diff --git a/404.html b/404.html
index a4377fd..5c332e5 100644
--- a/404.html
+++ b/404.html
@@ -24,7 +24,7 @@
     
     <a class="navbar-brand me-2" href="https://phipsonlab.github.io/SuperCellCyto/index.html">SuperCellCyto</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.99.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -70,7 +70,7 @@
 
     <footer><div class="pkgdown-footer-left">
   <p></p>
-<p>Developed by Givanna Putri, Belinda Phipson.</p>
+<p>Developed by Givanna Putri, George Howitt, Marsh-Wakefield Felix, Thomas Ashhurst, Belinda Phipson.</p>
 </div>
 
 <div class="pkgdown-footer-right">
diff --git a/CODE_OF_CONDUCT.html b/CODE_OF_CONDUCT.html
index 0978fa4..7421a6b 100644
--- a/CODE_OF_CONDUCT.html
+++ b/CODE_OF_CONDUCT.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="index.html">SuperCellCyto</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.99.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -117,7 +117,7 @@ <h2 id="attribution">Attribution<a class="anchor" aria-label="anchor" href="#att
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Givanna Putri, Belinda Phipson.</p>
+  <p></p><p>Developed by Givanna Putri, George Howitt, Marsh-Wakefield Felix, Thomas Ashhurst, Belinda Phipson.</p>
 </div>
 
 <div class="pkgdown-footer-right">
diff --git a/CONTRIBUTING.html b/CONTRIBUTING.html
index ee5204c..afd3c5f 100644
--- a/CONTRIBUTING.html
+++ b/CONTRIBUTING.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="index.html">SuperCellCyto</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.99.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -71,7 +71,7 @@ <h2 id="code-of-conduct">Code of Conduct<a class="anchor" aria-label="anchor" hr
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Givanna Putri, Belinda Phipson.</p>
+  <p></p><p>Developed by Givanna Putri, George Howitt, Marsh-Wakefield Felix, Thomas Ashhurst, Belinda Phipson.</p>
 </div>
 
 <div class="pkgdown-footer-right">
diff --git a/LICENSE-text.html b/LICENSE-text.html
index a9f53a5..2e4c917 100644
--- a/LICENSE-text.html
+++ b/LICENSE-text.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="index.html">SuperCellCyto</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.99.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -724,7 +724,7 @@
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Givanna Putri, Belinda Phipson.</p>
+  <p></p><p>Developed by Givanna Putri, George Howitt, Marsh-Wakefield Felix, Thomas Ashhurst, Belinda Phipson.</p>
 </div>
 
 <div class="pkgdown-footer-right">
diff --git a/articles/how_to_supercell.html b/articles/how_to_supercell.html
index 6b81025..fe9a8fc 100644
--- a/articles/how_to_supercell.html
+++ b/articles/how_to_supercell.html
@@ -26,7 +26,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">SuperCellCyto</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.99.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -141,20 +141,20 @@ <h2 id="preparing-your-dataset">Preparing your dataset<a class="anchor" aria-lab
 <span><span class="va">n_samples</span> <span class="op">&lt;-</span> <span class="fl">3</span></span>
 <span><span class="va">dat</span> <span class="op">&lt;-</span> <span class="fu">SuperCellCyto</span><span class="fu">::</span><span class="fu"><a href="../reference/simCytoData.html">simCytoData</a></span><span class="op">(</span>nmarkers <span class="op">=</span> <span class="va">n_markers</span>, ncells <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/rep.html" class="external-link">rep</a></span><span class="op">(</span><span class="fl">10000</span>, <span class="va">n_samples</span><span class="op">)</span><span class="op">)</span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/utils/head.html" class="external-link">head</a></span><span class="op">(</span><span class="va">dat</span><span class="op">)</span></span>
-<span><span class="co">#&gt;    Marker_1 Marker_2 Marker_3 Marker_4 Marker_5  Marker_6  Marker_7 Marker_8</span></span>
-<span><span class="co">#&gt; 1: 6.348896 12.76922 8.081814 14.82767 16.78516  8.327598  8.706527 6.917179</span></span>
-<span><span class="co">#&gt; 2: 7.045928 13.07817 6.584392 16.05816 16.73959  8.507928  5.878093 6.785206</span></span>
-<span><span class="co">#&gt; 3: 9.202952 12.56312 8.260834 16.10469 18.76380 10.285851  9.657357 7.576643</span></span>
-<span><span class="co">#&gt; 4: 6.453132 13.43010 8.273119 13.98670 17.26809  6.989559  8.875397 5.909259</span></span>
-<span><span class="co">#&gt; 5: 7.723788 13.59895 8.089639 14.18880 17.65259 10.306980  7.440030 6.269055</span></span>
-<span><span class="co">#&gt; 6: 8.712999 13.75800 7.982606 16.65486 16.62515  6.264880 10.946625 3.549110</span></span>
+<span><span class="co">#&gt;    Marker_1 Marker_2 Marker_3 Marker_4 Marker_5  Marker_6 Marker_7 Marker_8</span></span>
+<span><span class="co">#&gt; 1: 8.613145 12.27848 11.47877 15.85943 6.292417  9.371754 15.48644 8.502447</span></span>
+<span><span class="co">#&gt; 2: 8.444047 13.61751 15.42910 13.23521 7.337523 10.848695 16.41428 9.049085</span></span>
+<span><span class="co">#&gt; 3: 9.508217 13.27955 14.49741 15.32714 7.184697  9.229750 17.25246 7.224527</span></span>
+<span><span class="co">#&gt; 4: 8.727552 12.33650 13.08252 14.59704 6.344520 11.025034 16.08588 9.787236</span></span>
+<span><span class="co">#&gt; 5: 8.705266 11.54711 15.09200 12.14086 5.916500  9.986064 16.82250 7.815844</span></span>
+<span><span class="co">#&gt; 6: 9.809956 13.59739 14.26858 14.35567 7.930409 10.782738 17.08411 9.334017</span></span>
 <span><span class="co">#&gt;    Marker_9 Marker_10 Marker_11 Marker_12 Marker_13 Marker_14 Marker_15</span></span>
-<span><span class="co">#&gt; 1: 11.58426  12.88936  7.133000  18.24950  8.366636  5.202447  7.710622</span></span>
-<span><span class="co">#&gt; 2: 14.81178  12.12547  8.223698  18.35320  8.967888  5.452228 10.106635</span></span>
-<span><span class="co">#&gt; 3: 15.23759  11.56163  6.905385  17.91669 12.069136  8.134681  9.570365</span></span>
-<span><span class="co">#&gt; 4: 14.62778  10.85856  8.380473  16.59761 10.816997  7.746163 10.527337</span></span>
-<span><span class="co">#&gt; 5: 14.82137  11.43294  8.125290  16.37683 11.425650  8.851541 10.186497</span></span>
-<span><span class="co">#&gt; 6: 15.13292  12.60006  8.958321  17.62148 11.178471  9.192945  9.014084</span></span>
+<span><span class="co">#&gt; 1: 6.343346  8.611583  14.99405  13.14672  8.453014  4.727143  10.77041</span></span>
+<span><span class="co">#&gt; 2: 7.766555  9.717384  16.61520  13.32699  7.155631  4.038144  10.97021</span></span>
+<span><span class="co">#&gt; 3: 7.125987  8.541820  16.23950  14.86434  9.294373  5.023199  10.42803</span></span>
+<span><span class="co">#&gt; 4: 5.642658  7.652166  14.94492  12.52479  6.494958  3.826238  10.97673</span></span>
+<span><span class="co">#&gt; 5: 7.423770  7.872997  15.01467  14.43692  7.627948  3.731901  11.81326</span></span>
+<span><span class="co">#&gt; 6: 7.971358  9.705231  14.84054  13.69212  8.225082  4.368337  11.46224</span></span>
 <span><span class="co">#&gt;      Sample Cell_Id</span></span>
 <span><span class="co">#&gt; 1: Sample_1  Cell_1</span></span>
 <span><span class="co">#&gt; 2: Sample_1  Cell_2</span></span>
@@ -194,33 +194,33 @@ <h3 id="the-markers">The markers<a class="anchor" aria-label="anchor" href="#the
 <span></span>
 <span><span class="fu"><a href="https://rdrr.io/r/utils/head.html" class="external-link">head</a></span><span class="op">(</span><span class="va">dat</span><span class="op">[</span>, <span class="va">marker_cols_asinh</span>, with <span class="op">=</span> <span class="cn">FALSE</span><span class="op">]</span><span class="op">)</span></span>
 <span><span class="co">#&gt;    Marker_1_asinh Marker_2_asinh Marker_3_asinh Marker_4_asinh Marker_5_asinh</span></span>
-<span><span class="co">#&gt; 1:       1.059890       1.667045       1.257624       1.807491       1.925684</span></span>
-<span><span class="co">#&gt; 2:       1.143309       1.689341       1.088700       1.883327       1.923079</span></span>
-<span><span class="co">#&gt; 3:       1.369985       1.651909       1.276312       1.886090       2.032936</span></span>
-<span><span class="co">#&gt; 4:       1.072723       1.714186       1.277583       1.752334       1.952900</span></span>
-<span><span class="co">#&gt; 5:       1.219337       1.725903       1.258447       1.765853       1.974071</span></span>
-<span><span class="co">#&gt; 6:       1.322220       1.736824       1.247138       1.918218       1.916508</span></span>
+<span><span class="co">#&gt; 1:       1.312237       1.630650       1.568585       1.871444       1.052882</span></span>
+<span><span class="co">#&gt; 2:       1.295133       1.727183       1.845241       1.700499       1.176602</span></span>
+<span><span class="co">#&gt; 3:       1.398763       1.703628       1.786171       1.838936       1.159267</span></span>
+<span><span class="co">#&gt; 4:       1.323668       1.635018       1.689651       1.792648       1.059348</span></span>
+<span><span class="co">#&gt; 5:       1.321450       1.574029       1.824249       1.620219       1.005238</span></span>
+<span><span class="co">#&gt; 6:       1.426507       1.725796       1.771144       1.776888       1.241584</span></span>
 <span><span class="co">#&gt;    Marker_6_asinh Marker_7_asinh Marker_8_asinh Marker_9_asinh Marker_10_asinh</span></span>
-<span><span class="co">#&gt; 1:       1.283205       1.321576      1.1283165       1.576978        1.675770</span></span>
-<span><span class="co">#&gt; 2:       1.301624       1.000270      1.1127565       1.806475        1.619047</span></span>
-<span><span class="co">#&gt; 3:       1.468914       1.412562      1.2032360       1.833367        1.575182</span></span>
-<span><span class="co">#&gt; 4:       1.136768       1.338274      1.0043028       1.794638        1.517892</span></span>
-<span><span class="co">#&gt; 5:       1.470760       1.188092      1.0499715       1.807088        1.564918</span></span>
-<span><span class="co">#&gt; 6:       1.049451       1.525234      0.6606944       1.826820        1.654638</span></span>
+<span><span class="co">#&gt; 1:       1.385989       1.848771       1.301069      1.0592027        1.312080</span></span>
+<span><span class="co">#&gt; 2:       1.517067       1.904290       1.355198      1.2239756        1.418068</span></span>
+<span><span class="co">#&gt; 3:       1.372541       1.952030       1.163809      1.1525410        1.305053</span></span>
+<span><span class="co">#&gt; 4:       1.531730       1.884975       1.424442      0.9694039        1.211527</span></span>
+<span><span class="co">#&gt; 5:       1.442388       1.927814       1.229300      1.1862764        1.235444</span></span>
+<span><span class="co">#&gt; 6:       1.511531       1.942615       1.382431      1.2459434        1.416955</span></span>
 <span><span class="co">#&gt;    Marker_11_asinh Marker_12_asinh Marker_13_asinh Marker_14_asinh</span></span>
-<span><span class="co">#&gt; 1:        1.153346        2.006106        1.287217       0.9097161</span></span>
-<span><span class="co">#&gt; 2:        1.272459        2.011572        1.347318       0.9439046</span></span>
-<span><span class="co">#&gt; 3:        1.126934        1.988367        1.614743       1.2631736</span></span>
-<span><span class="co">#&gt; 4:        1.288636        1.914920        1.514410       1.2217659</span></span>
-<span><span class="co">#&gt; 5:        1.262190        1.902105        1.564333       1.3359296</span></span>
-<span><span class="co">#&gt; 6:        1.346385        1.972374        1.544332       1.3690291</span></span>
+<span><span class="co">#&gt; 1:        1.818070        1.694225        1.296046       0.8422546</span></span>
+<span><span class="co">#&gt; 2:        1.915935        1.706966        1.155942       0.7386142</span></span>
+<span><span class="co">#&gt; 3:        1.894055        1.809832        1.378680       0.8846507</span></span>
+<span><span class="co">#&gt; 4:        1.814957        1.649071        1.077836       0.7053006</span></span>
+<span><span class="co">#&gt; 5:        1.819374        1.782220        1.208874       0.6902489</span></span>
+<span><span class="co">#&gt; 6:        1.808313        1.732314        1.272603       0.7891656</span></span>
 <span><span class="co">#&gt;    Marker_15_asinh</span></span>
-<span><span class="co">#&gt; 1:        1.217905</span></span>
-<span><span class="co">#&gt; 2:        1.453133</span></span>
-<span><span class="co">#&gt; 3:        1.404534</span></span>
-<span><span class="co">#&gt; 4:        1.489831</span></span>
-<span><span class="co">#&gt; 5:        1.460193</span></span>
-<span><span class="co">#&gt; 6:        1.351808</span></span></code></pre></div>
+<span><span class="co">#&gt; 1:        1.510494</span></span>
+<span><span class="co">#&gt; 2:        1.527193</span></span>
+<span><span class="co">#&gt; 3:        1.481277</span></span>
+<span><span class="co">#&gt; 4:        1.527733</span></span>
+<span><span class="co">#&gt; 5:        1.594977</span></span>
+<span><span class="co">#&gt; 6:        1.567263</span></span></code></pre></div>
 <p>Breaking down the steps, we:</p>
 <ol style="list-style-type: decimal">
 <li>Identify the columns denoting the markers.</li>
@@ -372,33 +372,33 @@ <h3 id="supercell-expression-matrix">Supercell expression matrix<a class="anchor
 <div class="sourceCode" id="cb11"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/utils/head.html" class="external-link">head</a></span><span class="op">(</span><span class="va">supercells</span><span class="op">$</span><span class="va">supercell_expression_matrix</span><span class="op">)</span></span>
 <span><span class="co">#&gt;    Marker_1_asinh Marker_2_asinh Marker_3_asinh Marker_4_asinh Marker_5_asinh</span></span>
-<span><span class="co">#&gt; 1:       1.063102       1.615852       1.142469       1.795116       1.963970</span></span>
-<span><span class="co">#&gt; 2:       1.139560       1.592343       1.265137       1.762866       1.901955</span></span>
-<span><span class="co">#&gt; 3:       1.247001       1.642296       1.174917       1.825329       1.974449</span></span>
-<span><span class="co">#&gt; 4:       1.302365       1.659593       1.146650       1.804860       1.949896</span></span>
-<span><span class="co">#&gt; 5:       1.095591       1.638470       1.240644       1.794487       1.946385</span></span>
-<span><span class="co">#&gt; 6:       1.254591       1.654347       1.049694       1.806171       1.941226</span></span>
+<span><span class="co">#&gt; 1:       1.264766       1.684169       1.748368       1.788664      0.9973134</span></span>
+<span><span class="co">#&gt; 2:       1.300619       1.706533       1.743982       1.797021      1.1395078</span></span>
+<span><span class="co">#&gt; 3:       1.380854       1.663947       1.710558       1.759018      0.9370305</span></span>
+<span><span class="co">#&gt; 4:       1.172451       1.677868       1.737744       1.777133      0.9960757</span></span>
+<span><span class="co">#&gt; 5:       1.204584       1.704318       1.746477       1.796652      1.2397711</span></span>
+<span><span class="co">#&gt; 6:       1.345840       1.691527       1.747664       1.774837      0.8885885</span></span>
 <span><span class="co">#&gt;    Marker_6_asinh Marker_7_asinh Marker_8_asinh Marker_9_asinh Marker_10_asinh</span></span>
-<span><span class="co">#&gt; 1:       1.511784       1.474463       1.240553       1.784000        1.489342</span></span>
-<span><span class="co">#&gt; 2:       1.251121       1.331333       1.034064       1.785684        1.437205</span></span>
-<span><span class="co">#&gt; 3:       1.441164       1.272617       1.178466       1.823684        1.565898</span></span>
-<span><span class="co">#&gt; 4:       1.262037       1.376983       1.207233       1.828726        1.573047</span></span>
-<span><span class="co">#&gt; 5:       1.358139       1.246590       1.066354       1.793704        1.516685</span></span>
-<span><span class="co">#&gt; 6:       1.401569       1.277088       1.235338       1.808203        1.542743</span></span>
+<span><span class="co">#&gt; 1:       1.549610       1.913162       1.431371       1.131703        1.382052</span></span>
+<span><span class="co">#&gt; 2:       1.530629       1.921592       1.518904       1.050195        1.342751</span></span>
+<span><span class="co">#&gt; 3:       1.494180       1.896908       1.393027       1.093162        1.123521</span></span>
+<span><span class="co">#&gt; 4:       1.497948       1.929390       1.347520       1.033888        1.301701</span></span>
+<span><span class="co">#&gt; 5:       1.402733       1.920214       1.377683       1.003403        1.159945</span></span>
+<span><span class="co">#&gt; 6:       1.414919       1.899307       1.446175       1.120004        1.239734</span></span>
 <span><span class="co">#&gt;    Marker_11_asinh Marker_12_asinh Marker_13_asinh Marker_14_asinh</span></span>
-<span><span class="co">#&gt; 1:        1.225903        1.959363        1.583395        1.298039</span></span>
-<span><span class="co">#&gt; 2:        1.272995        1.904793        1.538821        1.296632</span></span>
-<span><span class="co">#&gt; 3:        1.178607        1.951651        1.633625        1.160451</span></span>
-<span><span class="co">#&gt; 4:        1.167396        1.929309        1.524944        1.039197</span></span>
-<span><span class="co">#&gt; 5:        1.215639        1.930916        1.435486        1.220823</span></span>
-<span><span class="co">#&gt; 6:        1.304864        1.957161        1.508904        1.304469</span></span>
+<span><span class="co">#&gt; 1:        1.830774        1.776173        1.215677       0.9110168</span></span>
+<span><span class="co">#&gt; 2:        1.832855        1.811177        1.278195       0.7774752</span></span>
+<span><span class="co">#&gt; 3:        1.793451        1.770732        1.252679       0.8274059</span></span>
+<span><span class="co">#&gt; 4:        1.812060        1.793503        1.282339       0.7681560</span></span>
+<span><span class="co">#&gt; 5:        1.823652        1.779230        1.278131       0.9229247</span></span>
+<span><span class="co">#&gt; 6:        1.809973        1.742942        1.297757       1.0299323</span></span>
 <span><span class="co">#&gt;    Marker_15_asinh   Sample                 SuperCellId</span></span>
-<span><span class="co">#&gt; 1:        1.470743 Sample_1 SuperCell_1_Sample_Sample_1</span></span>
-<span><span class="co">#&gt; 2:        1.436878 Sample_1 SuperCell_2_Sample_Sample_1</span></span>
-<span><span class="co">#&gt; 3:        1.460871 Sample_1 SuperCell_3_Sample_Sample_1</span></span>
-<span><span class="co">#&gt; 4:        1.457348 Sample_1 SuperCell_4_Sample_Sample_1</span></span>
-<span><span class="co">#&gt; 5:        1.430103 Sample_1 SuperCell_5_Sample_Sample_1</span></span>
-<span><span class="co">#&gt; 6:        1.410685 Sample_1 SuperCell_6_Sample_Sample_1</span></span></code></pre></div>
+<span><span class="co">#&gt; 1:        1.481482 Sample_1 SuperCell_1_Sample_Sample_1</span></span>
+<span><span class="co">#&gt; 2:        1.463727 Sample_1 SuperCell_2_Sample_Sample_1</span></span>
+<span><span class="co">#&gt; 3:        1.422810 Sample_1 SuperCell_3_Sample_Sample_1</span></span>
+<span><span class="co">#&gt; 4:        1.437886 Sample_1 SuperCell_4_Sample_Sample_1</span></span>
+<span><span class="co">#&gt; 5:        1.490259 Sample_1 SuperCell_5_Sample_Sample_1</span></span>
+<span><span class="co">#&gt; 6:        1.552772 Sample_1 SuperCell_6_Sample_Sample_1</span></span></code></pre></div>
 <p>Therein, we will have the following columns:</p>
 <div class="sourceCode" id="cb12"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/names.html" class="external-link">names</a></span><span class="op">(</span><span class="va">supercells</span><span class="op">$</span><span class="va">supercell_expression_matrix</span><span class="op">)</span></span>
@@ -454,12 +454,12 @@ <h3 id="supercell-cell-map">Supercell cell map<a class="anchor" aria-label="anch
 <div class="sourceCode" id="cb15"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/utils/head.html" class="external-link">head</a></span><span class="op">(</span><span class="va">supercells</span><span class="op">$</span><span class="va">supercell_cell_map</span><span class="op">)</span></span>
 <span><span class="co">#&gt;                      SuperCellID   Sample</span></span>
-<span><span class="co">#&gt; 1:  SuperCell_91_Sample_Sample_1 Sample_1</span></span>
-<span><span class="co">#&gt; 2: SuperCell_211_Sample_Sample_1 Sample_1</span></span>
-<span><span class="co">#&gt; 3: SuperCell_335_Sample_Sample_1 Sample_1</span></span>
-<span><span class="co">#&gt; 4: SuperCell_331_Sample_Sample_1 Sample_1</span></span>
-<span><span class="co">#&gt; 5: SuperCell_191_Sample_Sample_1 Sample_1</span></span>
-<span><span class="co">#&gt; 6: SuperCell_127_Sample_Sample_1 Sample_1</span></span></code></pre></div>
+<span><span class="co">#&gt; 1: SuperCell_427_Sample_Sample_1 Sample_1</span></span>
+<span><span class="co">#&gt; 2: SuperCell_194_Sample_Sample_1 Sample_1</span></span>
+<span><span class="co">#&gt; 3:  SuperCell_21_Sample_Sample_1 Sample_1</span></span>
+<span><span class="co">#&gt; 4:  SuperCell_40_Sample_Sample_1 Sample_1</span></span>
+<span><span class="co">#&gt; 5: SuperCell_138_Sample_Sample_1 Sample_1</span></span>
+<span><span class="co">#&gt; 6:  SuperCell_80_Sample_Sample_1 Sample_1</span></span></code></pre></div>
 <p>This map is very useful if we later need to expand the supercells
 out. Additionally, this is also the reason why we need to have a column
 in the dataset which uniquely identify each cell.</p>
@@ -570,29 +570,29 @@ <h2 id="controlling-the-supercells-granularity">Controlling the supercells’ gr
 <span><span class="op">)</span></span></code></pre></div>
 <div class="sourceCode" id="cb19"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/cbind.html" class="external-link">rbind</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/utils/head.html" class="external-link">head</a></span><span class="op">(</span><span class="va">supercell_expression_matrix</span>, n <span class="op">=</span> <span class="fl">3</span><span class="op">)</span>, <span class="fu"><a href="https://rdrr.io/r/utils/head.html" class="external-link">tail</a></span><span class="op">(</span><span class="va">supercell_expression_matrix</span>, n <span class="op">=</span> <span class="fl">3</span><span class="op">)</span><span class="op">)</span></span>
-<span><span class="co">#&gt;    Marker_1 Marker_2  Marker_3 Marker_4 Marker_5 Marker_6  Marker_7 Marker_8</span></span>
-<span><span class="co">#&gt; 1: 6.500549 10.04351  6.917036 9.754944 17.49099 12.47481  6.755746 14.99223</span></span>
-<span><span class="co">#&gt; 2: 6.219941 11.13442  4.471067 8.882971 18.96532 13.73045  6.488840 13.78131</span></span>
-<span><span class="co">#&gt; 3: 8.237611 11.50332  6.627568 8.507735 15.10083 14.65808  7.313123 14.58984</span></span>
-<span><span class="co">#&gt; 4: 8.406849 13.21608 14.083744 5.125704 15.79521 13.75427 12.986377 18.56063</span></span>
-<span><span class="co">#&gt; 5: 7.946571 12.70798 14.067321 8.803080 15.05481 13.42700 13.735859 21.44997</span></span>
-<span><span class="co">#&gt; 6: 9.128595 11.18219 12.629981 6.711764 16.48079 15.20647 14.924023 19.48714</span></span>
-<span><span class="co">#&gt;     Marker_9 Marker_10   Sample                   SuperCellId</span></span>
-<span><span class="co">#&gt; 1: 13.450022 10.328687 Sample_1   SuperCell_1_Sample_Sample_1</span></span>
-<span><span class="co">#&gt; 2: 11.831775 11.541301 Sample_1   SuperCell_2_Sample_Sample_1</span></span>
-<span><span class="co">#&gt; 3: 11.869873 12.023383 Sample_1   SuperCell_3_Sample_Sample_1</span></span>
-<span><span class="co">#&gt; 4:  8.369949  7.195550 Sample_2 SuperCell_498_Sample_Sample_2</span></span>
-<span><span class="co">#&gt; 5:  6.666370  7.260202 Sample_2 SuperCell_499_Sample_Sample_2</span></span>
-<span><span class="co">#&gt; 6:  7.481778  6.322925 Sample_2 SuperCell_500_Sample_Sample_2</span></span></code></pre></div>
+<span><span class="co">#&gt;     Marker_1  Marker_2  Marker_3 Marker_4 Marker_5  Marker_6 Marker_7 Marker_8</span></span>
+<span><span class="co">#&gt; 1: 12.597362 18.563892  7.939731 17.53654 20.16769 10.109291 20.08783 16.53032</span></span>
+<span><span class="co">#&gt; 2: 12.612559 19.527094  9.127097 16.71297 19.01135 10.000790 19.22212 17.74209</span></span>
+<span><span class="co">#&gt; 3: 11.751343 19.304758  8.749553 17.68459 20.15861 11.332877 18.80278 18.91703</span></span>
+<span><span class="co">#&gt; 4:  7.468624  6.504929 19.134719 16.50861 13.61697  5.884649 11.17226 13.54510</span></span>
+<span><span class="co">#&gt; 5:  4.423812  6.820534 16.989851 18.05577 15.46220  7.110407 14.37311 14.21248</span></span>
+<span><span class="co">#&gt; 6:  5.456059  6.600613 20.530448 17.10953 15.22410  7.192285 14.12641 14.91185</span></span>
+<span><span class="co">#&gt;    Marker_9 Marker_10   Sample                   SuperCellId</span></span>
+<span><span class="co">#&gt; 1: 13.37707 10.637396 Sample_1   SuperCell_1_Sample_Sample_1</span></span>
+<span><span class="co">#&gt; 2: 12.08168  9.940211 Sample_1   SuperCell_2_Sample_Sample_1</span></span>
+<span><span class="co">#&gt; 3: 11.57253 11.346711 Sample_1   SuperCell_3_Sample_Sample_1</span></span>
+<span><span class="co">#&gt; 4: 17.72237 12.940699 Sample_2 SuperCell_498_Sample_Sample_2</span></span>
+<span><span class="co">#&gt; 5: 17.39007 13.409344 Sample_2 SuperCell_499_Sample_Sample_2</span></span>
+<span><span class="co">#&gt; 6: 16.39534 12.380849 Sample_2 SuperCell_500_Sample_Sample_2</span></span></code></pre></div>
 <div class="sourceCode" id="cb20"><pre class="downlit sourceCode r">
 <code class="sourceCode R"><span><span class="fu"><a href="https://rdrr.io/r/base/cbind.html" class="external-link">rbind</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/utils/head.html" class="external-link">head</a></span><span class="op">(</span><span class="va">supercell_cell_map</span>, n <span class="op">=</span> <span class="fl">3</span><span class="op">)</span>, <span class="fu"><a href="https://rdrr.io/r/utils/head.html" class="external-link">tail</a></span><span class="op">(</span><span class="va">supercell_cell_map</span>, n <span class="op">=</span> <span class="fl">3</span><span class="op">)</span><span class="op">)</span></span>
 <span><span class="co">#&gt;                      SuperCellID     CellId   Sample</span></span>
-<span><span class="co">#&gt; 1: SuperCell_256_Sample_Sample_1     Cell_1 Sample_1</span></span>
-<span><span class="co">#&gt; 2:  SuperCell_77_Sample_Sample_1     Cell_2 Sample_1</span></span>
-<span><span class="co">#&gt; 3: SuperCell_421_Sample_Sample_1     Cell_3 Sample_1</span></span>
-<span><span class="co">#&gt; 4:  SuperCell_23_Sample_Sample_2 Cell_19998 Sample_2</span></span>
-<span><span class="co">#&gt; 5:  SuperCell_98_Sample_Sample_2 Cell_19999 Sample_2</span></span>
-<span><span class="co">#&gt; 6: SuperCell_227_Sample_Sample_2 Cell_20000 Sample_2</span></span></code></pre></div>
+<span><span class="co">#&gt; 1: SuperCell_949_Sample_Sample_1     Cell_1 Sample_1</span></span>
+<span><span class="co">#&gt; 2: SuperCell_735_Sample_Sample_1     Cell_2 Sample_1</span></span>
+<span><span class="co">#&gt; 3: SuperCell_190_Sample_Sample_1     Cell_3 Sample_1</span></span>
+<span><span class="co">#&gt; 4: SuperCell_163_Sample_Sample_2 Cell_19998 Sample_2</span></span>
+<span><span class="co">#&gt; 5:  SuperCell_95_Sample_Sample_2 Cell_19999 Sample_2</span></span>
+<span><span class="co">#&gt; 6: SuperCell_327_Sample_Sample_2 Cell_20000 Sample_2</span></span></code></pre></div>
 </div>
 <div class="section level2">
 <h2 id="using-runsupercellcyto-for-stratified-summarising">Using <code>runSuperCellCyto</code> for stratified summarising<a class="anchor" aria-label="anchor" href="#using-runsupercellcyto-for-stratified-summarising"></a>
@@ -690,7 +690,7 @@ <h2 id="session-information">Session information<a class="anchor" aria-label="an
 <span><span class="co">#&gt; [8] base     </span></span>
 <span><span class="co">#&gt; </span></span>
 <span><span class="co">#&gt; other attached packages:</span></span>
-<span><span class="co">#&gt; [1] BiocParallel_1.36.0  SuperCellCyto_0.99.0 BiocStyle_2.30.0    </span></span>
+<span><span class="co">#&gt; [1] BiocParallel_1.36.0 SuperCellCyto_0.1.0 BiocStyle_2.30.0   </span></span>
 <span><span class="co">#&gt; </span></span>
 <span><span class="co">#&gt; loaded via a namespace (and not attached):</span></span>
 <span><span class="co">#&gt;  [1] Matrix_1.6-1.1      jsonlite_1.8.8      compiler_4.3.2     </span></span>
@@ -717,7 +717,7 @@ <h2 id="session-information">Session information<a class="anchor" aria-label="an
 
