@@ -78,7 +78,7 @@

     <div id="navbar" class="navbar-collapse collapse">

       <ul class="nav navbar-nav">

         <li>

-  <a href="index.html">

+  <a href="http://celda.camplab.net/">

     <span class="fas fa-home fa-lg"></span>

   </a>

@@ -153,7 +153,7 @@ Content not found. Please use links in the navbar.

       <footer>

       <div class="copyright">

-  <p>Developed by Joshua Campbell, Sean Corbett, Yusuke Koga, Shiyi Yang, Eric Reed, Zhe Wang.</p>

+  <p>Developed by Joshua Campbell, Shiyi Yang, Zhe Wang, Sean Corbett, Yusuke Koga.</p>

 </div>

 <div class="pkgdown">

@@ -78,7 +78,7 @@

     <div id="navbar" class="navbar-collapse collapse">

       <ul class="nav navbar-nav">

         <li>

-  <a href="index.html">

+  <a href="http://celda.camplab.net/">

     <span class="fas fa-home fa-lg"></span>

   </a>

@@ -161,7 +161,7 @@

       <footer>

       <div class="copyright">

-  <p>Developed by Joshua Campbell, Sean Corbett, Yusuke Koga, Shiyi Yang, Eric Reed, Zhe Wang.</p>

+  <p>Developed by Joshua Campbell, Shiyi Yang, Zhe Wang, Sean Corbett, Yusuke Koga.</p>

 </div>

 <div class="pkgdown">

@@ -78,7 +78,7 @@

     <div id="navbar" class="navbar-collapse collapse">

       <ul class="nav navbar-nav">

         <li>

-  <a href="index.html">

+  <a href="http://celda.camplab.net/">

     <span class="fas fa-home fa-lg"></span>

   </a>

@@ -174,7 +174,7 @@ SOFTWARE.

       <footer>

       <div class="copyright">

-  <p>Developed by Joshua Campbell, Sean Corbett, Yusuke Koga, Shiyi Yang, Eric Reed, Zhe Wang.</p>

+  <p>Developed by Joshua Campbell, Shiyi Yang, Zhe Wang, Sean Corbett, Yusuke Koga.</p>

 </div>

 <div class="pkgdown">

@@ -5,13 +5,13 @@

 <meta charset="utf-8">

 <meta http-equiv="X-UA-Compatible" content="IE=edge">

 <meta name="viewport" content="width=device-width, initial-scale=1.0">

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+<title>Celda - Analysis of PBMC3K • celda</title>

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-<script src="../../pkgdown.js"></script><meta property="og:title" content="">

+<script src="../../pkgdown.js"></script><meta property="og:title" content="Celda - Analysis of PBMC3K">

 <meta property="og:description" content="celda">

 <!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>

 <script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>

@@ -38,7 +38,7 @@

     <div id="navbar" class="navbar-collapse collapse">

       <ul class="nav navbar-nav">

 <li>

-  <a href="../../index.html">

+  <a href="http://celda.camplab.net/">

     <span class="fas fa-home fa-lg"></span>

   </a>

@@ -91,12 +91,14 @@

-      </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">

+      </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">

 <div class="row">

   <div class="col-md-9 contents">

     <div class="page-header toc-ignore">

-      <h1 data-toc-skip></h1>

+      <h1 data-toc-skip>Celda - Analysis of PBMC3K</h1>

+                        <h4 class="author">Joshua Campbell, Zhe Wang</h4>

+            <h4 class="date">Compiled September 29, 2021</h4>

       <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>

       <div class="hidden name"><code>celda_pbmc3k.Rmd</code></div>

@@ -201,9 +203,9 @@

 <h2 class="hasAnchor">

 <a href="#bi-clustering-with-known-numbers-of-clusters" class="anchor"></a>Bi-clustering with known numbers of clusters</h2>

 <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>

-<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>

-<span id="cb15-2"><a href="#cb15-2"></a>altExpName &lt;-<span class="st"> "featureSubset"</span></span>

-<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>

+<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>

+<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>

+<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>

 <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>

 <div class="sourceCode" id="cb16"><pre class="downlit sourceCode r">

 <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

 <div id="session-information" class="section level1">

 <h1 class="hasAnchor">

 <a href="#session-information" class="anchor"></a>Session information</h1>

-<details><p><summary>sessionInfo()</summary></p>

-<pre><code>## R version 4.0.4 (2021-02-15)

+<details><summary>

+sessionInfo()

+</summary><pre><code>## R version 4.0.4 (2021-02-15)

