diff --git a/core/core.html b/core/core.html
index 9247cf5..6f9ef5d 100644
--- a/core/core.html
+++ b/core/core.html
@@ -341,7 +341,7 @@ <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">15 December, 2023</p>
+      <p class="date">18 December, 2023</p>
     </div>
   </div>
   
diff --git a/core/week-1/overview.html b/core/week-1/overview.html
index 27278e5..447a571 100644
--- a/core/week-1/overview.html
+++ b/core/week-1/overview.html
@@ -337,7 +337,7 @@ <h1 class="title">Overview</h1>
     <div>
     <div class="quarto-title-meta-heading">Published</div>
     <div class="quarto-title-meta-contents">
-      <p class="date">15 December, 2023</p>
+      <p class="date">18 December, 2023</p>
     </div>
   </div>
   
diff --git a/core/week-1/study_after_workshop.html b/core/week-1/study_after_workshop.html
index fea2baf..0e4854c 100644
--- a/core/week-1/study_after_workshop.html
+++ b/core/week-1/study_after_workshop.html
@@ -337,7 +337,7 @@ <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">15 December, 2023</p>
+      <p class="date">18 December, 2023</p>
     </div>
   </div>
   
diff --git a/core/week-1/study_before_workshop.html b/core/week-1/study_before_workshop.html
index c0ed27f..52e271b 100644
--- a/core/week-1/study_before_workshop.html
+++ b/core/week-1/study_before_workshop.html
@@ -330,7 +330,7 @@ <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">15 December, 2023</p>
+      <p class="date">18 December, 2023</p>
     </div>
   </div>
   
diff --git a/core/week-1/workshop.html b/core/week-1/workshop.html
index 2c7e5f6..18ee41d 100644
--- a/core/week-1/workshop.html
+++ b/core/week-1/workshop.html
@@ -390,7 +390,7 @@ <h1 class="title">Workshop</h1>
     <div>
     <div class="quarto-title-meta-heading">Published</div>
     <div class="quarto-title-meta-contents">
-      <p class="date">15 December, 2023</p>
+      <p class="date">18 December, 2023</p>
     </div>
   </div>
   
diff --git a/core/week-11/overview.html b/core/week-11/overview.html
index 563f313..5f57de4 100644
--- a/core/week-11/overview.html
+++ b/core/week-11/overview.html
@@ -337,7 +337,7 @@ <h1 class="title">Overview</h1>
     <div>
     <div class="quarto-title-meta-heading">Published</div>
     <div class="quarto-title-meta-contents">
-      <p class="date">15 December, 2023</p>
+      <p class="date">18 December, 2023</p>
     </div>
   </div>
   
diff --git a/core/week-11/study_after_workshop.html b/core/week-11/study_after_workshop.html
index 9e1c8a7..6748788 100644
--- a/core/week-11/study_after_workshop.html
+++ b/core/week-11/study_after_workshop.html
@@ -330,7 +330,7 @@ <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">15 December, 2023</p>
+      <p class="date">18 December, 2023</p>
     </div>
   </div>
   
diff --git a/core/week-11/study_before_workshop.html b/core/week-11/study_before_workshop.html
index 1934dd7..bbe8e22 100644
--- a/core/week-11/study_before_workshop.html
+++ b/core/week-11/study_before_workshop.html
@@ -384,7 +384,7 @@ <h1 class="title">Independent Study to prepare for workshop</h1>
 </div>
 </div>
 
-  <p class="date">15 December, 2023</p>
+  <p class="date">18 December, 2023</p>
 </section>
 <section id="module-assessment" class="slide level2">
 <h2>Module assessment</h2>
diff --git a/core/week-11/workshop.html b/core/week-11/workshop.html
index 96813b8..749ccbc 100644
--- a/core/week-11/workshop.html
+++ b/core/week-11/workshop.html
@@ -377,7 +377,7 @@ <h1 class="title">Workshop</h1>
     <div>
     <div class="quarto-title-meta-heading">Published</div>
     <div class="quarto-title-meta-contents">
-      <p class="date">15 December, 2023</p>
+      <p class="date">18 December, 2023</p>
     </div>
   </div>
   
diff --git a/core/week-2/overview.html b/core/week-2/overview.html
index 579348e..04d3a84 100644
--- a/core/week-2/overview.html
+++ b/core/week-2/overview.html
@@ -328,7 +328,7 @@ <h1 class="title">Overview</h1>
     <div>
     <div class="quarto-title-meta-heading">Published</div>
     <div class="quarto-title-meta-contents">
-      <p class="date">15 December, 2023</p>
+      <p class="date">18 December, 2023</p>
     </div>
   </div>
   
diff --git a/core/week-2/study_after_workshop.html b/core/week-2/study_after_workshop.html
index 778fac4..80f3037 100644
--- a/core/week-2/study_after_workshop.html
+++ b/core/week-2/study_after_workshop.html
@@ -328,7 +328,7 @@ <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">15 December, 2023</p>
+      <p class="date">18 December, 2023</p>
     </div>
   </div>
   
diff --git a/core/week-2/study_before_workshop.html b/core/week-2/study_before_workshop.html
index 10b7b77..278139e 100644
--- a/core/week-2/study_before_workshop.html
+++ b/core/week-2/study_before_workshop.html
@@ -444,7 +444,7 @@
 </div>
 </div>
 
-  <p class="date">15 December, 2023</p>
+  <p class="date">18 December, 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 1356e82..6993b1a 100644
--- a/core/week-2/workshop.html
+++ b/core/week-2/workshop.html
@@ -404,7 +404,7 @@ <h1 class="title">Workshop</h1>
     <div>
     <div class="quarto-title-meta-heading">Published</div>
     <div class="quarto-title-meta-contents">
-      <p class="date">15 December, 2023</p>
+      <p class="date">18 December, 2023</p>
     </div>
   </div>
   
@@ -493,18 +493,18 @@ <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 228
-drwxr-xr-x 2 runner docker  4096 Dec 15 12:46 data
-drwxr-xr-x 2 runner docker  4096 Dec 15 12:46 images
--rw-r--r-- 1 runner docker 27497 Dec 15 13:09 overview.html
--rw-r--r-- 1 runner docker  1597 Dec 15 12:46 overview.qmd
--rw-r--r-- 1 runner docker   184 Dec 15 12:46 study_after_workshop.qmd
--rw-r--r-- 1 runner docker 70988 Dec 15 13:09 study_before_workshop.html
--rw-r--r-- 1 runner docker  4807 Dec 15 12:46 study_before_workshop.ipynb
--rw-r--r-- 1 runner docker 13029 Dec 15 12:46 study_before_workshop.qmd
--rw-r--r-- 1 runner docker 58063 Dec 15 12:46 workshop.html
--rw-r--r-- 1 runner docker  8550 Dec 15 12:46 workshop.qmd
--rw-r--r-- 1 runner docker  8564 Dec 15 13:09 workshop.rmarkdown
-drwxr-xr-x 3 runner docker  4096 Dec 15 12:46 workshop_files</code></pre>
+drwxr-xr-x 2 runner docker  4096 Dec 18 15:28 data
+drwxr-xr-x 2 runner docker  4096 Dec 18 15:28 images
+-rw-r--r-- 1 runner docker 27497 Dec 18 15:30 overview.html
+-rw-r--r-- 1 runner docker  1597 Dec 18 15:28 overview.qmd
+-rw-r--r-- 1 runner docker   184 Dec 18 15:28 study_after_workshop.qmd
+-rw-r--r-- 1 runner docker 70988 Dec 18 15:30 study_before_workshop.html
+-rw-r--r-- 1 runner docker  4807 Dec 18 15:28 study_before_workshop.ipynb
+-rw-r--r-- 1 runner docker 13029 Dec 18 15:28 study_before_workshop.qmd
+-rw-r--r-- 1 runner docker 58063 Dec 18 15:28 workshop.html
+-rw-r--r-- 1 runner docker  8550 Dec 18 15:28 workshop.qmd
+-rw-r--r-- 1 runner docker  8564 Dec 18 15:30 workshop.rmarkdown
+drwxr-xr-x 3 runner docker  4096 Dec 18 15:28 workshop_files</code></pre>
 </div>
 </div>
 <p>You can use more than one option at once. The <code>-h</code> option stands for “human readable” and makes the file sizes easier to understand for humans:</p>
@@ -512,18 +512,18 @@ <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 228K
-drwxr-xr-x 2 runner docker 4.0K Dec 15 12:46 data
-drwxr-xr-x 2 runner docker 4.0K Dec 15 12:46 images
--rw-r--r-- 1 runner docker  27K Dec 15 13:09 overview.html
--rw-r--r-- 1 runner docker 1.6K Dec 15 12:46 overview.qmd
--rw-r--r-- 1 runner docker  184 Dec 15 12:46 study_after_workshop.qmd
--rw-r--r-- 1 runner docker  70K Dec 15 13:09 study_before_workshop.html
--rw-r--r-- 1 runner docker 4.7K Dec 15 12:46 study_before_workshop.ipynb
--rw-r--r-- 1 runner docker  13K Dec 15 12:46 study_before_workshop.qmd
--rw-r--r-- 1 runner docker  57K Dec 15 12:46 workshop.html
--rw-r--r-- 1 runner docker 8.4K Dec 15 12:46 workshop.qmd
--rw-r--r-- 1 runner docker 8.4K Dec 15 13:09 workshop.rmarkdown
-drwxr-xr-x 3 runner docker 4.0K Dec 15 12:46 workshop_files</code></pre>
+drwxr-xr-x 2 runner docker 4.0K Dec 18 15:28 data
+drwxr-xr-x 2 runner docker 4.0K Dec 18 15:28 images
+-rw-r--r-- 1 runner docker  27K Dec 18 15:30 overview.html
+-rw-r--r-- 1 runner docker 1.6K Dec 18 15:28 overview.qmd
+-rw-r--r-- 1 runner docker  184 Dec 18 15:28 study_after_workshop.qmd
+-rw-r--r-- 1 runner docker  70K Dec 18 15:30 study_before_workshop.html
+-rw-r--r-- 1 runner docker 4.7K Dec 18 15:28 study_before_workshop.ipynb
+-rw-r--r-- 1 runner docker  13K Dec 18 15:28 study_before_workshop.qmd
+-rw-r--r-- 1 runner docker  57K Dec 18 15:28 workshop.html
+-rw-r--r-- 1 runner docker 8.4K Dec 18 15:28 workshop.qmd
+-rw-r--r-- 1 runner docker 8.4K Dec 18 15:30 workshop.rmarkdown
+drwxr-xr-x 3 runner docker 4.0K Dec 18 15:28 workshop_files</code></pre>
 </div>
 </div>
 <p>The <code>-a</code> option stands for “all” and shows us all the files, including hidden files.</p>
@@ -531,20 +531,20 @@ <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 236K
-drwxr-xr-x 5 runner docker 4.0K Dec 15 13:09 .
-drwxr-xr-x 6 runner docker 4.0K Dec 15 13:09 ..
-drwxr-xr-x 2 runner docker 4.0K Dec 15 12:46 data
-drwxr-xr-x 2 runner docker 4.0K Dec 15 12:46 images
--rw-r--r-- 1 runner docker  27K Dec 15 13:09 overview.html
--rw-r--r-- 1 runner docker 1.6K Dec 15 12:46 overview.qmd
--rw-r--r-- 1 runner docker  184 Dec 15 12:46 study_after_workshop.qmd
--rw-r--r-- 1 runner docker  70K Dec 15 13:09 study_before_workshop.html
--rw-r--r-- 1 runner docker 4.7K Dec 15 12:46 study_before_workshop.ipynb
--rw-r--r-- 1 runner docker  13K Dec 15 12:46 study_before_workshop.qmd
--rw-r--r-- 1 runner docker  57K Dec 15 12:46 workshop.html
--rw-r--r-- 1 runner docker 8.4K Dec 15 12:46 workshop.qmd
--rw-r--r-- 1 runner docker 8.4K Dec 15 13:09 workshop.rmarkdown
-drwxr-xr-x 3 runner docker 4.0K Dec 15 12:46 workshop_files</code></pre>
+drwxr-xr-x 5 runner docker 4.0K Dec 18 15:30 .
+drwxr-xr-x 6 runner docker 4.0K Dec 18 15:30 ..
+drwxr-xr-x 2 runner docker 4.0K Dec 18 15:28 data
+drwxr-xr-x 2 runner docker 4.0K Dec 18 15:28 images
+-rw-r--r-- 1 runner docker  27K Dec 18 15:30 overview.html
+-rw-r--r-- 1 runner docker 1.6K Dec 18 15:28 overview.qmd
+-rw-r--r-- 1 runner docker  184 Dec 18 15:28 study_after_workshop.qmd
+-rw-r--r-- 1 runner docker  70K Dec 18 15:30 study_before_workshop.html
+-rw-r--r-- 1 runner docker 4.7K Dec 18 15:28 study_before_workshop.ipynb
+-rw-r--r-- 1 runner docker  13K Dec 18 15:28 study_before_workshop.qmd
+-rw-r--r-- 1 runner docker  57K Dec 18 15:28 workshop.html
+-rw-r--r-- 1 runner docker 8.4K Dec 18 15:28 workshop.qmd
+-rw-r--r-- 1 runner docker 8.4K Dec 18 15:30 workshop.rmarkdown
+drwxr-xr-x 3 runner docker 4.0K Dec 18 15:28 workshop_files</code></pre>
 </div>
 </div>
 <p>You can move about with the <code>cd</code> command, which stands for “change directory”. You can use it to move into a directory by specifying the path to the directory:</p>
diff --git a/core/week-6/overview.html b/core/week-6/overview.html
index f478109..b796f57 100644
--- a/core/week-6/overview.html
+++ b/core/week-6/overview.html
@@ -336,7 +336,7 @@ <h1 class="title">Overview</h1>
     <div>
     <div class="quarto-title-meta-heading">Published</div>
     <div class="quarto-title-meta-contents">
-      <p class="date">15 December, 2023</p>
+      <p class="date">18 December, 2023</p>
     </div>
   </div>
   
diff --git a/core/week-6/study_after_workshop.html b/core/week-6/study_after_workshop.html
index 2120b23..ef80bbd 100644
--- a/core/week-6/study_after_workshop.html
+++ b/core/week-6/study_after_workshop.html
@@ -330,7 +330,7 @@ <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">15 December, 2023</p>
+      <p class="date">18 December, 2023</p>
     </div>
   </div>
   
diff --git a/core/week-6/study_before_workshop.html b/core/week-6/study_before_workshop.html
index 7578923..e7e1d8d 100644
--- a/core/week-6/study_before_workshop.html
+++ b/core/week-6/study_before_workshop.html
@@ -330,7 +330,7 @@ <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">15 December, 2023</p>
+      <p class="date">18 December, 2023</p>
     </div>
   </div>
   
diff --git a/core/week-6/workshop.html b/core/week-6/workshop.html
index 17e825c..fdf741b 100644
--- a/core/week-6/workshop.html
+++ b/core/week-6/workshop.html
@@ -365,7 +365,7 @@ <h1 class="title">Workshop</h1>
     <div>
     <div class="quarto-title-meta-heading">Published</div>
     <div class="quarto-title-meta-contents">
-      <p class="date">15 December, 2023</p>
+      <p class="date">18 December, 2023</p>
     </div>
   </div>
   
diff --git a/images/images.html b/images/images.html
index 261651b..67620b9 100644
--- a/images/images.html
+++ b/images/images.html
@@ -209,7 +209,7 @@ <h1 class="title">Image Data Analysis for Group Project</h1>
     <div>
     <div class="quarto-title-meta-heading">Published</div>
     <div class="quarto-title-meta-contents">
-      <p class="date">15 December, 2023</p>
+      <p class="date">18 December, 2023</p>
     </div>
   </div>
   
diff --git a/index.html b/index.html
index de2cff6..76b2058 100644
--- a/index.html
+++ b/index.html
@@ -172,7 +172,7 @@ <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">15 December, 2023</p>
+      <p class="date">18 December, 2023</p>
     </div>
   </div>
   
