From cb1a543e97f21d3f91a267ba75534a5a2f629c58 Mon Sep 17 00:00:00 2001
From: 3mmaRand <7593411+3mmaRand@users.noreply.github.com>
Date: Sun, 8 Oct 2023 10:10:05 +0000
Subject: [PATCH] =?UTF-8?q?Deploying=20to=20gh-pages=20from=20@=203mmaRand?=
 =?UTF-8?q?/BIO00088H-data@de2becf56f05dd7cd13f9cbc375e3892d9201c64=20?=
 =?UTF-8?q?=F0=9F=9A=80?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 core/core.html                                |     2 +-
 core/week-1/overview.html                     |     2 +-
 core/week-1/study_after_workshop.html         |     2 +-
 core/week-1/study_before_workshop.html        |     2 +-
 core/week-1/workshop.html                     |     2 +-
 core/week-2/overview.html                     |     2 +-
 core/week-2/study_after_workshop.html         |     2 +-
 core/week-2/study_before_workshop.html        |     2 +-
 core/week-2/workshop.html                     |    60 +-
 core/week-6/overview.html                     |     2 +-
 core/week-6/study_after_workshop.html         |     2 +-
 core/week-6/study_before_workshop.html        |     2 +-
 core/week-6/workshop.html                     |     2 +-
 index.html                                    |     2 +-
 omics/omics.html                              |    10 +-
 omics/week-3/overview.html                    |    10 +-
 omics/week-3/study_after_workshop.html        |    10 +-
 omics/week-3/study_before_workshop.html       |     2 +-
 omics/week-3/workshop.html                    |    10 +-
 omics/week-4/data-raw/surfaceome_hspc.csv     |   281 +
 omics/week-4/data-raw/surfaceome_lthsc.csv    |   281 +
 omics/week-4/data-raw/surfaceome_prog.csv     |   281 +
 omics/week-4/data-raw/xlaevis_counts_S14.csv  | 11894 ++++++++++++++++
 omics/week-4/data-raw/xlaevis_counts_S20.csv  | 11894 ++++++++++++++++
 omics/week-4/data-raw/xlaevis_counts_S30.csv  | 11894 ++++++++++++++++
 omics/week-4/images/88H-exp-design-betsy.png  |   Bin 0 -> 32632 bytes
 .../week-4/images/88H-exp-design-jillian.png  |   Bin 0 -> 21955 bytes
 omics/week-4/overview.html                    |   592 +
 omics/week-4/study_after_workshop.html        |   558 +
 omics/week-4/study_before_workshop.html       |   953 ++
 omics/week-4/workshop.html                    |  2405 ++++
 .../figure-html/unnamed-chunk-14-1.png        |   Bin 0 -> 34496 bytes
 .../figure-html/unnamed-chunk-15-1.png        |   Bin 0 -> 49020 bytes
 .../figure-html/unnamed-chunk-19-1.png        |   Bin 0 -> 23882 bytes
 .../figure-html/unnamed-chunk-20-1.png        |   Bin 0 -> 24141 bytes
 .../figure-html/unnamed-chunk-23-1.png        |   Bin 0 -> 26123 bytes
 .../figure-html/unnamed-chunk-26-1.png        |   Bin 0 -> 35073 bytes
 .../figure-html/unnamed-chunk-5-1.png         |   Bin 0 -> 14073 bytes
 .../figure-html/unnamed-chunk-6-1.png         |   Bin 0 -> 17493 bytes
 .../figure-html/unnamed-chunk-7-1.png         |   Bin 0 -> 14547 bytes
 search.json                                   |   219 +-
 41 files changed, 41315 insertions(+), 65 deletions(-)
 create mode 100644 omics/week-4/data-raw/surfaceome_hspc.csv
 create mode 100644 omics/week-4/data-raw/surfaceome_lthsc.csv
 create mode 100644 omics/week-4/data-raw/surfaceome_prog.csv
 create mode 100644 omics/week-4/data-raw/xlaevis_counts_S14.csv
 create mode 100644 omics/week-4/data-raw/xlaevis_counts_S20.csv
 create mode 100644 omics/week-4/data-raw/xlaevis_counts_S30.csv
 create mode 100644 omics/week-4/images/88H-exp-design-betsy.png
 create mode 100644 omics/week-4/images/88H-exp-design-jillian.png
 create mode 100644 omics/week-4/overview.html
 create mode 100644 omics/week-4/study_after_workshop.html
 create mode 100644 omics/week-4/study_before_workshop.html
 create mode 100644 omics/week-4/workshop.html
 create mode 100644 omics/week-4/workshop_files/figure-html/unnamed-chunk-14-1.png
 create mode 100644 omics/week-4/workshop_files/figure-html/unnamed-chunk-15-1.png
 create mode 100644 omics/week-4/workshop_files/figure-html/unnamed-chunk-19-1.png
 create mode 100644 omics/week-4/workshop_files/figure-html/unnamed-chunk-20-1.png
 create mode 100644 omics/week-4/workshop_files/figure-html/unnamed-chunk-23-1.png
 create mode 100644 omics/week-4/workshop_files/figure-html/unnamed-chunk-26-1.png
 create mode 100644 omics/week-4/workshop_files/figure-html/unnamed-chunk-5-1.png
 create mode 100644 omics/week-4/workshop_files/figure-html/unnamed-chunk-6-1.png
 create mode 100644 omics/week-4/workshop_files/figure-html/unnamed-chunk-7-1.png

diff --git a/core/core.html b/core/core.html
index 46ac003..4178f54 100644
--- a/core/core.html
+++ b/core/core.html
@@ -269,7 +269,7 @@ <h1 class="title">Core Data Analysis</h1>
     <div>
     <div class="quarto-title-meta-heading">Published</div>
     <div class="quarto-title-meta-contents">
-      <p class="date">7 October, 2023</p>
+      <p class="date">8 October, 2023</p>
     </div>
   </div>
   
diff --git a/core/week-1/overview.html b/core/week-1/overview.html
index 11c4d67..de0783d 100644
--- a/core/week-1/overview.html
+++ b/core/week-1/overview.html
@@ -266,7 +266,7 @@ <h1 class="title">Overview</h1>
     <div>
     <div class="quarto-title-meta-heading">Published</div>
     <div class="quarto-title-meta-contents">
-      <p class="date">7 October, 2023</p>
+      <p class="date">8 October, 2023</p>
     </div>
   </div>
   
diff --git a/core/week-1/study_after_workshop.html b/core/week-1/study_after_workshop.html
index c8426c3..a754b7b 100644
--- a/core/week-1/study_after_workshop.html
+++ b/core/week-1/study_after_workshop.html
@@ -266,7 +266,7 @@ <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">7 October, 2023</p>
+      <p class="date">8 October, 2023</p>
     </div>
   </div>
   
diff --git a/core/week-1/study_before_workshop.html b/core/week-1/study_before_workshop.html
index 4cc096e..eb6f761 100644
--- a/core/week-1/study_before_workshop.html
+++ b/core/week-1/study_before_workshop.html
@@ -259,7 +259,7 @@ <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">7 October, 2023</p>
+      <p class="date">8 October, 2023</p>
     </div>
   </div>
   
diff --git a/core/week-1/workshop.html b/core/week-1/workshop.html
index d7b1abf..0edd77a 100644
--- a/core/week-1/workshop.html
+++ b/core/week-1/workshop.html
@@ -319,7 +319,7 @@ <h1 class="title">Workshop</h1>
     <div>
     <div class="quarto-title-meta-heading">Published</div>
     <div class="quarto-title-meta-contents">
-      <p class="date">7 October, 2023</p>
+      <p class="date">8 October, 2023</p>
     </div>
   </div>
   
diff --git a/core/week-2/overview.html b/core/week-2/overview.html
index 4b73168..48e5ca5 100644
--- a/core/week-2/overview.html
+++ b/core/week-2/overview.html
@@ -257,7 +257,7 @@ <h1 class="title">Overview</h1>
     <div>
     <div class="quarto-title-meta-heading">Published</div>
     <div class="quarto-title-meta-contents">
-      <p class="date">7 October, 2023</p>
+      <p class="date">8 October, 2023</p>
     </div>
   </div>
   
diff --git a/core/week-2/study_after_workshop.html b/core/week-2/study_after_workshop.html
index 644f643..4177035 100644
--- a/core/week-2/study_after_workshop.html
+++ b/core/week-2/study_after_workshop.html
@@ -257,7 +257,7 @@ <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">7 October, 2023</p>
+      <p class="date">8 October, 2023</p>
     </div>
   </div>
   
diff --git a/core/week-2/study_before_workshop.html b/core/week-2/study_before_workshop.html
index 5bd7b7d..3e7fd0c 100644
--- a/core/week-2/study_before_workshop.html
+++ b/core/week-2/study_before_workshop.html
@@ -423,7 +423,7 @@
 </div>
 </div>
 
-  <p class="date">7 October, 2023</p>
+  <p class="date">8 October, 2023</p>
 </section><section id="overview" class="slide level2"><h2>Overview</h2>
 <ul>
 <li>RStudio Projects revisited
diff --git a/core/week-2/workshop.html b/core/week-2/workshop.html
index 5a21467..6cee5b9 100644
--- a/core/week-2/workshop.html
+++ b/core/week-2/workshop.html
@@ -339,7 +339,7 @@ <h1 class="title">Workshop</h1>
     <div>
     <div class="quarto-title-meta-heading">Published</div>
     <div class="quarto-title-meta-contents">
-      <p class="date">7 October, 2023</p>
+      <p class="date">8 October, 2023</p>
     </div>
   </div>
   
@@ -425,15 +425,15 @@ <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 136
-drwxr-xr-x 2 runner docker  4096 Oct  7 10:56 data
-drwxr-xr-x 2 runner docker  4096 Oct  7 10:56 images
--rw-r--r-- 1 runner docker  1597 Oct  7 10:56 overview.qmd
--rw-r--r-- 1 runner docker   184 Oct  7 10:56 study_after_workshop.qmd
--rw-r--r-- 1 runner docker 69876 Oct  7 10:58 study_before_workshop.html
--rw-r--r-- 1 runner docker  4807 Oct  7 10:56 study_before_workshop.ipynb
--rw-r--r-- 1 runner docker 13029 Oct  7 10:56 study_before_workshop.qmd
--rw-r--r-- 1 runner docker  8550 Oct  7 10:56 workshop.qmd
--rw-r--r-- 1 runner docker  8564 Oct  7 10:58 workshop.rmarkdown</code></pre>
+drwxr-xr-x 2 runner docker  4096 Oct  8 10:06 data
+drwxr-xr-x 2 runner docker  4096 Oct  8 10:06 images
+-rw-r--r-- 1 runner docker  1597 Oct  8 10:06 overview.qmd
+-rw-r--r-- 1 runner docker   184 Oct  8 10:06 study_after_workshop.qmd
+-rw-r--r-- 1 runner docker 69876 Oct  8 10:09 study_before_workshop.html
+-rw-r--r-- 1 runner docker  4807 Oct  8 10:06 study_before_workshop.ipynb
+-rw-r--r-- 1 runner docker 13029 Oct  8 10:06 study_before_workshop.qmd
+-rw-r--r-- 1 runner docker  8550 Oct  8 10:06 workshop.qmd
+-rw-r--r-- 1 runner docker  8564 Oct  8 10:09 workshop.rmarkdown</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>
@@ -441,15 +441,15 @@ <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 136K
-drwxr-xr-x 2 runner docker 4.0K Oct  7 10:56 data
-drwxr-xr-x 2 runner docker 4.0K Oct  7 10:56 images
--rw-r--r-- 1 runner docker 1.6K Oct  7 10:56 overview.qmd
--rw-r--r-- 1 runner docker  184 Oct  7 10:56 study_after_workshop.qmd
--rw-r--r-- 1 runner docker  69K Oct  7 10:58 study_before_workshop.html
--rw-r--r-- 1 runner docker 4.7K Oct  7 10:56 study_before_workshop.ipynb
--rw-r--r-- 1 runner docker  13K Oct  7 10:56 study_before_workshop.qmd
--rw-r--r-- 1 runner docker 8.4K Oct  7 10:56 workshop.qmd
--rw-r--r-- 1 runner docker 8.4K Oct  7 10:58 workshop.rmarkdown</code></pre>
+drwxr-xr-x 2 runner docker 4.0K Oct  8 10:06 data
+drwxr-xr-x 2 runner docker 4.0K Oct  8 10:06 images
+-rw-r--r-- 1 runner docker 1.6K Oct  8 10:06 overview.qmd
+-rw-r--r-- 1 runner docker  184 Oct  8 10:06 study_after_workshop.qmd
+-rw-r--r-- 1 runner docker  69K Oct  8 10:09 study_before_workshop.html
+-rw-r--r-- 1 runner docker 4.7K Oct  8 10:06 study_before_workshop.ipynb
+-rw-r--r-- 1 runner docker  13K Oct  8 10:06 study_before_workshop.qmd
+-rw-r--r-- 1 runner docker 8.4K Oct  8 10:06 workshop.qmd
+-rw-r--r-- 1 runner docker 8.4K Oct  8 10:09 workshop.rmarkdown</code></pre>
 </div>
 </div>
 <p>The <code>-a</code> option stands for “all” and shows us all the files, including hidden files.</p>
@@ -457,17 +457,17 @@ <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 144K
-drwxr-xr-x 4 runner docker 4.0K Oct  7 10:58 .
-drwxr-xr-x 5 runner docker 4.0K Oct  7 10:58 ..
-drwxr-xr-x 2 runner docker 4.0K Oct  7 10:56 data
-drwxr-xr-x 2 runner docker 4.0K Oct  7 10:56 images
--rw-r--r-- 1 runner docker 1.6K Oct  7 10:56 overview.qmd
--rw-r--r-- 1 runner docker  184 Oct  7 10:56 study_after_workshop.qmd
--rw-r--r-- 1 runner docker  69K Oct  7 10:58 study_before_workshop.html
--rw-r--r-- 1 runner docker 4.7K Oct  7 10:56 study_before_workshop.ipynb
--rw-r--r-- 1 runner docker  13K Oct  7 10:56 study_before_workshop.qmd
--rw-r--r-- 1 runner docker 8.4K Oct  7 10:56 workshop.qmd
--rw-r--r-- 1 runner docker 8.4K Oct  7 10:58 workshop.rmarkdown</code></pre>
+drwxr-xr-x 4 runner docker 4.0K Oct  8 10:09 .
+drwxr-xr-x 5 runner docker 4.0K Oct  8 10:09 ..
+drwxr-xr-x 2 runner docker 4.0K Oct  8 10:06 data
+drwxr-xr-x 2 runner docker 4.0K Oct  8 10:06 images
+-rw-r--r-- 1 runner docker 1.6K Oct  8 10:06 overview.qmd
+-rw-r--r-- 1 runner docker  184 Oct  8 10:06 study_after_workshop.qmd
+-rw-r--r-- 1 runner docker  69K Oct  8 10:09 study_before_workshop.html
+-rw-r--r-- 1 runner docker 4.7K Oct  8 10:06 study_before_workshop.ipynb
+-rw-r--r-- 1 runner docker  13K Oct  8 10:06 study_before_workshop.qmd
+-rw-r--r-- 1 runner docker 8.4K Oct  8 10:06 workshop.qmd
+-rw-r--r-- 1 runner docker 8.4K Oct  8 10:09 workshop.rmarkdown</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-6/overview.html b/core/week-6/overview.html
index 19d90ed..3844045 100644
--- a/core/week-6/overview.html
+++ b/core/week-6/overview.html
@@ -257,7 +257,7 @@ <h1 class="title">Overview</h1>
     <div>
     <div class="quarto-title-meta-heading">Published</div>
     <div class="quarto-title-meta-contents">
-      <p class="date">7 October, 2023</p>
+      <p class="date">8 October, 2023</p>
     </div>
   </div>
   
diff --git a/core/week-6/study_after_workshop.html b/core/week-6/study_after_workshop.html
index 8380cc2..048ac2f 100644
--- a/core/week-6/study_after_workshop.html
+++ b/core/week-6/study_after_workshop.html
@@ -270,7 +270,7 @@ <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">7 October, 2023</p>
+      <p class="date">8 October, 2023</p>
     </div>
   </div>
   
diff --git a/core/week-6/study_before_workshop.html b/core/week-6/study_before_workshop.html
index c30b4c1..3c28547 100644
--- a/core/week-6/study_before_workshop.html
+++ b/core/week-6/study_before_workshop.html
@@ -257,7 +257,7 @@ <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">7 October, 2023</p>
+      <p class="date">8 October, 2023</p>
     </div>
   </div>
   
diff --git a/core/week-6/workshop.html b/core/week-6/workshop.html
index 074b67e..ef6223b 100644
--- a/core/week-6/workshop.html
+++ b/core/week-6/workshop.html
@@ -278,7 +278,7 @@ <h1 class="title">Workshop</h1>
     <div>
     <div class="quarto-title-meta-heading">Published</div>
     <div class="quarto-title-meta-contents">
-      <p class="date">7 October, 2023</p>
+      <p class="date">8 October, 2023</p>
     </div>
   </div>
   
diff --git a/index.html b/index.html
index df147e8..c7de006 100644
--- a/index.html
+++ b/index.html
@@ -120,7 +120,7 @@ <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">7 October, 2023</p>
+      <p class="date">8 October, 2023</p>
     </div>
   </div>
   
diff --git a/omics/omics.html b/omics/omics.html
index 82eff31..6f0c53b 100644
--- a/omics/omics.html
+++ b/omics/omics.html
@@ -174,25 +174,25 @@
       <ul id="quarto-sidebar-section-2" class="collapse list-unstyled sidebar-section depth1 show">  
           <li class="sidebar-item">
   <div class="sidebar-item-container"> 
-  <a href="../omics/week-4/overview.qmd" class="sidebar-item-text sidebar-link">
+  <a href="../omics/week-4/overview.html" class="sidebar-item-text sidebar-link">
  <span class="menu-text">About</span></a>
   </div>
 </li>
           <li class="sidebar-item">
   <div class="sidebar-item-container"> 
-  <a href="../omics/week-4/study_before_workshop.qmd" class="sidebar-item-text sidebar-link">
+  <a href="../omics/week-4/study_before_workshop.html" class="sidebar-item-text sidebar-link">
  <span class="menu-text">Prepare!</span></a>
   </div>
 </li>
           <li class="sidebar-item">
   <div class="sidebar-item-container"> 
-  <a href="../omics/week-4/workshop.qmd" class="sidebar-item-text sidebar-link">
+  <a href="../omics/week-4/workshop.html" class="sidebar-item-text sidebar-link">
  <span class="menu-text">Workshop</span></a>
   </div>
 </li>
           <li class="sidebar-item">
   <div class="sidebar-item-container"> 
-  <a href="../omics/week-4/study_after_workshop.qmd" class="sidebar-item-text sidebar-link">
+  <a href="../omics/week-4/study_after_workshop.html" class="sidebar-item-text sidebar-link">
  <span class="menu-text">Consolidate!</span></a>
   </div>
 </li>
@@ -269,7 +269,7 @@ <h1 class="title">Omics Data Analysis for Group Project</h1>
     <div>
     <div class="quarto-title-meta-heading">Published</div>
     <div class="quarto-title-meta-contents">
-      <p class="date">7 October, 2023</p>
+      <p class="date">8 October, 2023</p>
     </div>
   </div>
   
diff --git a/omics/week-3/overview.html b/omics/week-3/overview.html
index bcfe5d9..a0182df 100644
--- a/omics/week-3/overview.html
+++ b/omics/week-3/overview.html
@@ -174,25 +174,25 @@
       <ul id="quarto-sidebar-section-2" class="collapse list-unstyled sidebar-section depth1 show">  
           <li class="sidebar-item">
   <div class="sidebar-item-container"> 
-  <a href="../../omics/week-4/overview.qmd" class="sidebar-item-text sidebar-link">
+  <a href="../../omics/week-4/overview.html" class="sidebar-item-text sidebar-link">
  <span class="menu-text">About</span></a>
   </div>
 </li>
           <li class="sidebar-item">
   <div class="sidebar-item-container"> 
-  <a href="../../omics/week-4/study_before_workshop.qmd" class="sidebar-item-text sidebar-link">
+  <a href="../../omics/week-4/study_before_workshop.html" class="sidebar-item-text sidebar-link">
  <span class="menu-text">Prepare!</span></a>
   </div>
 </li>
           <li class="sidebar-item">
   <div class="sidebar-item-container"> 
-  <a href="../../omics/week-4/workshop.qmd" class="sidebar-item-text sidebar-link">
+  <a href="../../omics/week-4/workshop.html" class="sidebar-item-text sidebar-link">
  <span class="menu-text">Workshop</span></a>
   </div>
 </li>
           <li class="sidebar-item">
   <div class="sidebar-item-container"> 
-  <a href="../../omics/week-4/study_after_workshop.qmd" class="sidebar-item-text sidebar-link">
+  <a href="../../omics/week-4/study_after_workshop.html" class="sidebar-item-text sidebar-link">
  <span class="menu-text">Consolidate!</span></a>
   </div>
 </li>
@@ -266,7 +266,7 @@ <h1 class="title">Overview</h1>
     <div>
     <div class="quarto-title-meta-heading">Published</div>
     <div class="quarto-title-meta-contents">
-      <p class="date">7 October, 2023</p>
+      <p class="date">8 October, 2023</p>
     </div>
   </div>
   
