257   Median : 0.000   Median : 0.5816  \n Mean   : 2.095   Mean   : 2.2325   Mean   : 2.000   Mean   : 1.9972  \n 3rd Qu.: 2.578   3rd Qu.: 2.3442   3rd Qu.: 2.633   3rd Qu.: 2.2384  \n Max.   :11.429   Max.   :11.1776   Max.   :11.064   Max.   :11.5475  \n    HSPC_467         HSPC_468         HSPC_470         HSPC_471     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 1.177   Median : 0.649   Median : 0.000   Median : 0.000  \n Mean   : 1.866   Mean   : 2.130   Mean   : 1.774   Mean   : 2.279  \n 3rd Qu.: 2.258   3rd Qu.: 2.513   3rd Qu.: 1.931   3rd Qu.: 2.744  \n Max.   :10.632   Max.   :10.527   Max.   :10.781   Max.   :11.533  \n    HSPC_472         HSPC_473         HSPC_474         HSPC_475     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 0.000   Median : 0.000   Median : 0.000   Median : 0.000  \n Mean   : 2.265   Mean   : 2.168   Mean   : 2.016   Mean   : 2.339  \n 3rd Qu.: 2.982   3rd Qu.: 2.677   3rd Qu.: 2.061   3rd Qu.: 3.319  \n Max.   :11.795   Max.   :12.071   Max.   :11.732   Max.   :10.672  \n    HSPC_477          HSPC_478         HSPC_479         HSPC_480     \n Min.   : 0.0000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.0000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 0.6278   Median : 0.000   Median : 1.281   Median : 1.034  \n Mean   : 1.9910   Mean   : 2.068   Mean   : 2.175   Mean   : 2.239  \n 3rd Qu.: 1.6695   3rd Qu.: 3.402   3rd Qu.: 3.028   3rd Qu.: 2.642  \n Max.   :11.1171   Max.   :12.113   Max.   :11.277   Max.   :10.641  \n    HSPC_482         HSPC_483         HSPC_485          HSPC_486     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.0000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.0000   1st Qu.: 0.000  \n Median : 0.000   Median : 1.088   Median : 0.6036   Median : 1.411  \n Mean   : 1.998   Mean   : 2.454   Mean   : 2.3824   Mean   : 2.078  \n 3rd Qu.: 2.648   3rd Qu.: 3.006   3rd Qu.: 4.8213   3rd Qu.: 2.579  \n Max.   :13.948   Max.   :10.722   Max.   :11.8691   Max.   :10.155  \n    HSPC_488         HSPC_489         HSPC_490         HSPC_491     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 0.000   Median : 0.000   Median : 1.310   Median : 0.000  \n Mean   : 1.809   Mean   : 1.947   Mean   : 2.518   Mean   : 2.268  \n 3rd Qu.: 2.120   3rd Qu.: 2.330   3rd Qu.: 4.140   3rd Qu.: 3.300  \n Max.   :11.271   Max.   :11.518   Max.   :11.646   Max.   :10.366  \n    HSPC_492         HSPC_493         HSPC_494         HSPC_495     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 0.000   Median : 0.000   Median : 0.000   Median : 0.000  \n Mean   : 2.127   Mean   : 2.054   Mean   : 2.255   Mean   : 2.326  \n 3rd Qu.: 2.322   3rd Qu.: 3.060   3rd Qu.: 3.386   3rd Qu.: 3.812  \n Max.   :11.674   Max.   :10.404   Max.   :10.461   Max.   :10.304  \n    HSPC_496         HSPC_497         HSPC_498          HSPC_499     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.0000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.0000   1st Qu.: 0.000  \n Median : 0.000   Median : 1.945   Median : 0.5839   Median : 0.000  \n Mean   : 1.938   Mean   : 2.287   Mean   : 2.3731   Mean   : 2.045  \n 3rd Qu.: 2.227   3rd Qu.: 2.872   3rd Qu.: 3.6112   3rd Qu.: 2.358  \n Max.   :11.323   Max.   :11.873   Max.   :11.3264   Max.   :10.632  \n    HSPC_500         HSPC_501         HSPC_502          HSPC_503      \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.0000   Min.   : 0.0000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.0000   1st Qu.: 0.0000  \n Median : 0.000   Median : 0.000   Median : 0.9146   Median : 0.7789  \n Mean   : 2.199   Mean   : 2.209   Mean   : 2.2727   Mean   : 2.4495  \n 3rd Qu.: 2.678   3rd Qu.: 3.150   3rd Qu.: 2.8888   3rd Qu.: 5.4034  \n Max.   :11.665   Max.   :10.727   Max.   :11.4591   Max.   :11.5376  \n    HSPC_504         HSPC_505         HSPC_506         HSPC_507     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 0.000   Median : 0.000   Median : 0.000   Median : 0.000  \n Mean   : 2.137   Mean   : 2.132   Mean   : 2.017   Mean   : 2.314  \n 3rd Qu.: 3.035   3rd Qu.: 2.744   3rd Qu.: 2.794   3rd Qu.: 3.175  \n Max.   :11.625   Max.   :11.385   Max.   :11.467   Max.   :11.232  \n    HSPC_508          HSPC_509         HSPC_510         HSPC_512     \n Min.   : 0.0000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.0000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 0.2297   Median : 1.691   Median : 1.166   Median : 0.000  \n Mean   : 1.9265   Mean   : 2.548   Mean   : 2.319   Mean   : 2.482  \n 3rd Qu.: 0.8975   3rd Qu.: 4.397   3rd Qu.: 3.492   3rd Qu.: 3.753  \n Max.   :12.0747   Max.   :10.603   Max.   :10.885   Max.   :12.492  \n    HSPC_514         HSPC_515         HSPC_516          HSPC_518     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.0000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.0000   1st Qu.: 0.000  \n Median : 0.000   Median : 1.109   Median : 0.8853   Median : 0.000  \n Mean   : 2.295   Mean   : 2.298   Mean   : 2.5439   Mean   : 2.649  \n 3rd Qu.: 2.429   3rd Qu.: 2.560   3rd Qu.: 4.6629   3rd Qu.: 5.581  \n Max.   :11.783   Max.   :12.193   Max.   :12.1718   Max.   :11.838  \n    HSPC_520         HSPC_521         HSPC_522         HSPC_523      \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.0000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.0000  \n Median : 0.000   Median : 0.000   Median : 0.000   Median : 0.3648  \n Mean   : 2.295   Mean   : 2.348   Mean   : 2.529   Mean   : 1.9471  \n 3rd Qu.: 2.975   3rd Qu.: 3.375   3rd Qu.: 5.350   3rd Qu.: 1.5726  \n Max.   :12.289   Max.   :11.712   Max.   :10.364   Max.   :12.5906  \n    HSPC_524         HSPC_526         HSPC_527         HSPC_528     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 0.000   Median : 0.000   Median : 0.777   Median : 0.532  \n Mean   : 1.989   Mean   : 2.218   Mean   : 2.133   Mean   : 2.238  \n 3rd Qu.: 3.267   3rd Qu.: 2.431   3rd Qu.: 1.651   3rd Qu.: 2.095  \n Max.   :12.105   Max.   :10.870   Max.   :12.017   Max.   :12.183  \n    HSPC_530         HSPC_532         HSPC_533          HSPC_534     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.0000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.0000   1st Qu.: 0.000  \n Median : 0.000   Median : 0.000   Median : 0.7537   Median : 0.000  \n Mean   : 2.017   Mean   : 1.856   Mean   : 1.7546   Mean   : 2.183  \n 3rd Qu.: 2.514   3rd Qu.: 1.816   3rd Qu.: 1.3378   3rd Qu.: 2.311  \n Max.   :11.549   Max.   :11.255   Max.   :11.5862   Max.   :11.696  \n    HSPC_535         HSPC_537         HSPC_538         HSPC_539     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 0.000   Median : 0.000   Median : 0.000   Median : 0.000  \n Mean   : 2.122   Mean   : 2.010   Mean   : 2.501   Mean   : 2.463  \n 3rd Qu.: 2.733   3rd Qu.: 2.541   3rd Qu.: 4.886   3rd Qu.: 4.100  \n Max.   :10.793   Max.   :10.305   Max.   :11.359   Max.   :11.755  \n    HSPC_540          HSPC_541         HSPC_543         HSPC_544      \n Min.   : 0.0000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.0000  \n 1st Qu.: 0.0000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.0000  \n Median : 0.9898   Median : 2.362   Median : 0.000   Median : 0.8222  \n Mean   : 2.1775   Mean   : 2.613   Mean   : 2.275   Mean   : 2.8070  \n 3rd Qu.: 1.9846   3rd Qu.: 4.440   3rd Qu.: 2.690   3rd Qu.: 6.4209  \n Max.   :12.2963   Max.   :11.844   Max.   :10.983   Max.   :10.7976  \n    HSPC_545         HSPC_546         HSPC_547          HSPC_548     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.0000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.0000   1st Qu.: 0.000  \n Median : 1.485   Median : 0.000   Median : 0.6548   Median : 1.456  \n Mean   : 2.215   Mean   : 2.424   Mean   : 2.5255   Mean   : 2.415  \n 3rd Qu.: 2.677   3rd Qu.: 3.573   3rd Qu.: 2.8714   3rd Qu.: 2.639  \n Max.   :11.815   Max.   :11.235   Max.   :11.8801   Max.   :11.955  \n    HSPC_549         HSPC_550         HSPC_551         HSPC_552     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 0.000   Median : 1.750   Median : 1.287   Median : 1.226  \n Mean   : 2.149   Mean   : 2.592   Mean   : 2.680   Mean   : 2.236  \n 3rd Qu.: 2.289   3rd Qu.: 4.686   3rd Qu.: 4.007   3rd Qu.: 2.669  \n Max.   :11.827   Max.   :12.064   Max.   :11.874   Max.   :11.581  \n    HSPC_553          HSPC_554         HSPC_555         HSPC_556      \n Min.   : 0.0000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.0000  \n 1st Qu.: 0.0000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.0000  \n Median : 0.4709   Median : 0.000   Median : 0.000   Median : 0.9369  \n Mean   : 2.6931   Mean   : 2.090   Mean   : 1.903   Mean   : 2.4784  \n 3rd Qu.: 6.4420   3rd Qu.: 2.158   3rd Qu.: 2.579   3rd Qu.: 3.4024  \n Max.   :11.0566   Max.   :11.755   Max.   :11.245   Max.   :11.9838  \n    HSPC_557         HSPC_559         HSPC_560         HSPC_562     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 0.000   Median : 0.000   Median : 1.681   Median : 0.000  \n Mean   : 1.972   Mean   : 1.937   Mean   : 2.082   Mean   : 2.470  \n 3rd Qu.: 1.880   3rd Qu.: 2.411   3rd Qu.: 2.436   3rd Qu.: 4.148  \n Max.   :11.792   Max.   :11.871   Max.   :11.761   Max.   :11.958  \n    HSPC_563        HSPC_566         HSPC_567         HSPC_568     \n Min.   : 0.00   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.00   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 0.00   Median : 0.000   Median : 0.000   Median : 0.000  \n Mean   : 1.83   Mean   : 2.486   Mean   : 2.186   Mean   : 2.267  \n 3rd Qu.: 2.29   3rd Qu.: 3.577   3rd Qu.: 2.254   3rd Qu.: 2.957  \n Max.   :10.59   Max.   :12.452   Max.   :11.302   Max.   :10.851  \n    HSPC_569         HSPC_571         HSPC_573         HSPC_574      \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.0000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.0000  \n Median : 0.771   Median : 1.042   Median : 0.000   Median : 0.7547  \n Mean   : 2.283   Mean   : 2.213   Mean   : 2.089   Mean   : 2.3196  \n 3rd Qu.: 3.021   3rd Qu.: 2.879   3rd Qu.: 2.291   3rd Qu.: 5.6078  \n Max.   :10.720   Max.   :10.939   Max.   :11.397   Max.   :10.4741  \n    HSPC_575         HSPC_576         HSPC_577         HSPC_578     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 0.000   Median : 0.000   Median : 1.606   Median : 0.000  \n Mean   : 2.016   Mean   : 2.206   Mean   : 2.358   Mean   : 2.257  \n 3rd Qu.: 2.267   3rd Qu.: 2.741   3rd Qu.: 3.198   3rd Qu.: 2.923  \n Max.   :10.687   Max.   :11.201   Max.   :11.613   Max.   :12.323  \n    HSPC_579         HSPC_580          HSPC_582         HSPC_584     \n Min.   : 0.000   Min.   : 0.0000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.0000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 1.182   Median : 0.9442   Median : 0.000   Median : 0.000  \n Mean   : 2.472   Mean   : 2.4264   Mean   : 2.218   Mean   : 2.276  \n 3rd Qu.: 5.009   3rd Qu.: 3.5841   3rd Qu.: 3.332   3rd Qu.: 3.067  \n Max.   :11.096   Max.   :10.6790   Max.   :10.882   Max.   :10.954  \n    HSPC_585         HSPC_586          HSPC_589         HSPC_590     \n Min.   : 0.000   Min.   : 0.0000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.0000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 0.000   Median : 0.8915   Median : 0.000   Median : 1.192  \n Mean   : 2.034   Mean   : 2.0490   Mean   : 2.274   Mean   : 2.252  \n 3rd Qu.: 2.157   3rd Qu.: 1.8340   3rd Qu.: 3.655   3rd Qu.: 2.364  \n Max.   :11.956   Max.   :11.4729   Max.   :11.198   Max.   :10.673  \n    HSPC_592         HSPC_593         HSPC_594         HSPC_595     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 1.228   Median : 0.000   Median : 0.000   Median : 0.000  \n Mean   : 2.317   Mean   : 2.329   Mean   : 2.474   Mean   : 1.463  \n 3rd Qu.: 2.671   3rd Qu.: 3.263   3rd Qu.: 4.396   3rd Qu.: 1.757  \n Max.   :12.036   Max.   :10.626   Max.   :11.347   Max.   :11.286  \n    HSPC_596         HSPC_597         HSPC_598         HSPC_599     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 1.392   Median : 0.000   Median : 0.000   Median : 0.000  \n Mean   : 2.283   Mean   : 1.858   Mean   : 1.954   Mean   : 1.905  \n 3rd Qu.: 3.425   3rd Qu.: 2.296   3rd Qu.: 2.320   3rd Qu.: 2.497  \n Max.   :10.899   Max.   :11.002   Max.   :11.117   Max.   :11.248  \n    HSPC_600         HSPC_601         HSPC_602         HSPC_603     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 0.000   Median : 0.000   Median : 1.662   Median : 0.000  \n Mean   : 2.335   Mean   : 1.905   Mean   : 2.343   Mean   : 2.281  \n 3rd Qu.: 3.827   3rd Qu.: 2.376   3rd Qu.: 3.272   3rd Qu.: 3.048  \n Max.   :11.208   Max.   :11.022   Max.   :10.908   Max.   :11.464  \n    HSPC_604         HSPC_606         HSPC_607         HSPC_608     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 0.000   Median : 0.000   Median : 0.000   Median : 0.000  \n Mean   : 2.136   Mean   : 2.392   Mean   : 2.142   Mean   : 2.139  \n 3rd Qu.: 2.516   3rd Qu.: 4.726   3rd Qu.: 3.187   3rd Qu.: 2.885  \n Max.   :11.743   Max.   :11.210   Max.   :10.319   Max.   :10.802  \n    HSPC_610         HSPC_612         HSPC_613         HSPC_614     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 0.000   Median : 0.000   Median : 0.000   Median : 1.315  \n Mean   : 2.327   Mean   : 2.298   Mean   : 2.228   Mean   : 2.364  \n 3rd Qu.: 3.718   3rd Qu.: 3.138   3rd Qu.: 2.705   3rd Qu.: 3.136  \n Max.   :10.860   Max.   :11.564   Max.   :10.560   Max.   :11.824  \n    HSPC_615         HSPC_617          HSPC_618         HSPC_620     \n Min.   : 0.000   Min.   : 0.0000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.0000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 0.000   Median : 0.8525   Median : 0.000   Median : 0.000  \n Mean   : 1.964   Mean   : 2.2100   Mean   : 2.229   Mean   : 1.881  \n 3rd Qu.: 2.451   3rd Qu.: 2.3301   3rd Qu.: 2.885   3rd Qu.: 2.518  \n Max.   :11.058   Max.   :10.9434   Max.   :11.210   Max.   :11.388  \n    HSPC_623         HSPC_624          HSPC_625         HSPC_626     \n Min.   : 0.000   Min.   : 0.0000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.0000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 0.000   Median : 0.7201   Median : 0.000   Median : 0.000  \n Mean   : 2.563   Mean   : 2.0968   Mean   : 2.042   Mean   : 2.262  \n 3rd Qu.: 4.626   3rd Qu.: 1.8437   3rd Qu.: 2.938   3rd Qu.: 3.424  \n Max.   :10.954   Max.   :10.9459   Max.   :11.226   Max.   :11.770  \n    HSPC_627         HSPC_628         HSPC_629         HSPC_630     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 0.000   Median : 0.000   Median : 0.000   Median : 0.000  \n Mean   : 2.269   Mean   : 2.302   Mean   : 2.212   Mean   : 2.519  \n 3rd Qu.: 3.952   3rd Qu.: 2.875   3rd Qu.: 2.625   3rd Qu.: 4.511  \n Max.   :11.426   Max.   :11.792   Max.   :11.139   Max.   :11.519  \n    HSPC_631         HSPC_633         HSPC_634         HSPC_635     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 0.000   Median : 0.000   Median : 0.000   Median : 0.000  \n Mean   : 2.303   Mean   : 2.329   Mean   : 2.268   Mean   : 2.054  \n 3rd Qu.: 2.685   3rd Qu.: 3.619   3rd Qu.: 3.662   3rd Qu.: 2.629  \n Max.   :10.996   Max.   :12.011   Max.   :11.406   Max.   :11.178  \n    HSPC_636         HSPC_637          HSPC_638          HSPC_639      \n Min.   : 0.000   Min.   : 0.0000   Min.   : 0.0000   Min.   : 0.0000  \n 1st Qu.: 0.000   1st Qu.: 0.0000   1st Qu.: 0.0000   1st Qu.: 0.0000  \n Median : 0.000   Median : 0.9389   Median : 0.5101   Median : 0.9966  \n Mean   : 1.953   Mean   : 2.1351   Mean   : 1.6966   Mean   : 1.5879  \n 3rd Qu.: 2.129   3rd Qu.: 2.4817   3rd Qu.: 1.6879   3rd Qu.: 1.6840  \n Max.   :11.057   Max.   :11.1881   Max.   :10.8837   Max.   :10.9561  \n    HSPC_640         HSPC_641         HSPC_643         HSPC_644      \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.0000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.0000  \n Median : 1.025   Median : 1.000   Median : 1.706   Median : 0.4904  \n Mean   : 2.136   Mean   : 1.957   Mean   : 2.468   Mean   : 2.4726  \n 3rd Qu.: 2.119   3rd Qu.: 2.001   3rd Qu.: 3.329   3rd Qu.: 5.6227  \n Max.   :11.173   Max.   :11.056   Max.   :12.016   Max.   :11.0232  \n    HSPC_645          HSPC_646          HSPC_648         HSPC_649      \n Min.   : 0.0000   Min.   : 0.0000   Min.   : 0.000   Min.   : 0.0000  \n 1st Qu.: 0.0000   1st Qu.: 0.0000   1st Qu.: 0.000   1st Qu.: 0.0000  \n Median : 0.7157   Median : 0.9959   Median : 1.519   Median : 0.7139  \n Mean   : 2.3517   Mean   : 2.0594   Mean   : 2.267   Mean   : 2.3593  \n 3rd Qu.: 4.5630   3rd Qu.: 2.3154   3rd Qu.: 2.722   3rd Qu.: 4.1542  \n Max.   :10.9922   Max.   :11.6070   Max.   :11.243   Max.   :10.7707  \n    HSPC_651         HSPC_652         HSPC_654         HSPC_656    \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   :0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.:0.000  \n Median : 0.000   Median : 0.000   Median : 1.398   Median :0.000  \n Mean   : 2.550   Mean   : 1.764   Mean   : 2.108   Mean   :1.983  \n 3rd Qu.: 5.615   3rd Qu.: 2.038   3rd Qu.: 2.562   3rd Qu.:2.505  \n Max.   :11.202   Max.   :10.897   Max.   :10.367   Max.   :9.673  \n    HSPC_657         HSPC_658         HSPC_660        HSPC_661     \n Min.   : 0.000   Min.   : 0.000   Min.   :0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.:0.000   1st Qu.: 0.000  \n Median : 0.000   Median : 0.000   Median :1.253   Median : 1.491  \n Mean   : 1.839   Mean   : 2.319   Mean   :2.542   Mean   : 2.401  \n 3rd Qu.: 2.239   3rd Qu.: 4.021   3rd Qu.:5.274   3rd Qu.: 2.775  \n Max.   :12.132   Max.   :11.264   Max.   :9.852   Max.   :11.647  \n    HSPC_662         HSPC_663         HSPC_664          HSPC_665      \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.0000   Min.   : 0.0000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.0000   1st Qu.: 0.0000  \n Median : 0.000   Median : 0.000   Median : 0.9407   Median : 0.6452  \n Mean   : 2.298   Mean   : 2.726   Mean   : 2.4039   Mean   : 2.1211  \n 3rd Qu.: 2.939   3rd Qu.: 6.519   3rd Qu.: 3.4095   3rd Qu.: 2.0744  \n Max.   :11.277   Max.   :12.152   Max.   :10.9423   Max.   :12.0111  \n    HSPC_666         HSPC_667         HSPC_668         HSPC_669     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 1.130   Median : 0.000   Median : 0.000   Median : 0.000  \n Mean   : 2.815   Mean   : 2.075   Mean   : 2.245   Mean   : 1.992  \n 3rd Qu.: 6.359   3rd Qu.: 2.549   3rd Qu.: 2.407   3rd Qu.: 2.426  \n Max.   :11.052   Max.   :11.406   Max.   :11.061   Max.   :11.752  \n    HSPC_670         HSPC_671         HSPC_672         HSPC_673     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 0.000   Median : 0.000   Median : 0.000   Median : 0.000  \n Mean   : 2.336   Mean   : 2.349   Mean   : 2.041   Mean   : 2.148  \n 3rd Qu.: 3.188   3rd Qu.: 3.777   3rd Qu.: 2.057   3rd Qu.: 2.723  \n Max.   :11.021   Max.   :10.846   Max.   :11.212   Max.   :11.579  \n    HSPC_674         HSPC_676         HSPC_678         HSPC_679     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 0.000   Median : 0.000   Median : 1.525   Median : 1.531  \n Mean   : 1.801   Mean   : 2.239   Mean   : 2.298   Mean   : 2.133  \n 3rd Qu.: 1.892   3rd Qu.: 3.097   3rd Qu.: 3.089   3rd Qu.: 2.737  \n Max.   :10.875   Max.   :10.496   Max.   :12.125   Max.   :11.583  \n    HSPC_680         HSPC_681         HSPC_682         HSPC_683     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 0.000   Median : 1.037   Median : 1.043   Median : 1.180  \n Mean   : 2.573   Mean   : 2.277   Mean   : 2.586   Mean   : 2.498  \n 3rd Qu.: 4.165   3rd Qu.: 4.210   3rd Qu.: 5.432   3rd Qu.: 3.929  \n Max.   :11.100   Max.   :10.154   Max.   :11.095   Max.   :10.859  \n    HSPC_687         HSPC_689         HSPC_690         HSPC_692     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 2.091   Median : 0.000   Median : 0.000   Median : 0.000  \n Mean   : 2.136   Mean   : 2.489   Mean   : 2.686   Mean   : 2.285  \n 3rd Qu.: 2.911   3rd Qu.: 4.106   3rd Qu.: 5.055   3rd Qu.: 3.427  \n Max.   :11.380   Max.   :10.693   Max.   :10.408   Max.   :12.242  \n    HSPC_695          HSPC_696         HSPC_697         HSPC_698     \n Min.   : 0.0000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.0000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 0.2681   Median : 1.538   Median : 1.271   Median : 0.000  \n Mean   : 1.6151   Mean   : 2.688   Mean   : 2.529   Mean   : 2.531  \n 3rd Qu.: 0.6895   3rd Qu.: 5.560   3rd Qu.: 4.779   3rd Qu.: 4.387  \n Max.   :12.4139   Max.   :10.880   Max.   :10.292   Max.   :12.146  \n    HSPC_699         HSPC_700         HSPC_701         HSPC_702     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 0.000   Median : 0.000   Median : 1.157   Median : 0.000  \n Mean   : 2.586   Mean   : 2.402   Mean   : 2.401   Mean   : 2.723  \n 3rd Qu.: 4.595   3rd Qu.: 4.797   3rd Qu.: 3.889   3rd Qu.: 4.822  \n Max.   :11.389   Max.   :10.630   Max.   :11.750   Max.   :11.805  \n    HSPC_703         HSPC_704         HSPC_705          HSPC_706     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.0000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.0000   1st Qu.: 0.000  \n Median : 2.193   Median : 0.000   Median : 0.9795   Median : 1.273  \n Mean   : 2.543   Mean   : 2.598   Mean   : 2.5048   Mean   : 2.364  \n 3rd Qu.: 3.935   3rd Qu.: 4.335   3rd Qu.: 5.0680   3rd Qu.: 3.492  \n Max.   :11.710   Max.   :11.488   Max.   :11.3580   Max.   :10.447  \n    HSPC_707         HSPC_708         HSPC_709         HSPC_714     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 1.361   Median : 0.000   Median : 0.000   Median : 0.000  \n Mean   : 2.371   Mean   : 2.509   Mean   : 2.601   Mean   : 2.326  \n 3rd Qu.: 3.626   3rd Qu.: 3.832   3rd Qu.: 5.060   3rd Qu.: 3.324  \n Max.   :11.796   Max.   :10.865   Max.   :10.145   Max.   :11.126  \n    HSPC_716         HSPC_717         HSPC_719         HSPC_720      \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.0000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.0000  \n Median : 0.000   Median : 1.154   Median : 1.855   Median : 0.8206  \n Mean   : 2.325   Mean   : 2.302   Mean   : 2.519   Mean   : 2.5768  \n 3rd Qu.: 3.356   3rd Qu.: 2.833   3rd Qu.: 4.115   3rd Qu.: 5.5594  \n Max.   :11.812   Max.   :11.047   Max.   :12.237   Max.   :10.5895  \n    HSPC_721         HSPC_722         HSPC_723          HSPC_724      \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.0000   Min.   : 0.0000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.0000   1st Qu.: 0.0000  \n Median : 2.113   Median : 1.185   Median : 0.8421   Median : 0.6485  \n Mean   : 2.205   Mean   : 1.814   Mean   : 2.6174   Mean   : 1.9644  \n 3rd Qu.: 3.456   3rd Qu.: 2.269   3rd Qu.: 4.9545   3rd Qu.: 1.9402  \n Max.   :10.706   Max.   :10.709   Max.   :11.5956   Max.   :11.3505  \n    HSPC_725         HSPC_727         HSPC_729          HSPC_730     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.0000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.0000   1st Qu.: 0.000  \n Median : 1.577   Median : 1.576   Median : 0.9579   Median : 1.135  \n Mean   : 2.483   Mean   : 2.436   Mean   : 2.2448   Mean   : 2.445  \n 3rd Qu.: 3.741   3rd Qu.: 3.447   3rd Qu.: 2.7343   3rd Qu.: 3.475  \n Max.   :10.647   Max.   :11.512   Max.   :10.9657   Max.   :11.121  \n    HSPC_731         HSPC_732          HSPC_733         HSPC_734      \n Min.   : 0.000   Min.   : 0.0000   Min.   : 0.000   Min.   : 0.0000  \n 1st Qu.: 0.000   1st Qu.: 0.0000   1st Qu.: 0.000   1st Qu.: 0.0000  \n Median : 1.130   Median : 0.6937   Median : 1.436   Median : 0.7333  \n Mean   : 2.854   Mean   : 2.1051   Mean   : 2.489   Mean   : 2.5404  \n 3rd Qu.: 6.019   3rd Qu.: 2.0311   3rd Qu.: 3.738   3rd Qu.: 5.6282  \n Max.   :10.471   Max.   :11.0494   Max.   :10.929   Max.   :10.4547  \n    HSPC_735         HSPC_736          HSPC_737         HSPC_738     \n Min.   : 0.000   Min.   : 0.0000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.0000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 1.033   Median : 0.6789   Median : 1.185   Median : 1.514  \n Mean   : 2.389   Mean   : 2.0224   Mean   : 2.722   Mean   : 2.503  \n 3rd Qu.: 3.056   3rd Qu.: 2.0017   3rd Qu.: 5.669   3rd Qu.: 3.602  \n Max.   :10.866   Max.   :11.8100   Max.   :11.076   Max.   :10.473  \n    HSPC_740         HSPC_742          HSPC_743         HSPC_744     \n Min.   : 0.000   Min.   : 0.0000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.0000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 0.000   Median : 0.8437   Median : 1.122   Median : 1.213  \n Mean   : 2.506   Mean   : 1.8949   Mean   : 2.028   Mean   : 2.048  \n 3rd Qu.: 3.794   3rd Qu.: 1.7586   3rd Qu.: 2.840   3rd Qu.: 2.309  \n Max.   :10.618   Max.   :11.6327   Max.   :10.449   Max.   :10.598  \n    HSPC_745         HSPC_746         HSPC_747         HSPC_748     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 2.403   Median : 2.184   Median : 0.000   Median : 1.181  \n Mean   : 2.309   Mean   : 2.153   Mean   : 2.543   Mean   : 2.017  \n 3rd Qu.: 3.793   3rd Qu.: 3.016   3rd Qu.: 4.751   3rd Qu.: 2.264  \n Max.   :10.882   Max.   :10.988   Max.   :10.860   Max.   :12.153  \n    HSPC_749         HSPC_750         HSPC_751         HSPC_752     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 1.444   Median : 1.030   Median : 1.567   Median : 2.228  \n Mean   : 2.477   Mean   : 2.370   Mean   : 2.416   Mean   : 2.529  \n 3rd Qu.: 3.501   3rd Qu.: 3.052   3rd Qu.: 3.435   3rd Qu.: 3.976  \n Max.   :11.391   Max.   :11.167   Max.   :10.239   Max.   :10.586  \n    HSPC_753         HSPC_755         HSPC_756         HSPC_757     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 1.062   Median : 0.740   Median : 1.731   Median : 1.395  \n Mean   : 2.313   Mean   : 2.102   Mean   : 2.592   Mean   : 2.477  \n 3rd Qu.: 2.961   3rd Qu.: 2.509   3rd Qu.: 4.107   3rd Qu.: 3.253  \n Max.   :11.202   Max.   :10.559   Max.   :10.783   Max.   :10.973  \n    HSPC_758          HSPC_759          HSPC_760         HSPC_761      \n Min.   : 0.0000   Min.   : 0.0000   Min.   : 0.000   Min.   : 0.0000  \n 1st Qu.: 0.0000   1st Qu.: 0.0000   1st Qu.: 0.000   1st Qu.: 0.0000  \n Median : 0.8648   Median : 0.9415   Median : 1.052   Median : 0.6917  \n Mean   : 2.6819   Mean   : 2.1274   Mean   : 2.288   Mean   : 2.2992  \n 3rd Qu.: 4.7233   3rd Qu.: 2.2271   3rd Qu.: 2.404   3rd Qu.: 2.6015  \n Max.   :11.1096   Max.   :11.2534   Max.   :11.008   Max.   :11.7228  \n    HSPC_762         HSPC_764         HSPC_765         HSPC_766      \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.0000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.0000  \n Median : 1.271   Median : 1.784   Median : 2.116   Median : 0.9828  \n Mean   : 2.242   Mean   : 2.068   Mean   : 2.100   Mean   : 2.1721  \n 3rd Qu.: 2.734   3rd Qu.: 3.059   3rd Qu.: 2.939   3rd Qu.: 2.6115  \n Max.   :12.043   Max.   :11.003   Max.   :12.757   Max.   :10.2002  \n    HSPC_767          HSPC_768         HSPC_769         HSPC_770     \n Min.   : 0.0000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.0000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 0.6646   Median : 1.703   Median : 0.000   Median : 1.760  \n Mean   : 1.9552   Mean   : 2.365   Mean   : 2.080   Mean   : 2.343  \n 3rd Qu.: 1.9730   3rd Qu.: 3.325   3rd Qu.: 3.289   3rd Qu.: 3.122  \n Max.   :11.3033   Max.   :10.958   Max.   :11.176   Max.   :10.497  \n    HSPC_771         HSPC_772         HSPC_773          HSPC_774      \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.0000   Min.   : 0.0000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.0000   1st Qu.: 0.0000  \n Median : 1.178   Median : 1.601   Median : 0.9901   Median : 0.9736  \n Mean   : 2.527   Mean   : 2.283   Mean   : 1.8628   Mean   : 2.5263  \n 3rd Qu.: 3.342   3rd Qu.: 2.828   3rd Qu.: 1.9851   3rd Qu.: 5.4694  \n Max.   :11.156   Max.   :10.625   Max.   :10.7274   Max.   :10.7701  \n    HSPC_776         HSPC_777         HSPC_778        HSPC_780     \n Min.   : 0.000   Min.   : 0.000   Min.   :0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.:0.000   1st Qu.: 0.000  \n Median : 0.000   Median : 1.053   Median :1.435   Median : 1.178  \n Mean   : 2.315   Mean   : 2.110   Mean   :2.130   Mean   : 2.476  \n 3rd Qu.: 3.788   3rd Qu.: 2.673   3rd Qu.:3.488   3rd Qu.: 3.769  \n Max.   :11.105   Max.   :11.646   Max.   :9.535   Max.   :11.265  \n    HSPC_781         HSPC_782         HSPC_783         HSPC_784     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 0.000   Median : 0.000   Median : 0.000   Median : 1.050  \n Mean   : 1.911   Mean   : 2.416   Mean   : 2.254   Mean   : 2.175  \n 3rd Qu.: 2.884   3rd Qu.: 3.872   3rd Qu.: 2.548   3rd Qu.: 2.468  \n Max.   :11.445   Max.   :10.161   Max.   :10.970   Max.   :10.958  \n    HSPC_785         HSPC_786         HSPC_787         HSPC_788      \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.0000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.0000  \n Median : 0.000   Median : 1.148   Median : 0.000   Median : 0.9386  \n Mean   : 2.230   Mean   : 2.467   Mean   : 2.100   Mean   : 1.9749  \n 3rd Qu.: 2.466   3rd Qu.: 3.899   3rd Qu.: 2.991   3rd Qu.: 2.6662  \n Max.   :11.041   Max.   :11.080   Max.   :10.690   Max.   :11.1078  \n    HSPC_789         HSPC_790         HSPC_791         HSPC_794     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 1.181   Median : 1.353   Median : 1.790   Median : 1.113  \n Mean   : 2.225   Mean   : 2.255   Mean   : 2.699   Mean   : 2.225  \n 3rd Qu.: 2.876   3rd Qu.: 2.852   3rd Qu.: 4.931   3rd Qu.: 2.768  \n Max.   :11.245   Max.   :11.558   Max.   :11.104   Max.   :11.118  \n    HSPC_795          HSPC_796          HSPC_797          HSPC_798     \n Min.   : 0.0000   Min.   : 0.0000   Min.   : 0.0000   Min.   : 0.000  \n 1st Qu.: 0.0000   1st Qu.: 0.0000   1st Qu.: 0.0000   1st Qu.: 0.000  \n Median : 0.8317   Median : 0.7001   Median : 0.8722   Median : 1.531  \n Mean   : 2.3985   Mean   : 2.6865   Mean   : 2.6172   Mean   : 2.485  \n 3rd Qu.: 3.4461   3rd Qu.: 5.6688   3rd Qu.: 5.3078   3rd Qu.: 3.098  \n Max.   :11.0956   Max.   :11.0829   Max.   :11.4339   Max.   :10.933  \n    HSPC_799         HSPC_800         HSPC_801         HSPC_802     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 0.000   Median : 0.000   Median : 0.000   Median : 1.033  \n Mean   : 2.179   Mean   : 2.173   Mean   : 2.427   Mean   : 2.613  \n 3rd Qu.: 3.517   3rd Qu.: 2.865   3rd Qu.: 4.665   3rd Qu.: 3.780  \n Max.   :11.666   Max.   :11.263   Max.   :10.905   Max.   :10.864  \n    HSPC_803         HSPC_804         HSPC_806         HSPC_807      \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.0000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.0000  \n Median : 0.000   Median : 0.000   Median : 2.103   Median : 0.7501  \n Mean   : 2.395   Mean   : 2.301   Mean   : 2.222   Mean   : 2.2476  \n 3rd Qu.: 3.883   3rd Qu.: 3.167   3rd Qu.: 3.445   3rd Qu.: 2.3481  \n Max.   :10.766   Max.   :11.298   Max.   :10.326   Max.   :11.2700  \n    HSPC_808          HSPC_809         HSPC_810         HSPC_812      \n Min.   : 0.0000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.0000  \n 1st Qu.: 0.0000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.0000  \n Median : 0.6619   Median : 1.788   Median : 1.651   Median : 0.8459  \n Mean   : 2.1677   Mean   : 2.544   Mean   : 2.471   Mean   : 2.2960  \n 3rd Qu.: 2.5355   3rd Qu.: 3.730   3rd Qu.: 3.662   3rd Qu.: 2.6906  \n Max.   :10.9302   Max.   :11.791   Max.   :10.829   Max.   :11.5500  \n    HSPC_813          HSPC_814         HSPC_815          HSPC_816     \n Min.   : 0.0000   Min.   : 0.000   Min.   : 0.0000   Min.   : 0.000  \n 1st Qu.: 0.0000   1st Qu.: 0.000   1st Qu.: 0.0000   1st Qu.: 0.000  \n Median : 0.6278   Median : 1.110   Median : 0.9631   Median : 1.346  \n Mean   : 2.2448   Mean   : 2.762   Mean   : 2.4587   Mean   : 2.341  \n 3rd Qu.: 2.3066   3rd Qu.: 5.996   3rd Qu.: 3.4228   3rd Qu.: 2.842  \n Max.   :12.0043   Max.   :10.406   Max.   :11.4527   Max.   :11.151  \n    HSPC_818          HSPC_819         HSPC_820          HSPC_821     \n Min.   : 0.0000   Min.   : 0.000   Min.   : 0.0000   Min.   : 0.000  \n 1st Qu.: 0.0000   1st Qu.: 0.000   1st Qu.: 0.0000   1st Qu.: 0.000  \n Median : 0.9967   Median : 1.365   Median : 0.8099   Median : 1.382  \n Mean   : 2.3081   Mean   : 2.426   Mean   : 2.1063   Mean   : 2.532  \n 3rd Qu.: 2.9942   3rd Qu.: 3.632   3rd Qu.: 2.4643   3rd Qu.: 3.462  \n Max.   :11.9931   Max.   :10.672   Max.   :11.2412   Max.   :12.126  \n    HSPC_822         HSPC_824         HSPC_825         HSPC_826     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 1.387   Median : 0.000   Median : 1.386   Median : 1.324  \n Mean   : 2.503   Mean   : 2.084   Mean   : 2.162   Mean   : 2.398  \n 3rd Qu.: 3.799   3rd Qu.: 2.342   3rd Qu.: 2.897   3rd Qu.: 3.150  \n Max.   :11.892   Max.   :11.365   Max.   :11.498   Max.   :11.198  \n    HSPC_827         HSPC_828         HSPC_831         HSPC_832     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 1.746   Median : 1.003   Median : 1.304   Median : 1.035  \n Mean   : 2.239   Mean   : 2.145   Mean   : 2.589   Mean   : 2.384  \n 3rd Qu.: 2.638   3rd Qu.: 2.326   3rd Qu.: 3.866   3rd Qu.: 3.450  \n Max.   :12.101   Max.   :10.710   Max.   :10.839   Max.   :10.686  \n    HSPC_833          HSPC_834          HSPC_835         HSPC_836     \n Min.   : 0.0000   Min.   : 0.0000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.0000   1st Qu.: 0.0000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 0.7166   Median : 0.9245   Median : 1.006   Median : 0.000  \n Mean   : 2.3553   Mean   : 2.0872   Mean   : 2.552   Mean   : 2.471  \n 3rd Qu.: 3.9364   3rd Qu.: 2.4568   3rd Qu.: 4.034   3rd Qu.: 3.994  \n Max.   :11.1695   Max.   :11.1803   Max.   :11.779   Max.   :11.316  \n    HSPC_837         HSPC_838          HSPC_839         HSPC_840     \n Min.   : 0.000   Min.   : 0.0000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.0000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 1.312   Median : 0.9838   Median : 1.083   Median : 1.867  \n Mean   : 2.590   Mean   : 2.5281   Mean   : 2.380   Mean   : 2.548  \n 3rd Qu.: 4.443   3rd Qu.: 3.5551   3rd Qu.: 3.743   3rd Qu.: 3.609  \n Max.   :10.672   Max.   :11.2707   Max.   :10.966   Max.   :10.867  \n    HSPC_841         HSPC_842         HSPC_843         HSPC_844     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 1.439   Median : 1.774   Median : 1.257   Median : 1.584  \n Mean   : 2.408   Mean   : 2.380   Mean   : 2.845   Mean   : 2.627  \n 3rd Qu.: 3.494   3rd Qu.: 3.490   3rd Qu.: 6.768   3rd Qu.: 3.951  \n Max.   :10.930   Max.   :11.137   Max.   :11.933   Max.   :11.446  \n    HSPC_845         HSPC_846         HSPC_848         HSPC_849     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 1.227   Median : 1.401   Median : 0.000   Median : 1.602  \n Mean   : 2.464   Mean   : 2.240   Mean   : 2.152   Mean   : 2.402  \n 3rd Qu.: 3.377   3rd Qu.: 2.920   3rd Qu.: 2.554   3rd Qu.: 2.920  \n Max.   :10.535   Max.   :11.519   Max.   :11.266   Max.   :11.678  \n    HSPC_851         HSPC_852     \n Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 0.000   Median : 0.000  \n Mean   : 2.319   Mean   : 2.143  \n 3rd Qu.: 3.373   3rd Qu.: 2.901  \n Max.   :11.602   Max.   :11.469  \n\n\nHmmmm, did you get all that? Nope, me neither! We have 701 cells but we only have 6 samples for the frogs. We will need a different approach to get an overview but I find it is still useful to look at the few columns\n🎬 Get a quick overview the first 20 columns:\n\nsummary(hspc[1:20])\n\n ensembl_gene_id       HSPC_001         HSPC_002         HSPC_003      \n Length:280         Min.   : 0.000   Min.   : 0.000   Min.   : 0.0000  \n Class :character   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.0000  \n Mode  :character   Median : 0.000   Median : 0.000   Median : 0.9929  \n                    Mean   : 2.143   Mean   : 1.673   Mean   : 2.5964  \n                    3rd Qu.: 2.120   3rd Qu.: 2.239   3rd Qu.: 6.1559  \n                    Max.   :12.567   Max.   :11.976   Max.   :11.1138  \n    HSPC_004         HSPC_006         HSPC_008         HSPC_009    \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   :0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.:0.000  \n Median : 0.000   Median : 1.276   Median : 0.000   Median :0.000  \n Mean   : 1.851   Mean   : 2.338   Mean   : 2.375   Mean   :2.220  \n 3rd Qu.: 2.466   3rd Qu.: 3.536   3rd Qu.: 3.851   3rd Qu.:3.594  \n Max.   :11.133   Max.   :10.014   Max.   :11.574   Max.   :9.997  \n    HSPC_011         HSPC_012         HSPC_014         HSPC_015     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 0.000   Median : 1.750   Median : 0.000   Median : 0.000  \n Mean   : 2.285   Mean   : 2.431   Mean   : 2.295   Mean   : 2.515  \n 3rd Qu.: 3.193   3rd Qu.: 3.741   3rd Qu.: 3.150   3rd Qu.: 3.789  \n Max.   :11.260   Max.   :10.905   Max.   :11.051   Max.   :10.751  \n    HSPC_016          HSPC_017         HSPC_018         HSPC_020     \n Min.   : 0.0000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.0000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 0.9488   Median : 0.000   Median : 1.248   Median : 0.000  \n Mean   : 2.6115   Mean   : 2.146   Mean   : 2.710   Mean   : 2.509  \n 3rd Qu.: 5.9412   3rd Qu.: 2.357   3rd Qu.: 6.006   3rd Qu.: 4.470  \n Max.   :11.3082   Max.   :12.058   Max.   :11.894   Max.   :11.281  \n    HSPC_021         HSPC_022         HSPC_023         HSPC_024     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 0.000   Median : 0.000   Median : 0.000   Median : 0.000  \n Mean   : 2.170   Mean   : 2.287   Mean   : 2.314   Mean   : 2.195  \n 3rd Qu.: 2.996   3rd Qu.: 3.351   3rd Qu.: 2.749   3rd Qu.: 2.944  \n Max.   :10.709   Max.   :11.814   Max.   :12.113   Max.   :11.279  \n\n\nNotice that:\n\nthe maximum value is much less high than for the frogs and has decimals. That is because the mouse data are logged (to base 2) normalised counts, not raw counts as they are in the frog data set.\na minimum value of 0 appears in all 20 columns - perhaps that is true across the whole dataset (or at least common)\nat least some of the medians are zeros so there must be quite a lot of zeros\nthe few columns we can see are roughly similar\nit would not be very practical to plot the distributions of values in cell cell using facet_wrap().\n\nIn this data set, there is even more of an advantage of using the pivot_longer(), group_by() and summarise() approach. We will be able to open the dataframe in the Viewer and make plots to examine whether the distributions are similar across cells.\n🎬 Summarise all the cells:\n\nhspc_summary_samp &lt;- hspc |&gt;\n  pivot_longer(cols = -ensembl_gene_id,\n               names_to = \"cell\",\n               values_to = \"expr\") |&gt;\n  group_by(cell) |&gt;\n  summarise(min = min(expr),\n            lowerq = quantile(expr, 0.25),\n            mean = mean(expr),\n            median = median(expr),\n            sd = sd(expr),\n            upperq = quantile(expr, 0.75),\n            max = max(expr),\n            n_zero = sum(expr == 0))\n\nNotice that I have used cell as the column name rather than sample and expr (expression) rather than count. I’ve also added the standard deviation.\n🎬 View the hspc_summary_samp dataframe (click on it in the environment).\nAll cells have quite a few zeros and the lower quartile is 0 for all cells, i.e., every cell has many genes with zero expression.\nTo get a better understanding of the distribution of expressions in cells we can create a ggplot using the pointrange geom. Pointrange puts a dot at the mean and a line between a minimum and a maximum such as +/- one s.d. Not unlike a boxplot, but when you need the boxes too be very narrow!\n🎬 Create a pointrange plot.\n\nhspc_summary_samp |&gt; \n  ggplot(aes(x = cell, y = mean)) +\n  geom_pointrange(aes(ymin = mean - sd, \n                      ymax = mean + sd ),\n                  size = 0.1)\n\n\n\n\n\n\n\nYou will need to use the Zoom button to pop the plot window out so you can make it as wide as possible\nThe things to notice are:\n\nthe average expression in cells is similar for all cells. This is good to know - if some cells had much lower expression perhaps there is something wrong with them, or their sequencing, and they should be excluded.\nthe distributions are roughly similar in width too\n\nThe default order of cell is alphabetical. It can be easier to see these (non-) effects if we order the lines by the size of the mean.\n🎬 Order a pointrange plot with reorder(variable_to_order, order_by).\n\nhspc_summary_samp |&gt; \n  ggplot(aes(x = reorder(cell, mean), y = mean)) +\n  geom_pointrange(aes(ymin = mean - sd, \n                      ymax = mean + sd ),\n                  size = 0.1)\n\n\n\n\n\n\n\nreorder() arranges cell in increasing size of mean\n🎬 Write hspc_summary_samp to a file called “hspc_summary_samp.csv”:\nDistribution of values across the genes\n🐸 Frog genes\nThere are lots of genes in this dataset therefore we will take the same approach as that we took for the distributions across mouse cells. We will pivot the data to tidy and then summarise the counts for each gene.\n🎬 Summarise the counts for each genes:\n\ns30_summary_gene &lt;- s30 |&gt;\n  pivot_longer(cols = -xenbase_gene_id,\n               names_to = \"sample\",\n               values_to = \"count\") |&gt;\n  group_by(xenbase_gene_id) |&gt;\n  summarise(min = min(count),\n            lowerq = quantile(count, 0.25),\n            sd = sd(count),\n            mean = mean(count),\n            median = median(count),\n            upperq = quantile(count, 0.75),\n            max = max(count),\n            total = sum(count),\n            n_zero = sum(count == 0))\n\nI have calculated the values we used before with one addition: the sum of the counts (total).\n🎬 View the s30_summary_gene dataframe.\nNotice that we have:\n\na lot of genes with counts of zero in every sample\na lot of genes with zero counts in several of the samples\nsome very very low counts.\n\nThese should be filtered out because they are unreliable - or, at the least, uninformative. The goal of our downstream analysis will be to see if there is a signifcance difference in gene expression between the control and FGF-treated sibling. Since we have only three replicates in each group, having one or two unreliable, missing or zero values, makes such a determination impossible for a particular gene. We will use the total counts and the number of samples with non-zero values to filter our genes later.\nAs we have a lot of genes, it is again helpful to plot the mean counts with pointrange to get an overview. We will plot the log of the counts - we saw earlier that logging made it easier to understand the distribution of counts over such a wide range. We will also order the genes from lowest to highest mean count.\n🎬 Plot the logged mean counts for each gene in order of size using geom_pointrange():\n\ns30_summary_gene |&gt; \n  ggplot(aes(x = reorder(xenbase_gene_id, mean), y = log10(mean))) +\n  geom_pointrange(aes(ymin = log10(mean - sd), \n                      ymax = log10(mean + sd )),\n                  size = 0.1)\n\n\n\n\n\n\n\n(Remember, the warning is expected since we have zeros).\nYou can see we also have quite a few genes with means less than 1 (log below zero). Note that the variability between genes (average counts between 0 and 102586) is far greater than between samples (average counts from 260 to 426) which is exactly what we would expect to see.\n🎬 Write s30_summary_gene to a file called “s30_summary_gene.csv”:\n🐭 Mouse genes\nThere are fewer genes in this dataset, but still more than you can understand without the overview provided by a plot. We will again pivot the data to tidy and then summarise the expression for each gene.\n🎬 Summarise the expression for each genes:\n\nhspc_summary_gene &lt;- hspc |&gt;\n  pivot_longer(cols = -ensembl_gene_id,\n               names_to = \"cell\",\n               values_to = \"expr\") |&gt;\n  group_by(ensembl_gene_id) |&gt;\n  summarise(min = min(expr),\n            lowerq = quantile(expr, 0.25),\n            sd = sd(expr),\n            mean = mean(expr),\n            median = median(expr),\n            upperq = quantile(expr, 0.75),\n            max = max(expr),\n            total = sum(expr),\n            n_zero = sum(expr == 0))\n\n🎬 View the hspc_summary_gene dataframe. Remember these are normalised and logged (base 2) so we should not see very large values.\nNotice that we have:\n\nno genes with 0 in every cell\nvery few genes (9) with no zeros at all\nquite a few genes with zero in many cells but this matters less than zeros in the frog samples because we had just 6 samples and we have 701 cells.\n\nAs we have a lot of genes, it is again helpful to plot the mean expression with pointrange to get an overview. We do not need to log the values but ordering the genes will help.\n🎬 Plot the logged mean counts for each gene in order of size using geom_pointrange():\n\nhspc_summary_gene |&gt; \n  ggplot(aes(x = reorder(ensembl_gene_id, mean), y = mean)) +\n  geom_pointrange(aes(ymin = mean - sd, \n                      ymax = mean + sd),\n                  size = 0.1)\n\n\n\n\n\n\n\nNote again that the variability between genes (average expression between 0.02 and and 10.03) is far greater than between cells (average expression from1.46 to 3.18) which is expected.\n🎬 Write s30_summary_gene to a file called “s30_summary_gene.csv”:",
