Browse code

Changed vignettes to html instead of pdf

Joshua D. Campbell authored on 30/09/2021 01:14:49
Showing 128 changed files

... ...
@@ -78,7 +78,7 @@
78 78
     <div id="navbar" class="navbar-collapse collapse">
79 79
       <ul class="nav navbar-nav">
80 80
         <li>
81
-  <a href="index.html">
81
+  <a href="http://celda.camplab.net/">
82 82
     <span class="fas fa-home fa-lg"></span>
83 83
      
84 84
   </a>
... ...
@@ -153,7 +153,7 @@ Content not found. Please use links in the navbar.
153 153
 
154 154
       <footer>
155 155
       <div class="copyright">
156
-  <p>Developed by Joshua Campbell, Sean Corbett, Yusuke Koga, Shiyi Yang, Eric Reed, Zhe Wang.</p>
156
+  <p>Developed by Joshua Campbell, Shiyi Yang, Zhe Wang, Sean Corbett, Yusuke Koga.</p>
157 157
 </div>
158 158
 
159 159
 <div class="pkgdown">
... ...
@@ -78,7 +78,7 @@
78 78
     <div id="navbar" class="navbar-collapse collapse">
79 79
       <ul class="nav navbar-nav">
80 80
         <li>
81
-  <a href="index.html">
81
+  <a href="http://celda.camplab.net/">
82 82
     <span class="fas fa-home fa-lg"></span>
83 83
      
84 84
   </a>
... ...
@@ -161,7 +161,7 @@
161 161
 
162 162
       <footer>
163 163
       <div class="copyright">
164
-  <p>Developed by Joshua Campbell, Sean Corbett, Yusuke Koga, Shiyi Yang, Eric Reed, Zhe Wang.</p>
164
+  <p>Developed by Joshua Campbell, Shiyi Yang, Zhe Wang, Sean Corbett, Yusuke Koga.</p>
165 165
 </div>
166 166
 
167 167
 <div class="pkgdown">
... ...
@@ -78,7 +78,7 @@
78 78
     <div id="navbar" class="navbar-collapse collapse">
79 79
       <ul class="nav navbar-nav">
80 80
         <li>
81
-  <a href="index.html">
81
+  <a href="http://celda.camplab.net/">
82 82
     <span class="fas fa-home fa-lg"></span>
83 83
      
84 84
   </a>
... ...
@@ -174,7 +174,7 @@ SOFTWARE.
174 174
 
175 175
       <footer>
176 176
       <div class="copyright">
177
-  <p>Developed by Joshua Campbell, Sean Corbett, Yusuke Koga, Shiyi Yang, Eric Reed, Zhe Wang.</p>
177
+  <p>Developed by Joshua Campbell, Shiyi Yang, Zhe Wang, Sean Corbett, Yusuke Koga.</p>
178 178
 </div>
179 179
 
180 180
 <div class="pkgdown">
... ...
@@ -5,13 +5,13 @@
5 5
 <meta charset="utf-8">
6 6
 <meta http-equiv="X-UA-Compatible" content="IE=edge">
7 7
 <meta name="viewport" content="width=device-width, initial-scale=1.0">
8
-<title> • celda</title>
8
+<title>Celda - Analysis of PBMC3K • celda</title>
9 9
 <!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link href="https://cdnjs.cloudflare.com/ajax/libs/bootswatch/3.4.0/yeti/bootstrap.min.css" rel="stylesheet" crossorigin="anonymous">
10 10
 <script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../../bootstrap-toc.css">
11 11
 <script src="../../bootstrap-toc.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">
12 12
 <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">
13 13
 <!-- 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><!-- 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><!-- pkgdown --><link href="../../pkgdown.css" rel="stylesheet">
14
-<script src="../../pkgdown.js"></script><meta property="og:title" content="">
14
+<script src="../../pkgdown.js"></script><meta property="og:title" content="Celda - Analysis of PBMC3K">
15 15
 <meta property="og:description" content="celda">
16 16
 <!-- 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]>
17 17
 <script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
... ...
@@ -38,7 +38,7 @@
38 38
     <div id="navbar" class="navbar-collapse collapse">
39 39
       <ul class="nav navbar-nav">
40 40
 <li>
41
-  <a href="../../index.html">
41
+  <a href="http://celda.camplab.net/">
42 42
     <span class="fas fa-home fa-lg"></span>
43 43
      
44 44
   </a>
... ...
@@ -91,12 +91,14 @@
91 91
 
92 92
       
93 93
 
94
-      </header><script src="celda_pbmc3k_files/header-attrs-2.7/header-attrs.js"></script><script src="celda_pbmc3k_files/accessible-code-block-0.0.1/empty-anchor.js"></script><script src="celda_pbmc3k_files/kePrint-0.0.1/kePrint.js"></script><link href="celda_pbmc3k_files/lightable-0.0.1/lightable.css" rel="stylesheet">
94
+      </header><script src="celda_pbmc3k_files/header-attrs-2.7/header-attrs.js"></script><script src="celda_pbmc3k_files/kePrint-0.0.1/kePrint.js"></script><link href="celda_pbmc3k_files/lightable-0.0.1/lightable.css" rel="stylesheet">
95 95
 <div class="row">
96 96
   <div class="col-md-9 contents">
97 97
     <div class="page-header toc-ignore">
98
-      <h1 data-toc-skip></h1>
98
+      <h1 data-toc-skip>Celda - Analysis of PBMC3K</h1>
99
+                        <h4 class="author">Joshua Campbell, Zhe Wang</h4>
99 100
             
101
+            <h4 class="date">Compiled September 29, 2021</h4>
100 102
       
101 103
       <small class="dont-index">Source: <a href="https://github.com/campbio/celda/blob/master/vignettes/articles/celda_pbmc3k.Rmd"><code>vignettes/articles/celda_pbmc3k.Rmd</code></a></small>
102 104
       <div class="hidden name"><code>celda_pbmc3k.Rmd</code></div>
... ...
@@ -201,9 +203,9 @@
201 203
 <h2 class="hasAnchor">
202 204
 <a href="#bi-clustering-with-known-numbers-of-clusters" class="anchor"></a>Bi-clustering with known numbers of clusters</h2>
203 205
 <p>As mentioned earlier, celda is discrete Bayesian model that is able to simultaneously bi-cluster features into modules and cells into cell clusters. The primary bi-clustering model can be accessed with the function <code>celda_CG</code>. This function operates on a matrix stored as an alternative experiment in the <code>altExp</code> slot. If you did not perform feature selection as recommended in the previous section and your matrix of interest is not currently located in an <code>altExp</code> slot, the following code can be used to copy a matrix in the main assay slot to the <code>altExp</code> slot:</p>
204
-<div class="sourceCode" id="cb15"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb15-1"><a href="#cb15-1"></a>useAssay &lt;-<span class="st"> "counts"</span></span>
205
-<span id="cb15-2"><a href="#cb15-2"></a>altExpName &lt;-<span class="st"> "featureSubset"</span></span>
206
-<span id="cb15-3"><a href="#cb15-3"></a><span class="kw">altExp</span>(sce, altExpName) &lt;-<span class="st"> </span><span class="kw">assay</span>(sce, useAssay)<span class="st">`</span><span class="dt">. </span></span></code></pre></div>
206
+<div class="sourceCode" id="cb15"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb15-1"><a href="#cb15-1" aria-hidden="true" tabindex="-1"></a>useAssay <span class="ot">&lt;-</span> <span class="st">"counts"</span></span>
207
+<span id="cb15-2"><a href="#cb15-2" aria-hidden="true" tabindex="-1"></a>altExpName <span class="ot">&lt;-</span> <span class="st">"featureSubset"</span></span>
208
+<span id="cb15-3"><a href="#cb15-3" aria-hidden="true" tabindex="-1"></a><span class="fu">altExp</span>(sce, altExpName) <span class="ot">&lt;-</span> <span class="fu">assay</span>(sce, useAssay)<span class="st">`</span><span class="at">. </span></span></code></pre></div>
207 209
 <p>The two major adjustable parameters in this model are <code>L</code>, the number of modules, and <code>K</code>, the number of cell populations. The following code bi-clusters the PBMC3K dataset into 100 modules and 15 cell populations:</p>
208 210
 <div class="sourceCode" id="cb16"><pre class="downlit sourceCode r">
209 211
 <code class="sourceCode R"><span class="va">sce</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/celda_CG.html">celda_CG</a></span><span class="op">(</span><span class="va">sce</span>, L <span class="op">=</span> <span class="fl">100</span>, K <span class="op">=</span> <span class="fl">15</span>, useAssay <span class="op">=</span> <span class="va">useAssay</span>, altExpName <span class="op">=</span> <span class="va">altExpName</span><span class="op">)</span></code></pre></div>
... ...
@@ -28319,8 +28321,9 @@ RCE1
28319 28321
 <div id="session-information" class="section level1">
28320 28322
 <h1 class="hasAnchor">
28321 28323
 <a href="#session-information" class="anchor"></a>Session information</h1>
28322
-<details><p><summary>sessionInfo()</summary></p>
28323
-<pre><code>## R version 4.0.4 (2021-02-15)
28324
+<details><summary>
28325
+sessionInfo()
28326
+</summary><pre><code>## R version 4.0.4 (2021-02-15)
28324 28327
 ## Platform: x86_64-apple-darwin17.0 (64-bit)
28325 28328
 ## Running under: macOS Big Sur 10.16
28326 28329
 ## 
... ...
@@ -28338,7 +28341,7 @@ RCE1
28338 28341
 ## other attached packages:
28339 28342
 ##  [1] scater_1.18.6               kableExtra_1.3.4           
28340 28343
 ##  [3] knitr_1.31                  ggplot2_3.3.5              
28341
-##  [5] celda_1.9.2                 singleCellTK_2.1.3         
28344
+##  [5] celda_1.9.2                 singleCellTK_2.2.0         
28342 28345
 ##  [7] TENxPBMCData_1.8.0          HDF5Array_1.18.1           
28343 28346
 ##  [9] rhdf5_2.34.0                DelayedArray_0.16.2        
28344 28347
 ## [11] Matrix_1.3-2                SingleCellExperiment_1.12.0
... ...
@@ -28443,7 +28446,7 @@ RCE1
28443 28446
 
28444 28447
 
28445 28448
       <footer><div class="copyright">
28446
-  <p>Developed by Joshua Campbell, Sean Corbett, Yusuke Koga, Shiyi Yang, Eric Reed, Zhe Wang.</p>
28449
+  <p>Developed by Joshua Campbell, Shiyi Yang, Zhe Wang, Sean Corbett, Yusuke Koga.</p>
28447 28450
 </div>
28448 28451
 