     <footer><div class="pkgdown-footer-left">
   <p></p>
-<p>Developed by Givanna Putri, Belinda Phipson.</p>
+<p>Developed by Givanna Putri, George Howitt, Marsh-Wakefield Felix, Thomas Ashhurst, Belinda Phipson.</p>
 </div>
 
 <div class="pkgdown-footer-right">
diff --git a/articles/index.html b/articles/index.html
index 1881ed9..a93fde5 100644
--- a/articles/index.html
+++ b/articles/index.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">SuperCellCyto</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.99.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -54,7 +54,7 @@ <h3>All vignettes</h3>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Givanna Putri, Belinda Phipson.</p>
+  <p></p><p>Developed by Givanna Putri, George Howitt, Marsh-Wakefield Felix, Thomas Ashhurst, Belinda Phipson.</p>
 </div>
 
 <div class="pkgdown-footer-right">
diff --git a/authors.html b/authors.html
index 2eb1cb7..8d3f793 100644
--- a/authors.html
+++ b/authors.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="index.html">SuperCellCyto</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.99.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -50,6 +50,18 @@ <h2>Authors</h2>
           <p><strong>Givanna Putri</strong>. Author, maintainer. <a href="https://orcid.org/0000-0002-7399-8014" target="orcid.widget" aria-label="ORCID" class="external-link"><span class="fab fa-orcid orcid" aria-hidden="true"></span></a>
           </p>
         </li>
+        <li>
+          <p><strong>George Howitt</strong>. Author. 
+          </p>
+        </li>
+        <li>
+          <p><strong>Marsh-Wakefield Felix</strong>. Author. 
+          </p>
+        </li>
+        <li>
+          <p><strong>Thomas Ashhurst</strong>. Author. 
+          </p>
+        </li>
         <li>
           <p><strong>Belinda Phipson</strong>. Author. 
           </p>
@@ -60,15 +72,15 @@ <h2>Authors</h2>
       <h2 id="citation">Citation</h2>
       <p></p>
 
-      <p>Putri G, Phipson B (2024).
+      <p>Putri G, Howitt G, Felix M, Ashhurst T, Phipson B (2024).
 <em>SuperCellCyto: SuperCell For Cytometry Data</em>.
-R package version 0.99.0, <a href="https://phipsonlab.github.io/SuperCellCyto/">https://phipsonlab.github.io/SuperCellCyto/</a>. 
+R package version 0.1.0, <a href="https://phipsonlab.github.io/SuperCellCyto/">https://phipsonlab.github.io/SuperCellCyto/</a>. 
 </p>
       <pre>@Manual{,
   title = {SuperCellCyto: SuperCell For Cytometry Data},
-  author = {Givanna Putri and Belinda Phipson},
+  author = {Givanna Putri and George Howitt and Marsh-Wakefield Felix and Thomas Ashhurst and Belinda Phipson},
   year = {2024},
-  note = {R package version 0.99.0},
+  note = {R package version 0.1.0},
   url = {https://phipsonlab.github.io/SuperCellCyto/},
 }</pre>
     </div>
@@ -77,7 +89,7 @@ <h2 id="citation">Citation</h2>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Givanna Putri, Belinda Phipson.</p>
+  <p></p><p>Developed by Givanna Putri, George Howitt, Marsh-Wakefield Felix, Thomas Ashhurst, Belinda Phipson.</p>
 </div>
 
 <div class="pkgdown-footer-right">
diff --git a/index.html b/index.html
index a15036b..130b63a 100644
--- a/index.html
+++ b/index.html
@@ -32,7 +32,7 @@
     
     <a class="navbar-brand me-2" href="index.html">SuperCellCyto</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.99.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -71,7 +71,12 @@
 <p>
 <img src="reference/figures/supercellcyto.png" align="right" height="2px"></p>
 <p>SuperCellCyto is an extension of the <a href="https://github.com/GfellerLab/SuperCell" class="external-link">SuperCell R package</a>. Initially developed for scRNAseq data, SuperCell aggregates cells with similar transcriptomic profiles into “supercells” (also known as “metacells” in the scRNAseq literature).</p>
-<p>In SuperCellCyto, we have adapted the SuperCell package specifically for cytometry data and implemented within-sample supercell creation, preventing supercells from containing cells across multiple samples. Additionally, we have also introduced parallel processing capabilities using a careful load balancing strategy, allowing the simultaneous creation of supercells across multiple samples, which significantly speeds up computational time.</p>
+<p>In SuperCellCyto, we’ve tailored the SuperCell package to specifically cater to cytometry data:</p>
+<ol style="list-style-type: decimal">
+<li>Supercells are now aggregating cells that are similar marker expressions.</li>
+<li>Supercells are now created for each individual sample. This adaptation ensures that each supercell encompasses cells from exclusively one sample. By processing each sample independently, we prevent the intermixing of cells from different samples within supercells.</li>
+<li>Multiple samples can now be processed in parallel with a custom load balancing strategy. This enhancement enables simultaneous generation of supercells for multiple samples, significantly reducing the computational time required for processing large datasets.</li>
+</ol>
 <div class="section level2">
 <h2 id="vignettes-and-function-documentation">Vignettes and Function Documentation<a class="anchor" aria-label="anchor" href="#vignettes-and-function-documentation"></a>
 </h2>
@@ -87,15 +92,15 @@ <h2 id="citation">Citation<a class="anchor" aria-label="anchor" href="#citation"
 <div class="section level2">
 <h2 id="installation">Installation<a class="anchor" aria-label="anchor" href="#installation"></a>
 </h2>
-<p>The package can be installed using <code>devtools</code>:</p>
-<pre><code><span><span class="co"># Install devtools</span></span>
-<span><span class="fu"><a href="https://rdrr.io/r/utils/install.packages.html" class="external-link">install.packages</a></span><span class="op">(</span><span class="st">"devtools"</span><span class="op">)</span></span>
+<p>The package can be installed using <code>remotes</code>:</p>
+<pre><code><span><span class="co"># Install remotes</span></span>
+<span><span class="fu"><a href="https://rdrr.io/r/utils/install.packages.html" class="external-link">install.packages</a></span><span class="op">(</span><span class="st">"remotes"</span><span class="op">)</span></span>
 <span></span>
 <span><span class="co"># Install SuperCellCyto from this repository</span></span>
-<span><span class="fu">devtools</span><span class="fu">::</span><span class="fu"><a href="https://remotes.r-lib.org/reference/install_github.html" class="external-link">install_github</a></span><span class="op">(</span><span class="st">"phipsonlab/SuperCellCyto"</span><span class="op">)</span></span></code></pre>
+<span><span class="fu">remotes</span><span class="fu">::</span><span class="fu"><a href="https://remotes.r-lib.org/reference/install_github.html" class="external-link">install_github</a></span><span class="op">(</span><span class="st">"phipsonlab/SuperCellCyto"</span><span class="op">)</span></span></code></pre>
 <p>SuperCellCyto requires the <a href="https://github.com/GfellerLab/SuperCell" class="external-link">SuperCell R package</a> installed to run properly. If you use the installation command above, it should be, in theory, automatically installed. But in the case it doesn’t, please manually install it by running the following command beforehand:</p>
 <pre><code><span><span class="co"># Install SuperCell from their github repository</span></span>
-<span><span class="fu">devtools</span><span class="fu">::</span><span class="fu"><a href="https://remotes.r-lib.org/reference/install_github.html" class="external-link">install_github</a></span><span class="op">(</span><span class="st">"GfellerLab/SuperCell"</span><span class="op">)</span></span></code></pre>
+<span><span class="fu">remotes</span><span class="fu">::</span><span class="fu"><a href="https://remotes.r-lib.org/reference/install_github.html" class="external-link">install_github</a></span><span class="op">(</span><span class="st">"GfellerLab/SuperCell"</span><span class="op">)</span></span></code></pre>
 </div>
 <div class="section level2">
 <h2 id="code-of-conduct">Code of Conduct<a class="anchor" aria-label="anchor" href="#code-of-conduct"></a>
@@ -136,6 +141,9 @@ <h2 data-toc-skip>Citation</h2>
 <h2 data-toc-skip>Developers</h2>
 <ul class="list-unstyled">
 <li>Givanna Putri <br><small class="roles"> Author, maintainer </small> <a href="https://orcid.org/0000-0002-7399-8014" target="orcid.widget" aria-label="ORCID" class="external-link"><span class="fab fa-orcid orcid" aria-hidden="true"></span></a> </li>
+<li>George Howitt <br><small class="roles"> Author </small>  </li>
+<li>Marsh-Wakefield Felix <br><small class="roles"> Author </small>  </li>
+<li>Thomas Ashhurst <br><small class="roles"> Author </small>  </li>
 <li>Belinda Phipson <br><small class="roles"> Author </small>  </li>
 </ul>
 </div>
@@ -148,7 +156,7 @@ <h2 data-toc-skip>Developers</h2>
 
     <footer><div class="pkgdown-footer-left">
   <p></p>
-<p>Developed by Givanna Putri, Belinda Phipson.</p>
+<p>Developed by Givanna Putri, George Howitt, Marsh-Wakefield Felix, Thomas Ashhurst, Belinda Phipson.</p>
 </div>
 
 <div class="pkgdown-footer-right">
diff --git a/news/index.html b/news/index.html
index 3e071a5..41f0564 100644
--- a/news/index.html
+++ b/news/index.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">SuperCellCyto</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.99.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -62,7 +62,7 @@ <h2 class="pkg-version" data-toc-text="0.1.0" id="supercellcyto-010">SuperCellCy
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Givanna Putri, Belinda Phipson.</p>
+  <p></p><p>Developed by Givanna Putri, George Howitt, Marsh-Wakefield Felix, Thomas Ashhurst, Belinda Phipson.</p>
 </div>
 
 <div class="pkgdown-footer-right">
diff --git a/pkgdown.yml b/pkgdown.yml
index 9d0b3ec..1ee6c6e 100644
--- a/pkgdown.yml
+++ b/pkgdown.yml
@@ -3,7 +3,7 @@ pkgdown: 2.0.7
 pkgdown_sha: ~
 articles:
   how_to_supercell: how_to_supercell.html
-last_built: 2024-01-17T06:44Z
+last_built: 2024-01-18T01:34Z
 urls:
   reference: https://phipsonlab.github.io/SuperCellCyto/reference
   article: https://phipsonlab.github.io/SuperCellCyto/articles
diff --git a/reference/index.html b/reference/index.html
index e023b3d..980fb7c 100644
--- a/reference/index.html
+++ b/reference/index.html
@@ -10,7 +10,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">SuperCellCyto</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.99.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -63,18 +63,18 @@ <h2 id="all-functions">All functions<a class="anchor" aria-label="anchor" href="
           
           <code><a href="runSuperCellCyto.html">runSuperCellCyto()</a></code> 
         </dt>
-        <dd>Run SuperCell for cytometry data</dd>
+        <dd>Run SuperCellCyto on Cytometry Data</dd>
       </dl><dl><dt>
           
           <code><a href="simCytoData.html">simCytoData()</a></code> 
         </dt>
-        <dd>Simulates cytometry data</dd>
+        <dd>Simulate cytometry data</dd>
       </dl></div>
   </main></div>
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Givanna Putri, Belinda Phipson.</p>
+  <p></p><p>Developed by Givanna Putri, George Howitt, Marsh-Wakefield Felix, Thomas Ashhurst, Belinda Phipson.</p>
 </div>
 
 <div class="pkgdown-footer-right">
diff --git a/reference/recomputeSupercells.html b/reference/recomputeSupercells.html
index 0f08e7c..9d43235 100644
--- a/reference/recomputeSupercells.html
+++ b/reference/recomputeSupercells.html
@@ -1,11 +1,15 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Given a supercell object, recompute the supercell granularity using a different
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Given a supercell object, recreate the supercells using a different
 gamma value.
 Gamma value controls the number of supercells generated.
-The smaller the value, the more supercells you get, and vice versa."><title>Recompute supercells — recomputeSupercells • SuperCellCyto</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Recompute supercells — recomputeSupercells"><meta property="og:description" content="Given a supercell object, recompute the supercell granularity using a different
+The smaller the value, the more supercells you get, and vice versa.
+For this function to run, you need to have at least run runSuperCellCyto
+function once!"><title>Recompute supercells — recomputeSupercells • SuperCellCyto</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Recompute supercells — recomputeSupercells"><meta property="og:description" content="Given a supercell object, recreate the supercells using a different
 gamma value.
 Gamma value controls the number of supercells generated.
-The smaller the value, the more supercells you get, and vice versa."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
+The smaller the value, the more supercells you get, and vice versa.
+For this function to run, you need to have at least run runSuperCellCyto
+function once!"><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
 <script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
 <script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
 <![endif]--></head><body>
@@ -16,7 +20,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">SuperCellCyto</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.99.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -52,10 +56,12 @@
     </div>
 