+    "text": "Explore\nThe first task is to get an overview. We want to know\n\nare there any missing values? If so, how many and how are they distributed?\nhow may zeros are there and how are they distributed\ndoes it look as tough all the samples/cells were equally “successful”? Can we spot any problematic anomalies?\nwhat is the distribution of values?\n\nIf our data collection has gone well we would hope to see approximately the same average expression in each sample or cell of the same type. That is replicates should be similar. We would also expect to see that the average expression of genes varies. We might have genes which are zero in every cell/sample. We will want to to filter those out.\nWe get this overview by looking at:\n\nThe distribution of values across the whole dataset\nThe distribution of values across the sample/cells (i.e., averaged across genes). This allows us to see variation between samples/cells:\nThe distribution of values across the genes (i.e., averaged across samples/cells). This allows us to see variation between genes.\n\nDistribution of values across the whole dataset\nIn all data sets, the values are spread over multiple columns so in order to plot the distribution as a whole, we will need to first use pivot_longer() to put the data in ‘tidy’ format (Wickham2014-nl?) by stacking the columns. We could save a copy of the stacked data and then plot it, but here, I have just piped the stacked data straight into ggplot().\n🐸 Frogs\n🎬 Pivot the counts (stack the columns) so all the counts are in a single column (count) and pipe into ggplot() to create a histogram:\n\ns30 |&gt;\n  pivot_longer(cols = -xenbase_gene_id,\n               names_to = \"sample\",\n               values_to = \"count\") |&gt;\n  ggplot(aes(x = count)) +\n  geom_histogram()\n\n\n\n\n\n\n\nThis data is very skewed - there are so many low values that we can’t see the tiny bars for the higher values. Logging the counts is a way to make the distribution more visible.\n🎬 Repeat the plot on log of the counts.\n\ns30 |&gt;\n  pivot_longer(cols = -xenbase_gene_id,\n               names_to = \"sample\",\n               values_to = \"count\") |&gt;\n  ggplot(aes(x = log10(count))) +\n  geom_histogram()\n\n\n\n\n\n\n\nI’ve used base 10 only because it easy to convert to the original scale (1 is 10, 2 is 100, 3 is 1000 etc). The warning about rows being removed is expected - these are the counts of 0 since you can’t log a value of 0. The peak at zero suggests quite a few counts of 1. We would expect we would expect the distribution of counts to be roughly log normal because this is expression of all the genes in the genome1. That small peak near the low end suggests that these lower counts might be anomalies.\nThe excess number of low counts indicates we might want to create a cut off for quality control. The removal of low counts is a common processing step in ’omic data. We will revisit this after we have considered the distribution of counts across samples and genes.\n🐭 Mice\n🎬 Pivot the expression values (stack the columns) so all the counts are in a single column (expr) and pipe into ggplot() to create a histogram:\n\nhspc |&gt;\n  pivot_longer(cols = -ensembl_gene_id,\n               names_to = \"cell\",\n               values_to = \"expr\") |&gt; \n  ggplot(aes(x = expr)) +\n  geom_histogram()\n\n\n\n\n\n\n\nThis is a very striking distribution. Is it what we are expecting? Again,the excess number of low values is almost certainly anomalous. They will be inaccurate measure and we will want to exclude expression values below (about) 1. We will revisit this after we have considered the distribution of expression across cells and genes.\nWhat about the bimodal appearance of the the ‘real’ values? If we had the whole genome we would not expect to see such a pattern - we’d expect to see a roughly normal distribution2. However, this is a subset of the genome and the nature of the subsetting has had an influence here. These are a subset of cell surface proteins that show a significant difference between at least two of twelve cell subtypes. That is, all of these genes are either high or low.\nDistribution of values across the sample/cells\n🐸 Frog samples\nSummary statistics including the the number of NAs can be seen using the summary(). It is most helpful which you have up to about 30 columns. There is nothing special about the number 30, it is just that text summaries of a larger number of columns are difficult to grasp.\n🎬 Get a quick overview of the columns:\n\n# examine all the columns quickly\n# works well with smaller numbers of column\nsummary(s30)\n\n xenbase_gene_id       S30_C_1            S30_C_2            S30_C_3        \n Length:11893       Min.   :     0.0   Min.   :     0.0   Min.   :     0.0  \n Class :character   1st Qu.:    14.0   1st Qu.:    14.0   1st Qu.:    23.0  \n Mode  :character   Median :    70.0   Median :    75.0   Median :   107.0  \n                    Mean   :   317.1   Mean   :   335.8   Mean   :   426.3  \n                    3rd Qu.:   205.0   3rd Qu.:   220.0   3rd Qu.:   301.0  \n                    Max.   :101746.0   Max.   :118708.0   Max.   :117945.0  \n    S30_F_1            S30_F_2            S30_F_3       \n Min.   :     0.0   Min.   :     0.0   Min.   :    0.0  \n 1st Qu.:    19.0   1st Qu.:    17.0   1st Qu.:   16.0  \n Median :    88.0   Median :    84.0   Median :   69.0  \n Mean   :   376.2   Mean   :   376.5   Mean   :  260.4  \n 3rd Qu.:   251.0   3rd Qu.:   246.0   3rd Qu.:  187.0  \n Max.   :117573.0   Max.   :130672.0   Max.   :61531.0  \n\n\nNotice that: - the minimum count is 0 and the maximums are very high in all the columns - the medians are quite a lot lower than the means so the data are skewed (hump to the left, tail to the right) - there must be quite a lot of zeros - the columns are roughly similar and it doesn’t look like there is an anomalous replicate.\nTo find out how may zeros there are in a column we can make use of the fact that TRUE evaluates to 1 and FALSE evaluates to 0. This means sum(S30_C_1 == 0) gives the number of 0 in the S30_C_1 column\n🎬 Find the number of zeros in all six columns:\n\ns30 |&gt;\n  summarise(sum(S30_C_1 == 0),\n            sum(S30_C_2 == 0),\n            sum(S30_C_3 == 0),\n            sum(S30_F_1 == 0),\n            sum(S30_F_2 == 0),\n            sum(S30_F_3 == 0))\n\n# A tibble: 1 × 6\n  `sum(S30_C_1 == 0)` `sum(S30_C_2 == 0)` `sum(S30_C_3 == 0)`\n                &lt;int&gt;               &lt;int&gt;               &lt;int&gt;\n1                1340                1361                 998\n# ℹ 3 more variables: `sum(S30_F_1 == 0)` &lt;int&gt;, `sum(S30_F_2 == 0)` &lt;int&gt;,\n#   `sum(S30_F_3 == 0)` &lt;int&gt;\n\n\nThere is a better way of doing this that saves you having to repeat so much code - especially useful if you have a lot more than 6 columns. We can use pivot_longer() to put the data in tidy format and then use the group_by() and summarise() approach we have used extensively before.\n🎬 Find the number of zeros in all columns:\n\ns30 |&gt;\n  pivot_longer(cols = -xenbase_gene_id,\n               names_to = \"sample\",\n               values_to = \"count\") |&gt;\n  group_by(sample) |&gt;\n  summarise(n_zero = sum(count == 0))\n\n# A tibble: 6 × 2\n  sample  n_zero\n  &lt;chr&gt;    &lt;int&gt;\n1 S30_C_1   1340\n2 S30_C_2   1361\n3 S30_C_3    998\n4 S30_F_1   1210\n5 S30_F_2   1199\n6 S30_F_3    963\n\n\nYou could expand to get all the summary information\n🎬 Summarise all the samples:\n\ns30 |&gt;\n  pivot_longer(cols = -xenbase_gene_id,\n               names_to = \"sample\",\n               values_to = \"count\") |&gt;\n  group_by(sample) |&gt;\n  summarise(min = min(count),\n            lowerq = quantile(count, 0.25),\n            mean = mean(count),\n            median = median(count),\n            upperq = quantile(count, 0.75),\n            max = max(count),\n            n_zero = sum(count == 0))\n\n# A tibble: 6 × 8\n  sample    min lowerq  mean median upperq    max n_zero\n  &lt;chr&gt;   &lt;dbl&gt;  &lt;dbl&gt; &lt;dbl&gt;  &lt;dbl&gt;  &lt;dbl&gt;  &lt;dbl&gt;  &lt;int&gt;\n1 S30_C_1     0     14  317.     70    205 101746   1340\n2 S30_C_2     0     14  336.     75    220 118708   1361\n3 S30_C_3     0     23  426.    107    301 117945    998\n4 S30_F_1     0     19  376.     88    251 117573   1210\n5 S30_F_2     0     17  376.     84    246 130672   1199\n6 S30_F_3     0     16  260.     69    187  61531    963\n\n\nThe mean count ranges from 260 to 426.\nOne advantage this has over using summary() is that the output is a dataframe. For results, this is useful, and makes it easier to:\n\nwrite to file\nuse in ggplot()\n\nformat in a Quarto report\n\n🎬 Save the summary as a dataframe, s30_summary_samp.\nWe can write to file using write_csv()\n🎬 Write s30_summary_samp to a file called “s30_summary_samp.csv”:\n\nwrite_csv(s30_summary_samp, \n          file = \"data-processed/s30_summary_samp.csv\")\n\nPlotting the distribution of values is perhaps the easiest way to understand the data. We could plot each column separately or we can pipe the tidy format of data into ggplot() and make use of facet_wrap()\n🎬 Pivot the data and pipe into ggplot:\n\ns30 |&gt;\n  pivot_longer(cols = -xenbase_gene_id,\n               names_to = \"sample\",\n               values_to = \"count\") |&gt;\n  ggplot(aes(count)) +\n  geom_density() +\n  facet_wrap(. ~ sample, nrow = 3)\n\n\n\n\n\n\n\nWe have many values (11893) so we are not limited to using geom_histogram(). geom_density() gives us a smooth distribution.\nWe have many low values and a few very high ones which makes it tricky to see the distributions. Logging the counts will make these clearer.\n🎬 Repeat the graph but taking the base 10 log of the counts:\n\ns30 |&gt;\n  pivot_longer(cols = -xenbase_gene_id,\n               names_to = \"sample\",\n               values_to = \"count\") |&gt;\n  ggplot(aes(log10(count))) +\n  geom_density() +\n  facet_wrap(. ~ sample, nrow = 3)\n\n\n\n\n\n\n\nThe key information to take from these plots is:\n\nthe distributions are roughly similar in width, height, location and overall shape so it doesn’t look as though we have any suspect samples\nthe peak at zero suggests quite a few counts of 1.\nsince we would expect the distribution of counts in each sample to be roughly log normal so that the small rise near the low end, even before the peak at zero, suggests that these lower counts might be anomalies.\n\nThe excess number of low counts indicates we might want to create a cut off for quality control. The removal of low counts is a common processing step in ’omic data. We will revisit this after we have considered the distribution of counts across genes (averaged over the samples).\n🐭 Mouse cells\nWe used the summary() function to get an overview of the columns in the frog data. Let’s try that here.\n🎬 Get a quick overview of the columns:\n\nsummary(hspc)\n\n ensembl_gene_id       HSPC_001         HSPC_002         HSPC_003      \n Length:280         Min.   : 0.000   Min.   : 0.000   Min.   : 0.0000  \n Class :character   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.0000  \n Mode  :character   Median : 0.000   Median : 0.000   Median : 0.9929  \n                    Mean   : 2.143   Mean   : 1.673   Mean   : 2.5964  \n                    3rd Qu.: 2.120   3rd Qu.: 2.239   3rd Qu.: 6.1559  \n                    Max.   :12.567   Max.   :11.976   Max.   :11.1138  \n    HSPC_004         HSPC_006         HSPC_008         HSPC_009    \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   :0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.:0.000  \n Median : 0.000   Median : 1.276   Median : 0.000   Median :0.000  \n Mean   : 1.851   Mean   : 2.338   Mean   : 2.375   Mean   :2.220  \n 3rd Qu.: 2.466   3rd Qu.: 3.536   3rd Qu.: 3.851   3rd Qu.:3.594  \n Max.   :11.133   Max.   :10.014   Max.   :11.574   Max.   :9.997  \n    HSPC_011         HSPC_012         HSPC_014         HSPC_015     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 0.000   Median : 1.750   Median : 0.000   Median : 0.000  \n Mean   : 2.285   Mean   : 2.431   Mean   : 2.295   Mean   : 2.515  \n 3rd Qu.: 3.193   3rd Qu.: 3.741   3rd Qu.: 3.150   3rd Qu.: 3.789  \n Max.   :11.260   Max.   :10.905   Max.   :11.051   Max.   :10.751  \n    HSPC_016          HSPC_017         HSPC_018         HSPC_020     \n Min.   : 0.0000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.0000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 0.9488   Median : 0.000   Median : 1.248   Median : 0.000  \n Mean   : 2.6115   Mean   : 2.146   Mean   : 2.710   Mean   : 2.509  \n 3rd Qu.: 5.9412   3rd Qu.: 2.357   3rd Qu.: 6.006   3rd Qu.: 4.470  \n Max.   :11.3082   Max.   :12.058   Max.   :11.894   Max.   :11.281  \n    HSPC_021         HSPC_022         HSPC_023         HSPC_024     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 0.000   Median : 0.000   Median : 0.000   Median : 0.000  \n Mean   : 2.170   Mean   : 2.287   Mean   : 2.314   Mean   : 2.195  \n 3rd Qu.: 2.996   3rd Qu.: 3.351   3rd Qu.: 2.749   3rd Qu.: 2.944  \n Max.   :10.709   Max.   :11.814   Max.   :12.113   Max.   :11.279  \n    HSPC_025         HSPC_026         HSPC_027         HSPC_028     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 1.572   Median : 1.385   Median : 0.000   Median : 0.000  \n Mean   : 2.710   Mean   : 2.721   Mean   : 2.458   Mean   : 1.906  \n 3rd Qu.: 5.735   3rd Qu.: 6.392   3rd Qu.: 5.496   3rd Qu.: 2.037  \n Max.   :11.309   Max.   :10.865   Max.   :11.266   Max.   :10.777  \n    HSPC_030         HSPC_031          HSPC_033         HSPC_034      \n Min.   : 0.000   Min.   : 0.0000   Min.   : 0.000   Min.   : 0.0000  \n 1st Qu.: 0.000   1st Qu.: 0.0000   1st Qu.: 0.000   1st Qu.: 0.0000  \n Median : 1.119   Median : 0.9026   Median : 0.000   Median : 0.7984  \n Mean   : 2.338   Mean   : 2.3049   Mean   : 1.938   Mean   : 2.3220  \n 3rd Qu.: 3.005   3rd Qu.: 2.9919   3rd Qu.: 2.434   3rd Qu.: 4.8324  \n Max.   :11.391   Max.   :11.1748   Max.   :10.808   Max.   :10.6707  \n    HSPC_035         HSPC_036          HSPC_037         HSPC_038     \n Min.   : 0.000   Min.   : 0.0000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.0000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 0.000   Median : 0.8879   Median : 1.517   Median : 0.000  \n Mean   : 1.810   Mean   : 2.6918   Mean   : 2.327   Mean   : 2.212  \n 3rd Qu.: 2.175   3rd Qu.: 5.9822   3rd Qu.: 3.079   3rd Qu.: 2.867  \n Max.   :11.221   Max.   :11.3018   Max.   :11.399   Max.   :12.275  \n    HSPC_040         HSPC_041         HSPC_042          HSPC_043     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.0000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.0000   1st Qu.: 0.000  \n Median : 0.000   Median : 0.000   Median : 0.8673   Median : 1.342  \n Mean   : 2.509   Mean   : 2.492   Mean   : 2.3673   Mean   : 2.420  \n 3rd Qu.: 3.995   3rd Qu.: 3.943   3rd Qu.: 3.8371   3rd Qu.: 3.731  \n Max.   :11.863   Max.   :11.016   Max.   :11.4852   Max.   :11.123  \n    HSPC_044         HSPC_045         HSPC_046          HSPC_047     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.0000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.0000   1st Qu.: 0.000  \n Median : 0.000   Median : 0.000   Median : 0.8452   Median : 2.195  \n Mean   : 2.382   Mean   : 2.277   Mean   : 1.9707   Mean   : 2.498  \n 3rd Qu.: 3.998   3rd Qu.: 2.843   3rd Qu.: 2.0656   3rd Qu.: 3.937  \n Max.   :10.782   Max.   :10.629   Max.   :11.0311   Max.   :10.180  \n    HSPC_048         HSPC_049         HSPC_050         HSPC_051      \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.0000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.0000  \n Median : 1.108   Median : 1.275   Median : 0.000   Median : 0.9757  \n Mean   : 2.289   Mean   : 2.453   Mean   : 2.673   Mean   : 2.2693  \n 3rd Qu.: 2.988   3rd Qu.: 3.819   3rd Qu.: 5.772   3rd Qu.: 3.1644  \n Max.   :10.335   Max.   :11.844   Max.   :11.301   Max.   :10.8692  \n    HSPC_052         HSPC_053         HSPC_054         HSPC_055     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 1.509   Median : 0.818   Median : 0.000   Median : 0.000  \n Mean   : 2.561   Mean   : 2.684   Mean   : 2.107   Mean   : 1.959  \n 3rd Qu.: 4.644   3rd Qu.: 5.937   3rd Qu.: 2.568   3rd Qu.: 2.573  \n Max.   :11.674   Max.   :11.624   Max.   :10.770   Max.   :11.105  \n    HSPC_056         HSPC_057         HSPC_058         HSPC_060     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 0.000   Median : 0.000   Median : 1.399   Median : 0.000  \n Mean   : 2.295   Mean   : 2.430   Mean   : 2.296   Mean   : 2.112  \n 3rd Qu.: 3.721   3rd Qu.: 3.806   3rd Qu.: 3.199   3rd Qu.: 2.201  \n Max.   :11.627   Max.   :10.575   Max.   :11.134   Max.   :10.631  \n    HSPC_061         HSPC_062         HSPC_063         HSPC_064     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 0.000   Median : 0.000   Median : 1.515   Median : 1.101  \n Mean   : 1.934   Mean   : 2.129   Mean   : 2.508   Mean   : 2.696  \n 3rd Qu.: 2.489   3rd Qu.: 2.875   3rd Qu.: 4.895   3rd Qu.: 6.412  \n Max.   :11.190   Max.   :10.433   Max.   :10.994   Max.   :10.873  \n    HSPC_065          HSPC_066         HSPC_067         HSPC_068     \n Min.   : 0.0000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.0000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 0.4852   Median : 0.000   Median : 1.441   Median : 0.000  \n Mean   : 2.2676   Mean   : 2.136   Mean   : 2.480   Mean   : 2.449  \n 3rd Qu.: 3.8217   3rd Qu.: 2.632   3rd Qu.: 3.548   3rd Qu.: 4.517  \n Max.   :10.9023   Max.   :11.608   Max.   :11.147   Max.   :10.901  \n    HSPC_069         HSPC_070          HSPC_071          HSPC_072     \n Min.   : 0.000   Min.   : 0.0000   Min.   : 0.0000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.0000   1st Qu.: 0.0000   1st Qu.: 0.000  \n Median : 0.000   Median : 0.8949   Median : 0.9272   Median : 1.121  \n Mean   : 2.406   Mean   : 2.5826   Mean   : 2.2844   Mean   : 2.545  \n 3rd Qu.: 4.705   3rd Qu.: 5.4749   3rd Qu.: 3.2531   3rd Qu.: 4.939  \n Max.   :11.258   Max.   :11.6715   Max.   :10.7886   Max.   :11.397  \n    HSPC_073         HSPC_074        HSPC_075         HSPC_076     \n Min.   : 0.000   Min.   : 0.00   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.00   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 0.000   Median : 0.00   Median : 1.674   Median : 0.000  \n Mean   : 2.491   Mean   : 2.46   Mean   : 2.413   Mean   : 2.289  \n 3rd Qu.: 4.134   3rd Qu.: 3.40   3rd Qu.: 3.013   3rd Qu.: 2.550  \n Max.   :11.844   Max.   :11.66   Max.   :11.976   Max.   :12.136  \n    HSPC_077          HSPC_078         HSPC_079         HSPC_080     \n Min.   : 0.0000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.0000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 0.6624   Median : 1.492   Median : 1.384   Median : 1.013  \n Mean   : 2.4336   Mean   : 2.637   Mean   : 2.432   Mean   : 2.881  \n 3rd Qu.: 5.4937   3rd Qu.: 5.472   3rd Qu.: 3.617   3rd Qu.: 7.220  \n Max.   :11.6020   Max.   :10.673   Max.   :11.199   Max.   :11.836  \n    HSPC_081          HSPC_082         HSPC_083         HSPC_084     \n Min.   : 0.0000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.0000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 0.7671   Median : 0.000   Median : 1.896   Median : 1.128  \n Mean   : 1.9227   Mean   : 2.474   Mean   : 2.864   Mean   : 2.289  \n 3rd Qu.: 1.6349   3rd Qu.: 3.488   3rd Qu.: 5.101   3rd Qu.: 2.792  \n Max.   :11.4681   Max.   :11.962   Max.   :10.865   Max.   :11.834  \n    HSPC_085         HSPC_087         HSPC_088         HSPC_089     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 0.000   Median : 0.000   Median : 0.000   Median : 0.000  \n Mean   : 2.157   Mean   : 2.314   Mean   : 2.202   Mean   : 2.329  \n 3rd Qu.: 3.010   3rd Qu.: 3.245   3rd Qu.: 2.092   3rd Qu.: 3.246  \n Max.   :10.809   Max.   :10.976   Max.   :11.362   Max.   :11.301  \n    HSPC_090         HSPC_094         HSPC_095         HSPC_096    \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   :0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.:0.000  \n Median : 0.000   Median : 0.000   Median : 2.055   Median :0.000  \n Mean   : 2.286   Mean   : 2.186   Mean   : 2.756   Mean   :2.348  \n 3rd Qu.: 4.174   3rd Qu.: 2.002   3rd Qu.: 4.370   3rd Qu.:4.482  \n Max.   :11.124   Max.   :11.694   Max.   :11.385   Max.   :9.601  \n    HSPC_098         HSPC_099         HSPC_100         HSPC_101     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 0.000   Median : 0.000   Median : 0.000   Median : 1.007  \n Mean   : 2.209   Mean   : 2.082   Mean   : 2.313   Mean   : 2.587  \n 3rd Qu.: 3.354   3rd Qu.: 2.505   3rd Qu.: 2.775   3rd Qu.: 5.334  \n Max.   :11.070   Max.   :10.200   Max.   :11.452   Max.   :11.456  \n    HSPC_102         HSPC_103         HSPC_104         HSPC_105     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 1.111   Median : 0.000   Median : 0.000   Median : 0.000  \n Mean   : 2.210   Mean   : 2.853   Mean   : 2.099   Mean   : 1.893  \n 3rd Qu.: 2.993   3rd Qu.: 6.123   3rd Qu.: 2.720   3rd Qu.: 2.129  \n Max.   :11.153   Max.   :11.328   Max.   :10.746   Max.   :10.721  \n    HSPC_106         HSPC_107         HSPC_108         HSPC_109     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 0.000   Median : 0.000   Median : 0.000   Median : 1.595  \n Mean   : 1.980   Mean   : 2.279   Mean   : 2.296   Mean   : 2.420  \n 3rd Qu.: 2.425   3rd Qu.: 3.396   3rd Qu.: 3.361   3rd Qu.: 4.006  \n Max.   :10.919   Max.   :10.982   Max.   :11.744   Max.   :10.463  \n    HSPC_110         HSPC_111         HSPC_114          HSPC_115     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.0000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.0000   1st Qu.: 0.000  \n Median : 0.000   Median : 0.000   Median : 0.9173   Median : 2.349  \n Mean   : 2.159   Mean   : 1.800   Mean   : 1.8376   Mean   : 2.943  \n 3rd Qu.: 2.667   3rd Qu.: 2.214   3rd Qu.: 1.8741   3rd Qu.: 6.223  \n Max.   :11.121   Max.   :11.109   Max.   :10.4645   Max.   :11.124  \n    HSPC_117         HSPC_118         HSPC_119         HSPC_120     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 0.000   Median : 0.000   Median : 0.000   Median : 1.187  \n Mean   : 1.919   Mean   : 1.855   Mean   : 2.289   Mean   : 2.041  \n 3rd Qu.: 2.306   3rd Qu.: 2.387   3rd Qu.: 3.292   3rd Qu.: 2.610  \n Max.   :14.579   Max.   :11.119   Max.   :12.534   Max.   :11.438  \n    HSPC_121         HSPC_122         HSPC_123         HSPC_125     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 0.000   Median : 0.000   Median : 0.000   Median : 0.000  \n Mean   : 2.803   Mean   : 2.072   Mean   : 2.200   Mean   : 2.116  \n 3rd Qu.: 5.798   3rd Qu.: 2.140   3rd Qu.: 3.215   3rd Qu.: 2.409  \n Max.   :11.320   Max.   :11.013   Max.   :11.163   Max.   :11.368  \n    HSPC_126          HSPC_127         HSPC_130         HSPC_131     \n Min.   : 0.0000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.0000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 0.9381   Median : 1.147   Median : 0.000   Median : 0.000  \n Mean   : 2.0014   Mean   : 2.287   Mean   : 2.551   Mean   : 2.240  \n 3rd Qu.: 2.2215   3rd Qu.: 3.051   3rd Qu.: 3.968   3rd Qu.: 3.773  \n Max.   :10.9622   Max.   :11.028   Max.   :10.585   Max.   :11.216  \n    HSPC_132          HSPC_133         HSPC_134         HSPC_135     \n Min.   : 0.0000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.0000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 0.4438   Median : 2.234   Median : 0.000   Median : 0.000  \n Mean   : 2.1659   Mean   : 2.582   Mean   : 2.335   Mean   : 2.402  \n 3rd Qu.: 1.8512   3rd Qu.: 4.591   3rd Qu.: 3.659   3rd Qu.: 4.134  \n Max.   :10.6431   Max.   :10.730   Max.   :11.995   Max.   :11.573  \n    HSPC_136         HSPC_138          HSPC_139         HSPC_140     \n Min.   : 0.000   Min.   : 0.0000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.0000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 0.000   Median : 0.7062   Median : 2.078   Median : 0.000  \n Mean   : 2.546   Mean   : 2.1054   Mean   : 2.876   Mean   : 2.220  \n 3rd Qu.: 5.219   3rd Qu.: 1.8181   3rd Qu.: 4.604   3rd Qu.: 3.716  \n Max.   :11.281   Max.   :11.1177   Max.   :11.013   Max.   :10.893  \n    HSPC_141         HSPC_142         HSPC_143         HSPC_144     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 0.000   Median : 0.000   Median : 0.000   Median : 1.075  \n Mean   : 2.385   Mean   : 2.232   Mean   : 2.592   Mean   : 2.004  \n 3rd Qu.: 4.149   3rd Qu.: 2.523   3rd Qu.: 4.248   3rd Qu.: 2.441  \n Max.   :11.099   Max.   :11.902   Max.   :12.932   Max.   :11.121  \n    HSPC_146         HSPC_148         HSPC_149         HSPC_151      \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.0000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.0000  \n Median : 0.000   Median : 0.000   Median : 0.000   Median : 0.9711  \n Mean   : 2.418   Mean   : 2.385   Mean   : 2.314   Mean   : 2.4375  \n 3rd Qu.: 4.430   3rd Qu.: 3.288   3rd Qu.: 3.139   3rd Qu.: 3.2523  \n Max.   :10.385   Max.   :12.823   Max.   :10.910   Max.   :11.7148  \n    HSPC_152         HSPC_153         HSPC_154         HSPC_155     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 0.000   Median : 0.000   Median : 0.000   Median : 0.000  \n Mean   : 2.247   Mean   : 2.415   Mean   : 2.476   Mean   : 2.468  \n 3rd Qu.: 3.293   3rd Qu.: 3.524   3rd Qu.: 4.653   3rd Qu.: 3.621  \n Max.   :12.463   Max.   :12.205   Max.   :11.437   Max.   :11.207  \n    HSPC_156          HSPC_157         HSPC_158         HSPC_159     \n Min.   : 0.0000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.0000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 0.5545   Median : 1.993   Median : 0.000   Median : 0.000  \n Mean   : 2.2297   Mean   : 2.493   Mean   : 2.119   Mean   : 2.461  \n 3rd Qu.: 2.0977   3rd Qu.: 3.692   3rd Qu.: 2.930   3rd Qu.: 3.340  \n Max.   :11.2431   Max.   :10.539   Max.   :11.336   Max.   :11.123  \n    HSPC_161         HSPC_162          HSPC_164         HSPC_165     \n Min.   : 0.000   Min.   : 0.0000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.0000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 1.701   Median : 0.7152   Median : 0.000   Median : 0.000  \n Mean   : 2.533   Mean   : 2.3473   Mean   : 2.161   Mean   : 2.084  \n 3rd Qu.: 3.616   3rd Qu.: 2.4973   3rd Qu.: 2.553   3rd Qu.: 3.020  \n Max.   :11.429   Max.   :11.0065   Max.   :11.865   Max.   :10.282  \n    HSPC_166         HSPC_168         HSPC_169         HSPC_170     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 0.000   Median : 1.002   Median : 1.158   Median : 0.000  \n Mean   : 2.177   Mean   : 2.390   Mean   : 2.038   Mean   : 2.401  \n 3rd Qu.: 3.296   3rd Qu.: 4.701   3rd Qu.: 2.232   3rd Qu.: 3.703  \n Max.   :11.427   Max.   :10.393   Max.   :10.447   Max.   :11.288  \n    HSPC_171         HSPC_172          HSPC_173         HSPC_174     \n Min.   : 0.000   Min.   : 0.0000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.0000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 1.525   Median : 0.7679   Median : 0.000   Median : 1.257  \n Mean   : 2.312   Mean   : 2.3115   Mean   : 2.288   Mean   : 2.444  \n 3rd Qu.: 2.729   3rd Qu.: 3.7889   3rd Qu.: 3.037   3rd Qu.: 4.996  \n Max.   :10.468   Max.   :11.1442   Max.   :11.074   Max.   :11.095  \n    HSPC_175         HSPC_176         HSPC_177         HSPC_178     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 1.496   Median : 2.024   Median : 1.971   Median : 1.003  \n Mean   : 2.613   Mean   : 2.593   Mean   : 2.421   Mean   : 2.277  \n 3rd Qu.: 4.845   3rd Qu.: 4.092   3rd Qu.: 3.665   3rd Qu.: 2.812  \n Max.   :11.235   Max.   :10.379   Max.   :10.864   Max.   :10.979  \n    HSPC_179         HSPC_180         HSPC_181         HSPC_182     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 0.836   Median : 1.544   Median : 2.030   Median : 0.000  \n Mean   : 2.205   Mean   : 2.556   Mean   : 2.890   Mean   : 2.363  \n 3rd Qu.: 2.300   3rd Qu.: 4.798   3rd Qu.: 4.846   3rd Qu.: 3.779  \n Max.   :11.244   Max.   :10.802   Max.   :10.945   Max.   :10.399  \n    HSPC_183         HSPC_185         HSPC_186         HSPC_187     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 1.020   Median : 0.000   Median : 1.606   Median : 0.000  \n Mean   : 2.242   Mean   : 2.708   Mean   : 2.053   Mean   : 2.360  \n 3rd Qu.: 2.842   3rd Qu.: 4.855   3rd Qu.: 2.834   3rd Qu.: 3.541  \n Max.   :10.530   Max.   :11.079   Max.   :11.016   Max.   :10.923  \n    HSPC_189         HSPC_190         HSPC_191         HSPC_192     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 0.000   Median : 0.000   Median : 0.000   Median : 1.412  \n Mean   : 2.120   Mean   : 2.417   Mean   : 2.175   Mean   : 2.192  \n 3rd Qu.: 2.652   3rd Qu.: 5.226   3rd Qu.: 2.574   3rd Qu.: 2.669  \n Max.   :11.300   Max.   :11.023   Max.   :11.454   Max.   :10.225  \n    HSPC_193         HSPC_195          HSPC_196         HSPC_198     \n Min.   :0.0000   Min.   : 0.0000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.:0.0000   1st Qu.: 0.0000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median :0.9691   Median : 0.9175   Median : 1.379   Median : 1.105  \n Mean   :2.5448   Mean   : 2.7307   Mean   : 2.327   Mean   : 2.155  \n 3rd Qu.:5.1191   3rd Qu.: 5.8899   3rd Qu.: 2.625   3rd Qu.: 2.756  \n Max.   :9.8728   Max.   :10.4757   Max.   :11.319   Max.   :11.405  \n    HSPC_199         HSPC_200         HSPC_202          HSPC_203     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.0000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.0000   1st Qu.: 0.000  \n Median : 1.069   Median : 1.572   Median : 0.8045   Median : 1.311  \n Mean   : 1.909   Mean   : 2.346   Mean   : 2.1384   Mean   : 2.058  \n 3rd Qu.: 2.431   3rd Qu.: 2.791   3rd Qu.: 2.0569   3rd Qu.: 2.792  \n Max.   :11.377   Max.   :11.334   Max.   :11.0516   Max.   :10.852  \n    HSPC_204         HSPC_205         HSPC_206         HSPC_207      \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.0000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.0000  \n Median : 1.342   Median : 1.997   Median : 1.076   Median : 0.9235  \n Mean   : 2.716   Mean   : 2.520   Mean   : 2.426   Mean   : 2.2974  \n 3rd Qu.: 5.611   3rd Qu.: 4.244   3rd Qu.: 4.057   3rd Qu.: 2.6736  \n Max.   :10.269   Max.   :10.817   Max.   :11.866   Max.   :11.4287  \n    HSPC_208         HSPC_210         HSPC_211         HSPC_212     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 2.263   Median : 1.021   Median : 1.351   Median : 0.000  \n Mean   : 2.893   Mean   : 2.315   Mean   : 2.425   Mean   : 2.336  \n 3rd Qu.: 5.014   3rd Qu.: 2.676   3rd Qu.: 3.820   3rd Qu.: 3.443  \n Max.   :11.375   Max.   :12.208   Max.   :11.360   Max.   :11.808  \n    HSPC_213         HSPC_214          HSPC_215         HSPC_216      \n Min.   : 0.000   Min.   : 0.0000   Min.   : 0.000   Min.   : 0.0000  \n 1st Qu.: 0.000   1st Qu.: 0.0000   1st Qu.: 0.000   1st Qu.: 0.0000  \n Median : 1.270   Median : 0.9195   Median : 1.653   Median : 0.8022  \n Mean   : 2.483   Mean   : 2.1976   Mean   : 2.563   Mean   : 2.6010  \n 3rd Qu.: 4.903   3rd Qu.: 2.7139   3rd Qu.: 4.344   3rd Qu.: 6.0076  \n Max.   :11.548   Max.   :10.6947   Max.   :10.933   Max.   :11.2119  \n    HSPC_218         HSPC_219         HSPC_220         HSPC_221     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 0.000   Median : 1.027   Median : 0.000   Median : 1.269  \n Mean   : 2.467   Mean   : 2.291   Mean   : 2.449   Mean   : 2.641  \n 3rd Qu.: 3.980   3rd Qu.: 2.853   3rd Qu.: 4.486   3rd Qu.: 3.617  \n Max.   :11.654   Max.   :10.801   Max.   :10.410   Max.   :11.651  \n    HSPC_222         HSPC_223         HSPC_224         HSPC_225     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 1.449   Median : 0.000   Median : 0.000   Median : 0.000  \n Mean   : 2.262   Mean   : 2.271   Mean   : 2.492   Mean   : 2.585  \n 3rd Qu.: 3.271   3rd Qu.: 3.727   3rd Qu.: 3.769   3rd Qu.: 5.253  \n Max.   :11.133   Max.   :12.000   Max.   :11.114   Max.   :11.671  \n    HSPC_227         HSPC_228         HSPC_229         HSPC_230     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 0.000   Median : 0.000   Median : 2.484   Median : 0.000  \n Mean   : 2.492   Mean   : 2.370   Mean   : 2.742   Mean   : 2.586  \n 3rd Qu.: 3.692   3rd Qu.: 4.488   3rd Qu.: 4.836   3rd Qu.: 5.188  \n Max.   :10.815   Max.   :10.165   Max.   :11.143   Max.   :10.734  \n    HSPC_231         HSPC_232         HSPC_233         HSPC_235     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 0.000   Median : 1.869   Median : 1.254   Median : 0.000  \n Mean   : 2.379   Mean   : 2.264   Mean   : 2.531   Mean   : 2.552  \n 3rd Qu.: 4.787   3rd Qu.: 3.163   3rd Qu.: 3.925   3rd Qu.: 4.389  \n Max.   :10.790   Max.   :12.098   Max.   :11.533   Max.   :11.765  \n    HSPC_236         HSPC_237         HSPC_239         HSPC_240     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 0.000   Median : 0.000   Median : 2.207   Median : 0.892  \n Mean   : 2.205   Mean   : 2.457   Mean   : 2.656   Mean   : 2.049  \n 3rd Qu.: 3.748   3rd Qu.: 3.488   3rd Qu.: 4.904   3rd Qu.: 2.617  \n Max.   :10.234   Max.   :10.630   Max.   :10.858   Max.   :10.528  \n    HSPC_243         HSPC_244          HSPC_245         HSPC_246     \n Min.   : 0.000   Min.   : 0.0000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.0000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 1.118   Median : 0.7872   Median : 1.459   Median : 1.629  \n Mean   : 2.311   Mean   : 2.6638   Mean   : 2.360   Mean   : 2.321  \n 3rd Qu.: 2.574   3rd Qu.: 6.2395   3rd Qu.: 3.000   3rd Qu.: 3.229  \n Max.   :11.069   Max.   :10.0730   Max.   :11.297   Max.   :11.237  \n    HSPC_247        HSPC_248          HSPC_249         HSPC_250     \n Min.   :0.000   Min.   : 0.0000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.:0.000   1st Qu.: 0.0000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median :0.000   Median : 0.8453   Median : 0.000   Median : 1.278  \n Mean   :2.537   Mean   : 2.3719   Mean   : 1.803   Mean   : 2.751  \n 3rd Qu.:4.687   3rd Qu.: 3.3090   3rd Qu.: 2.335   3rd Qu.: 6.330  \n Max.   :9.821   Max.   :10.8128   Max.   :10.568   Max.   :11.256  \n    HSPC_251          HSPC_253         HSPC_254         HSPC_255      \n Min.   : 0.0000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.0000  \n 1st Qu.: 0.0000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.0000  \n Median : 0.9714   Median : 1.265   Median : 0.000   Median : 0.9098  \n Mean   : 2.5626   Mean   : 2.492   Mean   : 2.177   Mean   : 2.1878  \n 3rd Qu.: 4.9167   3rd Qu.: 4.185   3rd Qu.: 3.437   3rd Qu.: 2.4313  \n Max.   :11.1252   Max.   :10.435   Max.   :10.422   Max.   :10.7952  \n    HSPC_256          HSPC_257         HSPC_258         HSPC_261      \n Min.   : 0.0000   Min.   : 0.000   Min.   :0.0000   Min.   : 0.0000  \n 1st Qu.: 0.0000   1st Qu.: 0.000   1st Qu.:0.0000   1st Qu.: 0.0000  \n Median : 0.8248   Median : 1.241   Median :0.8526   Median : 0.5387  \n Mean   : 2.1051   Mean   : 2.630   Mean   :2.0295   Mean   : 2.1419  \n 3rd Qu.: 2.3331   3rd Qu.: 5.646   3rd Qu.:3.0784   3rd Qu.: 1.9352  \n Max.   :13.0375   Max.   :11.499   Max.   :9.9116   Max.   :11.3247  \n    HSPC_263         HSPC_264         HSPC_265         HSPC_266     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 1.538   Median : 1.426   Median : 1.883   Median : 1.839  \n Mean   : 2.613   Mean   : 2.374   Mean   : 3.177   Mean   : 2.833  \n 3rd Qu.: 4.485   3rd Qu.: 3.238   3rd Qu.: 5.702   3rd Qu.: 5.801  \n Max.   :10.571   Max.   :11.136   Max.   :12.436   Max.   :10.338  \n    HSPC_267          HSPC_268          HSPC_269          HSPC_270      \n Min.   : 0.0000   Min.   : 0.0000   Min.   : 0.0000   Min.   : 0.0000  \n 1st Qu.: 0.0000   1st Qu.: 0.0000   1st Qu.: 0.0000   1st Qu.: 0.0000  \n Median : 0.9675   Median : 0.7787   Median : 0.8632   Median : 0.9637  \n Mean   : 2.4910   Mean   : 2.5342   Mean   : 2.4029   Mean   : 2.6899  \n 3rd Qu.: 3.5345   3rd Qu.: 4.9871   3rd Qu.: 4.3176   3rd Qu.: 5.7266  \n Max.   :10.0139   Max.   :10.7848   Max.   :11.2689   Max.   :11.1648  \n    HSPC_271         HSPC_274         HSPC_275          HSPC_276     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.0000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.0000   1st Qu.: 0.000  \n Median : 1.352   Median : 1.730   Median : 0.5252   Median : 1.156  \n Mean   : 2.493   Mean   : 2.382   Mean   : 2.5375   Mean   : 2.485  \n 3rd Qu.: 4.430   3rd Qu.: 3.360   3rd Qu.: 5.7329   3rd Qu.: 4.623  \n Max.   :11.636   Max.   :11.165   Max.   :11.6234   Max.   :11.562  \n    HSPC_278         HSPC_279         HSPC_280         HSPC_281     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 0.000   Median : 1.487   Median : 1.608   Median : 2.611  \n Mean   : 2.161   Mean   : 2.497   Mean   : 2.580   Mean   : 2.737  \n 3rd Qu.: 2.270   3rd Qu.: 3.813   3rd Qu.: 3.985   3rd Qu.: 4.731  \n Max.   :11.734   Max.   :10.900   Max.   :11.673   Max.   :10.076  \n    HSPC_282          HSPC_283         HSPC_285          HSPC_286     \n Min.   : 0.0000   Min.   : 0.000   Min.   : 0.0000   Min.   : 0.000  \n 1st Qu.: 0.0000   1st Qu.: 0.000   1st Qu.: 0.0000   1st Qu.: 0.000  \n Median : 0.7021   Median : 1.911   Median : 0.8658   Median : 1.178  \n Mean   : 2.4272   Mean   : 2.534   Mean   : 2.4868   Mean   : 2.293  \n 3rd Qu.: 4.1254   3rd Qu.: 3.888   3rd Qu.: 5.3804   3rd Qu.: 2.597  \n Max.   :11.1094   Max.   :10.258   Max.   :10.5533   Max.   :11.112  \n    HSPC_287         HSPC_288          HSPC_289         HSPC_290     \n Min.   : 0.000   Min.   : 0.0000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.0000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 1.049   Median : 0.8548   Median : 1.953   Median : 1.176  \n Mean   : 2.775   Mean   : 2.6412   Mean   : 2.925   Mean   : 2.304  \n 3rd Qu.: 5.476   3rd Qu.: 5.4204   3rd Qu.: 5.613   3rd Qu.: 3.445  \n Max.   :10.925   Max.   :11.0814   Max.   :10.199   Max.   :11.094  \n    HSPC_291         HSPC_292         HSPC_293         HSPC_294      \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.0000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.0000  \n Median : 1.176   Median : 1.320   Median : 1.077   Median : 0.9161  \n Mean   : 2.662   Mean   : 2.534   Mean   : 2.538   Mean   : 2.4365  \n 3rd Qu.: 5.690   3rd Qu.: 4.297   3rd Qu.: 3.458   3rd Qu.: 4.8204  \n Max.   :12.255   Max.   :11.090   Max.   :10.987   Max.   :10.6135  \n    HSPC_295        HSPC_296         HSPC_297         HSPC_298     \n Min.   :0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.:0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median :1.479   Median : 2.157   Median : 2.444   Median : 1.281  \n Mean   :2.849   Mean   : 2.977   Mean   : 3.062   Mean   : 2.277  \n 3rd Qu.:5.282   3rd Qu.: 5.006   3rd Qu.: 5.005   3rd Qu.: 2.749  \n Max.   :9.986   Max.   :10.830   Max.   :11.009   Max.   :10.636  \n    HSPC_299         HSPC_300         HSPC_301         HSPC_302     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 1.716   Median : 1.163   Median : 2.235   Median : 2.240  \n Mean   : 2.597   Mean   : 2.346   Mean   : 2.739   Mean   : 2.890  \n 3rd Qu.: 3.762   3rd Qu.: 2.876   3rd Qu.: 4.593   3rd Qu.: 4.945  \n Max.   :11.663   Max.   :11.690   Max.   :10.364   Max.   :10.498  \n    HSPC_303          HSPC_304          HSPC_305         HSPC_306     \n Min.   : 0.0000   Min.   : 0.0000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.0000   1st Qu.: 0.0000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 0.8348   Median : 0.9727   Median : 1.152   Median : 1.303  \n Mean   : 2.3400   Mean   : 2.3710   Mean   : 2.469   Mean   : 2.496  \n 3rd Qu.: 3.2942   3rd Qu.: 2.9942   3rd Qu.: 3.300   3rd Qu.: 3.015  \n Max.   :10.3022   Max.   :11.7185   Max.   :11.051   Max.   :11.211  \n    HSPC_307        HSPC_308         HSPC_309         HSPC_310     \n Min.   :0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.:0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median :1.976   Median : 1.634   Median : 1.804   Median : 1.743  \n Mean   :2.873   Mean   : 2.812   Mean   : 2.892   Mean   : 2.874  \n 3rd Qu.:5.396   3rd Qu.: 5.089   3rd Qu.: 5.165   3rd Qu.: 5.004  \n Max.   :9.921   Max.   :10.527   Max.   :10.476   Max.   :11.107  \n    HSPC_312         HSPC_313         HSPC_314         HSPC_315     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 1.420   Median : 1.592   Median : 1.635   Median : 2.262  \n Mean   : 2.645   Mean   : 2.637   Mean   : 2.564   Mean   : 2.628  \n 3rd Qu.: 4.925   3rd Qu.: 4.257   3rd Qu.: 4.297   3rd Qu.: 4.092  \n Max.   :11.367   Max.   :10.644   Max.   :10.882   Max.   :12.140  \n    HSPC_317         HSPC_318         HSPC_320         HSPC_321     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 2.335   Median : 1.728   Median : 2.340   Median : 1.835  \n Mean   : 2.648   Mean   : 2.637   Mean   : 3.064   Mean   : 2.742  \n 3rd Qu.: 4.103   3rd Qu.: 4.483   3rd Qu.: 5.325   3rd Qu.: 4.340  \n Max.   :10.933   Max.   :11.712   Max.   :11.589   Max.   :11.695  \n    HSPC_322          HSPC_323         HSPC_324         HSPC_325     \n Min.   : 0.0000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.0000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 0.9842   Median : 0.989   Median : 1.088   Median : 2.132  \n Mean   : 2.5948   Mean   : 2.905   Mean   : 2.655   Mean   : 3.091  \n 3rd Qu.: 3.4619   3rd Qu.: 5.629   3rd Qu.: 3.772   3rd Qu.: 5.191  \n Max.   :11.9594   Max.   :12.267   Max.   :11.310   Max.   :11.134  \n    HSPC_326         HSPC_327         HSPC_328         HSPC_329     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 1.781   Median : 1.085   Median : 1.936   Median : 1.954  \n Mean   : 3.021   Mean   : 2.838   Mean   : 2.582   Mean   : 3.034  \n 3rd Qu.: 5.582   3rd Qu.: 6.388   3rd Qu.: 4.048   3rd Qu.: 5.497  \n Max.   :11.268   Max.   :11.433   Max.   :11.908   Max.   :10.927  \n    HSPC_330         HSPC_331         HSPC_332         HSPC_333     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 1.870   Median : 2.953   Median : 1.644   Median : 1.320  \n Mean   : 2.791   Mean   : 3.058   Mean   : 2.768   Mean   : 2.428  \n 3rd Qu.: 4.409   3rd Qu.: 5.118   3rd Qu.: 5.141   3rd Qu.: 2.985  \n Max.   :11.561   Max.   :10.855   Max.   :10.420   Max.   :11.946  \n    HSPC_334         HSPC_335         HSPC_336         HSPC_337     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 1.931   Median : 1.541   Median : 2.761   Median : 0.000  \n Mean   : 2.894   Mean   : 2.746   Mean   : 3.051   Mean   : 2.415  \n 3rd Qu.: 4.160   3rd Qu.: 4.461   3rd Qu.: 4.408   3rd Qu.: 4.188  \n Max.   :11.592   Max.   :11.076   Max.   :11.246   Max.   :10.205  \n    HSPC_338         HSPC_339         HSPC_341          HSPC_342      \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.0000   Min.   : 0.0000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.0000   1st Qu.: 0.0000  \n Median : 0.000   Median : 0.000   Median : 0.9553   Median : 0.4452  \n Mean   : 2.205   Mean   : 2.325   Mean   : 2.0823   Mean   : 2.4572  \n 3rd Qu.: 2.449   3rd Qu.: 3.136   3rd Qu.: 2.0118   3rd Qu.: 4.9582  \n Max.   :12.052   Max.   :11.858   Max.   :11.3855   Max.   :11.8066  \n    HSPC_343         HSPC_344         HSPC_345         HSPC_346      \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.0000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.0000  \n Median : 0.000   Median : 0.000   Median : 0.000   Median : 0.5197  \n Mean   : 2.363   Mean   : 2.290   Mean   : 1.984   Mean   : 2.5126  \n 3rd Qu.: 4.285   3rd Qu.: 3.238   3rd Qu.: 2.561   3rd Qu.: 5.2033  \n Max.   :11.422   Max.   :11.877   Max.   :10.939   Max.   :11.1527  \n    HSPC_348         HSPC_349         HSPC_350        HSPC_351     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.00   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.00   1st Qu.: 0.000  \n Median : 1.113   Median : 0.000   Median : 0.00   Median : 0.000  \n Mean   : 2.232   Mean   : 1.949   Mean   : 2.11   Mean   : 2.259  \n 3rd Qu.: 2.875   3rd Qu.: 2.784   3rd Qu.: 3.07   3rd Qu.: 3.214  \n Max.   :11.161   Max.   :10.720   Max.   :11.15   Max.   :10.912  \n    HSPC_352         HSPC_353         HSPC_354         HSPC_356     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 0.000   Median : 0.000   Median : 0.000   Median : 0.000  \n Mean   : 2.333   Mean   : 2.162   Mean   : 2.427   Mean   : 2.135  \n 3rd Qu.: 3.197   3rd Qu.: 2.819   3rd Qu.: 3.808   3rd Qu.: 2.709  \n Max.   :12.275   Max.   :11.351   Max.   :11.190   Max.   :10.662  \n    HSPC_358         HSPC_359         HSPC_360         HSPC_361     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 0.000   Median : 0.000   Median : 0.000   Median : 0.000  \n Mean   : 2.278   Mean   : 2.012   Mean   : 2.381   Mean   : 2.137  \n 3rd Qu.: 3.608   3rd Qu.: 1.460   3rd Qu.: 3.044   3rd Qu.: 2.875  \n Max.   :10.924   Max.   :11.678   Max.   :11.203   Max.   :10.847  \n    HSPC_362         HSPC_363         HSPC_365         HSPC_367     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 0.000   Median : 0.000   Median : 0.000   Median : 0.000  \n Mean   : 1.783   Mean   : 1.987   Mean   : 2.937   Mean   : 2.449  \n 3rd Qu.: 1.594   3rd Qu.: 2.750   3rd Qu.: 5.572   3rd Qu.: 3.936  \n Max.   :11.889   Max.   :10.389   Max.   :12.427   Max.   :11.081  \n    HSPC_368         HSPC_370          HSPC_371          HSPC_372     \n Min.   : 0.000   Min.   : 0.0000   Min.   : 0.0000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.0000   1st Qu.: 0.0000   1st Qu.: 0.000  \n Median : 0.000   Median : 0.7971   Median : 0.7613   Median : 0.000  \n Mean   : 1.877   Mean   : 2.7681   Mean   : 2.4278   Mean   : 2.487  \n 3rd Qu.: 2.018   3rd Qu.: 6.5358   3rd Qu.: 4.9578   3rd Qu.: 4.226  \n Max.   :11.523   Max.   :11.9636   Max.   :11.4223   Max.   :11.700  \n    HSPC_373         HSPC_374        HSPC_376         HSPC_377     \n Min.   : 0.000   Min.   : 0.00   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.00   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 0.000   Median : 0.00   Median : 0.000   Median : 0.000  \n Mean   : 2.330   Mean   : 2.21   Mean   : 2.625   Mean   : 2.456  \n 3rd Qu.: 3.784   3rd Qu.: 2.44   3rd Qu.: 4.365   3rd Qu.: 4.875  \n Max.   :11.672   Max.   :12.04   Max.   :12.011   Max.   :11.282  \n    HSPC_380         HSPC_382          HSPC_383         HSPC_386     \n Min.   : 0.000   Min.   : 0.0000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.0000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 0.000   Median : 0.9728   Median : 1.753   Median : 0.000  \n Mean   : 2.291   Mean   : 2.3318   Mean   : 2.307   Mean   : 2.351  \n 3rd Qu.: 2.403   3rd Qu.: 2.7605   3rd Qu.: 3.113   3rd Qu.: 3.704  \n Max.   :11.415   Max.   :11.3370   Max.   :11.592   Max.   :11.079  \n    HSPC_387         HSPC_388          HSPC_389         HSPC_390     \n Min.   : 0.000   Min.   : 0.0000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.0000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 0.000   Median : 0.9037   Median : 0.000   Median : 0.000  \n Mean   : 2.255   Mean   : 2.4969   Mean   : 2.081   Mean   : 2.131  \n 3rd Qu.: 3.151   3rd Qu.: 5.3587   3rd Qu.: 2.723   3rd Qu.: 2.738  \n Max.   :11.700   Max.   :10.9923   Max.   :11.868   Max.   :10.913  \n    HSPC_391         HSPC_392         HSPC_393         HSPC_395     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 0.000   Median : 0.000   Median : 0.000   Median : 0.000  \n Mean   : 2.026   Mean   : 2.356   Mean   : 2.063   Mean   : 1.779  \n 3rd Qu.: 2.126   3rd Qu.: 3.781   3rd Qu.: 2.163   3rd Qu.: 1.924  \n Max.   :12.021   Max.   :11.370   Max.   :10.530   Max.   :12.219  \n    HSPC_396         HSPC_398         HSPC_399         HSPC_400     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 0.000   Median : 0.000   Median : 0.000   Median : 0.000  \n Mean   : 2.164   Mean   : 2.309   Mean   : 1.831   Mean   : 2.091  \n 3rd Qu.: 2.681   3rd Qu.: 3.994   3rd Qu.: 1.844   3rd Qu.: 2.781  \n Max.   :11.292   Max.   :11.431   Max.   :11.343   Max.   :10.863  \n    HSPC_402         HSPC_403        HSPC_404         HSPC_405      \n Min.   : 0.000   Min.   : 0.00   Min.   : 0.000   Min.   : 0.0000  \n 1st Qu.: 0.000   1st Qu.: 0.00   1st Qu.: 0.000   1st Qu.: 0.0000  \n Median : 0.000   Median : 0.00   Median : 0.000   Median : 0.5496  \n Mean   : 2.343   Mean   : 2.06   Mean   : 1.878   Mean   : 2.3660  \n 3rd Qu.: 4.552   3rd Qu.: 2.45   3rd Qu.: 1.644   3rd Qu.: 2.5449  \n Max.   :11.444   Max.   :12.00   Max.   :11.188   Max.   :12.2605  \n    HSPC_406         HSPC_407         HSPC_408          HSPC_409     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.0000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.0000   1st Qu.: 0.000  \n Median : 0.000   Median : 0.565   Median : 0.5775   Median : 0.000  \n Mean   : 2.169   Mean   : 2.611   Mean   : 1.9174   Mean   : 2.234  \n 3rd Qu.: 2.606   3rd Qu.: 6.000   3rd Qu.: 1.3086   3rd Qu.: 3.044  \n Max.   :10.866   Max.   :11.296   Max.   :12.8185   Max.   :11.595  \n    HSPC_410         HSPC_411          HSPC_412          HSPC_413     \n Min.   : 0.000   Min.   : 0.0000   Min.   : 0.0000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.0000   1st Qu.: 0.0000   1st Qu.: 0.000  \n Median : 0.000   Median : 0.9059   Median : 0.6614   Median : 0.000  \n Mean   : 2.308   Mean   : 3.1194   Mean   : 3.0437   Mean   : 2.433  \n 3rd Qu.: 4.022   3rd Qu.: 7.7574   3rd Qu.: 7.4695   3rd Qu.: 3.329  \n Max.   :11.620   Max.   :12.0858   Max.   :11.5582   Max.   :12.549  \n    HSPC_415         HSPC_416         HSPC_417          HSPC_418     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.0000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.0000   1st Qu.: 0.000  \n Median : 0.000   Median : 0.000   Median : 0.7222   Median : 0.000  \n Mean   : 2.904   Mean   : 2.228   Mean   : 2.4242   Mean   : 2.508  \n 3rd Qu.: 5.531   3rd Qu.: 3.111   3rd Qu.: 3.0795   3rd Qu.: 3.249  \n Max.   :12.359   Max.   :11.338   Max.   :12.0314   Max.   :11.857  \n    HSPC_419          HSPC_420         HSPC_421         HSPC_422     \n Min.   : 0.0000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.0000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 0.6924   Median : 0.000   Median : 0.000   Median : 0.000  \n Mean   : 2.6246   Mean   : 2.514   Mean   : 2.075   Mean   : 2.552  \n 3rd Qu.: 4.8156   3rd Qu.: 5.709   3rd Qu.: 3.682   3rd Qu.: 5.382  \n Max.   :12.0526   Max.   :11.270   Max.   :10.250   Max.   :11.691  \n    HSPC_423        HSPC_424         HSPC_425         HSPC_426     \n Min.   : 0.00   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.00   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 0.00   Median : 0.000   Median : 1.016   Median : 0.000  \n Mean   : 2.12   Mean   : 2.225   Mean   : 2.658   Mean   : 2.235  \n 3rd Qu.: 1.55   3rd Qu.: 2.471   3rd Qu.: 6.474   3rd Qu.: 3.134  \n Max.   :11.56   Max.   :11.734   Max.   :11.303   Max.   :10.888  \n    HSPC_427         HSPC_431         HSPC_432        HSPC_435     \n Min.   : 0.000   Min.   : 0.000   Min.   :0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.:0.000   1st Qu.: 0.000  \n Median : 0.000   Median : 1.102   Median :0.000   Median : 1.098  \n Mean   : 1.829   Mean   : 2.360   Mean   :2.169   Mean   : 2.060  \n 3rd Qu.: 2.980   3rd Qu.: 3.640   3rd Qu.:3.261   3rd Qu.: 2.744  \n Max.   :10.517   Max.   :10.533   Max.   :9.911   Max.   :10.677  \n    HSPC_436          HSPC_440         HSPC_441         HSPC_442     \n Min.   : 0.0000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.0000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 0.4719   Median : 1.385   Median : 1.084   Median : 0.595  \n Mean   : 2.3880   Mean   : 1.712   Mean   : 2.265   Mean   : 2.109  \n 3rd Qu.: 4.3738   3rd Qu.: 2.079   3rd Qu.: 2.828   3rd Qu.: 2.193  \n Max.   :11.2839   Max.   :11.065   Max.   :11.152   Max.   :11.560  \n    HSPC_443          HSPC_444         HSPC_446         HSPC_447     \n Min.   : 0.0000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.0000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 0.7734   Median : 1.374   Median : 0.000   Median : 1.113  \n Mean   : 2.5663   Mean   : 2.262   Mean   : 1.475   Mean   : 2.446  \n 3rd Qu.: 4.9423   3rd Qu.: 2.952   3rd Qu.: 1.683   3rd Qu.: 4.733  \n Max.   :10.9262   Max.   :10.705   Max.   :10.545   Max.   :10.303  \n    HSPC_448         HSPC_449         HSPC_450         HSPC_451     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 1.139   Median : 1.344   Median : 0.000   Median : 1.759  \n Mean   : 2.396   Mean   : 2.164   Mean   : 1.946   Mean   : 1.806  \n 3rd Qu.: 3.660   3rd Qu.: 2.490   3rd Qu.: 2.483   3rd Qu.: 2.528  \n Max.   :11.091   Max.   :11.324   Max.   :10.397   Max.   :10.395  \n    HSPC_453          HSPC_454          HSPC_455         HSPC_456      \n Min.   : 0.0000   Min.   : 0.0000   Min.   : 0.000   Min.   : 0.0000  \n 1st Qu.: 0.0000   1st Qu.: 0.0000   1st Qu.: 0.000   1st Qu.: 0.0000  \n Median : 0.9321   Median : 0.5303   Median : 0.000   Median : 0.6497  \n Mean   : 2.4906   Mean   : 2.4477   Mean   : 2.379   Mean   : 2.4263  \n 3rd Qu.: 4.9604   3rd Qu.: 4.8773   3rd Qu.: 3.016   3rd Qu.: 5.4740  \n Max.   :10.5263   Max.   :11.1628   Max.   :11.437   Max.   :10.9787  \n    HSPC_457         HSPC_459         HSPC_460         HSPC_461     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 0.000   Median : 0.000   Median : 0.000   Median : 1.313  \n Mean   : 2.060   Mean   : 2.403   Mean   : 1.712   Mean   : 1.875  \n 3rd Qu.: 2.937   3rd Qu.: 3.029   3rd Qu.: 1.598   3rd Qu.: 2.104  \n Max.   :11.746   Max.   :12.135   Max.   :12.526   Max.   :10.210  \n    HSPC_462         HSPC_463          HSPC_465         HSPC_466      \n Min.   : 0.000   Min.   : 0.0000   Min.   : 0.000   Min.   : 0.0000  \n 1st Qu.: 0.000   1st Qu.: 0.0000   1st Qu.: 0.000   1st Qu.: 0.0000  \n Median : 0.000   Median : 0.7257   Median : 0.000   Median : 0.5816  \n Mean   : 2.095   Mean   : 2.2325   Mean   : 2.000   Mean   : 1.9972  \n 3rd Qu.: 2.578   3rd Qu.: 2.3442   3rd Qu.: 2.633   3rd Qu.: 2.2384  \n Max.   :11.429   Max.   :11.1776   Max.   :11.064   Max.   :11.5475  \n    HSPC_467         HSPC_468         HSPC_470         HSPC_471     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 1.177   Median : 0.649   Median : 0.000   Median : 0.000  \n Mean   : 1.866   Mean   : 2.130   Mean   : 1.774   Mean   : 2.279  \n 3rd Qu.: 2.258   3rd Qu.: 2.513   3rd Qu.: 1.931   3rd Qu.: 2.744  \n Max.   :10.632   Max.   :10.527   Max.   :10.781   Max.   :11.533  \n    HSPC_472         HSPC_473         HSPC_474         HSPC_475     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 0.000   Median : 0.000   Median : 0.000   Median : 0.000  \n Mean   : 2.265   Mean   : 2.168   Mean   : 2.016   Mean   : 2.339  \n 3rd Qu.: 2.982   3rd Qu.: 2.677   3rd Qu.: 2.061   3rd Qu.: 3.319  \n Max.   :11.795   Max.   :12.071   Max.   :11.732   Max.   :10.672  \n    HSPC_477          HSPC_478         HSPC_479         HSPC_480     \n Min.   : 0.0000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.0000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 0.6278   Median : 0.000   Median : 1.281   Median : 1.034  \n Mean   : 1.9910   Mean   : 2.068   Mean   : 2.175   Mean   : 2.239  \n 3rd Qu.: 1.6695   3rd Qu.: 3.402   3rd Qu.: 3.028   3rd Qu.: 2.642  \n Max.   :11.1171   Max.   :12.113   Max.   :11.277   Max.   :10.641  \n    HSPC_482         HSPC_483         HSPC_485          HSPC_486     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.0000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.0000   1st Qu.: 0.000  \n Median : 0.000   Median : 1.088   Median : 0.6036   Median : 1.411  \n Mean   : 1.998   Mean   : 2.454   Mean   : 2.3824   Mean   : 2.078  \n 3rd Qu.: 2.648   3rd Qu.: 3.006   3rd Qu.: 4.8213   3rd Qu.: 2.579  \n Max.   :13.948   Max.   :10.722   Max.   :11.8691   Max.   :10.155  \n    HSPC_488         HSPC_489         HSPC_490         HSPC_491     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 0.000   Median : 0.000   Median : 1.310   Median : 0.000  \n Mean   : 1.809   Mean   : 1.947   Mean   : 2.518   Mean   : 2.268  \n 3rd Qu.: 2.120   3rd Qu.: 2.330   3rd Qu.: 4.140   3rd Qu.: 3.300  \n Max.   :11.271   Max.   :11.518   Max.   :11.646   Max.   :10.366  \n    HSPC_492         HSPC_493         HSPC_494         HSPC_495     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 0.000   Median : 0.000   Median : 0.000   Median : 0.000  \n Mean   : 2.127   Mean   : 2.054   Mean   : 2.255   Mean   : 2.326  \n 3rd Qu.: 2.322   3rd Qu.: 3.060   3rd Qu.: 3.386   3rd Qu.: 3.812  \n Max.   :11.674   Max.   :10.404   Max.   :10.461   Max.   :10.304  \n    HSPC_496         HSPC_497         HSPC_498          HSPC_499     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.0000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.0000   1st Qu.: 0.000  \n Median : 0.000   Median : 1.945   Median : 0.5839   Median : 0.000  \n Mean   : 1.938   Mean   : 2.287   Mean   : 2.3731   Mean   : 2.045  \n 3rd Qu.: 2.227   3rd Qu.: 2.872   3rd Qu.: 3.6112   3rd Qu.: 2.358  \n Max.   :11.323   Max.   :11.873   Max.   :11.3264   Max.   :10.632  \n    HSPC_500         HSPC_501         HSPC_502          HSPC_503      \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.0000   Min.   : 0.0000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.0000   1st Qu.: 0.0000  \n Median : 0.000   Median : 0.000   Median : 0.9146   Median : 0.7789  \n Mean   : 2.199   Mean   : 2.209   Mean   : 2.2727   Mean   : 2.4495  \n 3rd Qu.: 2.678   3rd Qu.: 3.150   3rd Qu.: 2.8888   3rd Qu.: 5.4034  \n Max.   :11.665   Max.   :10.727   Max.   :11.4591   Max.   :11.5376  \n    HSPC_504         HSPC_505         HSPC_506         HSPC_507     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 0.000   Median : 0.000   Median : 0.000   Median : 0.000  \n Mean   : 2.137   Mean   : 2.132   Mean   : 2.017   Mean   : 2.314  \n 3rd Qu.: 3.035   3rd Qu.: 2.744   3rd Qu.: 2.794   3rd Qu.: 3.175  \n Max.   :11.625   Max.   :11.385   Max.   :11.467   Max.   :11.232  \n    HSPC_508          HSPC_509         HSPC_510         HSPC_512     \n Min.   : 0.0000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.0000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 0.2297   Median : 1.691   Median : 1.166   Median : 0.000  \n Mean   : 1.9265   Mean   : 2.548   Mean   : 2.319   Mean   : 2.482  \n 3rd Qu.: 0.8975   3rd Qu.: 4.397   3rd Qu.: 3.492   3rd Qu.: 3.753  \n Max.   :12.0747   Max.   :10.603   Max.   :10.885   Max.   :12.492  \n    HSPC_514         HSPC_515         HSPC_516          HSPC_518     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.0000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.0000   1st Qu.: 0.000  \n Median : 0.000   Median : 1.109   Median : 0.8853   Median : 0.000  \n Mean   : 2.295   Mean   : 2.298   Mean   : 2.5439   Mean   : 2.649  \n 3rd Qu.: 2.429   3rd Qu.: 2.560   3rd Qu.: 4.6629   3rd Qu.: 5.581  \n Max.   :11.783   Max.   :12.193   Max.   :12.1718   Max.   :11.838  \n    HSPC_520         HSPC_521         HSPC_522         HSPC_523      \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.0000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.0000  \n Median : 0.000   Median : 0.000   Median : 0.000   Median : 0.3648  \n Mean   : 2.295   Mean   : 2.348   Mean   : 2.529   Mean   : 1.9471  \n 3rd Qu.: 2.975   3rd Qu.: 3.375   3rd Qu.: 5.350   3rd Qu.: 1.5726  \n Max.   :12.289   Max.   :11.712   Max.   :10.364   Max.   :12.5906  \n    HSPC_524         HSPC_526         HSPC_527         HSPC_528     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 0.000   Median : 0.000   Median : 0.777   Median : 0.532  \n Mean   : 1.989   Mean   : 2.218   Mean   : 2.133   Mean   : 2.238  \n 3rd Qu.: 3.267   3rd Qu.: 2.431   3rd Qu.: 1.651   3rd Qu.: 2.095  \n Max.   :12.105   Max.   :10.870   Max.   :12.017   Max.   :12.183  \n    HSPC_530         HSPC_532         HSPC_533          HSPC_534     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.0000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.0000   1st Qu.: 0.000  \n Median : 0.000   Median : 0.000   Median : 0.7537   Median : 0.000  \n Mean   : 2.017   Mean   : 1.856   Mean   : 1.7546   Mean   : 2.183  \n 3rd Qu.: 2.514   3rd Qu.: 1.816   3rd Qu.: 1.3378   3rd Qu.: 2.311  \n Max.   :11.549   Max.   :11.255   Max.   :11.5862   Max.   :11.696  \n    HSPC_535         HSPC_537         HSPC_538         HSPC_539     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 0.000   Median : 0.000   Median : 0.000   Median : 0.000  \n Mean   : 2.122   Mean   : 2.010   Mean   : 2.501   Mean   : 2.463  \n 3rd Qu.: 2.733   3rd Qu.: 2.541   3rd Qu.: 4.886   3rd Qu.: 4.100  \n Max.   :10.793   Max.   :10.305   Max.   :11.359   Max.   :11.755  \n    HSPC_540          HSPC_541         HSPC_543         HSPC_544      \n Min.   : 0.0000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.0000  \n 1st Qu.: 0.0000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.0000  \n Median : 0.9898   Median : 2.362   Median : 0.000   Median : 0.8222  \n Mean   : 2.1775   Mean   : 2.613   Mean   : 2.275   Mean   : 2.8070  \n 3rd Qu.: 1.9846   3rd Qu.: 4.440   3rd Qu.: 2.690   3rd Qu.: 6.4209  \n Max.   :12.2963   Max.   :11.844   Max.   :10.983   Max.   :10.7976  \n    HSPC_545         HSPC_546         HSPC_547          HSPC_548     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.0000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.0000   1st Qu.: 0.000  \n Median : 1.485   Median : 0.000   Median : 0.6548   Median : 1.456  \n Mean   : 2.215   Mean   : 2.424   Mean   : 2.5255   Mean   : 2.415  \n 3rd Qu.: 2.677   3rd Qu.: 3.573   3rd Qu.: 2.8714   3rd Qu.: 2.639  \n Max.   :11.815   Max.   :11.235   Max.   :11.8801   Max.   :11.955  \n    HSPC_549         HSPC_550         HSPC_551         HSPC_552     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 0.000   Median : 1.750   Median : 1.287   Median : 1.226  \n Mean   : 2.149   Mean   : 2.592   Mean   : 2.680   Mean   : 2.236  \n 3rd Qu.: 2.289   3rd Qu.: 4.686   3rd Qu.: 4.007   3rd Qu.: 2.669  \n Max.   :11.827   Max.   :12.064   Max.   :11.874   Max.   :11.581  \n    HSPC_553          HSPC_554         HSPC_555         HSPC_556      \n Min.   : 0.0000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.0000  \n 1st Qu.: 0.0000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.0000  \n Median : 0.4709   Median : 0.000   Median : 0.000   Median : 0.9369  \n Mean   : 2.6931   Mean   : 2.090   Mean   : 1.903   Mean   : 2.4784  \n 3rd Qu.: 6.4420   3rd Qu.: 2.158   3rd Qu.: 2.579   3rd Qu.: 3.4024  \n Max.   :11.0566   Max.   :11.755   Max.   :11.245   Max.   :11.9838  \n    HSPC_557         HSPC_559         HSPC_560         HSPC_562     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 0.000   Median : 0.000   Median : 1.681   Median : 0.000  \n Mean   : 1.972   Mean   : 1.937   Mean   : 2.082   Mean   : 2.470  \n 3rd Qu.: 1.880   3rd Qu.: 2.411   3rd Qu.: 2.436   3rd Qu.: 4.148  \n Max.   :11.792   Max.   :11.871   Max.   :11.761   Max.   :11.958  \n    HSPC_563        HSPC_566         HSPC_567         HSPC_568     \n Min.   : 0.00   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.00   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 0.00   Median : 0.000   Median : 0.000   Median : 0.000  \n Mean   : 1.83   Mean   : 2.486   Mean   : 2.186   Mean   : 2.267  \n 3rd Qu.: 2.29   3rd Qu.: 3.577   3rd Qu.: 2.254   3rd Qu.: 2.957  \n Max.   :10.59   Max.   :12.452   Max.   :11.302   Max.   :10.851  \n    HSPC_569         HSPC_571         HSPC_573         HSPC_574      \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.0000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.0000  \n Median : 0.771   Median : 1.042   Median : 0.000   Median : 0.7547  \n Mean   : 2.283   Mean   : 2.213   Mean   : 2.089   Mean   : 2.3196  \n 3rd Qu.: 3.021   3rd Qu.: 2.879   3rd Qu.: 2.291   3rd Qu.: 5.6078  \n Max.   :10.720   Max.   :10.939   Max.   :11.397   Max.   :10.4741  \n    HSPC_575         HSPC_576         HSPC_577         HSPC_578     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 0.000   Median : 0.000   Median : 1.606   Median : 0.000  \n Mean   : 2.016   Mean   : 2.206   Mean   : 2.358   Mean   : 2.257  \n 3rd Qu.: 2.267   3rd Qu.: 2.741   3rd Qu.: 3.198   3rd Qu.: 2.923  \n Max.   :10.687   Max.   :11.201   Max.   :11.613   Max.   :12.323  \n    HSPC_579         HSPC_580          HSPC_582         HSPC_584     \n Min.   : 0.000   Min.   : 0.0000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.0000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 1.182   Median : 0.9442   Median : 0.000   Median : 0.000  \n Mean   : 2.472   Mean   : 2.4264   Mean   : 2.218   Mean   : 2.276  \n 3rd Qu.: 5.009   3rd Qu.: 3.5841   3rd Qu.: 3.332   3rd Qu.: 3.067  \n Max.   :11.096   Max.   :10.6790   Max.   :10.882   Max.   :10.954  \n    HSPC_585         HSPC_586          HSPC_589         HSPC_590     \n Min.   : 0.000   Min.   : 0.0000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.0000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 0.000   Median : 0.8915   Median : 0.000   Median : 1.192  \n Mean   : 2.034   Mean   : 2.0490   Mean   : 2.274   Mean   : 2.252  \n 3rd Qu.: 2.157   3rd Qu.: 1.8340   3rd Qu.: 3.655   3rd Qu.: 2.364  \n Max.   :11.956   Max.   :11.4729   Max.   :11.198   Max.   :10.673  \n    HSPC_592         HSPC_593         HSPC_594         HSPC_595     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 1.228   Median : 0.000   Median : 0.000   Median : 0.000  \n Mean   : 2.317   Mean   : 2.329   Mean   : 2.474   Mean   : 1.463  \n 3rd Qu.: 2.671   3rd Qu.: 3.263   3rd Qu.: 4.396   3rd Qu.: 1.757  \n Max.   :12.036   Max.   :10.626   Max.   :11.347   Max.   :11.286  \n    HSPC_596         HSPC_597         HSPC_598         HSPC_599     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 1.392   Median : 0.000   Median : 0.000   Median : 0.000  \n Mean   : 2.283   Mean   : 1.858   Mean   : 1.954   Mean   : 1.905  \n 3rd Qu.: 3.425   3rd Qu.: 2.296   3rd Qu.: 2.320   3rd Qu.: 2.497  \n Max.   :10.899   Max.   :11.002   Max.   :11.117   Max.   :11.248  \n    HSPC_600         HSPC_601         HSPC_602         HSPC_603     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 0.000   Median : 0.000   Median : 1.662   Median : 0.000  \n Mean   : 2.335   Mean   : 1.905   Mean   : 2.343   Mean   : 2.281  \n 3rd Qu.: 3.827   3rd Qu.: 2.376   3rd Qu.: 3.272   3rd Qu.: 3.048  \n Max.   :11.208   Max.   :11.022   Max.   :10.908   Max.   :11.464  \n    HSPC_604         HSPC_606         HSPC_607         HSPC_608     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 0.000   Median : 0.000   Median : 0.000   Median : 0.000  \n Mean   : 2.136   Mean   : 2.392   Mean   : 2.142   Mean   : 2.139  \n 3rd Qu.: 2.516   3rd Qu.: 4.726   3rd Qu.: 3.187   3rd Qu.: 2.885  \n Max.   :11.743   Max.   :11.210   Max.   :10.319   Max.   :10.802  \n    HSPC_610         HSPC_612         HSPC_613         HSPC_614     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 0.000   Median : 0.000   Median : 0.000   Median : 1.315  \n Mean   : 2.327   Mean   : 2.298   Mean   : 2.228   Mean   : 2.364  \n 3rd Qu.: 3.718   3rd Qu.: 3.138   3rd Qu.: 2.705   3rd Qu.: 3.136  \n Max.   :10.860   Max.   :11.564   Max.   :10.560   Max.   :11.824  \n    HSPC_615         HSPC_617          HSPC_618         HSPC_620     \n Min.   : 0.000   Min.   : 0.0000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.0000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 0.000   Median : 0.8525   Median : 0.000   Median : 0.000  \n Mean   : 1.964   Mean   : 2.2100   Mean   : 2.229   Mean   : 1.881  \n 3rd Qu.: 2.451   3rd Qu.: 2.3301   3rd Qu.: 2.885   3rd Qu.: 2.518  \n Max.   :11.058   Max.   :10.9434   Max.   :11.210   Max.   :11.388  \n    HSPC_623         HSPC_624          HSPC_625         HSPC_626     \n Min.   : 0.000   Min.   : 0.0000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.0000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 0.000   Median : 0.7201   Median : 0.000   Median : 0.000  \n Mean   : 2.563   Mean   : 2.0968   Mean   : 2.042   Mean   : 2.262  \n 3rd Qu.: 4.626   3rd Qu.: 1.8437   3rd Qu.: 2.938   3rd Qu.: 3.424  \n Max.   :10.954   Max.   :10.9459   Max.   :11.226   Max.   :11.770  \n    HSPC_627         HSPC_628         HSPC_629         HSPC_630     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 0.000   Median : 0.000   Median : 0.000   Median : 0.000  \n Mean   : 2.269   Mean   : 2.302   Mean   : 2.212   Mean   : 2.519  \n 3rd Qu.: 3.952   3rd Qu.: 2.875   3rd Qu.: 2.625   3rd Qu.: 4.511  \n Max.   :11.426   Max.   :11.792   Max.   :11.139   Max.   :11.519  \n    HSPC_631         HSPC_633         HSPC_634         HSPC_635     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 0.000   Median : 0.000   Median : 0.000   Median : 0.000  \n Mean   : 2.303   Mean   : 2.329   Mean   : 2.268   Mean   : 2.054  \n 3rd Qu.: 2.685   3rd Qu.: 3.619   3rd Qu.: 3.662   3rd Qu.: 2.629  \n Max.   :10.996   Max.   :12.011   Max.   :11.406   Max.   :11.178  \n    HSPC_636         HSPC_637          HSPC_638          HSPC_639      \n Min.   : 0.000   Min.   : 0.0000   Min.   : 0.0000   Min.   : 0.0000  \n 1st Qu.: 0.000   1st Qu.: 0.0000   1st Qu.: 0.0000   1st Qu.: 0.0000  \n Median : 0.000   Median : 0.9389   Median : 0.5101   Median : 0.9966  \n Mean   : 1.953   Mean   : 2.1351   Mean   : 1.6966   Mean   : 1.5879  \n 3rd Qu.: 2.129   3rd Qu.: 2.4817   3rd Qu.: 1.6879   3rd Qu.: 1.6840  \n Max.   :11.057   Max.   :11.1881   Max.   :10.8837   Max.   :10.9561  \n    HSPC_640         HSPC_641         HSPC_643         HSPC_644      \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.0000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.0000  \n Median : 1.025   Median : 1.000   Median : 1.706   Median : 0.4904  \n Mean   : 2.136   Mean   : 1.957   Mean   : 2.468   Mean   : 2.4726  \n 3rd Qu.: 2.119   3rd Qu.: 2.001   3rd Qu.: 3.329   3rd Qu.: 5.6227  \n Max.   :11.173   Max.   :11.056   Max.   :12.016   Max.   :11.0232  \n    HSPC_645          HSPC_646          HSPC_648         HSPC_649      \n Min.   : 0.0000   Min.   : 0.0000   Min.   : 0.000   Min.   : 0.0000  \n 1st Qu.: 0.0000   1st Qu.: 0.0000   1st Qu.: 0.000   1st Qu.: 0.0000  \n Median : 0.7157   Median : 0.9959   Median : 1.519   Median : 0.7139  \n Mean   : 2.3517   Mean   : 2.0594   Mean   : 2.267   Mean   : 2.3593  \n 3rd Qu.: 4.5630   3rd Qu.: 2.3154   3rd Qu.: 2.722   3rd Qu.: 4.1542  \n Max.   :10.9922   Max.   :11.6070   Max.   :11.243   Max.   :10.7707  \n    HSPC_651         HSPC_652         HSPC_654         HSPC_656    \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   :0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.:0.000  \n Median : 0.000   Median : 0.000   Median : 1.398   Median :0.000  \n Mean   : 2.550   Mean   : 1.764   Mean   : 2.108   Mean   :1.983  \n 3rd Qu.: 5.615   3rd Qu.: 2.038   3rd Qu.: 2.562   3rd Qu.:2.505  \n Max.   :11.202   Max.   :10.897   Max.   :10.367   Max.   :9.673  \n    HSPC_657         HSPC_658         HSPC_660        HSPC_661     \n Min.   : 0.000   Min.   : 0.000   Min.   :0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.:0.000   1st Qu.: 0.000  \n Median : 0.000   Median : 0.000   Median :1.253   Median : 1.491  \n Mean   : 1.839   Mean   : 2.319   Mean   :2.542   Mean   : 2.401  \n 3rd Qu.: 2.239   3rd Qu.: 4.021   3rd Qu.:5.274   3rd Qu.: 2.775  \n Max.   :12.132   Max.   :11.264   Max.   :9.852   Max.   :11.647  \n    HSPC_662         HSPC_663         HSPC_664          HSPC_665      \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.0000   Min.   : 0.0000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.0000   1st Qu.: 0.0000  \n Median : 0.000   Median : 0.000   Median : 0.9407   Median : 0.6452  \n Mean   : 2.298   Mean   : 2.726   Mean   : 2.4039   Mean   : 2.1211  \n 3rd Qu.: 2.939   3rd Qu.: 6.519   3rd Qu.: 3.4095   3rd Qu.: 2.0744  \n Max.   :11.277   Max.   :12.152   Max.   :10.9423   Max.   :12.0111  \n    HSPC_666         HSPC_667         HSPC_668         HSPC_669     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 1.130   Median : 0.000   Median : 0.000   Median : 0.000  \n Mean   : 2.815   Mean   : 2.075   Mean   : 2.245   Mean   : 1.992  \n 3rd Qu.: 6.359   3rd Qu.: 2.549   3rd Qu.: 2.407   3rd Qu.: 2.426  \n Max.   :11.052   Max.   :11.406   Max.   :11.061   Max.   :11.752  \n    HSPC_670         HSPC_671         HSPC_672         HSPC_673     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 0.000   Median : 0.000   Median : 0.000   Median : 0.000  \n Mean   : 2.336   Mean   : 2.349   Mean   : 2.041   Mean   : 2.148  \n 3rd Qu.: 3.188   3rd Qu.: 3.777   3rd Qu.: 2.057   3rd Qu.: 2.723  \n Max.   :11.021   Max.   :10.846   Max.   :11.212   Max.   :11.579  \n    HSPC_674         HSPC_676         HSPC_678         HSPC_679     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 0.000   Median : 0.000   Median : 1.525   Median : 1.531  \n Mean   : 1.801   Mean   : 2.239   Mean   : 2.298   Mean   : 2.133  \n 3rd Qu.: 1.892   3rd Qu.: 3.097   3rd Qu.: 3.089   3rd Qu.: 2.737  \n Max.   :10.875   Max.   :10.496   Max.   :12.125   Max.   :11.583  \n    HSPC_680         HSPC_681         HSPC_682         HSPC_683     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 0.000   Median : 1.037   Median : 1.043   Median : 1.180  \n Mean   : 2.573   Mean   : 2.277   Mean   : 2.586   Mean   : 2.498  \n 3rd Qu.: 4.165   3rd Qu.: 4.210   3rd Qu.: 5.432   3rd Qu.: 3.929  \n Max.   :11.100   Max.   :10.154   Max.   :11.095   Max.   :10.859  \n    HSPC_687         HSPC_689         HSPC_690         HSPC_692     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 2.091   Median : 0.000   Median : 0.000   Median : 0.000  \n Mean   : 2.136   Mean   : 2.489   Mean   : 2.686   Mean   : 2.285  \n 3rd Qu.: 2.911   3rd Qu.: 4.106   3rd Qu.: 5.055   3rd Qu.: 3.427  \n Max.   :11.380   Max.   :10.693   Max.   :10.408   Max.   :12.242  \n    HSPC_695          HSPC_696         HSPC_697         HSPC_698     \n Min.   : 0.0000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.0000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 0.2681   Median : 1.538   Median : 1.271   Median : 0.000  \n Mean   : 1.6151   Mean   : 2.688   Mean   : 2.529   Mean   : 2.531  \n 3rd Qu.: 0.6895   3rd Qu.: 5.560   3rd Qu.: 4.779   3rd Qu.: 4.387  \n Max.   :12.4139   Max.   :10.880   Max.   :10.292   Max.   :12.146  \n    HSPC_699         HSPC_700         HSPC_701         HSPC_702     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 0.000   Median : 0.000   Median : 1.157   Median : 0.000  \n Mean   : 2.586   Mean   : 2.402   Mean   : 2.401   Mean   : 2.723  \n 3rd Qu.: 4.595   3rd Qu.: 4.797   3rd Qu.: 3.889   3rd Qu.: 4.822  \n Max.   :11.389   Max.   :10.630   Max.   :11.750   Max.   :11.805  \n    HSPC_703         HSPC_704         HSPC_705          HSPC_706     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.0000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.0000   1st Qu.: 0.000  \n Median : 2.193   Median : 0.000   Median : 0.9795   Median : 1.273  \n Mean   : 2.543   Mean   : 2.598   Mean   : 2.5048   Mean   : 2.364  \n 3rd Qu.: 3.935   3rd Qu.: 4.335   3rd Qu.: 5.0680   3rd Qu.: 3.492  \n Max.   :11.710   Max.   :11.488   Max.   :11.3580   Max.   :10.447  \n    HSPC_707         HSPC_708         HSPC_709         HSPC_714     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 1.361   Median : 0.000   Median : 0.000   Median : 0.000  \n Mean   : 2.371   Mean   : 2.509   Mean   : 2.601   Mean   : 2.326  \n 3rd Qu.: 3.626   3rd Qu.: 3.832   3rd Qu.: 5.060   3rd Qu.: 3.324  \n Max.   :11.796   Max.   :10.865   Max.   :10.145   Max.   :11.126  \n    HSPC_716         HSPC_717         HSPC_719         HSPC_720      \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.0000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.0000  \n Median : 0.000   Median : 1.154   Median : 1.855   Median : 0.8206  \n Mean   : 2.325   Mean   : 2.302   Mean   : 2.519   Mean   : 2.5768  \n 3rd Qu.: 3.356   3rd Qu.: 2.833   3rd Qu.: 4.115   3rd Qu.: 5.5594  \n Max.   :11.812   Max.   :11.047   Max.   :12.237   Max.   :10.5895  \n    HSPC_721         HSPC_722         HSPC_723          HSPC_724      \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.0000   Min.   : 0.0000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.0000   1st Qu.: 0.0000  \n Median : 2.113   Median : 1.185   Median : 0.8421   Median : 0.6485  \n Mean   : 2.205   Mean   : 1.814   Mean   : 2.6174   Mean   : 1.9644  \n 3rd Qu.: 3.456   3rd Qu.: 2.269   3rd Qu.: 4.9545   3rd Qu.: 1.9402  \n Max.   :10.706   Max.   :10.709   Max.   :11.5956   Max.   :11.3505  \n    HSPC_725         HSPC_727         HSPC_729          HSPC_730     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.0000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.0000   1st Qu.: 0.000  \n Median : 1.577   Median : 1.576   Median : 0.9579   Median : 1.135  \n Mean   : 2.483   Mean   : 2.436   Mean   : 2.2448   Mean   : 2.445  \n 3rd Qu.: 3.741   3rd Qu.: 3.447   3rd Qu.: 2.7343   3rd Qu.: 3.475  \n Max.   :10.647   Max.   :11.512   Max.   :10.9657   Max.   :11.121  \n    HSPC_731         HSPC_732          HSPC_733         HSPC_734      \n Min.   : 0.000   Min.   : 0.0000   Min.   : 0.000   Min.   : 0.0000  \n 1st Qu.: 0.000   1st Qu.: 0.0000   1st Qu.: 0.000   1st Qu.: 0.0000  \n Median : 1.130   Median : 0.6937   Median : 1.436   Median : 0.7333  \n Mean   : 2.854   Mean   : 2.1051   Mean   : 2.489   Mean   : 2.5404  \n 3rd Qu.: 6.019   3rd Qu.: 2.0311   3rd Qu.: 3.738   3rd Qu.: 5.6282  \n Max.   :10.471   Max.   :11.0494   Max.   :10.929   Max.   :10.4547  \n    HSPC_735         HSPC_736          HSPC_737         HSPC_738     \n Min.   : 0.000   Min.   : 0.0000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.0000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 1.033   Median : 0.6789   Median : 1.185   Median : 1.514  \n Mean   : 2.389   Mean   : 2.0224   Mean   : 2.722   Mean   : 2.503  \n 3rd Qu.: 3.056   3rd Qu.: 2.0017   3rd Qu.: 5.669   3rd Qu.: 3.602  \n Max.   :10.866   Max.   :11.8100   Max.   :11.076   Max.   :10.473  \n    HSPC_740         HSPC_742          HSPC_743         HSPC_744     \n Min.   : 0.000   Min.   : 0.0000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.0000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 0.000   Median : 0.8437   Median : 1.122   Median : 1.213  \n Mean   : 2.506   Mean   : 1.8949   Mean   : 2.028   Mean   : 2.048  \n 3rd Qu.: 3.794   3rd Qu.: 1.7586   3rd Qu.: 2.840   3rd Qu.: 2.309  \n Max.   :10.618   Max.   :11.6327   Max.   :10.449   Max.   :10.598  \n    HSPC_745         HSPC_746         HSPC_747         HSPC_748     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 2.403   Median : 2.184   Median : 0.000   Median : 1.181  \n Mean   : 2.309   Mean   : 2.153   Mean   : 2.543   Mean   : 2.017  \n 3rd Qu.: 3.793   3rd Qu.: 3.016   3rd Qu.: 4.751   3rd Qu.: 2.264  \n Max.   :10.882   Max.   :10.988   Max.   :10.860   Max.   :12.153  \n    HSPC_749         HSPC_750         HSPC_751         HSPC_752     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 1.444   Median : 1.030   Median : 1.567   Median : 2.228  \n Mean   : 2.477   Mean   : 2.370   Mean   : 2.416   Mean   : 2.529  \n 3rd Qu.: 3.501   3rd Qu.: 3.052   3rd Qu.: 3.435   3rd Qu.: 3.976  \n Max.   :11.391   Max.   :11.167   Max.   :10.239   Max.   :10.586  \n    HSPC_753         HSPC_755         HSPC_756         HSPC_757     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 1.062   Median : 0.740   Median : 1.731   Median : 1.395  \n Mean   : 2.313   Mean   : 2.102   Mean   : 2.592   Mean   : 2.477  \n 3rd Qu.: 2.961   3rd Qu.: 2.509   3rd Qu.: 4.107   3rd Qu.: 3.253  \n Max.   :11.202   Max.   :10.559   Max.   :10.783   Max.   :10.973  \n    HSPC_758          HSPC_759          HSPC_760         HSPC_761      \n Min.   : 0.0000   Min.   : 0.0000   Min.   : 0.000   Min.   : 0.0000  \n 1st Qu.: 0.0000   1st Qu.: 0.0000   1st Qu.: 0.000   1st Qu.: 0.0000  \n Median : 0.8648   Median : 0.9415   Median : 1.052   Median : 0.6917  \n Mean   : 2.6819   Mean   : 2.1274   Mean   : 2.288   Mean   : 2.2992  \n 3rd Qu.: 4.7233   3rd Qu.: 2.2271   3rd Qu.: 2.404   3rd Qu.: 2.6015  \n Max.   :11.1096   Max.   :11.2534   Max.   :11.008   Max.   :11.7228  \n    HSPC_762         HSPC_764         HSPC_765         HSPC_766      \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.0000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.0000  \n Median : 1.271   Median : 1.784   Median : 2.116   Median : 0.9828  \n Mean   : 2.242   Mean   : 2.068   Mean   : 2.100   Mean   : 2.1721  \n 3rd Qu.: 2.734   3rd Qu.: 3.059   3rd Qu.: 2.939   3rd Qu.: 2.6115  \n Max.   :12.043   Max.   :11.003   Max.   :12.757   Max.   :10.2002  \n    HSPC_767          HSPC_768         HSPC_769         HSPC_770     \n Min.   : 0.0000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.0000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 0.6646   Median : 1.703   Median : 0.000   Median : 1.760  \n Mean   : 1.9552   Mean   : 2.365   Mean   : 2.080   Mean   : 2.343  \n 3rd Qu.: 1.9730   3rd Qu.: 3.325   3rd Qu.: 3.289   3rd Qu.: 3.122  \n Max.   :11.3033   Max.   :10.958   Max.   :11.176   Max.   :10.497  \n    HSPC_771         HSPC_772         HSPC_773          HSPC_774      \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.0000   Min.   : 0.0000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.0000   1st Qu.: 0.0000  \n Median : 1.178   Median : 1.601   Median : 0.9901   Median : 0.9736  \n Mean   : 2.527   Mean   : 2.283   Mean   : 1.8628   Mean   : 2.5263  \n 3rd Qu.: 3.342   3rd Qu.: 2.828   3rd Qu.: 1.9851   3rd Qu.: 5.4694  \n Max.   :11.156   Max.   :10.625   Max.   :10.7274   Max.   :10.7701  \n    HSPC_776         HSPC_777         HSPC_778        HSPC_780     \n Min.   : 0.000   Min.   : 0.000   Min.   :0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.:0.000   1st Qu.: 0.000  \n Median : 0.000   Median : 1.053   Median :1.435   Median : 1.178  \n Mean   : 2.315   Mean   : 2.110   Mean   :2.130   Mean   : 2.476  \n 3rd Qu.: 3.788   3rd Qu.: 2.673   3rd Qu.:3.488   3rd Qu.: 3.769  \n Max.   :11.105   Max.   :11.646   Max.   :9.535   Max.   :11.265  \n    HSPC_781         HSPC_782         HSPC_783         HSPC_784     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 0.000   Median : 0.000   Median : 0.000   Median : 1.050  \n Mean   : 1.911   Mean   : 2.416   Mean   : 2.254   Mean   : 2.175  \n 3rd Qu.: 2.884   3rd Qu.: 3.872   3rd Qu.: 2.548   3rd Qu.: 2.468  \n Max.   :11.445   Max.   :10.161   Max.   :10.970   Max.   :10.958  \n    HSPC_785         HSPC_786         HSPC_787         HSPC_788      \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.0000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.0000  \n Median : 0.000   Median : 1.148   Median : 0.000   Median : 0.9386  \n Mean   : 2.230   Mean   : 2.467   Mean   : 2.100   Mean   : 1.9749  \n 3rd Qu.: 2.466   3rd Qu.: 3.899   3rd Qu.: 2.991   3rd Qu.: 2.6662  \n Max.   :11.041   Max.   :11.080   Max.   :10.690   Max.   :11.1078  \n    HSPC_789         HSPC_790         HSPC_791         HSPC_794     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 1.181   Median : 1.353   Median : 1.790   Median : 1.113  \n Mean   : 2.225   Mean   : 2.255   Mean   : 2.699   Mean   : 2.225  \n 3rd Qu.: 2.876   3rd Qu.: 2.852   3rd Qu.: 4.931   3rd Qu.: 2.768  \n Max.   :11.245   Max.   :11.558   Max.   :11.104   Max.   :11.118  \n    HSPC_795          HSPC_796          HSPC_797          HSPC_798     \n Min.   : 0.0000   Min.   : 0.0000   Min.   : 0.0000   Min.   : 0.000  \n 1st Qu.: 0.0000   1st Qu.: 0.0000   1st Qu.: 0.0000   1st Qu.: 0.000  \n Median : 0.8317   Median : 0.7001   Median : 0.8722   Median : 1.531  \n Mean   : 2.3985   Mean   : 2.6865   Mean   : 2.6172   Mean   : 2.485  \n 3rd Qu.: 3.4461   3rd Qu.: 5.6688   3rd Qu.: 5.3078   3rd Qu.: 3.098  \n Max.   :11.0956   Max.   :11.0829   Max.   :11.4339   Max.   :10.933  \n    HSPC_799         HSPC_800         HSPC_801         HSPC_802     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 0.000   Median : 0.000   Median : 0.000   Median : 1.033  \n Mean   : 2.179   Mean   : 2.173   Mean   : 2.427   Mean   : 2.613  \n 3rd Qu.: 3.517   3rd Qu.: 2.865   3rd Qu.: 4.665   3rd Qu.: 3.780  \n Max.   :11.666   Max.   :11.263   Max.   :10.905   Max.   :10.864  \n    HSPC_803         HSPC_804         HSPC_806         HSPC_807      \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.0000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.0000  \n Median : 0.000   Median : 0.000   Median : 2.103   Median : 0.7501  \n Mean   : 2.395   Mean   : 2.301   Mean   : 2.222   Mean   : 2.2476  \n 3rd Qu.: 3.883   3rd Qu.: 3.167   3rd Qu.: 3.445   3rd Qu.: 2.3481  \n Max.   :10.766   Max.   :11.298   Max.   :10.326   Max.   :11.2700  \n    HSPC_808          HSPC_809         HSPC_810         HSPC_812      \n Min.   : 0.0000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.0000  \n 1st Qu.: 0.0000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.0000  \n Median : 0.6619   Median : 1.788   Median : 1.651   Median : 0.8459  \n Mean   : 2.1677   Mean   : 2.544   Mean   : 2.471   Mean   : 2.2960  \n 3rd Qu.: 2.5355   3rd Qu.: 3.730   3rd Qu.: 3.662   3rd Qu.: 2.6906  \n Max.   :10.9302   Max.   :11.791   Max.   :10.829   Max.   :11.5500  \n    HSPC_813          HSPC_814         HSPC_815          HSPC_816     \n Min.   : 0.0000   Min.   : 0.000   Min.   : 0.0000   Min.   : 0.000  \n 1st Qu.: 0.0000   1st Qu.: 0.000   1st Qu.: 0.0000   1st Qu.: 0.000  \n Median : 0.6278   Median : 1.110   Median : 0.9631   Median : 1.346  \n Mean   : 2.2448   Mean   : 2.762   Mean   : 2.4587   Mean   : 2.341  \n 3rd Qu.: 2.3066   3rd Qu.: 5.996   3rd Qu.: 3.4228   3rd Qu.: 2.842  \n Max.   :12.0043   Max.   :10.406   Max.   :11.4527   Max.   :11.151  \n    HSPC_818          HSPC_819         HSPC_820          HSPC_821     \n Min.   : 0.0000   Min.   : 0.000   Min.   : 0.0000   Min.   : 0.000  \n 1st Qu.: 0.0000   1st Qu.: 0.000   1st Qu.: 0.0000   1st Qu.: 0.000  \n Median : 0.9967   Median : 1.365   Median : 0.8099   Median : 1.382  \n Mean   : 2.3081   Mean   : 2.426   Mean   : 2.1063   Mean   : 2.532  \n 3rd Qu.: 2.9942   3rd Qu.: 3.632   3rd Qu.: 2.4643   3rd Qu.: 3.462  \n Max.   :11.9931   Max.   :10.672   Max.   :11.2412   Max.   :12.126  \n    HSPC_822         HSPC_824         HSPC_825         HSPC_826     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 1.387   Median : 0.000   Median : 1.386   Median : 1.324  \n Mean   : 2.503   Mean   : 2.084   Mean   : 2.162   Mean   : 2.398  \n 3rd Qu.: 3.799   3rd Qu.: 2.342   3rd Qu.: 2.897   3rd Qu.: 3.150  \n Max.   :11.892   Max.   :11.365   Max.   :11.498   Max.   :11.198  \n    HSPC_827         HSPC_828         HSPC_831         HSPC_832     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 1.746   Median : 1.003   Median : 1.304   Median : 1.035  \n Mean   : 2.239   Mean   : 2.145   Mean   : 2.589   Mean   : 2.384  \n 3rd Qu.: 2.638   3rd Qu.: 2.326   3rd Qu.: 3.866   3rd Qu.: 3.450  \n Max.   :12.101   Max.   :10.710   Max.   :10.839   Max.   :10.686  \n    HSPC_833          HSPC_834          HSPC_835         HSPC_836     \n Min.   : 0.0000   Min.   : 0.0000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.0000   1st Qu.: 0.0000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 0.7166   Median : 0.9245   Median : 1.006   Median : 0.000  \n Mean   : 2.3553   Mean   : 2.0872   Mean   : 2.552   Mean   : 2.471  \n 3rd Qu.: 3.9364   3rd Qu.: 2.4568   3rd Qu.: 4.034   3rd Qu.: 3.994  \n Max.   :11.1695   Max.   :11.1803   Max.   :11.779   Max.   :11.316  \n    HSPC_837         HSPC_838          HSPC_839         HSPC_840     \n Min.   : 0.000   Min.   : 0.0000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.0000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 1.312   Median : 0.9838   Median : 1.083   Median : 1.867  \n Mean   : 2.590   Mean   : 2.5281   Mean   : 2.380   Mean   : 2.548  \n 3rd Qu.: 4.443   3rd Qu.: 3.5551   3rd Qu.: 3.743   3rd Qu.: 3.609  \n Max.   :10.672   Max.   :11.2707   Max.   :10.966   Max.   :10.867  \n    HSPC_841         HSPC_842         HSPC_843         HSPC_844     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 1.439   Median : 1.774   Median : 1.257   Median : 1.584  \n Mean   : 2.408   Mean   : 2.380   Mean   : 2.845   Mean   : 2.627  \n 3rd Qu.: 3.494   3rd Qu.: 3.490   3rd Qu.: 6.768   3rd Qu.: 3.951  \n Max.   :10.930   Max.   :11.137   Max.   :11.933   Max.   :11.446  \n    HSPC_845         HSPC_846         HSPC_848         HSPC_849     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 1.227   Median : 1.401   Median : 0.000   Median : 1.602  \n Mean   : 2.464   Mean   : 2.240   Mean   : 2.152   Mean   : 2.402  \n 3rd Qu.: 3.377   3rd Qu.: 2.920   3rd Qu.: 2.554   3rd Qu.: 2.920  \n Max.   :10.535   Max.   :11.519   Max.   :11.266   Max.   :11.678  \n    HSPC_851         HSPC_852     \n Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 0.000   Median : 0.000  \n Mean   : 2.319   Mean   : 2.143  \n 3rd Qu.: 3.373   3rd Qu.: 2.901  \n Max.   :11.602   Max.   :11.469  \n\n\nHmmmm, did you get all that? Nope, me neither! We have 701 cells but we only have 6 samples for the frogs. We will need a different approach to get an overview but I find it is still useful to look at the few columns\n🎬 Get a quick overview the first 20 columns:\n\nsummary(hspc[1:20])\n\n ensembl_gene_id       HSPC_001         HSPC_002         HSPC_003      \n Length:280         Min.   : 0.000   Min.   : 0.000   Min.   : 0.0000  \n Class :character   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.0000  \n Mode  :character   Median : 0.000   Median : 0.000   Median : 0.9929  \n                    Mean   : 2.143   Mean   : 1.673   Mean   : 2.5964  \n                    3rd Qu.: 2.120   3rd Qu.: 2.239   3rd Qu.: 6.1559  \n                    Max.   :12.567   Max.   :11.976   Max.   :11.1138  \n    HSPC_004         HSPC_006         HSPC_008         HSPC_009    \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   :0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.:0.000  \n Median : 0.000   Median : 1.276   Median : 0.000   Median :0.000  \n Mean   : 1.851   Mean   : 2.338   Mean   : 2.375   Mean   :2.220  \n 3rd Qu.: 2.466   3rd Qu.: 3.536   3rd Qu.: 3.851   3rd Qu.:3.594  \n Max.   :11.133   Max.   :10.014   Max.   :11.574   Max.   :9.997  \n    HSPC_011         HSPC_012         HSPC_014         HSPC_015     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 0.000   Median : 1.750   Median : 0.000   Median : 0.000  \n Mean   : 2.285   Mean   : 2.431   Mean   : 2.295   Mean   : 2.515  \n 3rd Qu.: 3.193   3rd Qu.: 3.741   3rd Qu.: 3.150   3rd Qu.: 3.789  \n Max.   :11.260   Max.   :10.905   Max.   :11.051   Max.   :10.751  \n    HSPC_016          HSPC_017         HSPC_018         HSPC_020     \n Min.   : 0.0000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.0000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 0.9488   Median : 0.000   Median : 1.248   Median : 0.000  \n Mean   : 2.6115   Mean   : 2.146   Mean   : 2.710   Mean   : 2.509  \n 3rd Qu.: 5.9412   3rd Qu.: 2.357   3rd Qu.: 6.006   3rd Qu.: 4.470  \n Max.   :11.3082   Max.   :12.058   Max.   :11.894   Max.   :11.281  \n    HSPC_021         HSPC_022         HSPC_023         HSPC_024     \n Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  \n 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  \n Median : 0.000   Median : 0.000   Median : 0.000   Median : 0.000  \n Mean   : 2.170   Mean   : 2.287   Mean   : 2.314   Mean   : 2.195  \n 3rd Qu.: 2.996   3rd Qu.: 3.351   3rd Qu.: 2.749   3rd Qu.: 2.944  \n Max.   :10.709   Max.   :11.814   Max.   :12.113   Max.   :11.279  \n\n\nNotice that:\n\nthe maximum value is much less high than for the frogs and has decimals. That is because the mouse data are logged (to base 2) normalised counts, not raw counts as they are in the frog data set.\na minimum value of 0 appears in all 20 columns - perhaps that is true across the whole dataset (or at least common)\nat least some of the medians are zeros so there must be quite a lot of zeros\nthe few columns we can see are roughly similar\nit would not be very practical to plot the distributions of values in cell cell using facet_wrap().\n\nIn this data set, there is even more of an advantage of using the pivot_longer(), group_by() and summarise() approach. We will be able to open the dataframe in the Viewer and make plots to examine whether the distributions are similar across cells.\n🎬 Summarise all the cells:\n\nhspc_summary_samp &lt;- hspc |&gt;\n  pivot_longer(cols = -ensembl_gene_id,\n               names_to = \"cell\",\n               values_to = \"expr\") |&gt;\n  group_by(cell) |&gt;\n  summarise(min = min(expr),\n            lowerq = quantile(expr, 0.25),\n            mean = mean(expr),\n            median = median(expr),\n            sd = sd(expr),\n            upperq = quantile(expr, 0.75),\n            max = max(expr),\n            n_zero = sum(expr == 0))\n\nNotice that I have used cell as the column name rather than sample and expr (expression) rather than count. I’ve also added the standard deviation.\n🎬 View the hspc_summary_samp dataframe (click on it in the environment).\nAll cells have quite a few zeros and the lower quartile is 0 for all cells, i.e., every cell has many genes with zero expression.\nTo get a better understanding of the distribution of expressions in cells we can create a ggplot using the pointrange geom. Pointrange puts a dot at the mean and a line between a minimum and a maximum such as +/- one s.d. Not unlike a boxplot, but when you need the boxes too be very narrow!\n🎬 Create a pointrange plot.\n\nhspc_summary_samp |&gt; \n  ggplot(aes(x = cell, y = mean)) +\n  geom_pointrange(aes(ymin = mean - sd, \n                      ymax = mean + sd ),\n                  size = 0.1)\n\n\n\n\n\n\n\nYou will need to use the Zoom button to pop the plot window out so you can make it as wide as possible\nThe things to notice are:\n\nthe average expression in cells is similar for all cells. This is good to know - if some cells had much lower expression perhaps there is something wrong with them, or their sequencing, and they should be excluded.\nthe distributions are roughly similar in width too\n\nThe default order of cell is alphabetical. It can be easier to see these (non-) effects if we order the lines by the size of the mean.\n🎬 Order a pointrange plot with reorder(variable_to_order, order_by).\n\nhspc_summary_samp |&gt; \n  ggplot(aes(x = reorder(cell, mean), y = mean)) +\n  geom_pointrange(aes(ymin = mean - sd, \n                      ymax = mean + sd ),\n                  size = 0.1)\n\n\n\n\n\n\n\nreorder() arranges cell in increasing size of mean\n🎬 Write hspc_summary_samp to a file called “hspc_summary_samp.csv”:\nDistribution of values across the genes\n🐸 Frog genes\nThere are lots of genes in this dataset therefore we will take the same approach as that we took for the distributions across mouse cells. We will pivot the data to tidy and then summarise the counts for each gene.\n🎬 Summarise the counts for each genes:\n\ns30_summary_gene &lt;- s30 |&gt;\n  pivot_longer(cols = -xenbase_gene_id,\n               names_to = \"sample\",\n               values_to = \"count\") |&gt;\n  group_by(xenbase_gene_id) |&gt;\n  summarise(min = min(count),\n            lowerq = quantile(count, 0.25),\n            sd = sd(count),\n            mean = mean(count),\n            median = median(count),\n            upperq = quantile(count, 0.75),\n            max = max(count),\n            total = sum(count),\n            n_zero = sum(count == 0))\n\nI have calculated the values we used before with one addition: the sum of the counts (total).\n🎬 View the s30_summary_gene dataframe.\nNotice that we have:\n\na lot of genes with counts of zero in every sample\na lot of genes with zero counts in several of the samples\nsome very very low counts.\n\nThese should be filtered out because they are unreliable - or, at the least, uninformative. The goal of our downstream analysis will be to see if there is a significant difference in gene expression between the control and FGF-treated sibling. Since we have only three replicates in each group, having one or two unreliable, missing or zero values, makes such a determination impossible for a particular gene. We will use the total counts and the number of samples with non-zero values to filter our genes later.\nAs we have a lot of genes, it is again helpful to plot the mean counts with pointrange to get an overview. We will plot the log of the counts - we saw earlier that logging made it easier to understand the distribution of counts over such a wide range. We will also order the genes from lowest to highest mean count.\n🎬 Plot the logged mean counts for each gene in order of size using geom_pointrange():\n\ns30_summary_gene |&gt; \n  ggplot(aes(x = reorder(xenbase_gene_id, mean), y = log10(mean))) +\n  geom_pointrange(aes(ymin = log10(mean - sd), \n                      ymax = log10(mean + sd )),\n                  size = 0.1)\n\n\n\n\n\n\n\n(Remember, the warning is expected since we have zeros).\nYou can see we also have quite a few genes with means less than 1 (log below zero). Note that the variability between genes (average counts between 0 and 102586) is far greater than between samples (average counts from 260 to 426) which is exactly what we would expect to see.\n🎬 Write s30_summary_gene to a file called “s30_summary_gene.csv”:\n🐭 Mouse genes\nThere are fewer genes in this dataset, but still more than you can understand without the overview provided by a plot. We will again pivot the data to tidy and then summarise the expression for each gene.\n🎬 Summarise the expression for each genes:\n\nhspc_summary_gene &lt;- hspc |&gt;\n  pivot_longer(cols = -ensembl_gene_id,\n               names_to = \"cell\",\n               values_to = \"expr\") |&gt;\n  group_by(ensembl_gene_id) |&gt;\n  summarise(min = min(expr),\n            lowerq = quantile(expr, 0.25),\n            sd = sd(expr),\n            mean = mean(expr),\n            median = median(expr),\n            upperq = quantile(expr, 0.75),\n            max = max(expr),\n            total = sum(expr),\n            n_zero = sum(expr == 0))\n\n🎬 View the hspc_summary_gene dataframe. Remember these are normalised and logged (base 2) so we should not see very large values.\nNotice that we have:\n\nno genes with 0 in every cell\nvery few genes (9) with no zeros at all\nquite a few genes with zero in many cells but this matters less than zeros in the frog samples because we had just 6 samples and we have 701 cells.\n\nAs we have a lot of genes, it is again helpful to plot the mean expression with pointrange to get an overview. We do not need to log the values but ordering the genes will help.\n🎬 Plot the logged mean counts for each gene in order of size using geom_pointrange():\n\nhspc_summary_gene |&gt; \n  ggplot(aes(x = reorder(ensembl_gene_id, mean), y = mean)) +\n  geom_pointrange(aes(ymin = mean - sd, \n                      ymax = mean + sd),\n                  size = 0.1)\n\n\n\n\n\n\n\nNote again that the variability between genes (average expression between 0.02 and and 10.03) is far greater than between cells (average expression from1.46 to 3.18) which is expected.\n🎬 Write s30_summary_gene to a file called “s30_summary_gene.csv”:",
     "crumbs": [
       "Transcriptomics",
       "Week 3: Hello data!",
@@ -448,7 +448,7 @@
     "href": "transcriptomics/week-3/workshop.html#look-after-future-you",
     "title": "Workshop",
     "section": "🤗 Look after future you!",
-    "text": "🤗 Look after future you!\nYou need only do the section for your own project data\n🐸 Frogs and future you\n🎬 Create a new Project, frogs-88H, populated with folders and your data. Make a script file called cont-fgf-s30.R. This will a be commented analysis of the control vs FGF at S30 comparison. You will build on this each workshop and be able to use it as a template to examine other comparisons. Copy in the appropriate code and comments from workshop-1.R. Edit to improve your comments where your understanding has developed since you made them. Make sure you can close down RStudio, reopen it and run your whole script again.\n🐭 Mice and future you\n🎬 Create a new Project, mice-88H, populated with folders and your data. Make a script file called hspc-prog.R. This will a be commented analysis of the hspc cells vs the prog cells. At this point you will have only code for the hspc cells. You will build on this each workshop and be able to use it as a template to examine other comparisons. Copy in the appropriate code and comments from workshop-1.R. Edit to improve your comments where your understanding has developed since you made them. Make sure you can close down RStudio, reopen it and run your whole script again.\n🍂 xxxx and future you\nDo one of the other two examples.",
+    "text": "🤗 Look after future you!\nYou need only do the section for your own project data\n🐸 Frogs and future you\n🎬 Create a new Project, frogs-88H, populated with folders and your data. Make a script file called cont-fgf-s30.R. This will a be commented analysis of the control vs FGF at S30 comparison. You will build on this each workshop and be able to use it as a template to examine other comparisons. Copy in the appropriate code and comments from workshop-1.R. Edit to improve your comments where your understanding has developed since you made them. Make sure you can close down RStudio, reopen it and run your whole script again.\n🐭 Mice and future you\n🎬 Create a new Project, mice-88H, populated with folders and your data. Make a script file called hspc-prog.R. This will a be commented analysis of the hspc cells vs the prog cells. At this point you will have only code for the hspc cells. You will build on this each workshop and be able to use it as a template to examine other comparisons. Copy in the appropriate code and comments from workshop-1.R. Edit to improve your comments where your understanding has developed since you made them. Make sure you can close down RStudio, reopen it and run your whole script again.\n🍂 xxxx and future you",
     "crumbs": [
       "Transcriptomics",
       "Week 3: Hello data!",
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     "href": "core/week-2/workshop.html#reading",
     "title": "Workshop",
     "section": "Reading",
-    "text": "Reading\nCompletely optional suggestions for further reading\n\n\nProject-oriented workflow | What They Forgot to Teach You About R (Bryan et al., n.d.). Recommended if you still need convincing to use RStudio Projects\nTen simple rules for reproducible computational research (Sandve et al. 2013)\n\nGood enough practices in scientific computing (Wilson et al. 2017)\n\nExcuse Me, Do You Have a Moment to Talk About Version Control? (Bryan 2018)\n\n\nPages made with R (R Core Team 2024) and Quarto (allaire2022?)",
+    "text": "Reading\nCompletely optional suggestions for further reading\n\n\nProject-oriented workflow | What They Forgot to Teach You About R (Bryan et al., n.d.). Recommended if you still need convincing to use RStudio Projects\nTen simple rules for reproducible computational research (Sandve et al. 2013)\n\nGood enough practices in scientific computing (Wilson et al. 2017)\n\nExcuse Me, Do You Have a Moment to Talk About Version Control? (Bryan 2018)\n\n\nPages made with R (R Core Team 2024), Quarto (Allaire et al. 2024), knitr [Xie (2024); knitr2; knitr3], kableExtra (Zhu 2021)",
     "crumbs": [
       "Core Supporting Info",
       "Week 2: Supporting Information 1",
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     "href": "core/week-2/workshop.html#references",
     "title": "Workshop",
     "section": "References",
-    "text": "References\n\n\n\n🔗 About Core: Supporting Information 1\n\n\n\n\nBaggerly, Keith A, and Kevin R Coombes. 2009. “DERIVING CHEMOSENSITIVITY FROM CELL LINES: FORENSIC BIOINFORMATICS AND REPRODUCIBLE RESEARCH IN HIGH-THROUGHPUT BIOLOGY.” Ann. Appl. Stat. 3 (4): 1309–34. https://doi.org/10.2307/27801549.\n\n\nBryan, Jennifer. 2018. “Excuse Me, Do You Have a Moment to Talk about Version Control?” Am. Stat. 72 (1): 20–27. https://doi.org/10.1080/00031305.2017.1399928.\n\n\nBryan, Jennifer, Jim Hester, Shannon Pileggi, and E. David Aja. n.d. What They Forgot to Teach You about r. https://rstats.wtf/.\n\n\nMarkowetz, Florian. 2015. “Five Selfish Reasons to Work Reproducibly.” Genome Biol. 16 (December): 274. https://doi.org/10.1186/s13059-015-0850-7.\n\n\nNational Academies of Sciences, Engineering, Medicine, Policy, Global Affairs, Engineering, Medicine Committee on Science, Public Policy, Board on Research Data, et al. 2019. Understanding Reproducibility and Replicability. National Academies Press (US). https://www.ncbi.nlm.nih.gov/books/NBK547546/.\n\n\nOECD Global Science Forum. 2020. “Building Digital Workforce Capacity and Skills for Data-Intensive Science.” http://www.oecd.org/officialdocuments/publicdisplaydocumentpdf/?cote=DSTI/STP/GSF(2020)6/FINAL&docLanguage=En.\n\n\nR Core Team. 2024. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. https://www.R-project.org/.\n\n\nSandve, Geir Kjetil, Anton Nekrutenko, James Taylor, and Eivind Hovig. 2013. “Ten Simple Rules for Reproducible Computational Research.” PLoS Comput. Biol. 9 (10): e1003285. https://doi.org/10.1371/journal.pcbi.1003285.\n\n\nWilson, Greg, Jennifer Bryan, Karen Cranston, Justin Kitzes, Lex Nederbragt, and Tracy K Teal. 2017. “Good Enough Practices in Scientific Computing.” PLoS Comput. Biol. 13 (6): e1005510. https://doi.org/10.1371/journal.pcbi.1005510.",
+    "text": "References\n\n\n\n🔗 About Core: Supporting Information 1\n\n\n\n\nAllaire, J. J., Charles Teague, Carlos Scheidegger, Yihui Xie, and Christophe Dervieux. 2024. “Quarto.” https://doi.org/10.5281/zenodo.5960048.\n\n\nBaggerly, Keith A, and Kevin R Coombes. 2009. “DERIVING CHEMOSENSITIVITY FROM CELL LINES: FORENSIC BIOINFORMATICS AND REPRODUCIBLE RESEARCH IN HIGH-THROUGHPUT BIOLOGY.” Ann. Appl. Stat. 3 (4): 1309–34. https://doi.org/10.2307/27801549.\n\n\nBryan, Jennifer. 2018. “Excuse Me, Do You Have a Moment to Talk about Version Control?” Am. Stat. 72 (1): 20–27. https://doi.org/10.1080/00031305.2017.1399928.\n\n\nBryan, Jennifer, Jim Hester, Shannon Pileggi, and E. David Aja. n.d. What They Forgot to Teach You about r. https://rstats.wtf/.\n\n\nMarkowetz, Florian. 2015. “Five Selfish Reasons to Work Reproducibly.” Genome Biol. 16 (December): 274. https://doi.org/10.1186/s13059-015-0850-7.\n\n\nNational Academies of Sciences, Engineering, Medicine, Policy, Global Affairs, Engineering, Medicine Committee on Science, Public Policy, Board on Research Data, et al. 2019. Understanding Reproducibility and Replicability. National Academies Press (US). https://www.ncbi.nlm.nih.gov/books/NBK547546/.\n\n\nOECD Global Science Forum. 2020. “Building Digital Workforce Capacity and Skills for Data-Intensive Science.” http://www.oecd.org/officialdocuments/publicdisplaydocumentpdf/?cote=DSTI/STP/GSF(2020)6/FINAL&docLanguage=En.\n\n\nR Core Team. 2024. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. https://www.R-project.org/.\n\n\nSandve, Geir Kjetil, Anton Nekrutenko, James Taylor, and Eivind Hovig. 2013. “Ten Simple Rules for Reproducible Computational Research.” PLoS Comput. Biol. 9 (10): e1003285. https://doi.org/10.1371/journal.pcbi.1003285.\n\n\nWilson, Greg, Jennifer Bryan, Karen Cranston, Justin Kitzes, Lex Nederbragt, and Tracy K Teal. 2017. “Good Enough Practices in Scientific Computing.” PLoS Comput. Biol. 13 (6): e1005510. https://doi.org/10.1371/journal.pcbi.1005510.\n\n\nXie, Yihui. 2024. Knitr: A General-Purpose Package for Dynamic Report Generation in r. https://yihui.org/knitr/.\n\n\nZhu, Hao. 2021. “kableExtra: Construct Complex Table with ’Kable’ and Pipe Syntax.” https://CRAN.R-project.org/package=kableExtra.",
     "crumbs": [
       "Core Supporting Info",
       "Week 2: Supporting Information 1",
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     "href": "core/week-2-old/workshop.html#rstudio-terminal",
     "title": "Workshop",
     "section": "RStudio terminal",
-    "text": "RStudio terminal\nThe RStudio terminal is a convenient interface to the shell without leaving RStudio. It is useful for running commands that are not available in R. For example, you can use it to run other programs like fasqc, git, ftp, ssh\nNavigating your file system\nSeveral commands are frequently used to create, inspect, rename, and delete files and directories.\n$\nThe dollar sign is the prompt (like &gt; on the R console), which shows us that the shell is waiting for input.\nYou can find out where you are using the pwd command, which stands for “print working directory”.\n\npwd\n\n/home/runner/work/BIO00088H-data/BIO00088H-data/core/week-2-old\n\n\nYou can find out what you can see with ls which stands for “list”.\n\nls\n\ndata\nimages\noverview.qmd\nstudy_after_workshop.qmd\nstudy_before_workshop.ipynb\nstudy_before_workshop.qmd\nworkshop.html\nworkshop.qmd\nworkshop.rmarkdown\nworkshop_files\n\n\nYou might have noticed that unlike R, the commands do not have brackets after them. Instead, options (or switches) are given after the command. For example, we can modify the ls command to give us more information with the -l option, which stands for “long”.\n\nls -l\n\ntotal 128\ndrwxr-xr-x 2 runner docker  4096 Sep 13 11:06 data\ndrwxr-xr-x 2 runner docker  4096 Sep 13 11:06 images\n-rw-r--r-- 1 runner docker  1597 Sep 13 11:06 overview.qmd\n-rw-r--r-- 1 runner docker   184 Sep 13 11:06 study_after_workshop.qmd\n-rw-r--r-- 1 runner docker  4807 Sep 13 11:06 study_before_workshop.ipynb\n-rw-r--r-- 1 runner docker 13029 Sep 13 11:06 study_before_workshop.qmd\n-rw-r--r-- 1 runner docker 58063 Sep 13 11:06 workshop.html\n-rw-r--r-- 1 runner docker  8550 Sep 13 11:06 workshop.qmd\n-rw-r--r-- 1 runner docker  8577 Sep 13 11:29 workshop.rmarkdown\ndrwxr-xr-x 3 runner docker  4096 Sep 13 11:06 workshop_files\n\n\nYou can use more than one option at once. The -h option stands for “human readable” and makes the file sizes easier to understand for humans:\n\nls -hl\n\ntotal 128K\ndrwxr-xr-x 2 runner docker 4.0K Sep 13 11:06 data\ndrwxr-xr-x 2 runner docker 4.0K Sep 13 11:06 images\n-rw-r--r-- 1 runner docker 1.6K Sep 13 11:06 overview.qmd\n-rw-r--r-- 1 runner docker  184 Sep 13 11:06 study_after_workshop.qmd\n-rw-r--r-- 1 runner docker 4.7K Sep 13 11:06 study_before_workshop.ipynb\n-rw-r--r-- 1 runner docker  13K Sep 13 11:06 study_before_workshop.qmd\n-rw-r--r-- 1 runner docker  57K Sep 13 11:06 workshop.html\n-rw-r--r-- 1 runner docker 8.4K Sep 13 11:06 workshop.qmd\n-rw-r--r-- 1 runner docker 8.4K Sep 13 11:29 workshop.rmarkdown\ndrwxr-xr-x 3 runner docker 4.0K Sep 13 11:06 workshop_files\n\n\nThe -a option stands for “all” and shows us all the files, including hidden files.\n\nls -alh\n\ntotal 136K\ndrwxr-xr-x 5 runner docker 4.0K Sep 13 11:29 .\ndrwxr-xr-x 7 runner docker 4.0K Sep 13 11:06 ..\ndrwxr-xr-x 2 runner docker 4.0K Sep 13 11:06 data\ndrwxr-xr-x 2 runner docker 4.0K Sep 13 11:06 images\n-rw-r--r-- 1 runner docker 1.6K Sep 13 11:06 overview.qmd\n-rw-r--r-- 1 runner docker  184 Sep 13 11:06 study_after_workshop.qmd\n-rw-r--r-- 1 runner docker 4.7K Sep 13 11:06 study_before_workshop.ipynb\n-rw-r--r-- 1 runner docker  13K Sep 13 11:06 study_before_workshop.qmd\n-rw-r--r-- 1 runner docker  57K Sep 13 11:06 workshop.html\n-rw-r--r-- 1 runner docker 8.4K Sep 13 11:06 workshop.qmd\n-rw-r--r-- 1 runner docker 8.4K Sep 13 11:29 workshop.rmarkdown\ndrwxr-xr-x 3 runner docker 4.0K Sep 13 11:06 workshop_files\n\n\nYou can move about with the cd command, which stands for “change directory”. You can use it to move into a directory by specifying the path to the directory:\n\ncd data\npwd\ncd ..\npwd\ncd data\npwd\n\n/home/runner/work/BIO00088H-data/BIO00088H-data/core/week-2-old/data\n/home/runner/work/BIO00088H-data/BIO00088H-data/core/week-2-old\n/home/runner/work/BIO00088H-data/BIO00088H-data/core/week-2-old/data\n\n\nhead 1cq2.pdb\nHEADER    OXYGEN STORAGE/TRANSPORT                04-AUG-99   1CQ2              \nTITLE     NEUTRON STRUCTURE OF FULLY DEUTERATED SPERM WHALE MYOGLOBIN AT 2.0    \nTITLE    2 ANGSTROM                                                             \nCOMPND    MOL_ID: 1;                                                            \nCOMPND   2 MOLECULE: MYOGLOBIN;                                                 \nCOMPND   3 CHAIN: A;                                                            \nCOMPND   4 ENGINEERED: YES;                                                     \nCOMPND   5 OTHER_DETAILS: PROTEIN IS FULLY DEUTERATED                           \nSOURCE    MOL_ID: 1;                                                            \nSOURCE   2 ORGANISM_SCIENTIFIC: PHYSETER CATODON;      \nhead -20 data/1cq2.pdb\nHEADER    OXYGEN STORAGE/TRANSPORT                04-AUG-99   1CQ2              \nTITLE     NEUTRON STRUCTURE OF FULLY DEUTERATED SPERM WHALE MYOGLOBIN AT 2.0    \nTITLE    2 ANGSTROM                                                             \nCOMPND    MOL_ID: 1;                                                            \nCOMPND   2 MOLECULE: MYOGLOBIN;                                                 \nCOMPND   3 CHAIN: A;                                                            \nCOMPND   4 ENGINEERED: YES;                                                     \nCOMPND   5 OTHER_DETAILS: PROTEIN IS FULLY DEUTERATED                           \nSOURCE    MOL_ID: 1;                                                            \nSOURCE   2 ORGANISM_SCIENTIFIC: PHYSETER CATODON;                               \nSOURCE   3 ORGANISM_COMMON: SPERM WHALE;                                        \nSOURCE   4 ORGANISM_TAXID: 9755;                                                \nSOURCE   5 EXPRESSION_SYSTEM: ESCHERICHIA COLI;                                 \nSOURCE   6 EXPRESSION_SYSTEM_TAXID: 562;                                        \nSOURCE   7 EXPRESSION_SYSTEM_VECTOR_TYPE: PLASMID;                              \nSOURCE   8 EXPRESSION_SYSTEM_PLASMID: PET15A                                    \nKEYWDS    HELICAL, GLOBULAR, ALL-HYDROGEN CONTAINING STRUCTURE, OXYGEN STORAGE- \nKEYWDS   2 TRANSPORT COMPLEX                                                    \nEXPDTA    NEUTRON DIFFRACTION                                                   \nAUTHOR    F.SHU,V.RAMAKRISHNAN,B.P.SCHOENBORN   \nless 1cq2.pdb\nless is a program that displays the contents of a file, one page at a time. It is useful for viewing large files because it does not load the whole file into memory before displaying it. Instead, it reads and displays a few lines at a time. You can navigate forward through the file with the spacebar, and backwards with the b key. Press q to quit.\nA wildcard is a character that can be used as a substitute for any of a class of characters in a search, The most common wildcard characters are the asterisk (*) and the question mark (?).\nls *.csv\ncp stands for “copy”. You can copy a file from one directory to another by giving cp the path to the file you want to copy and the path to the destination directory.\ncp 1cq2.pdb copy_of_1cq2.pdb\ncp 1cq2.pdb ../copy_of_1cq2.pdb\ncp 1cq2.pdb ../bob.txt\nTo delete a file use the rm command, which stands for “remove”.\nrm ../bob.txt\nbut be careful because the file will be gone forever. There is no “are you sure?” or undo.\nTo move a file from one directory to another, use the mv command. mv works like cp except that it also deletes the original file.\nmv ../copy_of_1cq2.pdb .\nMake a directory\nmkdir mynewdir"
+    "text": "RStudio terminal\nThe RStudio terminal is a convenient interface to the shell without leaving RStudio. It is useful for running commands that are not available in R. For example, you can use it to run other programs like fasqc, git, ftp, ssh\nNavigating your file system\nSeveral commands are frequently used to create, inspect, rename, and delete files and directories.\n$\nThe dollar sign is the prompt (like &gt; on the R console), which shows us that the shell is waiting for input.\nYou can find out where you are using the pwd command, which stands for “print working directory”.\n\npwd\n\n/home/runner/work/BIO00088H-data/BIO00088H-data/core/week-2-old\n\n\nYou can find out what you can see with ls which stands for “list”.\n\nls\n\ndata\nimages\noverview.qmd\nstudy_after_workshop.qmd\nstudy_before_workshop.ipynb\nstudy_before_workshop.qmd\nworkshop.html\nworkshop.qmd\nworkshop.rmarkdown\nworkshop_files\n\n\nYou might have noticed that unlike R, the commands do not have brackets after them. Instead, options (or switches) are given after the command. For example, we can modify the ls command to give us more information with the -l option, which stands for “long”.\n\nls -l\n\ntotal 128\ndrwxr-xr-x 2 runner docker  4096 Sep 17 16:24 data\ndrwxr-xr-x 2 runner docker  4096 Sep 17 16:24 images\n-rw-r--r-- 1 runner docker  1597 Sep 17 16:24 overview.qmd\n-rw-r--r-- 1 runner docker   184 Sep 17 16:24 study_after_workshop.qmd\n-rw-r--r-- 1 runner docker  4807 Sep 17 16:24 study_before_workshop.ipynb\n-rw-r--r-- 1 runner docker 13029 Sep 17 16:24 study_before_workshop.qmd\n-rw-r--r-- 1 runner docker 58063 Sep 17 16:24 workshop.html\n-rw-r--r-- 1 runner docker  8550 Sep 17 16:24 workshop.qmd\n-rw-r--r-- 1 runner docker  8577 Sep 17 16:25 workshop.rmarkdown\ndrwxr-xr-x 3 runner docker  4096 Sep 17 16:24 workshop_files\n\n\nYou can use more than one option at once. The -h option stands for “human readable” and makes the file sizes easier to understand for humans:\n\nls -hl\n\ntotal 128K\ndrwxr-xr-x 2 runner docker 4.0K Sep 17 16:24 data\ndrwxr-xr-x 2 runner docker 4.0K Sep 17 16:24 images\n-rw-r--r-- 1 runner docker 1.6K Sep 17 16:24 overview.qmd\n-rw-r--r-- 1 runner docker  184 Sep 17 16:24 study_after_workshop.qmd\n-rw-r--r-- 1 runner docker 4.7K Sep 17 16:24 study_before_workshop.ipynb\n-rw-r--r-- 1 runner docker  13K Sep 17 16:24 study_before_workshop.qmd\n-rw-r--r-- 1 runner docker  57K Sep 17 16:24 workshop.html\n-rw-r--r-- 1 runner docker 8.4K Sep 17 16:24 workshop.qmd\n-rw-r--r-- 1 runner docker 8.4K Sep 17 16:25 workshop.rmarkdown\ndrwxr-xr-x 3 runner docker 4.0K Sep 17 16:24 workshop_files\n\n\nThe -a option stands for “all” and shows us all the files, including hidden files.\n\nls -alh\n\ntotal 136K\ndrwxr-xr-x 5 runner docker 4.0K Sep 17 16:25 .\ndrwxr-xr-x 7 runner docker 4.0K Sep 17 16:24 ..\ndrwxr-xr-x 2 runner docker 4.0K Sep 17 16:24 data\ndrwxr-xr-x 2 runner docker 4.0K Sep 17 16:24 images\n-rw-r--r-- 1 runner docker 1.6K Sep 17 16:24 overview.qmd\n-rw-r--r-- 1 runner docker  184 Sep 17 16:24 study_after_workshop.qmd\n-rw-r--r-- 1 runner docker 4.7K Sep 17 16:24 study_before_workshop.ipynb\n-rw-r--r-- 1 runner docker  13K Sep 17 16:24 study_before_workshop.qmd\n-rw-r--r-- 1 runner docker  57K Sep 17 16:24 workshop.html\n-rw-r--r-- 1 runner docker 8.4K Sep 17 16:24 workshop.qmd\n-rw-r--r-- 1 runner docker 8.4K Sep 17 16:25 workshop.rmarkdown\ndrwxr-xr-x 3 runner docker 4.0K Sep 17 16:24 workshop_files\n\n\nYou can move about with the cd command, which stands for “change directory”. You can use it to move into a directory by specifying the path to the directory:\n\ncd data\npwd\ncd ..\npwd\ncd data\npwd\n\n/home/runner/work/BIO00088H-data/BIO00088H-data/core/week-2-old/data\n/home/runner/work/BIO00088H-data/BIO00088H-data/core/week-2-old\n/home/runner/work/BIO00088H-data/BIO00088H-data/core/week-2-old/data\n\n\nhead 1cq2.pdb\nHEADER    OXYGEN STORAGE/TRANSPORT                04-AUG-99   1CQ2              \nTITLE     NEUTRON STRUCTURE OF FULLY DEUTERATED SPERM WHALE MYOGLOBIN AT 2.0    \nTITLE    2 ANGSTROM                                                             \nCOMPND    MOL_ID: 1;                                                            \nCOMPND   2 MOLECULE: MYOGLOBIN;                                                 \nCOMPND   3 CHAIN: A;                                                            \nCOMPND   4 ENGINEERED: YES;                                                     \nCOMPND   5 OTHER_DETAILS: PROTEIN IS FULLY DEUTERATED                           \nSOURCE    MOL_ID: 1;                                                            \nSOURCE   2 ORGANISM_SCIENTIFIC: PHYSETER CATODON;      \nhead -20 data/1cq2.pdb\nHEADER    OXYGEN STORAGE/TRANSPORT                04-AUG-99   1CQ2              \nTITLE     NEUTRON STRUCTURE OF FULLY DEUTERATED SPERM WHALE MYOGLOBIN AT 2.0    \nTITLE    2 ANGSTROM                                                             \nCOMPND    MOL_ID: 1;                                                            \nCOMPND   2 MOLECULE: MYOGLOBIN;                                                 \nCOMPND   3 CHAIN: A;                                                            \nCOMPND   4 ENGINEERED: YES;                                                     \nCOMPND   5 OTHER_DETAILS: PROTEIN IS FULLY DEUTERATED                           \nSOURCE    MOL_ID: 1;                                                            \nSOURCE   2 ORGANISM_SCIENTIFIC: PHYSETER CATODON;                               \nSOURCE   3 ORGANISM_COMMON: SPERM WHALE;                                        \nSOURCE   4 ORGANISM_TAXID: 9755;                                                \nSOURCE   5 EXPRESSION_SYSTEM: ESCHERICHIA COLI;                                 \nSOURCE   6 EXPRESSION_SYSTEM_TAXID: 562;                                        \nSOURCE   7 EXPRESSION_SYSTEM_VECTOR_TYPE: PLASMID;                              \nSOURCE   8 EXPRESSION_SYSTEM_PLASMID: PET15A                                    \nKEYWDS    HELICAL, GLOBULAR, ALL-HYDROGEN CONTAINING STRUCTURE, OXYGEN STORAGE- \nKEYWDS   2 TRANSPORT COMPLEX                                                    \nEXPDTA    NEUTRON DIFFRACTION                                                   \nAUTHOR    F.SHU,V.RAMAKRISHNAN,B.P.SCHOENBORN   \nless 1cq2.pdb\nless is a program that displays the contents of a file, one page at a time. It is useful for viewing large files because it does not load the whole file into memory before displaying it. Instead, it reads and displays a few lines at a time. You can navigate forward through the file with the spacebar, and backwards with the b key. Press q to quit.\nA wildcard is a character that can be used as a substitute for any of a class of characters in a search, The most common wildcard characters are the asterisk (*) and the question mark (?).\nls *.csv\ncp stands for “copy”. You can copy a file from one directory to another by giving cp the path to the file you want to copy and the path to the destination directory.\ncp 1cq2.pdb copy_of_1cq2.pdb\ncp 1cq2.pdb ../copy_of_1cq2.pdb\ncp 1cq2.pdb ../bob.txt\nTo delete a file use the rm command, which stands for “remove”.\nrm ../bob.txt\nbut be careful because the file will be gone forever. There is no “are you sure?” or undo.\nTo move a file from one directory to another, use the mv command. mv works like cp except that it also deletes the original file.\nmv ../copy_of_1cq2.pdb .\nMake a directory\nmkdir mynewdir"
   },
   {
     "objectID": "core/week-2-old/workshop.html#differences-between-r-and-python",
@@ -2185,7 +2185,7 @@
     "href": "transcriptomics/week-4/workshop.html#genes-expressed-in-one-treatment",
     "title": "Workshop",
     "section": "Genes expressed in one treatment",
-    "text": "Genes expressed in one treatment\nThe genes expressed in only one treatment group are those with zeros in all three replicates in one group and non-zero values in all three replicates in the other group. For example, those shown here:\n\n\n# A tibble: 3 × 7\n  xenbase_gene_id  S30_C_5 S30_C_6 S30_C_A S30_F_5 S30_F_6 S30_F_A\n  &lt;chr&gt;              &lt;dbl&gt;   &lt;dbl&gt;   &lt;dbl&gt;   &lt;dbl&gt;   &lt;dbl&gt;   &lt;dbl&gt;\n1 XB-GENE-1018260        0       0       0      10       2      16\n2 XB-GENE-17330117       0       0       0      13       4      17\n3 XB-GENE-17332184       0       0       0       6      19       6\n\n\nWe will use filter() to find these genes.\n🎬 Find the genes that are expressed only in the FGF-treated group:\n\ns30_fgf_only &lt;- s30_filtered |&gt; \n  filter(S30_C_5 == 0, \n         S30_C_6 == 0, \n         S30_C_A == 0, \n         S30_F_5 &gt; 0, \n         S30_F_6 &gt; 0, \n         S30_F_A &gt; 0)\n\n❓ How many genes are expressed only in the FGF-treated group?\n\n\n🎬 Now you find any genes that are expressed only in the control group.\n❓ Do the results make sense to you in light of what you know about the biology?\n\n\n\n\n\n\n\n🎬 Write to file (saved in results) all the genes that are expressed one group only.",
+    "text": "Genes expressed in one treatment\nThe genes expressed in only one treatment group are those with zeros in all three replicates in one group and non-zero values in all three replicates in the other group. For example, those shown here:\n\n\nError in `arrange()`:\nℹ In argument: `..1 = S30_C_3`.\nCaused by error:\n! object 'S30_C_3' not found\n\n\nWe will use filter() to find these genes.\n🎬 Find the genes that are expressed only in the FGF-treated group:\n\ns30_fgf_only &lt;- s30_filtered |&gt; \n  filter(S30_C_1 == 0, \n         S30_C_2 == 0, \n         S30_C_3 == 0, \n         S30_F_1 &gt; 0, \n         S30_F_2 &gt; 0, \n         S30_F_3 &gt; 0)\n\nError in `filter()`:\nℹ In argument: `S30_C_1 == 0`.\nCaused by error:\n! object 'S30_C_1' not found\n\n\n❓ How many genes are expressed only in the FGF-treated group?\n\n\n🎬 Now you find any genes that are expressed only in the control group.\n\n\nError in `filter()`:\nℹ In argument: `S30_C_1 &gt; 0`.\nCaused by error:\n! object 'S30_C_1' not found\n\n\n❓ Do the results make sense to you in light of what you know about the biology?\n\n\n\n\n\n\n\n🎬 Write to file (saved in results) all the genes that are expressed one group only.\n\n\nError in eval(expr, envir, enclos): object 's30_fgf_only' not found",
     "crumbs": [
       "Transcriptomics",
       "Week 4: Statistical Analysis",
@@ -2545,7 +2545,7 @@
     "href": "transcriptomics/week-4/study_before_workshop.html#packages-to-install-before-the-workshop",
     "title": "Independent Study to prepare for workshop",
     "section": "Packages to install before the workshop",
-    "text": "Packages to install before the workshop\nBiocManager from CRAN in the the normal way:\n\ninstall.packages(\"BiocManager\")\n\nDESeq2 from Bioconductor using BiocManager:\n\nBiocManager::install(\"DESeq2\")\n\nscran from Bioconductor using BiocManager:\n\nBiocManager::install(\"scran\")",
+    "text": "Packages to install before the workshop\nBiocManager from CRAN in the the normal way and set the version of Bioconductor packages to install:\n\ninstall.packages(\"BiocManager\")\nBiocManager::install(version = \"3.19\")\n\nDESeq2 from Bioconductor using BiocManager:\n\nBiocManager::install(\"DESeq2\")\n\nscran from Bioconductor using BiocManager:\n\nBiocManager::install(\"scran\")",
     "crumbs": [
       "Transcriptomics",
       "Week 4: Statistical Analysis",
@@ -2569,7 +2569,7 @@
     "href": "transcriptomics/week-4/study_before_workshop.html#references",
     "title": "Independent Study to prepare for workshop",
     "section": "References",
-    "text": "References\n\n\n\n🔗 About Transcriptomics 2: Statistical Analysis\n\n\n\n\nAbrams, Zachary B., Travis S. Johnson, Kun Huang, Philip R. O. Payne, and Kevin Coombes. 2019. “A Protocol to Evaluate RNA Sequencing Normalization Methods.” BMC Bioinformatics 20 (24): 679. https://doi.org/10.1186/s12859-019-3247-x.\n\n\nBenjamini, Yoav, and Yosef Hochberg. 1995. “Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing.” J. R. Stat. Soc. Series B Stat. Methodol. 57 (1): 289–300. http://www.jstor.org/stable/2346101.\n\n\nBullard, James H., Elizabeth Purdom, Kasper D. Hansen, and Sandrine Dudoit. 2010. “Evaluation of Statistical Methods for Normalization and Differential Expression in mRNA-Seq Experiments.” BMC Bioinformatics 11 (1): 94. https://doi.org/10.1186/1471-2105-11-94.\n\n\nChen, Yunshun, Aaron T. L. Lun, and Gordon K. Smyth. 2016. “From Reads to Genes to Pathways: Differential Expression Analysis of RNA-Seq Experiments Using Rsubread and the edgeR Quasi-Likelihood Pipeline.” https://doi.org/10.12688/f1000research.8987.2.\n\n\nDüren, Yannick, Johannes Lederer, and Li-Xuan Qin. 2022. “Depth Normalization of Small RNA Sequencing: Using Data and Biology to Select a Suitable Method.” Nucleic Acids Research 50 (10): e56. https://doi.org/10.1093/nar/gkac064.\n\n\nEvans, Ciaran, Johanna Hardin, and Daniel M Stoebel. 2017. “Selecting Between-Sample RNA-Seq Normalization Methods from the Perspective of Their Assumptions.” Briefings in Bioinformatics 19 (5): 776–92. https://doi.org/10.1093/bib/bbx008.\n\n\nLove, Michael I., Wolfgang Huber, and Simon Anders. 2014. “Moderated Estimation of Fold Change and Dispersion for RNA-Seq Data with DESeq2.” Genome Biology 15: 550. https://doi.org/10.1186/s13059-014-0550-8.\n\n\nLun, Aaron T. L., Davis J. McCarthy, and John C. Marioni. 2016. “A Step-by-Step Workflow for Low-Level Analysis of Single-Cell RNA-Seq Data with Bioconductor.” F1000Res. 5: 2122. https://doi.org/10.12688/f1000research.9501.2.\n\n\nLytal, Nicholas, Di Ran, and Lingling An. 2020. “Normalization Methods on Single-Cell RNA-Seq Data: An Empirical Survey.” Frontiers in Genetics 11. https://www.frontiersin.org/articles/10.3389/fgene.2020.00041.\n\n\nMcCarthy, Davis J., Yunshun Chen, and Gordon K. Smyth. 2012. “Differential Expression Analysis of Multifactor RNA-Seq Experiments with Respect to Biological Variation.” Nucleic Acids Research 40 (10): 4288–97. https://doi.org/10.1093/nar/gks042.\n\n\nRitchie, Matthew E., Belinda Phipson, Di Wu, Yifang Hu, Charity W. Law, Wei Shi, and Gordon K. Smyth. 2015. “Limma Powers Differential Expression Analyses for RNA-Sequencing and Microarray Studies.” Nucleic Acids Research 43 (7): e47. https://doi.org/10.1093/nar/gkv007.\n\n\nRobinson, Mark D., Davis J. McCarthy, and Gordon K. Smyth. 2010. “edgeR: A Bioconductor Package for Differential Expression Analysis of Digital Gene Expression Data.” Bioinformatics 26 (1): 139–40. https://doi.org/10.1093/bioinformatics/btp616.\n\n\nVallejos, Catalina A., Davide Risso, Antonio Scialdone, Sandrine Dudoit, and John C. Marioni. 2017. “Normalizing Single-Cell RNA Sequencing Data: Challenges and Opportunities.” Nature Methods 14 (6): 565–71. https://doi.org/10.1038/nmeth.4292.",
+    "text": "References\nPages made with R (R Core Team 2024), Quarto (Allaire et al. 2024), knitr [Xie (2024); knitr2; knitr3], kableExtra (Zhu 2021)\n\n\n\n🔗 About Transcriptomics 2: Statistical Analysis\n\n\n\n\nAbrams, Zachary B., Travis S. Johnson, Kun Huang, Philip R. O. Payne, and Kevin Coombes. 2019. “A Protocol to Evaluate RNA Sequencing Normalization Methods.” BMC Bioinformatics 20 (24): 679. https://doi.org/10.1186/s12859-019-3247-x.\n\n\nAllaire, J. J., Charles Teague, Carlos Scheidegger, Yihui Xie, and Christophe Dervieux. 2024. “Quarto.” https://doi.org/10.5281/zenodo.5960048.\n\n\nBenjamini, Yoav, and Yosef Hochberg. 1995. “Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing.” J. R. Stat. Soc. Series B Stat. Methodol. 57 (1): 289–300. http://www.jstor.org/stable/2346101.\n\n\nBullard, James H., Elizabeth Purdom, Kasper D. Hansen, and Sandrine Dudoit. 2010. “Evaluation of Statistical Methods for Normalization and Differential Expression in mRNA-Seq Experiments.” BMC Bioinformatics 11 (1): 94. https://doi.org/10.1186/1471-2105-11-94.\n\n\nChen, Yunshun, Aaron T. L. Lun, and Gordon K. Smyth. 2016. “From Reads to Genes to Pathways: Differential Expression Analysis of RNA-Seq Experiments Using Rsubread and the edgeR Quasi-Likelihood Pipeline.” https://doi.org/10.12688/f1000research.8987.2.\n\n\nDüren, Yannick, Johannes Lederer, and Li-Xuan Qin. 2022. “Depth Normalization of Small RNA Sequencing: Using Data and Biology to Select a Suitable Method.” Nucleic Acids Research 50 (10): e56. https://doi.org/10.1093/nar/gkac064.\n\n\nEvans, Ciaran, Johanna Hardin, and Daniel M Stoebel. 2017. “Selecting Between-Sample RNA-Seq Normalization Methods from the Perspective of Their Assumptions.” Briefings in Bioinformatics 19 (5): 776–92. https://doi.org/10.1093/bib/bbx008.\n\n\nLove, Michael I., Wolfgang Huber, and Simon Anders. 2014. “Moderated Estimation of Fold Change and Dispersion for RNA-Seq Data with DESeq2.” Genome Biology 15: 550. https://doi.org/10.1186/s13059-014-0550-8.\n\n\nLun, Aaron T. L., Davis J. McCarthy, and John C. Marioni. 2016. “A Step-by-Step Workflow for Low-Level Analysis of Single-Cell RNA-Seq Data with Bioconductor.” F1000Res. 5: 2122. https://doi.org/10.12688/f1000research.9501.2.\n\n\nLytal, Nicholas, Di Ran, and Lingling An. 2020. “Normalization Methods on Single-Cell RNA-Seq Data: An Empirical Survey.” Frontiers in Genetics 11. https://www.frontiersin.org/articles/10.3389/fgene.2020.00041.\n\n\nMcCarthy, Davis J., Yunshun Chen, and Gordon K. Smyth. 2012. “Differential Expression Analysis of Multifactor RNA-Seq Experiments with Respect to Biological Variation.” Nucleic Acids Research 40 (10): 4288–97. https://doi.org/10.1093/nar/gks042.\n\n\nR Core Team. 2024. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. https://www.R-project.org/.\n\n\nRitchie, Matthew E., Belinda Phipson, Di Wu, Yifang Hu, Charity W. Law, Wei Shi, and Gordon K. Smyth. 2015. “Limma Powers Differential Expression Analyses for RNA-Sequencing and Microarray Studies.” Nucleic Acids Research 43 (7): e47. https://doi.org/10.1093/nar/gkv007.\n\n\nRobinson, Mark D., Davis J. McCarthy, and Gordon K. Smyth. 2010. “edgeR: A Bioconductor Package for Differential Expression Analysis of Digital Gene Expression Data.” Bioinformatics 26 (1): 139–40. https://doi.org/10.1093/bioinformatics/btp616.\n\n\nVallejos, Catalina A., Davide Risso, Antonio Scialdone, Sandrine Dudoit, and John C. Marioni. 2017. “Normalizing Single-Cell RNA Sequencing Data: Challenges and Opportunities.” Nature Methods 14 (6): 565–71. https://doi.org/10.1038/nmeth.4292.\n\n\nXie, Yihui. 2024. Knitr: A General-Purpose Package for Dynamic Report Generation in r. https://yihui.org/knitr/.\n\n\nZhu, Hao. 2021. “kableExtra: Construct Complex Table with ’Kable’ and Pipe Syntax.” https://CRAN.R-project.org/package=kableExtra.",
     "crumbs": [
       "Transcriptomics",
       "Week 4: Statistical Analysis",
diff --git a/structures/structures.html b/structures/structures.html
index f41e725..f4f1953 100644
--- a/structures/structures.html
+++ b/structures/structures.html
@@ -217,7 +217,7 @@ <h1 class="title">Structure Data Analysis for Group Project</h1>
     <div>
     <div class="quarto-title-meta-heading">Published</div>
     <div class="quarto-title-meta-contents">
-      <p class="date">13 September, 2024</p>
+      <p class="date">17 September, 2024</p>
     </div>
   </div>
   