28449 28452
 <div class="pkgdown">
28450 28453
Binary files a/docs/articles/articles/celda_pbmc3k_files/figure-html/cell_clusters-1.png and b/docs/articles/articles/celda_pbmc3k_files/figure-html/cell_clusters-1.png differ
28451 28454
Binary files a/docs/articles/articles/celda_pbmc3k_files/figure-html/cell_identities-1.png and b/docs/articles/articles/celda_pbmc3k_files/figure-html/cell_identities-1.png differ
28452 28455
Binary files a/docs/articles/articles/celda_pbmc3k_files/figure-html/cell_split_perplexity-1.png and b/docs/articles/articles/celda_pbmc3k_files/figure-html/cell_split_perplexity-1.png differ
28453 28456
Binary files a/docs/articles/articles/celda_pbmc3k_files/figure-html/cell_split_perplexity-2.png and b/docs/articles/articles/celda_pbmc3k_files/figure-html/cell_split_perplexity-2.png differ
28454 28457
Binary files a/docs/articles/articles/celda_pbmc3k_files/figure-html/module_split_perplexity-1.png and b/docs/articles/articles/celda_pbmc3k_files/figure-html/module_split_perplexity-1.png differ
28455 28458
Binary files a/docs/articles/articles/celda_pbmc3k_files/figure-html/module_split_rpc-1.png and b/docs/articles/articles/celda_pbmc3k_files/figure-html/module_split_rpc-1.png differ
28456 28459
Binary files a/docs/articles/articles/celda_pbmc3k_files/figure-html/sctk_qc_plot-1.png and b/docs/articles/articles/celda_pbmc3k_files/figure-html/sctk_qc_plot-1.png differ
28457 28460
Binary files a/docs/articles/articles/celda_pbmc3k_files/figure-html/sctk_qc_plot-2.png and b/docs/articles/articles/celda_pbmc3k_files/figure-html/sctk_qc_plot-2.png differ
28458 28461
Binary files a/docs/articles/articles/celda_pbmc3k_files/figure-html/sctk_qc_plot-3.png and b/docs/articles/articles/celda_pbmc3k_files/figure-html/sctk_qc_plot-3.png differ
28459 28462
Binary files a/docs/articles/articles/celda_pbmc3k_files/figure-html/violin-1.png and b/docs/articles/articles/celda_pbmc3k_files/figure-html/violin-1.png differ
... ...
@@ -5,13 +5,13 @@
5 5
 <meta charset="utf-8">
6 6
 <meta http-equiv="X-UA-Compatible" content="IE=edge">
7 7
 <meta name="viewport" content="width=device-width, initial-scale=1.0">
8
-<title> • celda</title>
8
+<title>DecontX - Decontamination of PBMC4K • celda</title>
9 9
 <!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link href="https://cdnjs.cloudflare.com/ajax/libs/bootswatch/3.4.0/yeti/bootstrap.min.css" rel="stylesheet" crossorigin="anonymous">
10 10
 <script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../../bootstrap-toc.css">
11 11
 <script src="../../bootstrap-toc.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">
12 12
 <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">
13 13
 <!-- 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><!-- 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><!-- pkgdown --><link href="../../pkgdown.css" rel="stylesheet">
14
-<script src="../../pkgdown.js"></script><meta property="og:title" content="">
14
+<script src="../../pkgdown.js"></script><meta property="og:title" content="DecontX - Decontamination of PBMC4K">
15 15
 <meta property="og:description" content="celda">
16 16
 <!-- 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]>
17 17
 <script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
... ...
@@ -38,7 +38,7 @@
38 38
     <div id="navbar" class="navbar-collapse collapse">
39 39
       <ul class="nav navbar-nav">
40 40
 <li>
41
-  <a href="../../index.html">
41
+  <a href="http://celda.camplab.net/">
42 42
     <span class="fas fa-home fa-lg"></span>
43 43
      
44 44
   </a>
... ...
@@ -91,11 +91,13 @@
91 91
 
92 92
       
93 93
 
94
-      </header><script src="decontX_pbmc4k_files/header-attrs-2.7/header-attrs.js"></script><script src="decontX_pbmc4k_files/accessible-code-block-0.0.1/empty-anchor.js"></script><div class="row">
94
+      </header><script src="decontX_pbmc4k_files/header-attrs-2.7/header-attrs.js"></script><div class="row">
95 95
   <div class="col-md-9 contents">
96 96
     <div class="page-header toc-ignore">
97
-      <h1 data-toc-skip></h1>
97
+      <h1 data-toc-skip>DecontX - Decontamination of PBMC4K</h1>
98
+                        <h4 class="author">Joshua Campbell, Shiyi Yang, Zhe Wang, Yuan Yin</h4>
98 99
             
100
+            <h4 class="date">Compiled September 29, 2021</h4>
99 101
       
100 102
       <small class="dont-index">Source: <a href="https://github.com/campbio/celda/blob/master/vignettes/articles/decontX_pbmc4k.Rmd"><code>vignettes/articles/decontX_pbmc4k.Rmd</code></a></small>
101 103
       <div class="hidden name"><code>decontX_pbmc4k.Rmd</code></div>
... ...
@@ -383,7 +385,7 @@
383 385
 
384 386
 
385 387
       <footer><div class="copyright">
386
-  <p>Developed by Joshua Campbell, Sean Corbett, Yusuke Koga, Shiyi Yang, Eric Reed, Zhe Wang.</p>
388
+  <p>Developed by Joshua Campbell, Shiyi Yang, Zhe Wang, Sean Corbett, Yusuke Koga.</p>
387 389
 </div>
388 390
 
389 391
 <div class="pkgdown">
... ...
@@ -38,7 +38,7 @@
38 38
     <div id="navbar" class="navbar-collapse collapse">
39 39
       <ul class="nav navbar-nav">
40 40
 <li>
41
-  <a href="../../index.html">
41
+  <a href="http://celda.camplab.net/">
42 42
     <span class="fas fa-home fa-lg"></span>
43 43
      
44 44
   </a>
... ...
@@ -91,7 +91,7 @@
91 91
 
92 92
       
93 93
 
94
-      </header><script src="installation_files/header-attrs-2.7/header-attrs.js"></script><script src="installation_files/accessible-code-block-0.0.1/empty-anchor.js"></script><div class="row">
94
+      </header><script src="installation_files/header-attrs-2.7/header-attrs.js"></script><div class="row">
95 95
   <div class="col-md-9 contents">
96 96
     <div class="page-header toc-ignore">
97 97
       <h1 data-toc-skip></h1>
... ...
@@ -151,7 +151,7 @@
151 151
 
152 152
 
153 153
       <footer><div class="copyright">
154
-  <p>Developed by Joshua Campbell, Sean Corbett, Yusuke Koga, Shiyi Yang, Eric Reed, Zhe Wang.</p>
154
+  <p>Developed by Joshua Campbell, Shiyi Yang, Zhe Wang, Sean Corbett, Yusuke Koga.</p>
155 155
 </div>
156 156
 
157 157
 <div class="pkgdown">
... ...
@@ -31,20 +31,20 @@
31 31
       </button>
32 32
       <span class="navbar-brand">
33 33
         <a class="navbar-link" href="../index.html">celda</a>
34
-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.9.1</span>
34
+        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.9.2</span>
35 35
       </span>
36 36
     </div>
37 37
 
38 38
     <div id="navbar" class="navbar-collapse collapse">
39 39
       <ul class="nav navbar-nav">
40 40
 <li>
41
-  <a href="../index.html">
41
+  <a href="http://celda.camplab.net/">
42 42
     <span class="fas fa-home fa-lg"></span>
43 43
      
44 44
   </a>
45 45
 </li>
46 46
 <li>
47
-  <a href="../index.html">Installation</a>
47
+  <a href="../articles/articles/installation.html">Installation</a>
48 48
 </li>
49 49
 <li class="dropdown">
50 50
   <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false">
... ...
@@ -54,16 +54,19 @@
54 54
   </a>
55 55
   <ul class="dropdown-menu" role="menu">
56 56
 <li>
57
-      <a href="../articles/articles/celda_pbmc3k.html">Analysis of PBMC3K with Celda</a>
57
+      <a href="../articles/articles/celda_pbmc3k.html">Celda - Analysis of PBMC3K</a>
58 58
     </li>
59 59
     <li>
60
-      <a href="../articles/articles/decontX_pbmc4k.html">Decontamination of PBMC4K with DecontX</a>
60
+      <a href="../articles/articles/decontX_pbmc4k.html">DecontX - Decontamination of PBMC4K</a>
61 61
     </li>
62 62
   </ul>
63 63
 </li>
64 64
 <li>
65 65
   <a href="../reference/index.html">Reference</a>
66 66
 </li>
67
+<li>
68
+  <a href="../news/index.html">News</a>
69
+</li>
67 70
 <li>
68 71
   <a href="https://github.com/campbio/celda">
69 72
     <span class="fas fa-github"></span>
... ...
@@ -88,24 +91,23 @@
88 91
 
89 92
       
90 93
 
91
-      </header><script src="celda_files/header-attrs-2.7/header-attrs.js"></script><script src="celda_files/accessible-code-block-0.0.1/empty-anchor.js"></script><div class="row">
94
+      </header><script src="celda_files/header-attrs-2.7/header-attrs.js"></script><div class="row">
92 95
   <div class="col-md-9 contents">
93 96
     <div class="page-header toc-ignore">
94 97
       <h1 data-toc-skip>Analysis of single-cell genomic data with celda</h1>
95
-                        <h4 class="author">Sean Corbett</h4>
98
+                        <h4 class="author">Joshua Campbell</h4>
96 99
             <address class="author_afil">
97
-      Boston University School of Medicine<br><h4 class="author">Yusuke Koga</h4>
100
+      Boston University School of Medicine<br><a class="author_email" href="mailto:#"></a><a href="mailto:camp@bu.edu" class="email">camp@bu.edu</a>
101
+      </address>
102
+                              <h4 class="author">Zhe Wang</h4>
98 103
             <address class="author_afil">
99 104
       Boston University School of Medicine<br><h4 class="author">Shiyi Yang</h4>
100 105
             <address class="author_afil">
101
-      Boston University School of Medicine<br><h4 class="author">Zhe Wang</h4>
106
+      Boston University School of Medicine<br><h4 class="author">Sean Corbett</h4>
102 107
             <address class="author_afil">
103
-      Boston University School of Medicine<br><h4 class="author">Joshua Campbell</h4>
108
+      Boston University School of Medicine<br><h4 class="author">Yusuke Koga</h4>
104 109
             <address class="author_afil">
105
-      Boston University School of Medicine<br><a class="author_email" href="mailto:#"></a><a href="mailto:camp@bu.edu" class="email">camp@bu.edu</a>
106
-      </address>
107
-                  
108
-            <h4 class="date">2021-07-18</h4>
110
+      Boston University School of Medicine<br><h4 class="date">2021-09-29</h4>
109 111
       