diff --git a/omics/kelly/data-raw/VFAdata for BIO00088H R_studio.csv b/omics/kelly/data-raw/VFAdata for BIO00088H R_studio.csv
new file mode 100644
index 0000000..f806cd9
--- /dev/null
+++ b/omics/kelly/data-raw/VFAdata for BIO00088H R_studio.csv	
@@ -0,0 +1,76 @@
+Notes:,,,,,,,,,,,,,,,,,
+Each sample has three replicates,,,,,,,,,,,,,,,,,
+"Concentrations are listed here in mM (millimolar, or ""moles / L *  1000 millimoles / 1 moles"")",,,,,,,,,,,,,,,,,
+CN samples have straw biomass added to AD vials,,,,,,,,,,,,,,,,,
+NC samples have had water added to AD vials,,,,,,,,,,,,,,,,,
+,,,,,,,,,Data for mM to g/l calculations,,,,,,,,
+To calculate from this data,,Change in VFA with time,,,,,,,MW (g/mol),60.05,74.08,88.11,88.11,102.13,102.13,116.1583,116.1583
+,,"Recalculate the data into grams per liter, and change in VFA g/l with time",,,,,,,,Acetate,Propanoate,Isobutyrate,Butyrate,Isopentanoate,Pentanoate,Isohexanoate,Hexanoate
+,,"Calculate the percent representation of each VFA, by mM and by weight",,,,,,,,,,,,,,,
+,,"Useful graphs would include 1) mM or mg/l versus time for VFAs, 2) Delta mg/l versus time for each VFA",,,,,,,,,,,,,,,
+,,,,,,,,,,,,,,,,,
+,,,,,,,,,,,,,,,,,
+Sample - Replicate,Time (day),Acetate,Propanoate,Isobutyrate,Butyrate,Isopentanoate,Pentanoate,Isohexanoate,Hexanoate,,,,,,,,
+CN10-1,0,0,0,0,0,0,0,0,0,,,,,,,,
+CN10-2,0,0,0,0,0,0,0,0,0,,,,,,,,
+CN10-3,0,0,0,0,0,0,0,0,0,,,,,,,,
+CN10-1,1,17.239,1.126,0.733,0.538,0.52,0.076,0.025,0.007,,,,,,,,
+CN10-2,1,19.805,1.153,0.736,0.731,0.766,0.098,0.022,0.018,,,,,,,,
+CN10-3,1,21.043,1.235,0.742,0.717,0.793,0.07,0.019,0.023,,,,,,,,
+CN10-1,3,47.847,4.195,0.812,1.465,1.034,0.082,0.058,0.044,,,,,,,,
+CN10-2,3,56.541,4.596,1.193,1.452,1.257,0.096,0.012,0.081,,,,,,,,
+CN10-3,3,54.29,4.258,1.116,1.384,1.237,0.084,0.032,0.081,,,,,,,,
+CN10-1,5,66.582,7.176,1.449,2.096,1.137,0.284,0.053,0.027,,,,,,,,
+CN10-2,5,81.172,8.209,1.927,2.544,1.505,0.317,0.043,0.035,,,,,,,,
+CN10-3,5,70.528,6.831,1.629,2.202,1.285,0.257,0.016,0.029,,,,,,,,
+CN10-1,9,76.325,9.799,2.017,3.352,1.45,0.439,0.055,0.042,,,,,,,,
+CN10-2,9,98.902,11.816,2.675,4.346,1.888,0.579,0.043,0.054,,,,,,,,
+CN10-3,9,100.199,11.236,2.836,4.164,1.888,0.519,0.047,0.047,,,,,,,,
+CN10-1,11,82.171,10.703,2.485,3.976,1.625,0.591,0.057,0.08,,,,,,,,
+CN10-2,11,94.162,11.751,2.719,4.598,1.963,0.649,0.044,0.088,,,,,,,,
+CN10-3,11,89.181,10.756,2.493,4.137,1.835,0.561,0.034,0.064,,,,,,,,
+CN10-1,13,101.508,12.57,3.115,4.895,1.949,0.758,0.041,0.114,,,,,,,,
+CN10-2,13,108.601,13.698,3.31,5.505,2.072,0.814,0.047,0.146,,,,,,,,
+CN10-3,13,107.873,13.669,3.688,5.447,2.043,0.861,0.068,0.157,,,,,,,,
+CN10-1,16,100.422,14.564,3.045,5.623,2.105,0.862,0.063,0.126,,,,,,,,
+CN10-2,16,111.334,15.229,3.06,6.128,2.202,0.859,0.043,0.12,,,,,,,,
+CN10-3,16,112.261,15.044,3.423,6.106,2.271,0.858,0.046,0.121,,,,,,,,
+CN10-1,18,96.766,13.804,3.585,5.66,1.988,0.788,0.056,0.099,,,,,,,,
+CN10-2,18,104.135,14.584,3.38,6.173,2.157,0.827,0.047,0.108,,,,,,,,
+CN10-3,18,99.257,14.082,2.894,5.712,1.998,0.816,0.04,0.093,,,,,,,,
+CN10-1,20,90.968,13.733,3.266,5.697,1.974,0.804,0.055,0.119,,,,,,,,
+CN10-2,20,96.995,13.965,2.346,6.951,2.089,0.853,0.045,0.109,,,,,,,,
+CN10-3,20,107.952,14.742,3.668,6.546,2.227,0.973,0.042,0.108,,,,,,,,
+CN10-1,22,89.43,13.57,2.953,5.681,1.898,0.818,0.048,0.08,,,,,,,,
+CN10-2,22,104.397,14.584,2.946,7.454,2.237,0.918,0.041,0.096,,,,,,,,
+CN10-3,22,110.657,15.274,4.216,7.192,2.467,1.118,0.047,0.125,,,,,,,,
+NC1,1,3.594,0.982,0.395,0.206,0.246,0.031,0.006,0.005,,,,,,,,
+NC2,1,1.604,0.927,0.478,0.242,0.314,0.057,0.004,0.011,,,,,,,,
+NC3,1,1.503,0.867,0.416,0.236,0.295,0.033,0.003,0.01,,,,,,,,
+NC1,3,7.095,1.355,0.413,0.37,0.441,0.086,0.049,0.038,,,,,,,,
+NC2,3,7.441,1.353,0.574,0.41,0.503,0.091,0.05,0.05,,,,,,,,
+NC3,3,7.011,1.446,0.555,0.417,0.563,0.1,0.057,0.034,,,,,,,,
+NC1,5,7.676,1.341,0.68,0.373,0.539,0.075,0.024,0.014,,,,,,,,
+NC2,5,10.885,1.931,0.872,0.522,0.763,0.11,0.043,0.019,,,,,,,,
+NC3,5,8.89,1.558,0.783,0.426,0.641,0.087,0.037,0.015,,,,,,,,
+NC1,9,20.134,3.602,1.405,0.839,1.218,0.231,0.044,0.061,,,,,,,,
+NC2,9,19.093,3.479,1.497,0.885,1.112,0.176,0.033,0.032,,,,,,,,
+NC3,9,20.491,3.837,1.625,0.875,1.492,0.242,0.051,0.041,,,,,,,,
+NC1,11,19.002,3.324,1.56,0.785,1.366,0.201,0.043,0.042,,,,,,,,
+NC2,11,16.193,2.783,1.321,0.633,1.205,0.152,0.033,0.033,,,,,,,,
+NC3,11,11.179,2.143,1.079,0.493,0.951,0.11,0.025,0.022,,,,,,,,
+NC1,13,20.805,3.555,1.863,0.816,1.394,0.217,0.044,0.07,,,,,,,,
+NC2,13,20.702,3.572,1.915,0.788,1.446,0.208,0.044,0.07,,,,,,,,
+NC3,13,20.187,3.34,1.684,0.638,1.364,0.176,0.034,0.063,,,,,,,,
+NC1,16,23.457,4.129,1.754,0.891,1.542,0.208,0.039,0.052,,,,,,,,
+NC2,16,23.79,4.419,2.033,0.886,1.695,0.238,0.048,0.076,,,,,,,,
+NC3,16,19.692,4.146,1.901,0.682,1.449,0.174,0.045,0.057,,,,,,,,
+NC1,18,22.499,3.988,1.543,0.798,1.364,0.162,0.031,0.051,,,,,,,,
+NC2,18,26.354,4.468,1.776,0.856,1.568,0.19,0.041,0.069,,,,,,,,
+NC3,18,18.685,3.869,1.478,0.6,1.202,0.127,0.033,0.032,,,,,,,,
+NC1,20,20.639,3.968,1.626,0.815,1.363,0.167,0.034,0.05,,,,,,,,
+NC2,20,17.364,3.724,1.484,0.656,1.222,0.136,0.03,0.027,,,,,,,,
+NC3,20,20.528,4.211,1.55,0.635,1.312,0.124,0.041,0.033,,,,,,,,
+NC1,22,21.128,3.982,1.86,0.805,1.465,0.22,0.038,0.037,,,,,,,,
+NC2,22,24.633,4.165,1.706,0.738,1.524,0.155,0.045,0.39,,,,,,,,
+NC3,22,29.288,4.129,1.575,0.639,1.458,0.133,0.052,0.025,,,,,,,,
diff --git a/omics/kelly/workshop.html b/omics/kelly/workshop.html
index 3341531..7feb118 100644
--- a/omics/kelly/workshop.html
+++ b/omics/kelly/workshop.html
@@ -5,7 +5,7 @@
 <meta name="generator" content="quarto-1.3.450">
 <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=yes">
 <meta name="author" content="Emma Rand">
-<title>Data Analysis for Group Project - Kelly</title>
+<title>Data Analysis for Group Project - Workflow for VFA analysis</title>
 <style>
 code{white-space: pre-wrap;}
 span.smallcaps{font-variant: small-caps;}
@@ -330,24 +330,32 @@
 <li>
 <a href="#introduction" id="toc-introduction" class="nav-link active" data-scroll-target="#introduction">Introduction</a>
   <ul>
-<li><a href="#overview" id="toc-overview" class="nav-link" data-scroll-target="#overview">Overview</a></li>
-  <li><a href="#data-files" id="toc-data-files" class="nav-link" data-scroll-target="#data-files">Data files</a></li>
+<li><a href="#exact-information-supplied-by-kelly" id="toc-exact-information-supplied-by-kelly" class="nav-link" data-scroll-target="#exact-information-supplied-by-kelly">Exact information supplied by Kelly</a></li>
+  <li><a href="#emmas-worklflow-interpretation" id="toc-emmas-worklflow-interpretation" class="nav-link" data-scroll-target="#emmas-worklflow-interpretation">Emma’s Worklflow interpretation</a></li>
   <li><a href="#getting-started" id="toc-getting-started" class="nav-link" data-scroll-target="#getting-started">Getting started</a></li>
-  <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" id="toc-examine-the-data" class="nav-link" data-scroll-target="#examine-the-data">Examine the data</a></li>
   <li><a href="#import" id="toc-import" class="nav-link" data-scroll-target="#import">Import</a></li>
-  <li><a href="#graphs" id="toc-graphs" class="nav-link" data-scroll-target="#graphs">Graphs</a></li>
-  <li><a href="#view-the-relationship-between-samples-using-pca" id="toc-view-the-relationship-between-samples-using-pca" class="nav-link" data-scroll-target="#view-the-relationship-between-samples-using-pca">View the relationship between samples using PCA</a></li>
+  <li><a href="#calculate-change-in-vfa-gl-with-time" id="toc-calculate-change-in-vfa-gl-with-time" class="nav-link" data-scroll-target="#calculate-change-in-vfa-gl-with-time">1. Calculate <em>Change</em> in VFA g/l with time</a></li>
+  <li><a href="#recalculate-the-data-into-grams-per-litre" id="toc-recalculate-the-data-into-grams-per-litre" class="nav-link" data-scroll-target="#recalculate-the-data-into-grams-per-litre">2. Recalculate the data into grams per litre</a></li>
+  <li><a href="#calculate-the-percent-representation-of-each-vfa" id="toc-calculate-the-percent-representation-of-each-vfa" class="nav-link" data-scroll-target="#calculate-the-percent-representation-of-each-vfa">3. Calculate the percent representation of each VFA</a></li>
+  <li>
+<a href="#graphs-for-info-so-far" id="toc-graphs-for-info-so-far" class="nav-link" data-scroll-target="#graphs-for-info-so-far">Graphs for info so far</a>
+  <ul class="collapse">
+<li><a href="#view-the-relationship-between-samples-using-pca" id="toc-view-the-relationship-between-samples-using-pca" class="nav-link" data-scroll-target="#view-the-relationship-between-samples-using-pca">View the relationship between samples using PCA</a></li>
   <li><a href="#visualise-the-vfa-concentration-using-a-heatmap" id="toc-visualise-the-vfa-concentration-using-a-heatmap" class="nav-link" data-scroll-target="#visualise-the-vfa-concentration-using-a-heatmap">Visualise the VFA concentration using a heatmap</a></li>
   </ul>
+</li>
+  <li><a href="#calculate-the-flux---pending." id="toc-calculate-the-flux---pending." class="nav-link" data-scroll-target="#calculate-the-flux---pending.">4. Calculate the flux - pending.</a></li>
+  <li><a href="#graph-and-extract-the-reaction-rate---pending" id="toc-graph-and-extract-the-reaction-rate---pending" class="nav-link" data-scroll-target="#graph-and-extract-the-reaction-rate---pending">5. Graph and extract the reaction rate - pending</a></li>
+  </ul>
 </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">Kelly</h1>
-<p class="subtitle lead">VFAs</p>
+<h1 class="title">Workflow for VFA analysis</h1>
+<p class="subtitle lead">Kelly’s project</p>
 </div>
 
 
@@ -364,7 +372,7 @@ <h1 class="title">Kelly</h1>
     <div>
     <div class="quarto-title-meta-heading">Published</div>
     <div class="quarto-title-meta-contents">
-      <p class="date">15 December, 2023</p>
+      <p class="date">18 December, 2023</p>
     </div>
   </div>
   
@@ -373,31 +381,36 @@ <h1 class="title">Kelly</h1>
   
 
 </header><section id="introduction" class="level1"><h1>Introduction</h1>
-<section id="overview" class="level2"><h2 class="anchored" data-anchor-id="overview">Overview</h2>
-<p>VFAs from AD vials</p>
-<ul>
-<li><p>Two treatments: straw (CN10) and water (NC)</p></li>
-<li><p>10 time points: 1, 3, 5, 9, 11, 13, 16, 18, 20, 22</p></li>
-<li><p>three replicates per treatment per time point</p></li>
-<li><p>2 x 10 x 3 = 60 groups</p></li>
-<li><p>8 VFA with concentration in mM (millimolar): acetate, propanoate, isobutyrate, butyrate, isopentanoate, pentanoate, isohexanoate, hexanoate</p></li>
-</ul>
-<p>To calculate from this data</p>
-<ul>
-<li>Recalculate the data into grams per litre
+<p>I have some data and information from Kelly. I have interpreted it and written some code to do the calculations.</p>
+<p>However, Kelly hasn’t had a chance to look at it yet so I am providing the exact information and data he supplied along with my suggested workflow based on my interpretation of the data and info.</p>
+<section id="exact-information-supplied-by-kelly" class="level2"><h2 class="anchored" data-anchor-id="exact-information-supplied-by-kelly">Exact information supplied by Kelly</h2>
+<p><em>The <a href="data-raw/VFAdata for BIO00088H R_studio.csv">file is a CSV file</a>, with some notes on top and the data in the following order, post notes and headers. Please note that all chemical data is in millimolar. There are 62 rows of actual data.</em></p>
+<p><em>Sample Name – Replicate, Time (days), Acetate, Propanoate, Isobutyrate, Butyrate, Isopentanoate, Pentanoate, Isohexanoate, Hexanoate</em></p>
+<p><em>The students should be able to transform the data from mM to mg/L, and to g/L. To do this they only need to multiply the molecular weight of the compound (listed in the notes in the file) by the concentration in mM to get mg/L. Obviously to get g/L they will just divide by 1000. They should be able to graph the VFA concentrations with time.</em></p>
+<p><em>They should also be able to do a simple flux measurement, which is the change in VFA concentration over a period of time, divided by weight or volume of material. In this case it might be equal to == Delta(Acetate at 3 days - Acetate at 1 day)/Delta (3days - 1day)/50 mls sludge. This would provide a final flux with the units of mg acetate per ml sludge per day. Let me know if this isn’t clear.</em></p>
+<p><em>Perhaps more importantly they should be able to graph and extract the reaction rate, assuming a first order chemical/biological reaction and an exponential falloff rate. I found this as a starting point (https://martinlab.chem.umass.edu/r-fitting-data/) , but I assume Emma has something much more effective already in the pipeline.</em></p>
+</section><section id="emmas-worklflow-interpretation" class="level2"><h2 class="anchored" data-anchor-id="emmas-worklflow-interpretation">Emma’s Worklflow interpretation</h2>
+<p>I created these two data files from the original.</p>
+<ol type="1">
+<li>8 VFA in mM for 60 samples <a href="data-raw/vfa.csv">vfa.csv</a>. There were 63 rows of data in the original file. There were no time 0 for one treatment and all values were zero for the other treatment so I removed those.
 <ul>
-<li>convert to molar: 1 millimolar to molar = 0.001 molar</li>
-<li>multiply by the molecular weight of each VFA</li>
+<li>Two treatments: straw (CN10) and water (NC)</li>
+<li>10 time points: 1, 3, 5, 9, 11, 13, 16, 18, 20, 22</li>
+<li>three replicates per treatment per time point</li>
+<li>2 x 10 x 3 = 60 groups</li>
+<li>8 VFA with concentration in mM (millimolar): acetate, propanoate, isobutyrate, butyrate, isopentanoate, pentanoate, isohexanoate, hexanoate</li>
 </ul>
 </li>
+<li>Molecular weights for each VFA in grams per mole <a href="data-raw/mol_wt.txt">mol_wt.txt</a> VFAs from AD vials</li>
+</ol>
+<p>We need to:</p>
+<ol type="1">
 <li>Calculate <em>Change</em> in VFA g/l with time</li>
+<li>Recalculate the data into grams per litre - convert to molar: 1 millimolar to molar = 0.001 molar - multiply by the molecular weight of each VFA</li>
 <li>Calculate the percent representation of each VFA, by mM and by weight</li>
-</ul></section><section id="data-files" class="level2"><h2 class="anchored" data-anchor-id="data-files">Data files</h2>
-<ul>
-<li><p>8 VFA in mM for 60 samples <a href="data-raw/vfa.csv">vfa.csv</a></p></li>
-<li><p>Molecular weights for each VFA in grams per mole <a href="data-raw/mol_wt.txt">mol_wt.txt</a></p></li>
-</ul></section><section id="getting-started" class="level2"><h2 class="anchored" data-anchor-id="getting-started">Getting started</h2>
-</section><section id="set-up-a-project" class="level2"><h2 class="anchored" data-anchor-id="set-up-a-project">Set up a Project</h2>
+<li>Calculate the flux (change in VFA concentration over a period of time, divided by weight or volume of material) of each VFA, by mM and by weight</li>
+<li>Graph and extract the reaction rate, assuming a first order chemical/biological reaction and an exponential falloff rate</li>
+</ol></section><section id="getting-started" class="level2"><h2 class="anchored" data-anchor-id="getting-started">Getting started</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>
@@ -427,6 +440,7 @@ <h1 class="title">Kelly</h1>
 <span>           remove <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <p>The provided data is cumulative/absolute. We need to calculate the change in VFA with time. There is a function, <code><a href="https://dplyr.tidyverse.org/reference/lead-lag.html">lag()</a></code> that will help us do this. It will take the previous value and subtract it from the current value. We need to do that separately for each <code>sample_replicate</code> so we need to group by <code>sample_replicate</code> first. We also need to make sure the data is in the right order so we will arrange by <code>sample_replicate</code> and <code>time_day</code>.</p>
+</section><section id="calculate-change-in-vfa-gl-with-time" class="level2"><h2 class="anchored" data-anchor-id="calculate-change-in-vfa-gl-with-time">1. Calculate <em>Change</em> in VFA g/l with time</h2>
 <p>🎬 Create dataframe for the change in VFA</p>
 <div class="cell">
 <div class="sourceCode" id="cb4"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="va">vfa_delta</span> <span class="op">&lt;-</span> <span class="va">vfa_cummul</span> <span class="op">|&gt;</span> </span>
@@ -442,6 +456,7 @@ <h1 class="title">Kelly</h1>
 <span>           hexanoate <span class="op">=</span> <span class="va">hexanoate</span> <span class="op">-</span> <span class="fu"><a href="https://dplyr.tidyverse.org/reference/lead-lag.html">lag</a></span><span class="op">(</span><span class="va">hexanoate</span><span class="op">)</span><span class="op">)</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
 <p>Now we have two dataframes, one for the cumulative data and one for the change in VFA.</p>
+</section><section id="recalculate-the-data-into-grams-per-litre" class="level2"><h2 class="anchored" data-anchor-id="recalculate-the-data-into-grams-per-litre">2. Recalculate the data into grams per litre</h2>
 <p>To make conversions from mM to g/l we need to do mM * 0.001 * MW. We will import the molecular weight data, pivot the VFA data to long format and join the molecular weight data to the VFA data. Then we can calculate the g/l. We will do this for both the cumulative and delta dataframes.</p>
 <p>🎬 import molecular weight data</p>
 <div class="cell">
@@ -457,6 +472,12 @@ <h1 class="title">Kelly</h1>
 <span>  <span class="fu"><a href="https://dplyr.tidyverse.org/reference/mutate.html">mutate</a></span><span class="op">(</span>conc_g_l <span class="op">=</span> <span class="va">conc_mM</span> <span class="op">*</span> <span class="fl">0.001</span> <span class="op">*</span> <span class="va">mw</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 <code>vfa_cummul</code> to check you understand what you have done.</p>
+<p>Repeat for the delta data.</p>
+<p>🎬 Pivot the change data, <code>delta_vfa</code> to long format:</p>
+<p>View <code>vfa_delta</code> to check it looks like <code>vfa_cummul</code></p>
+<p>🎬 Join molecular weight to data and calculate g/l (mutate to convert to g/l * 0.001 * MW):</p>
+</section><section id="calculate-the-percent-representation-of-each-vfa" class="level2"><h2 class="anchored" data-anchor-id="calculate-the-percent-representation-of-each-vfa">3. Calculate the percent representation of each VFA</h2>
+<p>by mM and by weight</p>
 <p>🎬 Add a column which is the percent representation of each VFA for mM and g/l:</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">vfa_cummul</span> <span class="op">&lt;-</span> <span class="va">vfa_cummul</span> <span class="op">|&gt;</span> </span>
@@ -464,10 +485,7 @@ <h1 class="title">Kelly</h1>
 <span>  <span class="fu"><a href="https://dplyr.tidyverse.org/reference/mutate.html">mutate</a></span><span class="op">(</span>percent_conc_g_l <span class="op">=</span> <span class="va">conc_g_l</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">conc_g_l</span><span class="op">)</span> <span class="op">*</span> <span class="fl">100</span>,</span>
 <span>         percent_conc_mM <span class="op">=</span> <span class="va">conc_mM</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">conc_mM</span><span class="op">)</span> <span class="op">*</span> <span class="fl">100</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>🎬 Pivot the change data, <code>delta_vfa</code> to long format:</p>
-<p>View <code>vfa_delta</code> to check it looks like <code>vfa_cummul</code></p>
-<p>🎬 Join molecular weight to data and calculate g/l (mutate to convert to g/l * 0.001 * MW):</p>
-</section><section id="graphs" class="level2"><h2 class="anchored" data-anchor-id="graphs">Graphs</h2>
+</section><section id="graphs-for-info-so-far" class="level2"><h2 class="anchored" data-anchor-id="graphs-for-info-so-far">Graphs for info so far</h2>
 <p>🎬 Make summary data for graphing</p>
 <div class="cell">
 <div class="sourceCode" id="cb8"><pre class="downlit sourceCode r code-with-copy"><code class="sourceCode R"><span><span class="va">vfa_cummul_summary</span> <span class="op">&lt;-</span> <span class="va">vfa_cummul</span> <span class="op">|&gt;</span> </span>
@@ -544,8 +562,8 @@ <h1 class="title">Kelly</h1>
 <p><img src="workshop_files/figure-html/unnamed-chunk-15-1.png" class="img-fluid" width="672"></p>
 </div>
 </div>
-</section><section id="view-the-relationship-between-samples-using-pca" class="level2"><h2 class="anchored" data-anchor-id="view-the-relationship-between-samples-using-pca">View the relationship between samples using PCA</h2>
-<p>We have 8 genes in our dataset. PCA will allow us to plot our samples in the “VFA” space so we can see if treatments, time or replicate cluster.</p>
+<section id="view-the-relationship-between-samples-using-pca" class="level3"><h3 class="anchored" data-anchor-id="view-the-relationship-between-samples-using-pca">View the relationship between samples using PCA</h3>
+<p>We have 8 VFA in our dataset. PCA will allow us to plot our samples in the “VFA” space so we can see if treatments, time or replicate cluster.</p>
 <p>However, PCA expects a matrix with samples in rows and VFA, the variables, in columns. We will need to select the columns we need and pivot wider. Then convert to a matrix.</p>
 <p>🎬</p>
 <div class="cell">
@@ -1253,7 +1271,7 @@ <h1 class="title">Kelly</h1>
 </div>
 </div>
 <p>replicates are similar at the same time and treatment especially early as we might expect. PC is essentially an axis of time.</p>
-</section><section id="visualise-the-vfa-concentration-using-a-heatmap" class="level2"><h2 class="anchored" data-anchor-id="visualise-the-vfa-concentration-using-a-heatmap">Visualise the VFA concentration using a heatmap</h2>
+</section><section id="visualise-the-vfa-concentration-using-a-heatmap" class="level3"><h3 class="anchored" data-anchor-id="visualise-the-vfa-concentration-using-a-heatmap">Visualise the VFA concentration using a heatmap</h3>
 <p>We are going to create an interactive heatmap with the <strong><code>heatmaply</code></strong> <span class="citation" data-cites="heatmaply">(<a href="#ref-heatmaply" role="doc-biblioref">Galili et al. 2017</a>)</span> package. <strong><code>heatmaply</code></strong> takes a matrix as input so we can use <code>mat</code></p>
 <p>🎬 Set the rownames to the sample id whihcih is combination of <code>sample_replicate</code> and <code>time_day</code>:</p>
 <div class="cell">
@@ -1282,12 +1300,18 @@ <h1 class="title">Kelly</h1>
 <span>          labRow <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/colnames.html">rownames</a></span><span class="op">(</span><span class="va">mat</span><span class="op">)</span>,</span>
 <span>          heatmap_layers <span class="op">=</span> <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/theme.html">theme</a></span><span class="op">(</span>axis.line <span class="op">=</span> <span class="fu"><a href="https://ggplot2.tidyverse.org/reference/element.html">element_blank</a></span><span class="op">(</span><span class="op">)</span><span class="op">)</span><span class="op">)</span></span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
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 </div>
 </div>
 <p>The heatmap will open in the viewer pane (rather than the plot pane) because it is html. You can “Show in a new window” to see it in a larger format. You can also zoom in and out and pan around the heatmap and download it as a png. You might feel the colour bars is not adding much to the plot. You can remove it by setting <code>hide_colorbar = TRUE,</code> in the <code><a href="https://talgalili.github.io/heatmaply/reference/heatmaply.html">heatmaply()</a></code> function.</p>
 <p>One of the NC replicates at time = 22 is very different from the other replicates. The CN10 treatments cluster together at high time points. CN10 samples are more similar to NC samples early on. Most of the VFAs behave similarly with highest values later in the experiment for CN10 but isohexanoate and hexanoate differ. The difference might be because isohexanoate is especially low in the NC replicates at time = 1 and hexanoate is especially high in the NC replicate 2 at time = 22</p>
+</section></section><section id="calculate-the-flux---pending." class="level2"><h2 class="anchored" data-anchor-id="calculate-the-flux---pending.">4. Calculate the flux - pending.</h2>
+<p>Calculate the flux(change in VFA concentration over a period of time, divided by weight or volume of material) of each VFA, by mM and by weight.</p>
+<p>I’ve requested clarification: <em>for the flux measurements, do they need graphs of the rate of change wrt time? And is the sludge volume going to be a constant for all samples or something they measure and varies by vial?</em></p>
+</section><section id="graph-and-extract-the-reaction-rate---pending" class="level2"><h2 class="anchored" data-anchor-id="graph-and-extract-the-reaction-rate---pending">5. Graph and extract the reaction rate - pending</h2>
+<p>Graph and extract the reaction rate assuming a first order chemical/biological reaction and an exponential falloff rate</p>
+<p>I’ve requested clarification: <em>for the nonlinear least squares curve fitting, I assume x is time but I’m not clear what the Y variable is - concentration? or change in concentration? or rate of change of concentration?</em></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><section id="references" class="level1">
 