diff --git a/omics/week-3/study_after_workshop.html b/omics/week-3/study_after_workshop.html
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diff --git a/omics/week-4/data-raw/surfaceome_lthsc.csv b/omics/week-4/data-raw/surfaceome_lthsc.csv
new file mode 100644
index 0000000..460a05b
--- /dev/null
+++ b/omics/week-4/data-raw/surfaceome_lthsc.csv
@@ -0,0 +1,281 @@
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diff --git a/omics/week-4/data-raw/surfaceome_prog.csv b/omics/week-4/data-raw/surfaceome_prog.csv
new file mode 100644
index 0000000..1774a10
--- /dev/null
+++ b/omics/week-4/data-raw/surfaceome_prog.csv
@@ -0,0 +1,281 @@
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diff --git a/omics/week-4/data-raw/xlaevis_counts_S14.csv b/omics/week-4/data-raw/xlaevis_counts_S14.csv
new file mode 100644
index 0000000..65162c3
--- /dev/null
+++ b/omics/week-4/data-raw/xlaevis_counts_S14.csv
@@ -0,0 +1,11894 @@
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diff --git a/omics/week-4/data-raw/xlaevis_counts_S20.csv b/omics/week-4/data-raw/xlaevis_counts_S20.csv
new file mode 100644
index 0000000..17c1964
--- /dev/null
+++ b/omics/week-4/data-raw/xlaevis_counts_S20.csv
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diff --git a/omics/week-4/data-raw/xlaevis_counts_S30.csv b/omics/week-4/data-raw/xlaevis_counts_S30.csv
new file mode 100644
index 0000000..fe0770c
--- /dev/null
+++ b/omics/week-4/data-raw/xlaevis_counts_S30.csv
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+  <div class="sidebar-item-container"> 
+  <a href="../../omics/week-3/study_before_workshop.html" class="sidebar-item-text sidebar-link">
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+ <span class="menu-text">Consolidate!</span></a>
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+  <div class="sidebar-item-container"> 
+  <a href="../../omics/week-4/study_before_workshop.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text">Prepare!</span></a>
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+ <span class="menu-text">Consolidate!</span></a>
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+        <li class="sidebar-item sidebar-item-section">
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+            <a class="sidebar-item-text sidebar-link text-start" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-3" aria-expanded="true">
+ <span class="menu-text">Week 5: Visualising and Interpreting</span></a>
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+  <a href="../../omics/week-5/overview.qmd" class="sidebar-item-text sidebar-link">
+ <span class="menu-text">About</span></a>
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+</li>
+          <li class="sidebar-item">
+  <div class="sidebar-item-container"> 
+  <a href="../../omics/week-5/study_before_workshop.qmd" class="sidebar-item-text sidebar-link">
+ <span class="menu-text">Prepare!</span></a>
+  </div>
+</li>
+          <li class="sidebar-item">
+  <div class="sidebar-item-container"> 
+  <a href="../../omics/week-5/workshop.qmd" class="sidebar-item-text sidebar-link">
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+          <li class="sidebar-item">
+  <div class="sidebar-item-container"> 
+  <a href="../../omics/week-5/study_after_workshop.qmd" class="sidebar-item-text sidebar-link">
+ <span class="menu-text">Consolidate!</span></a>
+  </div>
+</li>
+      </ul>
+  </li>
+    </ul>
+    </div>
+</nav>
+<div id="quarto-sidebar-glass" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar,#quarto-sidebar-glass"></div>
+<!-- margin-sidebar -->
+    <div id="quarto-margin-sidebar" class="sidebar margin-sidebar">
+        <nav id="TOC" role="doc-toc" class="toc-active">
+    <h2 id="toc-title">On this page</h2>
+   
+  <ul>
+  <li><a href="#learning-objectives" id="toc-learning-objectives" class="nav-link active" data-scroll-target="#learning-objectives">Learning objectives</a></li>
+  <li><a href="#instructions" id="toc-instructions" class="nav-link" data-scroll-target="#instructions">Instructions</a></li>
+  </ul>
+</nav>
+    </div>
+<!-- main -->
+<main class="content" id="quarto-document-content">
+
+<header id="title-block-header" class="quarto-title-block default">
+<div class="quarto-title">
+<h1 class="title">Overview</h1>
+<p class="subtitle lead">Omics 1: 👋 Hello data!</p>
+</div>
+
+
+
+<div class="quarto-title-meta">
+
+    
+    <div>
+    <div class="quarto-title-meta-heading">Published</div>
+    <div class="quarto-title-meta-contents">
+      <p class="date">8 October, 2023</p>
+    </div>
+  </div>
+  
+    
+  </div>
+  
+
+</header>
+
+<p>This week you will meet your data. The independent study will concisely cover how these data were generated and how they have been processed before being given to you. There will also be an overview of the analysis we will carry out over three workshops. In the workshop, you will learn what steps to take to get a good understanding of ’omics data before you consider any statistical analysis. This is an often overlooked, but very valuable and informative, part of any data pipeline. It gives you the deep understanding of the data structures and values that you will need to code and trouble-shoot code, allows you to spot failed or problematic samples and informs your decisions on quality control.</p>
+<p>We suggest you sit together with your group in the workshop.</p>
+<section id="learning-objectives" class="level3">
+<h3 class="anchored" data-anchor-id="learning-objectives">Learning objectives</h3>
+<p>The successful student will be able to:</p>
+<ul>
+<li>explore ’omics data to find the number of rows and columns and know how these correspond to samples and variables</li>
+<li>explore the distribution of expression measures across whole data sets, across variables and across samples by summarising and plotting</li>
+<li>explain what distributions are expected and interpret the distributions they have</li>
+<li>explain on what basis we might filter out variables or samples</li>
+<li>import, explore and filter ’omics data reproducibly so they can understand and reuse their code in the future</li>
+</ul>
+</section>
+<section id="instructions" class="level3">
+<h3 class="anchored" data-anchor-id="instructions">Instructions</h3>
+<ol type="1">
+<li><p><a href="../../omics/week-4/study_before_workshop.html">Prepare</a></p>
+<ol type="i">
+<li>📖 Read how the data were generated and how they have been processed so far, install</li>
+</ol></li>
+<li><p><a href="../../omics/week-4/workshop.html">Workshop</a></p>
+<ol type="i">
+<li><p>💻 Set up a Project</p></li>
+<li><p>💻 Import data</p></li>
+<li><p>💻 Explore the distribution of values across samples/cells and across genes/species</p></li>
+<li><p>💻 Perform quality control</p></li>
+<li><p>💻 Look after future you!</p></li>
+</ol></li>
+<li><p><a href="../../omics/week-4/study_after_workshop.html">Consolidate</a></p>
+<ol type="i">
+<li>💻 Use the work you completed in the workshop as a template to apply to a new case.</li>
+</ol></li>
+</ol>
+
+
+</section>
+
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diff --git a/omics/week-4/study_after_workshop.html b/omics/week-4/study_after_workshop.html
new file mode 100644
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+    
+    <div>
+    <div class="quarto-title-meta-heading">Published</div>
+    <div class="quarto-title-meta-contents">
+      <p class="date">8 October, 2023</p>
+    </div>
+  </div>
+  
+    
+  </div>
+  
+
+</header>
+
+<p>You need only do the section for your own project data</p>
+<p>🐸 Frogs</p>
+<p>🎬 Open your <code>frogs-88H</code> Project. Make a new script and, using <code>cont-fgf-s30.R</code> as a template, repeat the analysis on one of the other comparisons.</p>
+<p>🐭 Mice</p>
+<p>🎬 Open your <code>mice-88H</code> Project. Open your <code>hspc-prog.R</code> script and, using you code working with the hspc cells as a template, repeat the analysis on the prog cells.</p>
+<p>🍂 xxxx</p>
+<p>🎬 Open your <code>xxxx-88H</code> Project. Make a new script and, using <code>xxxxxxx.R</code> as a template, repeat the analysis on xxxxxxxx</p>
+
+
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+</head>
+<body class="quarto-light">
+  <div class="reveal">
+    <div class="slides">
+
+<section id="title-slide" class="quarto-title-block center">
+  <h1 class="title">Independent Study to prepare for workshop</h1>
+  <p class="subtitle">Omics 1: 👋 Hello data!</p>
+
+<div class="quarto-title-authors">
+<div class="quarto-title-author">
+<div class="quarto-title-author-name">
+Emma Rand 
+</div>
+</div>
+</div>
+
+  <p class="date">8 October, 2023</p>
+</section>
+<section id="overview" class="slide level2">
+<h2>Overview</h2>
+<div>
+<ul>
+<li class="fragment"><p>Concise summary of the experimental design and aims</p></li>
+<li class="fragment"><p>What the raw data consist of</p></li>
+<li class="fragment"><p>What has been done to the data so far</p></li>
+<li class="fragment"><p>What steps we will take in the workshop</p></li>
+</ul>
+</div>
+</section>
+<section id="the-data" class="slide level2">
+<h2>The Data</h2>
+<p>There are three datasets</p>
+<ul>
+<li><p>🐸 transcriptomic data (bulk RNA-seq) from frog embryos.</p></li>
+<li><p>🐭 transcriptomic data (single cell RNA-seq) from stemcells</p></li>
+<li><p>🍂 ??????? Metabolomic / Metagenomic data from anaerobic digesters</p></li>
+</ul>
+</section>
+<section>
+<section id="experimental-design" class="title-slide slide level1 center">
+<h1>Experimental design</h1>
+
+</section>
+<section id="experimental-design-1" class="slide level2" data-auto-animate="true">
+<h2 data-id="quarto-animate-title">🐸 Experimental design</h2>
+
+<img data-src="images/88H-exp-design-betsy.png" width="700" class="r-stretch quarto-figure-center"><p class="caption">Schematic of frog development experiment</p></section>
+<section id="experimental-design-2" class="slide level2" data-auto-animate="true">
+<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</p></li>
+<li class="fragment"><p>two siblings from each fertilisation one control, on FGF treated</p></li>
+<li class="fragment"><p>sequenced at three time points</p></li>
+<li class="fragment"><p>3 x 2 x 3 = 18 groups</p></li>
+</ul>
+</div>
+</section>
+<section id="experimental-design-3" class="slide level2" data-auto-animate="true">
+<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>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>
+</ul>
+</div>
+</section>
+<section id="aim" class="slide level2">
+<h2>🐸 Aim</h2>
+<div>
+<ul>
+<li class="fragment"><p>find genes important in frog development</p></li>
+<li class="fragment"><p>Important means the genes that are differentially expressed between the control-treated and the FGF-treated siblings</p></li>
+<li class="fragment"><p>Differentially expressed means the expression in one group is significantly higher than in the other</p></li>
+</ul>
+</div>
+</section>
+<section id="guided-analysis" class="slide level2">
+<h2>🐸 Guided analysis</h2>
+<div>
+<ul>
+<li class="fragment"><p>The workshops will take you through comparing the control and FGF treated sibling at S30</p></li>
+<li class="fragment"><p>This is the “least interesting” comparison</p></li>
+<li class="fragment"><p>You will be guided to carefully document your work so you can apply the same methods to other comparisons</p></li>
+</ul>
+</div>
+</section>
+<section id="experimental-design-4" class="slide level2" data-auto-animate="true">
+<h2 data-id="quarto-animate-title">🐭 Experimental design</h2>
+
+<img data-src="images/88H-exp-design-jillian.png" width="700" class="r-stretch quarto-figure-center"><p class="caption">Schematic of stem cell experiment</p></section>
+<section id="experimental-design-5" class="slide level2" data-auto-animate="true">
+<h2 data-id="quarto-animate-title">🐭 Experimental design</h2>
+
+<img data-src="images/88H-exp-design-jillian.png" width="200" class="r-stretch quarto-figure-left"><p class="caption">Schematic of stem cell experiment</p><div>
+<ul>
+<li class="fragment"><p>Cells were sorted using flow cytometry on the basis of cell surface markers</p></li>
+<li class="fragment"><p>There are three cell types: LT-HSCs, HSPCs, Progs</p></li>
+<li class="fragment"><p>Many cells of each cell type were sequenced</p></li>
+</ul>
+</div>
+</section>
+<section id="experimental-design-6" class="slide level2" data-auto-animate="true">
+<h2 data-id="quarto-animate-title">🐭 Experimental design</h2>
+
+<img data-src="images/88H-exp-design-jillian.png" width="200" class="r-stretch quarto-figure-left"><p class="caption">Schematic of stem cell experiment</p><div>
+<ul>
+<li class="fragment"><p>There are three cell types: LT-HSCs, HSPCs, Progs <span style="color:#009900">These are the “treaments”</span></p></li>
+<li class="fragment"><p>Many cells of each type were sequenced: <span style="color:#009900">These are the replicates</span></p></li>
+<li class="fragment"><p><span style="color:#009900">155 LT-HSCs, 701 HSPCs, 798 Progs</span></p></li>
+</ul>
+</div>
+</section>
+<section id="aim-1" class="slide level2">
+<h2>🐭 Aim</h2>
+<div>
+<ul>
+<li class="fragment"><p>find genes for cell surface proteins that are important in stem cell identity</p></li>
+<li class="fragment"><p>Important means genes that are differentially expressed between at least two cell types</p></li>
+<li class="fragment"><p>Differentially expressed means the expression in one group is significantly higher than in the other</p></li>
+</ul>
+</div>
+</section>
+<section id="guided-analysis-1" class="slide level2">
+<h2>🐭 Guided analysis</h2>
+<div>
+<ul>
+<li class="fragment"><p>The workshops will take you through comparing the HSPC and Prog cells</p></li>
+<li class="fragment"><p>This is the “least interesting” comparison</p></li>
+<li class="fragment"><p>You will be guided to carefully document your work so you can apply the same methods to other comparisons</p></li>
+</ul>
+</div>
+</section></section>
+<section>
+<section id="the-raw-data" class="title-slide slide level1 center">
+<h1>The raw data</h1>
+
+</section>
+<section id="raw-sequence-data" class="slide level2">
+<h2>Raw Sequence data</h2>
+<div>
+<ul>
+<li class="fragment"><p>The raw data are “reads” from a sequencing machine.</p></li>
+<li class="fragment"><p>A read is sequence of DNA or RNA shorter than the whole genome or transcriptome</p></li>
+<li class="fragment"><p>The length of the reads depends on the type of sequencing machine</p>
+<ul>
+<li class="fragment">Short-read technologies e.g.&nbsp;Illumina have higher base accuracy but are harder to align</li>
+<li class="fragment">Long-read technologies e.g.&nbsp;Nanopore have lower base accuracy but are easier to align</li>
+</ul></li>
+</ul>
+</div>
+</section></section>
+<section>
+<section id="the-raw-data-1" class="title-slide slide level1 center">
+<h1>The raw data</h1>
+
+</section>
+<section id="raw-sequence-data-1" class="slide level2">
+<h2>Raw Sequence data</h2>
+<div>
+<ul>
+<li class="fragment"><p>Sequencing technology is constantly improving</p></li>
+<li class="fragment"><p>Optional: You can read more about Sequencing technologies in <a href="https://cloud-span.github.io/experimental_design00-overview/">Statistically useful experimental design</a> <span class="citation" data-cites="rand_statistically_2022">(<a href="#/references" role="doc-biblioref" onclick="">Rand and Forrester 2022</a>)</span></p></li>
+</ul>
+</div>
+</section>
+<section id="raw-sequence-data-2" class="slide level2">
+<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>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>
+</div>
+</section></section>
+<section>
+<section id="what-has-been-done-to-the-data-so-far" class="title-slide slide level1 center">
+<h1>What has been done to the data so far</h1>
+
+</section>
+<section id="general-steps" class="slide level2">
+<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>
+</ul>
+</div>
+</section>
+<section id="data" class="slide level2">
+<h2>🐸 Data</h2>
+<ul>
+<li><p>Unpublished (so far!)</p></li>
+<li><p>Expression for the whole transcriptome <a href="https://www.xenbase.org/xenbase/static-xenbase/ftpDatafiles.jsp"><em>X. laevis</em> v10.1 genome assembly</a></p></li>
+<li><p>Values are raw counts</p></li>
+<li><p>The statistical analysis method we will use <code>DESeq2</code> <span class="citation" data-cites="DESeq2">(<a href="#/references" role="doc-biblioref" onclick="">Love, Huber, and Anders 2014</a>)</span> requires raw counts and performs the normalisation itself</p></li>
+</ul>
+</section>
+<section id="data-1" class="slide level2">
+<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>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>
+</section></section>
+<section>
+<section id="workshops" class="title-slide slide level1 center">
+<h1>Workshops</h1>
+
+</section>
+<section id="workshops-1" class="slide level2">
+<h2>Workshops</h2>
+<ul>
+<li><p>Omics 1: Hello data Getting to know the data. Checking the distributions of values overall, across samples and across genes to check things are as we expect and detect genes/samples that need to be removed</p></li>
+<li><p>Omics 2: Statistical Analysis Identifying which genes are differentially expressed between treatments. This is the main analysis step. We will use different methods for bulk and single cell data.</p></li>
+<li><p>Omics 3: Visualising and Interpreting Production of volcano plots and heatmaps to visualise the results of the statistical analysis. We will also look at how to interpret the results and how to find out more about the genes of interest.</p></li>
+</ul>
+</section>
+<section id="references" class="slide level2 smaller scrollable">
+<h2>References</h2>
+
+<div class="footer footer-default">
+<p>🔗 <a href="https://3mmarand.github.io/BIO00088H-data/omics/week-3/overview.html">About Omics 1: Hello data!</a></p>
+</div>
+<div id="refs" class="references csl-bib-body hanging-indent" role="list">
+<div id="ref-DESeq2" class="csl-entry" role="listitem">
+Love, Michael I., Wolfgang Huber, and Simon Anders. 2014. <span>“Moderated Estimation of Fold Change and Dispersion for RNA-Seq Data with DESeq2”</span> 15: 550. <a href="https://doi.org/10.1186/s13059-014-0550-8">https://doi.org/10.1186/s13059-014-0550-8</a>.
+</div>
+<div id="ref-scran" class="csl-entry" role="listitem">
+Lun, Aaron T. L., Davis J. McCarthy, and John C. Marioni. 2016. <span>“A Step-by-Step Workflow for Low-Level Analysis of Single-Cell RNA-Seq Data with Bioconductor”</span> 5: 2122. <a href="https://doi.org/10.12688/f1000research.9501.2">https://doi.org/10.12688/f1000research.9501.2</a>.
+</div>
+<div id="ref-nestorowa2016" class="csl-entry" role="listitem">
+Nestorowa, Sonia, Fiona K. Hamey, Blanca Pijuan Sala, Evangelia Diamanti, Mairi Shepherd, Elisa Laurenti, Nicola K. Wilson, David G. Kent, and Berthold Göttgens. 2016. <span>“A Single-Cell Resolution Map of Mouse Hematopoietic Stem and Progenitor Cell Differentiation.”</span> <em>Blood</em> 128 (8): e20–31. <a href="https://doi.org/10.1182/blood-2016-05-716480">https://doi.org/10.1182/blood-2016-05-716480</a>.
+</div>
+<div id="ref-rand_statistically_2022" class="csl-entry" role="listitem">
+Rand, Emma, and Sarah Forrester. 2022. <span>“Statistically Useful Experimental Design.”</span> <a href="https://cloud-span.github.io/experimental_design00-overview/">https://cloud-span.github.io/experimental_design00-overview/</a>.
+</div>
+</div>
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+        <nav id="TOC" role="doc-toc" class="toc-active"><h2 id="toc-title">On this page</h2>
+   
+  <ul>
+<li>
+<a href="#introduction" id="toc-introduction" class="nav-link active" data-scroll-target="#introduction">Introduction</a>
+  <ul>
+<li><a href="#session-overview" id="toc-session-overview" class="nav-link" data-scroll-target="#session-overview">Session overview</a></li>
+  </ul>
+</li>
+  <li>
+<a href="#exercises" id="toc-exercises" class="nav-link" data-scroll-target="#exercises">Exercises</a>
+  <ul>
+<li><a href="#set-up-a-project" id="toc-set-up-a-project" class="nav-link" data-scroll-target="#set-up-a-project">Set up a Project</a></li>
+  <li><a href="#examine-the-data-in-a-spreadsheet" id="toc-examine-the-data-in-a-spreadsheet" class="nav-link" data-scroll-target="#examine-the-data-in-a-spreadsheet">Examine the data in a spreadsheet</a></li>
+  <li><a href="#import" id="toc-import" class="nav-link" data-scroll-target="#import">Import</a></li>
+  <li>
+<a href="#explore" id="toc-explore" class="nav-link" data-scroll-target="#explore">Explore</a>
+  <ul class="collapse">
+<li>
+<a href="#distribution-of-values-across-the-whole-dataset" id="toc-distribution-of-values-across-the-whole-dataset" class="nav-link" data-scroll-target="#distribution-of-values-across-the-whole-dataset">Distribution of values across the whole dataset</a>
+  <ul class="collapse">
+<li><a href="#frogs" id="toc-frogs" class="nav-link" data-scroll-target="#frogs">🐸 Frogs</a></li>
+  <li><a href="#mice" id="toc-mice" class="nav-link" data-scroll-target="#mice">🐭 Mice</a></li>
+  </ul>
+</li>
+  <li>
+<a href="#distribution-of-values-across-the-samplecells" id="toc-distribution-of-values-across-the-samplecells" class="nav-link" data-scroll-target="#distribution-of-values-across-the-samplecells">Distribution of values across the sample/cells</a>
+  <ul class="collapse">
+<li><a href="#frog-samples" id="toc-frog-samples" class="nav-link" data-scroll-target="#frog-samples">🐸 Frog samples</a></li>
+  <li><a href="#mouse-cells" id="toc-mouse-cells" class="nav-link" data-scroll-target="#mouse-cells">🐭 Mouse cells</a></li>
+  </ul>
+</li>
+  <li>
+<a href="#distribution-of-values-across-the-genes" id="toc-distribution-of-values-across-the-genes" class="nav-link" data-scroll-target="#distribution-of-values-across-the-genes">Distribution of values across the genes</a>
+  <ul class="collapse">
+<li><a href="#frog-genes" id="toc-frog-genes" class="nav-link" data-scroll-target="#frog-genes">🐸 Frog genes</a></li>
+  <li><a href="#mouse-genes" id="toc-mouse-genes" class="nav-link" data-scroll-target="#mouse-genes">🐭 Mouse genes</a></li>
+  </ul>
+</li>
+  </ul>
+</li>
+  <li>
+<a href="#filtering-for-qc" id="toc-filtering-for-qc" class="nav-link" data-scroll-target="#filtering-for-qc">Filtering for QC</a>
+  <ul class="collapse">
+<li><a href="#frog-filtering" id="toc-frog-filtering" class="nav-link" data-scroll-target="#frog-filtering">🐸 Frog filtering</a></li>
+  <li><a href="#mouse-filtering" id="toc-mouse-filtering" class="nav-link" data-scroll-target="#mouse-filtering">🐭 Mouse filtering</a></li>
+  </ul>
+</li>
+  <li>
+<a href="#look-after-future-you" id="toc-look-after-future-you" class="nav-link" data-scroll-target="#look-after-future-you">🤗 Look after future you!</a>
+  <ul class="collapse">
+<li><a href="#frogs-and-future-you" id="toc-frogs-and-future-you" class="nav-link" data-scroll-target="#frogs-and-future-you">🐸 Frogs and future you</a></li>
+  <li><a href="#mice-and-future-you" id="toc-mice-and-future-you" class="nav-link" data-scroll-target="#mice-and-future-you">🐭 Mice and future you</a></li>
+  <li><a href="#xxxx-and-future-you" id="toc-xxxx-and-future-you" class="nav-link" data-scroll-target="#xxxx-and-future-you">🍂 xxxx and future you</a></li>
+  </ul>
+</li>
+  </ul>
+</li>
+  <li><a href="#finished" id="toc-finished" class="nav-link" data-scroll-target="#finished">🥳 Finished</a></li>
+  <li><a href="#independent-study-following-the-workshop" id="toc-independent-study-following-the-workshop" class="nav-link" data-scroll-target="#independent-study-following-the-workshop">Independent study following the workshop</a></li>
+  <li><a href="#the-code-file" id="toc-the-code-file" class="nav-link" data-scroll-target="#the-code-file">The Code file</a></li>
+  <li><a href="#references" id="toc-references" class="nav-link" data-scroll-target="#references">References</a></li>
+  </ul></nav>
+    </div>
+<!-- main -->
+<main class="content" id="quarto-document-content"><header id="title-block-header" class="quarto-title-block default"><div class="quarto-title">
+<h1 class="title">Workshop</h1>
+<p class="subtitle lead">Omics 1: Hello data!</p>
+</div>
+
+
+
+<div class="quarto-title-meta">
+
+    <div>
+    <div class="quarto-title-meta-heading">Author</div>
+    <div class="quarto-title-meta-contents">
+             <p>Emma Rand </p>
+          </div>
+  </div>
+    
+    <div>
+    <div class="quarto-title-meta-heading">Published</div>
+    <div class="quarto-title-meta-contents">
+      <p class="date">8 October, 2023</p>
+    </div>
+  </div>
+  
+    
+  </div>
+  
+
+</header><section id="introduction" class="level1"><h1>Introduction</h1>
+<section id="session-overview" class="level2"><h2 class="anchored" data-anchor-id="session-overview">Session overview</h2>
+<p>In this workshop you will learn what steps to take to get a good understanding of your ’omics data before you consider any statistical analysis. This is an often overlooked, but very valuable and informative, part of any data pipeline. It gives you the deep understanding of the data structures and values that you will need to code and trouble-shoot code, allows you to spot failed or problematic samples and informs your decisions on quality control.</p>
+<p>You should examine <strong>all three data sets</strong> because the comparisons will give you a stronger understanding of your own project data.</p>
+</section></section><section id="exercises" class="level1"><h1>Exercises</h1>
+<section id="set-up-a-project" class="level2"><h2 class="anchored" data-anchor-id="set-up-a-project">Set up a Project</h2>
+<p>🎬 Start RStudio from the Start menu</p>
+<p>🎬 Make an RStudio project. Be deliberate about where you create it so that it is a good place for you</p>
+<p>🎬 Use the Files pane to make new folders for the data. I suggest <code>data-raw</code> and <code>data-processed</code></p>
+<p>🎬 Make a new script called <code>workshop-1.R</code> to carry out the rest of the work.</p>
+<p>🎬 Record what you do and what you find out. All of it!</p>
+<p>🎬 Load <code>tidyverse</code> <span class="citation" data-cites="tidyverse">(<a href="#ref-tidyverse" role="doc-biblioref">Wickham et al. 2019</a>)</span> for importing, summarising, plotting and filtering.</p>
+<div class="cell">
+<div class="sourceCode" id="cb1"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span><span class="op">(</span><span class="va"><a href="https://tidyverse.tidyverse.org">tidyverse</a></span><span class="op">)</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</div>
+</section><section id="examine-the-data-in-a-spreadsheet" class="level2"><h2 class="anchored" data-anchor-id="examine-the-data-in-a-spreadsheet">Examine the data in a spreadsheet</h2>
+<p>These are the three datasets. Each set compromises several files.</p>
+<p>🐸 Frog development data:</p>
+<ul>