110 112
       <small class="dont-index">Source: <a href="https://github.com/campbio/celda/blob/master/vignettes/celda.Rmd"><code>vignettes/celda.Rmd</code></a></small>
111 113
       <div class="hidden name"><code>celda.Rmd</code></div>
... ...
@@ -122,7 +124,7 @@
122 124
 <h1 class="hasAnchor">
123 125
 <a href="#introduction" class="anchor"></a>Introduction</h1>
124 126
 <p><strong>CE</strong>llular <strong>L</strong>atent <strong>D</strong>irichlet <strong>A</strong>llocation (celda) is a collection of Bayesian hierarchical models to perform feature and cell bi-clustering for count data generated by single-cell platforms. This algorithm is an extension of the Latent Dirichlet Allocation (LDA) topic modeling framework that has been popular in text mining applications and has shown good performance with sparse data. celda simultaneously clusters features (i.e. gene expression) into modules based on co-expression patterns across cells and cells into subpopulations based on the probabilities of the feature modules within each cell.</p>
125
-<p>Starting from Bioconductor release 3.12 (<code>celda</code> version 1.6.0), <code>celda</code> makes use of <em><a href="https://bioconductor.org/packages/3.12/SingleCellExperiment">SingleCellExperiment</a></em> (SCE) objects for storing data and results. In this vignette we will demonstrate how to use celda to perform cell and feature clustering with a simple, small simulated dataset. This vignette does not include upstream importing of data, quality control, or filtering. To see a more complete analysis of larger real-world datasets, visit [celda.camplab.net] for additional vignettes.</p>
127
+<p>Starting from Bioconductor release 3.12 (<code>celda</code> version 1.6.0), <code>celda</code> makes use of <em><a href="https://bioconductor.org/packages/3.12/SingleCellExperiment">SingleCellExperiment</a></em> (SCE) objects for storing data and results. In this vignette we will demonstrate how to use celda to perform cell and feature clustering with a simple, small simulated dataset. This vignette does not include upstream importing of data, quality control, or filtering. To see a more complete analysis of larger real-world datasets, visit <a href="https://www.camplab.net/celda/">camplab.net/celda</a> for additional vignettes.</p>
126 128
 </div>
127 129
 <div id="installation" class="section level1">
128 130
 <h1 class="hasAnchor">
... ...
@@ -151,10 +153,10 @@
151 153
 <div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">
152 154
 <code class="sourceCode R"><span class="va">simsce</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/simulateCells.html">simulateCells</a></span><span class="op">(</span><span class="st">"celda_CG"</span>,
153 155
     S <span class="op">=</span> <span class="fl">5</span>, K <span class="op">=</span> <span class="fl">5</span>, L <span class="op">=</span> <span class="fl">10</span>, G <span class="op">=</span> <span class="fl">200</span>, CRange <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">30</span>, <span class="fl">50</span><span class="op">)</span><span class="op">)</span></code></pre></div>
154
-<p>Starting from <em>celda</em> version 1.5.6. <code>celda</code> makes use of the <em><a href="https://bioconductor.org/packages/3.12/SingleCellExperiment">SingleCellExperiment</a></em> package. The <code>counts</code> assay slot in <code>simsce</code> contains the counts matrix. The dimensions of counts matrix:</p>
156
+<p>The <code>counts</code> assay slot in <code>simsce</code> contains the counts matrix. The dimensions of counts matrix:</p>
155 157
 <div class="sourceCode" id="cb5"><pre class="downlit sourceCode r">
156 158
 <code class="sourceCode R"><span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span><span class="op">(</span><span class="va">SingleCellExperiment</span><span class="op">)</span>
157
-<span class="fu"><a href="https://rdrr.io/r/base/dim.html">dim</a></span><span class="op">(</span><span class="fu">assay</span><span class="op">(</span><span class="va">simsce</span>, i <span class="op">=</span> <span class="st">"counts"</span><span class="op">)</span><span class="op">)</span></code></pre></div>
159
+<span class="fu"><a href="https://rdrr.io/r/base/dim.html">dim</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/pkg/SingleCellExperiment/man/assays.html">counts</a></span><span class="op">(</span><span class="va">simsce</span><span class="op">)</span><span class="op">)</span></code></pre></div>
158 160
 <pre><code>## [1] 200 207</code></pre>
159 161
 <p>Columns <code>celda_sample_label</code> and <code>celda_cell_cluster</code> in <code>colData(simsce)</code> contain sample labels and celda cell population cluster labels. Here are the numbers of cells in each subpopulation and in each sample:</p>
160 162
 <div class="sourceCode" id="cb7"><pre class="downlit sourceCode r">
... ...
@@ -180,6 +182,7 @@
180 182
 <p>A simple heuristic feature selection is performed to reduce the size of features used for clustering. To speed up the process, only features with at least 3 counts in at least 3 cells are included in downstream clustering for this data. A subset <code>SingleCellExperiment</code> object with filtered features is stored in <code><a href="https://rdrr.io/pkg/SingleCellExperiment/man/altExps.html">altExp(simsce, "featureSubset")</a></code> slot by default.</p>
181 183
 <div class="sourceCode" id="cb13"><pre class="downlit sourceCode r">
182 184
 <code class="sourceCode R"><span class="va">simsce</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/selectFeatures.html">selectFeatures</a></span><span class="op">(</span><span class="va">simsce</span><span class="op">)</span></code></pre></div>
185
+<p>If the number of features is still too large, then a smaller subset of features can be obtained by selecting the top number of most variable genes. For an example code, see the PBMC3K tutorial in the online celda <a href="https://www.camplab.net/celda">documentation</a>.</p>
183 186
 </div>
184 187
 <div id="performing-bi-clustering-with-celda" class="section level1">
185 188
 <h1 class="hasAnchor">
... ...
@@ -212,11 +215,6 @@
212 215
 ##   9   0  0  0  0  0  0  0  0  3  0
213 216
 ##   10  0  0  0  0  0  0  0  0  0 20</code></pre>
214 217
 </div>
215
-<div id="note-on-reproducibility" class="section level1">
216
-<h1 class="hasAnchor">
217
-<a href="#note-on-reproducibility" class="anchor"></a>Note on reproducibility</h1>
218
-<p>Many functions in <em>celda</em> make use of stochastic algorithms or procedures which require the use of random number generator (RNG) for simulation or sampling. To maintain reproducibility, all these functions use a <strong>default seed of 12345</strong> to make sure same results are generated each time one of these functions is called. Explicitly setting the <code>seed</code> arguments is needed for greater control and randomness.</p>
219
-</div>
220 218
 <div id="visualization" class="section level1">
221 219
 <h1 class="hasAnchor">
222 220
 <a href="#visualization" class="anchor"></a>Visualization</h1>
... ...
@@ -290,10 +288,10 @@
290 288
     yInit <span class="op">=</span> <span class="fu"><a href="../reference/celdaModules.html">celdaModules</a></span><span class="op">(</span><span class="va">moduleSplitSelect</span><span class="op">)</span><span class="op">)</span></code></pre></div>
291 289
 <div class="sourceCode" id="cb30"><pre class="downlit sourceCode r">
292 290
 <code class="sourceCode R"><span class="fu"><a href="../reference/plotGridSearchPerplexity.html">plotGridSearchPerplexity</a></span><span class="op">(</span><span class="va">cellSplit</span><span class="op">)</span></code></pre></div>
293
-<p><img src="celda_files/figure-html/unnamed-chunk-5-1.png" width="700"></p>
291
+<p><img src="celda_files/figure-html/rpc_cell-1.png" width="700"></p>
294 292
 <div class="sourceCode" id="cb31"><pre class="downlit sourceCode r">
295 293
 <code class="sourceCode R"><span class="fu"><a href="../reference/plotRPC.html">plotRPC</a></span><span class="op">(</span><span class="va">cellSplit</span><span class="op">)</span></code></pre></div>
296
-<p><img src="celda_files/figure-html/unnamed-chunk-5-2.png" width="700"></p>
294
+<p><img src="celda_files/figure-html/rpc_cell-2.png" width="700"></p>
297 295
 <p>In this plot, the perplexity for K stops decreasing at K = 5, with a final K/L combination of K = 5, L = 10. Generally, this method can be used to pick a reasonable <code>L</code> and a potential range of <code>K</code>. However, manual review of specific selections of <code>K</code> is often required to ensure results are biologically coherent.</p>
298 296
 <p>Once users have chosen the K/L parameters for further analysis, the <code>subsetCeldaList</code> function can be used to subset the celda list <em>SCE</em> object to a single model <em>SCE</em> object.</p>
299 297
 <div class="sourceCode" id="cb32"><pre class="downlit sourceCode r">
... ...
@@ -315,7 +313,7 @@
315 313
 <p>Setting <code>verbose</code> to <code>TRUE</code> will print the output of each model to a text file. These results can be visualized with <code>plotGridSearchPerplexity</code>. The major goal is to pick the lowest <code>K</code> and <code>L</code> combination with relatively good perplexity. In general, visual inspection of the plot can be used to select the number of modules (<code>L</code>) or cell populations (<code>K</code>) where the rate of decrease in the perplexity starts to drop off. <code>bestOnly = TRUE</code> indicates that only the chain with the best log likelihood will be returned for each K/L combination.</p>
316 314
 <div class="sourceCode" id="cb34"><pre class="downlit sourceCode r">
317 315
 <code class="sourceCode R"><span class="fu"><a href="../reference/plotGridSearchPerplexity.html">plotGridSearchPerplexity</a></span><span class="op">(</span><span class="va">cgs</span><span class="op">)</span></code></pre></div>
318
-<p><img src="celda_files/figure-html/unnamed-chunk-8-1.png" width="768"></p>
316
+<p><img src="celda_files/figure-html/plot_grid_search-1.png" width="768"></p>
319 317
 <p>In this example, the perplexity for <code>L</code> stops decreasing at L = 10 for the majority of <code>K</code> values. For the line corresponding to L = 10, the perplexity stops decreasing at K = 5. Thus L = 10 and K = 5 would be a good choice. Again, manual review of specific selections of K is often be required to ensure results are biologically coherent.</p>
320 318
 <p>Once users have chosen the K/L parameters for further analysis, the <code>subsetCeldaList</code> function can be used to subset the celda list <em>SCE</em> object to a single model <em>SCE</em> object.</p>
321 319
 <div class="sourceCode" id="cb35"><pre class="downlit sourceCode r">
... ...
@@ -342,18 +340,18 @@
342 340
 <h1 class="hasAnchor">
343 341
 <a href="#miscellaneous-utility-functions" class="anchor"></a>Miscellaneous utility functions</h1>
344 342
 <p>celda also contains several utility functions for the users’ convenience.</p>
345
-<div id="featuremodulelookup" class="section level2">
343
+<div id="finding-the-modules-for-feature-with-featuremodulelookup" class="section level2">