diff --git a/omics/omics.html b/omics/omics.html
index 02a0032..7e7720b 100644
--- a/omics/omics.html
+++ b/omics/omics.html
@@ -341,7 +341,7 @@ <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">15 December, 2023</p>
+      <p class="date">18 December, 2023</p>
     </div>
   </div>
   
diff --git a/omics/week-3/overview.html b/omics/week-3/overview.html
index 487d575..218a501 100644
--- a/omics/week-3/overview.html
+++ b/omics/week-3/overview.html
@@ -319,7 +319,7 @@ <h1 class="title">Overview</h1>
     <div>
     <div class="quarto-title-meta-heading">Published</div>
     <div class="quarto-title-meta-contents">
-      <p class="date">15 December, 2023</p>
+      <p class="date">18 December, 2023</p>
     </div>
   </div>
   
diff --git a/omics/week-3/study_after_workshop.html b/omics/week-3/study_after_workshop.html
index 40e6ad5..1ed5bd3 100644
--- a/omics/week-3/study_after_workshop.html
+++ b/omics/week-3/study_after_workshop.html
@@ -312,7 +312,7 @@ <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">15 December, 2023</p>
+      <p class="date">18 December, 2023</p>
     </div>
   </div>
   
diff --git a/omics/week-3/study_before_workshop.html b/omics/week-3/study_before_workshop.html
index 03848e8..8a34ce9 100644
--- a/omics/week-3/study_before_workshop.html
+++ b/omics/week-3/study_before_workshop.html
@@ -384,7 +384,7 @@ <h1 class="title">Independent Study to prepare for workshop</h1>
 </div>
 </div>
 
-  <p class="date">15 December, 2023</p>
+  <p class="date">18 December, 2023</p>
 </section>
 <section id="overview" class="slide level2">
 <h2>Overview</h2>
diff --git a/omics/week-3/workshop.html b/omics/week-3/workshop.html
index 1a09757..26f76d8 100644
--- a/omics/week-3/workshop.html
+++ b/omics/week-3/workshop.html
@@ -406,7 +406,7 @@ <h1 class="title">Workshop</h1>
     <div>
     <div class="quarto-title-meta-heading">Published</div>
     <div class="quarto-title-meta-contents">
-      <p class="date">15 December, 2023</p>
+      <p class="date">18 December, 2023</p>
     </div>
   </div>
   
diff --git a/omics/week-4/overview.html b/omics/week-4/overview.html
index 52b8d0d..a58d57e 100644
--- a/omics/week-4/overview.html
+++ b/omics/week-4/overview.html
@@ -339,7 +339,7 @@ <h1 class="title">Overview</h1>
     <div>
     <div class="quarto-title-meta-heading">Published</div>
     <div class="quarto-title-meta-contents">
-      <p class="date">15 December, 2023</p>
+      <p class="date">18 December, 2023</p>
     </div>
   </div>
   
diff --git a/omics/week-4/study_after_workshop.html b/omics/week-4/study_after_workshop.html
index 5c54c71..6039d67 100644
--- a/omics/week-4/study_after_workshop.html
+++ b/omics/week-4/study_after_workshop.html
@@ -312,7 +312,7 @@ <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">15 December, 2023</p>
+      <p class="date">18 December, 2023</p>
     </div>
   </div>
   
diff --git a/omics/week-4/study_before_workshop.html b/omics/week-4/study_before_workshop.html
index 021e2f3..421b49e 100644
--- a/omics/week-4/study_before_workshop.html
+++ b/omics/week-4/study_before_workshop.html
@@ -442,7 +442,7 @@
 </div>
 </div>
 
-  <p class="date">15 December, 2023</p>
+  <p class="date">18 December, 2023</p>
 </section><section id="overview" class="slide level2"><h2>Overview</h2>
 <p>In these slides we will:</p>
 <div>
diff --git a/omics/week-4/workshop.html b/omics/week-4/workshop.html
index fb3793a..c88a5d7 100644
--- a/omics/week-4/workshop.html
+++ b/omics/week-4/workshop.html
@@ -379,7 +379,7 @@ <h1 class="title">Workshop</h1>
     <div>
     <div class="quarto-title-meta-heading">Published</div>
     <div class="quarto-title-meta-contents">
-      <p class="date">15 December, 2023</p>
+      <p class="date">18 December, 2023</p>
     </div>
   </div>
   
diff --git a/omics/week-5/figures/prog-hspc-volcano.png b/omics/week-5/figures/prog-hspc-volcano.png
index 781e6ab..a3ce321 100644
Binary files a/omics/week-5/figures/prog-hspc-volcano.png and b/omics/week-5/figures/prog-hspc-volcano.png differ
diff --git a/omics/week-5/overview.html b/omics/week-5/overview.html
index 79adba6..869ddec 100644
--- a/omics/week-5/overview.html
+++ b/omics/week-5/overview.html
@@ -320,7 +320,7 @@ <h1 class="title">Overview</h1>
     <div>
     <div class="quarto-title-meta-heading">Published</div>
     <div class="quarto-title-meta-contents">
-      <p class="date">15 December, 2023</p>
+      <p class="date">18 December, 2023</p>
     </div>
   </div>
   
diff --git a/omics/week-5/study_after_workshop.html b/omics/week-5/study_after_workshop.html
index a819a44..861e59e 100644
--- a/omics/week-5/study_after_workshop.html
+++ b/omics/week-5/study_after_workshop.html
@@ -312,7 +312,7 @@ <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">15 December, 2023</p>
+      <p class="date">18 December, 2023</p>
     </div>
   </div>
   
diff --git a/omics/week-5/study_before_workshop.html b/omics/week-5/study_before_workshop.html
index b9c9bec..ab4879f 100644
--- a/omics/week-5/study_before_workshop.html
+++ b/omics/week-5/study_before_workshop.html
@@ -442,7 +442,7 @@
 </div>
 </div>
 
-  <p class="date">15 December, 2023</p>
+  <p class="date">18 December, 2023</p>
 </section><section id="overview" class="slide level2"><h2>Overview</h2>
 <p>In these slides we will:</p>
 <div>
diff --git a/omics/week-5/workshop.html b/omics/week-5/workshop.html
index 35a5709..7806769 100644
--- a/omics/week-5/workshop.html
+++ b/omics/week-5/workshop.html
@@ -384,7 +384,7 @@ <h1 class="title">Workshop</h1>
     <div>
     <div class="quarto-title-meta-heading">Published</div>
     <div class="quarto-title-meta-contents">
-      <p class="date">15 December, 2023</p>
+      <p class="date">18 December, 2023</p>
     </div>
   </div>
   