+<li><a href="data-raw/xlaevis_counts_S14.csv">xlaevis_counts_S14.csv</a></li>
+<li><a href="data-raw/xlaevis_counts_S20.csv">xlaevis_counts_S20.csv</a></li>
+<li><a href="data-raw/xlaevis_counts_S30.csv">xlaevis_counts_S30.csv</a></li>
+<li><a href="data-raw/meta_data.txt">meta_data.txt</a></li>
+</ul>
+<p>🐭 Stem cell data:</p>
+<ul>
+<li><a href="data-raw/surfaceome_hspc.csv">surfaceome_hspc.csv</a></li>
+<li><a href="data-raw/surfaceome_prog.csv">surfaceome_prog.csv</a></li>
+<li><a href="data-raw/surfaceome_lthsc.csv">surfaceome_lthsc.csv</a></li>
+</ul>
+<p>🍂 xxxx data:</p>
+<ul>
+<li>xxx</li>
+<li>xxx</li>
+</ul>
+<p>🎬 Save the files to <code>data-raw</code> and open them in Excel</p>
+<p>🎬 Answer the following questions:</p>
+<ul>
+<li>Describe how the sets of data are similar and how they are different.</li>
+<li>What is in the rows and columns of each file?</li>
+<li>How many rows and columns are there in each file? Are these the same? In all cases or some cases? Why?</li>
+<li>Google an id. Where does your search take you? How much information is available?</li>
+</ul>
+<p>🎬 Did you record all that??</p>
+</section><section id="import" class="level2"><h2 class="anchored" data-anchor-id="import">Import</h2>
+<p>Now let’s get the data into R and visualise it.</p>
+<p>🎬 Import <a href="data-raw/xlaevis_counts_S30.csv">xlaevis_counts_S30.csv</a>, <a href="data-raw/surfaceome_hspc.csv">surfaceome_hspc.csv</a> and xxxxxxxx</p>
+<div class="cell">
+<div class="sourceCode" id="cb2"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="co"># 🐸 import the s30 data</span></span>
+<span><span class="va">s30</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://readr.tidyverse.org/reference/read_delim.html">read_csv</a></span><span class="op">(</span><span class="st">"data-raw/xlaevis_counts_S30.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>
+<div class="cell">
+<div class="sourceCode" id="cb3"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="co"># 🐭 import the hspc data</span></span>
+<span><span class="va">hspc</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://readr.tidyverse.org/reference/read_delim.html">read_csv</a></span><span class="op">(</span><span class="st">"data-raw/surfaceome_hspc.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>
+<div class="cell">
+<div class="sourceCode" id="cb4"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="co"># 🍂 xxxx import the xxxx data</span></span>
+<span><span class="co"># prog &lt;- read_csv("")</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
+</div>
+<p>🎬 Check these have the number of rows and column you were expecting and that column types and names are as expected.</p>
+</section><section id="explore" class="level2"><h2 class="anchored" data-anchor-id="explore">Explore</h2>
+<p>The first task is to get an overview. We want to know</p>
+<ul>
+<li>are there any missing values? If so, how many and how are they distributed?</li>
+<li>how may zeros are there and how are they distributed</li>
+<li>does it look as tough all the samples/cells were equally “successful”? Can we spot any problematic anomalies?</li>
+<li>what is the distribution of values?</li>
+</ul>
+<p>If 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.</p>
+<p>We get this overview by looking at:</p>
+<ul>
+<li><p>The distribution of values across the whole dataset</p></li>
+<li><p>The distribution of values across the sample/cells (i.e., averaged across genes). This allows us to see variation between samples/cells:</p></li>
+<li><p>The distribution of values across the genes (i.e., averaged across samples/cells). This allows us to see variation between genes.</p></li>
+</ul>
+<section id="distribution-of-values-across-the-whole-dataset" class="level3"><h3 class="anchored" data-anchor-id="distribution-of-values-across-the-whole-dataset">Distribution of values across the whole dataset</h3>
+<p>In 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 <code><a href="https://tidyr.tidyverse.org/reference/pivot_longer.html">pivot_longer()</a></code> to put the data in <a href="https://3mmarand.github.io/BIO00017C-Data-Analysis-in-R-2020/workshops/02TestingDataTypesReadingInData.html#Tidy_format">‘tidy’ format</a> <span class="citation" data-cites="Wickham2014-nl">(<a href="#ref-Wickham2014-nl" role="doc-biblioref">Wickham 2014</a>)</span> by stacking the columns. We <em>could</em> save a copy of the stacked data and then plot it, but here, I have just piped the stacked data straight into <code><a href="https://ggplot2.tidyverse.org/reference/ggplot.html">ggplot()</a></code>.</p>
+<section id="frogs" class="level4"><h4 class="anchored" data-anchor-id="frogs">🐸 Frogs</h4>
+<p>🎬 Pivot the counts (stack the columns) so all the counts are in a single column (<code>count</code>) and pipe into <code><a href="https://ggplot2.tidyverse.org/reference/ggplot.html">ggplot()</a></code> to create a histogram:</p>
+<div class="cell">
+<div class="sourceCode" id="cb5"><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://tidyr.tidyverse.org/reference/pivot_longer.html">pivot_longer</a></span><span class="op">(</span>cols <span class="op">=</span> <span class="op">-</span><span class="va">xenbase_gene_id</span>,</span>
+<span>               names_to <span class="op">=</span> <span class="st">"sample"</span>,</span>
+<span>               values_to <span class="op">=</span> <span class="st">"count"</span><span class="op">)</span> <span class="op">|&gt;</span></span>
+<span>  <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/ggplot.html">ggplot</a></span><span class="op">(</span><span class="fu"><a href="https://ggplot2.tidyverse.org/reference/aes.html">aes</a></span><span class="op">(</span>x <span class="op">=</span> <span class="va">count</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/geom_histogram.html">geom_histogram</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-display">
+<p><img src="workshop_files/figure-html/unnamed-chunk-5-1.png" class="img-fluid" width="672"></p>
+</div>
+</div>
+<p>This 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.</p>
+<p>🎬 Repeat the plot on log of the counts.</p>
+<div class="cell">
+<div class="sourceCode" id="cb6"><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://tidyr.tidyverse.org/reference/pivot_longer.html">pivot_longer</a></span><span class="op">(</span>cols <span class="op">=</span> <span class="op">-</span><span class="va">xenbase_gene_id</span>,</span>
+<span>               names_to <span class="op">=</span> <span class="st">"sample"</span>,</span>
+<span>               values_to <span class="op">=</span> <span class="st">"count"</span><span class="op">)</span> <span class="op">|&gt;</span></span>
+<span>  <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/ggplot.html">ggplot</a></span><span class="op">(</span><span class="fu"><a href="https://ggplot2.tidyverse.org/reference/aes.html">aes</a></span><span class="op">(</span>x <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/Log.html">log10</a></span><span class="op">(</span><span class="va">count</span><span class="op">)</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/geom_histogram.html">geom_histogram</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-display">
+<p><img src="workshop_files/figure-html/unnamed-chunk-6-1.png" class="img-fluid" width="672"></p>
+</div>
+</div>
+<p>I’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 <em>all</em> the genes in the genome<a href="#fn1" class="footnote-ref" id="fnref1" role="doc-noteref"><sup>1</sup></a>. That small peak near the low end suggests that these lower counts might be anomalies.</p>
+<p>The 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.</p>
+</section><section id="mice" class="level4"><h4 class="anchored" data-anchor-id="mice">🐭 Mice</h4>
+<p>🎬 Pivot the expression values (stack the columns) so all the counts are in a single column (<code>expr</code>) and pipe into <code><a href="https://ggplot2.tidyverse.org/reference/ggplot.html">ggplot()</a></code> to create a histogram:</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">hspc</span> <span class="op">|&gt;</span></span>
+<span>  <span class="fu"><a href="https://tidyr.tidyverse.org/reference/pivot_longer.html">pivot_longer</a></span><span class="op">(</span>cols <span class="op">=</span> <span class="op">-</span><span class="va">ensembl_gene_id</span>,</span>
+<span>               names_to <span class="op">=</span> <span class="st">"cell"</span>,</span>
+<span>               values_to <span class="op">=</span> <span class="st">"expr"</span><span class="op">)</span> <span class="op">|&gt;</span> </span>
+<span>  <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/ggplot.html">ggplot</a></span><span class="op">(</span><span class="fu"><a href="https://ggplot2.tidyverse.org/reference/aes.html">aes</a></span><span class="op">(</span>x <span class="op">=</span> <span class="va">expr</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/geom_histogram.html">geom_histogram</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-display">
+<p><img src="workshop_files/figure-html/unnamed-chunk-7-1.png" class="img-fluid" width="672"></p>
+</div>
+</div>
+<p>This 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.</p>
+<p>What 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 distribution<a href="#fn2" class="footnote-ref" id="fnref2" role="doc-noteref"><sup>2</sup></a>. 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.</p>
+</section></section><section id="distribution-of-values-across-the-samplecells" class="level3"><h3 class="anchored" data-anchor-id="distribution-of-values-across-the-samplecells">Distribution of values across the sample/cells</h3>
+<section id="frog-samples" class="level4"><h4 class="anchored" data-anchor-id="frog-samples">🐸 Frog samples</h4>
+<p>Summary statistics including the the number of NAs can be seen using the <code><a href="https://rdrr.io/r/base/summary.html">summary()</a></code>. 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.</p>
+<p>🎬 Get a quick overview of the columns:</p>
+<div class="cell">
+<div class="sourceCode" id="cb8"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="co"># examine all the columns quickly</span></span>
+<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        
+ 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       
+ 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  
+ Mean   :   376.2   Mean   :   376.5   Mean   :  260.4  
+ 3rd Qu.:   251.0   3rd Qu.:   246.0   3rd Qu.:  187.0  
+ Max.   :117573.0   Max.   :130672.0   Max.   :61531.0  </code></pre>
+</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>🎬 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>
+<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)`
+                &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>
+</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>
+<p>🎬 Find the number of zeros in all columns:</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">s30</span> <span class="op">|&gt;</span></span>
+<span>  <span class="fu"><a href="https://tidyr.tidyverse.org/reference/pivot_longer.html">pivot_longer</a></span><span class="op">(</span>cols <span class="op">=</span> <span class="op">-</span><span class="va">xenbase_gene_id</span>,</span>
+<span>               names_to <span class="op">=</span> <span class="st">"sample"</span>,</span>
+<span>               values_to <span class="op">=</span> <span class="st">"count"</span><span class="op">)</span> <span class="op">|&gt;</span></span>
+<span>  <span class="fu"><a href="https://dplyr.tidyverse.org/reference/group_by.html">group_by</a></span><span class="op">(</span><span class="va">sample</span><span class="op">)</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>n_zero <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">count</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: 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>
+</div>
+</div>
+<p>You could expand to get all the summary information</p>
+<p>🎬 Summarise all the samples:</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">s30</span> <span class="op">|&gt;</span></span>
+<span>  <span class="fu"><a href="https://tidyr.tidyverse.org/reference/pivot_longer.html">pivot_longer</a></span><span class="op">(</span>cols <span class="op">=</span> <span class="op">-</span><span class="va">xenbase_gene_id</span>,</span>
+<span>               names_to <span class="op">=</span> <span class="st">"sample"</span>,</span>
+<span>               values_to <span class="op">=</span> <span class="st">"count"</span><span class="op">)</span> <span class="op">|&gt;</span></span>
+<span>  <span class="fu"><a href="https://dplyr.tidyverse.org/reference/group_by.html">group_by</a></span><span class="op">(</span><span class="va">sample</span><span class="op">)</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>min <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/Extremes.html">min</a></span><span class="op">(</span><span class="va">count</span><span class="op">)</span>,</span>
+<span>            lowerq <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/stats/quantile.html">quantile</a></span><span class="op">(</span><span class="va">count</span>, <span class="fl">0.25</span><span class="op">)</span>,</span>
+<span>            mean <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/mean.html">mean</a></span><span class="op">(</span><span class="va">count</span><span class="op">)</span>,</span>
+<span>            median <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/stats/median.html">median</a></span><span class="op">(</span><span class="va">count</span><span class="op">)</span>,</span>
+<span>            upperq <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/stats/quantile.html">quantile</a></span><span class="op">(</span><span class="va">count</span>, <span class="fl">0.75</span><span class="op">)</span>,</span>
+<span>            max <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/Extremes.html">max</a></span><span class="op">(</span><span class="va">count</span><span class="op">)</span>,</span>
+<span>            n_zero <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">count</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: 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>
+</div>
+</div>
+<p>The mean count ranges from 260 to 426.</p>
+<p>One advantage this has over using <code><a href="https://rdrr.io/r/base/summary.html">summary()</a></code> is that the output is a dataframe. For results, this is useful, and makes it easier to:</p>
+<ul>
+<li>write to file</li>
+<li>use in <code><a href="https://ggplot2.tidyverse.org/reference/ggplot.html">ggplot()</a></code>
+</li>
+<li>format in a Quarto report</li>
+</ul>
+<p>🎬 Save the summary as a dataframe, <code>s30_summary_samp</code>.</p>
+<p>We can write to file using <code><a href="https://readr.tidyverse.org/reference/write_delim.html">write_csv()</a></code></p>
+<p>🎬 Write <code>s30_summary_samp</code> to a file called “s30_summary_samp.csv”:</p>
+<div class="cell">
+<div class="sourceCode" id="cb16"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="fu"><a href="https://readr.tidyverse.org/reference/write_delim.html">write_csv</a></span><span class="op">(</span><span class="va">s30_summary_samp</span>, </span>
+<span>          file <span class="op">=</span> <span class="st">"data-processed/s30_summary_samp.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>
+<p>Plotting 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 <code><a href="https://ggplot2.tidyverse.org/reference/ggplot.html">ggplot()</a></code> and make use of <code><a href="https://ggplot2.tidyverse.org/reference/facet_wrap.html">facet_wrap()</a></code></p>
+<p>🎬 Pivot the data and pipe into <code>ggplot</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="va">s30</span> <span class="op">|&gt;</span></span>
+<span>  <span class="fu"><a href="https://tidyr.tidyverse.org/reference/pivot_longer.html">pivot_longer</a></span><span class="op">(</span>cols <span class="op">=</span> <span class="op">-</span><span class="va">xenbase_gene_id</span>,</span>
+<span>               names_to <span class="op">=</span> <span class="st">"sample"</span>,</span>
+<span>               values_to <span class="op">=</span> <span class="st">"count"</span><span class="op">)</span> <span class="op">|&gt;</span></span>
+<span>  <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/ggplot.html">ggplot</a></span><span class="op">(</span><span class="fu"><a href="https://ggplot2.tidyverse.org/reference/aes.html">aes</a></span><span class="op">(</span><span class="va">count</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/geom_density.html">geom_density</a></span><span class="op">(</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/facet_wrap.html">facet_wrap</a></span><span class="op">(</span><span class="va">.</span> <span class="op">~</span> <span class="va">sample</span>, nrow <span class="op">=</span> <span class="fl">3</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-display">
+<p><img src="workshop_files/figure-html/unnamed-chunk-14-1.png" class="img-fluid" width="672"></p>
+</div>
+</div>
+<p>We have many values (11893) so we are not limited to using <code><a href="https://ggplot2.tidyverse.org/reference/geom_histogram.html">geom_histogram()</a></code>. <code><a href="https://ggplot2.tidyverse.org/reference/geom_density.html">geom_density()</a></code> gives us a smooth distribution.</p>
+<p>We 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.</p>
+<p>🎬 Repeat the graph but taking the base 10 log of the counts:</p>
+<div class="cell">
+<div class="sourceCode" id="cb18"><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://tidyr.tidyverse.org/reference/pivot_longer.html">pivot_longer</a></span><span class="op">(</span>cols <span class="op">=</span> <span class="op">-</span><span class="va">xenbase_gene_id</span>,</span>
+<span>               names_to <span class="op">=</span> <span class="st">"sample"</span>,</span>
+<span>               values_to <span class="op">=</span> <span class="st">"count"</span><span class="op">)</span> <span class="op">|&gt;</span></span>
+<span>  <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/ggplot.html">ggplot</a></span><span class="op">(</span><span class="fu"><a href="https://ggplot2.tidyverse.org/reference/aes.html">aes</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/Log.html">log10</a></span><span class="op">(</span><span class="va">count</span><span class="op">)</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/geom_density.html">geom_density</a></span><span class="op">(</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/facet_wrap.html">facet_wrap</a></span><span class="op">(</span><span class="va">.</span> <span class="op">~</span> <span class="va">sample</span>, nrow <span class="op">=</span> <span class="fl">3</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-display">
+<p><img src="workshop_files/figure-html/unnamed-chunk-15-1.png" class="img-fluid" width="672"></p>
+</div>
+</div>
+<p>The key information to take from these plots is:</p>
+<ul>
+<li>the distributions are roughly similar in width, height, location and overall shape so it doesn’t look as though we have any suspect samples</li>
+<li>the peak at zero suggests quite a few counts of 1.</li>
+<li>since 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.</li>
+</ul>
+<p>The 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).</p>
+</section><section id="mouse-cells" class="level4"><h4 class="anchored" data-anchor-id="mouse-cells">🐭 Mouse cells</h4>
+<p>We used the <code><a href="https://rdrr.io/r/base/summary.html">summary()</a></code> function to get an overview of the columns in the frog data. Let’s try that here.</p>
+<p>🎬 Get a quick overview of the columns:</p>
+<div class="cell outputscroll">
+<div class="sourceCode" id="cb19"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/summary.html">summary</a></span><span class="op">(</span><span class="va">hspc</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> ensembl_gene_id       HSPC_001         HSPC_002         HSPC_003      
+ Length:280         Min.   : 0.000   Min.   : 0.000   Min.   : 0.0000  
+ Class :character   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.0000  
+ Mode  :character   Median : 0.000   Median : 0.000   Median : 0.9929  
+                    Mean   : 2.143   Mean   : 1.673   Mean   : 2.5964  
+                    3rd Qu.: 2.120   3rd Qu.: 2.239   3rd Qu.: 6.1559  
+                    Max.   :12.567   Max.   :11.976   Max.   :11.1138  
+    HSPC_004         HSPC_006         HSPC_008         HSPC_009    
+ Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   :0.000  
+ 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.:0.000  
+ Median : 0.000   Median : 1.276   Median : 0.000   Median :0.000  
+ Mean   : 1.851   Mean   : 2.338   Mean   : 2.375   Mean   :2.220  
+ 3rd Qu.: 2.466   3rd Qu.: 3.536   3rd Qu.: 3.851   3rd Qu.:3.594  
+ Max.   :11.133   Max.   :10.014   Max.   :11.574   Max.   :9.997  
+    HSPC_011         HSPC_012         HSPC_014         HSPC_015     
+ Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  
+ 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  
+ Median : 0.000   Median : 1.750   Median : 0.000   Median : 0.000  
+ Mean   : 2.285   Mean   : 2.431   Mean   : 2.295   Mean   : 2.515  
+ 3rd Qu.: 3.193   3rd Qu.: 3.741   3rd Qu.: 3.150   3rd Qu.: 3.789  
+ Max.   :11.260   Max.   :10.905   Max.   :11.051   Max.   :10.751  
+    HSPC_016          HSPC_017         HSPC_018         HSPC_020     
+ Min.   : 0.0000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  
+ 1st Qu.: 0.0000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  
+ Median : 0.9488   Median : 0.000   Median : 1.248   Median : 0.000  
+ Mean   : 2.6115   Mean   : 2.146   Mean   : 2.710   Mean   : 2.509  
+ 3rd Qu.: 5.9412   3rd Qu.: 2.357   3rd Qu.: 6.006   3rd Qu.: 4.470  
+ Max.   :11.3082   Max.   :12.058   Max.   :11.894   Max.   :11.281  
+    HSPC_021         HSPC_022         HSPC_023         HSPC_024     
+ Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  
+ 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  
+ Median : 0.000   Median : 0.000   Median : 0.000   Median : 0.000  
+ Mean   : 2.170   Mean   : 2.287   Mean   : 2.314   Mean   : 2.195  
+ 3rd Qu.: 2.996   3rd Qu.: 3.351   3rd Qu.: 2.749   3rd Qu.: 2.944  
+ Max.   :10.709   Max.   :11.814   Max.   :12.113   Max.   :11.279  
+    HSPC_025         HSPC_026         HSPC_027         HSPC_028     
+ Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  
+ 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  
+ Median : 1.572   Median : 1.385   Median : 0.000   Median : 0.000  
+ Mean   : 2.710   Mean   : 2.721   Mean   : 2.458   Mean   : 1.906  
+ 3rd Qu.: 5.735   3rd Qu.: 6.392   3rd Qu.: 5.496   3rd Qu.: 2.037  
+ Max.   :11.309   Max.   :10.865   Max.   :11.266   Max.   :10.777  
+    HSPC_030         HSPC_031          HSPC_033         HSPC_034      
+ Min.   : 0.000   Min.   : 0.0000   Min.   : 0.000   Min.   : 0.0000  
+ 1st Qu.: 0.000   1st Qu.: 0.0000   1st Qu.: 0.000   1st Qu.: 0.0000  
+ Median : 1.119   Median : 0.9026   Median : 0.000   Median : 0.7984  
+ Mean   : 2.338   Mean   : 2.3049   Mean   : 1.938   Mean   : 2.3220  
+ 3rd Qu.: 3.005   3rd Qu.: 2.9919   3rd Qu.: 2.434   3rd Qu.: 4.8324  
+ Max.   :11.391   Max.   :11.1748   Max.   :10.808   Max.   :10.6707  
+    HSPC_035         HSPC_036          HSPC_037         HSPC_038     
+ Min.   : 0.000   Min.   : 0.0000   Min.   : 0.000   Min.   : 0.000  
+ 1st Qu.: 0.000   1st Qu.: 0.0000   1st Qu.: 0.000   1st Qu.: 0.000  
+ Median : 0.000   Median : 0.8879   Median : 1.517   Median : 0.000  
+ Mean   : 1.810   Mean   : 2.6918   Mean   : 2.327   Mean   : 2.212  
+ 3rd Qu.: 2.175   3rd Qu.: 5.9822   3rd Qu.: 3.079   3rd Qu.: 2.867  
+ Max.   :11.221   Max.   :11.3018   Max.   :11.399   Max.   :12.275  
+    HSPC_040         HSPC_041         HSPC_042          HSPC_043     
+ Min.   : 0.000   Min.   : 0.000   Min.   : 0.0000   Min.   : 0.000  
+ 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.0000   1st Qu.: 0.000  
+ Median : 0.000   Median : 0.000   Median : 0.8673   Median : 1.342  
+ Mean   : 2.509   Mean   : 2.492   Mean   : 2.3673   Mean   : 2.420  
+ 3rd Qu.: 3.995   3rd Qu.: 3.943   3rd Qu.: 3.8371   3rd Qu.: 3.731  
+ Max.   :11.863   Max.   :11.016   Max.   :11.4852   Max.   :11.123  
+    HSPC_044         HSPC_045         HSPC_046          HSPC_047     
+ Min.   : 0.000   Min.   : 0.000   Min.   : 0.0000   Min.   : 0.000  
+ 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.0000   1st Qu.: 0.000  
+ Median : 0.000   Median : 0.000   Median : 0.8452   Median : 2.195  
+ Mean   : 2.382   Mean   : 2.277   Mean   : 1.9707   Mean   : 2.498  
+ 3rd Qu.: 3.998   3rd Qu.: 2.843   3rd Qu.: 2.0656   3rd Qu.: 3.937  
+ Max.   :10.782   Max.   :10.629   Max.   :11.0311   Max.   :10.180  
+    HSPC_048         HSPC_049         HSPC_050         HSPC_051      
+ Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.0000  
+ 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.0000  
+ Median : 1.108   Median : 1.275   Median : 0.000   Median : 0.9757  
+ Mean   : 2.289   Mean   : 2.453   Mean   : 2.673   Mean   : 2.2693  
+ 3rd Qu.: 2.988   3rd Qu.: 3.819   3rd Qu.: 5.772   3rd Qu.: 3.1644  
+ Max.   :10.335   Max.   :11.844   Max.   :11.301   Max.   :10.8692  
+    HSPC_052         HSPC_053         HSPC_054         HSPC_055     
+ Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  
+ 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  
+ Median : 1.509   Median : 0.818   Median : 0.000   Median : 0.000  
+ Mean   : 2.561   Mean   : 2.684   Mean   : 2.107   Mean   : 1.959  
+ 3rd Qu.: 4.644   3rd Qu.: 5.937   3rd Qu.: 2.568   3rd Qu.: 2.573  
+ Max.   :11.674   Max.   :11.624   Max.   :10.770   Max.   :11.105  
+    HSPC_056         HSPC_057         HSPC_058         HSPC_060     
+ Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  
+ 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  
+ Median : 0.000   Median : 0.000   Median : 1.399   Median : 0.000  
+ Mean   : 2.295   Mean   : 2.430   Mean   : 2.296   Mean   : 2.112  
+ 3rd Qu.: 3.721   3rd Qu.: 3.806   3rd Qu.: 3.199   3rd Qu.: 2.201  
+ Max.   :11.627   Max.   :10.575   Max.   :11.134   Max.   :10.631  
+    HSPC_061         HSPC_062         HSPC_063         HSPC_064     
+ Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  
+ 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  
+ Median : 0.000   Median : 0.000   Median : 1.515   Median : 1.101  
+ Mean   : 1.934   Mean   : 2.129   Mean   : 2.508   Mean   : 2.696  
+ 3rd Qu.: 2.489   3rd Qu.: 2.875   3rd Qu.: 4.895   3rd Qu.: 6.412  
+ Max.   :11.190   Max.   :10.433   Max.   :10.994   Max.   :10.873  
+    HSPC_065          HSPC_066         HSPC_067         HSPC_068     