     <div class="ref-description section level2">
-    <p>Given a supercell object, recompute the supercell granularity using a different
-gamma value.
-Gamma value controls the number of supercells generated.
+    <p>Given a supercell object, recreate the supercells using a different
+gamma value.</p>
+<p>Gamma value controls the number of supercells generated.
 The smaller the value, the more supercells you get, and vice versa.</p>
+<p>For this function to run, you need to have at least run <a href="runSuperCellCyto.html">runSuperCellCyto</a>
+function <strong>once</strong>!</p>
     </div>
 
     <div class="section level2">
@@ -73,10 +79,8 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
     <dl><dt>dt</dt>
-<dd><p>data.table object containing the cytometry data.
-Rows represent cells, columns represent markers.
-If this is not a data.table object, the function will warn you about it,
-and then try to convert it to a data.table object.</p></dd>
+<dd><p>A data.table object containing cytometry data where rows represent
+cells and columns represent markers.</p></dd>
 
 
 <dt>sc_objects</dt>
@@ -84,40 +88,34 @@ <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#argumen
 
 
 <dt>markers</dt>
-<dd><p>character vector specifying the markers in <code>dt</code>.</p></dd>
+<dd><p>A character vector identifying the markers to create supercells with.</p></dd>
 
 
 <dt>sample_colname</dt>
-<dd><p>character specifying the column in <code>dt</code> that denotes
-the sample of a cell.</p></dd>
+<dd><p>A character string identifying the column in <code>dt</code> that denotes the sample of a cell.</p></dd>
 
 
 <dt>cell_id_colname</dt>
-<dd><p>character specifying the column in <code>dt</code> that denotes
-the unique ID of a cell.</p></dd>
+<dd><p>A character string identifying the column in <code>dt</code> representing each cell's unique ID.</p></dd>
 
 
 <dt>gam</dt>
-<dd><p>numeric specifying the gamma value to be used by SuperCell.</p></dd>
+<dd><p>A numeric value specifying the gamma value which regulates the number of supercells generated.
+Defaults to 20.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
     
 
-<p>A list with the following components:</p><dl><dt><code>supercell_expression_matrix</code>:</dt>
-<dd><p>A data.table containing
-the marker expression of all the supercells.
-These are computed by taking the average marker expression of the cells
-captured by each supercell.</p></dd>
-
-<dt><code>supercell_cell_map</code>:</dt>
-<dd><p>A data.table showing which cell is
-captured by which supercell. This is very useful if you intend to work out
-which supercell captures which cell.</p></dd>
-
-
-</dl></div>
+<p>A list with the following components:</p><ul><li><p><code>supercell_expression_matrix</code>:  A data.table object that contains the marker expression for each supercell.
+These marker expressions are computed by calculating the mean of the marker expressions across all cells
+within each individual supercell.</p></li>
+<li><p><code>supercell_cell_map</code>: A data.table that maps each cell to its corresponding supercell.
+This table is essential for identifying the specific supercell each cell has been allocated to.
+It proves particularly useful for analyses that require one to expand the supercells
+to the individual cell level.</p></li>
+</ul></div>
     <div class="section level2">
     <h2 id="author">Author<a class="anchor" aria-label="anchor" href="#author"></a></h2>
     <p>Givanna Putri</p>
@@ -151,7 +149,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Givanna Putri, Belinda Phipson.</p>
+  <p></p><p>Developed by Givanna Putri, George Howitt, Marsh-Wakefield Felix, Thomas Ashhurst, Belinda Phipson.</p>
 </div>
 
 <div class="pkgdown-footer-right">
diff --git a/reference/runSuperCellCyto.html b/reference/runSuperCellCyto.html
index 6d3caa4..fee1021 100644
--- a/reference/runSuperCellCyto.html
+++ b/reference/runSuperCellCyto.html
@@ -1,19 +1,11 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content='Run SuperCell on cytometry data stored as a data.table object.
-This is a wrapper function around the SCImplify function in the
-SuperCell R package by Bilous et.al, 2022.
-We have enhanced it by adding the capacity to "supercell" multiple samples
-in parallel through the use of BiocParallel, and by adding support to
-"supercell" cytometry data.
-More explanations are given in various sections below, and are further
-expanded in our vignette.'><title>Run SuperCell for cytometry data — runSuperCellCyto • SuperCellCyto</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Run SuperCell for cytometry data — runSuperCellCyto"><meta property="og:description" content='Run SuperCell on cytometry data stored as a data.table object.
-This is a wrapper function around the SCImplify function in the
-SuperCell R package by Bilous et.al, 2022.
-We have enhanced it by adding the capacity to "supercell" multiple samples
-in parallel through the use of BiocParallel, and by adding support to
-"supercell" cytometry data.
-More explanations are given in various sections below, and are further
-expanded in our vignette.'><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="This function creates supercells for a cytometry data formatted
+as a data.table object using the SuperCellCyto algorithm.
+Please make sure you read additional details below to better understand
+what the function does and how it works."><title>Run SuperCellCyto on Cytometry Data — runSuperCellCyto • SuperCellCyto</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Run SuperCellCyto on Cytometry Data — runSuperCellCyto"><meta property="og:description" content="This function creates supercells for a cytometry data formatted
+as a data.table object using the SuperCellCyto algorithm.
+Please make sure you read additional details below to better understand
+what the function does and how it works."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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@@ -24,7 +16,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">SuperCellCyto</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.99.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -54,20 +46,16 @@
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Run SuperCell for cytometry data</h1>
+      <img src="" class="logo" alt=""><h1>Run SuperCellCyto on Cytometry Data</h1>
       
       <div class="d-none name"><code>runSuperCellCyto.Rd</code></div>
     </div>
 
     <div class="ref-description section level2">
-    <p>Run SuperCell on cytometry data stored as a data.table object.
-This is a wrapper function around the <code>SCImplify</code> function in the
-SuperCell R package by Bilous et.al, 2022.
-We have enhanced it by adding the capacity to "supercell" multiple samples
-in parallel through the use of BiocParallel, and by adding support to
-"supercell" cytometry data.
-More explanations are given in various sections below, and are further
-expanded in our vignette.</p>
+    <p>This function creates supercells for a cytometry data formatted
+as a data.table object using the SuperCellCyto algorithm.</p>
+<p>Please make sure you read additional details below to better understand
+what the function does and how it works.</p>
     </div>
 
     <div class="section level2">
@@ -87,109 +75,98 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
     <dl><dt>dt</dt>
-<dd><p>data.table object containing the cytometry data.
-Rows represent cells, columns represent markers.
-If this is not a data.table object, the function will warn you about it,
-and then try to convert it to a data.table object.</p></dd>
+<dd><p>A data.table object containing cytometry data where rows represent
+cells and columns represent markers.
+If this is a <a href="https://rdrr.io/r/base/data.frame.html" class="external-link">data.frame</a> object, the function will try to convert it to a data.table object.
+A warning message will be displayed when this happens.
+Otherwise, it will terminate.</p></dd>
 
 
 <dt>markers</dt>
-<dd><p>character vector specifying the markers to run SuperCell on.</p></dd>
+<dd><p>A character vector identifying the markers to create supercells with.</p></dd>
 
 
 <dt>sample_colname</dt>
-<dd><p>character specifying the column in <code>dt</code> that denotes
-the sample of a cell.</p></dd>
+<dd><p>A character string identifying the column in <code>dt</code> that denotes the sample of a cell.</p></dd>
 
 
 <dt>cell_id_colname</dt>
-<dd><p>character specifying the column in <code>dt</code> that denotes
-the unique ID of a cell.</p></dd>
+<dd><p>A character string identifying the column in <code>dt</code> representing each cell's unique ID.</p></dd>
 
 
 <dt>gam</dt>
-<dd><p>numeric specifying the gamma value to be used by SuperCell.
-Default to 20.</p></dd>
+<dd><p>A numeric value specifying the gamma value which regulates the number of supercells generated.
+Defaults to 20.</p></dd>
 
 
 <dt>k_knn</dt>
-<dd><p>numeric specifying the k value to be used by SuperCell's knn.
-Default to 5.</p></dd>
+<dd><p>A numeric value specifying the k value (number of neighbours) used
+to build the kNN network.
+Defaults to 5.</p></dd>
 
 
 <dt>BPPARAM</dt>
-<dd><p>A BiocParallelParam specifying how the samples
-should be "supercelled" in parallel.
-Default to SerialParam, meaning the samples will be processed in serial.
-Please go the section on parallel processing for more details
-on how to best supercell multiple samples in parallel.</p></dd>
+<dd><p>A BiocParallelParam object specifying parallel processing settings.
+Defaults to <code>SerialParam</code>, meaning the samples will be processed sequentially one after the other.
+Refer to additional details section below on parallel processing for more details.</p></dd>
 
 
 <dt>load_balancing</dt>
-<dd><p>logical whether load balancing should be performed when
-multiple samples are to "supercelled" in parallel.
-Please go the section on parallel processing for more details
-on how to best supercell multiple samples in parallel.
-Default to FALSE.</p></dd>
+<dd><p>A logical value  indicating whether to use a custom load balancing
+scheme when processing multiple samples in parallel.
+Defaults to FALSE.
+Refer to additional details section below on parallel processing for more details.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
     
 
-<p><code>runSuperCellCyto</code> will return a list with the following components:</p><dl><dt><code>supercell_object</code>:</dt>
-<dd><p>A list containing the object returned by
-SCimplify function. One object per sample.</p></dd>
-
-<dt><code>supercell_expression_matrix</code>:</dt>
-<dd><p>A data.table containing
-the marker expression of all the supercells.
-These are computed by taking the average marker expression of the cells
-captured by each supercell.</p></dd>
-
-<dt><code>supercell_cell_map</code>:</dt>
-<dd><p>A data.table showing which cell is
-captured by which supercell. This is very useful if you intend to work out
-which supercell captures which cell.</p></dd>
-
-
-</dl></div>
-    <div class="section level2">
-    <h2 id="supercell-multiple-samples-in-parallel">Supercell multiple samples in parallel<a class="anchor" aria-label="anchor" href="#supercell-multiple-samples-in-parallel"></a></h2>
-    
-
-<p>For the function to supercell multiple samples in parallel, you will need to supply
-a BiocParallelParam (as BPPARAM parameter) that leverages parallel evaluation,
-e.g., MulticoreParam or SnowParam.
-Importantly, it is also recommended to set their number of tasks (task parameter) as
-the number of samples you have, and to set the <code>load_balancing</code> parameter to TRUE.
-That way, the jobs that are handling the large samples are responsible (mostly)
-for only that sample, and that the small ones are handled by other jobs which are
-handling much smaller samples.</p>
-    </div>
+<p>A list with the following components:</p><ul><li><p><code>supercell_object</code>: A list containing the object returned by
+SCimplify function.
+One object per sample.
+This object is critical for recomputing supercells in the future.
+Hence do not discard it.</p></li>
+<li><p><code>supercell_expression_matrix</code>:  A data.table object that contains the marker expression for each supercell.
+These marker expressions are computed by calculating the mean of the marker expressions across all cells
+within each individual supercell.</p></li>
+<li><p><code>supercell_cell_map</code>: A data.table that maps each cell to its corresponding supercell.
+This table is essential for identifying the specific supercell each cell has been allocated to.
+It proves particularly useful for analyses that require one to expand the supercells
+to the individual cell level.</p></li>
+</ul></div>
     <div class="section level2">
-    <h2 id="what-is-cell-id-colname">What is <code>cell_id_colname</code><a class="anchor" aria-label="anchor" href="#what-is-cell-id-colname"></a></h2>
+    <h2 id="parallel-processing">Parallel Processing<a class="anchor" aria-label="anchor" href="#parallel-processing"></a></h2>
     
 
-<p>This is a column in <code>dt</code> containing a unique identifier for each cell.
-Commonly, you will have to manually create this column as a FCS file does not
-typically has a field which uniquely identify each cell.
-You can create this ID by giving the cells a numeric value of 1 to however many
-you have, and store this as a column in <code>dt</code>.
-If you don't know how to do this, refer to our vignette.</p>
+<p>SuperCellCyto can process multiple samples simultaneously in parallel.
+This can drastically bring down processing time for dataset with a large number of samples.
+To enable this feature, set the <code>BPPARAM</code> parameter to either a
+MulticoreParam object or a SnowParam object.
+Importantly, it is also recommended to set the number of tasks (i.e., the <code>task</code>
+parameter in either MulticoreParam or SnowParam object)
+to <strong>the number of samples in the dataset</strong>.</p>
+<p>Furthermore, we also recommend setting <code>load_balancing</code> parameter to TRUE.
+This ensures optimal distribution of samples across multiple cores, and is
+particularly important if your samples are of varying sizes (number of cells).</p>
     </div>
     <div class="section level2">
-    <h2 id="what-constitute-a-sample">What constitute a sample<a class="anchor" aria-label="anchor" href="#what-constitute-a-sample"></a></h2>
+    <h2 id="cell-id-and-sample-definitions">Cell ID and Sample Definitions<a class="anchor" aria-label="anchor" href="#cell-id-and-sample-definitions"></a></h2>
     
 
-<p>This function is designed such that all the samples are processed,
-independent of each other.
-Because of this, you can safely assume that each supercell will contain only
-cells from exactly one sample.</p>
-<p>The function will work out which cells come from which sample based on what is
-specified in the <code>sample_colname</code> column.
-This is why it is critical to specify what this column is.</p>
-<p>For most purposes, a sample represents a biological sample in your experiment.
+<p>The <code>cell_id_colname</code> parameter specifies the column in <code>dt</code> that denotes
+the unique identifier for each cell.
+It is perfectly normal to not have this column in your dataset by default.
+The good news is that <strong>it is trivial to create one</strong>.
+You can create a new vector containing a sequence of numbers from 1 to however
+many cells you have, and append this vector as a new column in your dataset.
+Refer to our vignette on how to do this.</p>
+<p>The <code>sample_colname</code> parameter specifies the column in <code>dt</code> that denotes
+the sample a cell came from.
+By default, SuperCellCyto creates supercells for each sample independent of other samples.
+This ensures each supercell to only contain cells from <strong>exactly</strong> one sample.</p>
+<p>What constitute a sample?
+For most purposes, a sample represents a biological sample in your experiment.
 You may be thinking, is it then possible to use this in a different context,
 say creating supercells for each population or cluster rather than a biological
 sample?
@@ -199,56 +176,49 @@ <h2 id="what-constitute-a-sample">What constitute a sample<a class="anchor" aria
     <h2 id="computing-pca">Computing PCA<a class="anchor" aria-label="anchor" href="#computing-pca"></a></h2>
     
 
-<p>By default, the function will start by computing PCA from all the markers
-specified in <code>markers</code> parameter, and that 10 PCs are computed.
-If there are less than 10 markers in the <code>markers</code> parameter, then the
-number of PCs are set to however many markers there are in the <code>markers</code> parameter.</p>
+<p>The function will start by computing PCA from all the markers
+specified in <code>markers</code> parameter.
+By default, the number of PCs calculated is set to 10.
+If there is less than 10 markers in the <code>markers</code> parameter, then the
+number of PCs is set to however many markers there are in the <code>markers</code> parameter.</p>
 <p>Notably, <em>no</em> scaling or transformation were done on the markers' expressions
 prior to computing the PCs.</p>
-<p><code>irlba</code> is not used to calculate PCA as cytometry data tend to only have a
-handful of features (markers) compared to scRNAseq data.
-Hence there is very little gain.</p>
+<p>The function does not use <code>irlba</code> to calculate PCA.
+There is very little gain to use it for cytometry data because of the relatively
+tiny number of features (markers) in the data.</p>
     </div>
     <div class="section level2">
-    <h2 id="setting-the-supercell-graining-level">Setting the supercell graining level<a class="anchor" aria-label="anchor" href="#setting-the-supercell-graining-level"></a></h2>
+    <h2 id="setting-supercell-s-resolution">Setting Supercell's Resolution<a class="anchor" aria-label="anchor" href="#setting-supercell-s-resolution"></a></h2>
     
 
-<p>How many supercells will I get for my dataset? or to phrase it in another way,
-can I estimate how many cells will be captured within each supercell?
-That depends on what you set the <code>gam</code> parameter to.</p>
-<p>The <code>gam</code> parameter is represented by the formula <code>gamma=n_cells/n_supercells</code>
+<p>The <code>gam</code> parameter influences the number of supercells created per sample.
+A lower <code>gam</code> value results in more, and thus generally smaller supercells, and vice versa.</p>
+<p>To estimate how many supercells we will get for our dataset, it is important
+to understand how the <code>gam</code> value is interpreted in the context of number of cells
+in a sample.</p>
+<p><code>gam=n_cells/n_supercells</code>
 where <code>n_cells</code> denotes the number of cells and <code>n_supercells</code> denotes the
-number of supercells to be created.
-By resolving this formula, we can roughly estimate how many supercells you will
-get at the end, and thus, <em>approximately</em> how many cells will be captured
-within each supercell.</p>
-<p>Generally, the smaller the <code>gam</code> parameter is, the more supercells
-you will get.
-Say for instance you have 10,000 cells.
-If <code>gam</code> is set to 10, you will end up with about 1,000 supercells, whereas
-if <code>gam</code> is set to 50, you will end up with about 200 supercells.</p>
-<p>Conversely, as you get more supercells (i.e. smaller <code>gam</code> value),
-the smaller their size will be.
-In other words, each of them will be capturing less cells.
-Importantly, one cannot expect all the supercells to be of the same size.
-Some will capture more/less cells than the other, and that is it not trivial
-to estimate how many will be captured beforehand.
-We may look into swapping the <code>gam</code> value to how many cells to be captured
-within each supercell <em>in the future</em>.
-More thoughts are required into whether this make sense.</p>
-<p>Lastly, for now, a <code>gam</code> value for all your samples
-(read the section above if you are not sure what I mean by samples here).
-<em>In the future</em>, we can perhaps look into setting different <code>gam</code> values
-for different samples.</p>
+number of supercells to be created.</p>
+<p>By resolving the formula above, we can roughly estimate how many supercells we will
+get per sample.
+For example, say we have 2 samples, sample A and B.
+Sample A has 10,000 cells, while sample B has 5,000 cells:</p><ul><li><p>If <code>gam</code> is set to 10, we will end up with 1,000 supercells for sample A
+and 500 supercells for sample B, a total of 1,500 supercells.</p></li>
+<li><p>If <code>gam</code> is set to 50, we will end up with 200 supercells for sample A
+and 100 supercells for sample B, a total of 300 supercells.</p></li>
+</ul><p><em>Importantly</em>, one cannot expect all the supercells to be of the same size.
+Some will capture more/less cells than others.
+It is not trivial to estimate how many cells will be captured in each supercell beforehand.</p>
     </div>
     <div class="section level2">
     <h2 id="computing-knn-network">Computing kNN network<a class="anchor" aria-label="anchor" href="#computing-knn-network"></a></h2>
     
 
-<p>The parameter <code>k_knn</code> governs and the k value used to compute the
-single-cell kNN network.
-Actual (not approximate) kNN network is created, and walktrap algorithm is
-used to form supercells from the kNN network.</p>
+<p>To create supercells, a kNN (k-Nearest Neighbors) network is constructed based
+on the <code>k_knn</code> parameter which dictates the number of neighbours (for each cell) used to create
+the network.
+An actual (not approximate) kNN network is created.</p>
+<p>A walktrap algorithm then uses this network to group cells into supercells.</p>
     </div>
     <div class="section level2">
     <h2 id="author">Author<a class="anchor" aria-label="anchor" href="#author"></a></h2>
@@ -278,7 +248,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Givanna Putri, Belinda Phipson.</p>
+  <p></p><p>Developed by Givanna Putri, George Howitt, Marsh-Wakefield Felix, Thomas Ashhurst, Belinda Phipson.</p>
 </div>
 