@@ -725,8 +725,8 @@ <h1 class="title">Workshop</h1>
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 <p>On the vertical axis are genes which are differentially expressed at the 0.01 level. On the horizontal axis are samples. We can see that the FGF-treated samples cluster together and the control samples cluster together. We can also see two clusters of genes; one of these shows genes upregulated (more yellow) in the FGF-treated samples (the pink cluster) and the other shows genes down regulated (more blue, the blue cluster) in the FGF-treated samples.</p>
@@ -1040,8 +1040,8 @@ <h1 class="title">Workshop</h1>
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Bsg<br>column: HSPC_422<br>value:  3.028e-02","row: Bsg<br>column: HSPC_275<br>value:  9.345e-01","row: Bsg<br>column: Prog_341<br>value:  5.513e-01","row: Bsg<br>column: Prog_829<br>value:  7.461e-01","row: Bsg<br>column: Prog_716<br>value: -6.037e-01","row: Bsg<br>column: HSPC_325<br>value:  4.337e-01","row: Bsg<br>column: HSPC_550<br>value: -4.899e-01","row: Bsg<br>column: HSPC_280<br>value:  2.873e-01","row: Bsg<br>column: HSPC_774<br>value: -1.036e+00","row: Bsg<br>column: HSPC_409<br>value:  2.567e-01","row: Bsg<br>column: HSPC_523<br>value: -3.505e+00","row: Bsg<br>column: Prog_040<br>value: -1.001e-01","row: Bsg<br>column: HSPC_806<br>value: -1.599e+00","row: Bsg<br>column: HSPC_575<br>value: -1.520e-01","row: Bsg<br>column: HSPC_530<br>value:  3.487e-01","row: Bsg<br>column: HSPC_676<br>value: -2.876e-01","row: Bsg<br>column: HSPC_543<br>value: -4.312e-01","row: Bsg<br>column: HSPC_631<br>value:  1.729e-01","row: Bsg<br>column: HSPC_075<br>value: -8.817e-01","row: Bsg<br>column: HSPC_706<br>value:  5.519e-01","row: Bsg<br>column: HSPC_819<br>value: -2.955e-01","row: Bsg<br>column: HSPC_589<br>value:  3.214e-01","row: Bsg<br>column: HSPC_496<br>value: -2.485e+00","row: Bsg<br>column: HSPC_122<br>value: -1.283e-01","row: Bsg<br>column: HSPC_018<br>value:  8.126e-02","row: Bsg<br>column: HSPC_486<br>value: -7.107e-01"],["row: Ncln<br>column: HSPC_232<br>value: -6.230e-01","row: Ncln<br>column: Prog_580<br>value:  1.145e+00","row: Ncln<br>column: Prog_088<br>value:  8.153e-01","row: Ncln<br>column: Prog_145<br>value: -1.202e+00","row: Ncln<br>column: Prog_707<br>value: -1.212e+00","row: Ncln<br>column: Prog_085<br>value: -1.595e+00","row: Ncln<br>column: Prog_318<br>value: -3.595e-01","row: Ncln<br>column: Prog_831<br>value:  8.253e-01","row: Ncln<br>column: Prog_430<br>value:  1.132e+00","row: Ncln<br>column: Prog_081<br>value:  8.523e-01","row: Ncln<br>column: Prog_466<br>value:  3.730e-01","row: Ncln<br>column: Prog_592<br>value:  1.532e+00","row: Ncln<br>column: Prog_697<br>value:  7.717e-01","row: Ncln<br>column: Prog_067<br>value:  1.924e-01","row: Ncln<br>column: Prog_254<br>value:  8.712e-01","row: Ncln<br>column: Prog_082<br>value:  7.435e-01","row: Ncln<br>column: Prog_842<br>value:  5.001e-01","row: Ncln<br>column: Prog_188<br>value:  2.219e-01","row: Ncln<br>column: Prog_239<br>value:  7.811e-01","row: Ncln<br>column: Prog_272<br>value: -1.068e+00","row: Ncln<br>column: Prog_241<br>value:  1.422e+00","row: Ncln<br>column: Prog_555<br>value: -1.429e+00","row: Ncln<br>column: Prog_607<br>value:  8.204e-01","row: Ncln<br>column: HSPC_110<br>value: -1.181e+00","row: Ncln<br>column: HSPC_773<br>value: -1.037e+00","row: Ncln<br>column: Prog_726<br>value:  1.003e+00","row: Ncln<br>column: Prog_362<br>value:  1.037e+00","row: Ncln<br>column: Prog_450<br>value:  1.271e+00","row: Ncln<br>column: HSPC_488<br>value: -7.275e-01","row: Ncln<br>column: HSPC_524<br>value:  7.040e-02","row: Ncln<br>column: 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-1.813e+00","row: Ncln<br>column: HSPC_018<br>value:  6.805e-01","row: Ncln<br>column: HSPC_486<br>value: -1.339e+00"],["row: Atp1b3<br>column: HSPC_232<br>value:  6.040e-01","row: Atp1b3<br>column: Prog_580<br>value:  3.088e-01","row: Atp1b3<br>column: Prog_088<br>value:  1.114e+00","row: Atp1b3<br>column: Prog_145<br>value:  9.341e-01","row: Atp1b3<br>column: Prog_707<br>value: -1.281e+00","row: Atp1b3<br>column: Prog_085<br>value: -2.133e+00","row: Atp1b3<br>column: Prog_318<br>value:  5.830e-01","row: Atp1b3<br>column: Prog_831<br>value:  1.905e-02","row: Atp1b3<br>column: Prog_430<br>value:  6.245e-01","row: Atp1b3<br>column: Prog_081<br>value: -5.726e-01","row: Atp1b3<br>column: Prog_466<br>value:  2.049e-01","row: Atp1b3<br>column: Prog_592<br>value:  7.543e-01","row: Atp1b3<br>column: Prog_697<br>value:  5.459e-01","row: Atp1b3<br>column: Prog_067<br>value:  6.642e-01","row: Atp1b3<br>column: Prog_254<br>value:  9.502e-01","row: Atp1b3<br>column: Prog_082<br>value:  7.024e-01","row: Atp1b3<br>column: Prog_842<br>value:  7.805e-01","row: Atp1b3<br>column: Prog_188<br>value:  6.699e-01","row: Atp1b3<br>column: Prog_239<br>value:  9.325e-01","row: Atp1b3<br>column: Prog_272<br>value:  1.188e+00","row: Atp1b3<br>column: Prog_241<br>value:  3.660e-01","row: Atp1b3<br>column: Prog_555<br>value: -1.695e+00","row: Atp1b3<br>column: Prog_607<br>value:  6.383e-01","row: Atp1b3<br>column: HSPC_110<br>value:  6.698e-01","row: Atp1b3<br>column: HSPC_773<br>value:  4.830e-02","row: Atp1b3<br>column: Prog_726<br>value:  1.051e-01","row: Atp1b3<br>column: Prog_362<br>value: -1.205e+00","row: Atp1b3<br>column: Prog_450<br>value:  4.255e-01","row: Atp1b3<br>column: HSPC_488<br>value: -3.577e-01","row: Atp1b3<br>column: HSPC_524<br>value: -1.487e-01","row: Atp1b3<br>column: HSPC_532<br>value:  1.580e-01","row: Atp1b3<br>column: Prog_529<br>value:  3.786e-01","row: Atp1b3<br>column: Prog_472<br>value:  8.514e-01","row: Atp1b3<br>column: Prog_372<br>value:  4.853e-01","row: Atp1b3<br>column: Prog_330<br>value:  8.474e-01","row: Atp1b3<br>column: HSPC_395<br>value: -1.960e+00","row: Atp1b3<br>column: HSPC_199<br>value: -1.300e+00","row: Atp1b3<br>column: Prog_437<br>value: -2.142e+00","row: Atp1b3<br>column: Prog_675<br>value: -1.877e+00","row: Atp1b3<br>column: Prog_141<br>value:  3.454e-01","row: Atp1b3<br>column: HSPC_044<br>value: -9.832e-01","row: Atp1b3<br>column: Prog_579<br>value:  3.320e-01","row: Atp1b3<br>column: HSPC_400<br>value:  1.417e+00","row: Atp1b3<br>column: HSPC_492<br>value:  8.593e-01","row: Atp1b3<br>column: HSPC_422<br>value: -2.018e-01","row: Atp1b3<br>column: HSPC_275<br>value:  1.328e+00","row: Atp1b3<br>column: Prog_341<br>value:  1.308e-01","row: Atp1b3<br>column: Prog_829<br>value:  9.985e-01","row: Atp1b3<br>column: Prog_716<br>value:  8.722e-01","row: Atp1b3<br>column: HSPC_325<br>value: -8.391e-02","row: Atp1b3<br>column: HSPC_550<br>value:  2.678e-01","row: Atp1b3<br>column: HSPC_280<br>value:  1.088e+00","row: Atp1b3<br>column: HSPC_774<br>value:  7.349e-01","row: Atp1b3<br>column: HSPC_409<br>value:  2.148e-01","row: Atp1b3<br>column: HSPC_523<br>value:  1.279e+00","row: Atp1b3<br>column: Prog_040<br>value: -7.599e-01","row: Atp1b3<br>column: HSPC_806<br>value: -1.534e+00","row: Atp1b3<br>column: HSPC_575<br>value:  1.897e-01","row: Atp1b3<br>column: HSPC_530<br>value: -1.560e+00","row: Atp1b3<br>column: HSPC_676<br>value: -1.446e+00","row: Atp1b3<br>column: HSPC_543<br>value: -1.692e+00","row: Atp1b3<br>column: HSPC_631<br>value:  6.002e-01","row: Atp1b3<br>column: HSPC_075<br>value: -1.743e+00","row: Atp1b3<br>column: HSPC_706<br>value:  5.284e-01","row: Atp1b3<br>column: HSPC_819<br>value: -1.287e+00","row: Atp1b3<br>column: HSPC_589<br>value: -1.076e+00","row: Atp1b3<br>column: HSPC_496<br>value: -1.777e+00","row: Atp1b3<br>column: HSPC_122<br>value: -2.435e-01","row: Atp1b3<br>column: HSPC_018<br>value:  6.114e-01","row: Atp1b3<br>column: HSPC_486<br>value:  7.080e-01"],["row: Cd44<br>column: HSPC_232<br>value: -1.600e+00","row: Cd44<br>column: Prog_580<br>value: -2.082e+00","row: Cd44<br>column: Prog_088<br>value: -7.477e-01","row: Cd44<br>column: Prog_145<br>value:  8.185e-01","row: Cd44<br>column: Prog_707<br>value: -1.138e-01","row: Cd44<br>column: Prog_085<br>value:  2.634e-01","row: Cd44<br>column: Prog_318<br>value:  4.317e-01","row: Cd44<br>column: Prog_831<br>value:  1.973e-01","row: Cd44<br>column: Prog_430<br>value:  1.098e+00","row: Cd44<br>column: Prog_081<br>value:  1.636e-01","row: Cd44<br>column: Prog_466<br>value:  8.196e-01","row: Cd44<br>column: Prog_592<br>value:  1.008e+00","row: Cd44<br>column: Prog_697<br>value:  1.132e-01","row: Cd44<br>column: Prog_067<br>value:  9.016e-01","row: Cd44<br>column: Prog_254<br>value:  6.361e-01","row: Cd44<br>column: Prog_082<br>value: -7.505e-01","row: Cd44<br>column: Prog_842<br>value:  7.222e-01","row: Cd44<br>column: Prog_188<br>value:  5.095e-01","row: Cd44<br>column: Prog_239<br>value:  6.522e-01","row: Cd44<br>column: Prog_272<br>value:  3.543e-01","row: Cd44<br>column: Prog_241<br>value:  5.261e-01","row: Cd44<br>column: Prog_555<br>value:  5.843e-01","row: Cd44<br>column: Prog_607<br>value:  7.720e-01","row: Cd44<br>column: HSPC_110<br>value:  6.098e-01","row: Cd44<br>column: HSPC_773<br>value:  6.741e-01","row: Cd44<br>column: Prog_726<br>value: -1.252e+00","row: Cd44<br>column: Prog_362<br>value:  8.317e-01","row: Cd44<br>column: Prog_450<br>value:  9.862e-01","row: Cd44<br>column: HSPC_488<br>value: -1.236e+00","row: Cd44<br>column: HSPC_524<br>value:  5.723e-01","row: Cd44<br>column: HSPC_532<br>value:  2.021e-01","row: Cd44<br>column: Prog_529<br>value:  1.427e-01","row: Cd44<br>column: Prog_472<br>value:  3.498e-01","row: Cd44<br>column: Prog_372<br>value: -1.979e+00","row: Cd44<br>column: Prog_330<br>value: -1.977e+00","row: Cd44<br>column: HSPC_395<br>value: -2.058e+00","row: Cd44<br>column: HSPC_199<br>value: -1.315e+00","row: Cd44<br>column: Prog_437<br>value: -3.028e-01","row: Cd44<br>column: Prog_675<br>value:  2.023e-01","row: Cd44<br>column: Prog_141<br>value:  9.033e-02","row: Cd44<br>column: HSPC_044<br>value:  4.401e-02","row: Cd44<br>column: Prog_579<br>value: -1.722e+00","row: Cd44<br>column: HSPC_400<br>value: -1.061e+00","row: Cd44<br>column: HSPC_492<br>value:  1.162e+00","row: Cd44<br>column: HSPC_422<br>value:  1.127e+00","row: Cd44<br>column: HSPC_275<br>value: -1.222e-01","row: Cd44<br>column: Prog_341<br>value: -1.532e+00","row: Cd44<br>column: Prog_829<br>value:  5.543e-03","row: Cd44<br>column: Prog_716<br>value:  1.058e+00","row: Cd44<br>column: HSPC_325<br>value:  9.504e-01","row: Cd44<br>column: HSPC_550<br>value:  6.701e-02","row: Cd44<br>column: HSPC_280<br>value:  1.329e+00","row: Cd44<br>column: HSPC_774<br>value:  6.526e-01","row: Cd44<br>column: HSPC_409<br>value:  1.125e+00","row: Cd44<br>column: HSPC_523<br>value:  7.223e-01","row: Cd44<br>column: Prog_040<br>value: 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 <p>It will take a minute to run and display. On the vertical axis are genes which are differentially expressed at the 0.01 level. On the horizontal axis are cells. We can see that cells of the same type don’t cluster that well together. We can also see two clusters of genes but the pattern of gene is not as clear as it was for the frogs and the correspondence with the cell clusters is not as strong.</p>
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+++ b/search.json
@@ -170,65 +170,86 @@
   {
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-    "title": "Kelly",
+    "title": "Workflow for VFA analysis",
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-    "text": "VFAs from AD vials\n\nTwo treatments: straw (CN10) and water (NC)\n10 time points: 1, 3, 5, 9, 11, 13, 16, 18, 20, 22\nthree replicates per treatment per time point\n2 x 10 x 3 = 60 groups\n8 VFA with concentration in mM (millimolar): acetate, propanoate, isobutyrate, butyrate, isopentanoate, pentanoate, isohexanoate, hexanoate\n\nTo calculate from this data\n\nRecalculate the data into grams per litre\n\nconvert to molar: 1 millimolar to molar = 0.001 molar\nmultiply by the molecular weight of each VFA\n\n\nCalculate Change in VFA g/l with time\nCalculate the percent representation of each VFA, by mM and by weight\n\n\n8 VFA in mM for 60 samples vfa.csv\nMolecular weights for each VFA in grams per mole mol_wt.txt\n\n\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 analysis.R to carry out the rest of the work.\n🎬 Load tidyverse (Wickham et al. 2019) for importing, summarising, plotting and filtering.\n\nlibrary(tidyverse)\n\n\n🎬 Save the files to data-raw. Open them and examine them. You may want to use Excel for the csv file.\n🎬 Answer the following questions:\n\nWhat is in the rows and columns of each file?\nHow many rows and columns are there in each file?\nHow are the data organised ?\n\n🎬 Import\n\nvfa_cummul &lt;- read_csv(\"data-raw/vfa.csv\") |&gt; janitor::clean_names()\n\n🎬 Split treatment and replicate to separate columns so there is a treatment column:\n\nvfa_cummul &lt;- vfa_cummul |&gt; \n  separate(col = sample_replicate, \n           into = c(\"treatment\", \"replicate\"), \n           sep = \"-\",\n           remove = FALSE)\n\nThe provided data is cumulative/absolute. We need to calculate the change in VFA with time. There is a function, lag() that will help us do this. It will take the previous value and subtract it from the current value. We need to do that separately for each sample_replicate so we need to group by sample_replicate first. We also need to make sure the data is in the right order so we will arrange by sample_replicate and time_day.\n🎬 Create dataframe for the change in VFA\n\nvfa_delta &lt;- vfa_cummul |&gt; \n    group_by(sample_replicate)  |&gt; \n    arrange(sample_replicate, time_day) |&gt;\n    mutate(acetate = acetate - lag(acetate),\n           propanoate = propanoate - lag(propanoate),\n           isobutyrate = isobutyrate - lag(isobutyrate),\n           butyrate = butyrate - lag(butyrate),\n           isopentanoate = isopentanoate - lag(isopentanoate),\n           pentanoate = pentanoate - lag(pentanoate),\n           isohexanoate = isohexanoate - lag(isohexanoate),\n           hexanoate = hexanoate - lag(hexanoate))\n\nNow we have two dataframes, one for the cumulative data and one for the change in VFA.\nTo make conversions from mM to g/l we need to do mM * 0.001 * MW. We will import the molecular weight data, pivot the VFA data to long format and join the molecular weight data to the VFA data. Then we can calculate the g/l. We will do this for both the cumulative and delta dataframes.\n🎬 import molecular weight data\n\nmol_wt &lt;- read_table(\"data-raw/mol_wt.txt\") |&gt;\n  mutate(vfa = tolower(vfa))\n\n🎬 Pivot the cumulative data to long format:\nView vfa_cummul to check you understand what you have done.\n🎬 Join molecular weight to data and calculate g/l (mutate to convert to g/l * 0.001 * MW):\n\nvfa_cummul &lt;- vfa_cummul |&gt; \n  left_join(mol_wt, by = \"vfa\") |&gt;\n  mutate(conc_g_l = conc_mM * 0.001 * mw)\n\nView vfa_cummul to check you understand what you have done.\n🎬 Add a column which is the percent representation of each VFA for mM and g/l:\n\nvfa_cummul &lt;- vfa_cummul |&gt; \n  group_by(sample_replicate, time_day) |&gt; \n  mutate(percent_conc_g_l = conc_g_l / sum(conc_g_l) * 100,\n         percent_conc_mM = conc_mM / sum(conc_mM) * 100)\n\n🎬 Pivot the change data, delta_vfa to long format:\nView vfa_delta to check it looks like vfa_cummul\n🎬 Join molecular weight to data and calculate g/l (mutate to convert to g/l * 0.001 * MW):\n\n🎬 Make summary data for graphing\n\nvfa_cummul_summary &lt;- vfa_cummul |&gt; \n  group_by(treatment, time_day, vfa) |&gt; \n  summarise(mean_g_l = mean(conc_g_l),\n            se_g_l = sd(conc_g_l)/sqrt(length(conc_g_l)),\n            mean_mM = mean(conc_mM),\n            se_mM = sd(conc_mM)/sqrt(length(conc_mM))) |&gt; \n  ungroup()\n\n\nvfa_delta_summary &lt;- vfa_delta |&gt; \n  group_by(treatment, time_day, vfa) |&gt; \n  summarise(mean_g_l = mean(conc_g_l),\n            se_g_l = sd(conc_g_l)/sqrt(length(conc_g_l)),\n            mean_mM = mean(conc_mM),\n            se_mM = sd(conc_mM)/sqrt(length(conc_mM))) |&gt; \n  ungroup()\n\n🎬 Graph the cumulative data, grams per litre:\n\nvfa_cummul_summary |&gt; \n  ggplot(aes(x = time_day, colour = vfa)) +\n  geom_line(aes(y = mean_g_l), \n            linewidth = 1) +\n  geom_errorbar(aes(ymin = mean_g_l - se_g_l,\n                    ymax = mean_g_l + se_g_l),\n                width = 0.5, \n                show.legend = F,\n                linewidth = 1) +\n  scale_color_viridis_d(name = NULL) +\n  scale_x_continuous(name = \"Time (days)\") +\n  scale_y_continuous(name = \"Mean VFA concentration (g/l)\") +\n  theme_bw() +\n  facet_wrap(~treatment) +\n  theme(strip.background = element_blank())\n\n\n\n\n🎬 Graph the change data, grams per litre:\n\nvfa_delta_summary |&gt; \n  ggplot(aes(x = time_day, colour = vfa)) +\n  geom_line(aes(y = mean_g_l), \n            linewidth = 1) +\n  geom_errorbar(aes(ymin = mean_g_l - se_g_l,\n                    ymax = mean_g_l + se_g_l),\n                width = 0.5, \n                show.legend = F,\n                linewidth = 1) +\n  scale_color_viridis_d(name = NULL) +\n  scale_x_continuous(name = \"Time (days)\") +\n  scale_y_continuous(name = \"Mean change in VFA concentration (g/l)\") +\n  theme_bw() +\n  facet_wrap(~treatment) +\n  theme(strip.background = element_blank())\n\n\n\n\n🎬 Graph the mean percent representation of each VFA g/l. Note geom_col() will plot proportion if we setposition = \"fill\"\n\nvfa_cummul_summary |&gt; \n  ggplot(aes(x = time_day, y = mean_g_l, fill = vfa)) +\n  geom_col(position = \"fill\") +\n  scale_fill_viridis_d(name = NULL) +\n  scale_x_continuous(name = \"Time (days)\") +\n  scale_y_continuous(name = \"Mean Proportion VFA\") +\n  theme_bw() +\n  facet_wrap(~treatment) +\n  theme(strip.background = element_blank())\n\n\n\n\n\nWe have 8 genes in our dataset. PCA will allow us to plot our samples in the “VFA” space so we can see if treatments, time or replicate cluster.\nHowever, PCA expects a matrix with samples in rows and VFA, the variables, in columns. We will need to select the columns we need and pivot wider. Then convert to a matrix.