346 344
 <h2 class="hasAnchor">
347
-<a href="#featuremodulelookup" class="anchor"></a>featureModuleLookup</h2>
345
+<a href="#finding-the-modules-for-feature-with-featuremodulelookup" class="anchor"></a>Finding the modules for feature with featureModuleLookup</h2>
348 346
 <p><code>featureModuleLookup</code> can be used to look up the module a specific feature was clustered to.</p>
349 347
 <div class="sourceCode" id="cb37"><pre class="downlit sourceCode r">
350 348
 <code class="sourceCode R"><span class="fu"><a href="../reference/featureModuleLookup.html">featureModuleLookup</a></span><span class="op">(</span><span class="va">sce</span>, feature <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="st">"Gene_99"</span><span class="op">)</span><span class="op">)</span></code></pre></div>
351 349
 <pre><code>## Gene_99 
352 350
 ##       4</code></pre>
353 351
 </div>
354
-<div id="recodeclusterz-recodeclustery" class="section level2">
352
+<div id="reordering-cluster-labels-with-recodeclusterz-recodeclustery" class="section level2">
355 353
 <h2 class="hasAnchor">
356
-<a href="#recodeclusterz-recodeclustery" class="anchor"></a>recodeClusterZ, recodeClusterY</h2>
354
+<a href="#reordering-cluster-labels-with-recodeclusterz-recodeclustery" class="anchor"></a>Reordering cluster labels with recodeClusterZ, recodeClusterY</h2>
357 355
 <p><code>recodeClusterZ</code> and <code>recodeClusterY</code> allows the user to recode the cell and feature cluster labels, respectively.</p>
358 356
 <div class="sourceCode" id="cb39"><pre class="downlit sourceCode r">
359 357
 <code class="sourceCode R"><span class="va">sceZRecoded</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/recodeClusterZ.html">recodeClusterZ</a></span><span class="op">(</span><span class="va">sce</span>,
... ...
@@ -362,29 +360,18 @@
362 360
 <div class="sourceCode" id="cb40"><pre class="downlit sourceCode r">
363 361
 <code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/r/base/table.html">table</a></span><span class="op">(</span><span class="fu"><a href="../reference/celdaClusters.html">celdaClusters</a></span><span class="op">(</span><span class="va">sce</span><span class="op">)</span>, <span class="fu"><a href="../reference/celdaClusters.html">celdaClusters</a></span><span class="op">(</span><span class="va">sceZRecoded</span><span class="op">)</span><span class="op">)</span></code></pre></div>
364 362
 <pre><code>##    
365
-##      2  1  3  4  5
366
-##   1 44  0  0  0  0
367
-##   2  0 42  0  0  0
363
+##      1  2  3  4  5
364
+##   1  0 44  0  0  0
365
+##   2 42  0  0  0  0
368 366
 ##   3  0  0 40  0  0
369 367
 ##   4  0  0  0 47  0
370 368
 ##   5  0  0  0  0 34</code></pre>
371 369
 </div>
372
-<div id="converting-from-previous-versions" class="section level2">
373
-<h2 class="hasAnchor">
374
-<a href="#converting-from-previous-versions" class="anchor"></a>Converting from previous versions</h2>
375
-<p>Previous versions of celda output custom objects called <code>celdaModel</code> (<code>celda_C</code>, <code>celda_G</code>, <code>celda_CG</code>) and <code>celdaList</code>. These objects are now deprecated and can be converted to SCE objects using the <code>celdatosce</code> function in <em><a href="https://bioconductor.org/packages/3.12/celda">celda</a></em>:</p>
376
-<div class="sourceCode" id="cb42"><pre class="downlit sourceCode r">
377
-<code class="sourceCode R"><span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span><span class="op">(</span><span class="va">celda</span><span class="op">)</span>
378
-<span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span><span class="op">(</span><span class="va">SingleCellExperiment</span><span class="op">)</span>
379
-<span class="fu"><a href="https://rdrr.io/r/utils/data.html">data</a></span><span class="op">(</span><span class="va">celdaCGSim</span>, <span class="va">celdaCGMod</span>, <span class="va">celdaCGGridSearchRes</span><span class="op">)</span>
380
-<span class="va">scegrid</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/celdatosce.html">celdatosce</a></span><span class="op">(</span><span class="va">celdaCGGridSearchRes</span>, <span class="va">celdaCGSim</span><span class="op">$</span><span class="va">counts</span><span class="op">)</span>
381
-<span class="va">scecg</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/celdatosce.html">celdatosce</a></span><span class="op">(</span><span class="va">celdaCGMod</span>, <span class="va">celdaCGSim</span><span class="op">$</span><span class="va">counts</span><span class="op">)</span></code></pre></div>
382
-</div>
383 370
 </div>
384 371
 <div id="session-information" class="section level1">
385 372
 <h1 class="hasAnchor">
386 373
 <a href="#session-information" class="anchor"></a>Session Information</h1>
387
-<div class="sourceCode" id="cb43"><pre class="downlit sourceCode r">
374
+<div class="sourceCode" id="cb42"><pre class="downlit sourceCode r">
388 375
 <code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/r/utils/sessionInfo.html">sessionInfo</a></span><span class="op">(</span><span class="op">)</span></code></pre></div>
389 376
 <pre><code>## R version 4.0.4 (2021-02-15)
390 377
 ## Platform: x86_64-apple-darwin17.0 (64-bit)
... ...
@@ -407,7 +394,7 @@
407 394
 ##  [5] GenomeInfoDb_1.26.4         IRanges_2.24.1             
408 395
 ##  [7] S4Vectors_0.28.1            BiocGenerics_0.36.0        
409 396
 ##  [9] MatrixGenerics_1.2.1        matrixStats_0.58.0         
410
-## [11] celda_1.9.1                
397
+## [11] celda_1.9.2                 BiocStyle_2.18.1           
411 398
 ## 
412 399
 ## loaded via a namespace (and not attached):
413 400
 ##   [1] bitops_1.0-6               fs_1.5.0                  
... ...
@@ -423,44 +410,44 @@
423 410
 ##  [21] assertive.properties_0.0-4 enrichR_3.0               
424 411
 ##  [23] DelayedArray_0.16.2        desc_1.3.0                
425 412
 ##  [25] labeling_0.4.2             assertive.files_0.0-2     
426
-##  [27] sass_0.3.1                 scales_1.1.1              
427
-##  [29] pkgdown_1.6.1              systemfonts_1.0.1         
428
-##  [31] stringr_1.4.0              digest_0.6.27             
429
-##  [33] rmarkdown_2.7              XVector_0.30.0            
430
-##  [35] assertive.numbers_0.0-2    pkgconfig_2.0.3           
431
-##  [37] htmltools_0.5.1.1          highr_0.8                 
432
-##  [39] fastmap_1.1.0              GlobalOptions_0.1.2       
433
-##  [41] rlang_0.4.10               FNN_1.1.3                 
434
-##  [43] shape_1.4.5                farver_2.1.0              
435
-##  [45] gridGraphics_0.5-1         jquerylib_0.1.3           
436
-##  [47] generics_0.1.0             combinat_0.0-8            
437
-##  [49] jsonlite_1.7.2             dplyr_1.0.5               
438
-##  [51] RCurl_1.98-1.2             magrittr_2.0.1            
439
-##  [53] GenomeInfoDbData_1.2.4     multipanelfigure_2.1.2    
440
-##  [55] Matrix_1.3-2               Rcpp_1.0.6                
441
-##  [57] munsell_0.5.0              fansi_0.4.2               
442
-##  [59] lifecycle_1.0.0            stringi_1.5.3             
443
-##  [61] assertive.base_0.0-9       yaml_2.2.1                
444
-##  [63] MCMCprecision_0.4.0        zlibbioc_1.36.0           
445
-##  [65] Rtsne_0.15                 plyr_1.8.6                
446
-##  [67] grid_4.0.4                 ggrepel_0.9.1             
447
-##  [69] crayon_1.4.1               lattice_0.20-41           
448
-##  [71] circlize_0.4.12            magick_2.7.0              
449
-##  [73] ComplexHeatmap_2.6.2       knitr_1.31                
450
-##  [75] pillar_1.5.1               rjson_0.2.20              
451
-##  [77] reshape2_1.4.4             codetools_0.2-18          
452
-##  [79] glue_1.4.2                 evaluate_0.14             
453
-##  [81] data.table_1.14.0          BiocManager_1.30.10       
454
-##  [83] png_0.1-7                  vctrs_0.3.6               
455
-##  [85] foreach_1.5.1              gtable_0.3.0              
456
-##  [87] purrr_0.3.4                clue_0.3-58               
457
-##  [89] assertthat_0.2.1           cachem_1.0.4              
458
-##  [91] ggplot2_3.3.5              xfun_0.22                 
459
-##  [93] assertive.types_0.0-3      RcppEigen_0.3.3.9.1       
460
-##  [95] ragg_1.1.3                 tibble_3.1.0              
461
-##  [97] iterators_1.0.13           memoise_2.0.0             
462
-##  [99] cluster_2.1.1              ellipsis_0.3.1            
463
-## [101] BiocStyle_2.18.1</code></pre>
413
+##  [27] bookdown_0.21              sass_0.3.1                
414
+##  [29] scales_1.1.1               pkgdown_1.6.1             
415
+##  [31] systemfonts_1.0.1          stringr_1.4.0             
416
+##  [33] digest_0.6.27              rmarkdown_2.7             
417
+##  [35] XVector_0.30.0             assertive.numbers_0.0-2   
418
+##  [37] pkgconfig_2.0.3            htmltools_0.5.1.1         
419
+##  [39] highr_0.8                  fastmap_1.1.0             
420
+##  [41] GlobalOptions_0.1.2        rlang_0.4.10              
421
+##  [43] FNN_1.1.3                  shape_1.4.5               
422
+##  [45] farver_2.1.0               gridGraphics_0.5-1        
423
+##  [47] jquerylib_0.1.3            generics_0.1.0            
424
+##  [49] combinat_0.0-8             jsonlite_1.7.2            
425
+##  [51] dplyr_1.0.5                RCurl_1.98-1.2            
426
+##  [53] magrittr_2.0.1             GenomeInfoDbData_1.2.4    
427
+##  [55] multipanelfigure_2.1.2     Matrix_1.3-2              
428
+##  [57] Rcpp_1.0.6                 munsell_0.5.0             
429
+##  [59] fansi_0.4.2                lifecycle_1.0.0           
430
+##  [61] stringi_1.5.3              assertive.base_0.0-9      
431
+##  [63] yaml_2.2.1                 MCMCprecision_0.4.0       
432
+##  [65] zlibbioc_1.36.0            Rtsne_0.15                
433
+##  [67] plyr_1.8.6                 grid_4.0.4                
434
+##  [69] ggrepel_0.9.1              crayon_1.4.1              
435
+##  [71] lattice_0.20-41            circlize_0.4.12           
436
+##  [73] magick_2.7.0               ComplexHeatmap_2.6.2      
437
+##  [75] knitr_1.31                 pillar_1.5.1              
438
+##  [77] rjson_0.2.20               reshape2_1.4.4            
439
+##  [79] codetools_0.2-18           glue_1.4.2                
440
+##  [81] evaluate_0.14              data.table_1.14.0         
441
+##  [83] BiocManager_1.30.10        png_0.1-7                 
442
+##  [85] vctrs_0.3.6                foreach_1.5.1             
443
+##  [87] gtable_0.3.0               purrr_0.3.4               
444
+##  [89] clue_0.3-58                assertthat_0.2.1          
445
+##  [91] cachem_1.0.4               ggplot2_3.3.5             
446
+##  [93] xfun_0.22                  assertive.types_0.0-3     
447
+##  [95] RcppEigen_0.3.3.9.1        ragg_1.1.3                
448
+##  [97] tibble_3.1.0               iterators_1.0.13          
449
+##  [99] memoise_2.0.0              cluster_2.1.1             
450
+## [101] ellipsis_0.3.1</code></pre>
464 451
 </div>
465 452
   </div>
466 453
 