+ Min.   : 0.0000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  
+ 1st Qu.: 0.0000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  
+ Median : 0.4852   Median : 0.000   Median : 1.441   Median : 0.000  
+ Mean   : 2.2676   Mean   : 2.136   Mean   : 2.480   Mean   : 2.449  
+ 3rd Qu.: 3.8217   3rd Qu.: 2.632   3rd Qu.: 3.548   3rd Qu.: 4.517  
+ Max.   :10.9023   Max.   :11.608   Max.   :11.147   Max.   :10.901  
+    HSPC_069         HSPC_070          HSPC_071          HSPC_072     
+ Min.   : 0.000   Min.   : 0.0000   Min.   : 0.0000   Min.   : 0.000  
+ 1st Qu.: 0.000   1st Qu.: 0.0000   1st Qu.: 0.0000   1st Qu.: 0.000  
+ Median : 0.000   Median : 0.8949   Median : 0.9272   Median : 1.121  
+ Mean   : 2.406   Mean   : 2.5826   Mean   : 2.2844   Mean   : 2.545  
+ 3rd Qu.: 4.705   3rd Qu.: 5.4749   3rd Qu.: 3.2531   3rd Qu.: 4.939  
+ Max.   :11.258   Max.   :11.6715   Max.   :10.7886   Max.   :11.397  
+    HSPC_073         HSPC_074        HSPC_075         HSPC_076     
+ Min.   : 0.000   Min.   : 0.00   Min.   : 0.000   Min.   : 0.000  
+ 1st Qu.: 0.000   1st Qu.: 0.00   1st Qu.: 0.000   1st Qu.: 0.000  
+ Median : 0.000   Median : 0.00   Median : 1.674   Median : 0.000  
+ Mean   : 2.491   Mean   : 2.46   Mean   : 2.413   Mean   : 2.289  
+ 3rd Qu.: 4.134   3rd Qu.: 3.40   3rd Qu.: 3.013   3rd Qu.: 2.550  
+ Max.   :11.844   Max.   :11.66   Max.   :11.976   Max.   :12.136  
+    HSPC_077          HSPC_078         HSPC_079         HSPC_080     
+ Min.   : 0.0000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  
+ 1st Qu.: 0.0000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  
+ Median : 0.6624   Median : 1.492   Median : 1.384   Median : 1.013  
+ Mean   : 2.4336   Mean   : 2.637   Mean   : 2.432   Mean   : 2.881  
+ 3rd Qu.: 5.4937   3rd Qu.: 5.472   3rd Qu.: 3.617   3rd Qu.: 7.220  
+ Max.   :11.6020   Max.   :10.673   Max.   :11.199   Max.   :11.836  
+    HSPC_081          HSPC_082         HSPC_083         HSPC_084     
+ Min.   : 0.0000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  
+ 1st Qu.: 0.0000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  
+ Median : 0.7671   Median : 0.000   Median : 1.896   Median : 1.128  
+ Mean   : 1.9227   Mean   : 2.474   Mean   : 2.864   Mean   : 2.289  
+ 3rd Qu.: 1.6349   3rd Qu.: 3.488   3rd Qu.: 5.101   3rd Qu.: 2.792  
+ Max.   :11.4681   Max.   :11.962   Max.   :10.865   Max.   :11.834  
+    HSPC_085         HSPC_087         HSPC_088         HSPC_089     
+ Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  
+ 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  
+ Median : 0.000   Median : 0.000   Median : 0.000   Median : 0.000  
+ Mean   : 2.157   Mean   : 2.314   Mean   : 2.202   Mean   : 2.329  
+ 3rd Qu.: 3.010   3rd Qu.: 3.245   3rd Qu.: 2.092   3rd Qu.: 3.246  
+ Max.   :10.809   Max.   :10.976   Max.   :11.362   Max.   :11.301  
+    HSPC_090         HSPC_094         HSPC_095         HSPC_096    
+ Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   :0.000  
+ 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.:0.000  
+ Median : 0.000   Median : 0.000   Median : 2.055   Median :0.000  
+ Mean   : 2.286   Mean   : 2.186   Mean   : 2.756   Mean   :2.348  
+ 3rd Qu.: 4.174   3rd Qu.: 2.002   3rd Qu.: 4.370   3rd Qu.:4.482  
+ Max.   :11.124   Max.   :11.694   Max.   :11.385   Max.   :9.601  
+    HSPC_098         HSPC_099         HSPC_100         HSPC_101     
+ Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  
+ 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  
+ Median : 0.000   Median : 0.000   Median : 0.000   Median : 1.007  
+ Mean   : 2.209   Mean   : 2.082   Mean   : 2.313   Mean   : 2.587  
+ 3rd Qu.: 3.354   3rd Qu.: 2.505   3rd Qu.: 2.775   3rd Qu.: 5.334  
+ Max.   :11.070   Max.   :10.200   Max.   :11.452   Max.   :11.456  
+    HSPC_102         HSPC_103         HSPC_104         HSPC_105     
+ Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  
+ 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  
+ Median : 1.111   Median : 0.000   Median : 0.000   Median : 0.000  
+ Mean   : 2.210   Mean   : 2.853   Mean   : 2.099   Mean   : 1.893  
+ 3rd Qu.: 2.993   3rd Qu.: 6.123   3rd Qu.: 2.720   3rd Qu.: 2.129  
+ Max.   :11.153   Max.   :11.328   Max.   :10.746   Max.   :10.721  
+    HSPC_106         HSPC_107         HSPC_108         HSPC_109     
+ Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  
+ 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  
+ Median : 0.000   Median : 0.000   Median : 0.000   Median : 1.595  
+ Mean   : 1.980   Mean   : 2.279   Mean   : 2.296   Mean   : 2.420  
+ 3rd Qu.: 2.425   3rd Qu.: 3.396   3rd Qu.: 3.361   3rd Qu.: 4.006  
+ Max.   :10.919   Max.   :10.982   Max.   :11.744   Max.   :10.463  
+    HSPC_110         HSPC_111         HSPC_114          HSPC_115     
+ Min.   : 0.000   Min.   : 0.000   Min.   : 0.0000   Min.   : 0.000  
+ 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.0000   1st Qu.: 0.000  
+ Median : 0.000   Median : 0.000   Median : 0.9173   Median : 2.349  
+ Mean   : 2.159   Mean   : 1.800   Mean   : 1.8376   Mean   : 2.943  
+ 3rd Qu.: 2.667   3rd Qu.: 2.214   3rd Qu.: 1.8741   3rd Qu.: 6.223  
+ Max.   :11.121   Max.   :11.109   Max.   :10.4645   Max.   :11.124  
+    HSPC_117         HSPC_118         HSPC_119         HSPC_120     
+ Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  
+ 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  
+ Median : 0.000   Median : 0.000   Median : 0.000   Median : 1.187  
+ Mean   : 1.919   Mean   : 1.855   Mean   : 2.289   Mean   : 2.041  
+ 3rd Qu.: 2.306   3rd Qu.: 2.387   3rd Qu.: 3.292   3rd Qu.: 2.610  
+ Max.   :14.579   Max.   :11.119   Max.   :12.534   Max.   :11.438  
+    HSPC_121         HSPC_122         HSPC_123         HSPC_125     
+ Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  
+ 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  
+ Median : 0.000   Median : 0.000   Median : 0.000   Median : 0.000  
+ Mean   : 2.803   Mean   : 2.072   Mean   : 2.200   Mean   : 2.116  
+ 3rd Qu.: 5.798   3rd Qu.: 2.140   3rd Qu.: 3.215   3rd Qu.: 2.409  
+ Max.   :11.320   Max.   :11.013   Max.   :11.163   Max.   :11.368  
+    HSPC_126          HSPC_127         HSPC_130         HSPC_131     
+ Min.   : 0.0000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  
+ 1st Qu.: 0.0000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  
+ Median : 0.9381   Median : 1.147   Median : 0.000   Median : 0.000  
+ Mean   : 2.0014   Mean   : 2.287   Mean   : 2.551   Mean   : 2.240  
+ 3rd Qu.: 2.2215   3rd Qu.: 3.051   3rd Qu.: 3.968   3rd Qu.: 3.773  
+ Max.   :10.9622   Max.   :11.028   Max.   :10.585   Max.   :11.216  
+    HSPC_132          HSPC_133         HSPC_134         HSPC_135     
+ Min.   : 0.0000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  
+ 1st Qu.: 0.0000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  
+ Median : 0.4438   Median : 2.234   Median : 0.000   Median : 0.000  
+ Mean   : 2.1659   Mean   : 2.582   Mean   : 2.335   Mean   : 2.402  
+ 3rd Qu.: 1.8512   3rd Qu.: 4.591   3rd Qu.: 3.659   3rd Qu.: 4.134  
+ Max.   :10.6431   Max.   :10.730   Max.   :11.995   Max.   :11.573  
+    HSPC_136         HSPC_138          HSPC_139         HSPC_140     
+ Min.   : 0.000   Min.   : 0.0000   Min.   : 0.000   Min.   : 0.000  
+ 1st Qu.: 0.000   1st Qu.: 0.0000   1st Qu.: 0.000   1st Qu.: 0.000  
+ Median : 0.000   Median : 0.7062   Median : 2.078   Median : 0.000  
+ Mean   : 2.546   Mean   : 2.1054   Mean   : 2.876   Mean   : 2.220  
+ 3rd Qu.: 5.219   3rd Qu.: 1.8181   3rd Qu.: 4.604   3rd Qu.: 3.716  
+ Max.   :11.281   Max.   :11.1177   Max.   :11.013   Max.   :10.893  
+    HSPC_141         HSPC_142         HSPC_143         HSPC_144     
+ Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  
+ 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  
+ Median : 0.000   Median : 0.000   Median : 0.000   Median : 1.075  
+ Mean   : 2.385   Mean   : 2.232   Mean   : 2.592   Mean   : 2.004  
+ 3rd Qu.: 4.149   3rd Qu.: 2.523   3rd Qu.: 4.248   3rd Qu.: 2.441  
+ Max.   :11.099   Max.   :11.902   Max.   :12.932   Max.   :11.121  
+    HSPC_146         HSPC_148         HSPC_149         HSPC_151      
+ Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.0000  
+ 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.0000  
+ Median : 0.000   Median : 0.000   Median : 0.000   Median : 0.9711  
+ Mean   : 2.418   Mean   : 2.385   Mean   : 2.314   Mean   : 2.4375  
+ 3rd Qu.: 4.430   3rd Qu.: 3.288   3rd Qu.: 3.139   3rd Qu.: 3.2523  
+ Max.   :10.385   Max.   :12.823   Max.   :10.910   Max.   :11.7148  
+    HSPC_152         HSPC_153         HSPC_154         HSPC_155     
+ Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  
+ 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  
+ Median : 0.000   Median : 0.000   Median : 0.000   Median : 0.000  
+ Mean   : 2.247   Mean   : 2.415   Mean   : 2.476   Mean   : 2.468  
+ 3rd Qu.: 3.293   3rd Qu.: 3.524   3rd Qu.: 4.653   3rd Qu.: 3.621  
+ Max.   :12.463   Max.   :12.205   Max.   :11.437   Max.   :11.207  
+    HSPC_156          HSPC_157         HSPC_158         HSPC_159     
+ Min.   : 0.0000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  
+ 1st Qu.: 0.0000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  
+ Median : 0.5545   Median : 1.993   Median : 0.000   Median : 0.000  
+ Mean   : 2.2297   Mean   : 2.493   Mean   : 2.119   Mean   : 2.461  
+ 3rd Qu.: 2.0977   3rd Qu.: 3.692   3rd Qu.: 2.930   3rd Qu.: 3.340  
+ Max.   :11.2431   Max.   :10.539   Max.   :11.336   Max.   :11.123  
+    HSPC_161         HSPC_162          HSPC_164         HSPC_165     
+ Min.   : 0.000   Min.   : 0.0000   Min.   : 0.000   Min.   : 0.000  
+ 1st Qu.: 0.000   1st Qu.: 0.0000   1st Qu.: 0.000   1st Qu.: 0.000  
+ Median : 1.701   Median : 0.7152   Median : 0.000   Median : 0.000  
+ Mean   : 2.533   Mean   : 2.3473   Mean   : 2.161   Mean   : 2.084  
+ 3rd Qu.: 3.616   3rd Qu.: 2.4973   3rd Qu.: 2.553   3rd Qu.: 3.020  
+ Max.   :11.429   Max.   :11.0065   Max.   :11.865   Max.   :10.282  
+    HSPC_166         HSPC_168         HSPC_169         HSPC_170     
+ Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  
+ 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  
+ Median : 0.000   Median : 1.002   Median : 1.158   Median : 0.000  
+ Mean   : 2.177   Mean   : 2.390   Mean   : 2.038   Mean   : 2.401  
+ 3rd Qu.: 3.296   3rd Qu.: 4.701   3rd Qu.: 2.232   3rd Qu.: 3.703  
+ Max.   :11.427   Max.   :10.393   Max.   :10.447   Max.   :11.288  
+    HSPC_171         HSPC_172          HSPC_173         HSPC_174     
+ Min.   : 0.000   Min.   : 0.0000   Min.   : 0.000   Min.   : 0.000  
+ 1st Qu.: 0.000   1st Qu.: 0.0000   1st Qu.: 0.000   1st Qu.: 0.000  
+ Median : 1.525   Median : 0.7679   Median : 0.000   Median : 1.257  
+ Mean   : 2.312   Mean   : 2.3115   Mean   : 2.288   Mean   : 2.444  
+ 3rd Qu.: 2.729   3rd Qu.: 3.7889   3rd Qu.: 3.037   3rd Qu.: 4.996  
+ Max.   :10.468   Max.   :11.1442   Max.   :11.074   Max.   :11.095  
+    HSPC_175         HSPC_176         HSPC_177         HSPC_178     
+ Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  
+ 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  
+ Median : 1.496   Median : 2.024   Median : 1.971   Median : 1.003  
+ Mean   : 2.613   Mean   : 2.593   Mean   : 2.421   Mean   : 2.277  
+ 3rd Qu.: 4.845   3rd Qu.: 4.092   3rd Qu.: 3.665   3rd Qu.: 2.812  
+ Max.   :11.235   Max.   :10.379   Max.   :10.864   Max.   :10.979  
+    HSPC_179         HSPC_180         HSPC_181         HSPC_182     
+ Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  
+ 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  
+ Median : 0.836   Median : 1.544   Median : 2.030   Median : 0.000  
+ Mean   : 2.205   Mean   : 2.556   Mean   : 2.890   Mean   : 2.363  
+ 3rd Qu.: 2.300   3rd Qu.: 4.798   3rd Qu.: 4.846   3rd Qu.: 3.779  
+ Max.   :11.244   Max.   :10.802   Max.   :10.945   Max.   :10.399  
+    HSPC_183         HSPC_185         HSPC_186         HSPC_187     
+ Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  
+ 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  
+ Median : 1.020   Median : 0.000   Median : 1.606   Median : 0.000  
+ Mean   : 2.242   Mean   : 2.708   Mean   : 2.053   Mean   : 2.360  
+ 3rd Qu.: 2.842   3rd Qu.: 4.855   3rd Qu.: 2.834   3rd Qu.: 3.541  
+ Max.   :10.530   Max.   :11.079   Max.   :11.016   Max.   :10.923  
+    HSPC_189         HSPC_190         HSPC_191         HSPC_192     
+ Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  
+ 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  
+ Median : 0.000   Median : 0.000   Median : 0.000   Median : 1.412  
+ Mean   : 2.120   Mean   : 2.417   Mean   : 2.175   Mean   : 2.192  
+ 3rd Qu.: 2.652   3rd Qu.: 5.226   3rd Qu.: 2.574   3rd Qu.: 2.669  
+ Max.   :11.300   Max.   :11.023   Max.   :11.454   Max.   :10.225  
+    HSPC_193         HSPC_195          HSPC_196         HSPC_198     
+ Min.   :0.0000   Min.   : 0.0000   Min.   : 0.000   Min.   : 0.000  
+ 1st Qu.:0.0000   1st Qu.: 0.0000   1st Qu.: 0.000   1st Qu.: 0.000  
+ Median :0.9691   Median : 0.9175   Median : 1.379   Median : 1.105  
+ Mean   :2.5448   Mean   : 2.7307   Mean   : 2.327   Mean   : 2.155  
+ 3rd Qu.:5.1191   3rd Qu.: 5.8899   3rd Qu.: 2.625   3rd Qu.: 2.756  
+ Max.   :9.8728   Max.   :10.4757   Max.   :11.319   Max.   :11.405  
+    HSPC_199         HSPC_200         HSPC_202          HSPC_203     
+ Min.   : 0.000   Min.   : 0.000   Min.   : 0.0000   Min.   : 0.000  
+ 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.0000   1st Qu.: 0.000  
+ Median : 1.069   Median : 1.572   Median : 0.8045   Median : 1.311  
+ Mean   : 1.909   Mean   : 2.346   Mean   : 2.1384   Mean   : 2.058  
+ 3rd Qu.: 2.431   3rd Qu.: 2.791   3rd Qu.: 2.0569   3rd Qu.: 2.792  
+ Max.   :11.377   Max.   :11.334   Max.   :11.0516   Max.   :10.852  
+    HSPC_204         HSPC_205         HSPC_206         HSPC_207      
+ Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.0000  
+ 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.0000  
+ Median : 1.342   Median : 1.997   Median : 1.076   Median : 0.9235  
+ Mean   : 2.716   Mean   : 2.520   Mean   : 2.426   Mean   : 2.2974  
+ 3rd Qu.: 5.611   3rd Qu.: 4.244   3rd Qu.: 4.057   3rd Qu.: 2.6736  
+ Max.   :10.269   Max.   :10.817   Max.   :11.866   Max.   :11.4287  
+    HSPC_208         HSPC_210         HSPC_211         HSPC_212     
+ Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  
+ 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  
+ Median : 2.263   Median : 1.021   Median : 1.351   Median : 0.000  
+ Mean   : 2.893   Mean   : 2.315   Mean   : 2.425   Mean   : 2.336  
+ 3rd Qu.: 5.014   3rd Qu.: 2.676   3rd Qu.: 3.820   3rd Qu.: 3.443  
+ Max.   :11.375   Max.   :12.208   Max.   :11.360   Max.   :11.808  
+    HSPC_213         HSPC_214          HSPC_215         HSPC_216      
+ Min.   : 0.000   Min.   : 0.0000   Min.   : 0.000   Min.   : 0.0000  
+ 1st Qu.: 0.000   1st Qu.: 0.0000   1st Qu.: 0.000   1st Qu.: 0.0000  
+ Median : 1.270   Median : 0.9195   Median : 1.653   Median : 0.8022  
+ Mean   : 2.483   Mean   : 2.1976   Mean   : 2.563   Mean   : 2.6010  
+ 3rd Qu.: 4.903   3rd Qu.: 2.7139   3rd Qu.: 4.344   3rd Qu.: 6.0076  
+ Max.   :11.548   Max.   :10.6947   Max.   :10.933   Max.   :11.2119  
+    HSPC_218         HSPC_219         HSPC_220         HSPC_221     
+ Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  
+ 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  
+ Median : 0.000   Median : 1.027   Median : 0.000   Median : 1.269  
+ Mean   : 2.467   Mean   : 2.291   Mean   : 2.449   Mean   : 2.641  
+ 3rd Qu.: 3.980   3rd Qu.: 2.853   3rd Qu.: 4.486   3rd Qu.: 3.617  
+ Max.   :11.654   Max.   :10.801   Max.   :10.410   Max.   :11.651  
+    HSPC_222         HSPC_223         HSPC_224         HSPC_225     
+ Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  
+ 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  
+ Median : 1.449   Median : 0.000   Median : 0.000   Median : 0.000  
+ Mean   : 2.262   Mean   : 2.271   Mean   : 2.492   Mean   : 2.585  
+ 3rd Qu.: 3.271   3rd Qu.: 3.727   3rd Qu.: 3.769   3rd Qu.: 5.253  
+ Max.   :11.133   Max.   :12.000   Max.   :11.114   Max.   :11.671  
+    HSPC_227         HSPC_228         HSPC_229         HSPC_230     
+ Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  
+ 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  
+ Median : 0.000   Median : 0.000   Median : 2.484   Median : 0.000  
+ Mean   : 2.492   Mean   : 2.370   Mean   : 2.742   Mean   : 2.586  
+ 3rd Qu.: 3.692   3rd Qu.: 4.488   3rd Qu.: 4.836   3rd Qu.: 5.188  
+ Max.   :10.815   Max.   :10.165   Max.   :11.143   Max.   :10.734  
+    HSPC_231         HSPC_232         HSPC_233         HSPC_235     
+ Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  
+ 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  
+ Median : 0.000   Median : 1.869   Median : 1.254   Median : 0.000  
+ Mean   : 2.379   Mean   : 2.264   Mean   : 2.531   Mean   : 2.552  
+ 3rd Qu.: 4.787   3rd Qu.: 3.163   3rd Qu.: 3.925   3rd Qu.: 4.389  
+ Max.   :10.790   Max.   :12.098   Max.   :11.533   Max.   :11.765  
+    HSPC_236         HSPC_237         HSPC_239         HSPC_240     
+ Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  
+ 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  
+ Median : 0.000   Median : 0.000   Median : 2.207   Median : 0.892  
+ Mean   : 2.205   Mean   : 2.457   Mean   : 2.656   Mean   : 2.049  
+ 3rd Qu.: 3.748   3rd Qu.: 3.488   3rd Qu.: 4.904   3rd Qu.: 2.617  
+ Max.   :10.234   Max.   :10.630   Max.   :10.858   Max.   :10.528  
+    HSPC_243         HSPC_244          HSPC_245         HSPC_246     
+ Min.   : 0.000   Min.   : 0.0000   Min.   : 0.000   Min.   : 0.000  
+ 1st Qu.: 0.000   1st Qu.: 0.0000   1st Qu.: 0.000   1st Qu.: 0.000  
+ Median : 1.118   Median : 0.7872   Median : 1.459   Median : 1.629  
+ Mean   : 2.311   Mean   : 2.6638   Mean   : 2.360   Mean   : 2.321  
+ 3rd Qu.: 2.574   3rd Qu.: 6.2395   3rd Qu.: 3.000   3rd Qu.: 3.229  
+ Max.   :11.069   Max.   :10.0730   Max.   :11.297   Max.   :11.237  
+    HSPC_247        HSPC_248          HSPC_249         HSPC_250     
+ Min.   :0.000   Min.   : 0.0000   Min.   : 0.000   Min.   : 0.000  
+ 1st Qu.:0.000   1st Qu.: 0.0000   1st Qu.: 0.000   1st Qu.: 0.000  
+ Median :0.000   Median : 0.8453   Median : 0.000   Median : 1.278  
+ Mean   :2.537   Mean   : 2.3719   Mean   : 1.803   Mean   : 2.751  
+ 3rd Qu.:4.687   3rd Qu.: 3.3090   3rd Qu.: 2.335   3rd Qu.: 6.330  
+ Max.   :9.821   Max.   :10.8128   Max.   :10.568   Max.   :11.256  
+    HSPC_251          HSPC_253         HSPC_254         HSPC_255      
+ Min.   : 0.0000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.0000  
+ 1st Qu.: 0.0000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.0000  
+ Median : 0.9714   Median : 1.265   Median : 0.000   Median : 0.9098  
+ Mean   : 2.5626   Mean   : 2.492   Mean   : 2.177   Mean   : 2.1878  
+ 3rd Qu.: 4.9167   3rd Qu.: 4.185   3rd Qu.: 3.437   3rd Qu.: 2.4313  
+ Max.   :11.1252   Max.   :10.435   Max.   :10.422   Max.   :10.7952  
+    HSPC_256          HSPC_257         HSPC_258         HSPC_261      
+ Min.   : 0.0000   Min.   : 0.000   Min.   :0.0000   Min.   : 0.0000  
+ 1st Qu.: 0.0000   1st Qu.: 0.000   1st Qu.:0.0000   1st Qu.: 0.0000  
+ Median : 0.8248   Median : 1.241   Median :0.8526   Median : 0.5387  
+ Mean   : 2.1051   Mean   : 2.630   Mean   :2.0295   Mean   : 2.1419  
+ 3rd Qu.: 2.3331   3rd Qu.: 5.646   3rd Qu.:3.0784   3rd Qu.: 1.9352  
+ Max.   :13.0375   Max.   :11.499   Max.   :9.9116   Max.   :11.3247  
+    HSPC_263         HSPC_264         HSPC_265         HSPC_266     
+ Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  
+ 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  
+ Median : 1.538   Median : 1.426   Median : 1.883   Median : 1.839  
+ Mean   : 2.613   Mean   : 2.374   Mean   : 3.177   Mean   : 2.833  
+ 3rd Qu.: 4.485   3rd Qu.: 3.238   3rd Qu.: 5.702   3rd Qu.: 5.801  
+ Max.   :10.571   Max.   :11.136   Max.   :12.436   Max.   :10.338  
+    HSPC_267          HSPC_268          HSPC_269          HSPC_270      
+ Min.   : 0.0000   Min.   : 0.0000   Min.   : 0.0000   Min.   : 0.0000  
+ 1st Qu.: 0.0000   1st Qu.: 0.0000   1st Qu.: 0.0000   1st Qu.: 0.0000  
+ Median : 0.9675   Median : 0.7787   Median : 0.8632   Median : 0.9637  
+ Mean   : 2.4910   Mean   : 2.5342   Mean   : 2.4029   Mean   : 2.6899  
+ 3rd Qu.: 3.5345   3rd Qu.: 4.9871   3rd Qu.: 4.3176   3rd Qu.: 5.7266  
+ Max.   :10.0139   Max.   :10.7848   Max.   :11.2689   Max.   :11.1648  
+    HSPC_271         HSPC_274         HSPC_275          HSPC_276     
+ Min.   : 0.000   Min.   : 0.000   Min.   : 0.0000   Min.   : 0.000  
+ 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.0000   1st Qu.: 0.000  
+ Median : 1.352   Median : 1.730   Median : 0.5252   Median : 1.156  
+ Mean   : 2.493   Mean   : 2.382   Mean   : 2.5375   Mean   : 2.485  
+ 3rd Qu.: 4.430   3rd Qu.: 3.360   3rd Qu.: 5.7329   3rd Qu.: 4.623  
+ Max.   :11.636   Max.   :11.165   Max.   :11.6234   Max.   :11.562  
+    HSPC_278         HSPC_279         HSPC_280         HSPC_281     
+ Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  
+ 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  
+ Median : 0.000   Median : 1.487   Median : 1.608   Median : 2.611  
+ Mean   : 2.161   Mean   : 2.497   Mean   : 2.580   Mean   : 2.737  
+ 3rd Qu.: 2.270   3rd Qu.: 3.813   3rd Qu.: 3.985   3rd Qu.: 4.731  
+ Max.   :11.734   Max.   :10.900   Max.   :11.673   Max.   :10.076  
+    HSPC_282          HSPC_283         HSPC_285          HSPC_286     
+ Min.   : 0.0000   Min.   : 0.000   Min.   : 0.0000   Min.   : 0.000  
+ 1st Qu.: 0.0000   1st Qu.: 0.000   1st Qu.: 0.0000   1st Qu.: 0.000  
+ Median : 0.7021   Median : 1.911   Median : 0.8658   Median : 1.178  
+ Mean   : 2.4272   Mean   : 2.534   Mean   : 2.4868   Mean   : 2.293  
+ 3rd Qu.: 4.1254   3rd Qu.: 3.888   3rd Qu.: 5.3804   3rd Qu.: 2.597  
+ Max.   :11.1094   Max.   :10.258   Max.   :10.5533   Max.   :11.112  
+    HSPC_287         HSPC_288          HSPC_289         HSPC_290     
+ Min.   : 0.000   Min.   : 0.0000   Min.   : 0.000   Min.   : 0.000  
+ 1st Qu.: 0.000   1st Qu.: 0.0000   1st Qu.: 0.000   1st Qu.: 0.000  
+ Median : 1.049   Median : 0.8548   Median : 1.953   Median : 1.176  
+ Mean   : 2.775   Mean   : 2.6412   Mean   : 2.925   Mean   : 2.304  
+ 3rd Qu.: 5.476   3rd Qu.: 5.4204   3rd Qu.: 5.613   3rd Qu.: 3.445  
+ Max.   :10.925   Max.   :11.0814   Max.   :10.199   Max.   :11.094  
+    HSPC_291         HSPC_292         HSPC_293         HSPC_294      
+ Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.0000  
+ 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.0000  
+ Median : 1.176   Median : 1.320   Median : 1.077   Median : 0.9161  
+ Mean   : 2.662   Mean   : 2.534   Mean   : 2.538   Mean   : 2.4365  
+ 3rd Qu.: 5.690   3rd Qu.: 4.297   3rd Qu.: 3.458   3rd Qu.: 4.8204  
+ Max.   :12.255   Max.   :11.090   Max.   :10.987   Max.   :10.6135  
+    HSPC_295        HSPC_296         HSPC_297         HSPC_298     
+ Min.   :0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  
+ 1st Qu.:0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  
+ Median :1.479   Median : 2.157   Median : 2.444   Median : 1.281  
+ Mean   :2.849   Mean   : 2.977   Mean   : 3.062   Mean   : 2.277  
+ 3rd Qu.:5.282   3rd Qu.: 5.006   3rd Qu.: 5.005   3rd Qu.: 2.749  
+ Max.   :9.986   Max.   :10.830   Max.   :11.009   Max.   :10.636  
+    HSPC_299         HSPC_300         HSPC_301         HSPC_302     
+ Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  
+ 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  
+ Median : 1.716   Median : 1.163   Median : 2.235   Median : 2.240  
+ Mean   : 2.597   Mean   : 2.346   Mean   : 2.739   Mean   : 2.890  
+ 3rd Qu.: 3.762   3rd Qu.: 2.876   3rd Qu.: 4.593   3rd Qu.: 4.945  
+ Max.   :11.663   Max.   :11.690   Max.   :10.364   Max.   :10.498  
+    HSPC_303          HSPC_304          HSPC_305         HSPC_306     
+ Min.   : 0.0000   Min.   : 0.0000   Min.   : 0.000   Min.   : 0.000  
+ 1st Qu.: 0.0000   1st Qu.: 0.0000   1st Qu.: 0.000   1st Qu.: 0.000  
+ Median : 0.8348   Median : 0.9727   Median : 1.152   Median : 1.303  
+ Mean   : 2.3400   Mean   : 2.3710   Mean   : 2.469   Mean   : 2.496  
+ 3rd Qu.: 3.2942   3rd Qu.: 2.9942   3rd Qu.: 3.300   3rd Qu.: 3.015  
+ Max.   :10.3022   Max.   :11.7185   Max.   :11.051   Max.   :11.211  
+    HSPC_307        HSPC_308         HSPC_309         HSPC_310     
+ Min.   :0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  
+ 1st Qu.:0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  
+ Median :1.976   Median : 1.634   Median : 1.804   Median : 1.743  
+ Mean   :2.873   Mean   : 2.812   Mean   : 2.892   Mean   : 2.874  
+ 3rd Qu.:5.396   3rd Qu.: 5.089   3rd Qu.: 5.165   3rd Qu.: 5.004  
+ Max.   :9.921   Max.   :10.527   Max.   :10.476   Max.   :11.107  
+    HSPC_312         HSPC_313         HSPC_314         HSPC_315     
+ Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  
+ 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  
+ Median : 1.420   Median : 1.592   Median : 1.635   Median : 2.262  
+ Mean   : 2.645   Mean   : 2.637   Mean   : 2.564   Mean   : 2.628  
+ 3rd Qu.: 4.925   3rd Qu.: 4.257   3rd Qu.: 4.297   3rd Qu.: 4.092  
+ Max.   :11.367   Max.   :10.644   Max.   :10.882   Max.   :12.140  
+    HSPC_317         HSPC_318         HSPC_320         HSPC_321     
+ Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  
+ 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  
+ Median : 2.335   Median : 1.728   Median : 2.340   Median : 1.835  
+ Mean   : 2.648   Mean   : 2.637   Mean   : 3.064   Mean   : 2.742  
+ 3rd Qu.: 4.103   3rd Qu.: 4.483   3rd Qu.: 5.325   3rd Qu.: 4.340  
+ Max.   :10.933   Max.   :11.712   Max.   :11.589   Max.   :11.695  
+    HSPC_322          HSPC_323         HSPC_324         HSPC_325     
+ Min.   : 0.0000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  
+ 1st Qu.: 0.0000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  
+ Median : 0.9842   Median : 0.989   Median : 1.088   Median : 2.132  
+ Mean   : 2.5948   Mean   : 2.905   Mean   : 2.655   Mean   : 3.091  
+ 3rd Qu.: 3.4619   3rd Qu.: 5.629   3rd Qu.: 3.772   3rd Qu.: 5.191  
+ Max.   :11.9594   Max.   :12.267   Max.   :11.310   Max.   :11.134  
+    HSPC_326         HSPC_327         HSPC_328         HSPC_329     
+ Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  
+ 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  
+ Median : 1.781   Median : 1.085   Median : 1.936   Median : 1.954  
+ Mean   : 3.021   Mean   : 2.838   Mean   : 2.582   Mean   : 3.034  
+ 3rd Qu.: 5.582   3rd Qu.: 6.388   3rd Qu.: 4.048   3rd Qu.: 5.497  
+ Max.   :11.268   Max.   :11.433   Max.   :11.908   Max.   :10.927  
+    HSPC_330         HSPC_331         HSPC_332         HSPC_333     
+ Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  
+ 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  
+ Median : 1.870   Median : 2.953   Median : 1.644   Median : 1.320  
+ Mean   : 2.791   Mean   : 3.058   Mean   : 2.768   Mean   : 2.428  
+ 3rd Qu.: 4.409   3rd Qu.: 5.118   3rd Qu.: 5.141   3rd Qu.: 2.985  
+ Max.   :11.561   Max.   :10.855   Max.   :10.420   Max.   :11.946  
+    HSPC_334         HSPC_335         HSPC_336         HSPC_337     
+ Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  
+ 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  
+ Median : 1.931   Median : 1.541   Median : 2.761   Median : 0.000  
+ Mean   : 2.894   Mean   : 2.746   Mean   : 3.051   Mean   : 2.415  
+ 3rd Qu.: 4.160   3rd Qu.: 4.461   3rd Qu.: 4.408   3rd Qu.: 4.188  
+ Max.   :11.592   Max.   :11.076   Max.   :11.246   Max.   :10.205  
+    HSPC_338         HSPC_339         HSPC_341          HSPC_342      
+ Min.   : 0.000   Min.   : 0.000   Min.   : 0.0000   Min.   : 0.0000  
+ 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.0000   1st Qu.: 0.0000  
+ Median : 0.000   Median : 0.000   Median : 0.9553   Median : 0.4452  
+ Mean   : 2.205   Mean   : 2.325   Mean   : 2.0823   Mean   : 2.4572  
+ 3rd Qu.: 2.449   3rd Qu.: 3.136   3rd Qu.: 2.0118   3rd Qu.: 4.9582  
+ Max.   :12.052   Max.   :11.858   Max.   :11.3855   Max.   :11.8066  
+    HSPC_343         HSPC_344         HSPC_345         HSPC_346      
+ Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.0000  
+ 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.0000  
+ Median : 0.000   Median : 0.000   Median : 0.000   Median : 0.5197  
+ Mean   : 2.363   Mean   : 2.290   Mean   : 1.984   Mean   : 2.5126  
+ 3rd Qu.: 4.285   3rd Qu.: 3.238   3rd Qu.: 2.561   3rd Qu.: 5.2033  
+ Max.   :11.422   Max.   :11.877   Max.   :10.939   Max.   :11.1527  
+    HSPC_348         HSPC_349         HSPC_350        HSPC_351     
+ Min.   : 0.000   Min.   : 0.000   Min.   : 0.00   Min.   : 0.000  
+ 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.00   1st Qu.: 0.000  
+ Median : 1.113   Median : 0.000   Median : 0.00   Median : 0.000  
+ Mean   : 2.232   Mean   : 1.949   Mean   : 2.11   Mean   : 2.259  
+ 3rd Qu.: 2.875   3rd Qu.: 2.784   3rd Qu.: 3.07   3rd Qu.: 3.214  
+ Max.   :11.161   Max.   :10.720   Max.   :11.15   Max.   :10.912  
+    HSPC_352         HSPC_353         HSPC_354         HSPC_356     
+ Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  
+ 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  
+ Median : 0.000   Median : 0.000   Median : 0.000   Median : 0.000  
+ Mean   : 2.333   Mean   : 2.162   Mean   : 2.427   Mean   : 2.135  
+ 3rd Qu.: 3.197   3rd Qu.: 2.819   3rd Qu.: 3.808   3rd Qu.: 2.709  
+ Max.   :12.275   Max.   :11.351   Max.   :11.190   Max.   :10.662  
+    HSPC_358         HSPC_359         HSPC_360         HSPC_361     
+ Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  
+ 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  
+ Median : 0.000   Median : 0.000   Median : 0.000   Median : 0.000  
+ Mean   : 2.278   Mean   : 2.012   Mean   : 2.381   Mean   : 2.137  
+ 3rd Qu.: 3.608   3rd Qu.: 1.460   3rd Qu.: 3.044   3rd Qu.: 2.875  
+ Max.   :10.924   Max.   :11.678   Max.   :11.203   Max.   :10.847  
+    HSPC_362         HSPC_363         HSPC_365         HSPC_367     
+ Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  
+ 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  
+ Median : 0.000   Median : 0.000   Median : 0.000   Median : 0.000  
+ Mean   : 1.783   Mean   : 1.987   Mean   : 2.937   Mean   : 2.449  
+ 3rd Qu.: 1.594   3rd Qu.: 2.750   3rd Qu.: 5.572   3rd Qu.: 3.936  
+ Max.   :11.889   Max.   :10.389   Max.   :12.427   Max.   :11.081  
+    HSPC_368         HSPC_370          HSPC_371          HSPC_372     
+ Min.   : 0.000   Min.   : 0.0000   Min.   : 0.0000   Min.   : 0.000  
+ 1st Qu.: 0.000   1st Qu.: 0.0000   1st Qu.: 0.0000   1st Qu.: 0.000  
+ Median : 0.000   Median : 0.7971   Median : 0.7613   Median : 0.000  
+ Mean   : 1.877   Mean   : 2.7681   Mean   : 2.4278   Mean   : 2.487  
+ 3rd Qu.: 2.018   3rd Qu.: 6.5358   3rd Qu.: 4.9578   3rd Qu.: 4.226  
+ Max.   :11.523   Max.   :11.9636   Max.   :11.4223   Max.   :11.700  
+    HSPC_373         HSPC_374        HSPC_376         HSPC_377     
+ Min.   : 0.000   Min.   : 0.00   Min.   : 0.000   Min.   : 0.000  
+ 1st Qu.: 0.000   1st Qu.: 0.00   1st Qu.: 0.000   1st Qu.: 0.000  
+ Median : 0.000   Median : 0.00   Median : 0.000   Median : 0.000  
+ Mean   : 2.330   Mean   : 2.21   Mean   : 2.625   Mean   : 2.456  
+ 3rd Qu.: 3.784   3rd Qu.: 2.44   3rd Qu.: 4.365   3rd Qu.: 4.875  
+ Max.   :11.672   Max.   :12.04   Max.   :12.011   Max.   :11.282  
+    HSPC_380         HSPC_382          HSPC_383         HSPC_386     
+ Min.   : 0.000   Min.   : 0.0000   Min.   : 0.000   Min.   : 0.000  
+ 1st Qu.: 0.000   1st Qu.: 0.0000   1st Qu.: 0.000   1st Qu.: 0.000  
+ Median : 0.000   Median : 0.9728   Median : 1.753   Median : 0.000  
+ Mean   : 2.291   Mean   : 2.3318   Mean   : 2.307   Mean   : 2.351  
+ 3rd Qu.: 2.403   3rd Qu.: 2.7605   3rd Qu.: 3.113   3rd Qu.: 3.704  
+ Max.   :11.415   Max.   :11.3370   Max.   :11.592   Max.   :11.079  
+    HSPC_387         HSPC_388          HSPC_389         HSPC_390     
+ Min.   : 0.000   Min.   : 0.0000   Min.   : 0.000   Min.   : 0.000  
+ 1st Qu.: 0.000   1st Qu.: 0.0000   1st Qu.: 0.000   1st Qu.: 0.000  
+ Median : 0.000   Median : 0.9037   Median : 0.000   Median : 0.000  
+ Mean   : 2.255   Mean   : 2.4969   Mean   : 2.081   Mean   : 2.131  
+ 3rd Qu.: 3.151   3rd Qu.: 5.3587   3rd Qu.: 2.723   3rd Qu.: 2.738  
+ Max.   :11.700   Max.   :10.9923   Max.   :11.868   Max.   :10.913  
+    HSPC_391         HSPC_392         HSPC_393         HSPC_395     
+ Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  
+ 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  
+ Median : 0.000   Median : 0.000   Median : 0.000   Median : 0.000  
+ Mean   : 2.026   Mean   : 2.356   Mean   : 2.063   Mean   : 1.779  
+ 3rd Qu.: 2.126   3rd Qu.: 3.781   3rd Qu.: 2.163   3rd Qu.: 1.924  
+ Max.   :12.021   Max.   :11.370   Max.   :10.530   Max.   :12.219  
+    HSPC_396         HSPC_398         HSPC_399         HSPC_400     
+ Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  
+ 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  
+ Median : 0.000   Median : 0.000   Median : 0.000   Median : 0.000  
+ Mean   : 2.164   Mean   : 2.309   Mean   : 1.831   Mean   : 2.091  
+ 3rd Qu.: 2.681   3rd Qu.: 3.994   3rd Qu.: 1.844   3rd Qu.: 2.781  
+ Max.   :11.292   Max.   :11.431   Max.   :11.343   Max.   :10.863  
+    HSPC_402         HSPC_403        HSPC_404         HSPC_405      
+ Min.   : 0.000   Min.   : 0.00   Min.   : 0.000   Min.   : 0.0000  
+ 1st Qu.: 0.000   1st Qu.: 0.00   1st Qu.: 0.000   1st Qu.: 0.0000  
+ Median : 0.000   Median : 0.00   Median : 0.000   Median : 0.5496  
+ Mean   : 2.343   Mean   : 2.06   Mean   : 1.878   Mean   : 2.3660  
+ 3rd Qu.: 4.552   3rd Qu.: 2.45   3rd Qu.: 1.644   3rd Qu.: 2.5449  
+ Max.   :11.444   Max.   :12.00   Max.   :11.188   Max.   :12.2605  
+    HSPC_406         HSPC_407         HSPC_408          HSPC_409     
+ Min.   : 0.000   Min.   : 0.000   Min.   : 0.0000   Min.   : 0.000  
+ 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.0000   1st Qu.: 0.000  
+ Median : 0.000   Median : 0.565   Median : 0.5775   Median : 0.000  
+ Mean   : 2.169   Mean   : 2.611   Mean   : 1.9174   Mean   : 2.234  
+ 3rd Qu.: 2.606   3rd Qu.: 6.000   3rd Qu.: 1.3086   3rd Qu.: 3.044  
+ Max.   :10.866   Max.   :11.296   Max.   :12.8185   Max.   :11.595  
+    HSPC_410         HSPC_411          HSPC_412          HSPC_413     
+ Min.   : 0.000   Min.   : 0.0000   Min.   : 0.0000   Min.   : 0.000  
+ 1st Qu.: 0.000   1st Qu.: 0.0000   1st Qu.: 0.0000   1st Qu.: 0.000  
+ Median : 0.000   Median : 0.9059   Median : 0.6614   Median : 0.000  
+ Mean   : 2.308   Mean   : 3.1194   Mean   : 3.0437   Mean   : 2.433  
+ 3rd Qu.: 4.022   3rd Qu.: 7.7574   3rd Qu.: 7.4695   3rd Qu.: 3.329  
+ Max.   :11.620   Max.   :12.0858   Max.   :11.5582   Max.   :12.549  
+    HSPC_415         HSPC_416         HSPC_417          HSPC_418     
+ Min.   : 0.000   Min.   : 0.000   Min.   : 0.0000   Min.   : 0.000  
+ 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.0000   1st Qu.: 0.000  
+ Median : 0.000   Median : 0.000   Median : 0.7222   Median : 0.000  
+ Mean   : 2.904   Mean   : 2.228   Mean   : 2.4242   Mean   : 2.508  
+ 3rd Qu.: 5.531   3rd Qu.: 3.111   3rd Qu.: 3.0795   3rd Qu.: 3.249  
+ Max.   :12.359   Max.   :11.338   Max.   :12.0314   Max.   :11.857  
+    HSPC_419          HSPC_420         HSPC_421         HSPC_422     
+ Min.   : 0.0000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  
+ 1st Qu.: 0.0000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  
+ Median : 0.6924   Median : 0.000   Median : 0.000   Median : 0.000  
+ Mean   : 2.6246   Mean   : 2.514   Mean   : 2.075   Mean   : 2.552  
+ 3rd Qu.: 4.8156   3rd Qu.: 5.709   3rd Qu.: 3.682   3rd Qu.: 5.382  
+ Max.   :12.0526   Max.   :11.270   Max.   :10.250   Max.   :11.691  
+    HSPC_423        HSPC_424         HSPC_425         HSPC_426     
+ Min.   : 0.00   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  
+ 1st Qu.: 0.00   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  
+ Median : 0.00   Median : 0.000   Median : 1.016   Median : 0.000  
+ Mean   : 2.12   Mean   : 2.225   Mean   : 2.658   Mean   : 2.235  
+ 3rd Qu.: 1.55   3rd Qu.: 2.471   3rd Qu.: 6.474   3rd Qu.: 3.134  
+ Max.   :11.56   Max.   :11.734   Max.   :11.303   Max.   :10.888  
+    HSPC_427         HSPC_431         HSPC_432        HSPC_435     
+ Min.   : 0.000   Min.   : 0.000   Min.   :0.000   Min.   : 0.000  
+ 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.:0.000   1st Qu.: 0.000  
+ Median : 0.000   Median : 1.102   Median :0.000   Median : 1.098  
+ Mean   : 1.829   Mean   : 2.360   Mean   :2.169   Mean   : 2.060  
+ 3rd Qu.: 2.980   3rd Qu.: 3.640   3rd Qu.:3.261   3rd Qu.: 2.744  
+ Max.   :10.517   Max.   :10.533   Max.   :9.911   Max.   :10.677  
+    HSPC_436          HSPC_440         HSPC_441         HSPC_442     
+ Min.   : 0.0000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  
+ 1st Qu.: 0.0000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  
+ Median : 0.4719   Median : 1.385   Median : 1.084   Median : 0.595  
+ Mean   : 2.3880   Mean   : 1.712   Mean   : 2.265   Mean   : 2.109  
+ 3rd Qu.: 4.3738   3rd Qu.: 2.079   3rd Qu.: 2.828   3rd Qu.: 2.193  
+ Max.   :11.2839   Max.   :11.065   Max.   :11.152   Max.   :11.560  
+    HSPC_443          HSPC_444         HSPC_446         HSPC_447     
+ Min.   : 0.0000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  
+ 1st Qu.: 0.0000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  
+ Median : 0.7734   Median : 1.374   Median : 0.000   Median : 1.113  
+ Mean   : 2.5663   Mean   : 2.262   Mean   : 1.475   Mean   : 2.446  
+ 3rd Qu.: 4.9423   3rd Qu.: 2.952   3rd Qu.: 1.683   3rd Qu.: 4.733  
+ Max.   :10.9262   Max.   :10.705   Max.   :10.545   Max.   :10.303  
+    HSPC_448         HSPC_449         HSPC_450         HSPC_451     
+ Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  
+ 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  
+ Median : 1.139   Median : 1.344   Median : 0.000   Median : 1.759  
+ Mean   : 2.396   Mean   : 2.164   Mean   : 1.946   Mean   : 1.806  
+ 3rd Qu.: 3.660   3rd Qu.: 2.490   3rd Qu.: 2.483   3rd Qu.: 2.528  
+ Max.   :11.091   Max.   :11.324   Max.   :10.397   Max.   :10.395  
+    HSPC_453          HSPC_454          HSPC_455         HSPC_456      
+ Min.   : 0.0000   Min.   : 0.0000   Min.   : 0.000   Min.   : 0.0000  
+ 1st Qu.: 0.0000   1st Qu.: 0.0000   1st Qu.: 0.000   1st Qu.: 0.0000  
+ Median : 0.9321   Median : 0.5303   Median : 0.000   Median : 0.6497  
+ Mean   : 2.4906   Mean   : 2.4477   Mean   : 2.379   Mean   : 2.4263  
+ 3rd Qu.: 4.9604   3rd Qu.: 4.8773   3rd Qu.: 3.016   3rd Qu.: 5.4740  
+ Max.   :10.5263   Max.   :11.1628   Max.   :11.437   Max.   :10.9787  
+    HSPC_457         HSPC_459         HSPC_460         HSPC_461     
+ Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  
+ 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  
+ Median : 0.000   Median : 0.000   Median : 0.000   Median : 1.313  
+ Mean   : 2.060   Mean   : 2.403   Mean   : 1.712   Mean   : 1.875  
+ 3rd Qu.: 2.937   3rd Qu.: 3.029   3rd Qu.: 1.598   3rd Qu.: 2.104  
+ Max.   :11.746   Max.   :12.135   Max.   :12.526   Max.   :10.210  
+    HSPC_462         HSPC_463          HSPC_465         HSPC_466      
+ Min.   : 0.000   Min.   : 0.0000   Min.   : 0.000   Min.   : 0.0000  
+ 1st Qu.: 0.000   1st Qu.: 0.0000   1st Qu.: 0.000   1st Qu.: 0.0000  
+ Median : 0.000   Median : 0.7257   Median : 0.000   Median : 0.5816  
+ Mean   : 2.095   Mean   : 2.2325   Mean   : 2.000   Mean   : 1.9972  
+ 3rd Qu.: 2.578   3rd Qu.: 2.3442   3rd Qu.: 2.633   3rd Qu.: 2.2384  
+ Max.   :11.429   Max.   :11.1776   Max.   :11.064   Max.   :11.5475  
+    HSPC_467         HSPC_468         HSPC_470         HSPC_471     
+ Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  
+ 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  
+ Median : 1.177   Median : 0.649   Median : 0.000   Median : 0.000  
+ Mean   : 1.866   Mean   : 2.130   Mean   : 1.774   Mean   : 2.279  
+ 3rd Qu.: 2.258   3rd Qu.: 2.513   3rd Qu.: 1.931   3rd Qu.: 2.744  
+ Max.   :10.632   Max.   :10.527   Max.   :10.781   Max.   :11.533  
+    HSPC_472         HSPC_473         HSPC_474         HSPC_475     
+ Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  
+ 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  
+ Median : 0.000   Median : 0.000   Median : 0.000   Median : 0.000  
+ Mean   : 2.265   Mean   : 2.168   Mean   : 2.016   Mean   : 2.339  
+ 3rd Qu.: 2.982   3rd Qu.: 2.677   3rd Qu.: 2.061   3rd Qu.: 3.319  
+ Max.   :11.795   Max.   :12.071   Max.   :11.732   Max.   :10.672  
+    HSPC_477          HSPC_478         HSPC_479         HSPC_480     
+ Min.   : 0.0000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  
+ 1st Qu.: 0.0000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  
+ Median : 0.6278   Median : 0.000   Median : 1.281   Median : 1.034  
+ Mean   : 1.9910   Mean   : 2.068   Mean   : 2.175   Mean   : 2.239  
+ 3rd Qu.: 1.6695   3rd Qu.: 3.402   3rd Qu.: 3.028   3rd Qu.: 2.642  
+ Max.   :11.1171   Max.   :12.113   Max.   :11.277   Max.   :10.641  
+    HSPC_482         HSPC_483         HSPC_485          HSPC_486     
+ Min.   : 0.000   Min.   : 0.000   Min.   : 0.0000   Min.   : 0.000  
+ 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.0000   1st Qu.: 0.000  
+ Median : 0.000   Median : 1.088   Median : 0.6036   Median : 1.411  
+ Mean   : 1.998   Mean   : 2.454   Mean   : 2.3824   Mean   : 2.078  
+ 3rd Qu.: 2.648   3rd Qu.: 3.006   3rd Qu.: 4.8213   3rd Qu.: 2.579  
+ Max.   :13.948   Max.   :10.722   Max.   :11.8691   Max.   :10.155  
+    HSPC_488         HSPC_489         HSPC_490         HSPC_491     
+ Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  
+ 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  
+ Median : 0.000   Median : 0.000   Median : 1.310   Median : 0.000  
+ Mean   : 1.809   Mean   : 1.947   Mean   : 2.518   Mean   : 2.268  
+ 3rd Qu.: 2.120   3rd Qu.: 2.330   3rd Qu.: 4.140   3rd Qu.: 3.300  
+ Max.   :11.271   Max.   :11.518   Max.   :11.646   Max.   :10.366  
+    HSPC_492         HSPC_493         HSPC_494         HSPC_495     
+ Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  
+ 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  
+ Median : 0.000   Median : 0.000   Median : 0.000   Median : 0.000  
+ Mean   : 2.127   Mean   : 2.054   Mean   : 2.255   Mean   : 2.326  
+ 3rd Qu.: 2.322   3rd Qu.: 3.060   3rd Qu.: 3.386   3rd Qu.: 3.812  
+ Max.   :11.674   Max.   :10.404   Max.   :10.461   Max.   :10.304  
+    HSPC_496         HSPC_497         HSPC_498          HSPC_499     
+ Min.   : 0.000   Min.   : 0.000   Min.   : 0.0000   Min.   : 0.000  
+ 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.0000   1st Qu.: 0.000  
+ Median : 0.000   Median : 1.945   Median : 0.5839   Median : 0.000  
+ Mean   : 1.938   Mean   : 2.287   Mean   : 2.3731   Mean   : 2.045  
+ 3rd Qu.: 2.227   3rd Qu.: 2.872   3rd Qu.: 3.6112   3rd Qu.: 2.358  
+ Max.   :11.323   Max.   :11.873   Max.   :11.3264   Max.   :10.632  
+    HSPC_500         HSPC_501         HSPC_502          HSPC_503      
+ Min.   : 0.000   Min.   : 0.000   Min.   : 0.0000   Min.   : 0.0000  
+ 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.0000   1st Qu.: 0.0000  
+ Median : 0.000   Median : 0.000   Median : 0.9146   Median : 0.7789  
+ Mean   : 2.199   Mean   : 2.209   Mean   : 2.2727   Mean   : 2.4495  
+ 3rd Qu.: 2.678   3rd Qu.: 3.150   3rd Qu.: 2.8888   3rd Qu.: 5.4034  
+ Max.   :11.665   Max.   :10.727   Max.   :11.4591   Max.   :11.5376  
+    HSPC_504         HSPC_505         HSPC_506         HSPC_507     
+ Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  
+ 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  
+ Median : 0.000   Median : 0.000   Median : 0.000   Median : 0.000  
+ Mean   : 2.137   Mean   : 2.132   Mean   : 2.017   Mean   : 2.314  
+ 3rd Qu.: 3.035   3rd Qu.: 2.744   3rd Qu.: 2.794   3rd Qu.: 3.175  
+ Max.   :11.625   Max.   :11.385   Max.   :11.467   Max.   :11.232  
+    HSPC_508          HSPC_509         HSPC_510         HSPC_512     
+ Min.   : 0.0000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  
+ 1st Qu.: 0.0000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  
+ Median : 0.2297   Median : 1.691   Median : 1.166   Median : 0.000  
+ Mean   : 1.9265   Mean   : 2.548   Mean   : 2.319   Mean   : 2.482  
+ 3rd Qu.: 0.8975   3rd Qu.: 4.397   3rd Qu.: 3.492   3rd Qu.: 3.753  
+ Max.   :12.0747   Max.   :10.603   Max.   :10.885   Max.   :12.492  
+    HSPC_514         HSPC_515         HSPC_516          HSPC_518     
+ Min.   : 0.000   Min.   : 0.000   Min.   : 0.0000   Min.   : 0.000  
+ 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.0000   1st Qu.: 0.000  
+ Median : 0.000   Median : 1.109   Median : 0.8853   Median : 0.000  
+ Mean   : 2.295   Mean   : 2.298   Mean   : 2.5439   Mean   : 2.649  
+ 3rd Qu.: 2.429   3rd Qu.: 2.560   3rd Qu.: 4.6629   3rd Qu.: 5.581  
+ Max.   :11.783   Max.   :12.193   Max.   :12.1718   Max.   :11.838  
+    HSPC_520         HSPC_521         HSPC_522         HSPC_523      
+ Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.0000  
+ 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.0000  
+ Median : 0.000   Median : 0.000   Median : 0.000   Median : 0.3648  
+ Mean   : 2.295   Mean   : 2.348   Mean   : 2.529   Mean   : 1.9471  
+ 3rd Qu.: 2.975   3rd Qu.: 3.375   3rd Qu.: 5.350   3rd Qu.: 1.5726  
+ Max.   :12.289   Max.   :11.712   Max.   :10.364   Max.   :12.5906  
+    HSPC_524         HSPC_526         HSPC_527         HSPC_528     
+ Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  
+ 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  
+ Median : 0.000   Median : 0.000   Median : 0.777   Median : 0.532  
+ Mean   : 1.989   Mean   : 2.218   Mean   : 2.133   Mean   : 2.238  
+ 3rd Qu.: 3.267   3rd Qu.: 2.431   3rd Qu.: 1.651   3rd Qu.: 2.095  
+ Max.   :12.105   Max.   :10.870   Max.   :12.017   Max.   :12.183  
+    HSPC_530         HSPC_532         HSPC_533          HSPC_534     
+ Min.   : 0.000   Min.   : 0.000   Min.   : 0.0000   Min.   : 0.000  
+ 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.0000   1st Qu.: 0.000  
+ Median : 0.000   Median : 0.000   Median : 0.7537   Median : 0.000  
+ Mean   : 2.017   Mean   : 1.856   Mean   : 1.7546   Mean   : 2.183  
+ 3rd Qu.: 2.514   3rd Qu.: 1.816   3rd Qu.: 1.3378   3rd Qu.: 2.311  
+ Max.   :11.549   Max.   :11.255   Max.   :11.5862   Max.   :11.696  
+    HSPC_535         HSPC_537         HSPC_538         HSPC_539     
+ Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  
+ 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  
+ Median : 0.000   Median : 0.000   Median : 0.000   Median : 0.000  
+ Mean   : 2.122   Mean   : 2.010   Mean   : 2.501   Mean   : 2.463  
+ 3rd Qu.: 2.733   3rd Qu.: 2.541   3rd Qu.: 4.886   3rd Qu.: 4.100  
+ Max.   :10.793   Max.   :10.305   Max.   :11.359   Max.   :11.755  
+    HSPC_540          HSPC_541         HSPC_543         HSPC_544      
+ Min.   : 0.0000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.0000  
+ 1st Qu.: 0.0000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.0000  
+ Median : 0.9898   Median : 2.362   Median : 0.000   Median : 0.8222  
+ Mean   : 2.1775   Mean   : 2.613   Mean   : 2.275   Mean   : 2.8070  
+ 3rd Qu.: 1.9846   3rd Qu.: 4.440   3rd Qu.: 2.690   3rd Qu.: 6.4209  
+ Max.   :12.2963   Max.   :11.844   Max.   :10.983   Max.   :10.7976  
+    HSPC_545         HSPC_546         HSPC_547          HSPC_548     
+ Min.   : 0.000   Min.   : 0.000   Min.   : 0.0000   Min.   : 0.000  
+ 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.0000   1st Qu.: 0.000  
+ Median : 1.485   Median : 0.000   Median : 0.6548   Median : 1.456  
+ Mean   : 2.215   Mean   : 2.424   Mean   : 2.5255   Mean   : 2.415  
+ 3rd Qu.: 2.677   3rd Qu.: 3.573   3rd Qu.: 2.8714   3rd Qu.: 2.639  
+ Max.   :11.815   Max.   :11.235   Max.   :11.8801   Max.   :11.955  
+    HSPC_549         HSPC_550         HSPC_551         HSPC_552     
+ Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  
+ 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  
+ Median : 0.000   Median : 1.750   Median : 1.287   Median : 1.226  
+ Mean   : 2.149   Mean   : 2.592   Mean   : 2.680   Mean   : 2.236  
+ 3rd Qu.: 2.289   3rd Qu.: 4.686   3rd Qu.: 4.007   3rd Qu.: 2.669  
+ Max.   :11.827   Max.   :12.064   Max.   :11.874   Max.   :11.581  
+    HSPC_553          HSPC_554         HSPC_555         HSPC_556      
+ Min.   : 0.0000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.0000  
+ 1st Qu.: 0.0000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.0000  
+ Median : 0.4709   Median : 0.000   Median : 0.000   Median : 0.9369  
+ Mean   : 2.6931   Mean   : 2.090   Mean   : 1.903   Mean   : 2.4784  
+ 3rd Qu.: 6.4420   3rd Qu.: 2.158   3rd Qu.: 2.579   3rd Qu.: 3.4024  
+ Max.   :11.0566   Max.   :11.755   Max.   :11.245   Max.   :11.9838  
+    HSPC_557         HSPC_559         HSPC_560         HSPC_562     
+ Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  
+ 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  
+ Median : 0.000   Median : 0.000   Median : 1.681   Median : 0.000  
+ Mean   : 1.972   Mean   : 1.937   Mean   : 2.082   Mean   : 2.470  
+ 3rd Qu.: 1.880   3rd Qu.: 2.411   3rd Qu.: 2.436   3rd Qu.: 4.148  
+ Max.   :11.792   Max.   :11.871   Max.   :11.761   Max.   :11.958  
+    HSPC_563        HSPC_566         HSPC_567         HSPC_568     
+ Min.   : 0.00   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  
+ 1st Qu.: 0.00   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  
+ Median : 0.00   Median : 0.000   Median : 0.000   Median : 0.000  
+ Mean   : 1.83   Mean   : 2.486   Mean   : 2.186   Mean   : 2.267  
+ 3rd Qu.: 2.29   3rd Qu.: 3.577   3rd Qu.: 2.254   3rd Qu.: 2.957  
+ Max.   :10.59   Max.   :12.452   Max.   :11.302   Max.   :10.851  
+    HSPC_569         HSPC_571         HSPC_573         HSPC_574      
+ Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.0000  
+ 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.0000  
+ Median : 0.771   Median : 1.042   Median : 0.000   Median : 0.7547  
+ Mean   : 2.283   Mean   : 2.213   Mean   : 2.089   Mean   : 2.3196  
+ 3rd Qu.: 3.021   3rd Qu.: 2.879   3rd Qu.: 2.291   3rd Qu.: 5.6078  
+ Max.   :10.720   Max.   :10.939   Max.   :11.397   Max.   :10.4741  
+    HSPC_575         HSPC_576         HSPC_577         HSPC_578     
+ Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  
+ 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  
+ Median : 0.000   Median : 0.000   Median : 1.606   Median : 0.000  
+ Mean   : 2.016   Mean   : 2.206   Mean   : 2.358   Mean   : 2.257  
+ 3rd Qu.: 2.267   3rd Qu.: 2.741   3rd Qu.: 3.198   3rd Qu.: 2.923  
+ Max.   :10.687   Max.   :11.201   Max.   :11.613   Max.   :12.323  
+    HSPC_579         HSPC_580          HSPC_582         HSPC_584     
+ Min.   : 0.000   Min.   : 0.0000   Min.   : 0.000   Min.   : 0.000  
+ 1st Qu.: 0.000   1st Qu.: 0.0000   1st Qu.: 0.000   1st Qu.: 0.000  
+ Median : 1.182   Median : 0.9442   Median : 0.000   Median : 0.000  
+ Mean   : 2.472   Mean   : 2.4264   Mean   : 2.218   Mean   : 2.276  
+ 3rd Qu.: 5.009   3rd Qu.: 3.5841   3rd Qu.: 3.332   3rd Qu.: 3.067  
+ Max.   :11.096   Max.   :10.6790   Max.   :10.882   Max.   :10.954  
+    HSPC_585         HSPC_586          HSPC_589         HSPC_590     
+ Min.   : 0.000   Min.   : 0.0000   Min.   : 0.000   Min.   : 0.000  
+ 1st Qu.: 0.000   1st Qu.: 0.0000   1st Qu.: 0.000   1st Qu.: 0.000  
+ Median : 0.000   Median : 0.8915   Median : 0.000   Median : 1.192  
+ Mean   : 2.034   Mean   : 2.0490   Mean   : 2.274   Mean   : 2.252  
+ 3rd Qu.: 2.157   3rd Qu.: 1.8340   3rd Qu.: 3.655   3rd Qu.: 2.364  
+ Max.   :11.956   Max.   :11.4729   Max.   :11.198   Max.   :10.673  
+    HSPC_592         HSPC_593         HSPC_594         HSPC_595     
+ Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  
+ 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  
+ Median : 1.228   Median : 0.000   Median : 0.000   Median : 0.000  
+ Mean   : 2.317   Mean   : 2.329   Mean   : 2.474   Mean   : 1.463  
+ 3rd Qu.: 2.671   3rd Qu.: 3.263   3rd Qu.: 4.396   3rd Qu.: 1.757  
+ Max.   :12.036   Max.   :10.626   Max.   :11.347   Max.   :11.286  
+    HSPC_596         HSPC_597         HSPC_598         HSPC_599     
+ Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  
+ 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  
+ Median : 1.392   Median : 0.000   Median : 0.000   Median : 0.000  
+ Mean   : 2.283   Mean   : 1.858   Mean   : 1.954   Mean   : 1.905  
+ 3rd Qu.: 3.425   3rd Qu.: 2.296   3rd Qu.: 2.320   3rd Qu.: 2.497  
+ Max.   :10.899   Max.   :11.002   Max.   :11.117   Max.   :11.248  
+    HSPC_600         HSPC_601         HSPC_602         HSPC_603     
+ Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  
+ 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  
+ Median : 0.000   Median : 0.000   Median : 1.662   Median : 0.000  
+ Mean   : 2.335   Mean   : 1.905   Mean   : 2.343   Mean   : 2.281  
+ 3rd Qu.: 3.827   3rd Qu.: 2.376   3rd Qu.: 3.272   3rd Qu.: 3.048  
+ Max.   :11.208   Max.   :11.022   Max.   :10.908   Max.   :11.464  
+    HSPC_604         HSPC_606         HSPC_607         HSPC_608     
+ Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  
+ 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  
+ Median : 0.000   Median : 0.000   Median : 0.000   Median : 0.000  
+ Mean   : 2.136   Mean   : 2.392   Mean   : 2.142   Mean   : 2.139  