\n🎬\n\nvfa_cummul_pca &lt;- vfa_cummul |&gt; \n  select(sample_replicate, \n         treatment, \n         replicate, \n         time_day, \n         vfa, \n         conc_g_l) |&gt; \n  pivot_wider(names_from = vfa, \n              values_from = conc_g_l)\n\n\nmat &lt;- vfa_cummul_pca |&gt; \n  ungroup() |&gt;\n  select(-sample_replicate, \n         -treatment, \n         -replicate, \n         -time_day) |&gt; \n  as.matrix()\n\n🎬 Perform PCA on the matrix:\n\npca &lt;- mat |&gt;\n  prcomp(scale. = TRUE, \n         rank. = 4) \n\nThe scale. argument tells prcomp() to scale the data to have a mean of 0 and a standard deviation of 1. The rank. argument tells prcomp() to only calculate the first 4 principal components. This is useful for visualisation as we can only plot in 2 or 3 dimensions. We can see the results of the PCA by viewing the summary() of the pca object.\n\nsummary(pca)\n\nImportance of first k=4 (out of 8) components:\n                          PC1    PC2     PC3     PC4\nStandard deviation     2.4977 0.9026 0.77959 0.45567\nProportion of Variance 0.7798 0.1018 0.07597 0.02595\nCumulative Proportion  0.7798 0.8816 0.95760 0.98355\n\n\nThe Proportion of Variance tells us how much of the variance is explained by each component. We can see that the first component explains 0.7798 of the variance, the second 0.1018, and the third 0.07597. Together the first three components explain nearly 96% of the total variance in the data. Plotting PC1 against PC2 will capture about 78% of the variance which is likely much better than we would get plotting any two VFA against each other. To plot the PC1 against PC2 we will need to extract the PC1 and PC2 score from the pca object and add labels for the samples.\n🎬 Create a dataframe of the PC1 and PC2 scores which are in pca$x and add the sample information from vfa_cummul_pca:\n\npca_labelled &lt;- data.frame(pca$x,\n                           sample_replicate = vfa_cummul_pca$sample_replicate,\n                           treatment = vfa_cummul_pca$treatment,\n                           replicate = vfa_cummul_pca$replicate,\n                           time_day = vfa_cummul_pca$time_day) \n\nThe dataframe should look like this:\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nPC1\nPC2\nPC3\nPC4\nsample_replicate\ntreatment\nreplicate\ntime_day\n\n\n\n-2.9592362\n0.6710553\n0.0068846\n-0.4453904\nCN10-1\nCN10\n1\n1\n\n\n-2.7153060\n0.7338367\n-0.2856872\n-0.2030110\nCN10-2\nCN10\n2\n1\n\n\n-2.7423102\n0.8246832\n-0.4964249\n-0.1434490\nCN10-3\nCN10\n3\n1\n\n\n-1.1909064\n-1.0360724\n1.1249513\n-0.7360599\nCN10-1\nCN10\n1\n3\n\n\n-1.3831563\n0.9572091\n-1.5561657\n0.0582755\nCN10-2\nCN10\n2\n3\n\n\n-1.1628940\n-0.0865412\n-0.6046780\n-0.1976743\nCN10-3\nCN10\n3\n3\n\n\n-0.2769661\n-0.2221055\n1.1579897\n-0.6079395\nCN10-1\nCN10\n1\n5\n\n\n0.3480962\n0.3612522\n0.5841649\n-0.0612366\nCN10-2\nCN10\n2\n5\n\n\n-0.7281116\n1.6179706\n-0.6430170\n0.0660727\nCN10-3\nCN10\n3\n5\n\n\n0.9333578\n-0.1339061\n1.0870945\n-0.4374103\nCN10-1\nCN10\n1\n9\n\n\n2.0277528\n0.6993342\n0.3850147\n0.0723540\nCN10-2\nCN10\n2\n9\n\n\n1.9931908\n0.5127260\n0.6605782\n0.1841974\nCN10-3\nCN10\n3\n9\n\n\n1.8365692\n-0.4189762\n0.7029015\n-0.3873133\nCN10-1\nCN10\n1\n11\n\n\n2.3313978\n0.3274834\n-0.0135608\n0.0264372\nCN10-2\nCN10\n2\n11\n\n\n1.5833035\n0.9263509\n-0.1909483\n0.1358320\nCN10-3\nCN10\n3\n11\n\n\n2.8498246\n0.3815854\n-0.4763500\n-0.0280281\nCN10-1\nCN10\n1\n13\n\n\n3.5652461\n-0.0836709\n-0.5948483\n-0.1612809\nCN10-2\nCN10\n2\n13\n\n\n4.1314944\n-1.2254642\n0.2699666\n-0.3152100\nCN10-3\nCN10\n3\n13\n\n\n3.7338024\n-0.6744610\n0.4344639\n-0.3736234\nCN10-1\nCN10\n1\n16\n\n\n3.6748427\n0.5202498\n-0.4333685\n-0.1607235\nCN10-2\nCN10\n2\n16\n\n\n3.9057053\n0.3599520\n-0.3049074\n0.0540037\nCN10-3\nCN10\n3\n16\n\n\n3.4561583\n-0.0996639\n0.4472090\n-0.0185889\nCN10-1\nCN10\n1\n18\n\n\n3.6354729\n0.3809673\n-0.0934957\n0.0018722\nCN10-2\nCN10\n2\n18\n\n\n2.9872250\n0.7890400\n-0.2361098\n-0.1628506\nCN10-3\nCN10\n3\n18\n\n\n3.3562231\n-0.2866224\n0.1331068\n-0.2056366\nCN10-1\nCN10\n1\n20\n\n\n3.2009943\n0.4795967\n-0.2092384\n-0.5962183\nCN10-2\nCN10\n2\n20\n\n\n3.9948127\n0.7772640\n-0.3181372\n0.1218382\nCN10-3\nCN10\n3\n20\n\n\n2.8874207\n0.4554681\n0.3106044\n-0.2220240\nCN10-1\nCN10\n1\n22\n\n\n3.6868864\n0.9681097\n-0.2174166\n-0.2246775\nCN10-2\nCN10\n2\n22\n\n\n4.8689622\n0.5218563\n-0.2906042\n0.3532981\nCN10-3\nCN10\n3\n22\n\n\n-3.8483418\n1.5205541\n-0.8809715\n-0.5306228\nNC-1\nNC\n1\n1\n\n\n-3.7653460\n1.5598499\n-1.0570798\n-0.4075397\nNC-2\nNC\n2\n1\n\n\n-3.8586309\n1.6044929\n-1.0936576\n-0.4292404\nNC-3\nNC\n3\n1\n\n\n-2.6934553\n-0.9198406\n0.7439841\n-0.9881115\nNC-1\nNC\n1\n3\n\n\n-2.5064076\n-1.0856761\n0.6334250\n-0.8999028\nNC-2\nNC\n2\n3\n\n\n-2.4097945\n-1.2731546\n1.1767665\n-0.8715948\nNC-3\nNC\n3\n3\n\n\n-3.0567309\n0.5804906\n-0.1391344\n-0.3701763\nNC-1\nNC\n1\n5\n\n\n-2.3511737\n-0.3692016\n0.7053757\n-0.3284113\nNC-2\nNC\n2\n5\n\n\n-2.6752311\n-0.0637855\n0.4692194\n-0.3841240\nNC-3\nNC\n3\n5\n\n\n-1.2335368\n-0.6717374\n0.2155285\n0.1060486\nNC-1\nNC\n1\n9\n\n\n-1.6550689\n0.1576557\n0.0687658\n0.2750388\nNC-2\nNC\n2\n9\n\n\n-0.8948103\n-0.8171884\n0.8062876\n0.5032756\nNC-3\nNC\n3\n9\n\n\n-1.2512737\n-0.4720993\n0.4071788\n0.4693106\nNC-1\nNC\n1\n11\n\n\n-1.8091407\n0.0552546\n0.0424090\n0.3918222\nNC-2\nNC\n2\n11\n\n\n-2.4225566\n0.4998948\n-0.1987773\n0.1959282\nNC-3\nNC\n3\n11\n\n\n-0.9193427\n-0.7741826\n0.0918984\n0.5089847\nNC-1\nNC\n1\n13\n\n\n-0.8800183\n-0.7850404\n0.0895146\n0.6050052\nNC-2\nNC\n2\n13\n\n\n-1.3075763\n-0.2525829\n-0.2993318\n0.5874269\nNC-3\nNC\n3\n13\n\n\n-0.9543813\n-0.3170305\n0.0885062\n0.7153071\nNC-1\nNC\n1\n16\n\n\n-0.4303679\n-0.9952374\n0.2038883\n0.8214647\nNC-2\nNC\n2\n16\n\n\n-0.9457300\n-0.7180646\n0.3081282\n0.6563748\nNC-3\nNC\n3\n16\n\n\n-1.3830063\n0.0614677\n-0.2805342\n0.5462137\nNC-1\nNC\n1\n18\n\n\n-0.7960522\n-0.5792768\n-0.0369684\n0.6621526\nNC-2\nNC\n2\n18\n\n\n-1.6822927\n0.1041656\n0.0634251\n0.4337240\nNC-3\nNC\n3\n18\n\n\n-1.3157478\n-0.0835664\n-0.1246253\n0.5599467\nNC-1\nNC\n1\n20\n\n\n-1.7425068\n0.3029227\n-0.0161466\n0.5134360\nNC-2\nNC\n2\n20\n\n\n-1.3970678\n-0.2923056\n0.4324586\n0.4765460\nNC-3\nNC\n3\n20\n\n\n-1.0777451\n-0.1232925\n0.2388682\n0.7585307\nNC-1\nNC\n1\n22\n\n\n0.4851039\n-4.1291445\n-4.0625050\n-0.4582436\nNC-2\nNC\n2\n22\n\n\n-1.0516226\n-0.7228479\n1.0641320\n0.4955951\nNC-3\nNC\n3\n22\n\n\n\n\n\n🎬 Plot PC1 against PC2 and colour by time and shape by treatment:\n\npca_labelled |&gt; \n  ggplot(aes(x = PC1, y = PC2, \n             colour = factor(time_day),\n             shape = treatment)) +\n  geom_point(size = 3) +\n  scale_colour_viridis_d(end = 0.95, begin = 0.15,\n                         name = \"Time\") +\n  scale_shape_manual(values = c(17, 19),\n                     name = NULL) +\n  theme_classic()\n\n\n\n\n🎬 Plot PC1 against PC2 and colour by time and facet treatment:\n\npca_labelled |&gt; \n  ggplot(aes(x = PC1, y = PC2, colour = factor(time_day))) +\n  geom_point(size = 3) +\n  scale_colour_viridis_d(end = 0.95, begin = 0.15,\n                         name = \"Time\") +\n  facet_wrap(~treatment, ncol = 1) +\n  theme_classic()\n\n\n\n\nreplicates are similar at the same time and treatment especially early as we might expect. PC is essentially an axis of time.\n\nWe are going to create an interactive heatmap with the heatmaply (Galili et al. 2017) package. heatmaply takes a matrix as input so we can use mat\n🎬 Set the rownames to the sample id whihcih is combination of sample_replicate and time_day:\n\nrownames(mat) &lt;- interaction(vfa_cummul_pca$sample_replicate, \n                             vfa_cummul_pca$time_day)\n\nYou might want to view the matrix by clicking on it in the environment pane.\n🎬 Load the heatmaply package:\n\nlibrary(heatmaply)\n\nWe need to tell the clustering algorithm how many clusters to create. We will set the number of clusters for the treatments to be 2 and the number of clusters for the vfa to be the same since it makes sense to see what clusters of genes correlate with the treatments.\n🎬 Set the number of clusters for the treatments and vfa:\n\nn_treatment_clusters &lt;- 2\nn_vfa_clusters &lt;- 2\n\n🎬 Create the heatmap:\n\nheatmaply(mat, \n          scale = \"column\",\n          k_col = n_vfa_clusters,\n          k_row = n_treatment_clusters,\n          fontsize_row = 7, fontsize_col = 10,\n          labCol = colnames(mat),\n          labRow = rownames(mat),\n          heatmap_layers = theme(axis.line = element_blank()))\n\n\n\n\n\nThe heatmap will open in the viewer pane (rather than the plot pane) because it is html. You can “Show in a new window” to see it in a larger format. You can also zoom in and out and pan around the heatmap and download it as a png. You might feel the colour bars is not adding much to the plot. You can remove it by setting hide_colorbar = TRUE, in the heatmaply() function.\nOne of the NC replicates at time = 22 is very different from the other replicates. The CN10 treatments cluster together at high time points. CN10 samples are more similar to NC samples early on. Most of the VFAs behave similarly with highest values later in the experiment for CN10 but isohexanoate and hexanoate differ. The difference might be because isohexanoate is especially low in the NC replicates at time = 1 and hexanoate is especially high in the NC replicate 2 at time = 22\nPages made with R (R Core Team 2023), Quarto (Allaire et al. 2022), knitr (Xie 2022), kableExtra (Zhu 2021)"
+    "text": "I have some data and information from Kelly. I have interpreted it and written some code to do the calculations.\nHowever, Kelly hasn’t had a chance to look at it yet so I am providing the exact information and data he supplied along with my suggested workflow based on my interpretation of the data and info.\n\nThe file is a CSV file, with some notes on top and the data in the following order, post notes and headers. Please note that all chemical data is in millimolar. There are 62 rows of actual data.\nSample Name – Replicate, Time (days), Acetate, Propanoate, Isobutyrate, Butyrate, Isopentanoate, Pentanoate, Isohexanoate, Hexanoate\nThe students should be able to transform the data from mM to mg/L, and to g/L. To do this they only need to multiply the molecular weight of the compound (listed in the notes in the file) by the concentration in mM to get mg/L. Obviously to get g/L they will just divide by 1000. They should be able to graph the VFA concentrations with time.\nThey should also be able to do a simple flux measurement, which is the change in VFA concentration over a period of time, divided by weight or volume of material. In this case it might be equal to == Delta(Acetate at 3 days - Acetate at 1 day)/Delta (3days - 1day)/50 mls sludge. This would provide a final flux with the units of mg acetate per ml sludge per day. Let me know if this isn’t clear.\nPerhaps more importantly they should be able to graph and extract the reaction rate, assuming a first order chemical/biological reaction and an exponential falloff rate. I found this as a starting point (https://martinlab.chem.umass.edu/r-fitting-data/) , but I assume Emma has something much more effective already in the pipeline.\n\nI created these two data files from the original.\n\n8 VFA in mM for 60 samples vfa.csv. There were 63 rows of data in the original file. There were no time 0 for one treatment and all values were zero for the other treatment so I removed those.\n\nTwo treatments: straw (CN10) and water (NC)\n10 time points: 1, 3, 5, 9, 11, 13, 16, 18, 20, 22\nthree replicates per treatment per time point\n2 x 10 x 3 = 60 groups\n8 VFA with concentration in mM (millimolar): acetate, propanoate, isobutyrate, butyrate, isopentanoate, pentanoate, isohexanoate, hexanoate\n\n\nMolecular weights for each VFA in grams per mole mol_wt.txt VFAs from AD vials\n\nWe need to:\n\nCalculate Change in VFA g/l with time\nRecalculate the data into grams per litre - convert to molar: 1 millimolar to molar = 0.001 molar - multiply by the molecular weight of each VFA\nCalculate the percent representation of each VFA, by mM and by weight\nCalculate the flux (change in VFA concentration over a period of time, divided by weight or volume of material) of each VFA, by mM and by weight\nGraph and extract the reaction rate, assuming a first order chemical/biological reaction and an exponential falloff rate\n\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 analysis.R to carry out the rest of the work.\n🎬 Load tidyverse (Wickham et al. 2019) for importing, summarising, plotting and filtering.\n\nlibrary(tidyverse)\n\n\n🎬 Save the files to data-raw. Open them and examine them. You may want to use Excel for the csv file.\n🎬 Answer the following questions:\n\nWhat is in the rows and columns of each file?\nHow many rows and columns are there in each file?\nHow are the data organised ?\n\n🎬 Import\n\nvfa_cummul &lt;- read_csv(\"data-raw/vfa.csv\") |&gt; janitor::clean_names()\n\n🎬 Split treatment and replicate to separate columns so there is a treatment column:\n\nvfa_cummul &lt;- vfa_cummul |&gt; \n  separate(col = sample_replicate, \n           into = c(\"treatment\", \"replicate\"), \n           sep = \"-\",\n           remove = FALSE)\n\nThe provided data is cumulative/absolute. We need to calculate the change in VFA with time. There is a function, lag() that will help us do this. It will take the previous value and subtract it from the current value. We need to do that separately for each sample_replicate so we need to group by sample_replicate first. We also need to make sure the data is in the right order so we will arrange by sample_replicate and time_day.\n\n🎬 Create dataframe for the change in VFA\n\nvfa_delta &lt;- vfa_cummul |&gt; \n    group_by(sample_replicate)  |&gt; \n    arrange(sample_replicate, time_day) |&gt;\n    mutate(acetate = acetate - lag(acetate),\n           propanoate = propanoate - lag(propanoate),\n           isobutyrate = isobutyrate - lag(isobutyrate),\n           butyrate = butyrate - lag(butyrate),\n           isopentanoate = isopentanoate - lag(isopentanoate),\n           pentanoate = pentanoate - lag(pentanoate),\n           isohexanoate = isohexanoate - lag(isohexanoate),\n           hexanoate = hexanoate - lag(hexanoate))\n\nNow we have two dataframes, one for the cumulative data and one for the change in VFA.\n\nTo make conversions from mM to g/l we need to do mM * 0.001 * MW. We will import the molecular weight data, pivot the VFA data to long format and join the molecular weight data to the VFA data. Then we can calculate the g/l. We will do this for both the cumulative and delta dataframes.\n🎬 import molecular weight data\n\nmol_wt &lt;- read_table(\"data-raw/mol_wt.txt\") |&gt;\n  mutate(vfa = tolower(vfa))\n\n🎬 Pivot the cumulative data to long format:\nView vfa_cummul to check you understand what you have done.\n🎬 Join molecular weight to data and calculate g/l (mutate to convert to g/l * 0.001 * MW):\n\nvfa_cummul &lt;- vfa_cummul |&gt; \n  left_join(mol_wt, by = \"vfa\") |&gt;\n  mutate(conc_g_l = conc_mM * 0.001 * mw)\n\nView vfa_cummul to check you understand what you have done.\nRepeat for the delta data.\n🎬 Pivot the change data, delta_vfa to long format:\nView vfa_delta to check it looks like vfa_cummul\n🎬 Join molecular weight to data and calculate g/l (mutate to convert to g/l * 0.001 * MW):\n\nby mM and by weight\n🎬 Add a column which is the percent representation of each VFA for mM and g/l:\n\nvfa_cummul &lt;- vfa_cummul |&gt; \n  group_by(sample_replicate, time_day) |&gt; \n  mutate(percent_conc_g_l = conc_g_l / sum(conc_g_l) * 100,\n         percent_conc_mM = conc_mM / sum(conc_mM) * 100)\n\n\n🎬 Make summary data for graphing\n\nvfa_cummul_summary &lt;- vfa_cummul |&gt; \n  group_by(treatment, time_day, vfa) |&gt; \n  summarise(mean_g_l = mean(conc_g_l),\n            se_g_l = sd(conc_g_l)/sqrt(length(conc_g_l)),\n            mean_mM = mean(conc_mM),\n            se_mM = sd(conc_mM)/sqrt(length(conc_mM))) |&gt; \n  ungroup()\n\n\nvfa_delta_summary &lt;- vfa_delta |&gt; \n  group_by(treatment, time_day, vfa) |&gt; \n  summarise(mean_g_l = mean(conc_g_l),\n            se_g_l = sd(conc_g_l)/sqrt(length(conc_g_l)),\n            mean_mM = mean(conc_mM),\n            se_mM = sd(conc_mM)/sqrt(length(conc_mM))) |&gt; \n  ungroup()\n\n🎬 Graph the cumulative data, grams per litre:\n\nvfa_cummul_summary |&gt; \n  ggplot(aes(x = time_day, colour = vfa)) +\n  geom_line(aes(y = mean_g_l), \n            linewidth = 1) +\n  geom_errorbar(aes(ymin = mean_g_l - se_g_l,\n                    ymax = mean_g_l + se_g_l),\n                width = 0.5, \n                show.legend = F,\n                linewidth = 1) +\n  scale_color_viridis_d(name = NULL) +\n  scale_x_continuous(name = \"Time (days)\") +\n  scale_y_continuous(name = \"Mean VFA concentration (g/l)\") +\n  theme_bw() +\n  facet_wrap(~treatment) +\n  theme(strip.background = element_blank())\n\n\n\n\n🎬 Graph the change data, grams per litre:\n\nvfa_delta_summary |&gt; \n  ggplot(aes(x = time_day, colour = vfa)) +\n  geom_line(aes(y = mean_g_l), \n            linewidth = 1) +\n  geom_errorbar(aes(ymin = mean_g_l - se_g_l,\n                    ymax = mean_g_l + se_g_l),\n                width = 0.5, \n                show.legend = F,\n                linewidth = 1) +\n  scale_color_viridis_d(name = NULL) +\n  scale_x_continuous(name = \"Time (days)\") +\n  scale_y_continuous(name = \"Mean change in VFA concentration (g/l)\") +\n  theme_bw() +\n  facet_wrap(~treatment) +\n  theme(strip.background = element_blank())\n\n\n\n\n🎬 Graph the mean percent representation of each VFA g/l. Note geom_col() will plot proportion if we setposition = \"fill\"\n\nvfa_cummul_summary |&gt; \n  ggplot(aes(x = time_day, y = mean_g_l, fill = vfa)) +\n  geom_col(position = \"fill\") +\n  scale_fill_viridis_d(name = NULL) +\n  scale_x_continuous(name = \"Time (days)\") +\n  scale_y_continuous(name = \"Mean Proportion VFA\") +\n  theme_bw() +\n  facet_wrap(~treatment) +\n  theme(strip.background = element_blank())\n\n\n\n\n\nWe have 8 VFA in our dataset. PCA will allow us to plot our samples in the “VFA” space so we can see if treatments, time or replicate cluster.