... ...
@@ -475,7 +462,7 @@
475 462
 
476 463
 
477 464
       <footer><div class="copyright">
478
-  <p>Developed by Joshua Campbell, Sean Corbett, Yusuke Koga, Shiyi Yang, Eric Reed, Zhe Wang.</p>
465
+  <p>Developed by Joshua Campbell, Shiyi Yang, Zhe Wang, Sean Corbett, Yusuke Koga.</p>
479 466
 </div>
480 467
 
481 468
 <div class="pkgdown">
482 469
Binary files a/docs/articles/celda_files/figure-html/module_split_rpc-1.png and b/docs/articles/celda_files/figure-html/module_split_rpc-1.png differ
483 470
new file mode 100644
484 471
Binary files /dev/null and b/docs/articles/celda_files/figure-html/plot_grid_search-1.png differ
485 472
new file mode 100644
486 473
Binary files /dev/null and b/docs/articles/celda_files/figure-html/rpc_cell-1.png differ
487 474
new file mode 100644
488 475
Binary files /dev/null and b/docs/articles/celda_files/figure-html/rpc_cell-2.png differ
489 476
Binary files a/docs/articles/celda_files/figure-html/unnamed-chunk-3-1.png and b/docs/articles/celda_files/figure-html/unnamed-chunk-3-1.png differ
490 477
Binary files a/docs/articles/celda_files/figure-html/unnamed-chunk-5-1.png and b/docs/articles/celda_files/figure-html/unnamed-chunk-5-1.png differ
491 478
Binary files a/docs/articles/celda_files/figure-html/unnamed-chunk-5-2.png and b/docs/articles/celda_files/figure-html/unnamed-chunk-5-2.png differ
492 479
Binary files a/docs/articles/celda_files/figure-html/unnamed-chunk-8-1.png and b/docs/articles/celda_files/figure-html/unnamed-chunk-8-1.png differ
493 480
new file mode 100644
... ...
@@ -0,0 +1,415 @@
1
+<!DOCTYPE html>
2
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en">
3
+<head>
4
+<meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
5
+<meta charset="utf-8">
6
+<meta http-equiv="X-UA-Compatible" content="IE=edge">
7
+<meta name="viewport" content="width=device-width, initial-scale=1.0">
8
+<title>Decontamination of ambient RNA in single-cell genomic data with DecontX • celda</title>
9
+<!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link href="https://cdnjs.cloudflare.com/ajax/libs/bootswatch/3.4.0/yeti/bootstrap.min.css" rel="stylesheet" crossorigin="anonymous">
10
+<script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css">
11
+<script src="../bootstrap-toc.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">
12
+<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">
13
+<!-- 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><!-- 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><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet">
14
+<script src="../pkgdown.js"></script><meta property="og:title" content="Decontamination of ambient RNA in single-cell genomic data with DecontX">
15
+<meta property="og:description" content="celda">
16
+<!-- 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]>
17
+<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
18
+<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
19
+<![endif]-->
20
+</head>
21
+<body data-spy="scroll" data-target="#toc">
22
+    <div class="container template-article">
23
+      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
24
+  <div class="container">
25
+    <div class="navbar-header">
26
+      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
27
+        <span class="sr-only">Toggle navigation</span>
28
+        <span class="icon-bar"></span>
29
+        <span class="icon-bar"></span>
30
+        <span class="icon-bar"></span>
31
+      </button>
32
+      <span class="navbar-brand">
33
+        <a class="navbar-link" href="../index.html">celda</a>
34
+        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.9.2</span>
35
+      </span>
36
+    </div>
37
+
38
+    <div id="navbar" class="navbar-collapse collapse">
39
+      <ul class="nav navbar-nav">
40
+<li>
41
+  <a href="http://celda.camplab.net/">
42
+    <span class="fas fa-home fa-lg"></span>
43
+     
44
+  </a>
45
+</li>
46
+<li>
47
+  <a href="../articles/articles/installation.html">Installation</a>
48
+</li>
49
+<li class="dropdown">
50
+  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false">
51
+    Vignettes
52
+     
53
+    <span class="caret"></span>
54
+  </a>
55
+  <ul class="dropdown-menu" role="menu">
56
+<li>
57
+      <a href="../articles/articles/celda_pbmc3k.html">Celda - Analysis of PBMC3K</a>
58
+    </li>
59
+    <li>
60
+      <a href="../articles/articles/decontX_pbmc4k.html">DecontX - Decontamination of PBMC4K</a>
61
+    </li>
62
+  </ul>
63
+</li>
64
+<li>
65
+  <a href="../reference/index.html">Reference</a>
66
+</li>
67
+<li>
68
+  <a href="../news/index.html">News</a>
69
+</li>
70
+<li>
71
+  <a href="https://github.com/campbio/celda">
72
+    <span class="fas fa-github"></span>
73
+     
74
+  </a>
75
+</li>
76
+      </ul>
77
+<ul class="nav navbar-nav navbar-right">
78
+<li>
79
+  <a href="https://github.com/campbio/celda/">
80
+    <span class="fab fa-github fa-lg"></span>
81
+     
82
+  </a>
83
+</li>
84
+      </ul>
85
+</div>
86
+<!--/.nav-collapse -->
87
+  </div>
88
+<!--/.container -->
89
+</div>
90
+<!--/.navbar -->
91
+
92
+      
93
+
94
+      </header><script src="decontX_files/header-attrs-2.7/header-attrs.js"></script><div class="row">
95
+  <div class="col-md-9 contents">
96
+    <div class="page-header toc-ignore">
97
+      <h1 data-toc-skip>Decontamination of ambient RNA in single-cell genomic data with DecontX</h1>
98
+                        <h4 class="author">Shiyi (Iris) Yang</h4>
99
+            <address class="author_afil">
100
+      Boston University School of Medicine<br><h4 class="author">Zhe Wang</h4>
101
+            <address class="author_afil">
102
+      Boston University School of Medicine<br><h4 class="author">Yuan Yin</h4>
103
+            <address class="author_afil">
104
+      Boston University School of Medicine<br><h4 class="author">Joshua Campbell</h4>
105
+            <address class="author_afil">
106
+      Boston University School of Medicine<br><a class="author_email" href="mailto:#"></a><a href="mailto:camp@bu.edu" class="email">camp@bu.edu</a>
107
+      </address>
108
+                  
109
+            <h4 class="date">2021-09-29</h4>
110
+      
111
+      <small class="dont-index">Source: <a href="https://github.com/campbio/celda/blob/master/vignettes/decontX.Rmd"><code>vignettes/decontX.Rmd</code></a></small>
112
+      <div class="hidden name"><code>decontX.Rmd</code></div>
113
+
114
+    </address>
115
+</address>
116
+</address>
117
+</div>
118
+
119
+    
120
+    
121
+<div id="introduction" class="section level1">
122
+<h1 class="hasAnchor">
123
+<a href="#introduction" class="anchor"></a>Introduction</h1>
124
+<p>Droplet-based microfluidic devices have become widely used to perform single-cell RNA sequencing (scRNA-seq). However, ambient RNA present in the cell suspension can be aberrantly counted along with a cell’s native mRNA and result in cross-contamination of transcripts between different cell populations. DecontX is a Bayesian method to estimate and remove contamination in individual cells. DecontX assumes the observed expression of a cell is a mixture of counts from two multinomial distributions: (1) a distribution of native transcript counts from the cell’s actual population and (2) a distribution of contaminating transcript counts from all other cell populations captured in the assay. Overall, computational decontamination of single cell counts can aid in downstream clustering and visualization.</p>
125
+<p>The package can be loaded using the <code>library</code> command.</p>
126
+<div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">
127
+<code class="sourceCode R"><span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span><span class="op">(</span><span class="va">celda</span><span class="op">)</span></code></pre></div>
128
+<p>DecontX can take either <code>SingleCellExperiment</code> object from package <a href="https://bioconductor.org/packages/release/bioc/html/SingleCellExperiment.html">SingleCellExperiment package</a> or a single counts matrix as input. <code>decontX</code> will attempt to convert any input matrix to class <code>dgCMatrix</code> from package <a href="https://cran.r-project.org/web/packages/Matrix/index.html">Matrix</a> before beginning any analyses.</p>
129
+</div>
130
+<div id="load-pbmc4k-data-from-10x" class="section level1">
131
+<h1 class="hasAnchor">
132
+<a href="#load-pbmc4k-data-from-10x" class="anchor"></a>Load PBMC4k data from 10X</h1>
133
+<p>We will utlize the 10X PBMC 4K dataset as an example. This can be easily retrieved from the package <a href="http://bioconductor.org/packages/release/data/experiment/html/TENxPBMCData.html">TENxPBMCData</a>. Make sure the the column names are set before running decontX.</p>
134
+<div class="sourceCode" id="cb2"><pre class="downlit sourceCode r">
135
+<code class="sourceCode R"><span class="co"># Install TENxPBMCData if is it not already</span>
136
+<span class="kw">if</span> <span class="op">(</span><span class="op">!</span><span class="fu"><a href="https://rdrr.io/r/base/ns-load.html">requireNamespace</a></span><span class="op">(</span><span class="st">"TENxPBMCData"</span>, quietly <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span>
137
+  <span class="kw">if</span> <span class="op">(</span><span class="op">!</span><span class="fu"><a href="https://rdrr.io/r/base/ns-load.html">requireNamespace</a></span><span class="op">(</span><span class="st">"BiocManager"</span>, quietly <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span><span class="op">)</span> <span class="op">{</span>
138
+    <span class="fu"><a href="https://rdrr.io/r/utils/install.packages.html">install.packages</a></span><span class="op">(</span><span class="st">"BiocManager"</span><span class="op">)</span>
139
+  <span class="op">}</span>
140
+  <span class="fu">BiocManager</span><span class="fu">::</span><span class="fu"><a href="https://rdrr.io/pkg/BiocManager/man/install.html">install</a></span><span class="op">(</span><span class="st">"TENxPBMCData"</span><span class="op">)</span>
141
+<span class="op">}</span>
142
+
143
+<span class="co"># Load PBMC data</span>
144
+<span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span><span class="op">(</span><span class="va">TENxPBMCData</span><span class="op">)</span>
145
+<span class="va">sce</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/TENxPBMCData/man/TENxPBMCData.html">TENxPBMCData</a></span><span class="op">(</span><span class="st">"pbmc4k"</span><span class="op">)</span>
146
+<span class="fu"><a href="https://rdrr.io/r/base/colnames.html">colnames</a></span><span class="op">(</span><span class="va">sce</span><span class="op">)</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/paste.html">paste</a></span><span class="op">(</span><span class="va">sce</span><span class="op">$</span><span class="va">Sample</span>, <span class="va">sce</span><span class="op">$</span><span class="va">Barcode</span>, sep <span class="op">=</span> <span class="st">"_"</span><span class="op">)</span>
147
+<span class="fu"><a href="https://rdrr.io/r/base/colnames.html">rownames</a></span><span class="op">(</span><span class="va">sce</span><span class="op">)</span> <span class="op">&lt;-</span> <span class="fu">rowData</span><span class="op">(</span><span class="va">sce</span><span class="op">)</span><span class="op">$</span><span class="va">Symbol_TENx</span></code></pre></div>
148
+</div>
149
+<div id="running-decontx" class="section level1">
150
+<h1 class="hasAnchor">
151
+<a href="#running-decontx" class="anchor"></a>Running decontX</h1>
152
+<p>A SingleCellExperiment (SCE) object or a sparse matrix containing the counts for filtered cells can be passed to decontX via the <code>x</code> parameter. There are two major ways to run decontX: with and without the raw/droplet matrix containing empty droplets. The raw/droplet matrix can be used to empirically estimate the distribution of ambient RNA, which is especially useful when cells that contributed to the ambient RNA are not accurately represented in the filtered count matrix containing the cells. For example, cells that were removed via flow cytometry or that were more sensitive to lysis during dissociation may have contributed to the ambient RNA but were not measured in the filtered/cell matrix. The raw/droplet matrix can be input as a sparse matrix or SCE object using the <code>background</code> parameter:</p>
153
+<div class="sourceCode" id="cb3"><pre class="downlit sourceCode r">
154
+<code class="sourceCode R"><span class="va">sce</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/decontX.html">decontX</a></span><span class="op">(</span><span class="va">sce</span>, background <span class="op">=</span> <span class="va">raw</span><span class="op">)</span></code></pre></div>
155
+<p>If cell/column names in the raw/droplet matrix are also found in the filtered counts matrix, then they will be excluded from the raw/droplet matrix before calculation of the ambient RNA distribution. If the raw matrix is not available, then <code>decontX</code> will estimate the contamination distribution for each cell cluster based on the profiles of the other cell clusters in the filtered dataset:</p>
156
+<div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">
157
+<code class="sourceCode R"><span class="va">sce</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/decontX.html">decontX</a></span><span class="op">(</span><span class="va">sce</span><span class="op">)</span></code></pre></div>
158
+<p>Note that in this case <code>decontX</code> will perform heuristic clustering to quickly define major cell clusters. However if you have your own cell cluster labels, they can be specified with the <code>z</code> parameter. If you supply a raw matrix via the <code>background</code> parameter, then the <code>z</code> parameter will not have an effect as clustering will not be performed.</p>
159