 <div class="pkgdown-footer-right">
diff --git a/reference/simCytoData.html b/reference/simCytoData.html
index 6c10fe7..1c8bf4c 100644
--- a/reference/simCytoData.html
+++ b/reference/simCytoData.html
@@ -1,8 +1,8 @@
 <!DOCTYPE html>
-<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Simulates some cytometry data for use in testing or documenting functions
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en"><head><meta http-equiv="Content-Type" content="text/html; charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><meta name="description" content="Simulate some cytometry data for use in testing or documenting functions
 which require some cytometry data.
 Please run set.seed before running the function if you want to ensure
-reproducibility."><title>Simulates cytometry data — simCytoData • SuperCellCyto</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Simulates cytometry data — simCytoData"><meta property="og:description" content="Simulates some cytometry data for use in testing or documenting functions
+reproducibility."><title>Simulate cytometry data — simCytoData • SuperCellCyto</title><script src="../deps/jquery-3.6.0/jquery-3.6.0.min.js"></script><meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"><link href="../deps/bootstrap-5.3.1/bootstrap.min.css" rel="stylesheet"><script src="../deps/bootstrap-5.3.1/bootstrap.bundle.min.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- bootstrap-toc --><script src="https://cdn.jsdelivr.net/gh/afeld/bootstrap-toc@v1.0.1/dist/bootstrap-toc.min.js" integrity="sha256-4veVQbu7//Lk5TSmc7YV48MxtMy98e26cf5MrgZYnwo=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- search --><script src="https://cdnjs.cloudflare.com/ajax/libs/fuse.js/6.4.6/fuse.js" integrity="sha512-zv6Ywkjyktsohkbp9bb45V6tEMoWhzFzXis+LrMehmJZZSys19Yxf1dopHx7WzIKxr5tK2dVcYmaCk2uqdjF4A==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/autocomplete.js/0.38.0/autocomplete.jquery.min.js" integrity="sha512-GU9ayf+66Xx2TmpxqJpliWbT5PiGYxpaG8rfnBEk1LL8l1KGkRShhngwdXK1UgqhAzWpZHSiYPc09/NwDQIGyg==" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mark.js/8.11.1/mark.min.js" integrity="sha512-5CYOlHXGh6QpOFA/TeTylKLWfB3ftPsde7AnmhuitiTX4K5SqCLBeKro6sPS8ilsz1Q4NRx3v8Ko2IBiszzdww==" crossorigin="anonymous"></script><!-- pkgdown --><script src="../pkgdown.js"></script><meta property="og:title" content="Simulate cytometry data — simCytoData"><meta property="og:description" content="Simulate some cytometry data for use in testing or documenting functions
 which require some cytometry data.
 Please run set.seed before running the function if you want to ensure
 reproducibility."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
@@ -16,7 +16,7 @@
     
     <a class="navbar-brand me-2" href="../index.html">SuperCellCyto</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.99.0</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">0.1.0</small>
 
     
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -46,13 +46,13 @@
 </nav><div class="container template-reference-topic">
 <div class="row">
   <main id="main" class="col-md-9"><div class="page-header">
-      <img src="" class="logo" alt=""><h1>Simulates cytometry data</h1>
+      <img src="" class="logo" alt=""><h1>Simulate cytometry data</h1>
       
       <div class="d-none name"><code>simCytoData.Rd</code></div>
     </div>
 
     <div class="ref-description section level2">
-    <p>Simulates some cytometry data for use in testing or documenting functions
+    <p>Simulate some cytometry data for use in testing or documenting functions
 which require some cytometry data.
 Please run <code>set.seed</code> before running the function if you want to ensure
 reproducibility.</p>
@@ -66,20 +66,20 @@ <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage">
     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
     <dl><dt>nmarkers</dt>
-<dd><p>numeric specifying number of markers to simulate.</p></dd>
+<dd><p>A numeric value specifying number of markers to simulate.</p></dd>
 
 
 <dt>ncells</dt>
-<dd><p>numeric vector the number of cells to simulate per sample.
-1 vector element per sample.#'</p></dd>
+<dd><p>A numeric vector specifying the number of cells to simulate per sample.
+1 vector element per sample.</p></dd>
 
 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>
     
 
-<p>A data.table containing the simulated data.
-Rows are cells, columns are markers.</p>
+<p>A data.table object containing the simulated cytometry data where rows represent
+cells and columns represent markers.</p>
     </div>
     <div class="section level2">
     <h2 id="author">Author<a class="anchor" aria-label="anchor" href="#author"></a></h2>
@@ -115,7 +115,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 
 
     <footer><div class="pkgdown-footer-left">
-  <p></p><p>Developed by Givanna Putri, Belinda Phipson.</p>
+  <p></p><p>Developed by Givanna Putri, George Howitt, Marsh-Wakefield Felix, Thomas Ashhurst, Belinda Phipson.</p>
 </div>
 