\nHowever, PCA expects a matrix with samples in rows and VFA, the variables, in columns. We will need to select the columns we need and pivot wider. Then convert to a matrix.\n🎬\n\nvfa_cummul_pca &lt;- vfa_cummul |&gt; \n  select(sample_replicate, \n         treatment, \n         replicate, \n         time_day, \n         vfa, \n         conc_g_l) |&gt; \n  pivot_wider(names_from = vfa, \n              values_from = conc_g_l)\n\n\nmat &lt;- vfa_cummul_pca |&gt; \n  ungroup() |&gt;\n  select(-sample_replicate, \n         -treatment, \n         -replicate, \n         -time_day) |&gt; \n  as.matrix()\n\n🎬 Perform PCA on the matrix:\n\npca &lt;- mat |&gt;\n  prcomp(scale. = TRUE, \n         rank. = 4) \n\nThe scale. argument tells prcomp() to scale the data to have a mean of 0 and a standard deviation of 1. The rank. argument tells prcomp() to only calculate the first 4 principal components. This is useful for visualisation as we can only plot in 2 or 3 dimensions. We can see the results of the PCA by viewing the summary() of the pca object.\n\nsummary(pca)\n\nImportance of first k=4 (out of 8) components:\n                          PC1    PC2     PC3     PC4\nStandard deviation     2.4977 0.9026 0.77959 0.45567\nProportion of Variance 0.7798 0.1018 0.07597 0.02595\nCumulative Proportion  0.7798 0.8816 0.95760 0.98355\n\n\nThe Proportion of Variance tells us how much of the variance is explained by each component. We can see that the first component explains 0.7798 of the variance, the second 0.1018, and the third 0.07597. Together the first three components explain nearly 96% of the total variance in the data. Plotting PC1 against PC2 will capture about 78% of the variance which is likely much better than we would get plotting any two VFA against each other. To plot the PC1 against PC2 we will need to extract the PC1 and PC2 score from the pca object and add labels for the samples.\n🎬 Create a dataframe of the PC1 and PC2 scores which are in pca$x and add the sample information from vfa_cummul_pca:\n\npca_labelled &lt;- data.frame(pca$x,\n                           sample_replicate = vfa_cummul_pca$sample_replicate,\n                           treatment = vfa_cummul_pca$treatment,\n                           replicate = vfa_cummul_pca$replicate,\n                           time_day = vfa_cummul_pca$time_day) \n\nThe dataframe should look like this:\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nPC1\nPC2\nPC3\nPC4\nsample_replicate\ntreatment\nreplicate\ntime_day\n\n\n\n-2.9592362\n0.6710553\n0.0068846\n-0.4453904\nCN10-1\nCN10\n1\n1\n\n\n-2.7153060\n0.7338367\n-0.2856872\n-0.2030110\nCN10-2\nCN10\n2\n1\n\n\n-2.7423102\n0.8246832\n-0.4964249\n-0.1434490\nCN10-3\nCN10\n3\n1\n\n\n-1.1909064\n-1.0360724\n1.1249513\n-0.7360599\nCN10-1\nCN10\n1\n3\n\n\n-1.3831563\n0.9572091\n-1.5561657\n0.0582755\nCN10-2\nCN10\n2\n3\n\n\n-1.1628940\n-0.0865412\n-0.6046780\n-0.1976743\nCN10-3\nCN10\n3\n3\n\n\n-0.2769661\n-0.2221055\n1.1579897\n-0.6079395\nCN10-1\nCN10\n1\n5\n\n\n0.3480962\n0.3612522\n0.5841649\n-0.0612366\nCN10-2\nCN10\n2\n5\n\n\n-0.7281116\n1.6179706\n-0.6430170\n0.0660727\nCN10-3\nCN10\n3\n5\n\n\n0.9333578\n-0.1339061\n1.0870945\n-0.4374103\nCN10-1\nCN10\n1\n9\n\n\n2.0277528\n0.6993342\n0.3850147\n0.0723540\nCN10-2\nCN10\n2\n9\n\n\n1.9931908\n0.5127260\n0.6605782\n0.1841974\nCN10-3\nCN10\n3\n9\n\n\n1.8365692\n-0.4189762\n0.7029015\n-0.3873133\nCN10-1\nCN10\n1\n11\n\n\n2.3313978\n0.3274834\n-0.0135608\n0.0264372\nCN10-2\nCN10\n2\n11\n\n\n1.5833035\n0.9263509\n-0.1909483\n0.1358320\nCN10-3\nCN10\n3\n11\n\n\n2.8498246\n0.3815854\n-0.4763500\n-0.0280281\nCN10-1\nCN10\n1\n13\n\n\n3.5652461\n-0.0836709\n-0.5948483\n-0.1612809\nCN10-2\nCN10\n2\n13\n\n\n4.1314944\n-1.2254642\n0.2699666\n-0.3152100\nCN10-3\nCN10\n3\n13\n\n\n3.7338024\n-0.6744610\n0.4344639\n-0.3736234\nCN10-1\nCN10\n1\n16\n\n\n3.6748427\n0.5202498\n-0.4333685\n-0.1607235\nCN10-2\nCN10\n2\n16\n\n\n3.9057053\n0.3599520\n-0.3049074\n0.0540037\nCN10-3\nCN10\n3\n16\n\n\n3.4561583\n-0.0996639\n0.4472090\n-0.0185889\nCN10-1\nCN10\n1\n18\n\n\n3.6354729\n0.3809673\n-0.0934957\n0.0018722\nCN10-2\nCN10\n2\n18\n\n\n2.9872250\n0.7890400\n-0.2361098\n-0.1628506\nCN10-3\nCN10\n3\n18\n\n\n3.3562231\n-0.2866224\n0.1331068\n-0.2056366\nCN10-1\nCN10\n1\n20\n\n\n3.2009943\n0.4795967\n-0.2092384\n-0.5962183\nCN10-2\nCN10\n2\n20\n\n\n3.9948127\n0.7772640\n-0.3181372\n0.1218382\nCN10-3\nCN10\n3\n20\n\n\n2.8874207\n0.4554681\n0.3106044\n-0.2220240\nCN10-1\nCN10\n1\n22\n\n\n3.6868864\n0.9681097\n-0.2174166\n-0.2246775\nCN10-2\nCN10\n2\n22\n\n\n4.8689622\n0.5218563\n-0.2906042\n0.3532981\nCN10-3\nCN10\n3\n22\n\n\n-3.8483418\n1.5205541\n-0.8809715\n-0.5306228\nNC-1\nNC\n1\n1\n\n\n-3.7653460\n1.5598499\n-1.0570798\n-0.4075397\nNC-2\nNC\n2\n1\n\n\n-3.8586309\n1.6044929\n-1.0936576\n-0.4292404\nNC-3\nNC\n3\n1\n\n\n-2.6934553\n-0.9198406\n0.7439841\n-0.9881115\nNC-1\nNC\n1\n3\n\n\n-2.5064076\n-1.0856761\n0.6334250\n-0.8999028\nNC-2\nNC\n2\n3\n\n\n-2.4097945\n-1.2731546\n1.1767665\n-0.8715948\nNC-3\nNC\n3\n3\n\n\n-3.0567309\n0.5804906\n-0.1391344\n-0.3701763\nNC-1\nNC\n1\n5\n\n\n-2.3511737\n-0.3692016\n0.7053757\n-0.3284113\nNC-2\nNC\n2\n5\n\n\n-2.6752311\n-0.0637855\n0.4692194\n-0.3841240\nNC-3\nNC\n3\n5\n\n\n-1.2335368\n-0.6717374\n0.2155285\n0.1060486\nNC-1\nNC\n1\n9\n\n\n-1.6550689\n0.1576557\n0.0687658\n0.2750388\nNC-2\nNC\n2\n9\n\n\n-0.8948103\n-0.8171884\n0.8062876\n0.5032756\nNC-3\nNC\n3\n9\n\n\n-1.2512737\n-0.4720993\n0.4071788\n0.4693106\nNC-1\nNC\n1\n11\n\n\n-1.8091407\n0.0552546\n0.0424090\n0.3918222\nNC-2\nNC\n2\n11\n\n\n-2.4225566\n0.4998948\n-0.1987773\n0.1959282\nNC-3\nNC\n3\n11\n\n\n-0.9193427\n-0.7741826\n0.0918984\n0.5089847\nNC-1\nNC\n1\n13\n\n\n-0.8800183\n-0.7850404\n0.0895146\n0.6050052\nNC-2\nNC\n2\n13\n\n\n-1.3075763\n-0.2525829\n-0.2993318\n0.5874269\nNC-3\nNC\n3\n13\n\n\n-0.9543813\n-0.3170305\n0.0885062\n0.7153071\nNC-1\nNC\n1\n16\n\n\n-0.4303679\n-0.9952374\n0.2038883\n0.8214647\nNC-2\nNC\n2\n16\n\n\n-0.9457300\n-0.7180646\n0.3081282\n0.6563748\nNC-3\nNC\n3\n16\n\n\n-1.3830063\n0.0614677\n-0.2805342\n0.5462137\nNC-1\nNC\n1\n18\n\n\n-0.7960522\n-0.5792768\n-0.0369684\n0.6621526\nNC-2\nNC\n2\n18\n\n\n-1.6822927\n0.1041656\n0.0634251\n0.4337240\nNC-3\nNC\n3\n18\n\n\n-1.3157478\n-0.0835664\n-0.1246253\n0.5599467\nNC-1\nNC\n1\n20\n\n\n-1.7425068\n0.3029227\n-0.0161466\n0.5134360\nNC-2\nNC\n2\n20\n\n\n-1.3970678\n-0.2923056\n0.4324586\n0.4765460\nNC-3\nNC\n3\n20\n\n\n-1.0777451\n-0.1232925\n0.2388682\n0.7585307\nNC-1\nNC\n1\n22\n\n\n0.4851039\n-4.1291445\n-4.0625050\n-0.4582436\nNC-2\nNC\n2\n22\n\n\n-1.0516226\n-0.7228479\n1.0641320\n0.4955951\nNC-3\nNC\n3\n22\n\n\n\n\n\n🎬 Plot PC1 against PC2 and colour by time and shape by treatment:\n\npca_labelled |&gt; \n  ggplot(aes(x = PC1, y = PC2, \n             colour = factor(time_day),\n             shape = treatment)) +\n  geom_point(size = 3) +\n  scale_colour_viridis_d(end = 0.95, begin = 0.15,\n                         name = \"Time\") +\n  scale_shape_manual(values = c(17, 19),\n                     name = NULL) +\n  theme_classic()\n\n\n\n\n🎬 Plot PC1 against PC2 and colour by time and facet treatment:\n\npca_labelled |&gt; \n  ggplot(aes(x = PC1, y = PC2, colour = factor(time_day))) +\n  geom_point(size = 3) +\n  scale_colour_viridis_d(end = 0.95, begin = 0.15,\n                         name = \"Time\") +\n  facet_wrap(~treatment, ncol = 1) +\n  theme_classic()\n\n\n\n\nreplicates are similar at the same time and treatment especially early as we might expect. PC is essentially an axis of time.\n\nWe are going to create an interactive heatmap with the heatmaply (Galili et al. 2017) package. heatmaply takes a matrix as input so we can use mat\n🎬 Set the rownames to the sample id whihcih is combination of sample_replicate and time_day:\n\nrownames(mat) &lt;- interaction(vfa_cummul_pca$sample_replicate, \n                             vfa_cummul_pca$time_day)\n\nYou might want to view the matrix by clicking on it in the environment pane.\n🎬 Load the heatmaply package:\n\nlibrary(heatmaply)\n\nWe need to tell the clustering algorithm how many clusters to create. We will set the number of clusters for the treatments to be 2 and the number of clusters for the vfa to be the same since it makes sense to see what clusters of genes correlate with the treatments.\n🎬 Set the number of clusters for the treatments and vfa:\n\nn_treatment_clusters &lt;- 2\nn_vfa_clusters &lt;- 2\n\n🎬 Create the heatmap:\n\nheatmaply(mat, \n          scale = \"column\",\n          k_col = n_vfa_clusters,\n          k_row = n_treatment_clusters,\n          fontsize_row = 7, fontsize_col = 10,\n          labCol = colnames(mat),\n          labRow = rownames(mat),\n          heatmap_layers = theme(axis.line = element_blank()))\n\n\n\n\n\nThe heatmap will open in the viewer pane (rather than the plot pane) because it is html. You can “Show in a new window” to see it in a larger format. You can also zoom in and out and pan around the heatmap and download it as a png. You might feel the colour bars is not adding much to the plot. You can remove it by setting hide_colorbar = TRUE, in the heatmaply() function.\nOne of the NC replicates at time = 22 is very different from the other replicates. The CN10 treatments cluster together at high time points. CN10 samples are more similar to NC samples early on. Most of the VFAs behave similarly with highest values later in the experiment for CN10 but isohexanoate and hexanoate differ. The difference might be because isohexanoate is especially low in the NC replicates at time = 1 and hexanoate is especially high in the NC replicate 2 at time = 22\n\nCalculate the flux(change in VFA concentration over a period of time, divided by weight or volume of material) of each VFA, by mM and by weight.\nI’ve requested clarification: for the flux measurements, do they need graphs of the rate of change wrt time? And is the sludge volume going to be a constant for all samples or something they measure and varies by vial?\n\nGraph and extract the reaction rate assuming a first order chemical/biological reaction and an exponential falloff rate\nI’ve requested clarification: for the nonlinear least squares curve fitting, I assume x is time but I’m not clear what the Y variable is - concentration? or change in concentration? or rate of change of concentration?\nPages made with R (R Core Team 2023), Quarto (Allaire et al. 2022), knitr (Xie 2022), kableExtra (Zhu 2021)"
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     "section": "",
-    "text": "VFAs from AD vials\n\nTwo treatments: straw (CN10) and water (NC)\n10 time points: 1, 3, 5, 9, 11, 13, 16, 18, 20, 22\nthree replicates per treatment per time point\n2 x 10 x 3 = 60 groups\n8 VFA with concentration in mM (millimolar): acetate, propanoate, isobutyrate, butyrate, isopentanoate, pentanoate, isohexanoate, hexanoate\n\nTo calculate from this data\n\nRecalculate the data into grams per litre\n\nconvert to molar: 1 millimolar to molar = 0.001 molar\nmultiply by the molecular weight of each VFA\n\n\nCalculate Change in VFA g/l with time\nCalculate the percent representation of each VFA, by mM and by weight"
+    "text": "The file is a CSV file, with some notes on top and the data in the following order, post notes and headers. Please note that all chemical data is in millimolar. There are 62 rows of actual data.\nSample Name – Replicate, Time (days), Acetate, Propanoate, Isobutyrate, Butyrate, Isopentanoate, Pentanoate, Isohexanoate, Hexanoate\nThe students should be able to transform the data from mM to mg/L, and to g/L. To do this they only need to multiply the molecular weight of the compound (listed in the notes in the file) by the concentration in mM to get mg/L. Obviously to get g/L they will just divide by 1000. They should be able to graph the VFA concentrations with time.\nThey should also be able to do a simple flux measurement, which is the change in VFA concentration over a period of time, divided by weight or volume of material. In this case it might be equal to == Delta(Acetate at 3 days - Acetate at 1 day)/Delta (3days - 1day)/50 mls sludge. This would provide a final flux with the units of mg acetate per ml sludge per day. Let me know if this isn’t clear.\nPerhaps more importantly they should be able to graph and extract the reaction rate, assuming a first order chemical/biological reaction and an exponential falloff rate. I found this as a starting point (https://martinlab.chem.umass.edu/r-fitting-data/) , but I assume Emma has something much more effective already in the pipeline."
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-    "text": "8 VFA in mM for 60 samples vfa.csv\nMolecular weights for each VFA in grams per mole mol_wt.txt"
+    "text": "I created these two data files from the original.\n\n8 VFA in mM for 60 samples vfa.csv. There were 63 rows of data in the original file. There were no time 0 for one treatment and all values were zero for the other treatment so I removed those.\n\nTwo treatments: straw (CN10) and water (NC)\n10 time points: 1, 3, 5, 9, 11, 13, 16, 18, 20, 22\nthree replicates per treatment per time point\n2 x 10 x 3 = 60 groups\n8 VFA with concentration in mM (millimolar): acetate, propanoate, isobutyrate, butyrate, isopentanoate, pentanoate, isohexanoate, hexanoate\n\n\nMolecular weights for each VFA in grams per mole mol_wt.txt VFAs from AD vials\n\nWe need to:\n\nCalculate Change in VFA g/l with time\nRecalculate the data into grams per litre - convert to molar: 1 millimolar to molar = 0.001 molar - multiply by the molecular weight of each VFA\nCalculate the percent representation of each VFA, by mM and by weight\nCalculate the flux (change in VFA concentration over a period of time, divided by weight or volume of material) of each VFA, by mM and by weight\nGraph and extract the reaction rate, assuming a first order chemical/biological reaction and an exponential falloff rate"
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     "text": "🎬 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 analysis.R to carry out the rest of the work.\n🎬 Load tidyverse (Wickham et al. 2019) for importing, summarising, plotting and filtering.\n\nlibrary(tidyverse)"
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-    "title": "Kelly",
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     "section": "",
     "text": "🎬 Save the files to data-raw. Open them and examine them. You may want to use Excel for the csv file.\n🎬 Answer the following questions:\n\nWhat is in the rows and columns of each file?\nHow many rows and columns are there in each file?\nHow are the data organised ?"
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-    "text": "🎬 Import\n\nvfa_cummul &lt;- read_csv(\"data-raw/vfa.csv\") |&gt; janitor::clean_names()\n\n🎬 Split treatment and replicate to separate columns so there is a treatment column:\n\nvfa_cummul &lt;- vfa_cummul |&gt; \n  separate(col = sample_replicate, \n           into = c(\"treatment\", \"replicate\"), \n           sep = \"-\",\n           remove = FALSE)\n\nThe provided data is cumulative/absolute. We need to calculate the change in VFA with time. There is a function, lag() that will help us do this. It will take the previous value and subtract it from the current value. We need to do that separately for each sample_replicate so we need to group by sample_replicate first. We also need to make sure the data is in the right order so we will arrange by sample_replicate and time_day.\n🎬 Create dataframe for the change in VFA\n\nvfa_delta &lt;- vfa_cummul |&gt; \n    group_by(sample_replicate)  |&gt; \n    arrange(sample_replicate, time_day) |&gt;\n    mutate(acetate = acetate - lag(acetate),\n           propanoate = propanoate - lag(propanoate),\n           isobutyrate = isobutyrate - lag(isobutyrate),\n           butyrate = butyrate - lag(butyrate),\n           isopentanoate = isopentanoate - lag(isopentanoate),\n           pentanoate = pentanoate - lag(pentanoate),\n           isohexanoate = isohexanoate - lag(isohexanoate),\n           hexanoate = hexanoate - lag(hexanoate))\n\nNow we have two dataframes, one for the cumulative data and one for the change in VFA.\nTo make conversions from mM to g/l we need to do mM * 0.001 * MW. We will import the molecular weight data, pivot the VFA data to long format and join the molecular weight data to the VFA data. Then we can calculate the g/l. We will do this for both the cumulative and delta dataframes.\n🎬 import molecular weight data\n\nmol_wt &lt;- read_table(\"data-raw/mol_wt.txt\") |&gt;\n  mutate(vfa = tolower(vfa))\n\n🎬 Pivot the cumulative data to long format:\nView vfa_cummul to check you understand what you have done.\n🎬 Join molecular weight to data and calculate g/l (mutate to convert to g/l * 0.001 * MW):\n\nvfa_cummul &lt;- vfa_cummul |&gt; \n  left_join(mol_wt, by = \"vfa\") |&gt;\n  mutate(conc_g_l = conc_mM * 0.001 * mw)\n\nView vfa_cummul to check you understand what you have done.\n🎬 Add a column which is the percent representation of each VFA for mM and g/l:\n\nvfa_cummul &lt;- vfa_cummul |&gt; \n  group_by(sample_replicate, time_day) |&gt; \n  mutate(percent_conc_g_l = conc_g_l / sum(conc_g_l) * 100,\n         percent_conc_mM = conc_mM / sum(conc_mM) * 100)\n\n🎬 Pivot the change data, delta_vfa to long format:\nView vfa_delta to check it looks like vfa_cummul\n🎬 Join molecular weight to data and calculate g/l (mutate to convert to g/l * 0.001 * MW):"