 ## Platform: x86_64-apple-darwin17.0 (64-bit)

 ## Running under: macOS Big Sur 10.16

 ## 

@@ -28338,7 +28341,7 @@ RCE1

 ## other attached packages:

 ##  [1] scater_1.18.6               kableExtra_1.3.4           

 ##  [3] knitr_1.31                  ggplot2_3.3.5              

-##  [5] celda_1.9.2                 singleCellTK_2.1.3         

+##  [5] celda_1.9.2                 singleCellTK_2.2.0         

 ##  [7] TENxPBMCData_1.8.0          HDF5Array_1.18.1           

 ##  [9] rhdf5_2.34.0                DelayedArray_0.16.2        

 ## [11] Matrix_1.3-2                SingleCellExperiment_1.12.0

@@ -28443,7 +28446,7 @@ RCE1

       <footer><div class="copyright">

-  <p>Developed by Joshua Campbell, Sean Corbett, Yusuke Koga, Shiyi Yang, Eric Reed, Zhe Wang.</p>

+  <p>Developed by Joshua Campbell, Shiyi Yang, Zhe Wang, Sean Corbett, Yusuke Koga.</p>

 </div>

 <div class="pkgdown">

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@@ -5,13 +5,13 @@

 <meta charset="utf-8">

 <meta http-equiv="X-UA-Compatible" content="IE=edge">

 <meta name="viewport" content="width=device-width, initial-scale=1.0">

-<title> • celda</title>

+<title>DecontX - Decontamination of PBMC4K • celda</title>

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-<script src="../../pkgdown.js"></script><meta property="og:title" content="">

+<script src="../../pkgdown.js"></script><meta property="og:title" content="DecontX - Decontamination of PBMC4K">

 <meta property="og:description" content="celda">

 <!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>

 <script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>

@@ -38,7 +38,7 @@

     <div id="navbar" class="navbar-collapse collapse">

       <ul class="nav navbar-nav">

 <li>

-  <a href="../../index.html">

+  <a href="http://celda.camplab.net/">

     <span class="fas fa-home fa-lg"></span>

   </a>

@@ -91,11 +91,13 @@

-      </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">

+      </header><script src="decontX_pbmc4k_files/header-attrs-2.7/header-attrs.js"></script><div class="row">

   <div class="col-md-9 contents">

     <div class="page-header toc-ignore">

-      <h1 data-toc-skip></h1>

+      <h1 data-toc-skip>DecontX - Decontamination of PBMC4K</h1>

+                        <h4 class="author">Joshua Campbell, Shiyi Yang, Zhe Wang, Yuan Yin</h4>

+            <h4 class="date">Compiled September 29, 2021</h4>

       <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>

       <div class="hidden name"><code>decontX_pbmc4k.Rmd</code></div>

@@ -383,7 +385,7 @@

       <footer><div class="copyright">

-  <p>Developed by Joshua Campbell, Sean Corbett, Yusuke Koga, Shiyi Yang, Eric Reed, Zhe Wang.</p>

+  <p>Developed by Joshua Campbell, Shiyi Yang, Zhe Wang, Sean Corbett, Yusuke Koga.</p>

 </div>

 <div class="pkgdown">

@@ -38,7 +38,7 @@

     <div id="navbar" class="navbar-collapse collapse">

       <ul class="nav navbar-nav">

 <li>

-  <a href="../../index.html">

+  <a href="http://celda.camplab.net/">

     <span class="fas fa-home fa-lg"></span>

   </a>

@@ -91,7 +91,7 @@

-      </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">

+      </header><script src="installation_files/header-attrs-2.7/header-attrs.js"></script><div class="row">

   <div class="col-md-9 contents">

     <div class="page-header toc-ignore">

       <h1 data-toc-skip></h1>

@@ -151,7 +151,7 @@

       <footer><div class="copyright">

-  <p>Developed by Joshua Campbell, Sean Corbett, Yusuke Koga, Shiyi Yang, Eric Reed, Zhe Wang.</p>

+  <p>Developed by Joshua Campbell, Shiyi Yang, Zhe Wang, Sean Corbett, Yusuke Koga.</p>

 </div>

 <div class="pkgdown">

@@ -31,20 +31,20 @@

       </button>

       <span class="navbar-brand">

         <a class="navbar-link" href="../index.html">celda</a>

-        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.9.1</span>

+        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.9.2</span>

       </span>

     </div>

     <div id="navbar" class="navbar-collapse collapse">

       <ul class="nav navbar-nav">

 <li>

-  <a href="../index.html">

+  <a href="http://celda.camplab.net/">

     <span class="fas fa-home fa-lg"></span>

   </a>

 </li>

 <li>

-  <a href="../index.html">Installation</a>

+  <a href="../articles/articles/installation.html">Installation</a>

 </li>

 <li class="dropdown">

   <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false">

@@ -54,16 +54,19 @@

   </a>

   <ul class="dropdown-menu" role="menu">

 <li>

-      <a href="../articles/articles/celda_pbmc3k.html">Analysis of PBMC3K with Celda</a>