diff --git a/transcriptomics/transcriptomics.html b/transcriptomics/transcriptomics.html
index 5f7ef53..3b3f6f0 100644
--- a/transcriptomics/transcriptomics.html
+++ b/transcriptomics/transcriptomics.html
@@ -329,7 +329,7 @@ <h1 class="title">Transcriptomics Data Analysis for Group Project</h1>
     <div>
     <div class="quarto-title-meta-heading">Published</div>
     <div class="quarto-title-meta-contents">
-      <p class="date">13 September, 2024</p>
+      <p class="date">17 September, 2024</p>
     </div>
   </div>
   
diff --git a/transcriptomics/week-3/data-raw/xlaevis_counts_S14.csv b/transcriptomics/week-3/data-raw/xlaevis_counts_S14.csv
index 65162c3..167ebfc 100644
--- a/transcriptomics/week-3/data-raw/xlaevis_counts_S14.csv
+++ b/transcriptomics/week-3/data-raw/xlaevis_counts_S14.csv
@@ -1,4 +1,4 @@
-xenbase_gene_id,S14_C_5,S14_C_6,S14_C_A,S14_F_5,S14_F_6,S14_F_A
+xenbase_gene_id,S14_C_1,S14_C_2,S14_C_A,S14_F_1,S14_F_2,S14_F_A
 XB-GENE-1000007,111,77,124,198,68,133
 XB-GENE-1000023,347,283,581,530,269,711
 XB-GENE-1000062,154,98,176,236,95,198
diff --git a/transcriptomics/week-3/data-raw/xlaevis_counts_S20.csv b/transcriptomics/week-3/data-raw/xlaevis_counts_S20.csv
index 17c1964..a1ac5ce 100644
--- a/transcriptomics/week-3/data-raw/xlaevis_counts_S20.csv
+++ b/transcriptomics/week-3/data-raw/xlaevis_counts_S20.csv
@@ -1,4 +1,4 @@
-xenbase_gene_id,S20_C_5,S20_C_6,S20_C_A,S20_F_5,S20_F_6,S20_F_A
+xenbase_gene_id,S20_C_1,S20_C_2,S20_C_A,S20_F_1,S20_F_2,S20_F_A
 XB-GENE-1000007,231,127,136,134,169,244
 XB-GENE-1000023,610,292,483,384,609,725
 XB-GENE-1000062,140,67,65,86,81,132
diff --git a/transcriptomics/week-3/data-raw/xlaevis_counts_S30.csv b/transcriptomics/week-3/data-raw/xlaevis_counts_S30.csv
index fe0770c..012e5aa 100644
--- a/transcriptomics/week-3/data-raw/xlaevis_counts_S30.csv
+++ b/transcriptomics/week-3/data-raw/xlaevis_counts_S30.csv
@@ -1,4 +1,4 @@
-xenbase_gene_id,S30_C_5,S30_C_6,S30_C_A,S30_F_5,S30_F_6,S30_F_A
+xenbase_gene_id,S30_C_1,S30_C_2,S30_C_3,S30_F_1,S30_F_2,S30_F_3
 XB-GENE-1000007,201,210,310,274,252,204
 XB-GENE-1000023,423,471,868,493,507,506
 XB-GENE-1000062,98,77,108,107,86,52
diff --git a/transcriptomics/week-3/images/88H-exp-design-betsy.png b/transcriptomics/week-3/images/88H-exp-design-betsy.png
index 42fd12e..2d7bc81 100644
Binary files a/transcriptomics/week-3/images/88H-exp-design-betsy.png and b/transcriptomics/week-3/images/88H-exp-design-betsy.png differ
diff --git a/transcriptomics/week-3/overview.html b/transcriptomics/week-3/overview.html
index f0f3265..71fb67f 100644
--- a/transcriptomics/week-3/overview.html
+++ b/transcriptomics/week-3/overview.html
@@ -306,7 +306,7 @@ <h1 class="title">Overview</h1>
     <div>
     <div class="quarto-title-meta-heading">Published</div>
     <div class="quarto-title-meta-contents">
-      <p class="date">13 September, 2024</p>
+      <p class="date">17 September, 2024</p>
     </div>
   </div>
   