+    "text": "🎬 Import\n\nvfa_cummul &lt;- read_csv(\"data-raw/vfa.csv\") |&gt; janitor::clean_names()\n\n🎬 Split treatment and replicate to separate columns so there is a treatment column:\n\nvfa_cummul &lt;- vfa_cummul |&gt; \n  separate(col = sample_replicate, \n           into = c(\"treatment\", \"replicate\"), \n           sep = \"-\",\n           remove = FALSE)\n\nThe provided data is cumulative/absolute. We need to calculate the change in VFA with time. There is a function, lag() that will help us do this. It will take the previous value and subtract it from the current value. We need to do that separately for each sample_replicate so we need to group by sample_replicate first. We also need to make sure the data is in the right order so we will arrange by sample_replicate and time_day."
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-    "text": "🎬 Make summary data for graphing\n\nvfa_cummul_summary &lt;- vfa_cummul |&gt; \n  group_by(treatment, time_day, vfa) |&gt; \n  summarise(mean_g_l = mean(conc_g_l),\n            se_g_l = sd(conc_g_l)/sqrt(length(conc_g_l)),\n            mean_mM = mean(conc_mM),\n            se_mM = sd(conc_mM)/sqrt(length(conc_mM))) |&gt; \n  ungroup()\n\n\nvfa_delta_summary &lt;- vfa_delta |&gt; \n  group_by(treatment, time_day, vfa) |&gt; \n  summarise(mean_g_l = mean(conc_g_l),\n            se_g_l = sd(conc_g_l)/sqrt(length(conc_g_l)),\n            mean_mM = mean(conc_mM),\n            se_mM = sd(conc_mM)/sqrt(length(conc_mM))) |&gt; \n  ungroup()\n\n🎬 Graph the cumulative data, grams per litre:\n\nvfa_cummul_summary |&gt; \n  ggplot(aes(x = time_day, colour = vfa)) +\n  geom_line(aes(y = mean_g_l), \n            linewidth = 1) +\n  geom_errorbar(aes(ymin = mean_g_l - se_g_l,\n                    ymax = mean_g_l + se_g_l),\n                width = 0.5, \n                show.legend = F,\n                linewidth = 1) +\n  scale_color_viridis_d(name = NULL) +\n  scale_x_continuous(name = \"Time (days)\") +\n  scale_y_continuous(name = \"Mean VFA concentration (g/l)\") +\n  theme_bw() +\n  facet_wrap(~treatment) +\n  theme(strip.background = element_blank())\n\n\n\n\n🎬 Graph the change data, grams per litre:\n\nvfa_delta_summary |&gt; \n  ggplot(aes(x = time_day, colour = vfa)) +\n  geom_line(aes(y = mean_g_l), \n            linewidth = 1) +\n  geom_errorbar(aes(ymin = mean_g_l - se_g_l,\n                    ymax = mean_g_l + se_g_l),\n                width = 0.5, \n                show.legend = F,\n                linewidth = 1) +\n  scale_color_viridis_d(name = NULL) +\n  scale_x_continuous(name = \"Time (days)\") +\n  scale_y_continuous(name = \"Mean change in VFA concentration (g/l)\") +\n  theme_bw() +\n  facet_wrap(~treatment) +\n  theme(strip.background = element_blank())\n\n\n\n\n🎬 Graph the mean percent representation of each VFA g/l. Note geom_col() will plot proportion if we setposition = \"fill\"\n\nvfa_cummul_summary |&gt; \n  ggplot(aes(x = time_day, y = mean_g_l, fill = vfa)) +\n  geom_col(position = \"fill\") +\n  scale_fill_viridis_d(name = NULL) +\n  scale_x_continuous(name = \"Time (days)\") +\n  scale_y_continuous(name = \"Mean Proportion VFA\") +\n  theme_bw() +\n  facet_wrap(~treatment) +\n  theme(strip.background = element_blank())"
+    "text": "🎬 Create dataframe for the change in VFA\n\nvfa_delta &lt;- vfa_cummul |&gt; \n    group_by(sample_replicate)  |&gt; \n    arrange(sample_replicate, time_day) |&gt;\n    mutate(acetate = acetate - lag(acetate),\n           propanoate = propanoate - lag(propanoate),\n           isobutyrate = isobutyrate - lag(isobutyrate),\n           butyrate = butyrate - lag(butyrate),\n           isopentanoate = isopentanoate - lag(isopentanoate),\n           pentanoate = pentanoate - lag(pentanoate),\n           isohexanoate = isohexanoate - lag(isohexanoate),\n           hexanoate = hexanoate - lag(hexanoate))\n\nNow we have two dataframes, one for the cumulative data and one for the change in VFA."
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-    "text": "We have 8 genes in our dataset. PCA will allow us to plot our samples in the “VFA” space so we can see if treatments, time or replicate cluster.\nHowever, PCA expects a matrix with samples in rows and VFA, the variables, in columns. We will need to select the columns we need and pivot wider. Then convert to a matrix.\n🎬\n\nvfa_cummul_pca &lt;- vfa_cummul |&gt; \n  select(sample_replicate, \n         treatment, \n         replicate, \n         time_day, \n         vfa, \n         conc_g_l) |&gt; \n  pivot_wider(names_from = vfa, \n              values_from = conc_g_l)\n\n\nmat &lt;- vfa_cummul_pca |&gt; \n  ungroup() |&gt;\n  select(-sample_replicate, \n         -treatment, \n         -replicate, \n         -time_day) |&gt; \n  as.matrix()\n\n🎬 Perform PCA on the matrix:\n\npca &lt;- mat |&gt;\n  prcomp(scale. = TRUE, \n         rank. = 4) \n\nThe scale. argument tells prcomp() to scale the data to have a mean of 0 and a standard deviation of 1. The rank. argument tells prcomp() to only calculate the first 4 principal components. This is useful for visualisation as we can only plot in 2 or 3 dimensions. We can see the results of the PCA by viewing the summary() of the pca object.\n\nsummary(pca)\n\nImportance of first k=4 (out of 8) components:\n                          PC1    PC2     PC3     PC4\nStandard deviation     2.4977 0.9026 0.77959 0.45567\nProportion of Variance 0.7798 0.1018 0.07597 0.02595\nCumulative Proportion  0.7798 0.8816 0.95760 0.98355\n\n\nThe Proportion of Variance tells us how much of the variance is explained by each component. We can see that the first component explains 0.7798 of the variance, the second 0.1018, and the third 0.07597. Together the first three components explain nearly 96% of the total variance in the data. Plotting PC1 against PC2 will capture about 78% of the variance which is likely much better than we would get plotting any two VFA against each other. To plot the PC1 against PC2 we will need to extract the PC1 and PC2 score from the pca object and add labels for the samples.\n🎬 Create a dataframe of the PC1 and PC2 scores which are in pca$x and add the sample information from vfa_cummul_pca:\n\npca_labelled &lt;- data.frame(pca$x,\n                           sample_replicate = vfa_cummul_pca$sample_replicate,\n                           treatment = vfa_cummul_pca$treatment,\n                           replicate = vfa_cummul_pca$replicate,\n                           time_day = vfa_cummul_pca$time_day) \n\nThe dataframe should look like this:\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nPC1\nPC2\nPC3\nPC4\nsample_replicate\ntreatment\nreplicate\ntime_day\n\n\n\n-2.9592362\n0.6710553\n0.0068846\n-0.4453904\nCN10-1\nCN10\n1\n1\n\n\n-2.7153060\n0.7338367\n-0.2856872\n-0.2030110\nCN10-2\nCN10\n2\n1\n\n\n-2.7423102\n0.8246832\n-0.4964249\n-0.1434490\nCN10-3\nCN10\n3\n1\n\n\n-1.1909064\n-1.0360724\n1.1249513\n-0.7360599\nCN10-1\nCN10\n1\n3\n\n\n-1.3831563\n0.9572091\n-1.5561657\n0.0582755\nCN10-2\nCN10\n2\n3\n\n\n-1.1628940\n-0.0865412\n-0.6046780\n-0.1976743\nCN10-3\nCN10\n3\n3\n\n\n-0.2769661\n-0.2221055\n1.1579897\n-0.6079395\nCN10-1\nCN10\n1\n5\n\n\n0.3480962\n0.3612522\n0.5841649\n-0.0612366\nCN10-2\nCN10\n2\n5\n\n\n-0.7281116\n1.6179706\n-0.6430170\n0.0660727\nCN10-3\nCN10\n3\n5\n\n\n0.9333578\n-0.1339061\n1.0870945\n-0.4374103\nCN10-1\nCN10\n1\n9\n\n\n2.0277528\n0.6993342\n0.3850147\n0.0723540\nCN10-2\nCN10\n2\n9\n\n\n1.9931908\n0.5127260\n0.6605782\n0.1841974\nCN10-3\nCN10\n3\n9\n\n\n1.8365692\n-0.4189762\n0.7029015\n-0.3873133\nCN10-1\nCN10\n1\n11\n\n\n2.3313978\n0.3274834\n-0.0135608\n0.0264372\nCN10-2\nCN10\n2\n11\n\n\n1.5833035\n0.9263509\n-0.1909483\n0.1358320\nCN10-3\nCN10\n3\n11\n\n\n2.8498246\n0.3815854\n-0.4763500\n-0.0280281\nCN10-1\nCN10\n1\n13\n\n\n3.5652461\n-0.0836709\n-0.5948483\n-0.1612809\nCN10-2\nCN10\n2\n13\n\n\n4.1314944\n-1.2254642\n0.2699666\n-0.3152100\nCN10-3\nCN10\n3\n13\n\n\n3.7338024\n-0.6744610\n0.4344639\n-0.3736234\nCN10-1\nCN10\n1\n16\n\n\n3.6748427\n0.5202498\n-0.4333685\n-0.1607235\nCN10-2\nCN10\n2\n16\n\n\n3.9057053\n0.3599520\n-0.3049074\n0.0540037\nCN10-3\nCN10\n3\n16\n\n\n3.4561583\n-0.0996639\n0.4472090\n-0.0185889\nCN10-1\nCN10\n1\n18\n\n\n3.6354729\n0.3809673\n-0.0934957\n0.0018722\nCN10-2\nCN10\n2\n18\n\n\n2.9872250\n0.7890400\n-0.2361098\n-0.1628506\nCN10-3\nCN10\n3\n18\n\n\n3.3562231\n-0.2866224\n0.1331068\n-0.2056366\nCN10-1\nCN10\n1\n20\n\n\n3.2009943\n0.4795967\n-0.2092384\n-0.5962183\nCN10-2\nCN10\n2\n20\n\n\n3.9948127\n0.7772640\n-0.3181372\n0.1218382\nCN10-3\nCN10\n3\n20\n\n\n2.8874207\n0.4554681\n0.3106044\n-0.2220240\nCN10-1\nCN10\n1\n22\n\n\n3.6868864\n0.9681097\n-0.2174166\n-0.2246775\nCN10-2\nCN10\n2\n22\n\n\n4.8689622\n0.5218563\n-0.2906042\n0.3532981\nCN10-3\nCN10\n3\n22\n\n\n-3.8483418\n1.5205541\n-0.8809715\n-0.5306228\nNC-1\nNC\n1\n1\n\n\n-3.7653460\n1.5598499\n-1.0570798\n-0.4075397\nNC-2\nNC\n2\n1\n\n\n-3.8586309\n1.6044929\n-1.0936576\n-0.4292404\nNC-3\nNC\n3\n1\n\n\n-2.6934553\n-0.9198406\n0.7439841\n-0.9881115\nNC-1\nNC\n1\n3\n\n\n-2.5064076\n-1.0856761\n0.6334250\n-0.8999028\nNC-2\nNC\n2\n3\n\n\n-2.4097945\n-1.2731546\n1.1767665\n-0.8715948\nNC-3\nNC\n3\n3\n\n\n-3.0567309\n0.5804906\n-0.1391344\n-0.3701763\nNC-1\nNC\n1\n5\n\n\n-2.3511737\n-0.3692016\n0.7053757\n-0.3284113\nNC-2\nNC\n2\n5\n\n\n-2.6752311\n-0.0637855\n0.4692194\n-0.3841240\nNC-3\nNC\n3\n5\n\n\n-1.2335368\n-0.6717374\n0.2155285\n0.1060486\nNC-1\nNC\n1\n9\n\n\n-1.6550689\n0.1576557\n0.0687658\n0.2750388\nNC-2\nNC\n2\n9\n\n\n-0.8948103\n-0.8171884\n0.8062876\n0.5032756\nNC-3\nNC\n3\n9\n\n\n-1.2512737\n-0.4720993\n0.4071788\n0.4693106\nNC-1\nNC\n1\n11\n\n\n-1.8091407\n0.0552546\n0.0424090\n0.3918222\nNC-2\nNC\n2\n11\n\n\n-2.4225566\n0.4998948\n-0.1987773\n0.1959282\nNC-3\nNC\n3\n11\n\n\n-0.9193427\n-0.7741826\n0.0918984\n0.5089847\nNC-1\nNC\n1\n13\n\n\n-0.8800183\n-0.7850404\n0.0895146\n0.6050052\nNC-2\nNC\n2\n13\n\n\n-1.3075763\n-0.2525829\n-0.2993318\n0.5874269\nNC-3\nNC\n3\n13\n\n\n-0.9543813\n-0.3170305\n0.0885062\n0.7153071\nNC-1\nNC\n1\n16\n\n\n-0.4303679\n-0.9952374\n0.2038883\n0.8214647\nNC-2\nNC\n2\n16\n\n\n-0.9457300\n-0.7180646\n0.3081282\n0.6563748\nNC-3\nNC\n3\n16\n\n\n-1.3830063\n0.0614677\n-0.2805342\n0.5462137\nNC-1\nNC\n1\n18\n\n\n-0.7960522\n-0.5792768\n-0.0369684\n0.6621526\nNC-2\nNC\n2\n18\n\n\n-1.6822927\n0.1041656\n0.0634251\n0.4337240\nNC-3\nNC\n3\n18\n\n\n-1.3157478\n-0.0835664\n-0.1246253\n0.5599467\nNC-1\nNC\n1\n20\n\n\n-1.7425068\n0.3029227\n-0.0161466\n0.5134360\nNC-2\nNC\n2\n20\n\n\n-1.3970678\n-0.2923056\n0.4324586\n0.4765460\nNC-3\nNC\n3\n20\n\n\n-1.0777451\n-0.1232925\n0.2388682\n0.7585307\nNC-1\nNC\n1\n22\n\n\n0.4851039\n-4.1291445\n-4.0625050\n-0.4582436\nNC-2\nNC\n2\n22\n\n\n-1.0516226\n-0.7228479\n1.0641320\n0.4955951\nNC-3\nNC\n3\n22\n\n\n\n\n\n🎬 Plot PC1 against PC2 and colour by time and shape by treatment:\n\npca_labelled |&gt; \n  ggplot(aes(x = PC1, y = PC2, \n             colour = factor(time_day),\n             shape = treatment)) +\n  geom_point(size = 3) +\n  scale_colour_viridis_d(end = 0.95, begin = 0.15,\n                         name = \"Time\") +\n  scale_shape_manual(values = c(17, 19),\n                     name = NULL) +\n  theme_classic()\n\n\n\n\n🎬 Plot PC1 against PC2 and colour by time and facet treatment:\n\npca_labelled |&gt; \n  ggplot(aes(x = PC1, y = PC2, colour = factor(time_day))) +\n  geom_point(size = 3) +\n  scale_colour_viridis_d(end = 0.95, begin = 0.15,\n                         name = \"Time\") +\n  facet_wrap(~treatment, ncol = 1) +\n  theme_classic()\n\n\n\n\nreplicates are similar at the same time and treatment especially early as we might expect. PC is essentially an axis of time."
+    "text": "To make conversions from mM to g/l we need to do mM * 0.001 * MW. We will import the molecular weight data, pivot the VFA data to long format and join the molecular weight data to the VFA data. Then we can calculate the g/l. We will do this for both the cumulative and delta dataframes.\n🎬 import molecular weight data\n\nmol_wt &lt;- read_table(\"data-raw/mol_wt.txt\") |&gt;\n  mutate(vfa = tolower(vfa))\n\n🎬 Pivot the cumulative data to long format:\nView vfa_cummul to check you understand what you have done.\n🎬 Join molecular weight to data and calculate g/l (mutate to convert to g/l * 0.001 * MW):\n\nvfa_cummul &lt;- vfa_cummul |&gt; \n  left_join(mol_wt, by = \"vfa\") |&gt;\n  mutate(conc_g_l = conc_mM * 0.001 * mw)\n\nView vfa_cummul to check you understand what you have done.\nRepeat for the delta data.\n🎬 Pivot the change data, delta_vfa to long format:\nView vfa_delta to check it looks like vfa_cummul\n🎬 Join molecular weight to data and calculate g/l (mutate to convert to g/l * 0.001 * MW):"
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     "section": "",
-    "text": "We are going to create an interactive heatmap with the heatmaply (Galili et al. 2017) package. heatmaply takes a matrix as input so we can use mat\n🎬 Set the rownames to the sample id whihcih is combination of sample_replicate and time_day:\n\nrownames(mat) &lt;- interaction(vfa_cummul_pca$sample_replicate, \n                             vfa_cummul_pca$time_day)\n\nYou might want to view the matrix by clicking on it in the environment pane.\n🎬 Load the heatmaply package:\n\nlibrary(heatmaply)\n\nWe need to tell the clustering algorithm how many clusters to create. We will set the number of clusters for the treatments to be 2 and the number of clusters for the vfa to be the same since it makes sense to see what clusters of genes correlate with the treatments.\n🎬 Set the number of clusters for the treatments and vfa:\n\nn_treatment_clusters &lt;- 2\nn_vfa_clusters &lt;- 2\n\n🎬 Create the heatmap:\n\nheatmaply(mat, \n          scale = \"column\",\n          k_col = n_vfa_clusters,\n          k_row = n_treatment_clusters,\n          fontsize_row = 7, fontsize_col = 10,\n          labCol = colnames(mat),\n          labRow = rownames(mat),\n          heatmap_layers = theme(axis.line = element_blank()))\n\n\n\n\n\nThe heatmap will open in the viewer pane (rather than the plot pane) because it is html. You can “Show in a new window” to see it in a larger format. You can also zoom in and out and pan around the heatmap and download it as a png. You might feel the colour bars is not adding much to the plot. You can remove it by setting hide_colorbar = TRUE, in the heatmaply() function.\nOne of the NC replicates at time = 22 is very different from the other replicates. The CN10 treatments cluster together at high time points. CN10 samples are more similar to NC samples early on. Most of the VFAs behave similarly with highest values later in the experiment for CN10 but isohexanoate and hexanoate differ. The difference might be because isohexanoate is especially low in the NC replicates at time = 1 and hexanoate is especially high in the NC replicate 2 at time = 22\nPages made with R (R Core Team 2023), Quarto (Allaire et al. 2022), knitr (Xie 2022), kableExtra (Zhu 2021)"
+    "text": "by mM and by weight\n🎬 Add a column which is the percent representation of each VFA for mM and g/l:\n\nvfa_cummul &lt;- vfa_cummul |&gt; \n  group_by(sample_replicate, time_day) |&gt; \n  mutate(percent_conc_g_l = conc_g_l / sum(conc_g_l) * 100,\n         percent_conc_mM = conc_mM / sum(conc_mM) * 100)"
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+    "title": "Workflow for VFA analysis",
+    "section": "",
+    "text": "🎬 Make summary data for graphing\n\nvfa_cummul_summary &lt;- vfa_cummul |&gt; \n  group_by(treatment, time_day, vfa) |&gt; \n  summarise(mean_g_l = mean(conc_g_l),\n            se_g_l = sd(conc_g_l)/sqrt(length(conc_g_l)),\n            mean_mM = mean(conc_mM),\n            se_mM = sd(conc_mM)/sqrt(length(conc_mM))) |&gt; \n  ungroup()\n\n\nvfa_delta_summary &lt;- vfa_delta |&gt; \n  group_by(treatment, time_day, vfa) |&gt; \n  summarise(mean_g_l = mean(conc_g_l),\n            se_g_l = sd(conc_g_l)/sqrt(length(conc_g_l)),\n            mean_mM = mean(conc_mM),\n            se_mM = sd(conc_mM)/sqrt(length(conc_mM))) |&gt; \n  ungroup()\n\n🎬 Graph the cumulative data, grams per litre:\n\nvfa_cummul_summary |&gt; \n  ggplot(aes(x = time_day, colour = vfa)) +\n  geom_line(aes(y = mean_g_l), \n            linewidth = 1) +\n  geom_errorbar(aes(ymin = mean_g_l - se_g_l,\n                    ymax = mean_g_l + se_g_l),\n                width = 0.5, \n                show.legend = F,\n                linewidth = 1) +\n  scale_color_viridis_d(name = NULL) +\n  scale_x_continuous(name = \"Time (days)\") +\n  scale_y_continuous(name = \"Mean VFA concentration (g/l)\") +\n  theme_bw() +\n  facet_wrap(~treatment) +\n  theme(strip.background = element_blank())\n\n\n\n\n🎬 Graph the change data, grams per litre:\n\nvfa_delta_summary |&gt; \n  ggplot(aes(x = time_day, colour = vfa)) +\n  geom_line(aes(y = mean_g_l), \n            linewidth = 1) +\n  geom_errorbar(aes(ymin = mean_g_l - se_g_l,\n                    ymax = mean_g_l + se_g_l),\n                width = 0.5, \n                show.legend = F,\n                linewidth = 1) +\n  scale_color_viridis_d(name = NULL) +\n  scale_x_continuous(name = \"Time (days)\") +\n  scale_y_continuous(name = \"Mean change in VFA concentration (g/l)\") +\n  theme_bw() +\n  facet_wrap(~treatment) +\n  theme(strip.background = element_blank())\n\n\n\n\n🎬 Graph the mean percent representation of each VFA g/l. Note geom_col() will plot proportion if we setposition = \"fill\"\n\nvfa_cummul_summary |&gt; \n  ggplot(aes(x = time_day, y = mean_g_l, fill = vfa)) +\n  geom_col(position = \"fill\") +\n  scale_fill_viridis_d(name = NULL) +\n  scale_x_continuous(name = \"Time (days)\") +\n  scale_y_continuous(name = \"Mean Proportion VFA\") +\n  theme_bw() +\n  facet_wrap(~treatment) +\n  theme(strip.background = element_blank())\n\n\n\n\n\nWe have 8 VFA in our dataset. PCA will allow us to plot our samples in the “VFA” space so we can see if treatments, time or replicate cluster.\nHowever, PCA expects a matrix with samples in rows and VFA, the variables, in columns. We will need to select the columns we need and pivot wider. Then convert to a matrix.