+<p>The contamination can be found in the <code>colData(sce)$decontX_contamination</code> and the decontaminated counts can be accessed with <code><a href="../reference/decontXcounts.html">decontXcounts(sce)</a></code>. If the input object was a matrix, make sure to save the output into a variable with a different name (e.g. result). The result object will be a list with contamination in <code>result$contamination</code> and the decontaminated counts in <code>result$decontXcounts</code>.</p>
160
+</div>
161
+<div id="plotting-decontx-results" class="section level1">
162
+<h1 class="hasAnchor">
163
+<a href="#plotting-decontx-results" class="anchor"></a>Plotting DecontX results</h1>
164
+<div id="cluster-labels-on-umap" class="section level2">
165
+<h2 class="hasAnchor">
166
+<a href="#cluster-labels-on-umap" class="anchor"></a>Cluster labels on UMAP</h2>
167
+<p>DecontX creates a UMAP which we can use to plot the cluster labels automatically identified in the analysis. Note that the clustering approach used here is designed to find “broad” cell types rather than individual cell subpopulations within a cell type.</p>
168
+<div class="sourceCode" id="cb5"><pre class="downlit sourceCode r">
169
+<code class="sourceCode R"><span class="va">umap</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/SingleCellExperiment/man/reducedDims.html">reducedDim</a></span><span class="op">(</span><span class="va">sce</span>, <span class="st">"decontX_UMAP"</span><span class="op">)</span>
170
+<span class="fu"><a href="../reference/plotDimReduceCluster.html">plotDimReduceCluster</a></span><span class="op">(</span>x <span class="op">=</span> <span class="va">sce</span><span class="op">$</span><span class="va">decontX_clusters</span>,
171
+    dim1 <span class="op">=</span> <span class="va">umap</span><span class="op">[</span>, <span class="fl">1</span><span class="op">]</span>, dim2 <span class="op">=</span> <span class="va">umap</span><span class="op">[</span>, <span class="fl">2</span><span class="op">]</span><span class="op">)</span></code></pre></div>
172
+<p><img src="decontX_files/figure-html/UMAP_Clusters-1.png" width="700"></p>
173
+</div>
174
+<div id="contamination-on-umap" class="section level2">
175
+<h2 class="hasAnchor">
176
+<a href="#contamination-on-umap" class="anchor"></a>Contamination on UMAP</h2>
177
+<p>The percentage of contamination in each cell can be plotting on the UMAP to visualize what what clusters may have higher levels of ambient RNA.</p>
178
+<div class="sourceCode" id="cb6"><pre class="downlit sourceCode r">
179
+<code class="sourceCode R"><span class="fu"><a href="../reference/plotDecontXContamination.html">plotDecontXContamination</a></span><span class="op">(</span><span class="va">sce</span><span class="op">)</span></code></pre></div>
180
+<p><img src="decontX_files/figure-html/plot_decon-1.png" width="700"></p>
181
+</div>
182
+<div id="expression-of-markers-on-umap" class="section level2">
183
+<h2 class="hasAnchor">
184
+<a href="#expression-of-markers-on-umap" class="anchor"></a>Expression of markers on UMAP</h2>
185
+<p>Known marker genes can also be plotted on the UMAP to identify the cell types for each cluster. We will use CD3D and CD3E for T-cells, LYZ, S100A8, and S100A9 for monocytes, CD79A, CD79B, and MS4A1 for B-cells, GNLY for NK-cells, and PPBP for megakaryocytes.</p>
186
+<div class="sourceCode" id="cb7"><pre class="downlit sourceCode r">
187
+<code class="sourceCode R"><span class="kw"><a href="https://rdrr.io/r/base/library.html">library</a></span><span class="op">(</span><span class="va"><a href="http://bioconductor.org/packages/scater/">scater</a></span><span class="op">)</span>
188
+<span class="va">sce</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/pkg/scuttle/man/logNormCounts.html">logNormCounts</a></span><span class="op">(</span><span class="va">sce</span><span class="op">)</span>
189
+<span class="fu"><a href="../reference/plotDimReduceFeature.html">plotDimReduceFeature</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/r/base/matrix.html">as.matrix</a></span><span class="op">(</span><span class="fu"><a href="https://rdrr.io/pkg/SingleCellExperiment/man/assays.html">logcounts</a></span><span class="op">(</span><span class="va">sce</span><span class="op">)</span><span class="op">)</span>,
190
+    dim1 <span class="op">=</span> <span class="va">umap</span><span class="op">[</span>, <span class="fl">1</span><span class="op">]</span>,
191
+    dim2 <span class="op">=</span> <span class="va">umap</span><span class="op">[</span>, <span class="fl">2</span><span class="op">]</span>,
192
+    features <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="st">"CD3D"</span>, <span class="st">"CD3E"</span>, <span class="st">"GNLY"</span>,
193
+        <span class="st">"LYZ"</span>, <span class="st">"S100A8"</span>, <span class="st">"S100A9"</span>,
194
+        <span class="st">"CD79A"</span>, <span class="st">"CD79B"</span>, <span class="st">"MS4A1"</span><span class="op">)</span>,
195
+    exactMatch <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></code></pre></div>
196
+<p><img src="decontX_files/figure-html/plot_feature-1.png" width="700"></p>
197
+</div>
198
+<div id="barplot-of-markers-detected-in-cell-clusters" class="section level2">
199
+<h2 class="hasAnchor">
200
+<a href="#barplot-of-markers-detected-in-cell-clusters" class="anchor"></a>Barplot of markers detected in cell clusters</h2>
201
+<p>The percetage of cells within a cluster that have detectable expression of marker genes can be displayed in a barplot. Markers for cell types need to be supplied in a named list. First, the detection of marker genes in the original <code>counts</code> assay is shown:</p>
202
+<div class="sourceCode" id="cb8"><pre class="downlit sourceCode r">
203
+<code class="sourceCode R"><span class="va">markers</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html">list</a></span><span class="op">(</span>Tcell_Markers <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="st">"CD3E"</span>, <span class="st">"CD3D"</span><span class="op">)</span>,
204
+    Bcell_Markers <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="st">"CD79A"</span>, <span class="st">"CD79B"</span>, <span class="st">"MS4A1"</span><span class="op">)</span>,
205
+    Monocyte_Markers <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="st">"S100A8"</span>, <span class="st">"S100A9"</span>, <span class="st">"LYZ"</span><span class="op">)</span>,
206
+    NKcell_Markers <span class="op">=</span> <span class="st">"GNLY"</span><span class="op">)</span>
207
+<span class="va">cellTypeMappings</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html">list</a></span><span class="op">(</span>Tcells <span class="op">=</span> <span class="fl">2</span>, Bcells <span class="op">=</span> <span class="fl">5</span>, Monocytes <span class="op">=</span> <span class="fl">1</span>, NKcells <span class="op">=</span> <span class="fl">6</span><span class="op">)</span>
208
+<span class="fu"><a href="../reference/plotDecontXMarkerPercentage.html">plotDecontXMarkerPercentage</a></span><span class="op">(</span><span class="va">sce</span>,
209
+    markers <span class="op">=</span> <span class="va">markers</span>,
210
+    groupClusters <span class="op">=</span> <span class="va">cellTypeMappings</span>,
211
+    assayName <span class="op">=</span> <span class="st">"counts"</span><span class="op">)</span></code></pre></div>
212
+<p><img src="decontX_files/figure-html/barplotCounts-1.png" width="700"></p>
213
+<p>We can then look to see how much decontX removed aberrant expression of marker genes in each cell type by changing the <code>assayName</code> to <code>decontXcounts</code>:</p>
214
+<div class="sourceCode" id="cb9"><pre class="downlit sourceCode r">
215
+<code class="sourceCode R"><span class="fu"><a href="../reference/plotDecontXMarkerPercentage.html">plotDecontXMarkerPercentage</a></span><span class="op">(</span><span class="va">sce</span>,
216
+    markers <span class="op">=</span> <span class="va">markers</span>,
217
+    groupClusters <span class="op">=</span> <span class="va">cellTypeMappings</span>,
218
+    assayName <span class="op">=</span> <span class="st">"decontXcounts"</span><span class="op">)</span></code></pre></div>
219
+<p><img src="decontX_files/figure-html/barplotDecontCounts-1.png" width="700"></p>
220
+<p>Percentages of marker genes detected in other cell types were reduced or completely removed. For example, the percentage of cells that expressed Monocyte marker genes was greatly reduced in T-cells, B-cells, and NK-cells. The original counts and decontamined counts can be plotted side-by-side by listing multiple assays in the <code>assayName</code> parameter. This option is only available if the data is stored in <code>SingleCellExperiment</code> object.</p>
221
+<div class="sourceCode" id="cb10"><pre class="downlit sourceCode r">
222
+<code class="sourceCode R"><span class="fu"><a href="../reference/plotDecontXMarkerPercentage.html">plotDecontXMarkerPercentage</a></span><span class="op">(</span><span class="va">sce</span>,
223
+    markers <span class="op">=</span> <span class="va">markers</span>,
224
+    groupClusters <span class="op">=</span> <span class="va">cellTypeMappings</span>,
225
+    assayName <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="st">"counts"</span>, <span class="st">"decontXcounts"</span><span class="op">)</span><span class="op">)</span></code></pre></div>
226
+<p><img src="decontX_files/figure-html/barplotBoth-1.png" width="700"></p>
227
+<p>Some helpful hints when using <code>plotDecontXMarkerPercentage</code>:</p>
228
+<ol style="list-style-type: decimal">
229
+<li>Cell clusters can be renamed and re-grouped using the <code>groupCluster</code> parameter, which also needs to be a named list. If <code>groupCluster</code> is used, cell clusters not included in the list will be excluded in the barplot. For example, if we wanted to group T-cells and NK-cells together, we could set <code>cellTypeMappings &lt;- list(NK_Tcells = c(2,6), Bcells = 5, Monocytes = 1)</code>
230
+</li>
231
+<li>The level a gene needs to be expressed to be considered detected in a cell can be adjusted using the <code>threshold</code> parameter.</li>
232
+<li>If you are not using a <code>SingleCellExperiment</code>, then you will need to supply the original counts matrix or the decontaminated counts matrix as the first argument to generate the barplots.</li>
233
+</ol>
234
+</div>
235
+<div id="violin-plot-to-compare-the-distributions-of-original-and-decontaminated-counts" class="section level2">
236
+<h2 class="hasAnchor">
237
+<a href="#violin-plot-to-compare-the-distributions-of-original-and-decontaminated-counts" class="anchor"></a>Violin plot to compare the distributions of original and decontaminated counts</h2>
238
+<p>Another useful way to assess the amount of decontamination is to view the expression of marker genes before and after <code>decontX</code> across cell types. Here we view the monocyte markers in each cell type. The violin plot shows that the markers have been removed from T-cells, B-cells, and NK-cells, but are largely unaffected in monocytes.</p>
239
+<div class="sourceCode" id="cb11"><pre class="downlit sourceCode r">
240
+<code class="sourceCode R"><span class="fu"><a href="../reference/plotDecontXMarkerExpression.html">plotDecontXMarkerExpression</a></span><span class="op">(</span><span class="va">sce</span>,
241
+    markers <span class="op">=</span> <span class="va">markers</span><span class="op">[[</span><span class="st">"Monocyte_Markers"</span><span class="op">]</span><span class="op">]</span>,
242
+    groupClusters <span class="op">=</span> <span class="va">cellTypeMappings</span>,
243
+    ncol <span class="op">=</span> <span class="fl">3</span><span class="op">)</span></code></pre></div>
244
+<p><img src="decontX_files/figure-html/plotDecontXMarkerExpression-1.png" width="700"></p>
245
+<p>Some helpful hints when using <code>plotDecontXMarkerExpression</code>:</p>
246
+<ol style="list-style-type: decimal">
247
+<li>
248
+<code>groupClusters</code> works the same way as in <code>plotDecontXMarkerPercentage</code>.</li>
249
+<li>This function will plot each pair of markers and clusters (or cell type specified by <code>groupClusters</code>). Therefore, you may want to keep the number of markers small in each plot and call the function multiple times for different sets of marker genes.</li>
250
+<li>You can also plot the individual points by setting <code>plotDots = TRUE</code> and/or log transform the points on the fly by setting <code>log1p = TRUE</code>.</li>
251
+<li>This function can plot any assay in a <code>SingleCellExperiment</code>. Therefore you could also examine normalized expression of the original and decontaminated counts. For example:</li>
252
+</ol>
253
+<div class="sourceCode" id="cb12"><pre class="downlit sourceCode r">
254
+<code class="sourceCode R"><span class="va">sce</span> <span class="op">&lt;-</span> <span class="fu">scater</span><span class="fu">::</span><span class="fu"><a href="https://rdrr.io/pkg/scuttle/man/logNormCounts.html">logNormCounts</a></span><span class="op">(</span><span class="va">sce</span>,
255
+    exprs_values <span class="op">=</span> <span class="st">"decontXcounts"</span>,
256
+    name <span class="op">=</span> <span class="st">"dlogcounts"</span><span class="op">)</span>
257
+
258
+<span class="fu"><a href="../reference/plotDecontXMarkerExpression.html">plotDecontXMarkerExpression</a></span><span class="op">(</span><span class="va">sce</span>,
259
+    markers <span class="op">=</span> <span class="va">markers</span><span class="op">[[</span><span class="st">"Monocyte_Markers"</span><span class="op">]</span><span class="op">]</span>,
260
+    groupClusters <span class="op">=</span> <span class="va">cellTypeMappings</span>,
261
+    ncol <span class="op">=</span> <span class="fl">3</span>,
262