+ 3rd Qu.: 2.516   3rd Qu.: 4.726   3rd Qu.: 3.187   3rd Qu.: 2.885  
+ Max.   :11.743   Max.   :11.210   Max.   :10.319   Max.   :10.802  
+    HSPC_610         HSPC_612         HSPC_613         HSPC_614     
+ Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  
+ 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  
+ Median : 0.000   Median : 0.000   Median : 0.000   Median : 1.315  
+ Mean   : 2.327   Mean   : 2.298   Mean   : 2.228   Mean   : 2.364  
+ 3rd Qu.: 3.718   3rd Qu.: 3.138   3rd Qu.: 2.705   3rd Qu.: 3.136  
+ Max.   :10.860   Max.   :11.564   Max.   :10.560   Max.   :11.824  
+    HSPC_615         HSPC_617          HSPC_618         HSPC_620     
+ Min.   : 0.000   Min.   : 0.0000   Min.   : 0.000   Min.   : 0.000  
+ 1st Qu.: 0.000   1st Qu.: 0.0000   1st Qu.: 0.000   1st Qu.: 0.000  
+ Median : 0.000   Median : 0.8525   Median : 0.000   Median : 0.000  
+ Mean   : 1.964   Mean   : 2.2100   Mean   : 2.229   Mean   : 1.881  
+ 3rd Qu.: 2.451   3rd Qu.: 2.3301   3rd Qu.: 2.885   3rd Qu.: 2.518  
+ Max.   :11.058   Max.   :10.9434   Max.   :11.210   Max.   :11.388  
+    HSPC_623         HSPC_624          HSPC_625         HSPC_626     
+ Min.   : 0.000   Min.   : 0.0000   Min.   : 0.000   Min.   : 0.000  
+ 1st Qu.: 0.000   1st Qu.: 0.0000   1st Qu.: 0.000   1st Qu.: 0.000  
+ Median : 0.000   Median : 0.7201   Median : 0.000   Median : 0.000  
+ Mean   : 2.563   Mean   : 2.0968   Mean   : 2.042   Mean   : 2.262  
+ 3rd Qu.: 4.626   3rd Qu.: 1.8437   3rd Qu.: 2.938   3rd Qu.: 3.424  
+ Max.   :10.954   Max.   :10.9459   Max.   :11.226   Max.   :11.770  
+    HSPC_627         HSPC_628         HSPC_629         HSPC_630     
+ Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  
+ 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  
+ Median : 0.000   Median : 0.000   Median : 0.000   Median : 0.000  
+ Mean   : 2.269   Mean   : 2.302   Mean   : 2.212   Mean   : 2.519  
+ 3rd Qu.: 3.952   3rd Qu.: 2.875   3rd Qu.: 2.625   3rd Qu.: 4.511  
+ Max.   :11.426   Max.   :11.792   Max.   :11.139   Max.   :11.519  
+    HSPC_631         HSPC_633         HSPC_634         HSPC_635     
+ Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  
+ 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  
+ Median : 0.000   Median : 0.000   Median : 0.000   Median : 0.000  
+ Mean   : 2.303   Mean   : 2.329   Mean   : 2.268   Mean   : 2.054  
+ 3rd Qu.: 2.685   3rd Qu.: 3.619   3rd Qu.: 3.662   3rd Qu.: 2.629  
+ Max.   :10.996   Max.   :12.011   Max.   :11.406   Max.   :11.178  
+    HSPC_636         HSPC_637          HSPC_638          HSPC_639      
+ Min.   : 0.000   Min.   : 0.0000   Min.   : 0.0000   Min.   : 0.0000  
+ 1st Qu.: 0.000   1st Qu.: 0.0000   1st Qu.: 0.0000   1st Qu.: 0.0000  
+ Median : 0.000   Median : 0.9389   Median : 0.5101   Median : 0.9966  
+ Mean   : 1.953   Mean   : 2.1351   Mean   : 1.6966   Mean   : 1.5879  
+ 3rd Qu.: 2.129   3rd Qu.: 2.4817   3rd Qu.: 1.6879   3rd Qu.: 1.6840  
+ Max.   :11.057   Max.   :11.1881   Max.   :10.8837   Max.   :10.9561  
+    HSPC_640         HSPC_641         HSPC_643         HSPC_644      
+ Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.0000  
+ 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.0000  
+ Median : 1.025   Median : 1.000   Median : 1.706   Median : 0.4904  
+ Mean   : 2.136   Mean   : 1.957   Mean   : 2.468   Mean   : 2.4726  
+ 3rd Qu.: 2.119   3rd Qu.: 2.001   3rd Qu.: 3.329   3rd Qu.: 5.6227  
+ Max.   :11.173   Max.   :11.056   Max.   :12.016   Max.   :11.0232  
+    HSPC_645          HSPC_646          HSPC_648         HSPC_649      
+ Min.   : 0.0000   Min.   : 0.0000   Min.   : 0.000   Min.   : 0.0000  
+ 1st Qu.: 0.0000   1st Qu.: 0.0000   1st Qu.: 0.000   1st Qu.: 0.0000  
+ Median : 0.7157   Median : 0.9959   Median : 1.519   Median : 0.7139  
+ Mean   : 2.3517   Mean   : 2.0594   Mean   : 2.267   Mean   : 2.3593  
+ 3rd Qu.: 4.5630   3rd Qu.: 2.3154   3rd Qu.: 2.722   3rd Qu.: 4.1542  
+ Max.   :10.9922   Max.   :11.6070   Max.   :11.243   Max.   :10.7707  
+    HSPC_651         HSPC_652         HSPC_654         HSPC_656    
+ Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   :0.000  
+ 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.:0.000  
+ Median : 0.000   Median : 0.000   Median : 1.398   Median :0.000  
+ Mean   : 2.550   Mean   : 1.764   Mean   : 2.108   Mean   :1.983  
+ 3rd Qu.: 5.615   3rd Qu.: 2.038   3rd Qu.: 2.562   3rd Qu.:2.505  
+ Max.   :11.202   Max.   :10.897   Max.   :10.367   Max.   :9.673  
+    HSPC_657         HSPC_658         HSPC_660        HSPC_661     
+ Min.   : 0.000   Min.   : 0.000   Min.   :0.000   Min.   : 0.000  
+ 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.:0.000   1st Qu.: 0.000  
+ Median : 0.000   Median : 0.000   Median :1.253   Median : 1.491  
+ Mean   : 1.839   Mean   : 2.319   Mean   :2.542   Mean   : 2.401  
+ 3rd Qu.: 2.239   3rd Qu.: 4.021   3rd Qu.:5.274   3rd Qu.: 2.775  
+ Max.   :12.132   Max.   :11.264   Max.   :9.852   Max.   :11.647  
+    HSPC_662         HSPC_663         HSPC_664          HSPC_665      
+ Min.   : 0.000   Min.   : 0.000   Min.   : 0.0000   Min.   : 0.0000  
+ 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.0000   1st Qu.: 0.0000  
+ Median : 0.000   Median : 0.000   Median : 0.9407   Median : 0.6452  
+ Mean   : 2.298   Mean   : 2.726   Mean   : 2.4039   Mean   : 2.1211  
+ 3rd Qu.: 2.939   3rd Qu.: 6.519   3rd Qu.: 3.4095   3rd Qu.: 2.0744  
+ Max.   :11.277   Max.   :12.152   Max.   :10.9423   Max.   :12.0111  
+    HSPC_666         HSPC_667         HSPC_668         HSPC_669     
+ Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  
+ 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  
+ Median : 1.130   Median : 0.000   Median : 0.000   Median : 0.000  
+ Mean   : 2.815   Mean   : 2.075   Mean   : 2.245   Mean   : 1.992  
+ 3rd Qu.: 6.359   3rd Qu.: 2.549   3rd Qu.: 2.407   3rd Qu.: 2.426  
+ Max.   :11.052   Max.   :11.406   Max.   :11.061   Max.   :11.752  
+    HSPC_670         HSPC_671         HSPC_672         HSPC_673     
+ Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  
+ 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  
+ Median : 0.000   Median : 0.000   Median : 0.000   Median : 0.000  
+ Mean   : 2.336   Mean   : 2.349   Mean   : 2.041   Mean   : 2.148  
+ 3rd Qu.: 3.188   3rd Qu.: 3.777   3rd Qu.: 2.057   3rd Qu.: 2.723  
+ Max.   :11.021   Max.   :10.846   Max.   :11.212   Max.   :11.579  
+    HSPC_674         HSPC_676         HSPC_678         HSPC_679     
+ Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  
+ 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  
+ Median : 0.000   Median : 0.000   Median : 1.525   Median : 1.531  
+ Mean   : 1.801   Mean   : 2.239   Mean   : 2.298   Mean   : 2.133  
+ 3rd Qu.: 1.892   3rd Qu.: 3.097   3rd Qu.: 3.089   3rd Qu.: 2.737  
+ Max.   :10.875   Max.   :10.496   Max.   :12.125   Max.   :11.583  
+    HSPC_680         HSPC_681         HSPC_682         HSPC_683     
+ Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  
+ 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  
+ Median : 0.000   Median : 1.037   Median : 1.043   Median : 1.180  
+ Mean   : 2.573   Mean   : 2.277   Mean   : 2.586   Mean   : 2.498  
+ 3rd Qu.: 4.165   3rd Qu.: 4.210   3rd Qu.: 5.432   3rd Qu.: 3.929  
+ Max.   :11.100   Max.   :10.154   Max.   :11.095   Max.   :10.859  
+    HSPC_687         HSPC_689         HSPC_690         HSPC_692     
+ Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  
+ 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  
+ Median : 2.091   Median : 0.000   Median : 0.000   Median : 0.000  
+ Mean   : 2.136   Mean   : 2.489   Mean   : 2.686   Mean   : 2.285  
+ 3rd Qu.: 2.911   3rd Qu.: 4.106   3rd Qu.: 5.055   3rd Qu.: 3.427  
+ Max.   :11.380   Max.   :10.693   Max.   :10.408   Max.   :12.242  
+    HSPC_695          HSPC_696         HSPC_697         HSPC_698     
+ Min.   : 0.0000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  
+ 1st Qu.: 0.0000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  
+ Median : 0.2681   Median : 1.538   Median : 1.271   Median : 0.000  
+ Mean   : 1.6151   Mean   : 2.688   Mean   : 2.529   Mean   : 2.531  
+ 3rd Qu.: 0.6895   3rd Qu.: 5.560   3rd Qu.: 4.779   3rd Qu.: 4.387  
+ Max.   :12.4139   Max.   :10.880   Max.   :10.292   Max.   :12.146  
+    HSPC_699         HSPC_700         HSPC_701         HSPC_702     
+ Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  
+ 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  
+ Median : 0.000   Median : 0.000   Median : 1.157   Median : 0.000  
+ Mean   : 2.586   Mean   : 2.402   Mean   : 2.401   Mean   : 2.723  
+ 3rd Qu.: 4.595   3rd Qu.: 4.797   3rd Qu.: 3.889   3rd Qu.: 4.822  
+ Max.   :11.389   Max.   :10.630   Max.   :11.750   Max.   :11.805  
+    HSPC_703         HSPC_704         HSPC_705          HSPC_706     
+ Min.   : 0.000   Min.   : 0.000   Min.   : 0.0000   Min.   : 0.000  
+ 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.0000   1st Qu.: 0.000  
+ Median : 2.193   Median : 0.000   Median : 0.9795   Median : 1.273  
+ Mean   : 2.543   Mean   : 2.598   Mean   : 2.5048   Mean   : 2.364  
+ 3rd Qu.: 3.935   3rd Qu.: 4.335   3rd Qu.: 5.0680   3rd Qu.: 3.492  
+ Max.   :11.710   Max.   :11.488   Max.   :11.3580   Max.   :10.447  
+    HSPC_707         HSPC_708         HSPC_709         HSPC_714     
+ Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  
+ 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  
+ Median : 1.361   Median : 0.000   Median : 0.000   Median : 0.000  
+ Mean   : 2.371   Mean   : 2.509   Mean   : 2.601   Mean   : 2.326  
+ 3rd Qu.: 3.626   3rd Qu.: 3.832   3rd Qu.: 5.060   3rd Qu.: 3.324  
+ Max.   :11.796   Max.   :10.865   Max.   :10.145   Max.   :11.126  
+    HSPC_716         HSPC_717         HSPC_719         HSPC_720      
+ Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.0000  
+ 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.0000  
+ Median : 0.000   Median : 1.154   Median : 1.855   Median : 0.8206  
+ Mean   : 2.325   Mean   : 2.302   Mean   : 2.519   Mean   : 2.5768  
+ 3rd Qu.: 3.356   3rd Qu.: 2.833   3rd Qu.: 4.115   3rd Qu.: 5.5594  
+ Max.   :11.812   Max.   :11.047   Max.   :12.237   Max.   :10.5895  
+    HSPC_721         HSPC_722         HSPC_723          HSPC_724      
+ Min.   : 0.000   Min.   : 0.000   Min.   : 0.0000   Min.   : 0.0000  
+ 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.0000   1st Qu.: 0.0000  
+ Median : 2.113   Median : 1.185   Median : 0.8421   Median : 0.6485  
+ Mean   : 2.205   Mean   : 1.814   Mean   : 2.6174   Mean   : 1.9644  
+ 3rd Qu.: 3.456   3rd Qu.: 2.269   3rd Qu.: 4.9545   3rd Qu.: 1.9402  
+ Max.   :10.706   Max.   :10.709   Max.   :11.5956   Max.   :11.3505  
+    HSPC_725         HSPC_727         HSPC_729          HSPC_730     
+ Min.   : 0.000   Min.   : 0.000   Min.   : 0.0000   Min.   : 0.000  
+ 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.0000   1st Qu.: 0.000  
+ Median : 1.577   Median : 1.576   Median : 0.9579   Median : 1.135  
+ Mean   : 2.483   Mean   : 2.436   Mean   : 2.2448   Mean   : 2.445  
+ 3rd Qu.: 3.741   3rd Qu.: 3.447   3rd Qu.: 2.7343   3rd Qu.: 3.475  
+ Max.   :10.647   Max.   :11.512   Max.   :10.9657   Max.   :11.121  
+    HSPC_731         HSPC_732          HSPC_733         HSPC_734      
+ Min.   : 0.000   Min.   : 0.0000   Min.   : 0.000   Min.   : 0.0000  
+ 1st Qu.: 0.000   1st Qu.: 0.0000   1st Qu.: 0.000   1st Qu.: 0.0000  
+ Median : 1.130   Median : 0.6937   Median : 1.436   Median : 0.7333  
+ Mean   : 2.854   Mean   : 2.1051   Mean   : 2.489   Mean   : 2.5404  
+ 3rd Qu.: 6.019   3rd Qu.: 2.0311   3rd Qu.: 3.738   3rd Qu.: 5.6282  
+ Max.   :10.471   Max.   :11.0494   Max.   :10.929   Max.   :10.4547  
+    HSPC_735         HSPC_736          HSPC_737         HSPC_738     
+ Min.   : 0.000   Min.   : 0.0000   Min.   : 0.000   Min.   : 0.000  
+ 1st Qu.: 0.000   1st Qu.: 0.0000   1st Qu.: 0.000   1st Qu.: 0.000  
+ Median : 1.033   Median : 0.6789   Median : 1.185   Median : 1.514  
+ Mean   : 2.389   Mean   : 2.0224   Mean   : 2.722   Mean   : 2.503  
+ 3rd Qu.: 3.056   3rd Qu.: 2.0017   3rd Qu.: 5.669   3rd Qu.: 3.602  
+ Max.   :10.866   Max.   :11.8100   Max.   :11.076   Max.   :10.473  
+    HSPC_740         HSPC_742          HSPC_743         HSPC_744     
+ Min.   : 0.000   Min.   : 0.0000   Min.   : 0.000   Min.   : 0.000  
+ 1st Qu.: 0.000   1st Qu.: 0.0000   1st Qu.: 0.000   1st Qu.: 0.000  
+ Median : 0.000   Median : 0.8437   Median : 1.122   Median : 1.213  
+ Mean   : 2.506   Mean   : 1.8949   Mean   : 2.028   Mean   : 2.048  
+ 3rd Qu.: 3.794   3rd Qu.: 1.7586   3rd Qu.: 2.840   3rd Qu.: 2.309  
+ Max.   :10.618   Max.   :11.6327   Max.   :10.449   Max.   :10.598  
+    HSPC_745         HSPC_746         HSPC_747         HSPC_748     
+ Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  
+ 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  
+ Median : 2.403   Median : 2.184   Median : 0.000   Median : 1.181  
+ Mean   : 2.309   Mean   : 2.153   Mean   : 2.543   Mean   : 2.017  
+ 3rd Qu.: 3.793   3rd Qu.: 3.016   3rd Qu.: 4.751   3rd Qu.: 2.264  
+ Max.   :10.882   Max.   :10.988   Max.   :10.860   Max.   :12.153  
+    HSPC_749         HSPC_750         HSPC_751         HSPC_752     
+ Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  
+ 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  
+ Median : 1.444   Median : 1.030   Median : 1.567   Median : 2.228  
+ Mean   : 2.477   Mean   : 2.370   Mean   : 2.416   Mean   : 2.529  
+ 3rd Qu.: 3.501   3rd Qu.: 3.052   3rd Qu.: 3.435   3rd Qu.: 3.976  
+ Max.   :11.391   Max.   :11.167   Max.   :10.239   Max.   :10.586  
+    HSPC_753         HSPC_755         HSPC_756         HSPC_757     
+ Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  
+ 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  
+ Median : 1.062   Median : 0.740   Median : 1.731   Median : 1.395  
+ Mean   : 2.313   Mean   : 2.102   Mean   : 2.592   Mean   : 2.477  
+ 3rd Qu.: 2.961   3rd Qu.: 2.509   3rd Qu.: 4.107   3rd Qu.: 3.253  
+ Max.   :11.202   Max.   :10.559   Max.   :10.783   Max.   :10.973  
+    HSPC_758          HSPC_759          HSPC_760         HSPC_761      
+ Min.   : 0.0000   Min.   : 0.0000   Min.   : 0.000   Min.   : 0.0000  
+ 1st Qu.: 0.0000   1st Qu.: 0.0000   1st Qu.: 0.000   1st Qu.: 0.0000  
+ Median : 0.8648   Median : 0.9415   Median : 1.052   Median : 0.6917  
+ Mean   : 2.6819   Mean   : 2.1274   Mean   : 2.288   Mean   : 2.2992  
+ 3rd Qu.: 4.7233   3rd Qu.: 2.2271   3rd Qu.: 2.404   3rd Qu.: 2.6015  
+ Max.   :11.1096   Max.   :11.2534   Max.   :11.008   Max.   :11.7228  
+    HSPC_762         HSPC_764         HSPC_765         HSPC_766      
+ Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.0000  
+ 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.0000  
+ Median : 1.271   Median : 1.784   Median : 2.116   Median : 0.9828  
+ Mean   : 2.242   Mean   : 2.068   Mean   : 2.100   Mean   : 2.1721  
+ 3rd Qu.: 2.734   3rd Qu.: 3.059   3rd Qu.: 2.939   3rd Qu.: 2.6115  
+ Max.   :12.043   Max.   :11.003   Max.   :12.757   Max.   :10.2002  
+    HSPC_767          HSPC_768         HSPC_769         HSPC_770     
+ Min.   : 0.0000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  
+ 1st Qu.: 0.0000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  
+ Median : 0.6646   Median : 1.703   Median : 0.000   Median : 1.760  
+ Mean   : 1.9552   Mean   : 2.365   Mean   : 2.080   Mean   : 2.343  
+ 3rd Qu.: 1.9730   3rd Qu.: 3.325   3rd Qu.: 3.289   3rd Qu.: 3.122  
+ Max.   :11.3033   Max.   :10.958   Max.   :11.176   Max.   :10.497  
+    HSPC_771         HSPC_772         HSPC_773          HSPC_774      
+ Min.   : 0.000   Min.   : 0.000   Min.   : 0.0000   Min.   : 0.0000  
+ 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.0000   1st Qu.: 0.0000  
+ Median : 1.178   Median : 1.601   Median : 0.9901   Median : 0.9736  
+ Mean   : 2.527   Mean   : 2.283   Mean   : 1.8628   Mean   : 2.5263  
+ 3rd Qu.: 3.342   3rd Qu.: 2.828   3rd Qu.: 1.9851   3rd Qu.: 5.4694  
+ Max.   :11.156   Max.   :10.625   Max.   :10.7274   Max.   :10.7701  
+    HSPC_776         HSPC_777         HSPC_778        HSPC_780     
+ Min.   : 0.000   Min.   : 0.000   Min.   :0.000   Min.   : 0.000  
+ 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.:0.000   1st Qu.: 0.000  
+ Median : 0.000   Median : 1.053   Median :1.435   Median : 1.178  
+ Mean   : 2.315   Mean   : 2.110   Mean   :2.130   Mean   : 2.476  
+ 3rd Qu.: 3.788   3rd Qu.: 2.673   3rd Qu.:3.488   3rd Qu.: 3.769  
+ Max.   :11.105   Max.   :11.646   Max.   :9.535   Max.   :11.265  
+    HSPC_781         HSPC_782         HSPC_783         HSPC_784     
+ Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  
+ 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  
+ Median : 0.000   Median : 0.000   Median : 0.000   Median : 1.050  
+ Mean   : 1.911   Mean   : 2.416   Mean   : 2.254   Mean   : 2.175  
+ 3rd Qu.: 2.884   3rd Qu.: 3.872   3rd Qu.: 2.548   3rd Qu.: 2.468  
+ Max.   :11.445   Max.   :10.161   Max.   :10.970   Max.   :10.958  
+    HSPC_785         HSPC_786         HSPC_787         HSPC_788      
+ Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.0000  
+ 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.0000  
+ Median : 0.000   Median : 1.148   Median : 0.000   Median : 0.9386  
+ Mean   : 2.230   Mean   : 2.467   Mean   : 2.100   Mean   : 1.9749  
+ 3rd Qu.: 2.466   3rd Qu.: 3.899   3rd Qu.: 2.991   3rd Qu.: 2.6662  
+ Max.   :11.041   Max.   :11.080   Max.   :10.690   Max.   :11.1078  
+    HSPC_789         HSPC_790         HSPC_791         HSPC_794     
+ Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  
+ 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  
+ Median : 1.181   Median : 1.353   Median : 1.790   Median : 1.113  
+ Mean   : 2.225   Mean   : 2.255   Mean   : 2.699   Mean   : 2.225  
+ 3rd Qu.: 2.876   3rd Qu.: 2.852   3rd Qu.: 4.931   3rd Qu.: 2.768  
+ Max.   :11.245   Max.   :11.558   Max.   :11.104   Max.   :11.118  
+    HSPC_795          HSPC_796          HSPC_797          HSPC_798     
+ Min.   : 0.0000   Min.   : 0.0000   Min.   : 0.0000   Min.   : 0.000  
+ 1st Qu.: 0.0000   1st Qu.: 0.0000   1st Qu.: 0.0000   1st Qu.: 0.000  
+ Median : 0.8317   Median : 0.7001   Median : 0.8722   Median : 1.531  
+ Mean   : 2.3985   Mean   : 2.6865   Mean   : 2.6172   Mean   : 2.485  
+ 3rd Qu.: 3.4461   3rd Qu.: 5.6688   3rd Qu.: 5.3078   3rd Qu.: 3.098  
+ Max.   :11.0956   Max.   :11.0829   Max.   :11.4339   Max.   :10.933  
+    HSPC_799         HSPC_800         HSPC_801         HSPC_802     
+ Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  
+ 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  
+ Median : 0.000   Median : 0.000   Median : 0.000   Median : 1.033  
+ Mean   : 2.179   Mean   : 2.173   Mean   : 2.427   Mean   : 2.613  
+ 3rd Qu.: 3.517   3rd Qu.: 2.865   3rd Qu.: 4.665   3rd Qu.: 3.780  
+ Max.   :11.666   Max.   :11.263   Max.   :10.905   Max.   :10.864  
+    HSPC_803         HSPC_804         HSPC_806         HSPC_807      
+ Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.0000  
+ 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.0000  
+ Median : 0.000   Median : 0.000   Median : 2.103   Median : 0.7501  
+ Mean   : 2.395   Mean   : 2.301   Mean   : 2.222   Mean   : 2.2476  
+ 3rd Qu.: 3.883   3rd Qu.: 3.167   3rd Qu.: 3.445   3rd Qu.: 2.3481  
+ Max.   :10.766   Max.   :11.298   Max.   :10.326   Max.   :11.2700  
+    HSPC_808          HSPC_809         HSPC_810         HSPC_812      
+ Min.   : 0.0000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.0000  
+ 1st Qu.: 0.0000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.0000  
+ Median : 0.6619   Median : 1.788   Median : 1.651   Median : 0.8459  
+ Mean   : 2.1677   Mean   : 2.544   Mean   : 2.471   Mean   : 2.2960  
+ 3rd Qu.: 2.5355   3rd Qu.: 3.730   3rd Qu.: 3.662   3rd Qu.: 2.6906  
+ Max.   :10.9302   Max.   :11.791   Max.   :10.829   Max.   :11.5500  
+    HSPC_813          HSPC_814         HSPC_815          HSPC_816     
+ Min.   : 0.0000   Min.   : 0.000   Min.   : 0.0000   Min.   : 0.000  
+ 1st Qu.: 0.0000   1st Qu.: 0.000   1st Qu.: 0.0000   1st Qu.: 0.000  
+ Median : 0.6278   Median : 1.110   Median : 0.9631   Median : 1.346  
+ Mean   : 2.2448   Mean   : 2.762   Mean   : 2.4587   Mean   : 2.341  
+ 3rd Qu.: 2.3066   3rd Qu.: 5.996   3rd Qu.: 3.4228   3rd Qu.: 2.842  
+ Max.   :12.0043   Max.   :10.406   Max.   :11.4527   Max.   :11.151  
+    HSPC_818          HSPC_819         HSPC_820          HSPC_821     
+ Min.   : 0.0000   Min.   : 0.000   Min.   : 0.0000   Min.   : 0.000  
+ 1st Qu.: 0.0000   1st Qu.: 0.000   1st Qu.: 0.0000   1st Qu.: 0.000  
+ Median : 0.9967   Median : 1.365   Median : 0.8099   Median : 1.382  
+ Mean   : 2.3081   Mean   : 2.426   Mean   : 2.1063   Mean   : 2.532  
+ 3rd Qu.: 2.9942   3rd Qu.: 3.632   3rd Qu.: 2.4643   3rd Qu.: 3.462  
+ Max.   :11.9931   Max.   :10.672   Max.   :11.2412   Max.   :12.126  
+    HSPC_822         HSPC_824         HSPC_825         HSPC_826     
+ Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  
+ 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  
+ Median : 1.387   Median : 0.000   Median : 1.386   Median : 1.324  
+ Mean   : 2.503   Mean   : 2.084   Mean   : 2.162   Mean   : 2.398  
+ 3rd Qu.: 3.799   3rd Qu.: 2.342   3rd Qu.: 2.897   3rd Qu.: 3.150  
+ Max.   :11.892   Max.   :11.365   Max.   :11.498   Max.   :11.198  
+    HSPC_827         HSPC_828         HSPC_831         HSPC_832     
+ Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  
+ 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  
+ Median : 1.746   Median : 1.003   Median : 1.304   Median : 1.035  
+ Mean   : 2.239   Mean   : 2.145   Mean   : 2.589   Mean   : 2.384  
+ 3rd Qu.: 2.638   3rd Qu.: 2.326   3rd Qu.: 3.866   3rd Qu.: 3.450  
+ Max.   :12.101   Max.   :10.710   Max.   :10.839   Max.   :10.686  
+    HSPC_833          HSPC_834          HSPC_835         HSPC_836     
+ Min.   : 0.0000   Min.   : 0.0000   Min.   : 0.000   Min.   : 0.000  
+ 1st Qu.: 0.0000   1st Qu.: 0.0000   1st Qu.: 0.000   1st Qu.: 0.000  
+ Median : 0.7166   Median : 0.9245   Median : 1.006   Median : 0.000  
+ Mean   : 2.3553   Mean   : 2.0872   Mean   : 2.552   Mean   : 2.471  
+ 3rd Qu.: 3.9364   3rd Qu.: 2.4568   3rd Qu.: 4.034   3rd Qu.: 3.994  
+ Max.   :11.1695   Max.   :11.1803   Max.   :11.779   Max.   :11.316  
+    HSPC_837         HSPC_838          HSPC_839         HSPC_840     
+ Min.   : 0.000   Min.   : 0.0000   Min.   : 0.000   Min.   : 0.000  
+ 1st Qu.: 0.000   1st Qu.: 0.0000   1st Qu.: 0.000   1st Qu.: 0.000  
+ Median : 1.312   Median : 0.9838   Median : 1.083   Median : 1.867  
+ Mean   : 2.590   Mean   : 2.5281   Mean   : 2.380   Mean   : 2.548  
+ 3rd Qu.: 4.443   3rd Qu.: 3.5551   3rd Qu.: 3.743   3rd Qu.: 3.609  
+ Max.   :10.672   Max.   :11.2707   Max.   :10.966   Max.   :10.867  
+    HSPC_841         HSPC_842         HSPC_843         HSPC_844     
+ Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  
+ 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  
+ Median : 1.439   Median : 1.774   Median : 1.257   Median : 1.584  
+ Mean   : 2.408   Mean   : 2.380   Mean   : 2.845   Mean   : 2.627  
+ 3rd Qu.: 3.494   3rd Qu.: 3.490   3rd Qu.: 6.768   3rd Qu.: 3.951  
+ Max.   :10.930   Max.   :11.137   Max.   :11.933   Max.   :11.446  
+    HSPC_845         HSPC_846         HSPC_848         HSPC_849     
+ Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  
+ 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  
+ Median : 1.227   Median : 1.401   Median : 0.000   Median : 1.602  
+ Mean   : 2.464   Mean   : 2.240   Mean   : 2.152   Mean   : 2.402  
+ 3rd Qu.: 3.377   3rd Qu.: 2.920   3rd Qu.: 2.554   3rd Qu.: 2.920  
+ Max.   :10.535   Max.   :11.519   Max.   :11.266   Max.   :11.678  
+    HSPC_851         HSPC_852     
+ Min.   : 0.000   Min.   : 0.000  
+ 1st Qu.: 0.000   1st Qu.: 0.000  
+ Median : 0.000   Median : 0.000  
+ Mean   : 2.319   Mean   : 2.143  
+ 3rd Qu.: 3.373   3rd Qu.: 2.901  
+ Max.   :11.602   Max.   :11.469  </code></pre>
+</div>
+</div>
+<p>Hmmmm, 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</p>
+<p>🎬 Get a quick overview the first 20 columns:</p>
+<div class="cell outputscroll">
+<div class="sourceCode" id="cb21"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/summary.html">summary</a></span><span class="op">(</span><span class="va">hspc</span><span class="op">[</span><span class="fl">1</span><span class="op">:</span><span class="fl">20</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> ensembl_gene_id       HSPC_001         HSPC_002         HSPC_003      
+ Length:280         Min.   : 0.000   Min.   : 0.000   Min.   : 0.0000  
+ Class :character   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.0000  
+ Mode  :character   Median : 0.000   Median : 0.000   Median : 0.9929  
+                    Mean   : 2.143   Mean   : 1.673   Mean   : 2.5964  
+                    3rd Qu.: 2.120   3rd Qu.: 2.239   3rd Qu.: 6.1559  
+                    Max.   :12.567   Max.   :11.976   Max.   :11.1138  
+    HSPC_004         HSPC_006         HSPC_008         HSPC_009    
+ Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   :0.000  
+ 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.:0.000  
+ Median : 0.000   Median : 1.276   Median : 0.000   Median :0.000  
+ Mean   : 1.851   Mean   : 2.338   Mean   : 2.375   Mean   :2.220  
+ 3rd Qu.: 2.466   3rd Qu.: 3.536   3rd Qu.: 3.851   3rd Qu.:3.594  
+ Max.   :11.133   Max.   :10.014   Max.   :11.574   Max.   :9.997  
+    HSPC_011         HSPC_012         HSPC_014         HSPC_015     
+ Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  
+ 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  
+ Median : 0.000   Median : 1.750   Median : 0.000   Median : 0.000  
+ Mean   : 2.285   Mean   : 2.431   Mean   : 2.295   Mean   : 2.515  
+ 3rd Qu.: 3.193   3rd Qu.: 3.741   3rd Qu.: 3.150   3rd Qu.: 3.789  
+ Max.   :11.260   Max.   :10.905   Max.   :11.051   Max.   :10.751  
+    HSPC_016          HSPC_017         HSPC_018         HSPC_020     
+ Min.   : 0.0000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  
+ 1st Qu.: 0.0000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  
+ Median : 0.9488   Median : 0.000   Median : 1.248   Median : 0.000  
+ Mean   : 2.6115   Mean   : 2.146   Mean   : 2.710   Mean   : 2.509  
+ 3rd Qu.: 5.9412   3rd Qu.: 2.357   3rd Qu.: 6.006   3rd Qu.: 4.470  
+ Max.   :11.3082   Max.   :12.058   Max.   :11.894   Max.   :11.281  
+    HSPC_021         HSPC_022         HSPC_023         HSPC_024     
+ Min.   : 0.000   Min.   : 0.000   Min.   : 0.000   Min.   : 0.000  
+ 1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000   1st Qu.: 0.000  
+ Median : 0.000   Median : 0.000   Median : 0.000   Median : 0.000  
+ Mean   : 2.170   Mean   : 2.287   Mean   : 2.314   Mean   : 2.195  