+      <a href="../articles/articles/celda_pbmc3k.html">Celda - Analysis of PBMC3K</a>

     </li>

     <li>

-      <a href="../articles/articles/decontX_pbmc4k.html">Decontamination of PBMC4K with DecontX</a>

+      <a href="../articles/articles/decontX_pbmc4k.html">DecontX - Decontamination of PBMC4K</a>

     </li>

   </ul>

 </li>

 <li>

   <a href="../reference/index.html">Reference</a>

 </li>

+<li>

+  <a href="../news/index.html">News</a>

+</li>

 <li>

   <a href="https://github.com/campbio/celda">

     <span class="fas fa-github"></span>

@@ -88,24 +91,23 @@

-      </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">

+      </header><script src="celda_files/header-attrs-2.7/header-attrs.js"></script><div class="row">

   <div class="col-md-9 contents">

     <div class="page-header toc-ignore">

       <h1 data-toc-skip>Analysis of single-cell genomic data with celda</h1>

-                        <h4 class="author">Sean Corbett</h4>

+                        <h4 class="author">Joshua Campbell</h4>

             <address class="author_afil">

-      Boston University School of Medicine<br><h4 class="author">Yusuke Koga</h4>

+      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>

+      </address>

+                              <h4 class="author">Zhe Wang</h4>

             <address class="author_afil">

       Boston University School of Medicine<br><h4 class="author">Shiyi Yang</h4>

             <address class="author_afil">

-      Boston University School of Medicine<br><h4 class="author">Zhe Wang</h4>

+      Boston University School of Medicine<br><h4 class="author">Sean Corbett</h4>

             <address class="author_afil">

-      Boston University School of Medicine<br><h4 class="author">Joshua Campbell</h4>

+      Boston University School of Medicine<br><h4 class="author">Yusuke Koga</h4>

             <address class="author_afil">

-      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>

-      </address>

-                  

-            <h4 class="date">2021-07-18</h4>

+      Boston University School of Medicine<br><h4 class="date">2021-09-29</h4>

       <small class="dont-index">Source: <a href="https://github.com/campbio/celda/blob/master/vignettes/celda.Rmd"><code>vignettes/celda.Rmd</code></a></small>

       <div class="hidden name"><code>celda.Rmd</code></div>

@@ -122,7 +124,7 @@

 <h1 class="hasAnchor">

 <a href="#introduction" class="anchor"></a>Introduction</h1>

 <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>

-<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>

+<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>

 </div>

 <div id="installation" class="section level1">

 <h1 class="hasAnchor">

@@ -151,10 +153,10 @@

 <div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">

 <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>,

     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>

-<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>

+<p>The <code>counts</code> assay slot in <code>simsce</code> contains the counts matrix. The dimensions of counts matrix:</p>

 <div class="sourceCode" id="cb5"><pre class="downlit sourceCode r">

 <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>

-<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>

+<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>

 <pre><code>## [1] 200 207</code></pre>

 <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>

 <div class="sourceCode" id="cb7"><pre class="downlit sourceCode r">

@@ -180,6 +182,7 @@

 <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>

 <div class="sourceCode" id="cb13"><pre class="downlit sourceCode r">

 <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>

+<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>

 </div>

 <div id="performing-bi-clustering-with-celda" class="section level1">

 <h1 class="hasAnchor">

@@ -212,11 +215,6 @@

 ##   9   0  0  0  0  0  0  0  0  3  0

 ##   10  0  0  0  0  0  0  0  0  0 20</code></pre>

 </div>

-<div id="note-on-reproducibility" class="section level1">

-<h1 class="hasAnchor">

-<a href="#note-on-reproducibility" class="anchor"></a>Note on reproducibility</h1>