 <div class="pkgdown-footer-right">
diff --git a/search.json b/search.json
index 0a94450..d19eb5b 100644
--- a/search.json
+++ b/search.json
@@ -1 +1 @@
-[{"path":[]},{"path":"https://phipsonlab.github.io/SuperCellCyto/CODE_OF_CONDUCT.html","id":"our-pledge","dir":"","previous_headings":"","what":"Our Pledge","title":"Contributor Covenant Code of Conduct","text":"members, contributors, leaders pledge make participation community harassment-free experience everyone, regardless age, body size, visible invisible disability, ethnicity, sex characteristics, gender identity expression, level experience, education, socio-economic status, nationality, personal appearance, race, caste, color, religion, sexual identity orientation. pledge act interact ways contribute open, welcoming, diverse, inclusive, healthy community.","code":""},{"path":"https://phipsonlab.github.io/SuperCellCyto/CODE_OF_CONDUCT.html","id":"our-standards","dir":"","previous_headings":"","what":"Our Standards","title":"Contributor Covenant Code of Conduct","text":"Examples behavior contributes positive environment community include: Demonstrating empathy kindness toward people respectful differing opinions, viewpoints, experiences Giving gracefully accepting constructive feedback Accepting responsibility apologizing affected mistakes, learning experience Focusing best just us individuals, overall community Examples unacceptable behavior include: use sexualized language imagery, sexual attention advances kind Trolling, insulting derogatory comments, personal political attacks Public private harassment Publishing others’ private information, physical email address, without explicit permission conduct reasonably considered inappropriate professional setting","code":""},{"path":"https://phipsonlab.github.io/SuperCellCyto/CODE_OF_CONDUCT.html","id":"enforcement-responsibilities","dir":"","previous_headings":"","what":"Enforcement Responsibilities","title":"Contributor Covenant Code of Conduct","text":"Community leaders responsible clarifying enforcing standards acceptable behavior take appropriate fair corrective action response behavior deem inappropriate, threatening, offensive, harmful. Community leaders right responsibility remove, edit, reject comments, commits, code, wiki edits, issues, contributions aligned Code Conduct, communicate reasons moderation decisions appropriate.","code":""},{"path":"https://phipsonlab.github.io/SuperCellCyto/CODE_OF_CONDUCT.html","id":"scope","dir":"","previous_headings":"","what":"Scope","title":"Contributor Covenant Code of Conduct","text":"Code Conduct applies within community spaces, also applies individual officially representing community public spaces. Examples representing community include using official e-mail address, posting via official social media account, acting appointed representative online offline event.","code":""},{"path":"https://phipsonlab.github.io/SuperCellCyto/CODE_OF_CONDUCT.html","id":"enforcement","dir":"","previous_headings":"","what":"Enforcement","title":"Contributor Covenant Code of Conduct","text":"Instances abusive, harassing, otherwise unacceptable behavior may reported community leaders responsible enforcement putri.g@wehi.edu.au. complaints reviewed investigated promptly fairly. community leaders obligated respect privacy security reporter incident.","code":""},{"path":"https://phipsonlab.github.io/SuperCellCyto/CODE_OF_CONDUCT.html","id":"enforcement-guidelines","dir":"","previous_headings":"","what":"Enforcement Guidelines","title":"Contributor Covenant Code of Conduct","text":"Community leaders follow Community Impact Guidelines determining consequences action deem violation Code Conduct:","code":""},{"path":"https://phipsonlab.github.io/SuperCellCyto/CODE_OF_CONDUCT.html","id":"id_1-correction","dir":"","previous_headings":"Enforcement Guidelines","what":"1. Correction","title":"Contributor Covenant Code of Conduct","text":"Community Impact: Use inappropriate language behavior deemed unprofessional unwelcome community. Consequence: private, written warning community leaders, providing clarity around nature violation explanation behavior inappropriate. public apology may requested.","code":""},{"path":"https://phipsonlab.github.io/SuperCellCyto/CODE_OF_CONDUCT.html","id":"id_2-warning","dir":"","previous_headings":"Enforcement Guidelines","what":"2. Warning","title":"Contributor Covenant Code of Conduct","text":"Community Impact: violation single incident series actions. Consequence: warning consequences continued behavior. interaction people involved, including unsolicited interaction enforcing Code Conduct, specified period time. includes avoiding interactions community spaces well external channels like social media. Violating terms may lead temporary permanent ban.","code":""},{"path":"https://phipsonlab.github.io/SuperCellCyto/CODE_OF_CONDUCT.html","id":"id_3-temporary-ban","dir":"","previous_headings":"Enforcement Guidelines","what":"3. Temporary Ban","title":"Contributor Covenant Code of Conduct","text":"Community Impact: serious violation community standards, including sustained inappropriate behavior. Consequence: temporary ban sort interaction public communication community specified period time. public private interaction people involved, including unsolicited interaction enforcing Code Conduct, allowed period. Violating terms may lead permanent ban.","code":""},{"path":"https://phipsonlab.github.io/SuperCellCyto/CODE_OF_CONDUCT.html","id":"id_4-permanent-ban","dir":"","previous_headings":"Enforcement Guidelines","what":"4. Permanent Ban","title":"Contributor Covenant Code of Conduct","text":"Community Impact: Demonstrating pattern violation community standards, including sustained inappropriate behavior, harassment individual, aggression toward disparagement classes individuals. Consequence: permanent ban sort public interaction within community.","code":""},{"path":"https://phipsonlab.github.io/SuperCellCyto/CODE_OF_CONDUCT.html","id":"attribution","dir":"","previous_headings":"","what":"Attribution","title":"Contributor Covenant Code of Conduct","text":"Code Conduct adapted Contributor Covenant, version 2.1, available https://www.contributor-covenant.org/version/2/1/code_of_conduct.html. Community Impact Guidelines inspired [Mozilla’s code conduct enforcement ladder][https://github.com/mozilla/inclusion]. answers common questions code conduct, see FAQ https://www.contributor-covenant.org/faq. Translations available https://www.contributor-covenant.org/translations.","code":""},{"path":"https://phipsonlab.github.io/SuperCellCyto/CONTRIBUTING.html","id":null,"dir":"","previous_headings":"","what":"Contributing to SuperCellCyto","title":"Contributing to SuperCellCyto","text":"outlines propose change SuperCellCyto.","code":""},{"path":"https://phipsonlab.github.io/SuperCellCyto/CONTRIBUTING.html","id":"making-changes-to-the-package","dir":"","previous_headings":"","what":"Making changes to the package","title":"Contributing to SuperCellCyto","text":"want make change, please first file issue make sure someone team agrees ’s needed. ’ve found bug, please file issue illustrates bug minimal reprex (also help write unit test, needed).","code":""},{"path":"https://phipsonlab.github.io/SuperCellCyto/CONTRIBUTING.html","id":"pull-request-process","dir":"","previous_headings":"Making changes to the package","what":"Pull request process","title":"Contributing to SuperCellCyto","text":"Fork package clone onto computer. Install development dependencies devtools::install_dev_deps(). Create Git branch pull request (PR). can use usethis::pr_init(\"brief-description--change\"). Make changes, commit git. Make sure unit tests passing. can use devtools::test(). Create PR. Make sure title PR briefly describe change, body PR contain Fixes #issue-number.","code":""},{"path":"https://phipsonlab.github.io/SuperCellCyto/CONTRIBUTING.html","id":"code-of-conduct","dir":"","previous_headings":"","what":"Code of Conduct","title":"Contributing to SuperCellCyto","text":"Please note SuperCellCyto project released Contributor Code Conduct. contributing project agree abide terms.","code":""},{"path":"https://phipsonlab.github.io/SuperCellCyto/articles/how_to_supercell.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"How to create supercells","text":"vignette describes steps reduce size vast high-dimensional cytometry data using SuperCellCyto, R package based SuperCell R package David Gfeller lab University Lausanne. Please note ’re still actively updating vignette (fact package ), welcome feedbacks improve . myriad ways use SuperCell. try cover many use cases possible, bound miss something. case, please reach github repository creating Github issue.","code":""},{"path":"https://phipsonlab.github.io/SuperCellCyto/articles/how_to_supercell.html","id":"installation","dir":"Articles","previous_headings":"","what":"Installation","title":"How to create supercells","text":"install SuprCellCyto, need use devtools package CRAN. can install devtools using install.packages(\"devtools\") command. Thereafter, can install SuperCellCyto using devtools::install_github(\"phipsonlab/SuperCellCyto\"). SuperCellCyto requires SuperCell R package installed run properly. use devtools::install_github command install SuperCellCyto, , theory, automatically installed. case doesn’t, can manually install using devtools::install_github(\"GfellerLab/SuperCell\").","code":""},{"path":"https://phipsonlab.github.io/SuperCellCyto/articles/how_to_supercell.html","id":"preparing-your-dataset","dir":"Articles","previous_headings":"","what":"Preparing your dataset","title":"How to create supercells","text":"function creates supercells called runSuperCellCyto, operates data.table object, enhanced version R native data.frame. may add support SummarizedExperiment flowFrame object future enough demands . raw data stored csv file, can import data.table object using fread function. raw data stored across multiple csv files FCS files (common cytometry), need help Spectre R package import adata.table object. Specifically, need : Run read.files function read FCS csv files. Run .merge.files merge resulting data.table objects one. unsure steps work , look example Spectre vignette. Using vignette , csv files, can run steps vignette , changing InputDirectory variable. FCS files, need change file.type parameter read.files function .fcs. vignette, simulate toy data using simCytoData function. several things note dataset. Let’s go one one sub-section .","code":"n_markers <- 15 n_samples <- 3 dat <- SuperCellCyto::simCytoData(nmarkers = n_markers, ncells = rep(10000, n_samples)) head(dat) #>    Marker_1 Marker_2 Marker_3 Marker_4 Marker_5  Marker_6  Marker_7 Marker_8 #> 1: 6.348896 12.76922 8.081814 14.82767 16.78516  8.327598  8.706527 6.917179 #> 2: 7.045928 13.07817 6.584392 16.05816 16.73959  8.507928  5.878093 6.785206 #> 3: 9.202952 12.56312 8.260834 16.10469 18.76380 10.285851  9.657357 7.576643 #> 4: 6.453132 13.43010 8.273119 13.98670 17.26809  6.989559  8.875397 5.909259 #> 5: 7.723788 13.59895 8.089639 14.18880 17.65259 10.306980  7.440030 6.269055 #> 6: 8.712999 13.75800 7.982606 16.65486 16.62515  6.264880 10.946625 3.549110 #>    Marker_9 Marker_10 Marker_11 Marker_12 Marker_13 Marker_14 Marker_15 #> 1: 11.58426  12.88936  7.133000  18.24950  8.366636  5.202447  7.710622 #> 2: 14.81178  12.12547  8.223698  18.35320  8.967888  5.452228 10.106635 #> 3: 15.23759  11.56163  6.905385  17.91669 12.069136  8.134681  9.570365 #> 4: 14.62778  10.85856  8.380473  16.59761 10.816997  7.746163 10.527337 #> 5: 14.82137  11.43294  8.125290  16.37683 11.425650  8.851541 10.186497 #> 6: 15.13292  12.60006  8.958321  17.62148 11.178471  9.192945  9.014084 #>      Sample Cell_Id #> 1: Sample_1  Cell_1 #> 2: Sample_1  Cell_2 #> 3: Sample_1  Cell_3 #> 4: Sample_1  Cell_4 #> 5: Sample_1  Cell_5 #> 6: Sample_1  Cell_6"},{"path":"https://phipsonlab.github.io/SuperCellCyto/articles/how_to_supercell.html","id":"the-markers","dir":"Articles","previous_headings":"Preparing your dataset","what":"The markers","title":"How to create supercells","text":"runSuperCellCyto function perform data transformation scaling. Thus, must ensure dataset already appropriately transformed using either arc-sinh transformation linear binning (using FlowJo). tutorial explains data transformation process great detail: (https://wiki.centenary.org.au/display/SPECTRE/Data+transformation). Please read unsure transform data. toy dataset, transform data using arc-sinh transformation implementation provided base R asinh function: Breaking steps, : Identify columns denoting markers. Set co-factor 5. transformation store dat_asinh variable. Set dat_asinh column name reflect values column (marker) haas undergone arc-sinh transformation. Combine dat dat_asinh using cbind.","code":"# Specify which columns are the markers to transform marker_cols <- paste0(\"Marker_\", seq_len(n_markers)) # The co-factor for arc-sinh cofactor <- 5  # Do the transformation dat_asinh <- asinh(dat[, marker_cols, with = FALSE] / cofactor)  # Rename the new columns marker_cols_asinh <- paste0(marker_cols, \"_asinh\") names(dat_asinh) <- marker_cols_asinh  # Add them our previously loaded data dat <- cbind(dat, dat_asinh)  head(dat[, marker_cols_asinh, with = FALSE]) #>    Marker_1_asinh Marker_2_asinh Marker_3_asinh Marker_4_asinh Marker_5_asinh #> 1:       1.059890       1.667045       1.257624       1.807491       1.925684 #> 2:       1.143309       1.689341       1.088700       1.883327       1.923079 #> 3:       1.369985       1.651909       1.276312       1.886090       2.032936 #> 4:       1.072723       1.714186       1.277583       1.752334       1.952900 #> 5:       1.219337       1.725903       1.258447       1.765853       1.974071 #> 6:       1.322220       1.736824       1.247138       1.918218       1.916508 #>    Marker_6_asinh Marker_7_asinh Marker_8_asinh Marker_9_asinh Marker_10_asinh #> 1:       1.283205       1.321576      1.1283165       1.576978        1.675770 #> 2:       1.301624       1.000270      1.1127565       1.806475        1.619047 #> 3:       1.468914       1.412562      1.2032360       1.833367        1.575182 #> 4:       1.136768       1.338274      1.0043028       1.794638        1.517892 #> 5:       1.470760       1.188092      1.0499715       1.807088        1.564918 #> 6:       1.049451       1.525234      0.6606944       1.826820        1.654638 #>    Marker_11_asinh Marker_12_asinh Marker_13_asinh Marker_14_asinh #> 1:        1.153346        2.006106        1.287217       0.9097161 #> 2:        1.272459        2.011572        1.347318       0.9439046 #> 3:        1.126934        1.988367        1.614743       1.2631736 #> 4:        1.288636        1.914920        1.514410       1.2217659 #> 5:        1.262190        1.902105        1.564333       1.3359296 #> 6:        1.346385        1.972374        1.544332       1.3690291 #>    Marker_15_asinh #> 1:        1.217905 #> 2:        1.453133 #> 3:        1.404534 #> 4:        1.489831 #> 5:        1.460193 #> 6:        1.351808"},{"path":"https://phipsonlab.github.io/SuperCellCyto/articles/how_to_supercell.html","id":"cell-id-column","dir":"Articles","previous_headings":"Preparing your dataset","what":"Cell id column","title":"How to create supercells","text":"create supercell, must provide column uniquely identify cell, akin Cell_Id column toy data generated : purpose cell id allow SuperCell uniquely identify cell dataset. ID come super handy later /need work cells belong supercells. Generally, need create ID . dataset won’t come ID already embedded . simple cell id can made concatenating word Cell row number. Something like following: , store cell id column called Cell_id_dummy. values Cell_1, Cell_2, way Cell_x x number cells dataset. Note, can name cell id column however like, id, cell_identity, etc. Importantly, need make sure note column name need pass runSuperCellCyto function later.","code":"head(dat$Cell_Id, n = 10) #>  [1] \"Cell_1\"  \"Cell_2\"  \"Cell_3\"  \"Cell_4\"  \"Cell_5\"  \"Cell_6\"  \"Cell_7\"  #>  [8] \"Cell_8\"  \"Cell_9\"  \"Cell_10\" dat$Cell_id_dummy <- paste0(\"Cell_\", seq_len(nrow(dat))) head(dat$Cell_id_dummy, n = 10) #>  [1] \"Cell_1\"  \"Cell_2\"  \"Cell_3\"  \"Cell_4\"  \"Cell_5\"  \"Cell_6\"  \"Cell_7\"  #>  [8] \"Cell_8\"  \"Cell_9\"  \"Cell_10\""},{"path":"https://phipsonlab.github.io/SuperCellCyto/articles/how_to_supercell.html","id":"sample-column","dir":"Articles","previous_headings":"Preparing your dataset","what":"Sample column","title":"How to create supercells","text":"notice toy data , column called Sample. default, column refers biological sample cells come . toy data , 3 samples, Sample_1, Sample_2, Sample_3: 10,000 cells per sample: create supercells, necessary Sample column dataset. can name column however like, Samp, Cell_Samp. However, make sure note column name need pass runSuperCellCyto function. [Creating supercells][#creating-supercells] section. 1 biological sample dataset? matter. still need column dataset, pass column name runSuperCellCyto function. difference column 1 unique value. need ? ensure supercell contains cells exactly 1 sample. , general, make sense mix cells different biological samples one supercell. Additionally (important), runSuperCellCyto function can process samples parallel set BPPARAM parameter BiocParallelParam class leverage parallel processing. Running runSuperCellCyto parallel section . However, want supercell contain cells different biological samples, need create new Sample column containing exactly 1 unique value, pass column name runSuperCellCyto function. : may wonder whether possible use SuperCellCyto reduce number cells captured cluster (cell type) can make UMAP/tSNE plot crowded? Commonly cytometry, use stratified sampling subsample clusters drawing UMAP/tSNE plot avoid overcrowding . short answer , yes can. See Using runSuperCellCyto stratified summarising section information.","code":"unique(dat$Sample) #> [1] \"Sample_1\" \"Sample_2\" \"Sample_3\" table(dat$Sample) #>  #> Sample_1 Sample_2 Sample_3  #>    10000    10000    10000"},{"path":"https://phipsonlab.github.io/SuperCellCyto/articles/how_to_supercell.html","id":"creating-supercells","dir":"Articles","previous_headings":"","what":"Creating supercells","title":"How to create supercells","text":"Now imported data, let’s create supercells. First, let’s store markers, sample, cell id column variables: pass , together dataset runSuperCellCyto function create supercells: Now let’s dig deeper object created: list containing 3 elements:","code":"markers_col <- paste0(\"Marker_\", seq_len(n_markers), \"_asinh\") sample_col <- \"Sample\" cell_id_col <- \"Cell_Id_dummy\" supercells <- runSuperCellCyto(   dt = dat,   markers = markers_col,   sample_colname = sample_col,   cell_id_colname = cell_id_col ) #> Warning in SCimplify(X = mt, genes.use = rownames(mt), do.scale = FALSE, : colnames(X) is Null,  #> Gene expression matrix X is expected to have cellIDs as colnames!  #> CellIDs will be created automatically in a form 'cell_i'  #> Warning in SCimplify(X = mt, genes.use = rownames(mt), do.scale = FALSE, : colnames(X) is Null,  #> Gene expression matrix X is expected to have cellIDs as colnames!  #> CellIDs will be created automatically in a form 'cell_i'  #> Warning in SCimplify(X = mt, genes.use = rownames(mt), do.scale = FALSE, : colnames(X) is Null,  #> Gene expression matrix X is expected to have cellIDs as colnames!  #> CellIDs will be created automatically in a form 'cell_i' class(supercells) #> [1] \"list\" names(supercells) #> [1] \"supercell_expression_matrix\" \"supercell_cell_map\"          #> [3] \"supercell_object\""},{"path":"https://phipsonlab.github.io/SuperCellCyto/articles/how_to_supercell.html","id":"supercell-object","dir":"Articles","previous_headings":"Creating supercells","what":"Supercell object","title":"How to create supercells","text":"supercell_object contains metadata used create supercells. list, element contains metadata used create supercells sample. come handy need debug supercells later line.","code":""},{"path":"https://phipsonlab.github.io/SuperCellCyto/articles/how_to_supercell.html","id":"supercell-expression-matrix","dir":"Articles","previous_headings":"Creating supercells","what":"Supercell expression matrix","title":"How to create supercells","text":"supercell_expression_matrix contains marker expression supercell. calculated taking average marker expression cells contained within supercell. Therein, following columns: markers previously specified markers_col variable. column (Sample case) denoting sample supercell belongs , (note column name stored sample_col variable). SuperCellId column denoting unique ID supercell.","code":"head(supercells$supercell_expression_matrix) #>    Marker_1_asinh Marker_2_asinh Marker_3_asinh Marker_4_asinh Marker_5_asinh #> 1:       1.063102       1.615852       1.142469       1.795116       1.963970 #> 2:       1.139560       1.592343       1.265137       1.762866       1.901955 #> 3:       1.247001       1.642296       1.174917       1.825329       1.974449 #> 4:       1.302365       1.659593       1.146650       1.804860       1.949896 #> 5:       1.095591       1.638470       1.240644       1.794487       1.946385 #> 6:       1.254591       1.654347       1.049694       1.806171       1.941226 #>    Marker_6_asinh Marker_7_asinh Marker_8_asinh Marker_9_asinh Marker_10_asinh #> 1:       1.511784       1.474463       1.240553       1.784000        1.489342 #> 2:       1.251121       1.331333       1.034064       1.785684        1.437205 #> 3:       1.441164       1.272617       1.178466       1.823684        1.565898 #> 4:       1.262037       1.376983       1.207233       1.828726        1.573047 #> 5:       1.358139       1.246590       1.066354       1.793704        1.516685 #> 6:       1.401569       1.277088       1.235338       1.808203        1.542743 #>    Marker_11_asinh Marker_12_asinh Marker_13_asinh Marker_14_asinh #> 1:        1.225903        1.959363        1.583395        1.298039 #> 2:        1.272995        1.904793        1.538821        1.296632 #> 3:        1.178607        1.951651        1.633625        1.160451 #> 4:        1.167396        1.929309        1.524944        1.039197 #> 5:        1.215639        1.930916        1.435486        1.220823 #> 6:        1.304864        1.957161        1.508904        1.304469 #>    Marker_15_asinh   Sample                 SuperCellId #> 1:        1.470743 Sample_1 SuperCell_1_Sample_Sample_1 #> 2:        1.436878 Sample_1 SuperCell_2_Sample_Sample_1 #> 3:        1.460871 Sample_1 SuperCell_3_Sample_Sample_1 #> 4:        1.457348 Sample_1 SuperCell_4_Sample_Sample_1 #> 5:        1.430103 Sample_1 SuperCell_5_Sample_Sample_1 #> 6:        1.410685 Sample_1 SuperCell_6_Sample_Sample_1 names(supercells$supercell_expression_matrix) #>  [1] \"Marker_1_asinh\"  \"Marker_2_asinh\"  \"Marker_3_asinh\"  \"Marker_4_asinh\"  #>  [5] \"Marker_5_asinh\"  \"Marker_6_asinh\"  \"Marker_7_asinh\"  \"Marker_8_asinh\"  #>  [9] \"Marker_9_asinh\"  \"Marker_10_asinh\" \"Marker_11_asinh\" \"Marker_12_asinh\" #> [13] \"Marker_13_asinh\" \"Marker_14_asinh\" \"Marker_15_asinh\" \"Sample\"          #> [17] \"SuperCellId\""},{"path":"https://phipsonlab.github.io/SuperCellCyto/articles/how_to_supercell.html","id":"supercellid","dir":"Articles","previous_headings":"Creating supercells > Supercell expression matrix","what":"SuperCellId","title":"How to create supercells","text":"Let’s look SuperCellId: Let’s break one , SuperCell_1_Sample_Sample_1. SuperCell_1 numbering (1 however many supercells sample) used uniquely identify supercell sample. Notably, may encounter (SuperCell_1, SuperCell_2) repeated across different samples, e.g., 3 supercells’ id pre-fixed SuperCell_1, make equal one another! SuperCell_1_Sample_Sample_1 contain cells Sample_1 SuperCell_1_Sample_Sample_2 contain cells Sample_2. now, may noticed appended sample name supercell id. aids differentiating supercells different samples.","code":"head(unique(supercells$supercell_expression_matrix$SuperCellId)) #> [1] \"SuperCell_1_Sample_Sample_1\" \"SuperCell_2_Sample_Sample_1\" #> [3] \"SuperCell_3_Sample_Sample_1\" \"SuperCell_4_Sample_Sample_1\" #> [5] \"SuperCell_5_Sample_Sample_1\" \"SuperCell_6_Sample_Sample_1\" supercell_ids <- unique(supercells$supercell_expression_matrix$SuperCellId) supercell_ids[grep(\"SuperCell_1_\", supercell_ids)] #> [1] \"SuperCell_1_Sample_Sample_1\" \"SuperCell_1_Sample_Sample_2\" #> [3] \"SuperCell_1_Sample_Sample_3\""},{"path":"https://phipsonlab.github.io/SuperCellCyto/articles/how_to_supercell.html","id":"supercell-cell-map","dir":"Articles","previous_headings":"Creating supercells","what":"Supercell cell map","title":"How to create supercells","text":"supercell_cell_map maps cell dataset supercell belongs . map useful later need expand supercells . Additionally, also reason need column dataset uniquely identify cell.","code":"head(supercells$supercell_cell_map) #>                      SuperCellID   Sample #> 1:  SuperCell_91_Sample_Sample_1 Sample_1 #> 2: SuperCell_211_Sample_Sample_1 Sample_1 #> 3: SuperCell_335_Sample_Sample_1 Sample_1 #> 4: SuperCell_331_Sample_Sample_1 Sample_1 #> 5: SuperCell_191_Sample_Sample_1 Sample_1 #> 6: SuperCell_127_Sample_Sample_1 Sample_1"},{"path":"https://phipsonlab.github.io/SuperCellCyto/articles/how_to_supercell.html","id":"running-runsupercellcyto-in-parallel","dir":"Articles","previous_headings":"","what":"Running runSuperCellCyto in parallel","title":"How to create supercells","text":"default, runSuperCellCyto process sample one . sample processed independent one another, can process parallel. , need create BiocParallelParam object leverages parallel processing. Additionally, also set number tasks number samples, set load_balancing parameter TRUE jobs supercelling large samples assigned small samples (instead given supercelling smaller samples). Notably, set workers total number cores computer, render computer useless anything else (might blow RAM). find total number cores computer, can use parallel’s detectCores.","code":"n_cores <- detectCores() supercell_par <- runSuperCellCyto(   dt = dat,   markers = markers_col,   sample_colname = sample_col,   cell_id_colname = cell_id_col,   