diff --git a/transcriptomics/week-3/study_after_workshop.html b/transcriptomics/week-3/study_after_workshop.html
index 5193c09..70b6df5 100644
--- a/transcriptomics/week-3/study_after_workshop.html
+++ b/transcriptomics/week-3/study_after_workshop.html
@@ -299,7 +299,7 @@ <h1 class="title">Independent Study to consolidate this week</h1>
     <div>
     <div class="quarto-title-meta-heading">Published</div>
     <div class="quarto-title-meta-contents">
-      <p class="date">13 September, 2024</p>
+      <p class="date">17 September, 2024</p>
     </div>
   </div>
   
diff --git a/transcriptomics/week-3/study_before_workshop.html b/transcriptomics/week-3/study_before_workshop.html
index 05c3803..eb9ce84 100644
--- a/transcriptomics/week-3/study_before_workshop.html
+++ b/transcriptomics/week-3/study_before_workshop.html
@@ -388,7 +388,7 @@ <h1 class="title">Independent Study to prepare for workshop</h1>
 </div>
 </div>
 
-  <p class="date">13 September, 2024</p>
+  <p class="date">17 September, 2024</p>
 </section>
 <section id="overview" class="slide level2">
 <h2>Overview</h2>
@@ -436,7 +436,7 @@ <h2 data-id="quarto-animate-title">🐸 Experimental design</h2>
 
 <img data-src="images/88H-exp-design-betsy.png" width="200" class="r-stretch quarto-figure-left"><p class="caption">Schematic of frog development experiment</p><div>
 <ul>
-<li class="fragment"><p>3 fertilisations. <span style="color:#009900">These are the replicates, <code>.5</code>, <code>.6</code>, <code>A</code></span></p></li>
+<li class="fragment"><p>3 fertilisations. <span style="color:#009900">These are the replicates, <code>1</code>, <code>2</code>, <code>3</code></span></p></li>
 <li class="fragment"><p>two siblings from each fertilisation one control, one FGF treated. <span style="color:#009900">The treatments are paired</span></p></li>
 <li class="fragment"><p>sequenced at three time points. <span style="color:#009900">S14, S20, S30</span></p></li>
 <li class="fragment"><p>3 x 2 x 3 = 18 groups</p></li>
@@ -546,7 +546,7 @@ <h2>Raw Sequence data</h2>
 <h2>Raw Sequence data</h2>
 <div>
 <ul>
-<li class="fragment"><p>The RNA-seq data are from an Illumina machine 150-300bp; Metagenomic data are often Nanopore 10,000 - 30000bp</p></li>
+<li class="fragment"><p>The RNA-seq data are from an Illumina machine 150-300bp</p></li>
 <li class="fragment"><p>Reads are in FASTQ files</p></li>
 <li class="fragment"><p>FASTQ files contain the sequence of each read and a quality score for each base</p></li>
 </ul>
@@ -562,9 +562,9 @@ <h2>General steps</h2>
 <div>
 <ul>
 <li class="fragment"><p>Reads are filtered and trimmed on the basis of the quality score</p></li>
-<li class="fragment"><p>They are then aligned/pseudo-aligned to a reference genome/transcriptome or, in metagenomics, assembled de novo.</p></li>
-<li class="fragment"><p>Reads are then counted to quantify the expression or number of genomes in metagenomics</p></li>
-<li class="fragment"><p>Counts are normalised to account for differences in sequencing depth and gene/transcript/genome length before statistical analysis</p></li>
+<li class="fragment"><p>They are then aligned/pseudo-aligned to a reference genome/transcriptome</p></li>
+<li class="fragment"><p>Reads are then counted to quantify the expression</p></li>
+<li class="fragment"><p>Counts will need to be normalised to account for differences in sequencing depth and gene/transcript/ length before, or as part of, statistical analysis.</p></li>
 </ul>
 </div>
 </section>
@@ -581,7 +581,7 @@ <h2>🐸 Data</h2>
 <h2>🐭 Data</h2>
 <ul>
 <li><p>Published in <span class="citation" data-cites="nestorowa2016">Nestorowa et al. (<a href="#/references" role="doc-biblioref" onclick="">2016</a>)</span></p></li>
-<li><p>Expression for a subset of genes, the surfaceome</p></li>
+<li><p>Expression for a <strong>subset of genes</strong>, the surfaceome</p></li>
 <li><p>Values are log2 normalised values</p></li>
 <li><p>The statistical analysis method we will use <code>scran</code> <span class="citation" data-cites="scran">(<a href="#/references" role="doc-biblioref" onclick="">Lun, McCarthy, and Marioni 2016</a>)</span> requires normalised values</p></li>
 </ul>
diff --git a/transcriptomics/week-3/workshop.html b/transcriptomics/week-3/workshop.html
index 52addb0..f5ebe23 100644
--- a/transcriptomics/week-3/workshop.html
+++ b/transcriptomics/week-3/workshop.html
@@ -394,7 +394,7 @@ <h1 class="title">Workshop</h1>
     <div>
     <div class="quarto-title-meta-heading">Published</div>
     <div class="quarto-title-meta-contents">
-      <p class="date">13 September, 2024</p>
+      <p class="date">17 September, 2024</p>
     </div>
   </div>
   