\n🎬\n\nvfa_cummul_pca &lt;- vfa_cummul |&gt; \n  select(sample_replicate, \n         treatment, \n         replicate, \n         time_day, \n         vfa, \n         conc_g_l) |&gt; \n  pivot_wider(names_from = vfa, \n              values_from = conc_g_l)\n\n\nmat &lt;- vfa_cummul_pca |&gt; \n  ungroup() |&gt;\n  select(-sample_replicate, \n         -treatment, \n         -replicate, \n         -time_day) |&gt; \n  as.matrix()\n\n🎬 Perform PCA on the matrix:\n\npca &lt;- mat |&gt;\n  prcomp(scale. = TRUE, \n         rank. = 4) \n\nThe scale. argument tells prcomp() to scale the data to have a mean of 0 and a standard deviation of 1. The rank. argument tells prcomp() to only calculate the first 4 principal components. This is useful for visualisation as we can only plot in 2 or 3 dimensions. We can see the results of the PCA by viewing the summary() of the pca object.\n\nsummary(pca)\n\nImportance of first k=4 (out of 8) components:\n                          PC1    PC2     PC3     PC4\nStandard deviation     2.4977 0.9026 0.77959 0.45567\nProportion of Variance 0.7798 0.1018 0.07597 0.02595\nCumulative Proportion  0.7798 0.8816 0.95760 0.98355\n\n\nThe Proportion of Variance tells us how much of the variance is explained by each component. We can see that the first component explains 0.7798 of the variance, the second 0.1018, and the third 0.07597. Together the first three components explain nearly 96% of the total variance in the data. Plotting PC1 against PC2 will capture about 78% of the variance which is likely much better than we would get plotting any two VFA against each other. To plot the PC1 against PC2 we will need to extract the PC1 and PC2 score from the pca object and add labels for the samples.\n🎬 Create a dataframe of the PC1 and PC2 scores which are in pca$x and add the sample information from vfa_cummul_pca:\n\npca_labelled &lt;- data.frame(pca$x,\n                           sample_replicate = vfa_cummul_pca$sample_replicate,\n                           treatment = vfa_cummul_pca$treatment,\n                           replicate = vfa_cummul_pca$replicate,\n                           time_day = vfa_cummul_pca$time_day) \n\nThe dataframe should look like this:\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nPC1\nPC2\nPC3\nPC4\nsample_replicate\ntreatment\nreplicate\ntime_day\n\n\n\n-2.9592362\n0.6710553\n0.0068846\n-0.4453904\nCN10-1\nCN10\n1\n1\n\n\n-2.7153060\n0.7338367\n-0.2856872\n-0.2030110\nCN10-2\nCN10\n2\n1\n\n\n-2.7423102\n0.8246832\n-0.4964249\n-0.1434490\nCN10-3\nCN10\n3\n1\n\n\n-1.1909064\n-1.0360724\n1.1249513\n-0.7360599\nCN10-1\nCN10\n1\n3\n\n\n-1.3831563\n0.9572091\n-1.5561657\n0.0582755\nCN10-2\nCN10\n2\n3\n\n\n-1.1628940\n-0.0865412\n-0.6046780\n-0.1976743\nCN10-3\nCN10\n3\n3\n\n\n-0.2769661\n-0.2221055\n1.1579897\n-0.6079395\nCN10-1\nCN10\n1\n5\n\n\n0.3480962\n0.3612522\n0.5841649\n-0.0612366\nCN10-2\nCN10\n2\n5\n\n\n-0.7281116\n1.6179706\n-0.6430170\n0.0660727\nCN10-3\nCN10\n3\n5\n\n\n0.9333578\n-0.1339061\n1.0870945\n-0.4374103\nCN10-1\nCN10\n1\n9\n\n\n2.0277528\n0.6993342\n0.3850147\n0.0723540\nCN10-2\nCN10\n2\n9\n\n\n1.9931908\n0.5127260\n0.6605782\n0.1841974\nCN10-3\nCN10\n3\n9\n\n\n1.8365692\n-0.4189762\n0.7029015\n-0.3873133\nCN10-1\nCN10\n1\n11\n\n\n2.3313978\n0.3274834\n-0.0135608\n0.0264372\nCN10-2\nCN10\n2\n11\n\n\n1.5833035\n0.9263509\n-0.1909483\n0.1358320\nCN10-3\nCN10\n3\n11\n\n\n2.8498246\n0.3815854\n-0.4763500\n-0.0280281\nCN10-1\nCN10\n1\n13\n\n\n3.5652461\n-0.0836709\n-0.5948483\n-0.1612809\nCN10-2\nCN10\n2\n13\n\n\n4.1314944\n-1.2254642\n0.2699666\n-0.3152100\nCN10-3\nCN10\n3\n13\n\n\n3.7338024\n-0.6744610\n0.4344639\n-0.3736234\nCN10-1\nCN10\n1\n16\n\n\n3.6748427\n0.5202498\n-0.4333685\n-0.1607235\nCN10-2\nCN10\n2\n16\n\n\n3.9057053\n0.3599520\n-0.3049074\n0.0540037\nCN10-3\nCN10\n3\n16\n\n\n3.4561583\n-0.0996639\n0.4472090\n-0.0185889\nCN10-1\nCN10\n1\n18\n\n\n3.6354729\n0.3809673\n-0.0934957\n0.0018722\nCN10-2\nCN10\n2\n18\n\n\n2.9872250\n0.7890400\n-0.2361098\n-0.1628506\nCN10-3\nCN10\n3\n18\n\n\n3.3562231\n-0.2866224\n0.1331068\n-0.2056366\nCN10-1\nCN10\n1\n20\n\n\n3.2009943\n0.4795967\n-0.2092384\n-0.5962183\nCN10-2\nCN10\n2\n20\n\n\n3.9948127\n0.7772640\n-0.3181372\n0.1218382\nCN10-3\nCN10\n3\n20\n\n\n2.8874207\n0.4554681\n0.3106044\n-0.2220240\nCN10-1\nCN10\n1\n22\n\n\n3.6868864\n0.9681097\n-0.2174166\n-0.2246775\nCN10-2\nCN10\n2\n22\n\n\n4.8689622\n0.5218563\n-0.2906042\n0.3532981\nCN10-3\nCN10\n3\n22\n\n\n-3.8483418\n1.5205541\n-0.8809715\n-0.5306228\nNC-1\nNC\n1\n1\n\n\n-3.7653460\n1.5598499\n-1.0570798\n-0.4075397\nNC-2\nNC\n2\n1\n\n\n-3.8586309\n1.6044929\n-1.0936576\n-0.4292404\nNC-3\nNC\n3\n1\n\n\n-2.6934553\n-0.9198406\n0.7439841\n-0.9881115\nNC-1\nNC\n1\n3\n\n\n-2.5064076\n-1.0856761\n0.6334250\n-0.8999028\nNC-2\nNC\n2\n3\n\n\n-2.4097945\n-1.2731546\n1.1767665\n-0.8715948\nNC-3\nNC\n3\n3\n\n\n-3.0567309\n0.5804906\n-0.1391344\n-0.3701763\nNC-1\nNC\n1\n5\n\n\n-2.3511737\n-0.3692016\n0.7053757\n-0.3284113\nNC-2\nNC\n2\n5\n\n\n-2.6752311\n-0.0637855\n0.4692194\n-0.3841240\nNC-3\nNC\n3\n5\n\n\n-1.2335368\n-0.6717374\n0.2155285\n0.1060486\nNC-1\nNC\n1\n9\n\n\n-1.6550689\n0.1576557\n0.0687658\n0.2750388\nNC-2\nNC\n2\n9\n\n\n-0.8948103\n-0.8171884\n0.8062876\n0.5032756\nNC-3\nNC\n3\n9\n\n\n-1.2512737\n-0.4720993\n0.4071788\n0.4693106\nNC-1\nNC\n1\n11\n\n\n-1.8091407\n0.0552546\n0.0424090\n0.3918222\nNC-2\nNC\n2\n11\n\n\n-2.4225566\n0.4998948\n-0.1987773\n0.1959282\nNC-3\nNC\n3\n11\n\n\n-0.9193427\n-0.7741826\n0.0918984\n0.5089847\nNC-1\nNC\n1\n13\n\n\n-0.8800183\n-0.7850404\n0.0895146\n0.6050052\nNC-2\nNC\n2\n13\n\n\n-1.3075763\n-0.2525829\n-0.2993318\n0.5874269\nNC-3\nNC\n3\n13\n\n\n-0.9543813\n-0.3170305\n0.0885062\n0.7153071\nNC-1\nNC\n1\n16\n\n\n-0.4303679\n-0.9952374\n0.2038883\n0.8214647\nNC-2\nNC\n2\n16\n\n\n-0.9457300\n-0.7180646\n0.3081282\n0.6563748\nNC-3\nNC\n3\n16\n\n\n-1.3830063\n0.0614677\n-0.2805342\n0.5462137\nNC-1\nNC\n1\n18\n\n\n-0.7960522\n-0.5792768\n-0.0369684\n0.6621526\nNC-2\nNC\n2\n18\n\n\n-1.6822927\n0.1041656\n0.0634251\n0.4337240\nNC-3\nNC\n3\n18\n\n\n-1.3157478\n-0.0835664\n-0.1246253\n0.5599467\nNC-1\nNC\n1\n20\n\n\n-1.7425068\n0.3029227\n-0.0161466\n0.5134360\nNC-2\nNC\n2\n20\n\n\n-1.3970678\n-0.2923056\n0.4324586\n0.4765460\nNC-3\nNC\n3\n20\n\n\n-1.0777451\n-0.1232925\n0.2388682\n0.7585307\nNC-1\nNC\n1\n22\n\n\n0.4851039\n-4.1291445\n-4.0625050\n-0.4582436\nNC-2\nNC\n2\n22\n\n\n-1.0516226\n-0.7228479\n1.0641320\n0.4955951\nNC-3\nNC\n3\n22\n\n\n\n\n\n🎬 Plot PC1 against PC2 and colour by time and shape by treatment:\n\npca_labelled |&gt; \n  ggplot(aes(x = PC1, y = PC2, \n             colour = factor(time_day),\n             shape = treatment)) +\n  geom_point(size = 3) +\n  scale_colour_viridis_d(end = 0.95, begin = 0.15,\n                         name = \"Time\") +\n  scale_shape_manual(values = c(17, 19),\n                     name = NULL) +\n  theme_classic()\n\n\n\n\n🎬 Plot PC1 against PC2 and colour by time and facet treatment:\n\npca_labelled |&gt; \n  ggplot(aes(x = PC1, y = PC2, colour = factor(time_day))) +\n  geom_point(size = 3) +\n  scale_colour_viridis_d(end = 0.95, begin = 0.15,\n                         name = \"Time\") +\n  facet_wrap(~treatment, ncol = 1) +\n  theme_classic()\n\n\n\n\nreplicates are similar at the same time and treatment especially early as we might expect. PC is essentially an axis of time.\n\nWe are going to create an interactive heatmap with the heatmaply (Galili et al. 2017) package. heatmaply takes a matrix as input so we can use mat\n🎬 Set the rownames to the sample id whihcih is combination of sample_replicate and time_day:\n\nrownames(mat) &lt;- interaction(vfa_cummul_pca$sample_replicate, \n                             vfa_cummul_pca$time_day)\n\nYou might want to view the matrix by clicking on it in the environment pane.\n🎬 Load the heatmaply package:\n\nlibrary(heatmaply)\n\nWe need to tell the clustering algorithm how many clusters to create. We will set the number of clusters for the treatments to be 2 and the number of clusters for the vfa to be the same since it makes sense to see what clusters of genes correlate with the treatments.\n🎬 Set the number of clusters for the treatments and vfa:\n\nn_treatment_clusters &lt;- 2\nn_vfa_clusters &lt;- 2\n\n🎬 Create the heatmap:\n\nheatmaply(mat, \n          scale = \"column\",\n          k_col = n_vfa_clusters,\n          k_row = n_treatment_clusters,\n          fontsize_row = 7, fontsize_col = 10,\n          labCol = colnames(mat),\n          labRow = rownames(mat),\n          heatmap_layers = theme(axis.line = element_blank()))\n\n\n\n\n\nThe heatmap will open in the viewer pane (rather than the plot pane) because it is html. You can “Show in a new window” to see it in a larger format. You can also zoom in and out and pan around the heatmap and download it as a png. You might feel the colour bars is not adding much to the plot. You can remove it by setting hide_colorbar = TRUE, in the heatmaply() function.\nOne of the NC replicates at time = 22 is very different from the other replicates. The CN10 treatments cluster together at high time points. CN10 samples are more similar to NC samples early on. Most of the VFAs behave similarly with highest values later in the experiment for CN10 but isohexanoate and hexanoate differ. The difference might be because isohexanoate is especially low in the NC replicates at time = 1 and hexanoate is especially high in the NC replicate 2 at time = 22"
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+    "href": "omics/kelly/workshop.html#calculate-the-flux---pending.",
+    "title": "Workflow for VFA analysis",
+    "section": "",
+    "text": "Calculate the flux(change in VFA concentration over a period of time, divided by weight or volume of material) of each VFA, by mM and by weight.\nI’ve requested clarification: for the flux measurements, do they need graphs of the rate of change wrt time? And is the sludge volume going to be a constant for all samples or something they measure and varies by vial?"
+  },
+  {
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+    "href": "omics/kelly/workshop.html#graph-and-extract-the-reaction-rate---pending",
+    "title": "Workflow for VFA analysis",
+    "section": "",
+    "text": "Graph and extract the reaction rate assuming a first order chemical/biological reaction and an exponential falloff rate\nI’ve requested clarification: for the nonlinear least squares curve fitting, I assume x is time but I’m not clear what the Y variable is - concentration? or change in concentration? or rate of change of concentration?\nPages made with R (R Core Team 2023), Quarto (Allaire et al. 2022), knitr (Xie 2022), kableExtra (Zhu 2021)"
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@@ -1040,7 +1061,7 @@
     "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.html\noverview.qmd\nstudy_after_workshop.qmd\nstudy_before_workshop.html\nstudy_before_workshop.ipynb\nstudy_before_workshop.qmd\nworkshop.html\nworkshop.qmd\nworkshop.rmarkdown\nworkshop_files\n\n\nYou might have noticed that unlike R, the commands do not have brackets after them. Instead, options (or switches) are given after the command. For example, we can modify the ls command to give us more information with the -l option, which stands for “long”.\n\nls -l\n\ntotal 228\ndrwxr-xr-x 2 runner docker  4096 Dec 15 12:46 data\ndrwxr-xr-x 2 runner docker  4096 Dec 15 12:46 images\n-rw-r--r-- 1 runner docker 27497 Dec 15 13:09 overview.html\n-rw-r--r-- 1 runner docker  1597 Dec 15 12:46 overview.qmd\n-rw-r--r-- 1 runner docker   184 Dec 15 12:46 study_after_workshop.qmd\n-rw-r--r-- 1 runner docker 70988 Dec 15 13:09 study_before_workshop.html\n-rw-r--r-- 1 runner docker  4807 Dec 15 12:46 study_before_workshop.ipynb\n-rw-r--r-- 1 runner docker 13029 Dec 15 12:46 study_before_workshop.qmd\n-rw-r--r-- 1 runner docker 58063 Dec 15 12:46 workshop.html\n-rw-r--r-- 1 runner docker  8550 Dec 15 12:46 workshop.qmd\n-rw-r--r-- 1 runner docker  8564 Dec 15 13:09 workshop.rmarkdown\ndrwxr-xr-x 3 runner docker  4096 Dec 15 12:46 workshop_files\n\n\nYou can use more than one option at once. The -h option stands for “human readable” and makes the file sizes easier to understand for humans:\n\nls -hl\n\ntotal 228K\ndrwxr-xr-x 2 runner docker 4.0K Dec 15 12:46 data\ndrwxr-xr-x 2 runner docker 4.0K Dec 15 12:46 images\n-rw-r--r-- 1 runner docker  27K Dec 15 13:09 overview.html\n-rw-r--r-- 1 runner docker 1.6K Dec 15 12:46 overview.qmd\n-rw-r--r-- 1 runner docker  184 Dec 15 12:46 study_after_workshop.qmd\n-rw-r--r-- 1 runner docker  70K Dec 15 13:09 study_before_workshop.html\n-rw-r--r-- 1 runner docker 4.7K Dec 15 12:46 study_before_workshop.ipynb\n-rw-r--r-- 1 runner docker  13K Dec 15 12:46 study_before_workshop.qmd\n-rw-r--r-- 1 runner docker  57K Dec 15 12:46 workshop.html\n-rw-r--r-- 1 runner docker 8.4K Dec 15 12:46 workshop.qmd\n-rw-r--r-- 1 runner docker 8.4K Dec 15 13:09 workshop.rmarkdown\ndrwxr-xr-x 3 runner docker 4.0K Dec 15 12:46 workshop_files\n\n\nThe -a option stands for “all” and shows us all the files, including hidden files.\n\nls -alh\n\ntotal 236K\ndrwxr-xr-x 5 runner docker 4.0K Dec 15 13:09 .\ndrwxr-xr-x 6 runner docker 4.0K Dec 15 13:09 ..\ndrwxr-xr-x 2 runner docker 4.0K Dec 15 12:46 data\ndrwxr-xr-x 2 runner docker 4.0K Dec 15 12:46 images\n-rw-r--r-- 1 runner docker  27K Dec 15 13:09 overview.html\n-rw-r--r-- 1 runner docker 1.6K Dec 15 12:46 overview.qmd\n-rw-r--r-- 1 runner docker  184 Dec 15 12:46 study_after_workshop.qmd\n-rw-r--r-- 1 runner docker  70K Dec 15 13:09 study_before_workshop.html\n-rw-r--r-- 1 runner docker 4.7K Dec 15 12:46 study_before_workshop.ipynb\n-rw-r--r-- 1 runner docker  13K Dec 15 12:46 study_before_workshop.qmd\n-rw-r--r-- 1 runner docker  57K Dec 15 12:46 workshop.html\n-rw-r--r-- 1 runner docker 8.4K Dec 15 12:46 workshop.qmd\n-rw-r--r-- 1 runner docker 8.4K Dec 15 13:09 workshop.rmarkdown\ndrwxr-xr-x 3 runner docker 4.0K Dec 15 12:46 workshop_files\n\n\nYou can move about with the cd command, which stands for “change directory”. You can use it to move into a directory by specifying the path to the directory:\n\ncd data\npwd\ncd ..\npwd\ncd data\npwd\n\n/home/runner/work/BIO00088H-data/BIO00088H-data/core/week-2/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.html\noverview.qmd\nstudy_after_workshop.qmd\nstudy_before_workshop.html\nstudy_before_workshop.ipynb\nstudy_before_workshop.qmd\nworkshop.html\nworkshop.qmd\nworkshop.rmarkdown\nworkshop_files\n\n\nYou might have noticed that unlike R, the commands do not have brackets after them. Instead, options (or switches) are given after the command. For example, we can modify the ls command to give us more information with the -l option, which stands for “long”.\n\nls -l\n\ntotal 228\ndrwxr-xr-x 2 runner docker  4096 Dec 18 15:28 data\ndrwxr-xr-x 2 runner docker  4096 Dec 18 15:28 images\n-rw-r--r-- 1 runner docker 27497 Dec 18 15:30 overview.html\n-rw-r--r-- 1 runner docker  1597 Dec 18 15:28 overview.qmd\n-rw-r--r-- 1 runner docker   184 Dec 18 15:28 study_after_workshop.qmd\n-rw-r--r-- 1 runner docker 70988 Dec 18 15:30 study_before_workshop.html\n-rw-r--r-- 1 runner docker  4807 Dec 18 15:28 study_before_workshop.ipynb\n-rw-r--r-- 1 runner docker 13029 Dec 18 15:28 study_before_workshop.qmd\n-rw-r--r-- 1 runner docker 58063 Dec 18 15:28 workshop.html\n-rw-r--r-- 1 runner docker  8550 Dec 18 15:28 workshop.qmd\n-rw-r--r-- 1 runner docker  8564 Dec 18 15:30 workshop.rmarkdown\ndrwxr-xr-x 3 runner docker  4096 Dec 18 15:28 workshop_files\n\n\nYou can use more than one option at once. The -h option stands for “human readable” and makes the file sizes easier to understand for humans:\n\nls -hl\n\ntotal 228K\ndrwxr-xr-x 2 runner docker 4.0K Dec 18 15:28 data\ndrwxr-xr-x 2 runner docker 4.0K Dec 18 15:28 images\n-rw-r--r-- 1 runner docker  27K Dec 18 15:30 overview.html\n-rw-r--r-- 1 runner docker 1.6K Dec 18 15:28 overview.qmd\n-rw-r--r-- 1 runner docker  184 Dec 18 15:28 study_after_workshop.qmd\n-rw-r--r-- 1 runner docker  70K Dec 18 15:30 study_before_workshop.html\n-rw-r--r-- 1 runner docker 4.7K Dec 18 15:28 study_before_workshop.ipynb\n-rw-r--r-- 1 runner docker  13K Dec 18 15:28 study_before_workshop.qmd\n-rw-r--r-- 1 runner docker  57K Dec 18 15:28 workshop.html\n-rw-r--r-- 1 runner docker 8.4K Dec 18 15:28 workshop.qmd\n-rw-r--r-- 1 runner docker 8.4K Dec 18 15:30 workshop.rmarkdown\ndrwxr-xr-x 3 runner docker 4.0K Dec 18 15:28 workshop_files\n\n\nThe -a option stands for “all” and shows us all the files, including hidden files.\n\nls -alh\n\ntotal 236K\ndrwxr-xr-x 5 runner docker 4.0K Dec 18 15:30 .\ndrwxr-xr-x 6 runner docker 4.0K Dec 18 15:30 ..\ndrwxr-xr-x 2 runner docker 4.0K Dec 18 15:28 data\ndrwxr-xr-x 2 runner docker 4.0K Dec 18 15:28 images\n-rw-r--r-- 1 runner docker  27K Dec 18 15:30 overview.html\n-rw-r--r-- 1 runner docker 1.6K Dec 18 15:28 overview.qmd\n-rw-r--r-- 1 runner docker  184 Dec 18 15:28 study_after_workshop.qmd\n-rw-r--r-- 1 runner docker  70K Dec 18 15:30 study_before_workshop.html\n-rw-r--r-- 1 runner docker 4.7K Dec 18 15:28 study_before_workshop.ipynb\n-rw-r--r-- 1 runner docker  13K Dec 18 15:28 study_before_workshop.qmd\n-rw-r--r-- 1 runner docker  57K Dec 18 15:28 workshop.html\n-rw-r--r-- 1 runner docker 8.4K Dec 18 15:28 workshop.qmd\n-rw-r--r-- 1 runner docker 8.4K Dec 18 15:30 workshop.rmarkdown\ndrwxr-xr-x 3 runner docker 4.0K Dec 18 15:28 workshop_files\n\n\nYou can move about with the cd command, which stands for “change directory”. You can use it to move into a directory by specifying the path to the directory:\n\ncd data\npwd\ncd ..\npwd\ncd data\npwd\n\n/home/runner/work/BIO00088H-data/BIO00088H-data/core/week-2/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"
   },
   {
     "objectID": "core/week-2/workshop.html#differences-between-r-and-python",
diff --git a/structures/structures.html b/structures/structures.html
index bf75fb1..3d624a8 100644
--- a/structures/structures.html
+++ b/structures/structures.html
@@ -208,7 +208,7 @@ <h1 class="title">Structure Data Analysis for Group Project</h1>
     <div>
     <div class="quarto-title-meta-heading">Published</div>
     <div class="quarto-title-meta-contents">
-      <p class="date">15 December, 2023</p>
+      <p class="date">18 December, 2023</p>
     </div>
   </div>