BPPARAM = MulticoreParam(     workers = n_cores - 1,     tasks = n_samples   ),   load_balancing = TRUE )"},{"path":"https://phipsonlab.github.io/SuperCellCyto/articles/how_to_supercell.html","id":"controlling-the-supercells-granularity","dir":"Articles","previous_headings":"","what":"Controlling the supercells’ granularity","title":"How to create supercells","text":"described runSuperCellCyto function’s documentation, let’s briefly go . runSuperCellCyto function equipped various parameters can customise alter composition supercells. one likely used gam parameter. gam parameter controls many supercells generate, indirectly, many cells captured within supercell. parameter resolved following formula gam=n_cells/n_supercells n_cell denotes number cells n_supercells denotes number supercells. general, larger gam parameter set , less supercells get. Say instance 10,000 cells. gam set 10, end 1,000 supercells, whereas gam set 50, end 200 supercells. may noticed, reading sections , runSuperCellCyto ran sample independent , can set 1 value gam parameter. Indeed, now, gam value used across samples, depending many cells sample, end different number supercells sample. instance, say 10,000 cells sample 1, 100,000 cells sample 2. gam set 10, sample 1, get 1,000 supercells (10,000/10) sample 2, get 10,000 supercells (100,000/10). future, may add ability specify different gam value different samples. now, want , need break data multiple data.table objects, containing data 1 sample, run runSuperCellCyto function different gam parameter value. Something like following: Subsequently, extract combine supercell_expression_matrix supercell_cell_map, need use rbind:","code":"n_markers <- 10 dat <- simCytoData(nmarkers = n_markers) markers_col <- paste0(\"Marker_\", seq_len(n_markers)) sample_col <- \"Sample\" cell_id_col <- \"Cell_Id\"  samples <- unique(dat[[sample_col]]) gam_values <- c(10, 20, 10)  supercells_diff_gam <- lapply(seq_len(length(samples)), function(i) {   sample <- samples[i]   gam <- gam_values[i]   dat_samp <- dat[dat$Sample == sample, ]   supercell_samp <- runSuperCellCyto(     dt = dat_samp,     markers = markers_col,     sample_colname = sample_col,     cell_id_colname = cell_id_col,     gam = gam   )   return(supercell_samp) }) supercell_expression_matrix <- do.call(   \"rbind\", lapply(supercells_diff_gam, function(x) x[[\"supercell_expression_matrix\"]]) )  supercell_cell_map <- do.call(   \"rbind\", lapply(supercells_diff_gam, function(x) x[[\"supercell_cell_map\"]]) ) rbind(head(supercell_expression_matrix, n = 3), tail(supercell_expression_matrix, n = 3)) #>    Marker_1 Marker_2  Marker_3 Marker_4 Marker_5 Marker_6  Marker_7 Marker_8 #> 1: 6.500549 10.04351  6.917036 9.754944 17.49099 12.47481  6.755746 14.99223 #> 2: 6.219941 11.13442  4.471067 8.882971 18.96532 13.73045  6.488840 13.78131 #> 3: 8.237611 11.50332  6.627568 8.507735 15.10083 14.65808  7.313123 14.58984 #> 4: 8.406849 13.21608 14.083744 5.125704 15.79521 13.75427 12.986377 18.56063 #> 5: 7.946571 12.70798 14.067321 8.803080 15.05481 13.42700 13.735859 21.44997 #> 6: 9.128595 11.18219 12.629981 6.711764 16.48079 15.20647 14.924023 19.48714 #>     Marker_9 Marker_10   Sample                   SuperCellId #> 1: 13.450022 10.328687 Sample_1   SuperCell_1_Sample_Sample_1 #> 2: 11.831775 11.541301 Sample_1   SuperCell_2_Sample_Sample_1 #> 3: 11.869873 12.023383 Sample_1   SuperCell_3_Sample_Sample_1 #> 4:  8.369949  7.195550 Sample_2 SuperCell_498_Sample_Sample_2 #> 5:  6.666370  7.260202 Sample_2 SuperCell_499_Sample_Sample_2 #> 6:  7.481778  6.322925 Sample_2 SuperCell_500_Sample_Sample_2 rbind(head(supercell_cell_map, n = 3), tail(supercell_cell_map, n = 3)) #>                      SuperCellID     CellId   Sample #> 1: SuperCell_256_Sample_Sample_1     Cell_1 Sample_1 #> 2:  SuperCell_77_Sample_Sample_1     Cell_2 Sample_1 #> 3: SuperCell_421_Sample_Sample_1     Cell_3 Sample_1 #> 4:  SuperCell_23_Sample_Sample_2 Cell_19998 Sample_2 #> 5:  SuperCell_98_Sample_Sample_2 Cell_19999 Sample_2 #> 6: SuperCell_227_Sample_Sample_2 Cell_20000 Sample_2"},{"path":"https://phipsonlab.github.io/SuperCellCyto/articles/how_to_supercell.html","id":"using-runsupercellcyto-for-stratified-summarising","dir":"Articles","previous_headings":"","what":"Using runSuperCellCyto for stratified summarising","title":"How to create supercells","text":"previously mentioned, can use runSuperCellCyto perform stratified summarising, .e., summarise (well, meaningfully sub-sample) cluster cell type. , need change sample column denotes cell type cluster cell belongs . example, let’s first cluster toy data k-means: perform stratified summarising, supply cluster column (kmeans_clusters example ), runSuperCellCyto’s sample_colname parameter. Now, look supercell_expression_matrix, row (supercell) denoted cluster belongs , biological sample came : look number supercells created check many cells cluster, find , cluster, get approximately n_cells/20 20 gam parameter value used runSuperCellCyto (default).","code":"set.seed(42)  # Simulate some data dat <- simCytoData() markers_col <- paste0(\"Marker_\", seq_len(10)) cell_id_col <- \"Cell_Id\"  # Run kmeans clust <- kmeans(   x = dat[, markers_col, with = FALSE],   centers = 5 )  clust_col <- \"kmeans_clusters\" dat[[clust_col]] <- paste0(\"cluster_\", clust$cluster) supercells <- runSuperCellCyto(   dt = dat,   markers = markers_col,   sample_colname = clust_col,   cell_id_colname = cell_id_col ) # Inspect the top 3 and bottom 3 of the expression matrix and some columns. rbind(   head(supercells$supercell_expression_matrix, n = 3),   tail(supercells$supercell_expression_matrix, n = 3) )[, c(\"kmeans_clusters\", \"SuperCellId\", \"Marker_10\")] #>    kmeans_clusters                    SuperCellId Marker_10 #> 1:       cluster_4   SuperCell_1_Sample_cluster_4  14.64662 #> 2:       cluster_4   SuperCell_2_Sample_cluster_4  14.66858 #> 3:       cluster_4   SuperCell_3_Sample_cluster_4  14.41837 #> 4:       cluster_5 SuperCell_498_Sample_cluster_5  16.99003 #> 5:       cluster_5 SuperCell_499_Sample_cluster_5  17.09864 #> 6:       cluster_5 SuperCell_500_Sample_cluster_5  15.85447 # Compute how many cells per cluster, and divide by 20, the gamma value. table(dat$kmeans_clusters) / 20 #>  #> cluster_1 cluster_2 cluster_3 cluster_4 cluster_5  #>    120.25    130.30    119.75    129.70    500.00 table(supercells$supercell_expression_matrix$kmeans_clusters) #>  #> cluster_1 cluster_2 cluster_3 cluster_4 cluster_5  #>       120       130       120       130       500"},{"path":"https://phipsonlab.github.io/SuperCellCyto/articles/how_to_supercell.html","id":"session-information","dir":"Articles","previous_headings":"","what":"Session information","title":"How to create supercells","text":"","code":"sessionInfo() #> R version 4.3.2 (2023-10-31) #> Platform: x86_64-pc-linux-gnu (64-bit) #> Running under: Ubuntu 22.04.3 LTS #>  #> Matrix products: default #> BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3  #> LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0 #>  #> locale: #>  [1] LC_CTYPE=C.UTF-8       LC_NUMERIC=C           LC_TIME=C.UTF-8        #>  [4] LC_COLLATE=C.UTF-8     LC_MONETARY=C.UTF-8    LC_MESSAGES=C.UTF-8    #>  [7] LC_PAPER=C.UTF-8       LC_NAME=C              LC_ADDRESS=C           #> [10] LC_TELEPHONE=C         LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C    #>  #> time zone: UTC #> tzcode source: system (glibc) #>  #> attached base packages: #> [1] parallel  stats     graphics  grDevices utils     datasets  methods   #> [8] base      #>  #> other attached packages: #> [1] BiocParallel_1.36.0  SuperCellCyto_0.99.0 BiocStyle_2.30.0     #>  #> loaded via a namespace (and not attached): #>  [1] Matrix_1.6-1.1      jsonlite_1.8.8      compiler_4.3.2      #>  [4] BiocManager_1.30.22 Rcpp_1.0.12         stringr_1.5.1       #>  [7] SuperCell_1.0       jquerylib_0.1.4     systemfonts_1.0.5   #> [10] textshaping_0.3.7   yaml_2.3.8          fastmap_1.1.1       #> [13] lattice_0.21-9      plyr_1.8.9          R6_2.5.1            #> [16] igraph_1.6.0        knitr_1.45          bookdown_0.37       #> [19] desc_1.4.3          bslib_0.6.1         rlang_1.1.3         #> [22] cachem_1.0.8        stringi_1.8.3       RANN_2.6.1          #> [25] xfun_0.41           fs_1.6.3            sass_0.4.8          #> [28] memoise_2.0.1       cli_3.6.2           pkgdown_2.0.7       #> [31] magrittr_2.0.3      digest_0.6.34       grid_4.3.2          #> [34] lifecycle_1.0.4     vctrs_0.6.5         evaluate_0.23       #> [37] glue_1.7.0          data.table_1.14.10  codetools_0.2-19    #> [40] ragg_1.2.7          rmarkdown_2.25      purrr_1.0.2         #> [43] pkgconfig_2.0.3     tools_4.3.2         htmltools_0.5.7"},{"path":"https://phipsonlab.github.io/SuperCellCyto/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Givanna Putri. Author, maintainer. Belinda Phipson. Author.","code":""},{"path":"https://phipsonlab.github.io/SuperCellCyto/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Putri G, Phipson B (2024). SuperCellCyto: SuperCell Cytometry Data. R package version 0.99.0, https://phipsonlab.github.io/SuperCellCyto/.","code":"@Manual{,   title = {SuperCellCyto: SuperCell For Cytometry Data},   author = {Givanna Putri and Belinda Phipson},   year = {2024},   note = {R package version 0.99.0},   url = {https://phipsonlab.github.io/SuperCellCyto/}, }"},{"path":"https://phipsonlab.github.io/SuperCellCyto/index.html","id":"supercellcyto","dir":"","previous_headings":"","what":"SuperCell For Cytometry Data","title":"SuperCell For Cytometry Data","text":"SuperCellCyto extension SuperCell R package. Initially developed scRNAseq data, SuperCell aggregates cells similar transcriptomic profiles “supercells” (also known “metacells” scRNAseq literature). SuperCellCyto, adapted SuperCell package specifically cytometry data implemented within-sample supercell creation, preventing supercells containing cells across multiple samples. Additionally, also introduced parallel processing capabilities using careful load balancing strategy, allowing simultaneous creation supercells across multiple samples, significantly speeds computational time.","code":""},{"path":"https://phipsonlab.github.io/SuperCellCyto/index.html","id":"vignettes-and-function-documentation","dir":"","previous_headings":"","what":"Vignettes and Function Documentation","title":"SuperCell For Cytometry Data","text":"understand SuperCellCyto can integrated workflow, head Articles page. Click dropdown arrow Articles link navbar top website. vignettes provide step--step guides, practical examples, use-case scenarios demonstrate package’s application various research contexts. Documentation usage function SuperCellCyto package available Reference page. Click Reference link navbar top website.","code":""},{"path":"https://phipsonlab.github.io/SuperCellCyto/index.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"SuperCell For Cytometry Data","text":"use SuperCellCyto study, please kindly cite preprint bioRxiv: Putri, G. H., Howitt, G., Marsh-Wakefield, F., Ashhurst, T. M., & Phipson, B. (2023). SuperCellCyto: enabling efficient analysis large scale cytometry datasets. bioRxiv; DOI: https://doi.org/10.1101/2023.08.14.553168","code":""},{"path":"https://phipsonlab.github.io/SuperCellCyto/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"SuperCell For Cytometry Data","text":"package can installed using devtools: SuperCellCyto requires SuperCell R package installed run properly. use installation command , , theory, automatically installed. case doesn’t, please manually install running following command beforehand:","code":"# Install devtools install.packages(\"devtools\")  # Install SuperCellCyto from this repository devtools::install_github(\"phipsonlab/SuperCellCyto\") # Install SuperCell from their github repository devtools::install_github(\"GfellerLab/SuperCell\")"},{"path":"https://phipsonlab.github.io/SuperCellCyto/index.html","id":"code-of-conduct","dir":"","previous_headings":"","what":"Code of Conduct","title":"SuperCell For Cytometry Data","text":"Please note SuperCellCyto project released Contributor Code Conduct. contributing project, agree abide terms.","code":""},{"path":"https://phipsonlab.github.io/SuperCellCyto/index.html","id":"contribution-guide","dir":"","previous_headings":"","what":"Contribution guide","title":"SuperCell For Cytometry Data","text":"Please visit Contributing Guide","code":""},{"path":"https://phipsonlab.github.io/SuperCellCyto/reference/recomputeSupercells.html","id":null,"dir":"Reference","previous_headings":"","what":"Recompute supercells — recomputeSupercells","title":"Recompute supercells — recomputeSupercells","text":"Given supercell object, recompute supercell granularity using different gamma value. Gamma value controls number supercells generated. smaller value, supercells get, vice versa.","code":""},{"path":"https://phipsonlab.github.io/SuperCellCyto/reference/recomputeSupercells.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Recompute supercells — recomputeSupercells","text":"","code":"recomputeSupercells(   dt,   sc_objects,   markers,   sample_colname,   cell_id_colname,   gam = 20 )"},{"path":"https://phipsonlab.github.io/SuperCellCyto/reference/recomputeSupercells.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Recompute supercells — recomputeSupercells","text":"dt data.table object containing cytometry data. Rows represent cells, columns represent markers. data.table object, function warn , try convert data.table object. sc_objects supercell_object returned runSuperCellCyto function. markers character vector specifying markers dt. sample_colname character specifying column dt denotes sample cell. cell_id_colname character specifying column dt denotes unique ID cell. gam numeric specifying gamma value used SuperCell.","code":""},{"path":"https://phipsonlab.github.io/SuperCellCyto/reference/recomputeSupercells.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Recompute supercells — recomputeSupercells","text":"list following components: supercell_expression_matrix: data.table containing marker expression supercells. computed taking average marker expression cells captured supercell. supercell_cell_map: data.table showing cell captured supercell. useful intend work supercell captures cell.","code":""},{"path":"https://phipsonlab.github.io/SuperCellCyto/reference/recomputeSupercells.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Recompute supercells — recomputeSupercells","text":"Givanna Putri","code":""},{"path":"https://phipsonlab.github.io/SuperCellCyto/reference/recomputeSupercells.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Recompute supercells — recomputeSupercells","text":"","code":"set.seed(42) cyto_dat <- simCytoData(10, rep(1000, 3)) markers <- paste0(\"Marker_\", seq_len(10)) out_gam20 <- runSuperCellCyto(     dt = cyto_dat,     markers = markers,     sample_colname = \"Sample\",     cell_id_colname = \"Cell_Id\",     gam = 20 ) recomputed_sc <- recomputeSupercells(     dt = cyto_dat,     sc_objects = out_gam20$supercell_object,     markers = markers,     sample_colname = \"Sample\",     cell_id_colname = \"Cell_Id\",     gam = 50 )"},{"path":"https://phipsonlab.github.io/SuperCellCyto/reference/runSuperCellCyto.html","id":null,"dir":"Reference","previous_headings":"","what":"Run SuperCell for cytometry data — runSuperCellCyto","title":"Run SuperCell for cytometry data — runSuperCellCyto","text":"Run SuperCell cytometry data stored data.table object. wrapper function around SCImplify function SuperCell R package Bilous et.al, 2022. enhanced adding capacity \"supercell\" multiple samples parallel use BiocParallel, adding support \"supercell\" cytometry data. explanations given various sections , expanded vignette.","code":""},{"path":"https://phipsonlab.github.io/SuperCellCyto/reference/runSuperCellCyto.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Run SuperCell for cytometry data — runSuperCellCyto","text":"","code":"runSuperCellCyto(   dt,   markers,   sample_colname,   cell_id_colname,   gam = 20,   k_knn = 5,   BPPARAM = SerialParam(),   load_balancing = FALSE )"},{"path":"https://phipsonlab.github.io/SuperCellCyto/reference/runSuperCellCyto.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Run SuperCell for cytometry data — runSuperCellCyto","text":"dt data.table object containing cytometry data. Rows represent cells, columns represent markers. data.table object, function warn , try convert data.table object. markers character vector specifying markers run SuperCell . sample_colname character specifying column dt denotes sample cell. cell_id_colname character specifying column dt denotes unique ID cell. gam numeric specifying gamma value used SuperCell. Default 20. k_knn numeric specifying k value used SuperCell's knn. Default 5. BPPARAM BiocParallelParam specifying samples \"supercelled\" parallel. Default SerialParam, meaning samples processed serial. Please go section parallel processing details best supercell multiple samples parallel. load_balancing logical whether load balancing performed multiple samples \"supercelled\" parallel. Please go section parallel processing details best supercell multiple samples parallel. Default FALSE.","code":""},{"path":"https://phipsonlab.github.io/SuperCellCyto/reference/runSuperCellCyto.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Run SuperCell for cytometry data — runSuperCellCyto","text":"runSuperCellCyto return list following components: supercell_object: list containing object returned SCimplify function. One object per sample. supercell_expression_matrix: data.table containing marker expression supercells. computed taking average marker expression cells captured supercell. supercell_cell_map: data.table showing cell captured supercell. useful intend work supercell captures cell.","code":""},{"path":"https://phipsonlab.github.io/SuperCellCyto/reference/runSuperCellCyto.html","id":"supercell-multiple-samples-in-parallel","dir":"Reference","previous_headings":"","what":"Supercell multiple samples in parallel","title":"Run SuperCell for cytometry data — runSuperCellCyto","text":"function supercell multiple samples parallel, need supply BiocParallelParam (BPPARAM parameter) leverages parallel evaluation, e.g., MulticoreParam SnowParam. Importantly, also recommended set number tasks (task parameter) number samples , set load_balancing parameter TRUE. way, jobs handling large samples responsible (mostly) sample, small ones handled jobs handling much smaller samples.","code":""},{"path":"https://phipsonlab.github.io/SuperCellCyto/reference/runSuperCellCyto.html","id":"what-is-cell-id-colname","dir":"Reference","previous_headings":"","what":"What is cell_id_colname","title":"Run SuperCell for cytometry data — runSuperCellCyto","text":"column dt containing unique identifier cell. Commonly, manually create column FCS file typically field uniquely identify cell. can create ID giving cells numeric value 1 however many , store column dt. know , refer vignette.","code":""},{"path":"https://phipsonlab.github.io/SuperCellCyto/reference/runSuperCellCyto.html","id":"what-constitute-a-sample","dir":"Reference","previous_headings":"","what":"What constitute a sample","title":"Run SuperCell for cytometry data — runSuperCellCyto","text":"function designed samples processed, independent . , can safely assume supercell contain cells exactly one sample. function work cells come sample based specified sample_colname column. critical specify column . purposes, sample represents biological sample experiment. may thinking, possible use different context, say creating supercells population cluster rather biological sample? short answer yes, address vignette.","code":""},{"path":"https://phipsonlab.github.io/SuperCellCyto/reference/runSuperCellCyto.html","id":"computing-pca","dir":"Reference","previous_headings":"","what":"Computing PCA","title":"Run SuperCell for cytometry data — runSuperCellCyto","text":"default, function start computing PCA markers specified markers parameter, 10 PCs computed. less 10 markers markers parameter, number PCs set however many markers markers parameter. Notably, scaling transformation done markers' expressions prior computing PCs. irlba used calculate PCA cytometry data tend handful features (markers) compared scRNAseq data. Hence little gain.","code":""},{"path":"https://phipsonlab.github.io/SuperCellCyto/reference/runSuperCellCyto.html","id":"setting-the-supercell-graining-level","dir":"Reference","previous_headings":"","what":"Setting the supercell graining level","title":"Run SuperCell for cytometry data — runSuperCellCyto","text":"many supercells get dataset? phrase another way, can estimate many cells captured within supercell? depends set gam parameter . gam parameter represented formula gamma=n_cells/n_supercells n_cells denotes number cells n_supercells denotes number supercells created. resolving formula, can roughly estimate many supercells get end, thus, approximately many cells captured within supercell. Generally, smaller gam parameter , supercells get. Say instance 10,000 cells. gam set 10, end 1,000 supercells, whereas gam set 50, end 200 supercells. Conversely, get supercells (.e. smaller gam value), smaller size . words, capturing less cells. Importantly, one expect supercells size. capture /less cells , trivial estimate many captured beforehand. may look swapping gam value many cells captured within supercell future. thoughts required whether make sense. Lastly, now, gam value samples (read section sure mean samples ). future, can perhaps look setting different gam values different samples.","code":""},{"path":"https://phipsonlab.github.io/SuperCellCyto/reference/runSuperCellCyto.html","id":"computing-knn-network","dir":"Reference","previous_headings":"","what":"Computing kNN network","title":"Run SuperCell for cytometry data — runSuperCellCyto","text":"parameter k_knn governs k value used compute single-cell kNN network. Actual (approximate) kNN network created, walktrap algorithm used form supercells kNN network.","code":""},{"path":"https://phipsonlab.github.io/SuperCellCyto/reference/runSuperCellCyto.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Run SuperCell for cytometry data — runSuperCellCyto","text":"Givanna Putri","code":""},{"path":"https://phipsonlab.github.io/SuperCellCyto/reference/runSuperCellCyto.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Run SuperCell for cytometry data — runSuperCellCyto","text":"","code":"# Simulate some data set.seed(42) cyto_dat <- simCytoData(nmarkers = 10, ncells = rep(2000,2))  # Setup the columns designating the markers, samples, and cell IDs marker_col <- paste0(\"Marker_\", seq_len(10)) sample_col <- \"Sample\" cell_id_col <- \"Cell_Id\"  supercell_dat <- runSuperCellCyto(     cyto_dat, marker_col,     sample_col, cell_id_col )"},{"path":"https://phipsonlab.github.io/SuperCellCyto/reference/simCytoData.html","id":null,"dir":"Reference","previous_headings":"","what":"Simulates cytometry data — simCytoData","title":"Simulates cytometry data — simCytoData","text":"Simulates cytometry data use testing documenting functions require cytometry data. Please run set.seed running function want ensure reproducibility.","code":""},{"path":"https://phipsonlab.github.io/SuperCellCyto/reference/simCytoData.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Simulates cytometry data — simCytoData","text":"","code":"simCytoData(nmarkers = 10, ncells = rep(10000, 2))"},{"path":"https://phipsonlab.github.io/SuperCellCyto/reference/simCytoData.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Simulates cytometry data — simCytoData","text":"nmarkers numeric specifying number markers simulate. ncells numeric vector number cells simulate per sample. 1 vector element per sample.#'","code":""},{"path":"https://phipsonlab.github.io/SuperCellCyto/reference/simCytoData.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Simulates cytometry data — simCytoData","text":"data.table containing simulated data. Rows cells, columns markers.","code":""},{"path":"https://phipsonlab.github.io/SuperCellCyto/reference/simCytoData.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Simulates cytometry data — simCytoData","text":"Givanna Putri","code":""},{"path":"https://phipsonlab.github.io/SuperCellCyto/reference/simCytoData.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Simulates cytometry data — simCytoData","text":"","code":"set.seed(42) cyto_dat <- simCytoData() head(cyto_dat) #>    Marker_1 Marker_2  Marker_3 Marker_4 Marker_5 Marker_6 Marker_7 Marker_8 #> 1: 18.61597 17.43348  9.213419 18.40881 13.68860 12.77402 15.24751 7.140615 #> 2: 20.23361 19.82792  9.740510 18.16383 13.97108 14.25898 16.05032 5.863752 #> 3: 18.62743 17.76816 11.175358 18.59606 16.08534 13.69734 16.71550 4.750240 #> 4: 20.74051 19.94771  9.797939 18.43590 13.92092 12.64266 15.55953 8.173464 #> 5: 18.65938 18.87830  8.916928 17.83295 15.62066 11.37118 16.08417 6.664707 #> 6: 20.02696 20.76054  9.262685 16.89373 15.92339 13.40002 16.13880 6.862717 #>    Marker_9 Marker_10   Sample Cell_Id #> 1: 14.37555  14.18484 Sample_1  Cell_1 #> 2: 15.58007  15.56981 Sample_1  Cell_2 #> 3: 14.16156  14.92222 Sample_1  Cell_3 #> 4: 14.26637  15.10761 Sample_1  Cell_4 #> 5: 15.12219  15.64231 Sample_1  Cell_5 #> 6: 15.85313  15.52535 Sample_1  Cell_6 dim(cyto_dat) #> [1] 20000    12"},{"path":"https://phipsonlab.github.io/SuperCellCyto/news/index.html","id":"supercellcyto-010","dir":"Changelog","previous_headings":"","what":"SuperCellCyto 0.1.0","title":"SuperCellCyto 0.1.0","text":"first release SuperCellCyto. also version SuperCellCyto used preprint:","code":"@article{     putri2023supercellcyto,     title={SuperCellCyto: enabling efficient analysis of large scale cytometry datasets},     author={Putri, Givanna H and Howitt, George and Marsh-Wakefield, Felix and Ashhurst, Thomas Myles and Phipson, Belinda},     journal={bioRxiv},     pages={2023--08},     year={2023},     publisher={Cold Spring Harbor Laboratory} }"}]