@@ -540,14 +540,14 @@ <h1 class="title">Workshop</h1>
 <span><span class="co"># works well with smaller numbers of column</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/base/summary.html">summary</a></span><span class="op">(</span><span class="va">s30</span><span class="op">)</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-stdout">
-<pre><code> xenbase_gene_id       S30_C_5            S30_C_6            S30_C_A        
+<pre><code> xenbase_gene_id       S30_C_1            S30_C_2            S30_C_3        
  Length:11893       Min.   :     0.0   Min.   :     0.0   Min.   :     0.0  
  Class :character   1st Qu.:    14.0   1st Qu.:    14.0   1st Qu.:    23.0  
  Mode  :character   Median :    70.0   Median :    75.0   Median :   107.0  
                     Mean   :   317.1   Mean   :   335.8   Mean   :   426.3  
                     3rd Qu.:   205.0   3rd Qu.:   220.0   3rd Qu.:   301.0  
                     Max.   :101746.0   Max.   :118708.0   Max.   :117945.0  
-    S30_F_5            S30_F_6            S30_F_A       
+    S30_F_1            S30_F_2            S30_F_3       
  Min.   :     0.0   Min.   :     0.0   Min.   :    0.0  
  1st Qu.:    19.0   1st Qu.:    17.0   1st Qu.:   16.0  
  Median :    88.0   Median :    84.0   Median :   69.0  
@@ -557,23 +557,23 @@ <h1 class="title">Workshop</h1>
 </div>
 </div>
 <p>Notice that: - the minimum count is 0 and the maximums are very high in all the columns - the medians are quite a lot lower than the means so the data are skewed (hump to the left, tail to the right) - there must be quite a lot of zeros - the columns are roughly similar and it doesn’t look like there is an anomalous replicate.</p>
-<p>To find out how may zeros there are in a column we can make use of the fact that <code>TRUE</code> evaluates to 1 and <code>FALSE</code> evaluates to 0. This means <code>sum(S30_C_5 == 0)</code> gives the number of 0 in the <code>S30_C_5</code> column</p>
+<p>To find out how may zeros there are in a column we can make use of the fact that <code>TRUE</code> evaluates to 1 and <code>FALSE</code> evaluates to 0. This means <code>sum(S30_C_1 == 0)</code> gives the number of 0 in the <code>S30_C_1</code> column</p>
 <p>🎬 Find the number of zeros in all six columns:</p>
 <div class="cell">
 <div class="sourceCode" id="cb10"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="va">s30</span> <span class="op">|&gt;</span></span>
-<span>  <span class="fu"><a href="https://dplyr.tidyverse.org/reference/summarise.html">summarise</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/sum.html">sum</a></span><span class="op">(</span><span class="va">S30_C_5</span> <span class="op">==</span> <span class="fl">0</span><span class="op">)</span>,</span>
-<span>            <span class="fu"><a href="https://rdrr.io/r/base/sum.html">sum</a></span><span class="op">(</span><span class="va">S30_C_6</span> <span class="op">==</span> <span class="fl">0</span><span class="op">)</span>,</span>
-<span>            <span class="fu"><a href="https://rdrr.io/r/base/sum.html">sum</a></span><span class="op">(</span><span class="va">S30_C_A</span> <span class="op">==</span> <span class="fl">0</span><span class="op">)</span>,</span>
-<span>            <span class="fu"><a href="https://rdrr.io/r/base/sum.html">sum</a></span><span class="op">(</span><span class="va">S30_F_5</span> <span class="op">==</span> <span class="fl">0</span><span class="op">)</span>,</span>
-<span>            <span class="fu"><a href="https://rdrr.io/r/base/sum.html">sum</a></span><span class="op">(</span><span class="va">S30_F_6</span> <span class="op">==</span> <span class="fl">0</span><span class="op">)</span>,</span>
-<span>            <span class="fu"><a href="https://rdrr.io/r/base/sum.html">sum</a></span><span class="op">(</span><span class="va">S30_F_A</span> <span class="op">==</span> <span class="fl">0</span><span class="op">)</span><span class="op">)</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<span>  <span class="fu"><a href="https://dplyr.tidyverse.org/reference/summarise.html">summarise</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/sum.html">sum</a></span><span class="op">(</span><span class="va">S30_C_1</span> <span class="op">==</span> <span class="fl">0</span><span class="op">)</span>,</span>
+<span>            <span class="fu"><a href="https://rdrr.io/r/base/sum.html">sum</a></span><span class="op">(</span><span class="va">S30_C_2</span> <span class="op">==</span> <span class="fl">0</span><span class="op">)</span>,</span>
+<span>            <span class="fu"><a href="https://rdrr.io/r/base/sum.html">sum</a></span><span class="op">(</span><span class="va">S30_C_3</span> <span class="op">==</span> <span class="fl">0</span><span class="op">)</span>,</span>
+<span>            <span class="fu"><a href="https://rdrr.io/r/base/sum.html">sum</a></span><span class="op">(</span><span class="va">S30_F_1</span> <span class="op">==</span> <span class="fl">0</span><span class="op">)</span>,</span>
+<span>            <span class="fu"><a href="https://rdrr.io/r/base/sum.html">sum</a></span><span class="op">(</span><span class="va">S30_F_2</span> <span class="op">==</span> <span class="fl">0</span><span class="op">)</span>,</span>
+<span>            <span class="fu"><a href="https://rdrr.io/r/base/sum.html">sum</a></span><span class="op">(</span><span class="va">S30_F_3</span> <span class="op">==</span> <span class="fl">0</span><span class="op">)</span><span class="op">)</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-stdout">
 <pre><code># A tibble: 1 × 6
-  `sum(S30_C_5 == 0)` `sum(S30_C_6 == 0)` `sum(S30_C_A == 0)`
+  `sum(S30_C_1 == 0)` `sum(S30_C_2 == 0)` `sum(S30_C_3 == 0)`
                 &lt;int&gt;               &lt;int&gt;               &lt;int&gt;
 1                1340                1361                 998
-# ℹ 3 more variables: `sum(S30_F_5 == 0)` &lt;int&gt;, `sum(S30_F_6 == 0)` &lt;int&gt;,
-#   `sum(S30_F_A == 0)` &lt;int&gt;</code></pre>
+# ℹ 3 more variables: `sum(S30_F_1 == 0)` &lt;int&gt;, `sum(S30_F_2 == 0)` &lt;int&gt;,
+#   `sum(S30_F_3 == 0)` &lt;int&gt;</code></pre>
 </div>
 </div>
 <p>There is a better way of doing this that saves you having to repeat so much code - especially useful if you have a lot more than 6 columns. We can use <code><a href="https://tidyr.tidyverse.org/reference/pivot_longer.html">pivot_longer()</a></code> to put the data in tidy format and then use the <code><a href="https://dplyr.tidyverse.org/reference/group_by.html">group_by()</a></code> and <code><a href="https://dplyr.tidyverse.org/reference/summarise.html">summarise()</a></code> approach we have used extensively before.</p>
@@ -589,12 +589,12 @@ <h1 class="title">Workshop</h1>
 <pre><code># A tibble: 6 × 2
   sample  n_zero
   &lt;chr&gt;    &lt;int&gt;
-1 S30_C_5   1340
-2 S30_C_6   1361
-3 S30_C_A    998
-4 S30_F_5   1210
-5 S30_F_6   1199
-6 S30_F_A    963</code></pre>
+1 S30_C_1   1340
+2 S30_C_2   1361
+3 S30_C_3    998
+4 S30_F_1   1210
+5 S30_F_2   1199
+6 S30_F_3    963</code></pre>
 </div>
 </div>
 <p>You could expand to get all the summary information</p>
@@ -616,12 +616,12 @@ <h1 class="title">Workshop</h1>
 <pre><code># A tibble: 6 × 8
   sample    min lowerq  mean median upperq    max n_zero
   &lt;chr&gt;   &lt;dbl&gt;  &lt;dbl&gt; &lt;dbl&gt;  &lt;dbl&gt;  &lt;dbl&gt;  &lt;dbl&gt;  &lt;int&gt;
-1 S30_C_5     0     14  317.     70    205 101746   1340
-2 S30_C_6     0     14  336.     75    220 118708   1361
-3 S30_C_A     0     23  426.    107    301 117945    998
-4 S30_F_5     0     19  376.     88    251 117573   1210
-5 S30_F_6     0     17  376.     84    246 130672   1199
-6 S30_F_A     0     16  260.     69    187  61531    963</code></pre>
+1 S30_C_1     0     14  317.     70    205 101746   1340
+2 S30_C_2     0     14  336.     75    220 118708   1361
+3 S30_C_3     0     23  426.    107    301 117945    998
+4 S30_F_1     0     19  376.     88    251 117573   1210
+5 S30_F_2     0     17  376.     84    246 130672   1199
+6 S30_F_3     0     16  260.     69    187  61531    963</code></pre>
 </div>
 </div>
 <p>The mean count ranges from 260 to 426.</p>
@@ -2057,7 +2057,7 @@ <h1 class="title">Workshop</h1>
 <li>a lot of genes with zero counts in several of the samples</li>
 <li>some very very low counts.</li>
 </ul>
-<p>These should be filtered out because they are unreliable - or, at the least, uninformative. The goal of our downstream analysis will be to see if there is a signifcance difference in gene expression between the control and FGF-treated sibling. Since we have only three replicates in each group, having one or two unreliable, missing or zero values, makes such a determination impossible for a particular gene. We will use the total counts and the number of samples with non-zero values to filter our genes later.</p>
+<p>These should be filtered out because they are unreliable - or, at the least, uninformative. The goal of our downstream analysis will be to see if there is a significant difference in gene expression between the control and FGF-treated sibling. Since we have only three replicates in each group, having one or two unreliable, missing or zero values, makes such a determination impossible for a particular gene. We will use the total counts and the number of samples with non-zero values to filter our genes later.</p>
 <p>As we have a lot of genes, it is again helpful to plot the mean counts with pointrange to get an overview. We will plot the log of the counts - we saw earlier that logging made it easier to understand the distribution of counts over such a wide range. We will also order the genes from lowest to highest mean count.</p>
 <p>🎬 Plot the logged mean counts for each gene in order of size using <code><a href="https://ggplot2.tidyverse.org/reference/geom_linerange.html">geom_pointrange()</a></code>:</p>
 <div class="cell">
@@ -2154,29 +2154,31 @@ <h1 class="title">Workshop</h1>
 </section><section id="mice-and-future-you" class="level3"><h3 class="anchored" data-anchor-id="mice-and-future-you">🐭 Mice and future you</h3>
 <p>🎬 Create a new Project, <code>mice-88H</code>, populated with folders and your data. Make a script file called <code>hspc-prog.R</code>. This will a be commented analysis of the hspc cells vs the prog cells. At this point you will have only code for the hspc cells. You will build on this each workshop and be able to use it as a template to examine other comparisons. Copy in the appropriate code and comments from <code>workshop-1.R</code>. Edit to improve your comments where your understanding has developed since you made them. Make sure you can close down RStudio, reopen it and run your whole script again.</p>
 </section><section id="xxxx-and-future-you" class="level3"><h3 class="anchored" data-anchor-id="xxxx-and-future-you">🍂 xxxx and future you</h3>
-<p><strong>Do one of the other two examples.</strong></p>
-<!-- 🎬 Create a new Project, `xxxx-88H`, populated with folders and your -->
-<!-- data. Make a script file called `xxxx.R`. This will a be commented -->
-<!-- analysis of t xxxxxxxxxxxx. -->
 </section></section></section><section id="finished" class="level1"><h1>🥳 Finished</h1>
 <p>Well Done!</p>
 </section><section id="independent-study-following-the-workshop" class="level1"><h1>Independent study following the workshop</h1>
-<p><a href="../../transcriptomics/week-3/study_after_workshop.html">Consolidate</a></p>
+<p><a href="study_3fter_workshop.qmd">Consolidate</a></p>
 </section><section id="the-code-file" class="level1"><h1>The Code file</h1>
 <p>These contain all the code needed in the workshop even where it is not visible on the webpage.</p>
 <p>The <a href="../../transcriptomics/week-3/workshop.html">workshop.qmd</a> file is the file I use to compile the practical. Qmd stands for Quarto markdown. It allows code and ordinary text to be interleaved to produce well-formatted reports including webpages. Right-click on the link and choose Save-As to download. You will be able to open the Qmd file in RStudio. Alternatively, <a href="https://github.com/3mmaRand/">View in Browser</a>. Coding and thinking answers are marked with <code>#---CODING ANSWER---</code> and <code>#---THINKING ANSWER---</code></p>
-<p>Pages made with R <span class="citation" data-cites="R-core">(<a href="#ref-R-core" role="doc-biblioref">R Core Team 2024</a>)</span>, Quarto <span class="citation" data-cites="allaire2022">(<a href="#ref-allaire2022" role="doc-biblioref"><strong>allaire2022?</strong></a>)</span>, <code>knitr</code> <span class="citation" data-cites="knitr">(<a href="#ref-knitr" role="doc-biblioref"><strong>knitr?</strong></a>)</span>, <code>kableExtra</code> <span class="citation" data-cites="kableExtra">(<a href="#ref-kableExtra" role="doc-biblioref">Zhu 2021</a>)</span></p>
+<p>Pages made with R <span class="citation" data-cites="R-core">(<a href="#ref-R-core" role="doc-biblioref">R Core Team 2024</a>)</span>, Quarto <span class="citation" data-cites="Allaire_Quarto_2024">(<a href="#ref-Allaire_Quarto_2024" role="doc-biblioref">Allaire et al. 2024</a>)</span>, <code>knitr</code> [<span class="citation" data-cites="knitr1">Xie (<a href="#ref-knitr1" role="doc-biblioref">2024</a>)</span>; knitr2; knitr3], <code>kableExtra</code> <span class="citation" data-cites="kableExtra">(<a href="#ref-kableExtra" role="doc-biblioref">Zhu 2021</a>)</span></p>
 </section><section id="references" class="level1">
 
 
 
 </section><div id="quarto-appendix" class="default"><section class="quarto-appendix-contents" role="doc-bibliography" id="quarto-bibliography"><h2 class="anchored quarto-appendix-heading">References</h2><div id="refs" class="references csl-bib-body hanging-indent" data-entry-spacing="0" role="list">
+<div id="ref-Allaire_Quarto_2024" class="csl-entry" role="listitem">
+Allaire, J. J., Charles Teague, Carlos Scheidegger, Yihui Xie, and Christophe Dervieux. 2024. <span>“<span>Quarto</span>.”</span> <a href="https://doi.org/10.5281/zenodo.5960048">https://doi.org/10.5281/zenodo.5960048</a>.
+</div>
 <div id="ref-R-core" class="csl-entry" role="listitem">
 R Core Team. 2024. <em>R: A Language and Environment for Statistical Computing</em>. Vienna, Austria: R Foundation for Statistical Computing. <a href="https://www.R-project.org/">https://www.R-project.org/</a>.
 </div>
 <div id="ref-tidyverse" class="csl-entry" role="listitem">
 Wickham, Hadley, Mara Averick, Jennifer Bryan, Winston Chang, Lucy D’Agostino McGowan, Romain François, Garrett Grolemund, et al. 2019. <span>“Welcome to the <span class="nocase">tidyverse</span>.”</span> <em>Journal of Open Source Software</em> 4 (43): 1686. <a href="https://doi.org/10.21105/joss.01686">https://doi.org/10.21105/joss.01686</a>.
 </div>
+<div id="ref-knitr1" class="csl-entry" role="listitem">
+Xie, Yihui. 2024. <em>Knitr: A General-Purpose Package for Dynamic Report Generation in r</em>. <a href="https://yihui.org/knitr/">https://yihui.org/knitr/</a>.
+</div>
 <div id="ref-kableExtra" class="csl-entry" role="listitem">
 Zhu, Hao. 2021. <span>“kableExtra: Construct Complex Table with ’Kable’ and Pipe Syntax.”</span> <a href="https://CRAN.R-project.org/package=kableExtra">https://CRAN.R-project.org/package=kableExtra</a>.
 </div>
diff --git a/transcriptomics/week-3/workshop_files/figure-html/unnamed-chunk-14-1.png b/transcriptomics/week-3/workshop_files/figure-html/unnamed-chunk-14-1.png
index 28c68da..f691864 100644
Binary files a/transcriptomics/week-3/workshop_files/figure-html/unnamed-chunk-14-1.png and b/transcriptomics/week-3/workshop_files/figure-html/unnamed-chunk-14-1.png differ
diff --git a/transcriptomics/week-3/workshop_files/figure-html/unnamed-chunk-15-1.png b/transcriptomics/week-3/workshop_files/figure-html/unnamed-chunk-15-1.png
index 78c9285..860be1f 100644
Binary files a/transcriptomics/week-3/workshop_files/figure-html/unnamed-chunk-15-1.png and b/transcriptomics/week-3/workshop_files/figure-html/unnamed-chunk-15-1.png differ
diff --git a/transcriptomics/week-4/overview.html b/transcriptomics/week-4/overview.html
index 10767cb..c103282 100644
--- a/transcriptomics/week-4/overview.html
+++ b/transcriptomics/week-4/overview.html
@@ -327,7 +327,7 @@ <h1 class="title">Overview</h1>
     <div>
     <div class="quarto-title-meta-heading">Published</div>
     <div class="quarto-title-meta-contents">
-      <p class="date">13 September, 2024</p>
+      <p class="date">17 September, 2024</p>
     </div>
   </div>
   
diff --git a/transcriptomics/week-4/study_after_workshop.html b/transcriptomics/week-4/study_after_workshop.html
index 28eed7b..2b7027b 100644
--- a/transcriptomics/week-4/study_after_workshop.html
+++ b/transcriptomics/week-4/study_after_workshop.html
@@ -299,7 +299,7 @@ <h1 class="title">Independent Study to consolidate this week</h1>
     <div>
     <div class="quarto-title-meta-heading">Published</div>
     <div class="quarto-title-meta-contents">
-      <p class="date">13 September, 2024</p>
+      <p class="date">17 September, 2024</p>
     </div>
   </div>
   
diff --git a/transcriptomics/week-4/study_before_workshop.html b/transcriptomics/week-4/study_before_workshop.html
index f45760b..a69bdc1 100644
--- a/transcriptomics/week-4/study_before_workshop.html
+++ b/transcriptomics/week-4/study_before_workshop.html
@@ -446,7 +446,7 @@
 </div>
 </div>
 
-  <p class="date">13 September, 2024</p>
+  <p class="date">17 September, 2024</p>
 </section><section id="overview" class="slide level2"><h2>Overview</h2>
 <p>In these slides we will:</p>
 <div>
@@ -639,9 +639,10 @@
 <li><p>Values are log2 normalised values</p></li>
 <li><p>The statistical analysis method we will use <strong><code>scran</code></strong> <span class="citation" data-cites="scran">(<a href="#/references" role="doc-biblioref" onclick="">Lun, McCarthy, and Marioni 2016</a>)</span> requires normalised values</p></li>
 </ul></section><section id="packages-to-install-before-the-workshop" class="slide level2"><h2>Packages to install before the workshop</h2>
-<p><strong><code>BiocManager</code></strong> from CRAN in the the normal way:</p>
+<p><strong><code>BiocManager</code></strong> from CRAN in the the normal way and set the version of Bioconductor packages to install:</p>
 <div class="cell">
-<div class="sourceCode" id="cb1"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/utils/install.packages.html">install.packages</a></span><span class="op">(</span><span class="st">"BiocManager"</span><span class="op">)</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="sourceCode" id="cb1"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/utils/install.packages.html">install.packages</a></span><span class="op">(</span><span class="st">"BiocManager"</span><span class="op">)</span></span>
+<span><span class="fu">BiocManager</span><span class="fu">::</span><span class="fu"><a href="https://bioconductor.github.io/BiocManager/reference/install.html">install</a></span><span class="op">(</span>version <span class="op">=</span> <span class="st">"3.19"</span><span class="op">)</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <p><strong><code>DESeq2</code></strong> from Bioconductor using BiocManager:</p>
 <div class="cell">
@@ -659,6 +660,7 @@
 <li><p>Transcriptomics 2: Statistical Analysis Identifying which genes are differentially expressed between treatments.</p></li>
 <li><p>Transcriptomics 3: Visualising and Interpreting. PCA, Volcano plots and heatmaps to visualise results. Interpreting the results and finding out more about genes of interest.</p></li>
 </ul></section><section id="references" class="slide level2 smaller scrollable"><h2>References</h2>
+<p>Pages made with R <span class="citation" data-cites="R-core">(<a href="#/references" role="doc-biblioref" onclick="">R Core Team 2024</a>)</span>, Quarto <span class="citation" data-cites="Allaire_Quarto_2024">(<a href="#/references" role="doc-biblioref" onclick="">Allaire et al. 2024</a>)</span>, <code>knitr</code> [<span class="citation" data-cites="knitr1">Xie (<a href="#/references" role="doc-biblioref" onclick="">2024</a>)</span>; knitr2; knitr3], <code>kableExtra</code> <span class="citation" data-cites="kableExtra">(<a href="#/references" role="doc-biblioref" onclick="">Zhu 2021</a>)</span></p>
 
 <div class="quarto-auto-generated-content">
 <div class="footer footer-default">
@@ -669,6 +671,9 @@
 <div id="ref-abrams2019" class="csl-entry" role="listitem">
 Abrams, Zachary B., Travis S. Johnson, Kun Huang, Philip R. O. Payne, and Kevin Coombes. 2019. <span>“A Protocol to Evaluate RNA Sequencing Normalization Methods.”</span> <em>BMC Bioinformatics</em> 20 (24): 679. <a href="https://doi.org/10.1186/s12859-019-3247-x">https://doi.org/10.1186/s12859-019-3247-x</a>.
 </div>
+<div id="ref-Allaire_Quarto_2024" class="csl-entry" role="listitem">
+Allaire, J. J., Charles Teague, Carlos Scheidegger, Yihui Xie, and Christophe Dervieux. 2024. <span>“<span>Quarto</span>.”</span> <a href="https://doi.org/10.5281/zenodo.5960048">https://doi.org/10.5281/zenodo.5960048</a>.
+</div>
 <div id="ref-benjamini1995" class="csl-entry" role="listitem">
 Benjamini, Yoav, and Yosef Hochberg. 1995. <span>“Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing.”</span> <em>J. R. Stat. Soc. Series B Stat. Methodol.</em> 57 (1): 289–300. <a href="http://www.jstor.org/stable/2346101">http://www.jstor.org/stable/2346101</a>.
 </div>
@@ -696,6 +701,9 @@
 <div id="ref-mccarthy2012" class="csl-entry" role="listitem">
 McCarthy, Davis J., Yunshun Chen, and Gordon K. Smyth. 2012. <span>“Differential Expression Analysis of Multifactor RNA-Seq Experiments with Respect to Biological Variation.”</span> <em>Nucleic Acids Research</em> 40 (10): 4288–97. <a href="https://doi.org/10.1093/nar/gks042">https://doi.org/10.1093/nar/gks042</a>.
 </div>
+<div id="ref-R-core" class="csl-entry" role="listitem">
+R Core Team. 2024. <em>R: A Language and Environment for Statistical Computing</em>. Vienna, Austria: R Foundation for Statistical Computing. <a href="https://www.R-project.org/">https://www.R-project.org/</a>.
+</div>
 <div id="ref-ritchie2015" class="csl-entry" role="listitem">
 Ritchie, Matthew E., Belinda Phipson, Di Wu, Yifang Hu, Charity W. Law, Wei Shi, and Gordon K. Smyth. 2015. <span>“Limma Powers Differential Expression Analyses for RNA-Sequencing and Microarray Studies.”</span> <em>Nucleic Acids Research</em> 43 (7): e47. <a href="https://doi.org/10.1093/nar/gkv007">https://doi.org/10.1093/nar/gkv007</a>.
 </div>
@@ -705,6 +713,12 @@
 <div id="ref-vallejos2017" class="csl-entry" role="listitem">
 Vallejos, Catalina A., Davide Risso, Antonio Scialdone, Sandrine Dudoit, and John C. Marioni. 2017. <span>“Normalizing Single-Cell RNA Sequencing Data: Challenges and Opportunities.”</span> <em>Nature Methods</em> 14 (6): 565–71. <a href="https://doi.org/10.1038/nmeth.4292">https://doi.org/10.1038/nmeth.4292</a>.
 </div>
+<div id="ref-knitr1" class="csl-entry" role="listitem">
+Xie, Yihui. 2024. <em>Knitr: A General-Purpose Package for Dynamic Report Generation in r</em>. <a href="https://yihui.org/knitr/">https://yihui.org/knitr/</a>.
+</div>
+<div id="ref-kableExtra" class="csl-entry" role="listitem">
+Zhu, Hao. 2021. <span>“kableExtra: Construct Complex Table with ’Kable’ and Pipe Syntax.”</span> <a href="https://CRAN.R-project.org/package=kableExtra">https://CRAN.R-project.org/package=kableExtra</a>.
+</div>
 </div>
 </section></section>
 </div>
diff --git a/transcriptomics/week-4/workshop.html b/transcriptomics/week-4/workshop.html
index 80ea8eb..414c992 100644
--- a/transcriptomics/week-4/workshop.html
+++ b/transcriptomics/week-4/workshop.html
@@ -367,7 +367,7 @@ <h1 class="title">Workshop</h1>
     <div>
     <div class="quarto-title-meta-heading">Published</div>
     <div class="quarto-title-meta-contents">
-      <p class="date">13 September, 2024</p>
+      <p class="date">17 September, 2024</p>
     </div>
   </div>
   