+ 3rd Qu.: 2.996   3rd Qu.: 3.351   3rd Qu.: 2.749   3rd Qu.: 2.944  
+ Max.   :10.709   Max.   :11.814   Max.   :12.113   Max.   :11.279  </code></pre>
+</div>
+</div>
+<p>Notice that:</p>
+<ul>
+<li>the 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.</li>
+<li>a minimum value of 0 appears in all 20 columns - perhaps that is true across the whole dataset (or at least common)</li>
+<li>at least some of the medians are zeros so there must be quite a lot of zeros</li>
+<li>the few columns we can see are roughly similar</li>
+<li>it would not be very practical to plot the distributions of values in cell cell using <code><a href="https://ggplot2.tidyverse.org/reference/facet_wrap.html">facet_wrap()</a></code>.</li>
+</ul>
+<p>In this data set, there is even more of an advantage of using the <code><a href="https://tidyr.tidyverse.org/reference/pivot_longer.html">pivot_longer()</a></code>, <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 will be able to open the dataframe in the Viewer and make plots to examine whether the distributions are similar across cells.</p>
+<p>🎬 Summarise all the cells:</p>
+<div class="cell">
+<div class="sourceCode" id="cb23"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="va">hspc_summary_samp</span> <span class="op">&lt;-</span> <span class="va">hspc</span> <span class="op">|&gt;</span></span>
+<span>  <span class="fu"><a href="https://tidyr.tidyverse.org/reference/pivot_longer.html">pivot_longer</a></span><span class="op">(</span>cols <span class="op">=</span> <span class="op">-</span><span class="va">ensembl_gene_id</span>,</span>
+<span>               names_to <span class="op">=</span> <span class="st">"cell"</span>,</span>
+<span>               values_to <span class="op">=</span> <span class="st">"expr"</span><span class="op">)</span> <span class="op">|&gt;</span></span>
+<span>  <span class="fu"><a href="https://dplyr.tidyverse.org/reference/group_by.html">group_by</a></span><span class="op">(</span><span class="va">cell</span><span class="op">)</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>min <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/Extremes.html">min</a></span><span class="op">(</span><span class="va">expr</span><span class="op">)</span>,</span>
+<span>            lowerq <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/stats/quantile.html">quantile</a></span><span class="op">(</span><span class="va">expr</span>, <span class="fl">0.25</span><span class="op">)</span>,</span>
+<span>            mean <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/mean.html">mean</a></span><span class="op">(</span><span class="va">expr</span><span class="op">)</span>,</span>
+<span>            median <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/stats/median.html">median</a></span><span class="op">(</span><span class="va">expr</span><span class="op">)</span>,</span>
+<span>            sd <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/stats/sd.html">sd</a></span><span class="op">(</span><span class="va">expr</span><span class="op">)</span>,</span>
+<span>            upperq <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/stats/quantile.html">quantile</a></span><span class="op">(</span><span class="va">expr</span>, <span class="fl">0.75</span><span class="op">)</span>,</span>
+<span>            max <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/Extremes.html">max</a></span><span class="op">(</span><span class="va">expr</span><span class="op">)</span>,</span>
+<span>            n_zero <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">expr</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>
+<p>Notice that I have used <code>cell</code> as the column name rather than <code>sample</code> and <code>expr</code> (expression) rather than <code>count</code>. I’ve also added the standard deviation.</p>
+<p>🎬 View the <code>hspc_summary_samp</code> dataframe (click on it in the environment).</p>
+<p>All cells have quite a few zeros and the lower quartile is 0 for all cells, <em>i.e.</em>, every cell has many genes with zero expression.</p>
+<p>To 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!</p>
+<p>🎬 Create a pointrange plot.</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">hspc_summary_samp</span> <span class="op">|&gt;</span> </span>
+<span>  <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/ggplot.html">ggplot</a></span><span class="op">(</span><span class="fu"><a href="https://ggplot2.tidyverse.org/reference/aes.html">aes</a></span><span class="op">(</span>x <span class="op">=</span> <span class="va">cell</span>, y <span class="op">=</span> <span class="va">mean</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/geom_linerange.html">geom_pointrange</a></span><span class="op">(</span><span class="fu"><a href="https://ggplot2.tidyverse.org/reference/aes.html">aes</a></span><span class="op">(</span>ymin <span class="op">=</span> <span class="va">mean</span> <span class="op">-</span> <span class="va">sd</span>, </span>
+<span>                      ymax <span class="op">=</span> <span class="va">mean</span> <span class="op">+</span> <span class="va">sd</span> <span class="op">)</span>,</span>
+<span>                  size <span class="op">=</span> <span class="fl">0.1</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-display">
+<p><img src="workshop_files/figure-html/unnamed-chunk-19-1.png" class="img-fluid" width="672"></p>
+</div>
+</div>
+<p>You will need to use the Zoom button to pop the plot window out so you can make it as wide as possible</p>
+<p>The things to notice are:</p>
+<ul>
+<li>the 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.</li>
+<li>the distributions are roughly similar in width too</li>
+</ul>
+<p>The default order of <code>cell</code> is alphabetical. It can be easier to see these (non-) effects if we order the lines by the size of the mean.</p>
+<p>🎬 Order a pointrange plot with <code>reorder(variable_to_order, order_by)</code>.</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">hspc_summary_samp</span> <span class="op">|&gt;</span> </span>
+<span>  <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/ggplot.html">ggplot</a></span><span class="op">(</span><span class="fu"><a href="https://ggplot2.tidyverse.org/reference/aes.html">aes</a></span><span class="op">(</span>x <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/stats/reorder.factor.html">reorder</a></span><span class="op">(</span><span class="va">cell</span>, <span class="va">mean</span><span class="op">)</span>, y <span class="op">=</span> <span class="va">mean</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/geom_linerange.html">geom_pointrange</a></span><span class="op">(</span><span class="fu"><a href="https://ggplot2.tidyverse.org/reference/aes.html">aes</a></span><span class="op">(</span>ymin <span class="op">=</span> <span class="va">mean</span> <span class="op">-</span> <span class="va">sd</span>, </span>
+<span>                      ymax <span class="op">=</span> <span class="va">mean</span> <span class="op">+</span> <span class="va">sd</span> <span class="op">)</span>,</span>
+<span>                  size <span class="op">=</span> <span class="fl">0.1</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-display">
+<p><img src="workshop_files/figure-html/unnamed-chunk-20-1.png" class="img-fluid" width="672"></p>
+</div>
+</div>
+<p><code><a href="https://rdrr.io/r/stats/reorder.factor.html">reorder()</a></code> arranges <code>cell</code> in increasing size of <code>mean</code></p>
+<p>🎬 Write <code>hspc_summary_samp</code> to a file called “hspc_summary_samp.csv”:</p>
+</section></section><section id="distribution-of-values-across-the-genes" class="level3"><h3 class="anchored" data-anchor-id="distribution-of-values-across-the-genes">Distribution of values across the genes</h3>
+<section id="frog-genes" class="level4"><h4 class="anchored" data-anchor-id="frog-genes">🐸 Frog genes</h4>
+<p>There 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.</p>
+<p>🎬 Summarise the counts for each genes:</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">s30_summary_gene</span> <span class="op">&lt;-</span> <span class="va">s30</span> <span class="op">|&gt;</span></span>
+<span>  <span class="fu"><a href="https://tidyr.tidyverse.org/reference/pivot_longer.html">pivot_longer</a></span><span class="op">(</span>cols <span class="op">=</span> <span class="op">-</span><span class="va">xenbase_gene_id</span>,</span>
+<span>               names_to <span class="op">=</span> <span class="st">"sample"</span>,</span>
+<span>               values_to <span class="op">=</span> <span class="st">"count"</span><span class="op">)</span> <span class="op">|&gt;</span></span>
+<span>  <span class="fu"><a href="https://dplyr.tidyverse.org/reference/group_by.html">group_by</a></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://dplyr.tidyverse.org/reference/summarise.html">summarise</a></span><span class="op">(</span>min <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/Extremes.html">min</a></span><span class="op">(</span><span class="va">count</span><span class="op">)</span>,</span>
+<span>            lowerq <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/stats/quantile.html">quantile</a></span><span class="op">(</span><span class="va">count</span>, <span class="fl">0.25</span><span class="op">)</span>,</span>
+<span>            sd <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/stats/sd.html">sd</a></span><span class="op">(</span><span class="va">count</span><span class="op">)</span>,</span>
+<span>            mean <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/mean.html">mean</a></span><span class="op">(</span><span class="va">count</span><span class="op">)</span>,</span>
+<span>            median <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/stats/median.html">median</a></span><span class="op">(</span><span class="va">count</span><span class="op">)</span>,</span>
+<span>            upperq <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/stats/quantile.html">quantile</a></span><span class="op">(</span><span class="va">count</span>, <span class="fl">0.75</span><span class="op">)</span>,</span>
+<span>            max <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/Extremes.html">max</a></span><span class="op">(</span><span class="va">count</span><span class="op">)</span>,</span>
+<span>            total <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">count</span><span class="op">)</span>,</span>
+<span>            n_zero <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">count</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>
+<p>I have calculated the values we used before with one addition: the sum of the counts (<code>total</code>).</p>
+<p>🎬 View the <code>s30_summary_gene</code> dataframe.</p>
+<p>Notice that we have:</p>
+<ul>
+<li>a lot of genes with counts of zero in every sample</li>
+<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>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">
+<div class="sourceCode" id="cb27"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="va">s30_summary_gene</span> <span class="op">|&gt;</span> </span>
+<span>  <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/ggplot.html">ggplot</a></span><span class="op">(</span><span class="fu"><a href="https://ggplot2.tidyverse.org/reference/aes.html">aes</a></span><span class="op">(</span>x <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/stats/reorder.factor.html">reorder</a></span><span class="op">(</span><span class="va">xenbase_gene_id</span>, <span class="va">mean</span><span class="op">)</span>, y <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/Log.html">log10</a></span><span class="op">(</span><span class="va">mean</span><span class="op">)</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/geom_linerange.html">geom_pointrange</a></span><span class="op">(</span><span class="fu"><a href="https://ggplot2.tidyverse.org/reference/aes.html">aes</a></span><span class="op">(</span>ymin <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/Log.html">log10</a></span><span class="op">(</span><span class="va">mean</span> <span class="op">-</span> <span class="va">sd</span><span class="op">)</span>, </span>
+<span>                      ymax <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/Log.html">log10</a></span><span class="op">(</span><span class="va">mean</span> <span class="op">+</span> <span class="va">sd</span> <span class="op">)</span><span class="op">)</span>,</span>
+<span>                  size <span class="op">=</span> <span class="fl">0.1</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-display">
+<p><img src="workshop_files/figure-html/unnamed-chunk-23-1.png" class="img-fluid" width="672"></p>
+</div>
+</div>
+<p>(Remember, the warning is expected since we have zeros).</p>
+<p>You 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.</p>
+<p>🎬 Write <code>s30_summary_gene</code> to a file called “s30_summary_gene.csv”:</p>
+</section><section id="mouse-genes" class="level4"><h4 class="anchored" data-anchor-id="mouse-genes">🐭 Mouse genes</h4>
+<p>There 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.</p>
+<p>🎬 Summarise the expression for each genes:</p>
+<div class="cell">
+<div class="sourceCode" id="cb28"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="va">hspc_summary_gene</span> <span class="op">&lt;-</span> <span class="va">hspc</span> <span class="op">|&gt;</span></span>
+<span>  <span class="fu"><a href="https://tidyr.tidyverse.org/reference/pivot_longer.html">pivot_longer</a></span><span class="op">(</span>cols <span class="op">=</span> <span class="op">-</span><span class="va">ensembl_gene_id</span>,</span>
+<span>               names_to <span class="op">=</span> <span class="st">"cell"</span>,</span>
+<span>               values_to <span class="op">=</span> <span class="st">"expr"</span><span class="op">)</span> <span class="op">|&gt;</span></span>
+<span>  <span class="fu"><a href="https://dplyr.tidyverse.org/reference/group_by.html">group_by</a></span><span class="op">(</span><span class="va">ensembl_gene_id</span><span class="op">)</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>min <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/Extremes.html">min</a></span><span class="op">(</span><span class="va">expr</span><span class="op">)</span>,</span>
+<span>            lowerq <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/stats/quantile.html">quantile</a></span><span class="op">(</span><span class="va">expr</span>, <span class="fl">0.25</span><span class="op">)</span>,</span>
+<span>            sd <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/stats/sd.html">sd</a></span><span class="op">(</span><span class="va">expr</span><span class="op">)</span>,</span>
+<span>            mean <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/mean.html">mean</a></span><span class="op">(</span><span class="va">expr</span><span class="op">)</span>,</span>
+<span>            median <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/stats/median.html">median</a></span><span class="op">(</span><span class="va">expr</span><span class="op">)</span>,</span>
+<span>            upperq <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/stats/quantile.html">quantile</a></span><span class="op">(</span><span class="va">expr</span>, <span class="fl">0.75</span><span class="op">)</span>,</span>
+<span>            max <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/Extremes.html">max</a></span><span class="op">(</span><span class="va">expr</span><span class="op">)</span>,</span>
+<span>            total <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">expr</span><span class="op">)</span>,</span>
+<span>            n_zero <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">expr</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>
+<p>🎬 View the <code>hspc_summary_gene</code> dataframe. Remember these are normalised and logged (base 2) so we should not see very large values.</p>
+<p>Notice that we have:</p>
+<ul>
+<li>no genes with 0 in every cell</li>
+<li>very few genes (9) with no zeros at all</li>
+<li>quite 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.</li>
+</ul>
+<p>As 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.</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">
+<div class="sourceCode" id="cb29"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="va">hspc_summary_gene</span> <span class="op">|&gt;</span> </span>
+<span>  <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/ggplot.html">ggplot</a></span><span class="op">(</span><span class="fu"><a href="https://ggplot2.tidyverse.org/reference/aes.html">aes</a></span><span class="op">(</span>x <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/stats/reorder.factor.html">reorder</a></span><span class="op">(</span><span class="va">ensembl_gene_id</span>, <span class="va">mean</span><span class="op">)</span>, y <span class="op">=</span> <span class="va">mean</span><span class="op">)</span><span class="op">)</span> <span class="op">+</span></span>
+<span>  <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/geom_linerange.html">geom_pointrange</a></span><span class="op">(</span><span class="fu"><a href="https://ggplot2.tidyverse.org/reference/aes.html">aes</a></span><span class="op">(</span>ymin <span class="op">=</span> <span class="va">mean</span> <span class="op">-</span> <span class="va">sd</span>, </span>
+<span>                      ymax <span class="op">=</span> <span class="va">mean</span> <span class="op">+</span> <span class="va">sd</span><span class="op">)</span>,</span>
+<span>                  size <span class="op">=</span> <span class="fl">0.1</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-display">
+<p><img src="workshop_files/figure-html/unnamed-chunk-26-1.png" class="img-fluid" width="672"></p>
+</div>
+</div>
+<p>Note 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.</p>
+<p>🎬 Write <code>s30_summary_gene</code> to a file called “s30_summary_gene.csv”:</p>
+</section></section></section><section id="filtering-for-qc" class="level2"><h2 class="anchored" data-anchor-id="filtering-for-qc">Filtering for QC</h2>
+<section id="frog-filtering" class="level3"><h3 class="anchored" data-anchor-id="frog-filtering">🐸 Frog filtering</h3>
+<p>Our samples look to be similarly well sequenced. There are no samples we should remove. However, some genes are not express or the expression values are so low in for a gene that they are uninformative. We will filter the <code>s30_summary_gene</code> dataframe to obtain a list of <code>xenbase_gene_id</code> we can use to filter <code>s30</code>.</p>
+<p>My suggestion is to include only the genes with counts in at least 3 samples<a href="#fn3" class="footnote-ref" id="fnref3" role="doc-noteref"><sup>3</sup></a> and those with total counts above 20.</p>
+<p>🎬 Filter the summary by gene dataframe:</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">s30_summary_gene_filtered</span> <span class="op">&lt;-</span> <span class="va">s30_summary_gene</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">total</span> <span class="op">&gt;</span> <span class="fl">20</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="va">n_zero</span> <span class="op">&lt;</span> <span class="fl">4</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>🎬 Write the filtered summary by gene to file:</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://readr.tidyverse.org/reference/write_delim.html">write_csv</a></span><span class="op">(</span><span class="va">s30_summary_gene_filtered</span>, </span>
+<span>          file <span class="op">=</span> <span class="st">"data-processed/s30_summary_gene_filtered.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>
+<p>🎬 Use the list of <code>xenbase_gene_id</code> in the filtered summary to filter the original dataset:</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">s30_filtered</span> <span class="op">&lt;-</span> <span class="va">s30</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">xenbase_gene_id</span> <span class="op"><a href="https://rdrr.io/r/base/match.html">%in%</a></span>  <span class="va">s30_summary_gene_filtered</span><span class="op">$</span><span class="va">xenbase_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>
+<p>🎬 Write the filtered original to file:</p>
+<div class="cell">
+<div class="sourceCode" id="cb33"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="fu"><a href="https://readr.tidyverse.org/reference/write_delim.html">write_csv</a></span><span class="op">(</span><span class="va">s30_filtered</span>, </span>
+<span>          file <span class="op">=</span> <span class="st">"data-processed/s30_filtered.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>
+</section><section id="mouse-filtering" class="level3"><h3 class="anchored" data-anchor-id="mouse-filtering">🐭 Mouse filtering</h3>
+<p>We will take a different approach to filtering the single cell data. For the Frog samples we are examining the control and the FGF treated samples. This means have a low number of counts overall means the gene is not really expressed (detected) in any condition, and filtering out those genes is removing things that definitely are not interesting. For the mice, we have examined only one cell type but will be making comparisons between cells types. It may be that low expression of a gene in this cell type tells us something if that gene is highly expressed in another cell type. Instead, we will make statistical comparisons between the cell types and then filter based on overall expression, the difference in expression between cell types and whether that difference is significant.</p>
+<p>The number of “replicates” is also important. When you have only three in each group it is not possible to make statistical comparisons when several replicates are zero. This is less of an issue with single cell data.</p>
+</section></section><section id="look-after-future-you" class="level2"><h2 class="anchored" data-anchor-id="look-after-future-you">🤗 Look after future you!</h2>
+<p><strong>You need only do the section for your own project data</strong></p>
+<section id="frogs-and-future-you" class="level3"><h3 class="anchored" data-anchor-id="frogs-and-future-you">🐸 Frogs and future you</h3>
+<p>🎬 Create a new Project, <code>frogs-88H</code>, populated with folders and your data. Make a script file called <code>cont-fgf-s30.R</code>. 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 <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="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>🎬 Create a new Project, <code>xxxx-88H</code>, populated with folders and your data. Make a script file called <code>xxxx.R</code>. This will a be commented analysis of t xxxxxxxxxxxx.</p>
+</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="../../omics/week-4/study_after_workshop.html">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="../../omics/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/">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 2023</a>)</span>, Quarto <span class="citation" data-cites="allaire2022">(<a href="#ref-allaire2022" role="doc-biblioref">Allaire et al. 2022</a>)</span>, <code>knitr</code> <span class="citation" data-cites="knitr">(<a href="#ref-knitr" role="doc-biblioref">Xie 2022</a>)</span>, <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"><h2 class="anchored quarto-appendix-heading">References</h2><div id="refs" class="references csl-bib-body hanging-indent" role="list">
+<div id="ref-allaire2022" class="csl-entry" role="listitem">
+Allaire, J. J., Charles Teague, Carlos Scheidegger, Yihui Xie, and Christophe Dervieux. 2022. <em>Quarto</em>. <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. 2023. <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-Wickham2014-nl" class="csl-entry" role="listitem">
+Wickham, Hadley. 2014. <span>“Tidy Data.”</span> <em>Journal of Statistical Software, Articles</em> 59 (10): 1–23. <a href="https://vita.had.co.nz/papers/tidy-data.pdf">https://vita.had.co.nz/papers/tidy-data.pdf</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></span>Tidyverse<span></span>”</span> 4: 1686. <a href="https://doi.org/10.21105/joss.01686">https://doi.org/10.21105/joss.01686</a>.
+</div>
+<div id="ref-knitr" class="csl-entry" role="listitem">
+Xie, Yihui. 2022. <span>“Knitr: A General-Purpose Package for Dynamic Report Generation in r.”</span> <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 id="footnotes" class="footnotes footnotes-end-of-document" role="doc-endnotes"><h2 class="anchored quarto-appendix-heading">Footnotes</h2>
+<ol>
+<li id="fn1"><p>This a result of the <a href="https://en.wikipedia.org/wiki/Central_limit_theorem">Central limit theorem</a>,one consequence of which is that adding together lots of distributions - whatever distributions they are - will tend to a normal distribution.<a href="#fnref1" class="footnote-back" role="doc-backlink">↩︎</a></p></li>
+<li id="fn2"><p>This a result of the <a href="https://en.wikipedia.org/wiki/Central_limit_theorem">Central limit theorem</a>,one consequence of which is that adding together lots of distributions - whatever distributions they are - will tend to a normal distribution.<a href="#fnref2" class="footnote-back" role="doc-backlink">↩︎</a></p></li>
+<li id="fn3"><p>I chose three because that would keep [0, 0, 0] [#,#,#]. This is difference we cannot test statistically, but which would matter biologically.<a href="#fnref3" class="footnote-back" role="doc-backlink">↩︎</a></p></li>
+</ol></section></div></main><!-- /main --><script id="quarto-html-after-body" type="application/javascript">
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+    // don't keep focus
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+    // flash "checked"
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+    button.setAttribute("title", "Copied!");
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+      content: contentFn,
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+      arrow: false,
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+      interactiveBorder: 10,
+      theme: 'quarto',
+      placement: 'bottom-start'
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+    window.tippy(el, config); 
+  }
+  const noterefs = window.document.querySelectorAll('a[role="doc-noteref"]');
+  for (var i=0; i<noterefs.length; i++) {
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+    tippyHover(ref, function() {
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+      try { href = new URL(href).hash; } catch {}
+      const id = href.replace(/^#\/?/, "");
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+      }
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+      return undefined;