-<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>

-</div>

 <div id="visualization" class="section level1">

 <h1 class="hasAnchor">

 <a href="#visualization" class="anchor"></a>Visualization</h1>

@@ -290,10 +288,10 @@

     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>

 <div class="sourceCode" id="cb30"><pre class="downlit sourceCode r">

 <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>

-<p><img src="celda_files/figure-html/unnamed-chunk-5-1.png" width="700"></p>

+<p><img src="celda_files/figure-html/rpc_cell-1.png" width="700"></p>

 <div class="sourceCode" id="cb31"><pre class="downlit sourceCode r">

 <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>

-<p><img src="celda_files/figure-html/unnamed-chunk-5-2.png" width="700"></p>

+<p><img src="celda_files/figure-html/rpc_cell-2.png" width="700"></p>

 <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>

 <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>

 <div class="sourceCode" id="cb32"><pre class="downlit sourceCode r">

@@ -315,7 +313,7 @@

 <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>

 <div class="sourceCode" id="cb34"><pre class="downlit sourceCode r">

 <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>

-<p><img src="celda_files/figure-html/unnamed-chunk-8-1.png" width="768"></p>

+<p><img src="celda_files/figure-html/plot_grid_search-1.png" width="768"></p>

 <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>

 <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>

 <div class="sourceCode" id="cb35"><pre class="downlit sourceCode r">

@@ -342,18 +340,18 @@

 <h1 class="hasAnchor">

 <a href="#miscellaneous-utility-functions" class="anchor"></a>Miscellaneous utility functions</h1>

 <p>celda also contains several utility functions for the users’ convenience.</p>

-<div id="featuremodulelookup" class="section level2">

+<div id="finding-the-modules-for-feature-with-featuremodulelookup" class="section level2">

 <h2 class="hasAnchor">

-<a href="#featuremodulelookup" class="anchor"></a>featureModuleLookup</h2>

+<a href="#finding-the-modules-for-feature-with-featuremodulelookup" class="anchor"></a>Finding the modules for feature with featureModuleLookup</h2>

 <p><code>featureModuleLookup</code> can be used to look up the module a specific feature was clustered to.</p>

 <div class="sourceCode" id="cb37"><pre class="downlit sourceCode r">

 <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>

 <pre><code>## Gene_99 

 ##       4</code></pre>

 </div>

-<div id="recodeclusterz-recodeclustery" class="section level2">

+<div id="reordering-cluster-labels-with-recodeclusterz-recodeclustery" class="section level2">

 <h2 class="hasAnchor">

-<a href="#recodeclusterz-recodeclustery" class="anchor"></a>recodeClusterZ, recodeClusterY</h2>

+<a href="#reordering-cluster-labels-with-recodeclusterz-recodeclustery" class="anchor"></a>Reordering cluster labels with recodeClusterZ, recodeClusterY</h2>

 <p><code>recodeClusterZ</code> and <code>recodeClusterY</code> allows the user to recode the cell and feature cluster labels, respectively.</p>

 <div class="sourceCode" id="cb39"><pre class="downlit sourceCode r">

 <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 @@

 <div class="sourceCode" id="cb40"><pre class="downlit sourceCode r">

 <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>

 <pre><code>##    

-##      2  1  3  4  5

-##   1 44  0  0  0  0

-##   2  0 42  0  0  0

+##      1  2  3  4  5

+##   1  0 44  0  0  0

+##   2 42  0  0  0  0

 ##   3  0  0 40  0  0

 ##   4  0  0  0 47  0

 ##   5  0  0  0  0 34</code></pre>

 </div>

-<div id="converting-from-previous-versions" class="section level2">

-<h2 class="hasAnchor">

-<a href="#converting-from-previous-versions" class="anchor"></a>Converting from previous versions</h2>

-<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>

-<div class="sourceCode" id="cb42"><pre class="downlit sourceCode r">

-<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>

-<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>

-<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>

-<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>

-<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>

-</div>

 </div>

 <div id="session-information" class="section level1">

 <h1 class="hasAnchor">

 <a href="#session-information" class="anchor"></a>Session Information</h1>

-<div class="sourceCode" id="cb43"><pre class="downlit sourceCode r">

+<div class="sourceCode" id="cb42"><pre class="downlit sourceCode r">

 <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>

 <pre><code>## R version 4.0.4 (2021-02-15)