@@ -429,30 +429,42 @@ <h1 class="title">Workshop</h1>
 </section><section id="genes-expressed-in-one-treatment" class="level2"><h2 class="anchored" data-anchor-id="genes-expressed-in-one-treatment">Genes expressed in one treatment</h2>
 <p>The genes expressed in only one treatment group are those with zeros in all three replicates in one group and non-zero values in all three replicates in the other group. For example, those shown here:</p>
 <div class="cell">
-<div class="cell-output cell-output-stdout">
-<pre><code># A tibble: 3 × 7
-  xenbase_gene_id  S30_C_5 S30_C_6 S30_C_A S30_F_5 S30_F_6 S30_F_A
-  &lt;chr&gt;              &lt;dbl&gt;   &lt;dbl&gt;   &lt;dbl&gt;   &lt;dbl&gt;   &lt;dbl&gt;   &lt;dbl&gt;
-1 XB-GENE-1018260        0       0       0      10       2      16
-2 XB-GENE-17330117       0       0       0      13       4      17
-3 XB-GENE-17332184       0       0       0       6      19       6</code></pre>
+<div class="cell-output cell-output-error">
+<pre><code>Error in `arrange()`:
+ℹ In argument: `..1 = S30_C_3`.
+Caused by error:
+! object 'S30_C_3' not found</code></pre>
 </div>
 </div>
 <p>We will use <code><a href="https://dplyr.tidyverse.org/reference/filter.html">filter()</a></code> to find these genes.</p>
 <p>🎬 Find the genes that are expressed only in the FGF-treated group:</p>
 <div class="cell">
 <div class="sourceCode" id="cb7"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="va">s30_fgf_only</span> <span class="op">&lt;-</span> <span class="va">s30_filtered</span> <span class="op">|&gt;</span> </span>
-<span>  <span class="fu"><a href="https://dplyr.tidyverse.org/reference/filter.html">filter</a></span><span class="op">(</span><span class="va">S30_C_5</span> <span class="op">==</span> <span class="fl">0</span>, </span>
-<span>         <span class="va">S30_C_6</span> <span class="op">==</span> <span class="fl">0</span>, </span>
-<span>         <span class="va">S30_C_A</span> <span class="op">==</span> <span class="fl">0</span>, </span>
-<span>         <span class="va">S30_F_5</span> <span class="op">&gt;</span> <span class="fl">0</span>, </span>
-<span>         <span class="va">S30_F_6</span> <span class="op">&gt;</span> <span class="fl">0</span>, </span>
-<span>         <span class="va">S30_F_A</span> <span class="op">&gt;</span> <span class="fl">0</span><span class="op">)</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<span>  <span class="fu"><a href="https://dplyr.tidyverse.org/reference/filter.html">filter</a></span><span class="op">(</span><span class="va">S30_C_1</span> <span class="op">==</span> <span class="fl">0</span>, </span>
+<span>         <span class="va">S30_C_2</span> <span class="op">==</span> <span class="fl">0</span>, </span>
+<span>         <span class="va">S30_C_3</span> <span class="op">==</span> <span class="fl">0</span>, </span>
+<span>         <span class="va">S30_F_1</span> <span class="op">&gt;</span> <span class="fl">0</span>, </span>
+<span>         <span class="va">S30_F_2</span> <span class="op">&gt;</span> <span class="fl">0</span>, </span>
+<span>         <span class="va">S30_F_3</span> <span class="op">&gt;</span> <span class="fl">0</span><span class="op">)</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="cell-output cell-output-error">
+<pre><code>Error in `filter()`:
+ℹ In argument: `S30_C_1 == 0`.
+Caused by error:
+! object 'S30_C_1' not found</code></pre>
+</div>
 </div>
 <p>❓ How many genes are expressed only in the FGF-treated group?</p>
 <!-- #---THINKING ANSWER--- -->
 <!-- There are 26 genes expressed only in the FGF-treated group. -->
 <p>🎬 Now you find any genes that are expressed only in the control group.</p>
+<div class="cell">
+<div class="cell-output cell-output-error">
+<pre><code>Error in `filter()`:
+ℹ In argument: `S30_C_1 &gt; 0`.
+Caused by error:
+! object 'S30_C_1' not found</code></pre>
+</div>
+</div>
 <p>❓ Do the results make sense to you in light of what you know about the biology?</p>
 <!-- #---THINKING ANSWER--- -->
 <!-- there are no genes expressed in the control group but not in the FGF-treated group -->
@@ -462,6 +474,11 @@ <h1 class="title">Workshop</h1>
 <!-- Domain expertise is important when analysing data so discuss your results with Betsy  -->
 <!-- and your group. -->
 <p>🎬 Write to file (saved in <code>results</code>) all the genes that are expressed one group only.</p>
+<div class="cell">
+<div class="cell-output cell-output-error">
+<pre><code>Error in eval(expr, envir, enclos): object 's30_fgf_only' not found</code></pre>
+</div>
+</div>
 </section><section id="create-deseqdataset-object" class="level2"><h2 class="anchored" data-anchor-id="create-deseqdataset-object">Create DESeqDataSet object</h2>
 <p>🎬 Load the DESeq2 package:</p>
 <p>A DEseqDataSet object is a custom data type that is used by <strong><code>DESeq2</code></strong>. Custom data types are common in the Bioconductor<a href="#fn1" class="footnote-ref" id="fnref1" role="doc-noteref"><sup>1</sup></a> packages. They are used to store data in a way that is useful for the analysis. These data types typically have data, transformed data, metadata and experimental designs within them.</p>
@@ -475,13 +492,13 @@ <h1 class="title">Workshop</h1>
 <p>The counts must in a matrix rather than a dataframe. Unlike a dataframe, a matrix has columns of all the same type. That is, it will contain only the counts. The gene ids are given as row names rather than a column. The <code><a href="https://rdrr.io/r/base/matrix.html">matrix()</a></code> function will create a matrix from a dataframe of columns of the same type and the <code><a href="https://dplyr.tidyverse.org/reference/select.html">select()</a></code> function can be used to remove the gene ids column.</p>
 <p>🎬 Create a matrix of the counts:</p>
 <div class="cell">
-<div class="sourceCode" id="cb8"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="va">s30_count_mat</span> <span class="op">&lt;-</span> <span class="va">s30_filtered</span> <span class="op">|&gt;</span></span>
+<div class="sourceCode" id="cb11"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="va">s30_count_mat</span> <span class="op">&lt;-</span> <span class="va">s30_filtered</span> <span class="op">|&gt;</span></span>
 <span>  <span class="fu"><a href="https://dplyr.tidyverse.org/reference/select.html">select</a></span><span class="op">(</span><span class="op">-</span><span class="va">xenbase_gene_id</span><span class="op">)</span> <span class="op">|&gt;</span></span>
 <span>  <span class="fu"><a href="https://rdrr.io/r/base/matrix.html">as.matrix</a></span><span class="op">(</span><span class="op">)</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <p>🎬 Add the gene ids as row names to the matrix:</p>
 <div class="cell">
-<div class="sourceCode" id="cb9"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="co"># add the row names to the matrix</span></span>
+<div class="sourceCode" id="cb12"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="co"># add the row names to the matrix</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/base/colnames.html">rownames</a></span><span class="op">(</span><span class="va">s30_count_mat</span><span class="op">)</span> <span class="op">&lt;-</span> <span class="va">s30_filtered</span><span class="op">$</span><span class="va">xenbase_gene_id</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <p>You might want to view the matrix.</p>
@@ -489,19 +506,19 @@ <h1 class="title">Workshop</h1>
 <p>🎬 Make a folder called <code>meta</code> and save the file to it.</p>
 <p>🎬 Read the metadata into a dataframe:</p>
 <div class="cell">
-<div class="sourceCode" id="cb10"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="va">meta</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://readr.tidyverse.org/reference/read_table.html">read_table</a></span><span class="op">(</span><span class="st">"meta/frog_meta_data.txt"</span><span class="op">)</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="sourceCode" id="cb13"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="va">meta</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://readr.tidyverse.org/reference/read_table.html">read_table</a></span><span class="op">(</span><span class="st">"meta/frog_meta_data.txt"</span><span class="op">)</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <p>🎬 Examine the resulting dataframe.</p>
 <p>We need to add the sample names as row names to the metadata dataframe. This is because the DESeqDataSet object will use the row names to match the samples in the metadata to the samples in the counts matrix.</p>
 <p>🎬 Add the sample names as row names to the metadata dataframe:</p>
 <div class="cell">
-<div class="sourceCode" id="cb11"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/row.names.html">row.names</a></span><span class="op">(</span><span class="va">meta</span><span class="op">)</span> <span class="op">&lt;-</span> <span class="va">meta</span><span class="op">$</span><span class="va">sample_id</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="sourceCode" id="cb14"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/row.names.html">row.names</a></span><span class="op">(</span><span class="va">meta</span><span class="op">)</span> <span class="op">&lt;-</span> <span class="va">meta</span><span class="op">$</span><span class="va">sample_id</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <p>(you will get a warning message but you can ignore it)</p>
 <p>We are dealing only with the S30 data so we need to remove the samples that are not in the S30 data.</p>
 <p>🎬 Filter the metadata to keep only the S30 information:</p>
 <div class="cell">
-<div class="sourceCode" id="cb12"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="va">meta_S30</span> <span class="op">&lt;-</span> <span class="va">meta</span> <span class="op">|&gt;</span></span>
+<div class="sourceCode" id="cb15"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="va">meta_S30</span> <span class="op">&lt;-</span> <span class="va">meta</span> <span class="op">|&gt;</span></span>
 <span>  <span class="fu">dplyr</span><span class="fu">::</span><span class="fu"><a href="https://dplyr.tidyverse.org/reference/filter.html">filter</a></span><span class="op">(</span><span class="va">stage</span> <span class="op">==</span> <span class="st">"stage_30"</span><span class="op">)</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <div class="cell">
@@ -520,7 +537,7 @@ <h1 class="title">Workshop</h1>
 <p>We can now create the DESeqDataSet object. The design formula describes the statistical model You should notice that it is the same sort of formula we used in <code><a href="https://rdrr.io/r/stats/t.test.html">t.test()</a></code>, <code><a href="https://rdrr.io/r/stats/lm.html">lm()</a></code>,<code><a href="https://rdrr.io/r/stats/glm.html">glm()</a></code> etc. The <code>~</code> indicates that the left hand side is the response variable (in this case counts) and the right hand side are the explanatory variables. We are interested in the difference between the treatments but we include <code>sibling_rep</code> to account for the fact that the data are paired. The names of the columns in the count matrix have to match the names in the metadata dataframe and the names of the explanatory variables in the design formula have to match the names of columns in the metadata.</p>
 <p>🎬 Create the DESeqDataSet object:</p>
 <div class="cell">
-<div class="sourceCode" id="cb14"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="va">dds</span> <span class="op">&lt;-</span> <span class="fu">DESeqDataSetFromMatrix</span><span class="op">(</span>countData <span class="op">=</span> <span class="va">s30_count_mat</span>,</span>
+<div class="sourceCode" id="cb17"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="va">dds</span> <span class="op">&lt;-</span> <span class="fu">DESeqDataSetFromMatrix</span><span class="op">(</span>countData <span class="op">=</span> <span class="va">s30_count_mat</span>,</span>
 <span>                              colData <span class="op">=</span> <span class="va">meta_S30</span>,</span>
 <span>                              design <span class="op">=</span> <span class="op">~</span> <span class="va">treatment</span> <span class="op">+</span> <span class="va">sibling_rep</span><span class="op">)</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
@@ -529,14 +546,14 @@ <h1 class="title">Workshop</h1>
 <p>The counts are in <code>dds@assays@data@listData[["counts"]]</code> and the metadata are in <code>dds@colData</code> but the easiest way to see them is to use the <code>counts()</code> and <code>colData()</code> functions from the <strong><code>DESeq2</code></strong> package.</p>
 <p>🎬 View the counts:</p>
 <div class="cell">
-<div class="sourceCode" id="cb15"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="fu">counts</span><span class="op">(</span><span class="va">dds</span><span class="op">)</span> <span class="op">|&gt;</span> <span class="fu"><a href="https://rdrr.io/r/utils/View.html">View</a></span><span class="op">(</span><span class="op">)</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="sourceCode" id="cb18"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="fu">counts</span><span class="op">(</span><span class="va">dds</span><span class="op">)</span> <span class="op">|&gt;</span> <span class="fu"><a href="https://rdrr.io/r/utils/View.html">View</a></span><span class="op">(</span><span class="op">)</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-error">
 <pre><code>Error in .External2(C_dataviewer, x, title): unable to start data viewer</code></pre>
 </div>
 </div>
 <p>You should be able to see that this is the same as in <code>s30_count_mat</code>.</p>
 <div class="cell">
-<div class="sourceCode" id="cb17"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="fu">colData</span><span class="op">(</span><span class="va">dds</span><span class="op">)</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="sourceCode" id="cb20"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="fu">colData</span><span class="op">(</span><span class="va">dds</span><span class="op">)</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-stdout">
 <pre><code>DataFrame with 6 rows and 4 columns
           sample_id       stage treatment sibling_rep
@@ -553,11 +570,11 @@ <h1 class="title">Workshop</h1>
 <p>The normalised counts are the counts that have been transformed to account for the library size (i.e., the total number of reads in a sample) and the gene length. We have to first estimate the normalisation factors and store them in the DESeqDataSet object and then we can get the normalised counts.</p>
 <p>🎬 Estimate the factors for normalisation and store them in the DESeqDataSet object:</p>
 <div class="cell">
-<div class="sourceCode" id="cb19"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="va">dds</span> <span class="op">&lt;-</span> <span class="fu">estimateSizeFactors</span><span class="op">(</span><span class="va">dds</span><span class="op">)</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="sourceCode" id="cb22"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="va">dds</span> <span class="op">&lt;-</span> <span class="fu">estimateSizeFactors</span><span class="op">(</span><span class="va">dds</span><span class="op">)</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <p>🎬 Look at the factors (just for information):</p>
 <div class="cell">
-<div class="sourceCode" id="cb20"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="fu">sizeFactors</span><span class="op">(</span><span class="va">dds</span><span class="op">)</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="sourceCode" id="cb23"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="fu">sizeFactors</span><span class="op">(</span><span class="va">dds</span><span class="op">)</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-stdout">
 <pre><code>  S30_C_5   S30_C_6   S30_C_A   S30_F_5   S30_F_6   S30_F_A 
 0.8812200 0.9454600 1.2989886 1.0881870 1.0518961 0.8322894 </code></pre>
@@ -566,12 +583,12 @@ <h1 class="title">Workshop</h1>
 <p>To get the normalised counts we again used the <code>counts()</code> function but this time we use the <code>normalized=TRUE</code> argument.</p>
 <p>🎬 Save the normalised to a matrix:</p>
 <div class="cell">
-<div class="sourceCode" id="cb22"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="va">normalised_counts</span> <span class="op">&lt;-</span> <span class="fu">counts</span><span class="op">(</span><span class="va">dds</span>, normalized <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="sourceCode" id="cb25"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="va">normalised_counts</span> <span class="op">&lt;-</span> <span class="fu">counts</span><span class="op">(</span><span class="va">dds</span>, normalized <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <p>We will write the normalised counts to a file so that we can use them in the future.</p>
 <p>🎬 Make a dataframe of the normalised counts, add a column for the gene ids and write to file:</p>
 <div class="cell">
-<div class="sourceCode" id="cb23"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/data.frame.html">data.frame</a></span><span class="op">(</span><span class="va">normalised_counts</span>,</span>
+<div class="sourceCode" id="cb26"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/data.frame.html">data.frame</a></span><span class="op">(</span><span class="va">normalised_counts</span>,</span>
 <span>           xenbase_gene_id <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/row.names.html">row.names</a></span><span class="op">(</span><span class="va">normalised_counts</span><span class="op">)</span><span class="op">)</span> <span class="op">|&gt;</span></span>
 <span>  <span class="fu"><a href="https://readr.tidyverse.org/reference/write_delim.html">write_csv</a></span><span class="op">(</span>file <span class="op">=</span> <span class="st">"results/S30_normalised_counts.csv"</span><span class="op">)</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
@@ -579,13 +596,13 @@ <h1 class="title">Workshop</h1>
 <p>We used the <code>DESeq()</code> function to do the differential expression analysis. This function fits the statistical model to the data and then uses the model to calculate the significance of the difference between the treatments. It again stored the results in the DESseqDataSet object. Note that the differential expression needs the raw (unnormalised counts) as it does its own normalisation as part of the process.</p>
 <p>🎬 Run the differential expression analysis:</p>
 <div class="cell">
-<div class="sourceCode" id="cb24"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="va">dds</span> <span class="op">&lt;-</span> <span class="fu">DESeq</span><span class="op">(</span><span class="va">dds</span><span class="op">)</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="sourceCode" id="cb27"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="va">dds</span> <span class="op">&lt;-</span> <span class="fu">DESeq</span><span class="op">(</span><span class="va">dds</span><span class="op">)</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <p>The function will take only a few moments to run on this data but can take longer for bigger datasets.</p>
 <p>We need to define the contrasts we want to test. We want to test the difference between the treatments so we will define the contrast as <code>FGF</code> and <code>control</code>.</p>
 <p>🎬 Define the contrast:</p>
 <div class="cell">
-<div class="sourceCode" id="cb25"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="va">contrast_fgf</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="st">"treatment"</span>, <span class="st">"FGF"</span>, <span class="st">"control"</span><span class="op">)</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="sourceCode" id="cb28"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="va">contrast_fgf</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="st">"treatment"</span>, <span class="st">"FGF"</span>, <span class="st">"control"</span><span class="op">)</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <p>Note that <code>treatment</code> is the name of the column in the metadata dataframe and <code>FGF</code> and <code>control</code> are the names of the levels in the <code>treatment</code> column. By putting them in the order <code>FGF</code> , <code>control</code> we are saying the fold change will be FGF / control. If we had put them in the order <code>control</code>, <code>FGF</code> we would have got the fold change as control / FGF. This means:</p>
 <ul>
@@ -594,13 +611,13 @@ <h1 class="title">Workshop</h1>
 </ul>
 <p>🎬 Extract the results from the DESseqDataSet object:</p>
 <div class="cell">
-<div class="sourceCode" id="cb26"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="va">results_fgf</span> <span class="op">&lt;-</span> <span class="fu">results</span><span class="op">(</span><span class="va">dds</span>,</span>
+<div class="sourceCode" id="cb29"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="va">results_fgf</span> <span class="op">&lt;-</span> <span class="fu">results</span><span class="op">(</span><span class="va">dds</span>,</span>
 <span>                       contrast <span class="op">=</span> <span class="va">contrast_fgf</span><span class="op">)</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <p>This will give us the log2 fold change and p-value for the contrast.</p>
 <p>🎬 Save the results to a file:</p>
 <div class="cell">
-<div class="sourceCode" id="cb27"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/data.frame.html">data.frame</a></span><span class="op">(</span><span class="va">results_fgf</span>,</span>
+<div class="sourceCode" id="cb30"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/data.frame.html">data.frame</a></span><span class="op">(</span><span class="va">results_fgf</span>,</span>
 <span>           xenbase_gene_id <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/row.names.html">row.names</a></span><span class="op">(</span><span class="va">results_fgf</span><span class="op">)</span><span class="op">)</span> <span class="op">|&gt;</span> </span>
 <span>  <span class="fu"><a href="https://readr.tidyverse.org/reference/write_delim.html">write_csv</a></span><span class="op">(</span>file <span class="op">=</span> <span class="st">"results/S30_results.csv"</span><span class="op">)</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
@@ -619,7 +636,7 @@ <h1 class="title">Workshop</h1>
 <p>We need to combine the two datasets of 701 and 798 cells into one dataset of 1499 cells, i.e., 1499 columns. The number of rows is the number of genes, 280. Before combining, we must make sure genes in the same order in both dataframes or we would be comparing the expression of one gene in one cell type to the expression of a different gene in the other cell type!</p>
 <p>🎬 Check the gene ids are in the same order in both dataframes:</p>
 <div class="cell">
-<div class="sourceCode" id="cb28"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/identical.html">identical</a></span><span class="op">(</span><span class="va">prog</span><span class="op">$</span><span class="va">ensembl_gene_id</span>, <span class="va">hspc</span><span class="op">$</span><span class="va">ensembl_gene_id</span><span class="op">)</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="sourceCode" id="cb31"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/identical.html">identical</a></span><span class="op">(</span><span class="va">prog</span><span class="op">$</span><span class="va">ensembl_gene_id</span>, <span class="va">hspc</span><span class="op">$</span><span class="va">ensembl_gene_id</span><span class="op">)</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-stdout">
 <pre><code>[1] TRUE</code></pre>
 </div>
@@ -627,11 +644,11 @@ <h1 class="title">Workshop</h1>
 <p><strong><code>scran</code></strong> can use a matrix or a dataframe of counts but theses must be log normalised counts. If using a dataframe, the columns must only contain the expression values (not the gene ids).</p>
 <p>🎬 Combine the two dataframes (minus the gene ids) into one dataframe called <code>prog_hspc</code>:</p>
 <div class="cell">
-<div class="sourceCode" id="cb30"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="va">prog_hspc</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://dplyr.tidyverse.org/reference/bind_cols.html">bind_cols</a></span><span class="op">(</span><span class="va">prog</span><span class="op">[</span><span class="op">-</span><span class="fl">1</span><span class="op">]</span>, <span class="va">hspc</span><span class="op">[</span><span class="op">-</span><span class="fl">1</span><span class="op">]</span><span class="op">)</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="sourceCode" id="cb33"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="va">prog_hspc</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://dplyr.tidyverse.org/reference/bind_cols.html">bind_cols</a></span><span class="op">(</span><span class="va">prog</span><span class="op">[</span><span class="op">-</span><span class="fl">1</span><span class="op">]</span>, <span class="va">hspc</span><span class="op">[</span><span class="op">-</span><span class="fl">1</span><span class="op">]</span><span class="op">)</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <p>🎬 Now add the gene ids as the row names:</p>
 <div class="cell">
-<div class="sourceCode" id="cb31"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/row.names.html">row.names</a></span><span class="op">(</span><span class="va">prog_hspc</span><span class="op">)</span> <span class="op">&lt;-</span> <span class="va">prog</span><span class="op">$</span><span class="va">ensembl_gene_id</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="sourceCode" id="cb34"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/row.names.html">row.names</a></span><span class="op">(</span><span class="va">prog_hspc</span><span class="op">)</span> <span class="op">&lt;-</span> <span class="va">prog</span><span class="op">$</span><span class="va">ensembl_gene_id</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 </section><section id="filter-to-remove-unexpressed-genes" class="level2"><h2 class="anchored" data-anchor-id="filter-to-remove-unexpressed-genes">Filter to remove unexpressed genes</h2>
 <p>In this dataset, we will not see and genes that are not expressed in any of the cells because we are using a specific subset of the transcriptome that was deliberately selected. However, we will go through how to do this because it is an important step in most analyses.</p>
@@ -639,7 +656,7 @@ <h1 class="title">Workshop</h1>
 <p>Where the sum of all the values in the rows is zero, all the entries must be zero. We can use this to find the filter the genes that are not expressed in any of the cells. To do row wise aggregates such as the sum across rows we can use the <code><a href="https://dplyr.tidyverse.org/reference/rowwise.html">rowwise()</a></code> function. <code><a href="https://dplyr.tidyverse.org/reference/c_across.html">c_across()</a></code> allows us to use the colon notation <code>Prog_001:HSPC_852</code> in <code><a href="https://rdrr.io/r/base/sum.html">sum()</a></code> rather than having to list all the column names: <code>sum(Prog_001, Prog_002, Prog_002, Prog_004,.....)</code></p>
 <p>🎬 Find the genes that are 0 in every column of the prog_hspc dataframe:</p>
 <div class="cell">
-<div class="sourceCode" id="cb32"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="va">prog_hspc</span> <span class="op">|&gt;</span> </span>
+<div class="sourceCode" id="cb35"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="va">prog_hspc</span> <span class="op">|&gt;</span> </span>
 <span>  <span class="fu"><a href="https://dplyr.tidyverse.org/reference/rowwise.html">rowwise</a></span><span class="op">(</span><span class="op">)</span> <span class="op">|&gt;</span> </span>
 <span>  <span class="fu"><a href="https://dplyr.tidyverse.org/reference/filter.html">filter</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/sum.html">sum</a></span><span class="op">(</span><span class="fu"><a href="https://dplyr.tidyverse.org/reference/c_across.html">c_across</a></span><span class="op">(</span><span class="va">Prog_001</span><span class="op">:</span><span class="va">HSPC_852</span><span class="op">)</span><span class="op">)</span> <span class="op">==</span> <span class="fl">0</span><span class="op">)</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-stdout">
@@ -661,7 +678,7 @@ <h1 class="title">Workshop</h1>
 <p>We might also examine the genes which are least expressed.</p>
 <p>🎬 Find ten least expressed genes:</p>
 <div class="cell">
-<div class="sourceCode" id="cb34"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/colSums.html">rowSums</a></span><span class="op">(</span><span class="va">prog_hspc</span><span class="op">)</span> <span class="op">|&gt;</span> <span class="fu"><a href="https://rdrr.io/r/base/sort.html">sort</a></span><span class="op">(</span><span class="op">)</span> <span class="op">|&gt;</span> <span class="fu"><a href="https://rdrr.io/r/utils/head.html">head</a></span><span class="op">(</span><span class="fl">10</span><span class="op">)</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+<div class="sourceCode" id="cb37"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/colSums.html">rowSums</a></span><span class="op">(</span><span class="va">prog_hspc</span><span class="op">)</span> <span class="op">|&gt;</span> <span class="fu"><a href="https://rdrr.io/r/base/sort.html">sort</a></span><span class="op">(</span><span class="op">)</span> <span class="op">|&gt;</span> <span class="fu"><a href="https://rdrr.io/r/utils/head.html">head</a></span><span class="op">(</span><span class="fl">10</span><span class="op">)</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-stdout">
 <pre><code>ENSMUSG00000041046 ENSMUSG00000012428 ENSMUSG00000022225 ENSMUSG00000027863 
           30.70322           35.35796           50.45975           61.27461 
@@ -680,7 +697,7 @@ <h1 class="title">Workshop</h1>
 <p>To find the genes that are expressed in only one cell type, we can use the same approach as above but only sum the columns for one cell type.</p>
 <p>🎬 Find the genes that are 0 in every column for the HSPC cells:</p>
 <div class="cell">
-<div class="sourceCode" id="cb36"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="va">prog_hspc</span> <span class="op">|&gt;</span> </span>
+<div class="sourceCode" id="cb39"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="va">prog_hspc</span> <span class="op">|&gt;</span> </span>
 <span>  <span class="fu"><a href="https://dplyr.tidyverse.org/reference/rowwise.html">rowwise</a></span><span class="op">(</span><span class="op">)</span> <span class="op">|&gt;</span> </span>
 <span>  <span class="fu"><a href="https://dplyr.tidyverse.org/reference/filter.html">filter</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/sum.html">sum</a></span><span class="op">(</span><span class="fu"><a href="https://dplyr.tidyverse.org/reference/c_across.html">c_across</a></span><span class="op">(</span><span class="va">HSPC_001</span><span class="op">:</span><span class="va">HSPC_852</span><span class="op">)</span><span class="op">)</span> <span class="op">==</span> <span class="fl">0</span><span class="op">)</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 <div class="cell-output cell-output-stdout">
@@ -720,14 +737,14 @@ <h1 class="title">Workshop</h1>
 <p>The meta data needed for the frog data was information about which columns were in which treatment group and which sibling group and we had that information in a file. Similarly, here we need information on which columns are from which cell type. Instead of having this is a file, we will create a vector that indicates which column belongs to which cell type.</p>
 <p>🎬 Create a vector that indicates which column belongs to which cell type:</p>
 <div class="cell">
-<div class="sourceCode" id="cb39"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="va">cell_type</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/rep.html">rep</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="st">"prog"</span>,<span class="st">"hspc"</span><span class="op">)</span>, </span>
+<div class="sourceCode" id="cb42"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="va">cell_type</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/rep.html">rep</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="st">"prog"</span>,<span class="st">"hspc"</span><span class="op">)</span>, </span>
 <span>                 times <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/length.html">length</a></span><span class="op">(</span><span class="va">prog</span><span class="op">)</span> <span class="op">-</span> <span class="fl">1</span>,</span>
 <span>                           <span class="fu"><a href="https://rdrr.io/r/base/length.html">length</a></span><span class="op">(</span><span class="va">hspc</span><span class="op">)</span> <span class="op">-</span> <span class="fl">1</span><span class="op">)</span><span class="op">)</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <p>The number of times each cell type is repeated is the number of columns in that cell type minus 1. This is because we have removed the column with the gene ids. Do check that the length of the <code>cell_type</code> vector is the same as the number of columns in the <code>prog_hspc</code> dataframe.</p>
 <p>🎬 Run the differential expression analysis:</p>
 <div class="cell">
-<div class="sourceCode" id="cb40"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="va">res_prog_hspc</span> <span class="op">&lt;-</span> <span class="fu">findMarkers</span><span class="op">(</span><span class="va">prog_hspc</span>, </span>
+<div class="sourceCode" id="cb43"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="va">res_prog_hspc</span> <span class="op">&lt;-</span> <span class="fu">findMarkers</span><span class="op">(</span><span class="va">prog_hspc</span>, </span>
 <span>                             <span class="va">cell_type</span><span class="op">)</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <p><code>findMarkers()</code> is the function that runs the differential expression analysis. The first argument is the dataframe containing the data. The second argument is the vector indicating which columns are in which cell type. It gives us two dataframes of the results - rather unnecessarily. One is the results with fold changes that are Prog/HSPC and the other is the results with fold changes that are HSPC/Prog. These have the same magnitude, just a different sign</p>
@@ -897,7 +914,7 @@ <h1 class="title">Workshop</h1>
 </div>
 <p>🎬 Write the results to file:</p>
 <div class="cell">
-<div class="sourceCode" id="cb41"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/data.frame.html">data.frame</a></span><span class="op">(</span><span class="va">res_prog_hspc</span><span class="op">$</span><span class="va">prog</span>, </span>
+<div class="sourceCode" id="cb44"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/data.frame.html">data.frame</a></span><span class="op">(</span><span class="va">res_prog_hspc</span><span class="op">$</span><span class="va">prog</span>, </span>
 <span>           ensembl_gene_id <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/row.names.html">row.names</a></span><span class="op">(</span><span class="va">res_prog_hspc</span><span class="op">$</span><span class="va">prog</span><span class="op">)</span><span class="op">)</span> <span class="op">|&gt;</span> </span>
 <span>  <span class="fu"><a href="https://readr.tidyverse.org/reference/write_delim.html">write_csv</a></span><span class="op">(</span><span class="st">"results/prog_hspc_results.csv"</span><span class="op">)</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
@@ -917,12 +934,15 @@ <h1 class="title">Workshop</h1>
 </section><section id="the-code-file" class="level1"><h1>The Code file</h1>
 <p>These contain all the code needed in the workshop even where it is not visible on the webpage.</p>
 <p>The <a href="../../transcriptomics/week-4/workshop.html">workshop.qmd</a> file is the file I use to compile the practical. Qmd stands for Quarto markdown. It allows code and ordinary text to be interleaved to produce well-formatted reports including webpages. Right-click on the link and choose Save-As to download. You will be able to open the Qmd file in RStudio. Alternatively, <a href="https://github.com/3mmaRand/BIO00088H-data/blob/main/transcriptomics/week-4/workshop.qmd">View in Browser</a>. Coding and thinking answers are marked with <code>#---CODING ANSWER---</code> and <code>#---THINKING ANSWER---</code></p>
-<p>Pages made with R <span class="citation" data-cites="R-core">(<a href="#ref-R-core" role="doc-biblioref">R Core Team 2024</a>)</span>, Quarto <span class="citation" data-cites="allaire2022">(<a href="#ref-allaire2022" role="doc-biblioref"><strong>allaire2022?</strong></a>)</span>, <code>knitr</code> <span class="citation" data-cites="knitr">(<a href="#ref-knitr" role="doc-biblioref"><strong>knitr?</strong></a>)</span>, <code>kableExtra</code> <span class="citation" data-cites="kableExtra">(<a href="#ref-kableExtra" role="doc-biblioref">Zhu 2021</a>)</span></p>
+<p>Pages made with R <span class="citation" data-cites="R-core">(<a href="#ref-R-core" role="doc-biblioref">R Core Team 2024</a>)</span>, Quarto <span class="citation" data-cites="Allaire_Quarto_2024">(<a href="#ref-Allaire_Quarto_2024" role="doc-biblioref">Allaire et al. 2024</a>)</span>, <code>knitr</code> [<span class="citation" data-cites="knitr1">Xie (<a href="#ref-knitr1" role="doc-biblioref">2024</a>)</span>; knitr2; knitr3], <code>kableExtra</code> <span class="citation" data-cites="kableExtra">(<a href="#ref-kableExtra" role="doc-biblioref">Zhu 2021</a>)</span></p>
 </section><section id="references" class="level1">
 
 
 
 </section><div id="quarto-appendix" class="default"><section class="quarto-appendix-contents" role="doc-bibliography" id="quarto-bibliography"><h2 class="anchored quarto-appendix-heading">References</h2><div id="refs" class="references csl-bib-body hanging-indent" data-entry-spacing="0" role="list">
+<div id="ref-Allaire_Quarto_2024" class="csl-entry" role="listitem">
+Allaire, J. J., Charles Teague, Carlos Scheidegger, Yihui Xie, and Christophe Dervieux. 2024. <span>“<span>Quarto</span>.”</span> <a href="https://doi.org/10.5281/zenodo.5960048">https://doi.org/10.5281/zenodo.5960048</a>.
+</div>
 <div id="ref-fs" class="csl-entry" role="listitem">
 Hester, Jim, Hadley Wickham, and Gábor Csárdi. 2023. <span>“Fs: Cross-Platform File System Operations Based on ’Libuv’.”</span>
 </div>
@@ -941,6 +961,9 @@ <h1 class="title">Workshop</h1>
 <div id="ref-tidyverse" class="csl-entry" role="listitem">
 Wickham, Hadley, Mara Averick, Jennifer Bryan, Winston Chang, Lucy D’Agostino McGowan, Romain François, Garrett Grolemund, et al. 2019. <span>“Welcome to the <span class="nocase">tidyverse</span>.”</span> <em>Journal of Open Source Software</em> 4 (43): 1686. <a href="https://doi.org/10.21105/joss.01686">https://doi.org/10.21105/joss.01686</a>.
 </div>
+<div id="ref-knitr1" class="csl-entry" role="listitem">
+Xie, Yihui. 2024. <em>Knitr: A General-Purpose Package for Dynamic Report Generation in r</em>. <a href="https://yihui.org/knitr/">https://yihui.org/knitr/</a>.
+</div>
 <div id="ref-kableExtra" class="csl-entry" role="listitem">
 Zhu, Hao. 2021. <span>“kableExtra: Construct Complex Table with ’Kable’ and Pipe Syntax.”</span> <a href="https://CRAN.R-project.org/package=kableExtra">https://CRAN.R-project.org/package=kableExtra</a>.
 </div>
diff --git a/transcriptomics/week-5/figures/prog-hspc-volcano.png b/transcriptomics/week-5/figures/prog-hspc-volcano.png
index c352f29..6b7e592 100644
Binary files a/transcriptomics/week-5/figures/prog-hspc-volcano.png and b/transcriptomics/week-5/figures/prog-hspc-volcano.png differ
diff --git a/transcriptomics/week-5/overview.html b/transcriptomics/week-5/overview.html
index 79f4aa1..3a3e3cf 100644
--- a/transcriptomics/week-5/overview.html
+++ b/transcriptomics/week-5/overview.html
@@ -307,7 +307,7 @@ <h1 class="title">Overview</h1>
     <div>
     <div class="quarto-title-meta-heading">Published</div>
     <div class="quarto-title-meta-contents">
-      <p class="date">13 September, 2024</p>
+      <p class="date">17 September, 2024</p>
     </div>
   </div>
   
diff --git a/transcriptomics/week-5/study_after_workshop.html b/transcriptomics/week-5/study_after_workshop.html
index d7e8fa0..6db013f 100644
--- a/transcriptomics/week-5/study_after_workshop.html
+++ b/transcriptomics/week-5/study_after_workshop.html
@@ -299,7 +299,7 @@ <h1 class="title">Independent Study to consolidate this week</h1>
     <div>
     <div class="quarto-title-meta-heading">Published</div>
     <div class="quarto-title-meta-contents">
-      <p class="date">13 September, 2024</p>
+      <p class="date">17 September, 2024</p>
     </div>
   </div>
   
diff --git a/transcriptomics/week-5/study_before_workshop.html b/transcriptomics/week-5/study_before_workshop.html
index e4d2df4..8cd011b 100644
--- a/transcriptomics/week-5/study_before_workshop.html
+++ b/transcriptomics/week-5/study_before_workshop.html
@@ -446,7 +446,7 @@
 </div>
 </div>
 
-  <p class="date">13 September, 2024</p>
+  <p class="date">17 September, 2024</p>
 </section><section id="overview" class="slide level2"><h2>Overview</h2>
 <p>In these slides we will:</p>
 <div>
diff --git a/transcriptomics/week-5/workshop.html b/transcriptomics/week-5/workshop.html
index 16ab6e2..46c716c 100644
--- a/transcriptomics/week-5/workshop.html
+++ b/transcriptomics/week-5/workshop.html
@@ -373,7 +373,7 @@ <h1 class="title">Workshop</h1>
     <div>
     <div class="quarto-title-meta-heading">Published</div>
     <div class="quarto-title-meta-contents">
-      <p class="date">13 September, 2024</p>
+      <p class="date">17 September, 2024</p>
     </div>
   </div>
   
@@ -432,12 +432,12 @@ <h1 class="title">Workshop</h1>
 <p>🎬 Remind yourself what is in the rows and columns and the structure of the dataframes (perhaps using <code><a href="https://pillar.r-lib.org/reference/glimpse.html">glimpse()</a></code>)</p>
 <!-- #---THINKING ANSWER--- -->
 <!-- The normalised counts dataframe has 10,136 rows and 7 columns: which arethe sample names and the xenbase gene ids. -->
-<!-- S30_C_5          -->
-<!-- S30_C_6          -->
-<!-- S30_C_A          -->
-<!-- S30_F_5          -->
-<!-- S30_F_6          -->
-<!-- S30_F_A          -->
+<!-- S30_C_1          -->
+<!-- S30_C_2          -->
+<!-- S30_C_3          -->
+<!-- S30_F_1          -->
+<!-- S30_F_2          -->
+<!-- S30_F_3          -->
 <!-- xenbase_gene_id  -->
 <!-- #---THINKING ANSWER--- -->
 <!-- the results dataframe contains the fold change and p values for each gene. -->
@@ -718,8 +718,8 @@ <h1 class="title">Workshop</h1>
 <span>          labRow <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/colnames.html">rownames</a></span><span class="op">(</span><span class="va">mat</span><span class="op">)</span>,</span>
 <span>          heatmap_layers <span class="op">=</span> <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/theme.html">theme</a></span><span class="op">(</span>axis.line <span class="op">=</span> <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/element.html">element_blank</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
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 <p>On the vertical axis are genes which are differentially expressed at the 0.01 level. On the horizontal axis are samples. We can see that the FGF-treated samples cluster together and the control samples cluster together. We can also see two clusters of genes; one of these shows genes upregulated (more yellow) in the FGF-treated samples (the pink cluster) and the other shows genes down regulated (more blue, the blue cluster) in the FGF-treated samples.</p>
@@ -1039,8 +1039,8 @@ <h1 class="title">Workshop</h1>
 <span>          labRow <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/colnames.html">rownames</a></span><span class="op">(</span><span class="va">mat</span><span class="op">)</span>,</span>
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 </div>
 <p>It will take a minute to run and display. On the vertical axis are genes which are differentially expressed at the 0.01 level. On the horizontal axis are cells. We can see that cells of the same type don’t cluster that well together. We can also see two clusters of genes but the pattern of gene is not as clear as it was for the frogs and the correspondence with the cell clusters is not as strong.</p>
@@ -1119,12 +1119,15 @@ <h1 class="title">Workshop</h1>
 </section><section id="the-code-file" class="level1"><h1>The Code file</h1>
 <p>These contain all the code needed in the workshop even where it is not visible on the webpage.</p>
 <p>The <a href="../../transcriptomics/week-5/workshop.html">workshop.qmd</a> file is the file I use to compile the practical. Qmd stands for Quarto markdown. It allows code and ordinary text to be interleaved to produce well-formatted reports including webpages. Right-click on the link and choose Save-As to download. You will be able to open the Qmd file in RStudio. Alternatively, <a href="https://github.com/3mmaRand/BIO00088H-data/blob/main/transcriptomics/week-4/workshop.qmd">View in Browser</a>. Coding and thinking answers are marked with <code>#---CODING ANSWER---</code> and <code>#---THINKING ANSWER---</code></p>
-<p>Pages made with R <span class="citation" data-cites="R-core">(<a href="#ref-R-core" role="doc-biblioref">R Core Team 2024</a>)</span>, Quarto <span class="citation" data-cites="allaire2022">(<a href="#ref-allaire2022" role="doc-biblioref"><strong>allaire2022?</strong></a>)</span>, <code>knitr</code> <span class="citation" data-cites="knitr">(<a href="#ref-knitr" role="doc-biblioref"><strong>knitr?</strong></a>)</span>, <code>kableExtra</code> <span class="citation" data-cites="kableExtra">(<a href="#ref-kableExtra" role="doc-biblioref">Zhu 2021</a>)</span></p>
+<p>Pages made with R <span class="citation" data-cites="R-core">(<a href="#ref-R-core" role="doc-biblioref">R Core Team 2024</a>)</span>, Quarto <span class="citation" data-cites="Allaire_Quarto_2024">(<a href="#ref-Allaire_Quarto_2024" role="doc-biblioref">Allaire et al. 2024</a>)</span>, <code>knitr</code> [<span class="citation" data-cites="knitr1">Xie (<a href="#ref-knitr1" role="doc-biblioref">2024</a>)</span>; knitr2; knitr3], <code>kableExtra</code> <span class="citation" data-cites="kableExtra">(<a href="#ref-kableExtra" role="doc-biblioref">Zhu 2021</a>)</span></p>
 </section><section id="references" class="level1">
 
 
 
 </section><div id="quarto-appendix" class="default"><section class="quarto-appendix-contents" role="doc-bibliography" id="quarto-bibliography"><h2 class="anchored quarto-appendix-heading">References</h2><div id="refs" class="references csl-bib-body hanging-indent" data-entry-spacing="0" role="list">
+<div id="ref-Allaire_Quarto_2024" class="csl-entry" role="listitem">
+Allaire, J. J., Charles Teague, Carlos Scheidegger, Yihui Xie, and Christophe Dervieux. 2024. <span>“<span>Quarto</span>.”</span> <a href="https://doi.org/10.5281/zenodo.5960048">https://doi.org/10.5281/zenodo.5960048</a>.
+</div>
 <div id="ref-birney2004" class="csl-entry" role="listitem">
 Birney, Ewan, T. Daniel Andrews, Paul Bevan, Mario Caccamo, Yuan Chen, Laura Clarke, Guy Coates, et al. 2004. <span>“An Overview of Ensembl.”</span> <em>Genome Research</em> 14 (5): 925–28. <a href="https://doi.org/10.1101/gr.1860604">https://doi.org/10.1101/gr.1860604</a>.
 </div>
@@ -1155,6 +1158,9 @@ <h1 class="title">Workshop</h1>
 <div id="ref-readxl" class="csl-entry" role="listitem">
 Wickham, Hadley, and Jennifer Bryan. 2023. <em>Readxl: Read Excel Files</em>. <a href="https://readxl.tidyverse.org">https://readxl.tidyverse.org</a>.
 </div>
+<div id="ref-knitr1" class="csl-entry" role="listitem">
+Xie, Yihui. 2024. <em>Knitr: A General-Purpose Package for Dynamic Report Generation in r</em>. <a href="https://yihui.org/knitr/">https://yihui.org/knitr/</a>.
+</div>
 <div id="ref-kableExtra" class="csl-entry" role="listitem">
 Zhu, Hao. 2021. <span>“kableExtra: Construct Complex Table with ’Kable’ and Pipe Syntax.”</span> <a href="https://CRAN.R-project.org/package=kableExtra">https://CRAN.R-project.org/package=kableExtra</a>.
 </div>
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