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+  var bibliorefs = window.document.querySelectorAll('a[role="doc-biblioref"]');
+  for (var i=0; i<bibliorefs.length; i++) {
+    const ref = bibliorefs[i];
+    const citeInfo = findCites(ref);
+    if (citeInfo) {
+      tippyHover(citeInfo.el, function() {
+        var popup = window.document.createElement('div');
+        citeInfo.cites.forEach(function(cite) {
+          var citeDiv = window.document.createElement('div');
+          citeDiv.classList.add('hanging-indent');
+          citeDiv.classList.add('csl-entry');
+          var biblioDiv = window.document.getElementById('ref-' + cite);
+          if (biblioDiv) {
+            citeDiv.innerHTML = biblioDiv.innerHTML;
+          }
+          popup.appendChild(citeDiv);
+        });
+        return popup.innerHTML;
+      });
+    }
+  }
+    var localhostRegex = new RegExp(/^(?:http|https):\/\/localhost\:?[0-9]*\//);
+      var filterRegex = new RegExp("^(?:http:|https:)\/\/3mmarand\.github\.io\/BIO00088H-data");
+    var isInternal = (href) => {
+        return filterRegex.test(href) || localhostRegex.test(href);
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+    // Inspect non-navigation links and adorn them if external
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+      if (!isInternal(link.href)) {
+          // target, if specified
+          link.setAttribute("target", "_blank");
+      }
+    }
+});
+</script>
+</div> <!-- /content -->
+<footer class="footer"><div class="nav-footer">
+    <div class="nav-footer-left">Rand E (2023). Data Analysis for Group Project. <a href="https://3mmarand.github.io/BIO00088H-data/" class="uri">https://3mmarand.github.io/BIO00088H-data/</a>.</div>   
+    <div class="nav-footer-center">
+      &nbsp;
+    </div>
+    <div class="nav-footer-right">
+      <ul class="footer-items list-unstyled">
+<li class="nav-item compact">
+    <a class="nav-link" href="https://twitter.com/er_13">
+      <i class="bi bi-twitter" role="img" aria-label="Twitter">
+</i> 
+    </a>
+  </li>  
+    <li class="nav-item compact">
+    <a class="nav-link" href="https://github.com/3mmaRand/BIO00088H-data">
+      <i class="bi bi-github" role="img" aria-label="BIO00088H-data GitHub">
+</i> 
+    </a>
+  </li>  
+</ul>
+</div>
+  </div>
+</footer>
+
+
+</body></html>
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@@ -314,6 +314,153 @@
     "section": "",
     "text": "before\n\nrecap what we have\nvolcano plot described\nGO terms\n\n\nworkshop\n\nopen project import the data\nrevise!\nvolcano plot\nkable table? knitr::kable(df) |&gt; kableExtra::kable_styling()\nannotating with go terms\n\nafter\n\ndocument what you have done\nrepeat on another comparison"
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+    "text": "This week you will meet your data. The independent study will concisely cover how these data were generated and how they have been processed before being given to you. There will also be an overview of the analysis we will carry out over three workshops. In the workshop, you will learn what steps to take to get a good understanding of ’omics data before you consider any statistical analysis. This is an often overlooked, but very valuable and informative, part of any data pipeline. It gives you the deep understanding of the data structures and values that you will need to code and trouble-shoot code, allows you to spot failed or problematic samples and informs your decisions on quality control.\nWe suggest you sit together with your group in the workshop.\n\nLearning objectives\nThe successful student will be able to:\n\nexplore ’omics data to find the number of rows and columns and know how these correspond to samples and variables\nexplore the distribution of expression measures across whole data sets, across variables and across samples by summarising and plotting\nexplain what distributions are expected and interpret the distributions they have\nexplain on what basis we might filter out variables or samples\nimport, explore and filter ’omics data reproducibly so they can understand and reuse their code in the future\n\n\n\nInstructions\n\nPrepare\n\n📖 Read how the data were generated and how they have been processed so far, install\n\nWorkshop\n\n💻 Set up a Project\n💻 Import data\n💻 Explore the distribution of values across samples/cells and across genes/species\n💻 Perform quality control\n💻 Look after future you!\n\nConsolidate\n\n💻 Use the work you completed in the workshop as a template to apply to a new case."
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+    "title": "Independent Study to prepare for workshop",
+    "section": "Overview",
+    "text": "Overview\n\n\nConcise summary of the experimental design and aims\nWhat the raw data consist of\nWhat has been done to the data so far\nWhat steps we will take in the workshop"
+  },
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+    "objectID": "omics/week-4/study_before_workshop.html#the-data",
+    "href": "omics/week-4/study_before_workshop.html#the-data",
+    "title": "Independent Study to prepare for workshop",
+    "section": "The Data",
+    "text": "The Data\nThere are three datasets\n\n🐸 transcriptomic data (bulk RNA-seq) from frog embryos.\n🐭 transcriptomic data (single cell RNA-seq) from stemcells\n🍂 ??????? Metabolomic / Metagenomic data from anaerobic digesters"
+  },
+  {
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+    "title": "Independent Study to prepare for workshop",
+    "section": "🐸 Experimental design",
+    "text": "🐸 Experimental design\n\nSchematic of frog development experiment"
+  },
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+    "title": "Independent Study to prepare for workshop",
+    "section": "🐸 Experimental design",
+    "text": "🐸 Experimental design\n\nSchematic of frog development experiment\n\n3 fertilisations\ntwo siblings from each fertilisation one control, on FGF treated\nsequenced at three time points\n3 x 2 x 3 = 18 groups"
+  },
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+    "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"
+  },
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+    "href": "omics/week-4/study_before_workshop.html#aim",
+    "title": "Independent Study to prepare for workshop",
+    "section": "🐸 Aim",
+    "text": "🐸 Aim\n\n\nfind genes important in frog development\nImportant means the genes that are differentially expressed between the control-treated and the FGF-treated siblings\nDifferentially expressed means the expression in one group is significantly higher than in the other"
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+    "title": "Independent Study to prepare for workshop",
+    "section": "🐸 Guided analysis",
+    "text": "🐸 Guided analysis\n\n\nThe workshops will take you through comparing the control and FGF treated sibling at S30\nThis is the “least interesting” comparison\nYou will be guided to carefully document your work so you can apply the same methods to other comparisons"
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+    "section": "🐭 Experimental design",
+    "text": "🐭 Experimental design\n\nSchematic of stem cell experiment"
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+    "section": "🐭 Experimental design",
+    "text": "🐭 Experimental design\n\nSchematic of stem cell experiment\n\nCells were sorted using flow cytometry on the basis of cell surface markers\nThere are three cell types: LT-HSCs, HSPCs, Progs\nMany cells of each cell type were sequenced"
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+    "title": "Independent Study to prepare for workshop",
+    "section": "🐭 Experimental design",
+    "text": "🐭 Experimental design\n\nSchematic of stem cell experiment\n\nThere are three cell types: LT-HSCs, HSPCs, Progs These are the “treaments”\nMany cells of each type were sequenced: These are the replicates\n155 LT-HSCs, 701 HSPCs, 798 Progs"
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+    "href": "omics/week-4/study_before_workshop.html#aim-1",
+    "title": "Independent Study to prepare for workshop",
+    "section": "🐭 Aim",
+    "text": "🐭 Aim\n\n\nfind genes for cell surface proteins that are important in stem cell identity\nImportant means genes that are differentially expressed between at least two cell types\nDifferentially expressed means the expression in one group is significantly higher than in the other"
+  },
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+    "title": "Independent Study to prepare for workshop",
+    "section": "🐭 Guided analysis",
+    "text": "🐭 Guided analysis\n\n\nThe workshops will take you through comparing the HSPC and Prog cells\nThis is the “least interesting” comparison\nYou will be guided to carefully document your work so you can apply the same methods to other comparisons"
+  },
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+    "href": "omics/week-4/study_before_workshop.html#raw-sequence-data",
+    "title": "Independent Study to prepare for workshop",
+    "section": "Raw Sequence data",
+    "text": "Raw Sequence data\n\n\nThe raw data are “reads” from a sequencing machine.\nA read is sequence of DNA or RNA shorter than the whole genome or transcriptome\nThe length of the reads depends on the type of sequencing machine\n\nShort-read technologies e.g. Illumina have higher base accuracy but are harder to align\nLong-read technologies e.g. Nanopore have lower base accuracy but are easier to align"
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+    "href": "omics/week-4/study_before_workshop.html#raw-sequence-data-1",
+    "title": "Independent Study to prepare for workshop",
+    "section": "Raw Sequence data",
+    "text": "Raw Sequence data\n\n\nSequencing technology is constantly improving\nOptional: You can read more about Sequencing technologies in Statistically useful experimental design (Rand and Forrester 2022)"
+  },
+  {
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+    "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"
+  },
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+    "href": "omics/week-4/study_before_workshop.html#general-steps",
+    "title": "Independent Study to prepare for workshop",
+    "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"
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+    "href": "omics/week-4/study_before_workshop.html#data",
+    "title": "Independent Study to prepare for workshop",
+    "section": "🐸 Data",
+    "text": "🐸 Data\n\nUnpublished (so far!)\nExpression for the whole transcriptome X. laevis v10.1 genome assembly\nValues are raw counts\nThe statistical analysis method we will use DESeq2 (Love, Huber, and Anders 2014) requires raw counts and performs the normalisation itself"
+  },
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+    "href": "omics/week-4/study_before_workshop.html#data-1",
+    "title": "Independent Study to prepare for workshop",
+    "section": "🐭 Data",
+    "text": "🐭 Data\n\nPublished in Nestorowa et al. (2016)\nExpression for a subset of genes, the surfaceome\nValues are log2 normalised values\nThe statistical analysis method we will use scran (Lun, McCarthy, and Marioni 2016) requires normalised values"
+  },
+  {
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+    "href": "omics/week-4/study_before_workshop.html#workshops-1",
+    "title": "Independent Study to prepare for workshop",
+    "section": "Workshops",
+    "text": "Workshops\n\nOmics 1: Hello data Getting to know the data. Checking the distributions of values overall, across samples and across genes to check things are as we expect and detect genes/samples that need to be removed\nOmics 2: Statistical Analysis Identifying which genes are differentially expressed between treatments. This is the main analysis step. We will use different methods for bulk and single cell data.\nOmics 3: Visualising and Interpreting Production of volcano plots and heatmaps to visualise the results of the statistical analysis. We will also look at how to interpret the results and how to find out more about the genes of interest."
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+    "href": "omics/week-4/study_before_workshop.html#references",
+    "title": "Independent Study to prepare for workshop",
+    "section": "References",
+    "text": "References\n\n\n🔗 About Omics 1: Hello data!\n\n\n\nLove, Michael I., Wolfgang Huber, and Simon Anders. 2014. “Moderated Estimation of Fold Change and Dispersion for RNA-Seq Data with DESeq2” 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” 5: 2122. https://doi.org/10.12688/f1000research.9501.2.\n\n\nNestorowa, Sonia, Fiona K. Hamey, Blanca Pijuan Sala, Evangelia Diamanti, Mairi Shepherd, Elisa Laurenti, Nicola K. Wilson, David G. Kent, and Berthold Göttgens. 2016. “A Single-Cell Resolution Map of Mouse Hematopoietic Stem and Progenitor Cell Differentiation.” Blood 128 (8): e20–31. https://doi.org/10.1182/blood-2016-05-716480.\n\n\nRand, Emma, and Sarah Forrester. 2022. “Statistically Useful Experimental Design.” https://cloud-span.github.io/experimental_design00-overview/."
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     "text": "You need only do the section for your own project data\n🐸 Frogs\n🎬 Open your frogs-88H Project. Make a new script and, using cont-fgf-s30.R as a template, repeat the analysis on one of the other comparisons.\n🐭 Mice\n🎬 Open your mice-88H Project. Open your hspc-prog.R script and, using you code working with the hspc cells as a template, repeat the analysis on the prog cells.\n🍂 xxxx\n🎬 Open your xxxx-88H Project. Make a new script and, using xxxxxxx.R as a template, repeat the analysis on xxxxxxxx"
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+    "text": "In this workshop you will learn what steps to take to get a good understanding of your ’omics data before you consider any statistical analysis. This is an often overlooked, but very valuable and informative, part of any data pipeline. It gives you the deep understanding of the data structures and values that you will need to code and trouble-shoot code, allows you to spot failed or problematic samples and informs your decisions on quality control.\nYou should examine all three data sets because the comparisons will give you a stronger understanding of your own project data."
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+    "text": "In this workshop you will learn what steps to take to get a good understanding of your ’omics data before you consider any statistical analysis. This is an often overlooked, but very valuable and informative, part of any data pipeline. It gives you the deep understanding of the data structures and values that you will need to code and trouble-shoot code, allows you to spot failed or problematic samples and informs your decisions on quality control.\nYou should examine all three data sets because the comparisons will give you a stronger understanding of your own project data."
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+  {
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+    "title": "Workshop",
+    "section": "Set up a Project",
+    "text": "Set up a Project\n🎬 Start RStudio from the Start menu\n🎬 Make an RStudio project. Be deliberate about where you create it so that it is a good place for you\n🎬 Use the Files pane to make new folders for the data. I suggest data-raw and data-processed\n🎬 Make a new script called workshop-1.R to carry out the rest of the work.\n🎬 Record what you do and what you find out. All of it!\n🎬 Load tidyverse (Wickham et al. 2019) for importing, summarising, plotting and filtering.\n\nlibrary(tidyverse)"
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+    "section": "Examine the data in a spreadsheet",
+    "text": "Examine the data in a spreadsheet\nThese are the three datasets. Each set compromises several files.\n🐸 Frog development data:\n\nxlaevis_counts_S14.csv\nxlaevis_counts_S20.csv\nxlaevis_counts_S30.csv\nmeta_data.txt\n\n🐭 Stem cell data:\n\nsurfaceome_hspc.csv\nsurfaceome_prog.csv\nsurfaceome_lthsc.csv\n\n🍂 xxxx data:\n\nxxx\nxxx\n\n🎬 Save the files to data-raw and open them in Excel\n🎬 Answer the following questions:\n\nDescribe how the sets of data are similar and how they are different.\nWhat is in the rows and columns of each file?\nHow many rows and columns are there in each file? Are these the same? In all cases or some cases? Why?\nGoogle an id. Where does your search take you? How much information is available?\n\n🎬 Did you record all that??"
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+    "section": "Import",
+    "text": "Import\nNow let’s get the data into R and visualise it.\n🎬 Import xlaevis_counts_S30.csv, surfaceome_hspc.csv and xxxxxxxx\n\n# 🐸 import the s30 data\ns30 &lt;- read_csv(\"data-raw/xlaevis_counts_S30.csv\")\n\n\n# 🐭 import the hspc data\nhspc &lt;- read_csv(\"data-raw/surfaceome_hspc.csv\")\n\n\n# 🍂 xxxx import the xxxx data\n# prog &lt;- read_csv(\"\")\n\n🎬 Check these have the number of rows and column you were expecting and that column types and names are as expected."
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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 (Wickham 2014) 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\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\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\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\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\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\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\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(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\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”:"
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+    "href": "omics/week-4/workshop.html#filtering-for-qc",
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+    "section": "Filtering for QC",
+    "text": "Filtering for QC\n🐸 Frog filtering\nOur samples look to be similarly well sequenced. There are no samples we should remove. However, some genes are not express or the expression values are so low in for a gene that they are uninformative. We will filter the s30_summary_gene dataframe to obtain a list of xenbase_gene_id we can use to filter s30.\nMy suggestion is to include only the genes with counts in at least 3 samples3 and those with total counts above 20.\n🎬 Filter the summary by gene dataframe:\n\ns30_summary_gene_filtered &lt;- s30_summary_gene |&gt; \n  filter(total &gt; 20) |&gt; \n  filter(n_zero &lt; 4)\n\n🎬 Write the filtered summary by gene to file:\n\nwrite_csv(s30_summary_gene_filtered, \n          file = \"data-processed/s30_summary_gene_filtered.csv\")\n\n🎬 Use the list of xenbase_gene_id in the filtered summary to filter the original dataset:\n\ns30_filtered &lt;- s30 |&gt; \n  filter(xenbase_gene_id %in%  s30_summary_gene_filtered$xenbase_gene_id)\n\n🎬 Write the filtered original to file:\n\nwrite_csv(s30_filtered, \n          file = \"data-processed/s30_filtered.csv\")\n\n🐭 Mouse filtering\nWe will take a different approach to filtering the single cell data. For the Frog samples we are examining the control and the FGF treated samples. This means have a low number of counts overall means the gene is not really expressed (detected) in any condition, and filtering out those genes is removing things that definitely are not interesting. For the mice, we have examined only one cell type but will be making comparisons between cells types. It may be that low expression of a gene in this cell type tells us something if that gene is highly expressed in another cell type. Instead, we will make statistical comparisons between the cell types and then filter based on overall expression, the difference in expression between cell types and whether that difference is significant.\nThe number of “replicates” is also important. When you have only three in each group it is not possible to make statistical comparisons when several replicates are zero. This is less of an issue with single cell data."
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+    "href": "omics/week-4/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\n🎬 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."
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+  {
+    "objectID": "omics/week-4/workshop.html#footnotes",
+    "href": "omics/week-4/workshop.html#footnotes",
+    "title": "Workshop",
+    "section": "Footnotes",
+    "text": "Footnotes\n\nThis a result of the Central limit theorem,one consequence of which is that adding together lots of distributions - whatever distributions they are - will tend to a normal distribution.↩︎\nThis a result of the Central limit theorem,one consequence of which is that adding together lots of distributions - whatever distributions they are - will tend to a normal distribution.↩︎\nI chose three because that would keep [0, 0, 0] [#,#,#]. This is difference we cannot test statistically, but which would matter biologically.↩︎"
+  },
+  {
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+    "href": "omics/week-4/study_after_workshop.html",
+    "title": "Independent Study to consolidate this week",
+    "section": "",
+    "text": "You need only do the section for your own project data\n🐸 Frogs\n🎬 Open your frogs-88H Project. Make a new script and, using cont-fgf-s30.R as a template, repeat the analysis on one of the other comparisons.\n🐭 Mice\n🎬 Open your mice-88H Project. Open your hspc-prog.R script and, using you code working with the hspc cells as a template, repeat the analysis on the prog cells.\n🍂 xxxx\n🎬 Open your xxxx-88H Project. Make a new script and, using xxxxxxx.R as a template, repeat the analysis on xxxxxxxx"
+  },
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@@ -620,7 +837,7 @@
     "href": "core/week-2/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\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.html\nstudy_before_workshop.ipynb\nstudy_before_workshop.qmd\nworkshop.qmd\nworkshop.rmarkdown\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 136\ndrwxr-xr-x 2 runner docker  4096 Oct  7 10:56 data\ndrwxr-xr-x 2 runner docker  4096 Oct  7 10:56 images\n-rw-r--r-- 1 runner docker  1597 Oct  7 10:56 overview.qmd\n-rw-r--r-- 1 runner docker   184 Oct  7 10:56 study_after_workshop.qmd\n-rw-r--r-- 1 runner docker 69876 Oct  7 10:58 study_before_workshop.html\n-rw-r--r-- 1 runner docker  4807 Oct  7 10:56 study_before_workshop.ipynb\n-rw-r--r-- 1 runner docker 13029 Oct  7 10:56 study_before_workshop.qmd\n-rw-r--r-- 1 runner docker  8550 Oct  7 10:56 workshop.qmd\n-rw-r--r-- 1 runner docker  8564 Oct  7 10:58 workshop.rmarkdown\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 136K\ndrwxr-xr-x 2 runner docker 4.0K Oct  7 10:56 data\ndrwxr-xr-x 2 runner docker 4.0K Oct  7 10:56 images\n-rw-r--r-- 1 runner docker 1.6K Oct  7 10:56 overview.qmd\n-rw-r--r-- 1 runner docker  184 Oct  7 10:56 study_after_workshop.qmd\n-rw-r--r-- 1 runner docker  69K Oct  7 10:58 study_before_workshop.html\n-rw-r--r-- 1 runner docker 4.7K Oct  7 10:56 study_before_workshop.ipynb\n-rw-r--r-- 1 runner docker  13K Oct  7 10:56 study_before_workshop.qmd\n-rw-r--r-- 1 runner docker 8.4K Oct  7 10:56 workshop.qmd\n-rw-r--r-- 1 runner docker 8.4K Oct  7 10:58 workshop.rmarkdown\n\n\nThe -a option stands for “all” and shows us all the files, including hidden files.\n\nls -alh\n\ntotal 144K\ndrwxr-xr-x 4 runner docker 4.0K Oct  7 10:58 .\ndrwxr-xr-x 5 runner docker 4.0K Oct  7 10:58 ..\ndrwxr-xr-x 2 runner docker 4.0K Oct  7 10:56 data\ndrwxr-xr-x 2 runner docker 4.0K Oct  7 10:56 images\n-rw-r--r-- 1 runner docker 1.6K Oct  7 10:56 overview.qmd\n-rw-r--r-- 1 runner docker  184 Oct  7 10:56 study_after_workshop.qmd\n-rw-r--r-- 1 runner docker  69K Oct  7 10:58 study_before_workshop.html\n-rw-r--r-- 1 runner docker 4.7K Oct  7 10:56 study_before_workshop.ipynb\n-rw-r--r-- 1 runner docker  13K Oct  7 10:56 study_before_workshop.qmd\n-rw-r--r-- 1 runner docker 8.4K Oct  7 10:56 workshop.qmd\n-rw-r--r-- 1 runner docker 8.4K Oct  7 10:58 workshop.rmarkdown\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/data\n/home/runner/work/BIO00088H-data/BIO00088H-data/core/week-2\n/home/runner/work/BIO00088H-data/BIO00088H-data/core/week-2/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\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.html\nstudy_before_workshop.ipynb\nstudy_before_workshop.qmd\nworkshop.qmd\nworkshop.rmarkdown\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 136\ndrwxr-xr-x 2 runner docker  4096 Oct  8 10:06 data\ndrwxr-xr-x 2 runner docker  4096 Oct  8 10:06 images\n-rw-r--r-- 1 runner docker  1597 Oct  8 10:06 overview.qmd\n-rw-r--r-- 1 runner docker   184 Oct  8 10:06 study_after_workshop.qmd\n-rw-r--r-- 1 runner docker 69876 Oct  8 10:09 study_before_workshop.html\n-rw-r--r-- 1 runner docker  4807 Oct  8 10:06 study_before_workshop.ipynb\n-rw-r--r-- 1 runner docker 13029 Oct  8 10:06 study_before_workshop.qmd\n-rw-r--r-- 1 runner docker  8550 Oct  8 10:06 workshop.qmd\n-rw-r--r-- 1 runner docker  8564 Oct  8 10:09 workshop.rmarkdown\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 136K\ndrwxr-xr-x 2 runner docker 4.0K Oct  8 10:06 data\ndrwxr-xr-x 2 runner docker 4.0K Oct  8 10:06 images\n-rw-r--r-- 1 runner docker 1.6K Oct  8 10:06 overview.qmd\n-rw-r--r-- 1 runner docker  184 Oct  8 10:06 study_after_workshop.qmd\n-rw-r--r-- 1 runner docker  69K Oct  8 10:09 study_before_workshop.html\n-rw-r--r-- 1 runner docker 4.7K Oct  8 10:06 study_before_workshop.ipynb\n-rw-r--r-- 1 runner docker  13K Oct  8 10:06 study_before_workshop.qmd\n-rw-r--r-- 1 runner docker 8.4K Oct  8 10:06 workshop.qmd\n-rw-r--r-- 1 runner docker 8.4K Oct  8 10:09 workshop.rmarkdown\n\n\nThe -a option stands for “all” and shows us all the files, including hidden files.\n\nls -alh\n\ntotal 144K\ndrwxr-xr-x 4 runner docker 4.0K Oct  8 10:09 .\ndrwxr-xr-x 5 runner docker 4.0K Oct  8 10:09 ..\ndrwxr-xr-x 2 runner docker 4.0K Oct  8 10:06 data\ndrwxr-xr-x 2 runner docker 4.0K Oct  8 10:06 images\n-rw-r--r-- 1 runner docker 1.6K Oct  8 10:06 overview.qmd\n-rw-r--r-- 1 runner docker  184 Oct  8 10:06 study_after_workshop.qmd\n-rw-r--r-- 1 runner docker  69K Oct  8 10:09 study_before_workshop.html\n-rw-r--r-- 1 runner docker 4.7K Oct  8 10:06 study_before_workshop.ipynb\n-rw-r--r-- 1 runner docker  13K Oct  8 10:06 study_before_workshop.qmd\n-rw-r--r-- 1 runner docker 8.4K Oct  8 10:06 workshop.qmd\n-rw-r--r-- 1 runner docker 8.4K Oct  8 10:09 workshop.rmarkdown\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/data\n/home/runner/work/BIO00088H-data/BIO00088H-data/core/week-2\n/home/runner/work/BIO00088H-data/BIO00088H-data/core/week-2/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"
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