+    assayName <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="st">"logcounts"</span>, <span class="st">"dlogcounts"</span><span class="op">)</span><span class="op">)</span></code></pre></div>
263
+</div>
264
+</div>
265
+<div id="other-important-notes" class="section level1">
266
+<h1 class="hasAnchor">
267
+<a href="#other-important-notes" class="anchor"></a>Other important notes</h1>
268
+<div id="choosing-appropriate-cell-clusters" class="section level2">
269
+<h2 class="hasAnchor">
270
+<a href="#choosing-appropriate-cell-clusters" class="anchor"></a>Choosing appropriate cell clusters</h2>
271
+<p>The ability of DecontX to accurately identify contamination is dependent on the cell cluster labels. DecontX assumes that contamination for a cell cluster comes from combination of counts from all other clusters. The default clustering approach used by DecontX tends to select fewer clusters that represent broader cell types. For example, all T-cells tend to be clustered together rather than splitting naive and cytotoxic T-cells into separate clusters. Custom cell type labels can be suppled via the <code>z</code> parameter if some cells are not being clustered appropriately by the default method.</p>
272
+</div>
273
+<div id="adjusting-the-priors-to-influence-contamination-estimates" class="section level2">
274
+<h2 class="hasAnchor">
275
+<a href="#adjusting-the-priors-to-influence-contamination-estimates" class="anchor"></a>Adjusting the priors to influence contamination estimates</h2>
276
+<p>There are ways to force <code>decontX</code> to estimate more or less contamination across a dataset by manipulating the priors. The <code>delta</code> parameter is a numeric vector of length two. It is the concentration parameter for the Dirichlet distribution which serves as the prior for the proportions of native and contamination counts in each cell. The first element is the prior for the proportion of native counts while the second element is the prior for the proportion of contamination counts. These essentially act as pseudocounts for the native and contamination in each cell. If <code>estimateDelta = TRUE</code>, <code>delta</code> is only used to produce a random sample of proportions for an initial value of contamination in each cell. Then <code>delta</code> is updated in each iteration. If <code>estimateDelta = FALSE</code>, then <code>delta</code> is fixed with these values for the entire inference procedure. Fixing <code>delta</code> and setting a high number in the second element will force <code>decontX</code> to be more aggressive and estimate higher levels of contamination in each cell at the expense of potentially removing native expression. For example, in the previous PBMC example, we can see what the estimated <code>delta</code> was by looking in the estimates:</p>
277
+<div class="sourceCode" id="cb13"><pre class="downlit sourceCode r">
278
+<code class="sourceCode R"><span class="fu">metadata</span><span class="op">(</span><span class="va">sce</span><span class="op">)</span><span class="op">$</span><span class="va">decontX</span><span class="op">$</span><span class="va">estimates</span><span class="op">$</span><span class="va">all_cells</span><span class="op">$</span><span class="va">delta</span></code></pre></div>
279
+<pre><code>## [1] 9.287164 1.038217</code></pre>
280
+<p>Setting a higher value in the second element of delta and <code>estimateDelta = FALSE</code> will force <code>decontX</code> to estimate higher levels of contamination per cell:</p>
281
+<div class="sourceCode" id="cb15"><pre class="downlit sourceCode r">
282
+<code class="sourceCode R"><span class="va">sce.delta</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/decontX.html">decontX</a></span><span class="op">(</span><span class="va">sce</span>, delta <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span><span class="op">(</span><span class="fl">9</span>, <span class="fl">20</span><span class="op">)</span>, estimateDelta <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span>
283
+
284
+<span class="fu"><a href="https://rdrr.io/r/graphics/plot.default.html">plot</a></span><span class="op">(</span><span class="va">sce</span><span class="op">$</span><span class="va">decontX_contamination</span>, <span class="va">sce.delta</span><span class="op">$</span><span class="va">decontX_contamination</span>,
285
+     xlab <span class="op">=</span> <span class="st">"DecontX estimated priors"</span>,
286
+     ylab <span class="op">=</span> <span class="st">"Setting priors to estimate higher contamination"</span><span class="op">)</span>
287
+<span class="fu"><a href="https://rdrr.io/r/graphics/abline.html">abline</a></span><span class="op">(</span><span class="fl">0</span>, <span class="fl">1</span>, col <span class="op">=</span> <span class="st">"red"</span>, lwd <span class="op">=</span> <span class="fl">2</span><span class="op">)</span></code></pre></div>
288
+<p><img src="decontX_files/figure-html/newDecontX-1.png" width="700"></p>
289
+</div>
290
+</div>
291
+<div id="session-information" class="section level1">
292
+<h1 class="hasAnchor">
293
+<a href="#session-information" class="anchor"></a>Session Information</h1>
294
+<div class="sourceCode" id="cb16"><pre class="downlit sourceCode r">
295
+<code class="sourceCode R"><span class="fu"><a href="https://rdrr.io/r/utils/sessionInfo.html">sessionInfo</a></span><span class="op">(</span><span class="op">)</span></code></pre></div>
296
+<pre><code>## R version 4.0.4 (2021-02-15)
297
+## Platform: x86_64-apple-darwin17.0 (64-bit)
298
+## Running under: macOS Big Sur 10.16
299
+## 
300
+## Matrix products: default
301
+## BLAS:   /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
302
+## LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib
303
+## 
304
+## locale:
305
+## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
306
+## 
307
+## attached base packages:
308
+## [1] parallel  stats4    stats     graphics  grDevices utils     datasets 
309
+## [8] methods   base     
310
+## 
311
+## other attached packages:
312
+##  [1] scater_1.18.6               ggplot2_3.3.5              
313
+##  [3] TENxPBMCData_1.8.0          HDF5Array_1.18.1           
314
+##  [5] rhdf5_2.34.0                DelayedArray_0.16.2        
315
+##  [7] Matrix_1.3-2                SingleCellExperiment_1.12.0
316
+##  [9] SummarizedExperiment_1.20.0 Biobase_2.50.0             
317
+## [11] GenomicRanges_1.42.0        GenomeInfoDb_1.26.4        
318
+## [13] IRanges_2.24.1              S4Vectors_0.28.1           
319
+## [15] BiocGenerics_0.36.0         MatrixGenerics_1.2.1       
320
+## [17] matrixStats_0.58.0          celda_1.9.2                
321
+## [19] BiocStyle_2.18.1           
322
+## 
323
+## loaded via a namespace (and not attached):
324
+##   [1] AnnotationHub_2.22.0          BiocFileCache_1.14.0         
325
+##   [3] systemfonts_1.0.1             RcppEigen_0.3.3.9.1          
326
+##   [5] plyr_1.8.6                    assertive.files_0.0-2        
327
+##   [7] enrichR_3.0                   multipanelfigure_2.1.2       
328
+##   [9] BiocParallel_1.24.1           digest_0.6.27                
329
+##  [11] foreach_1.5.1                 htmltools_0.5.1.1            
330
+##  [13] viridis_0.5.1                 magick_2.7.0                 
331
+##  [15] fansi_0.4.2                   magrittr_2.0.1               
332
+##  [17] memoise_2.0.0                 assertive.numbers_0.0-2      
333
+##  [19] doParallel_1.0.16             pkgdown_1.6.1                
334
+##  [21] colorspace_2.0-0              blob_1.2.1                   
335
+##  [23] rappdirs_0.3.3                ggrepel_0.9.1                
336
+##  [25] textshaping_0.3.5             xfun_0.22                    
337
+##  [27] dplyr_1.0.5                   crayon_1.4.1                 
338
+##  [29] RCurl_1.98-1.2                jsonlite_1.7.2               
339
+##  [31] iterators_1.0.13              glue_1.4.2                   
340
+##  [33] gtable_0.3.0                  zlibbioc_1.36.0              
341
+##  [35] XVector_0.30.0                BiocSingular_1.6.0           
342
+##  [37] Rhdf5lib_1.12.1               scales_1.1.1                 
343
+##  [39] DBI_1.1.1                     Rcpp_1.0.6                   
344
+##  [41] viridisLite_0.3.0             xtable_1.8-4                 
345
+##  [43] gridGraphics_0.5-1            bit_4.0.4                    
346
+##  [45] rsvd_1.0.3                    httr_1.4.2                   
347
+##  [47] RColorBrewer_1.1-2            ellipsis_0.3.1               
348
+##  [49] farver_2.1.0                  pkgconfig_2.0.3              
349
+##  [51] scuttle_1.0.4                 sass_0.3.1                   
350
+##  [53] uwot_0.1.10                   dbplyr_2.1.0                 
351
+##  [55] utf8_1.2.1                    labeling_0.4.2               
352
+##  [57] tidyselect_1.1.0              rlang_0.4.10                 
353
+##  [59] reshape2_1.4.4                later_1.1.0.1                
354
+##  [61] AnnotationDbi_1.52.0          munsell_0.5.0                
355
+##  [63] BiocVersion_3.12.0            tools_4.0.4                  
356
+##  [65] cachem_1.0.4                  dbscan_1.1-6                 
357
+##  [67] generics_0.1.0                RSQLite_2.2.4                
358
+##  [69] ExperimentHub_1.16.0          evaluate_0.14                
359
+##  [71] stringr_1.4.0                 fastmap_1.1.0                
360
+##  [73] yaml_2.2.1                    ragg_1.1.3                   
361
+##  [75] knitr_1.31                    bit64_4.0.5                  
362
+##  [77] fs_1.5.0                      purrr_0.3.4                  
363
+##  [79] sparseMatrixStats_1.2.1       mime_0.10                    
364
+##  [81] compiler_4.0.4                beeswarm_0.3.1               
365
+##  [83] curl_4.3                      interactiveDisplayBase_1.28.0
366
+##  [85] tibble_3.1.0                  bslib_0.2.4                  
367
+##  [87] stringi_1.5.3                 highr_0.8                    
368
+##  [89] RSpectra_0.16-0               desc_1.3.0                   
369
+##  [91] lattice_0.20-41               assertive.base_0.0-9         
370
+##  [93] vctrs_0.3.6                   pillar_1.5.1                 
371
+##  [95] lifecycle_1.0.0               rhdf5filters_1.2.0           
372
+##  [97] BiocManager_1.30.10           combinat_0.0-8               
373
+##  [99] jquerylib_0.1.3               RcppAnnoy_0.0.18             
374
+## [101] BiocNeighbors_1.8.2           data.table_1.14.0            
375
+## [103] bitops_1.0-6                  irlba_2.3.3                  
376
+## [105] httpuv_1.5.5                  assertive.types_0.0-3        
377
+## [107] R6_2.5.0                      bookdown_0.21                
378
+## [109] assertive.properties_0.0-4    promises_1.2.0.1             
379
+## [111] gridExtra_2.3                 vipor_0.4.5                  
380
+## [113] codetools_0.2-18              MCMCprecision_0.4.0          
381
+## [115] assertthat_0.2.1              rprojroot_2.0.2              
382
+## [117] rjson_0.2.20                  withr_2.4.1                  
383
+## [119] GenomeInfoDbData_1.2.4        grid_4.0.4                   
384
+## [121] beachmat_2.6.4                rmarkdown_2.7                
385
+## [123] DelayedMatrixStats_1.12.3     Rtsne_0.15                   
386
+## [125] shiny_1.6.0                   ggbeeswarm_0.6.0</code></pre>
387
+</div>
388
+  </div>
389
+
390
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
391
+
392
+        <nav id="toc" data-toggle="toc"><h2 data-toc-skip>Contents</h2>
393
+    </nav>
394
+</div>
395
+
396
+</div>
397
+
398
+
399
+
400
+      <footer><div class="copyright">
401
+  <p>Developed by Joshua Campbell, Shiyi Yang, Zhe Wang, Sean Corbett, Yusuke Koga.</p>
402
+</div>
403
+
404
+<div class="pkgdown">
405
+  <p>Site built with <a href="https://pkgdown.r-lib.org/">pkgdown</a> 1.6.1.</p>
406
+</div>
407
+
408
+      </footer>
409
+</div>
410
+
411
+  
412
+
413
+
414
+  </body>
415
+</html>
0 416
new file mode 100644
1 417
Binary files /dev/null and b/docs/articles/decontX_files/figure-html/UMAP_Clusters-1.png differ
2 418
new file mode 100644
3 419
Binary files /dev/null and b/docs/articles/decontX_files/figure-html/barplotBoth-1.png differ
4 420
new file mode 100644
5 421
Binary files /dev/null and b/docs/articles/decontX_files/figure-html/barplotCounts-1.png differ
6 422
new file mode 100644
7 423
Binary files /dev/null and b/docs/articles/decontX_files/figure-html/barplotDecontCounts-1.png differ
8 424
new file mode 100644
9 425
Binary files /dev/null and b/docs/articles/decontX_files/figure-html/newDecontX-1.png differ
10 426
new file mode 100644
11 427
Binary files /dev/null and b/docs/articles/decontX_files/figure-html/plotDecontXMarkerExpression-1.png differ
12 428
new file mode 100644
13 429
Binary files /dev/null and b/docs/articles/decontX_files/figure-html/plot_decon-1.png differ
14 430
new file mode 100644
15 431
Binary files /dev/null and b/docs/articles/decontX_files/figure-html/plot_feature-1.png differ
16 432
new file mode 100644
... ...
@@ -0,0 +1,12 @@
1
+// Pandoc 2.9 adds attributes on both header and div. We remove the former (to
2
+// be compatible with the behavior of Pandoc < 2.8).
3
+document.addEventListener('DOMContentLoaded', function(e) {
4
+  var hs = document.querySelectorAll("div.section[class*='level'] > :first-child");
5
+  var i, h, a;
6
+  for (i = 0; i < hs.length; i++) {
7
+    h = hs[i];
8
+    if (!/^h[1-6]$/i.test(h.tagName)) continue;  // it should be a header h1-h6
9
+    a = h.attributes;
10
+    while (a.length > 0) h.removeAttribute(a[0].name);
11
+  }
12
+});
... ...
@@ -78,7 +78,7 @@
78 78
     <div id="navbar" class="navbar-collapse collapse">
79 79
       <ul class="nav navbar-nav">
80 80
         <li>
81
-  <a href="../index.html">
81
+  <a href="http://celda.camplab.net/">
82 82
     <span class="fas fa-home fa-lg"></span>
83 83
      