+[{"path":[]},{"path":"https://phipsonlab.github.io/SuperCellCyto/CODE_OF_CONDUCT.html","id":"our-pledge","dir":"","previous_headings":"","what":"Our Pledge","title":"Contributor Covenant Code of Conduct","text":"members, contributors, leaders pledge make participation community harassment-free experience everyone, regardless age, body size, visible invisible disability, ethnicity, sex characteristics, gender identity expression, level experience, education, socio-economic status, nationality, personal appearance, race, caste, color, religion, sexual identity orientation. pledge act interact ways contribute open, welcoming, diverse, inclusive, healthy community.","code":""},{"path":"https://phipsonlab.github.io/SuperCellCyto/CODE_OF_CONDUCT.html","id":"our-standards","dir":"","previous_headings":"","what":"Our Standards","title":"Contributor Covenant Code of Conduct","text":"Examples behavior contributes positive environment community include: Demonstrating empathy kindness toward people respectful differing opinions, viewpoints, experiences Giving gracefully accepting constructive feedback Accepting responsibility apologizing affected mistakes, learning experience Focusing best just us individuals, overall community Examples unacceptable behavior include: use sexualized language imagery, sexual attention advances kind Trolling, insulting derogatory comments, personal political attacks Public private harassment Publishing others’ private information, physical email address, without explicit permission conduct reasonably considered inappropriate professional setting","code":""},{"path":"https://phipsonlab.github.io/SuperCellCyto/CODE_OF_CONDUCT.html","id":"enforcement-responsibilities","dir":"","previous_headings":"","what":"Enforcement Responsibilities","title":"Contributor Covenant Code of Conduct","text":"Community leaders responsible clarifying enforcing standards acceptable behavior take appropriate fair corrective action response behavior deem inappropriate, threatening, offensive, harmful. Community leaders right responsibility remove, edit, reject comments, commits, code, wiki edits, issues, contributions aligned Code Conduct, communicate reasons moderation decisions appropriate.","code":""},{"path":"https://phipsonlab.github.io/SuperCellCyto/CODE_OF_CONDUCT.html","id":"scope","dir":"","previous_headings":"","what":"Scope","title":"Contributor Covenant Code of Conduct","text":"Code Conduct applies within community spaces, also applies individual officially representing community public spaces. Examples representing community include using official e-mail address, posting via official social media account, acting appointed representative online offline event.","code":""},{"path":"https://phipsonlab.github.io/SuperCellCyto/CODE_OF_CONDUCT.html","id":"enforcement","dir":"","previous_headings":"","what":"Enforcement","title":"Contributor Covenant Code of Conduct","text":"Instances abusive, harassing, otherwise unacceptable behavior may reported community leaders responsible enforcement putri.g@wehi.edu.au. complaints reviewed investigated promptly fairly. community leaders obligated respect privacy security reporter incident.","code":""},{"path":"https://phipsonlab.github.io/SuperCellCyto/CODE_OF_CONDUCT.html","id":"enforcement-guidelines","dir":"","previous_headings":"","what":"Enforcement Guidelines","title":"Contributor Covenant Code of Conduct","text":"Community leaders follow Community Impact Guidelines determining consequences action deem violation Code Conduct:","code":""},{"path":"https://phipsonlab.github.io/SuperCellCyto/CODE_OF_CONDUCT.html","id":"id_1-correction","dir":"","previous_headings":"Enforcement Guidelines","what":"1. Correction","title":"Contributor Covenant Code of Conduct","text":"Community Impact: Use inappropriate language behavior deemed unprofessional unwelcome community. Consequence: private, written warning community leaders, providing clarity around nature violation explanation behavior inappropriate. public apology may requested.","code":""},{"path":"https://phipsonlab.github.io/SuperCellCyto/CODE_OF_CONDUCT.html","id":"id_2-warning","dir":"","previous_headings":"Enforcement Guidelines","what":"2. Warning","title":"Contributor Covenant Code of Conduct","text":"Community Impact: violation single incident series actions. Consequence: warning consequences continued behavior. interaction people involved, including unsolicited interaction enforcing Code Conduct, specified period time. includes avoiding interactions community spaces well external channels like social media. Violating terms may lead temporary permanent ban.","code":""},{"path":"https://phipsonlab.github.io/SuperCellCyto/CODE_OF_CONDUCT.html","id":"id_3-temporary-ban","dir":"","previous_headings":"Enforcement Guidelines","what":"3. Temporary Ban","title":"Contributor Covenant Code of Conduct","text":"Community Impact: serious violation community standards, including sustained inappropriate behavior. Consequence: temporary ban sort interaction public communication community specified period time. public private interaction people involved, including unsolicited interaction enforcing Code Conduct, allowed period. Violating terms may lead permanent ban.","code":""},{"path":"https://phipsonlab.github.io/SuperCellCyto/CODE_OF_CONDUCT.html","id":"id_4-permanent-ban","dir":"","previous_headings":"Enforcement Guidelines","what":"4. Permanent Ban","title":"Contributor Covenant Code of Conduct","text":"Community Impact: Demonstrating pattern violation community standards, including sustained inappropriate behavior, harassment individual, aggression toward disparagement classes individuals. Consequence: permanent ban sort public interaction within community.","code":""},{"path":"https://phipsonlab.github.io/SuperCellCyto/CODE_OF_CONDUCT.html","id":"attribution","dir":"","previous_headings":"","what":"Attribution","title":"Contributor Covenant Code of Conduct","text":"Code Conduct adapted Contributor Covenant, version 2.1, available https://www.contributor-covenant.org/version/2/1/code_of_conduct.html. Community Impact Guidelines inspired [Mozilla’s code conduct enforcement ladder][https://github.com/mozilla/inclusion]. answers common questions code conduct, see FAQ https://www.contributor-covenant.org/faq. Translations available https://www.contributor-covenant.org/translations.","code":""},{"path":"https://phipsonlab.github.io/SuperCellCyto/CONTRIBUTING.html","id":null,"dir":"","previous_headings":"","what":"Contributing to SuperCellCyto","title":"Contributing to SuperCellCyto","text":"outlines propose change SuperCellCyto.","code":""},{"path":"https://phipsonlab.github.io/SuperCellCyto/CONTRIBUTING.html","id":"making-changes-to-the-package","dir":"","previous_headings":"","what":"Making changes to the package","title":"Contributing to SuperCellCyto","text":"want make change, please first file issue make sure someone team agrees ’s needed. ’ve found bug, please file issue illustrates bug minimal reprex (also help write unit test, needed).","code":""},{"path":"https://phipsonlab.github.io/SuperCellCyto/CONTRIBUTING.html","id":"pull-request-process","dir":"","previous_headings":"Making changes to the package","what":"Pull request process","title":"Contributing to SuperCellCyto","text":"Fork package clone onto computer. Install development dependencies devtools::install_dev_deps(). Create Git branch pull request (PR). can use usethis::pr_init(\"brief-description--change\"). Make changes, commit git. Make sure unit tests passing. can use devtools::test(). Create PR. Make sure title PR briefly describe change, body PR contain Fixes #issue-number.","code":""},{"path":"https://phipsonlab.github.io/SuperCellCyto/CONTRIBUTING.html","id":"code-of-conduct","dir":"","previous_headings":"","what":"Code of Conduct","title":"Contributing to SuperCellCyto","text":"Please note SuperCellCyto project released Contributor Code Conduct. contributing project agree abide terms.","code":""},{"path":"https://phipsonlab.github.io/SuperCellCyto/articles/how_to_supercell.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"How to create supercells","text":"vignette describes steps reduce size vast high-dimensional cytometry data using SuperCellCyto, R package based SuperCell R package David Gfeller lab University Lausanne. Please note ’re still actively updating vignette (fact package ), welcome feedbacks improve . myriad ways use SuperCell. try cover many use cases possible, bound miss something. case, please reach github repository creating Github issue.","code":""},{"path":"https://phipsonlab.github.io/SuperCellCyto/articles/how_to_supercell.html","id":"installation","dir":"Articles","previous_headings":"","what":"Installation","title":"How to create supercells","text":"install SuprCellCyto, need use devtools package CRAN. can install devtools using install.packages(\"devtools\") command. Thereafter, can install SuperCellCyto using devtools::install_github(\"phipsonlab/SuperCellCyto\"). SuperCellCyto requires SuperCell R package installed run properly. use devtools::install_github command install SuperCellCyto, , theory, automatically installed. case doesn’t, can manually install using devtools::install_github(\"GfellerLab/SuperCell\").","code":""},{"path":"https://phipsonlab.github.io/SuperCellCyto/articles/how_to_supercell.html","id":"preparing-your-dataset","dir":"Articles","previous_headings":"","what":"Preparing your dataset","title":"How to create supercells","text":"function creates supercells called runSuperCellCyto, operates data.table object, enhanced version R native data.frame. may add support SummarizedExperiment flowFrame object future enough demands . raw data stored csv file, can import data.table object using fread function. raw data stored across multiple csv files FCS files (common cytometry), need help Spectre R package import adata.table object. Specifically, need : Run read.files function read FCS csv files. Run .merge.files merge resulting data.table objects one. unsure steps work , look example Spectre vignette. Using vignette , csv files, can run steps vignette , changing InputDirectory variable. FCS files, need change file.type parameter read.files function .fcs. vignette, simulate toy data using simCytoData function. several things note dataset. Let’s go one one sub-section .","code":"n_markers <- 15 n_samples <- 3 dat <- SuperCellCyto::simCytoData(nmarkers = n_markers, ncells = rep(10000, n_samples)) head(dat) #>    Marker_1 Marker_2 Marker_3 Marker_4 Marker_5  Marker_6 Marker_7 Marker_8 #> 1: 8.613145 12.27848 11.47877 15.85943 6.292417  9.371754 15.48644 8.502447 #> 2: 8.444047 13.61751 15.42910 13.23521 7.337523 10.848695 16.41428 9.049085 #> 3: 9.508217 13.27955 14.49741 15.32714 7.184697  9.229750 17.25246 7.224527 #> 4: 8.727552 12.33650 13.08252 14.59704 6.344520 11.025034 16.08588 9.787236 #> 5: 8.705266 11.54711 15.09200 12.14086 5.916500  9.986064 16.82250 7.815844 #> 6: 9.809956 13.59739 14.26858 14.35567 7.930409 10.782738 17.08411 9.334017 #>    Marker_9 Marker_10 Marker_11 Marker_12 Marker_13 Marker_14 Marker_15 #> 1: 6.343346  8.611583  14.99405  13.14672  8.453014  4.727143  10.77041 #> 2: 7.766555  9.717384  16.61520  13.32699  7.155631  4.038144  10.97021 #> 3: 7.125987  8.541820  16.23950  14.86434  9.294373  5.023199  10.42803 #> 4: 5.642658  7.652166  14.94492  12.52479  6.494958  3.826238  10.97673 #> 5: 7.423770  7.872997  15.01467  14.43692  7.627948  3.731901  11.81326 #> 6: 7.971358  9.705231  14.84054  13.69212  8.225082  4.368337  11.46224 #>      Sample Cell_Id #> 1: Sample_1  Cell_1 #> 2: Sample_1  Cell_2 #> 3: Sample_1  Cell_3 #> 4: Sample_1  Cell_4 #> 5: Sample_1  Cell_5 #> 6: Sample_1  Cell_6"},{"path":"https://phipsonlab.github.io/SuperCellCyto/articles/how_to_supercell.html","id":"the-markers","dir":"Articles","previous_headings":"Preparing your dataset","what":"The markers","title":"How to create supercells","text":"runSuperCellCyto function perform data transformation scaling. Thus, must ensure dataset already appropriately transformed using either arc-sinh transformation linear binning (using FlowJo). tutorial explains data transformation process great detail: (https://wiki.centenary.org.au/display/SPECTRE/Data+transformation). Please read unsure transform data. toy dataset, transform data using arc-sinh transformation implementation provided base R asinh function: Breaking steps, : Identify columns denoting markers. Set co-factor 5. transformation store dat_asinh variable. Set dat_asinh column name reflect values column (marker) haas undergone arc-sinh transformation. Combine dat dat_asinh using cbind.","code":"# Specify which columns are the markers to transform marker_cols <- paste0(\"Marker_\", seq_len(n_markers)) # The co-factor for arc-sinh cofactor <- 5  # Do the transformation dat_asinh <- asinh(dat[, marker_cols, with = FALSE] / cofactor)  # Rename the new columns marker_cols_asinh <- paste0(marker_cols, \"_asinh\") names(dat_asinh) <- marker_cols_asinh  # Add them our previously loaded data dat <- cbind(dat, dat_asinh)  head(dat[, marker_cols_asinh, with = FALSE]) #>    Marker_1_asinh Marker_2_asinh Marker_3_asinh Marker_4_asinh Marker_5_asinh #> 1:       1.312237       1.630650       1.568585       1.871444       1.052882 #> 2:       1.295133       1.727183       1.845241       1.700499       1.176602 #> 3:       1.398763       1.703628       1.786171       1.838936       1.159267 #> 4:       1.323668       1.635018       1.689651       1.792648       1.059348 #> 5:       1.321450       1.574029       1.824249       1.620219       1.005238 #> 6:       1.426507       1.725796       1.771144       1.776888       1.241584 #>    Marker_6_asinh Marker_7_asinh Marker_8_asinh Marker_9_asinh Marker_10_asinh #> 1:       1.385989       1.848771       1.301069      1.0592027        1.312080 #> 2:       1.517067       1.904290       1.355198      1.2239756        1.418068 #> 3:       1.372541       1.952030       1.163809      1.1525410        1.305053 #> 4:       1.531730       1.884975       1.424442      0.9694039        1.211527 #> 5:       1.442388       1.927814       1.229300      1.1862764        1.235444 #> 6:       1.511531       1.942615       1.382431      1.2459434        1.416955 #>    Marker_11_asinh Marker_12_asinh Marker_13_asinh Marker_14_asinh #> 1:        1.818070        1.694225        1.296046       0.8422546 #> 2:        1.915935        1.706966        1.155942       0.7386142 #> 3:        1.894055        1.809832        1.378680       0.8846507 #> 4:        1.814957        1.649071        1.077836       0.7053006 #> 5:        1.819374        1.782220        1.208874       0.6902489 #> 6:        1.808313        1.732314        1.272603       0.7891656 #>    Marker_15_asinh #> 1:        1.510494 #> 2:        1.527193 #> 3:        1.481277 #> 4:        1.527733 #> 5:        1.594977 #> 6:        1.567263"},{"path":"https://phipsonlab.github.io/SuperCellCyto/articles/how_to_supercell.html","id":"cell-id-column","dir":"Articles","previous_headings":"Preparing your dataset","what":"Cell id column","title":"How to create supercells","text":"create supercell, must provide column uniquely identify cell, akin Cell_Id column toy data generated : purpose cell id allow SuperCell uniquely identify cell dataset. ID come super handy later /need work cells belong supercells. Generally, need create ID . dataset won’t come ID already embedded . simple cell id can made concatenating word Cell row number. Something like following: , store cell id column called Cell_id_dummy. values Cell_1, Cell_2, way Cell_x x number cells dataset. Note, can name cell id column however like, id, cell_identity, etc. Importantly, need make sure note column name need pass runSuperCellCyto function later.","code":"head(dat$Cell_Id, n = 10) #>  [1] \"Cell_1\"  \"Cell_2\"  \"Cell_3\"  \"Cell_4\"  \"Cell_5\"  \"Cell_6\"  \"Cell_7\"  #>  [8] \"Cell_8\"  \"Cell_9\"  \"Cell_10\" dat$Cell_id_dummy <- paste0(\"Cell_\", seq_len(nrow(dat))) head(dat$Cell_id_dummy, n = 10) #>  [1] \"Cell_1\"  \"Cell_2\"  \"Cell_3\"  \"Cell_4\"  \"Cell_5\"  \"Cell_6\"  \"Cell_7\"  #>  [8] \"Cell_8\"  \"Cell_9\"  \"Cell_10\""},{"path":"https://phipsonlab.github.io/SuperCellCyto/articles/how_to_supercell.html","id":"sample-column","dir":"Articles","previous_headings":"Preparing your dataset","what":"Sample column","title":"How to create supercells","text":"notice toy data , column called Sample. default, column refers biological sample cells come . toy data , 3 samples, Sample_1, Sample_2, Sample_3: 10,000 cells per sample: create supercells, necessary Sample column dataset. can name column however like, Samp, Cell_Samp. However, make sure note column name need pass runSuperCellCyto function. [Creating supercells][#creating-supercells] section. 1 biological sample dataset? matter. still need column dataset, pass column name runSuperCellCyto function. difference column 1 unique value. need ? ensure supercell contains cells exactly 1 sample. , general, make sense mix cells different biological samples one supercell. Additionally (important), runSuperCellCyto function can process samples parallel set BPPARAM parameter BiocParallelParam class leverage parallel processing. Running runSuperCellCyto parallel section . However, want supercell contain cells different biological samples, need create new Sample column containing exactly 1 unique value, pass column name runSuperCellCyto function. : may wonder whether possible use SuperCellCyto reduce number cells captured cluster (cell type) can make UMAP/tSNE plot crowded? Commonly cytometry, use stratified sampling subsample clusters drawing UMAP/tSNE plot avoid overcrowding . short answer , yes can. See Using runSuperCellCyto stratified summarising section information.","code":"unique(dat$Sample) #> [1] \"Sample_1\" \"Sample_2\" \"Sample_3\" table(dat$Sample) #>  #> Sample_1 Sample_2 Sample_3  #>    10000    10000    10000"},{"path":"https://phipsonlab.github.io/SuperCellCyto/articles/how_to_supercell.html","id":"creating-supercells","dir":"Articles","previous_headings":"","what":"Creating supercells","title":"How to create supercells","text":"Now imported data, let’s create supercells. First, let’s store markers, sample, cell id column variables: pass , together dataset runSuperCellCyto function create supercells: Now let’s dig deeper object created: list containing 3 elements:","code":"markers_col <- paste0(\"Marker_\", seq_len(n_markers), \"_asinh\") sample_col <- \"Sample\" cell_id_col <- \"Cell_Id_dummy\" supercells <- runSuperCellCyto(   dt = dat,   markers = markers_col,   sample_colname = sample_col,   cell_id_colname = cell_id_col ) #> Warning in SCimplify(X = mt, genes.use = rownames(mt), do.scale = FALSE, : colnames(X) is Null,  #> Gene expression matrix X is expected to have cellIDs as colnames!  #> CellIDs will be created automatically in a form 'cell_i'  #> Warning in SCimplify(X = mt, genes.use = rownames(mt), do.scale = FALSE, : colnames(X) is Null,  #> Gene expression matrix X is expected to have cellIDs as colnames!  #> CellIDs will be created automatically in a form 'cell_i'  #> Warning in SCimplify(X = mt, genes.use = rownames(mt), do.scale = FALSE, : colnames(X) is Null,  #> Gene expression matrix X is expected to have cellIDs as colnames!  #> CellIDs will be created automatically in a form 'cell_i' class(supercells) #> [1] \"list\" names(supercells) #> [1] \"supercell_expression_matrix\" \"supercell_cell_map\"          #> [3] \"supercell_object\""},{"path":"https://phipsonlab.github.io/SuperCellCyto/articles/how_to_supercell.html","id":"supercell-object","dir":"Articles","previous_headings":"Creating supercells","what":"Supercell object","title":"How to create supercells","text":"supercell_object contains metadata used create supercells. list, element contains metadata used create supercells sample. come handy need debug supercells later line.","code":""},{"path":"https://phipsonlab.github.io/SuperCellCyto/articles/how_to_supercell.html","id":"supercell-expression-matrix","dir":"Articles","previous_headings":"Creating supercells","what":"Supercell expression matrix","title":"How to create supercells","text":"supercell_expression_matrix contains marker expression supercell. calculated taking average marker expression cells contained within supercell. Therein, following columns: markers previously specified markers_col variable. column (Sample case) denoting sample supercell belongs , (note column name stored sample_col variable). SuperCellId column denoting unique ID supercell.","code":"head(supercells$supercell_expression_matrix) #>    Marker_1_asinh Marker_2_asinh Marker_3_asinh Marker_4_asinh Marker_5_asinh #> 1:       1.264766       1.684169       1.748368       1.788664      0.9973134 #> 2:       1.300619       1.706533       1.743982       1.797021      1.1395078 #> 3:       1.380854       1.663947       1.710558       1.759018      0.9370305 #> 4:       1.172451       1.677868       1.737744       1.777133      0.9960757 #> 5:       1.204584       1.704318       1.746477       1.796652      1.2397711 #> 6:       1.345840       1.691527       1.747664       1.774837      0.8885885 #>    Marker_6_asinh Marker_7_asinh Marker_8_asinh Marker_9_asinh Marker_10_asinh #> 1:       1.549610       1.913162       1.431371       1.131703        1.382052 #> 2:       1.530629       1.921592       1.518904       1.050195        1.342751 #> 3:       1.494180       1.896908       1.393027       1.093162        1.123521 #> 4:       1.497948       1.929390       1.347520       1.033888        1.301701 #> 5:       1.402733       1.920214       1.377683       1.003403        1.159945 #> 6:       1.414919       1.899307       1.446175       1.120004        1.239734 #>    Marker_11_asinh Marker_12_asinh Marker_13_asinh Marker_14_asinh #> 1:        1.830774        1.776173        1.215677       0.9110168 #> 2:        1.832855        1.811177        1.278195       0.7774752 #> 3:        1.793451        1.770732        1.252679       0.8274059 #> 4:        1.812060        1.793503        1.282339       0.7681560 #> 5:        1.823652        1.779230        1.278131       0.9229247 #> 6:        1.809973        1.742942        1.297757       1.0299323 #>    Marker_15_asinh   Sample                 SuperCellId #> 1:        1.481482 Sample_1 SuperCell_1_Sample_Sample_1 #> 2:        1.463727 Sample_1 SuperCell_2_Sample_Sample_1 #> 3:        1.422810 Sample_1 SuperCell_3_Sample_Sample_1 #> 4:        1.437886 Sample_1 SuperCell_4_Sample_Sample_1 #> 5:        1.490259 Sample_1 SuperCell_5_Sample_Sample_1 #> 6:        1.552772 Sample_1 SuperCell_6_Sample_Sample_1 names(supercells$supercell_expression_matrix) #>  [1] \"Marker_1_asinh\"  \"Marker_2_asinh\"  \"Marker_3_asinh\"  \"Marker_4_asinh\"  #>  [5] \"Marker_5_asinh\"  \"Marker_6_asinh\"  \"Marker_7_asinh\"  \"Marker_8_asinh\"  #>  [9] \"Marker_9_asinh\"  \"Marker_10_asinh\" \"Marker_11_asinh\" \"Marker_12_asinh\" #> [13] \"Marker_13_asinh\" \"Marker_14_asinh\" \"Marker_15_asinh\" \"Sample\"          #> [17] \"SuperCellId\""},{"path":"https://phipsonlab.github.io/SuperCellCyto/articles/how_to_supercell.html","id":"supercellid","dir":"Articles","previous_headings":"Creating supercells > Supercell expression matrix","what":"SuperCellId","title":"How to create supercells","text":"Let’s look SuperCellId: Let’s break one , SuperCell_1_Sample_Sample_1. SuperCell_1 numbering (1 however many supercells sample) used uniquely identify supercell sample. Notably, may encounter (SuperCell_1, SuperCell_2) repeated across different samples, e.g., 3 supercells’ id pre-fixed SuperCell_1, make equal one another! SuperCell_1_Sample_Sample_1 contain cells Sample_1 SuperCell_1_Sample_Sample_2 contain cells Sample_2. now, may noticed appended sample name supercell id. aids differentiating supercells different samples.","code":"head(unique(supercells$supercell_expression_matrix$SuperCellId)) #> [1] \"SuperCell_1_Sample_Sample_1\" \"SuperCell_2_Sample_Sample_1\" #> [3] \"SuperCell_3_Sample_Sample_1\" \"SuperCell_4_Sample_Sample_1\" #> [5] \"SuperCell_5_Sample_Sample_1\" \"SuperCell_6_Sample_Sample_1\" supercell_ids <- unique(supercells$supercell_expression_matrix$SuperCellId) supercell_ids[grep(\"SuperCell_1_\", supercell_ids)] #> [1] \"SuperCell_1_Sample_Sample_1\" \"SuperCell_1_Sample_Sample_2\" #> [3] \"SuperCell_1_Sample_Sample_3\""},{"path":"https://phipsonlab.github.io/SuperCellCyto/articles/how_to_supercell.html","id":"supercell-cell-map","dir":"Articles","previous_headings":"Creating supercells","what":"Supercell cell map","title":"How to create supercells","text":"supercell_cell_map maps cell dataset supercell belongs . map useful later need expand supercells . Additionally, also reason need column dataset uniquely identify cell.","code":"head(supercells$supercell_cell_map) #>                      SuperCellID   Sample #> 1: SuperCell_427_Sample_Sample_1 Sample_1 #> 2: SuperCell_194_Sample_Sample_1 Sample_1 #> 3:  SuperCell_21_Sample_Sample_1 Sample_1 #> 4:  SuperCell_40_Sample_Sample_1 Sample_1 #> 5: SuperCell_138_Sample_Sample_1 Sample_1 #> 6:  SuperCell_80_Sample_Sample_1 Sample_1"},{"path":"https://phipsonlab.github.io/SuperCellCyto/articles/how_to_supercell.html","id":"running-runsupercellcyto-in-parallel","dir":"Articles","previous_headings":"","what":"Running runSuperCellCyto in parallel","title":"How to create supercells","text":"default, runSuperCellCyto process sample one . sample processed independent one another, can process parallel. , need create BiocParallelParam object leverages parallel processing. Additionally, also set number tasks number samples, set load_balancing parameter TRUE jobs supercelling large samples assigned small samples (instead given supercelling smaller samples). Notably, set workers total number cores computer, render computer useless anything else (might blow RAM). find total number cores computer, can use parallel’s detectCores.","code":"n_cores <- detectCores() supercell_par <- runSuperCellCyto(   dt = dat,   markers = markers_col,   sample_colname = sample_col,   cell_id_colname = cell_id_col,   BPPARAM = MulticoreParam(     workers = n_cores - 1,     tasks = n_samples   ),   load_balancing = TRUE )"},{"path":"https://phipsonlab.github.io/SuperCellCyto/articles/how_to_supercell.html","id":"controlling-the-supercells-granularity","dir":"Articles","previous_headings":"","what":"Controlling the supercells’ granularity","title":"How to create supercells","text":"described