84 84
   </a>
... ...
@@ -142,12 +142,16 @@
142 142
       <p class="section-desc"></p>
143 143
 
144 144
       <dl>
145
-        <dt><a href="articles/celda_pbmc3k.html">UNKNOWN TITLE</a></dt>
145
+        <dt><a href="articles/celda_pbmc3k.html">Celda - Analysis of PBMC3K</a></dt>
146 146
         <dd></dt>
147
-        <dt><a href="articles/decontX_pbmc4k.html">UNKNOWN TITLE</a></dt>
147
+        <dt><a href="articles/decontX_pbmc4k.html">DecontX - Decontamination of PBMC4K</a></dt>
148 148
         <dd></dt>
149 149
         <dt><a href="articles/installation.html">UNKNOWN TITLE</a></dt>
150 150
         <dd></dt>
151
+        <dt><a href="celda.html">Analysis of single-cell genomic data with celda</a></dt>
152
+        <dd></dt>
153
+        <dt><a href="decontX.html">Decontamination of ambient RNA in single-cell genomic data with DecontX</a></dt>
154
+        <dd></dt>
151 155
       </dl>
152 156
     </div>
153 157
   </div>
... ...
@@ -156,7 +160,7 @@
156 160
 
157 161
       <footer>
158 162
       <div class="copyright">
159
-  <p>Developed by Joshua Campbell, Sean Corbett, Yusuke Koga, Shiyi Yang, Eric Reed, Zhe Wang.</p>
163
+  <p>Developed by Joshua Campbell, Shiyi Yang, Zhe Wang, Sean Corbett, Yusuke Koga.</p>
160 164
 </div>
161 165
 
162 166
 <div class="pkgdown">
... ...
@@ -78,7 +78,7 @@
78 78
     <div id="navbar" class="navbar-collapse collapse">
79 79
       <ul class="nav navbar-nav">
80 80
         <li>
81
-  <a href="index.html">
81
+  <a href="http://celda.camplab.net/">
82 82
     <span class="fas fa-home fa-lg"></span>
83 83
      
84 84
   </a>
... ...
@@ -143,23 +143,19 @@
143 143
         </p>
144 144
       </li>
145 145
       <li>
146
-        <p><strong>Sean Corbett</strong>. Author. 
147
-        </p>
148
-      </li>
149
-      <li>
150
-        <p><strong>Yusuke Koga</strong>. Author. 
146
+        <p><strong>Shiyi Yang</strong>. Author. 
151 147
         </p>
152 148
       </li>
153 149
       <li>
154
-        <p><strong>Shiyi Yang</strong>. Author. 
150
+        <p><strong>Zhe Wang</strong>. Author. 
155 151
         </p>
156 152
       </li>
157 153
       <li>
158
-        <p><strong>Eric Reed</strong>. Author. 
154
+        <p><strong>Sean Corbett</strong>. Author. 
159 155
         </p>
160 156
       </li>
161 157
       <li>
162
-        <p><strong>Zhe Wang</strong>. Author. 
158
+        <p><strong>Yusuke Koga</strong>. Author. 
163 159
         </p>
164 160
       </li>
165 161
     </ul>
... ...
@@ -172,7 +168,7 @@
172 168
 
173 169
       <footer>
174 170
       <div class="copyright">
175
-  <p>Developed by Joshua Campbell, Sean Corbett, Yusuke Koga, Shiyi Yang, Eric Reed, Zhe Wang.</p>
171
+  <p>Developed by Joshua Campbell, Shiyi Yang, Zhe Wang, Sean Corbett, Yusuke Koga.</p>
176 172
 </div>
177 173
 
178 174
 <div class="pkgdown">
... ...
@@ -44,7 +44,7 @@
44 44
     <div id="navbar" class="navbar-collapse collapse">
45 45
       <ul class="nav navbar-nav">
46 46
 <li>
47
-  <a href="index.html">
47
+  <a href="http://celda.camplab.net/">
48 48
     <span class="fas fa-home fa-lg"></span>
49 49
      
50 50
   </a>
... ...
@@ -131,8 +131,8 @@ install_github("campbio/celda")</a></code></pre>
131 131
 <pre><code><a href="https://devtools.r-lib.org/">library(devtools)
132 132
 install_github("campbio/celda", build_vignettes = TRUE)</a></code></pre>
133 133
 <p>Note that installation may take an extra 5-10 minutes for building of the vignettes. The Celda and DecontX vignettes can then be accessed via the following commands:</p>
134
-<pre><code>vignette("celda")
135
-vignette("decontX")</code></pre>
134
+<pre><code><a href="articles/celda.html">vignette("celda")
135
+vignette("decontX")</a></code></pre>
136 136
 </div>
137 137
 <div id="for-developers" class="section level2">
138 138
 <h2 class="hasAnchor">
... ...
@@ -174,18 +174,17 @@ vignette("decontX")</code></pre>
174 174
 <h2>Developers</h2>
175 175
 <ul class="list-unstyled">
176 176
 <li>Joshua Campbell <br><small class="roles"> Author, maintainer </small>  </li>
177
-<li>Sean Corbett <br><small class="roles"> Author </small>  </li>
178
-<li>Yusuke Koga <br><small class="roles"> Author </small>  </li>
179 177
 <li>Shiyi Yang <br><small class="roles"> Author </small>  </li>
180
-<li>Eric Reed <br><small class="roles"> Author </small>  </li>
181 178
 <li>Zhe Wang <br><small class="roles"> Author </small>  </li>
179
+<li>Sean Corbett <br><small class="roles"> Author </small>  </li>
180
+<li>Yusuke Koga <br><small class="roles"> Author </small>  </li>
182 181
 </ul>
183 182
 </div>
184 183
 
185 184
   <div class="dev-status">
186 185
 <h2>Dev status</h2>
187 186
 <ul class="list-unstyled">
188
-<li><a href="https://travis-ci.org/campbio/celda"><img src="https://travis-ci.org/campbio/celda.svg?branch=master" alt="Build Status"></a></li>
187
+<li><a href="https://github.com/campbio/celda/actions"><img src="https://github.com/campbio/celda/workflows/R-CMD-check/badge.svg" alt="R-CMD-check"></a></li>
189 188
 <li><a href="https://coveralls.io/github/campbio/celda?branch=master"><img src="https://coveralls.io/repos/github/campbio/celda/badge.svg?branch=master" alt="Coverage Status"></a></li>
190 189
 </ul>
191 190
 </div>
... ...
@@ -194,7 +193,7 @@ vignette("decontX")</code></pre>
194 193
 
195 194
 
196 195
       <footer><div class="copyright">
197
-  <p>Developed by Joshua Campbell, Sean Corbett, Yusuke Koga, Shiyi Yang, Eric Reed, Zhe Wang.</p>
196
+  <p>Developed by Joshua Campbell, Shiyi Yang, Zhe Wang, Sean Corbett, Yusuke Koga.</p>
198 197
 </div>
199 198
 
200 199
 <div class="pkgdown">
... ...
@@ -78,7 +78,7 @@
78 78
     <div id="navbar" class="navbar-collapse collapse">
79 79
       <ul class="nav navbar-nav">
80 80
         <li>
81
-  <a href="../index.html">
81
+  <a href="http://celda.camplab.net/">
82 82
     <span class="fas fa-home fa-lg"></span>
83 83
      
84 84
   </a>
... ...
@@ -163,7 +163,7 @@
163 163
 
164 164
       <footer>
165 165
       <div class="copyright">
166
-  <p>Developed by Joshua Campbell, Sean Corbett, Yusuke Koga, Shiyi Yang, Eric Reed, Zhe Wang.</p>
166
+  <p>Developed by Joshua Campbell, Shiyi Yang, Zhe Wang, Sean Corbett, Yusuke Koga.</p>
167 167
 </div>
168 168
 
169 169
 <div class="pkgdown">
... ...
@@ -1,9 +1,11 @@
1
-pandoc: 2.9.2.1
1
+pandoc: 2.11.4
2 2
 pkgdown: 1.6.1
3 3
 pkgdown_sha: ~
4 4
 articles:
5 5
   articles/celda_pbmc3k: celda_pbmc3k.html
6 6
   articles/decontX_pbmc4k: decontX_pbmc4k.html
7 7
   articles/installation: installation.html
8
-last_built: 2021-07-19T14:59Z
8
+  celda: celda.html
9
+  decontX: decontX.html
10
+last_built: 2021-09-29T16:24Z
9 11
 
... ...
@@ -80,7 +80,7 @@
80 80
     <div id="navbar" class="navbar-collapse collapse">
81 81
       <ul class="nav navbar-nav">
82 82
         <li>
83
-  <a href="../index.html">
83
+  <a href="http://celda.camplab.net/">
84 84
     <span class="fas fa-home fa-lg"></span>
85 85
      
86 86
   </a>
... ...
@@ -184,7 +184,7 @@
184 184
 
185 185
       <footer>
186 186
       <div class="copyright">
187
-  <p>Developed by Joshua Campbell, Sean Corbett, Yusuke Koga, Shiyi Yang, Eric Reed, Zhe Wang.</p>
187
+  <p>Developed by Joshua Campbell, Shiyi Yang, Zhe Wang, Sean Corbett, Yusuke Koga.</p>
188 188
 </div>
189 189
 
190 190
 <div class="pkgdown">
... ...
@@ -79,7 +79,7 @@
79 79
     <div id="navbar" class="navbar-collapse collapse">
80 80
       <ul class="nav navbar-nav">
81 81
         <li>
82
-  <a href="../index.html">
82
+  <a href="http://celda.camplab.net/">
83 83
     <span class="fas fa-home fa-lg"></span>
84 84
      
85 85
   </a>
... ...
@@ -162,7 +162,7 @@
162 162
 
163 163
       <footer>
164 164
       <div class="copyright">
165
-  <p>Developed by Joshua Campbell, Sean Corbett, Yusuke Koga, Shiyi Yang, Eric Reed, Zhe Wang.</p>
165
+  <p>Developed by Joshua Campbell, Shiyi Yang, Zhe Wang, Sean Corbett, Yusuke Koga.</p>
166 166
 </div>
167 167
 
168 168
 <div class="pkgdown">
... ...
@@ -80,7 +80,7 @@
80 80
     <div id="navbar" class="navbar-collapse collapse">
81 81
       <ul class="nav navbar-nav">
82 82
         <li>
83
-  <a href="../index.html">
83
+  <a href="http://celda.camplab.net/">
84 84
     <span class="fas fa-home fa-lg"></span>
85 85
      
86 86
   </a>
... ...
@@ -192,7 +192,7 @@ to use. Default "featureSubset".</p></td>
192 192
 
193 193
       <footer>
194 194
       <div class="copyright">
195
-  <p>Developed by Joshua Campbell, Sean Corbett, Yusuke Koga, Shiyi Yang, Eric Reed, Zhe Wang.</p>
195
+  <p>Developed by Joshua Campbell, Shiyi Yang, Zhe Wang, Sean Corbett, Yusuke Koga.</p>
196 196
 </div>
197 197
 
198 198
 <div class="pkgdown">
... ...
@@ -79,7 +79,7 @@
79 79
     <div id="navbar" class="navbar-collapse collapse">
80 80
       <ul class="nav navbar-nav">
81 81
         <li>
82
-  <a href="../index.html">
82
+  <a href="http://celda.camplab.net/">
83 83
     <span class="fas fa-home fa-lg"></span>
84 84
      
85 85
   </a>
... ...
@@ -165,7 +165,7 @@
165 165
 
166 166
       <footer>
167 167
       <div class="copyright">
168
-  <p>Developed by Joshua Campbell, Sean Corbett, Yusuke Koga, Shiyi Yang, Eric Reed, Zhe Wang.</p>
168
+  <p>Developed by Joshua Campbell, Shiyi Yang, Zhe Wang, Sean Corbett, Yusuke Koga.</p>
169 169
 </div>
170 170
 
171 171
 <div class="pkgdown">
... ...
@@ -79,7 +79,7 @@
79 79
     <div id="navbar" class="navbar-collapse collapse">
80 80
       <ul class="nav navbar-nav">
81 81
         <li>
82
-  <a href="../index.html">
82
+  <a href="http://celda.camplab.net/">
83 83
     <span class="fas fa-home fa-lg"></span>
84 84
      
85 85
   </a>
... ...
@@ -162,7 +162,7 @@
162 162
 
163 163
       <footer>
164 164
       <div class="copyright">
165
-  <p>Developed by Joshua Campbell, Sean Corbett, Yusuke Koga, Shiyi Yang, Eric Reed, Zhe Wang.</p>
165
+  <p>Developed by Joshua Campbell, Shiyi Yang, Zhe Wang, Sean Corbett, Yusuke Koga.</p>
166 166
 </div>
167 167
 
168 168
 <div class="pkgdown">
... ...
@@ -80,7 +80,7 @@
80