runSuperCellCyto function’s documentation, let’s briefly go . runSuperCellCyto function equipped various parameters can customise alter composition supercells. one likely used gam parameter. gam parameter controls many supercells generate, indirectly, many cells captured within supercell. parameter resolved following formula gam=n_cells/n_supercells n_cell denotes number cells n_supercells denotes number supercells. general, larger gam parameter set , less supercells get. Say instance 10,000 cells. gam set 10, end 1,000 supercells, whereas gam set 50, end 200 supercells. may noticed, reading sections , runSuperCellCyto ran sample independent , can set 1 value gam parameter. Indeed, now, gam value used across samples, depending many cells sample, end different number supercells sample. instance, say 10,000 cells sample 1, 100,000 cells sample 2. gam set 10, sample 1, get 1,000 supercells (10,000/10) sample 2, get 10,000 supercells (100,000/10). future, may add ability specify different gam value different samples. now, want , need break data multiple data.table objects, containing data 1 sample, run runSuperCellCyto function different gam parameter value. Something like following: Subsequently, extract combine supercell_expression_matrix supercell_cell_map, need use rbind:","code":"n_markers <- 10 dat <- simCytoData(nmarkers = n_markers) markers_col <- paste0(\"Marker_\", seq_len(n_markers)) sample_col <- \"Sample\" cell_id_col <- \"Cell_Id\"  samples <- unique(dat[[sample_col]]) gam_values <- c(10, 20, 10)  supercells_diff_gam <- lapply(seq_len(length(samples)), function(i) {   sample <- samples[i]   gam <- gam_values[i]   dat_samp <- dat[dat$Sample == sample, ]   supercell_samp <- runSuperCellCyto(     dt = dat_samp,     markers = markers_col,     sample_colname = sample_col,     cell_id_colname = cell_id_col,     gam = gam   )   return(supercell_samp) }) supercell_expression_matrix <- do.call(   \"rbind\", lapply(supercells_diff_gam, function(x) x[[\"supercell_expression_matrix\"]]) )  supercell_cell_map <- do.call(   \"rbind\", lapply(supercells_diff_gam, function(x) x[[\"supercell_cell_map\"]]) ) rbind(head(supercell_expression_matrix, n = 3), tail(supercell_expression_matrix, n = 3)) #>     Marker_1  Marker_2  Marker_3 Marker_4 Marker_5  Marker_6 Marker_7 Marker_8 #> 1: 12.597362 18.563892  7.939731 17.53654 20.16769 10.109291 20.08783 16.53032 #> 2: 12.612559 19.527094  9.127097 16.71297 19.01135 10.000790 19.22212 17.74209 #> 3: 11.751343 19.304758  8.749553 17.68459 20.15861 11.332877 18.80278 18.91703 #> 4:  7.468624  6.504929 19.134719 16.50861 13.61697  5.884649 11.17226 13.54510 #> 5:  4.423812  6.820534 16.989851 18.05577 15.46220  7.110407 14.37311 14.21248 #> 6:  5.456059  6.600613 20.530448 17.10953 15.22410  7.192285 14.12641 14.91185 #>    Marker_9 Marker_10   Sample                   SuperCellId #> 1: 13.37707 10.637396 Sample_1   SuperCell_1_Sample_Sample_1 #> 2: 12.08168  9.940211 Sample_1   SuperCell_2_Sample_Sample_1 #> 3: 11.57253 11.346711 Sample_1   SuperCell_3_Sample_Sample_1 #> 4: 17.72237 12.940699 Sample_2 SuperCell_498_Sample_Sample_2 #> 5: 17.39007 13.409344 Sample_2 SuperCell_499_Sample_Sample_2 #> 6: 16.39534 12.380849 Sample_2 SuperCell_500_Sample_Sample_2 rbind(head(supercell_cell_map, n = 3), tail(supercell_cell_map, n = 3)) #>                      SuperCellID     CellId   Sample #> 1: SuperCell_949_Sample_Sample_1     Cell_1 Sample_1 #> 2: SuperCell_735_Sample_Sample_1     Cell_2 Sample_1 #> 3: SuperCell_190_Sample_Sample_1     Cell_3 Sample_1 #> 4: SuperCell_163_Sample_Sample_2 Cell_19998 Sample_2 #> 5:  SuperCell_95_Sample_Sample_2 Cell_19999 Sample_2 #> 6: SuperCell_327_Sample_Sample_2 Cell_20000 Sample_2"},{"path":"https://phipsonlab.github.io/SuperCellCyto/articles/how_to_supercell.html","id":"using-runsupercellcyto-for-stratified-summarising","dir":"Articles","previous_headings":"","what":"Using runSuperCellCyto for stratified summarising","title":"How to create supercells","text":"previously mentioned, can use runSuperCellCyto perform stratified summarising, .e., summarise (well, meaningfully sub-sample) cluster cell type. , need change sample column denotes cell type cluster cell belongs . example, let’s first cluster toy data k-means: perform stratified summarising, supply cluster column (kmeans_clusters example ), runSuperCellCyto’s sample_colname parameter. Now, look supercell_expression_matrix, row (supercell) denoted cluster belongs , biological sample came : look number supercells created check many cells cluster, find , cluster, get approximately n_cells/20 20 gam parameter value used runSuperCellCyto (default).","code":"set.seed(42)  # Simulate some data dat <- simCytoData() markers_col <- paste0(\"Marker_\", seq_len(10)) cell_id_col <- \"Cell_Id\"  # Run kmeans clust <- kmeans(   x = dat[, markers_col, with = FALSE],   centers = 5 )  clust_col <- \"kmeans_clusters\" dat[[clust_col]] <- paste0(\"cluster_\", clust$cluster) supercells <- runSuperCellCyto(   dt = dat,   markers = markers_col,   sample_colname = clust_col,   cell_id_colname = cell_id_col ) # Inspect the top 3 and bottom 3 of the expression matrix and some columns. rbind(   head(supercells$supercell_expression_matrix, n = 3),   tail(supercells$supercell_expression_matrix, n = 3) )[, c(\"kmeans_clusters\", \"SuperCellId\", \"Marker_10\")] #>    kmeans_clusters                    SuperCellId Marker_10 #> 1:       cluster_4   SuperCell_1_Sample_cluster_4  14.64662 #> 2:       cluster_4   SuperCell_2_Sample_cluster_4  14.66858 #> 3:       cluster_4   SuperCell_3_Sample_cluster_4  14.41837 #> 4:       cluster_5 SuperCell_498_Sample_cluster_5  16.99003 #> 5:       cluster_5 SuperCell_499_Sample_cluster_5  17.09864 #> 6:       cluster_5 SuperCell_500_Sample_cluster_5  15.85447 # Compute how many cells per cluster, and divide by 20, the gamma value. table(dat$kmeans_clusters) / 20 #>  #> cluster_1 cluster_2 cluster_3 cluster_4 cluster_5  #>    120.25    130.30    119.75    129.70    500.00 table(supercells$supercell_expression_matrix$kmeans_clusters) #>  #> cluster_1 cluster_2 cluster_3 cluster_4 cluster_5  #>       120       130       120       130       500"},{"path":"https://phipsonlab.github.io/SuperCellCyto/articles/how_to_supercell.html","id":"session-information","dir":"Articles","previous_headings":"","what":"Session information","title":"How to create supercells","text":"","code":"sessionInfo() #> R version 4.3.2 (2023-10-31) #> Platform: x86_64-pc-linux-gnu (64-bit) #> Running under: Ubuntu 22.04.3 LTS #>  #> Matrix products: default #> BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3  #> LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0 #>  #> locale: #>  [1] LC_CTYPE=C.UTF-8       LC_NUMERIC=C           LC_TIME=C.UTF-8        #>  [4] LC_COLLATE=C.UTF-8     LC_MONETARY=C.UTF-8    LC_MESSAGES=C.UTF-8    #>  [7] LC_PAPER=C.UTF-8       LC_NAME=C              LC_ADDRESS=C           #> [10] LC_TELEPHONE=C         LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C    #>  #> time zone: UTC #> tzcode source: system (glibc) #>  #> attached base packages: #> [1] parallel  stats     graphics  grDevices utils     datasets  methods   #> [8] base      #>  #> other attached packages: #> [1] BiocParallel_1.36.0 SuperCellCyto_0.1.0 BiocStyle_2.30.0    #>  #> loaded via a namespace (and not attached): #>  [1] Matrix_1.6-1.1      jsonlite_1.8.8      compiler_4.3.2      #>  [4] BiocManager_1.30.22 Rcpp_1.0.12         stringr_1.5.1       #>  [7] SuperCell_1.0       jquerylib_0.1.4     systemfonts_1.0.5   #> [10] textshaping_0.3.7   yaml_2.3.8          fastmap_1.1.1       #> [13] lattice_0.21-9      plyr_1.8.9          R6_2.5.1            #> [16] igraph_1.6.0        knitr_1.45          bookdown_0.37       #> [19] desc_1.4.3          bslib_0.6.1         rlang_1.1.3         #> [22] cachem_1.0.8        stringi_1.8.3       RANN_2.6.1          #> [25] xfun_0.41           fs_1.6.3            sass_0.4.8          #> [28] memoise_2.0.1       cli_3.6.2           pkgdown_2.0.7       #> [31] magrittr_2.0.3      digest_0.6.34       grid_4.3.2          #> [34] lifecycle_1.0.4     vctrs_0.6.5         evaluate_0.23       #> [37] glue_1.7.0          data.table_1.14.10  codetools_0.2-19    #> [40] ragg_1.2.7          rmarkdown_2.25      purrr_1.0.2         #> [43] pkgconfig_2.0.3     tools_4.3.2         htmltools_0.5.7"},{"path":"https://phipsonlab.github.io/SuperCellCyto/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Givanna Putri. Author, maintainer. George Howitt. Author. Marsh-Wakefield Felix. Author. Thomas Ashhurst. Author. Belinda Phipson. Author.","code":""},{"path":"https://phipsonlab.github.io/SuperCellCyto/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Putri G, Howitt G, Felix M, Ashhurst T, Phipson B (2024). SuperCellCyto: SuperCell Cytometry Data. R package version 0.1.0, https://phipsonlab.github.io/SuperCellCyto/.","code":"@Manual{,   title = {SuperCellCyto: SuperCell For Cytometry Data},   author = {Givanna Putri and George Howitt and Marsh-Wakefield Felix and Thomas Ashhurst and Belinda Phipson},   year = {2024},   note = {R package version 0.1.0},   url = {https://phipsonlab.github.io/SuperCellCyto/}, }"},{"path":"https://phipsonlab.github.io/SuperCellCyto/index.html","id":"supercellcyto","dir":"","previous_headings":"","what":"SuperCell For Cytometry Data","title":"SuperCell For Cytometry Data","text":"SuperCellCyto extension SuperCell R package. Initially developed scRNAseq data, SuperCell aggregates cells similar transcriptomic profiles “supercells” (also known “metacells” scRNAseq literature). SuperCellCyto, ’ve tailored SuperCell package specifically cater cytometry data: Supercells now aggregating cells similar marker expressions. Supercells now created individual sample. adaptation ensures supercell encompasses cells exclusively one sample. processing sample independently, prevent intermixing cells different samples within supercells. Multiple samples can now processed parallel custom load balancing strategy. enhancement enables simultaneous generation supercells multiple samples, significantly reducing computational time required processing large datasets.","code":""},{"path":"https://phipsonlab.github.io/SuperCellCyto/index.html","id":"vignettes-and-function-documentation","dir":"","previous_headings":"","what":"Vignettes and Function Documentation","title":"SuperCell For Cytometry Data","text":"understand SuperCellCyto can integrated workflow, head Articles page. Click dropdown arrow Articles link navbar top website. vignettes provide step--step guides, practical examples, use-case scenarios demonstrate package’s application various research contexts. Documentation usage function SuperCellCyto package available Reference page. Click Reference link navbar top website.","code":""},{"path":"https://phipsonlab.github.io/SuperCellCyto/index.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"SuperCell For Cytometry Data","text":"use SuperCellCyto study, please kindly cite preprint bioRxiv: Putri, G. H., Howitt, G., Marsh-Wakefield, F., Ashhurst, T. M., & Phipson, B. (2023). SuperCellCyto: enabling efficient analysis large scale cytometry datasets. bioRxiv; DOI: https://doi.org/10.1101/2023.08.14.553168","code":""},{"path":"https://phipsonlab.github.io/SuperCellCyto/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"SuperCell For Cytometry Data","text":"package can installed using remotes: SuperCellCyto requires SuperCell R package installed run properly. use installation command , , theory, automatically installed. case doesn’t, please manually install running following command beforehand:","code":"# Install remotes install.packages(\"remotes\")  # Install SuperCellCyto from this repository remotes::install_github(\"phipsonlab/SuperCellCyto\") # Install SuperCell from their github repository remotes::install_github(\"GfellerLab/SuperCell\")"},{"path":"https://phipsonlab.github.io/SuperCellCyto/index.html","id":"code-of-conduct","dir":"","previous_headings":"","what":"Code of Conduct","title":"SuperCell For Cytometry Data","text":"Please note SuperCellCyto project released Contributor Code Conduct. contributing project, agree abide terms.","code":""},{"path":"https://phipsonlab.github.io/SuperCellCyto/index.html","id":"contribution-guide","dir":"","previous_headings":"","what":"Contribution guide","title":"SuperCell For Cytometry Data","text":"Please visit Contributing Guide","code":""},{"path":"https://phipsonlab.github.io/SuperCellCyto/reference/recomputeSupercells.html","id":null,"dir":"Reference","previous_headings":"","what":"Recompute supercells — recomputeSupercells","title":"Recompute supercells — recomputeSupercells","text":"Given supercell object, recreate supercells using different gamma value. Gamma value controls number supercells generated. smaller value, supercells get, vice versa. function run, need least run runSuperCellCyto function !","code":""},{"path":"https://phipsonlab.github.io/SuperCellCyto/reference/recomputeSupercells.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Recompute supercells — recomputeSupercells","text":"","code":"recomputeSupercells(   dt,   sc_objects,   markers,   sample_colname,   cell_id_colname,   gam = 20 )"},{"path":"https://phipsonlab.github.io/SuperCellCyto/reference/recomputeSupercells.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Recompute supercells — recomputeSupercells","text":"dt data.table object containing cytometry data rows represent cells columns represent markers. sc_objects supercell_object returned runSuperCellCyto function. markers character vector identifying markers create supercells . sample_colname character string identifying column dt denotes sample cell. cell_id_colname character string identifying column dt representing cell's unique ID. gam numeric value specifying gamma value regulates number supercells generated. Defaults 20.","code":""},{"path":"https://phipsonlab.github.io/SuperCellCyto/reference/recomputeSupercells.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Recompute supercells — recomputeSupercells","text":"list following components: supercell_expression_matrix:  data.table object contains marker expression supercell. marker expressions computed calculating mean marker expressions across cells within individual supercell. supercell_cell_map: data.table maps cell corresponding supercell. table essential identifying specific supercell cell allocated . proves particularly useful analyses require one expand supercells individual cell level.","code":""},{"path":"https://phipsonlab.github.io/SuperCellCyto/reference/recomputeSupercells.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Recompute supercells — recomputeSupercells","text":"Givanna Putri","code":""},{"path":"https://phipsonlab.github.io/SuperCellCyto/reference/recomputeSupercells.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Recompute supercells — recomputeSupercells","text":"","code":"set.seed(42) cyto_dat <- simCytoData(10, rep(1000, 3)) markers <- paste0(\"Marker_\", seq_len(10)) out_gam20 <- runSuperCellCyto(     dt = cyto_dat,     markers = markers,     sample_colname = \"Sample\",     cell_id_colname = \"Cell_Id\",     gam = 20 ) recomputed_sc <- recomputeSupercells(     dt = cyto_dat,     sc_objects = out_gam20$supercell_object,     markers = markers,     sample_colname = \"Sample\",     cell_id_colname = \"Cell_Id\",     gam = 50 )"},{"path":"https://phipsonlab.github.io/SuperCellCyto/reference/runSuperCellCyto.html","id":null,"dir":"Reference","previous_headings":"","what":"Run SuperCellCyto on Cytometry Data — runSuperCellCyto","title":"Run SuperCellCyto on Cytometry Data — runSuperCellCyto","text":"function creates supercells cytometry data formatted data.table object using SuperCellCyto algorithm. Please make sure read additional details better understand function works.","code":""},{"path":"https://phipsonlab.github.io/SuperCellCyto/reference/runSuperCellCyto.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Run SuperCellCyto on Cytometry Data — runSuperCellCyto","text":"","code":"runSuperCellCyto(   dt,   markers,   sample_colname,   cell_id_colname,   gam = 20,   k_knn = 5,   BPPARAM = SerialParam(),   load_balancing = FALSE )"},{"path":"https://phipsonlab.github.io/SuperCellCyto/reference/runSuperCellCyto.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Run SuperCellCyto on Cytometry Data — runSuperCellCyto","text":"dt data.table object containing cytometry data rows represent cells columns represent markers. data.frame object, function try convert data.table object. warning message displayed happens. Otherwise, terminate. markers character vector identifying markers create supercells . sample_colname character string identifying column dt denotes sample cell. cell_id_colname character string identifying column dt representing cell's unique ID. gam numeric value specifying gamma value regulates number supercells generated. Defaults 20. k_knn numeric value specifying k value (number neighbours) used build kNN network. Defaults 5. BPPARAM BiocParallelParam object specifying parallel processing settings. Defaults SerialParam, meaning samples processed sequentially one . Refer additional details section parallel processing details. load_balancing logical value  indicating whether use custom load balancing scheme processing multiple samples parallel. Defaults FALSE. Refer additional details section parallel processing details.","code":""},{"path":"https://phipsonlab.github.io/SuperCellCyto/reference/runSuperCellCyto.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Run SuperCellCyto on Cytometry Data — runSuperCellCyto","text":"list following components: supercell_object: list containing object returned SCimplify function. One object per sample. object critical recomputing supercells future. Hence discard . supercell_expression_matrix:  data.table object contains marker expression supercell. marker expressions computed calculating mean marker expressions across cells within individual supercell. supercell_cell_map: data.table maps cell corresponding supercell. table essential identifying specific supercell cell allocated . proves particularly useful analyses require one expand supercells individual cell level.","code":""},{"path":"https://phipsonlab.github.io/SuperCellCyto/reference/runSuperCellCyto.html","id":"parallel-processing","dir":"Reference","previous_headings":"","what":"Parallel Processing","title":"Run SuperCellCyto on Cytometry Data — runSuperCellCyto","text":"SuperCellCyto can process multiple samples simultaneously parallel. can drastically bring processing time dataset large number samples. enable feature, set BPPARAM parameter either MulticoreParam object SnowParam object. Importantly, also recommended set number tasks (.e., task parameter either MulticoreParam SnowParam object) number samples dataset. Furthermore, also recommend setting load_balancing parameter TRUE. ensures optimal distribution samples across multiple cores, particularly important samples varying sizes (number cells).","code":""},{"path":"https://phipsonlab.github.io/SuperCellCyto/reference/runSuperCellCyto.html","id":"cell-id-and-sample-definitions","dir":"Reference","previous_headings":"","what":"Cell ID and Sample Definitions","title":"Run SuperCellCyto on Cytometry Data — runSuperCellCyto","text":"cell_id_colname parameter specifies column dt denotes unique identifier cell. perfectly normal column dataset default. good news trivial create one. can create new vector containing sequence numbers 1 however many cells , append vector new column dataset. Refer vignette . sample_colname parameter specifies column dt denotes sample cell came . default, SuperCellCyto creates supercells sample independent samples. ensures supercell contain cells exactly one sample. constitute sample? purposes, sample represents biological sample experiment. may thinking, possible use different context, say creating supercells population cluster rather biological sample? short answer yes, address vignette.","code":""},{"path":"https://phipsonlab.github.io/SuperCellCyto/reference/runSuperCellCyto.html","id":"computing-pca","dir":"Reference","previous_headings":"","what":"Computing PCA","title":"Run SuperCellCyto on Cytometry Data — runSuperCellCyto","text":"function start computing PCA markers specified markers parameter. default, number PCs calculated set 10. less 10 markers markers parameter, number PCs set however many markers markers parameter. Notably, scaling transformation done markers' expressions prior computing PCs. function use irlba calculate PCA. little gain use cytometry data relatively tiny number features (markers) data.","code":""},{"path":"https://phipsonlab.github.io/SuperCellCyto/reference/runSuperCellCyto.html","id":"setting-supercell-s-resolution","dir":"Reference","previous_headings":"","what":"Setting Supercell's Resolution","title":"Run SuperCellCyto on Cytometry Data — runSuperCellCyto","text":"gam parameter influences number supercells created per sample. lower gam value results , thus generally smaller supercells, vice versa. estimate many supercells get dataset, important understand gam value interpreted context number cells sample. gam=n_cells/n_supercells n_cells denotes number cells n_supercells denotes number supercells created. resolving formula , can roughly estimate many supercells get per sample. example, say 2 samples, sample B. Sample 10,000 cells, sample B 5,000 cells: gam set 10, end 1,000 supercells sample 500 supercells sample B, total 1,500 supercells. gam set 50, end 200 supercells sample 100 supercells sample B, total 300 supercells. Importantly, one expect supercells size. capture /less cells others. trivial estimate many cells captured supercell beforehand.","code":""},{"path":"https://phipsonlab.github.io/SuperCellCyto/reference/runSuperCellCyto.html","id":"computing-knn-network","dir":"Reference","previous_headings":"","what":"Computing kNN network","title":"Run SuperCellCyto on Cytometry Data — runSuperCellCyto","text":"create supercells, kNN (k-Nearest Neighbors) network constructed based k_knn parameter dictates number neighbours (cell) used create network. actual (approximate) kNN network created. walktrap algorithm uses network group cells supercells.","code":""},{"path":"https://phipsonlab.github.io/SuperCellCyto/reference/runSuperCellCyto.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Run SuperCellCyto on Cytometry Data — runSuperCellCyto","text":"Givanna Putri","code":""},{"path":"https://phipsonlab.github.io/SuperCellCyto/reference/runSuperCellCyto.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Run SuperCellCyto on Cytometry Data — runSuperCellCyto","text":"","code":"# Simulate some data set.seed(42) cyto_dat <- simCytoData(nmarkers = 10, ncells = rep(2000,2))  # Setup the columns designating the markers, samples, and cell IDs marker_col <- paste0(\"Marker_\", seq_len(10)) sample_col <- \"Sample\" cell_id_col <- \"Cell_Id\"  supercell_dat <- runSuperCellCyto(     cyto_dat, marker_col,     sample_col, cell_id_col )"},{"path":"https://phipsonlab.github.io/SuperCellCyto/reference/simCytoData.html","id":null,"dir":"Reference","previous_headings":"","what":"Simulate cytometry data — simCytoData","title":"Simulate cytometry data — simCytoData","text":"Simulate cytometry data use testing documenting functions require cytometry data. Please run set.seed running function want ensure reproducibility.","code":""},{"path":"https://phipsonlab.github.io/SuperCellCyto/reference/simCytoData.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Simulate cytometry data — simCytoData","text":"","code":"simCytoData(nmarkers = 10, ncells = rep(10000, 2))"},{"path":"https://phipsonlab.github.io/SuperCellCyto/reference/simCytoData.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Simulate cytometry data — simCytoData","text":"nmarkers numeric value specifying number markers simulate. ncells numeric vector specifying number cells simulate per sample. 1 vector element per sample.","code":""},{"path":"https://phipsonlab.github.io/SuperCellCyto/reference/simCytoData.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Simulate cytometry data — simCytoData","text":"data.table object containing simulated cytometry data rows represent cells columns represent markers.","code":""},{"path":"https://phipsonlab.github.io/SuperCellCyto/reference/simCytoData.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Simulate cytometry data — simCytoData","text":"Givanna Putri","code":""},{"path":"https://phipsonlab.github.io/SuperCellCyto/reference/simCytoData.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Simulate cytometry data — simCytoData","text":"","code":"set.seed(42) cyto_dat <- simCytoData() head(cyto_dat) #>    Marker_1 Marker_2  Marker_3 Marker_4 Marker_5 Marker_6 Marker_7 Marker_8 #> 1: 18.61597 17.43348  9.213419 18.40881 13.68860 12.77402 15.24751 7.140615 #> 2: 20.23361 19.82792  9.740510 18.16383 13.97108 14.25898 16.05032 5.863752 #> 3: 18.62743 17.76816 11.175358 18.59606 16.08534 13.69734 16.71550 4.750240 #> 4: 20.74051 19.94771  9.797939 18.43590 13.92092 12.64266 15.55953 8.173464 #> 5: 18.65938 18.87830  8.916928 17.83295 15.62066 11.37118 16.08417 6.664707 #> 6: 20.02696 20.76054  9.262685 16.89373 15.92339 13.40002 16.13880 6.862717 #>    Marker_9 Marker_10   Sample Cell_Id #> 1: 14.37555  14.18484 Sample_1  Cell_1 #> 2: 15.58007  15.56981 Sample_1  Cell_2 #> 3: 14.16156  14.92222 Sample_1  Cell_3 #> 4: 14.26637  15.10761 Sample_1  Cell_4 #> 5: 15.12219  15.64231 Sample_1  Cell_5 #> 6: 15.85313  15.52535 Sample_1  Cell_6 dim(cyto_dat) #> [1] 20000    12"},{"path":"https://phipsonlab.github.io/SuperCellCyto/news/index.html","id":"supercellcyto-010","dir":"Changelog","previous_headings":"","what":"SuperCellCyto 0.1.0","title":"SuperCellCyto 0.1.0","text":"first release SuperCellCyto. also version SuperCellCyto used preprint:","code":"@article{     putri2023supercellcyto,     title={SuperCellCyto: enabling efficient analysis of large scale cytometry datasets},     author={Putri, Givanna H and Howitt, George and Marsh-Wakefield, Felix and Ashhurst, Thomas Myles and Phipson, Belinda},     journal={bioRxiv},     pages={2023--08},     year={2023},     publisher={Cold Spring Harbor Laboratory} }"}]