 ## Platform: x86_64-apple-darwin17.0 (64-bit)

@@ -407,7 +394,7 @@

 ##  [5] GenomeInfoDb_1.26.4         IRanges_2.24.1             

 ##  [7] S4Vectors_0.28.1            BiocGenerics_0.36.0        

 ##  [9] MatrixGenerics_1.2.1        matrixStats_0.58.0         

-## [11] celda_1.9.1                

+## [11] celda_1.9.2                 BiocStyle_2.18.1           

 ## 

 ## loaded via a namespace (and not attached):

 ##   [1] bitops_1.0-6               fs_1.5.0                  

@@ -423,44 +410,44 @@

 ##  [21] assertive.properties_0.0-4 enrichR_3.0               

 ##  [23] DelayedArray_0.16.2        desc_1.3.0                

 ##  [25] labeling_0.4.2             assertive.files_0.0-2     

-##  [27] sass_0.3.1                 scales_1.1.1              

-##  [29] pkgdown_1.6.1              systemfonts_1.0.1         

-##  [31] stringr_1.4.0              digest_0.6.27             

-##  [33] rmarkdown_2.7              XVector_0.30.0            

-##  [35] assertive.numbers_0.0-2    pkgconfig_2.0.3           

-##  [37] htmltools_0.5.1.1          highr_0.8                 

-##  [39] fastmap_1.1.0              GlobalOptions_0.1.2       

-##  [41] rlang_0.4.10               FNN_1.1.3                 

-##  [43] shape_1.4.5                farver_2.1.0              

-##  [45] gridGraphics_0.5-1         jquerylib_0.1.3           

-##  [47] generics_0.1.0             combinat_0.0-8            

-##  [49] jsonlite_1.7.2             dplyr_1.0.5               

-##  [51] RCurl_1.98-1.2             magrittr_2.0.1            

-##  [53] GenomeInfoDbData_1.2.4     multipanelfigure_2.1.2    

-##  [55] Matrix_1.3-2               Rcpp_1.0.6                

-##  [57] munsell_0.5.0              fansi_0.4.2               

-##  [59] lifecycle_1.0.0            stringi_1.5.3             

-##  [61] assertive.base_0.0-9       yaml_2.2.1                

-##  [63] MCMCprecision_0.4.0        zlibbioc_1.36.0           

-##  [65] Rtsne_0.15                 plyr_1.8.6                

-##  [67] grid_4.0.4                 ggrepel_0.9.1             

-##  [69] crayon_1.4.1               lattice_0.20-41           

-##  [71] circlize_0.4.12            magick_2.7.0              

-##  [73] ComplexHeatmap_2.6.2       knitr_1.31                

-##  [75] pillar_1.5.1               rjson_0.2.20              

-##  [77] reshape2_1.4.4             codetools_0.2-18          

-##  [79] glue_1.4.2                 evaluate_0.14             

-##  [81] data.table_1.14.0          BiocManager_1.30.10       

-##  [83] png_0.1-7                  vctrs_0.3.6               

-##  [85] foreach_1.5.1              gtable_0.3.0              

-##  [87] purrr_0.3.4                clue_0.3-58               

-##  [89] assertthat_0.2.1           cachem_1.0.4              

-##  [91] ggplot2_3.3.5              xfun_0.22                 

-##  [93] assertive.types_0.0-3      RcppEigen_0.3.3.9.1       

-##  [95] ragg_1.1.3                 tibble_3.1.0              

-##  [97] iterators_1.0.13           memoise_2.0.0             

-##  [99] cluster_2.1.1              ellipsis_0.3.1            

-## [101] BiocStyle_2.18.1</code></pre>

+##  [27] bookdown_0.21              sass_0.3.1                

+##  [29] scales_1.1.1               pkgdown_1.6.1             

+##  [31] systemfonts_1.0.1          stringr_1.4.0             

+##  [33] digest_0.6.27              rmarkdown_2.7             

+##  [35] XVector_0.30.0             assertive.numbers_0.0-2   

+##  [37] pkgconfig_2.0.3            htmltools_0.5.1.1         

+##  [39] highr_0.8                  fastmap_1.1.0             

+##  [41] GlobalOptions_0.1.2        rlang_0.4.10              

+##  [43] FNN_1.1.3                  shape_1.4.5               

+##  [45] farver_2.1.0               gridGraphics_0.5-1        

+##  [47] jquerylib_0.1.3            generics_0.1.0            

+##  [49] combinat_0.0-8             jsonlite_1.7.2            

+##  [51] dplyr_1.0.5                RCurl_1.98-1.2            

+##  [53] magrittr_2.0.1             GenomeInfoDbData_1.2.4    

+##  [55] multipanelfigure_2.1.2     Matrix_1.3-2              

+##  [57] Rcpp_1.0.6                 munsell_0.5.0             

+##  [59] fansi_0.4.2                lifecycle_1.0.0           

+##  [61] stringi_1.5.3              assertive.base_0.0-9      

+##  [63] yaml_2.2.1                 MCMCprecision_0.4.0       

+##  [65] zlibbioc_1.36.0            Rtsne_0.15                

+##  [67] plyr_1.8.6                 grid_4.0.4                

+##  [69] ggrepel_0.9.1              crayon_1.4.1              

+##  [71] lattice_0.20-41            circlize_0.4.12           

+##  [73] magick_2.7.0               ComplexHeatmap_2.6.2      

+##  [75] knitr_1.31                 pillar_1.5.1              

+##  [77] rjson_0.2.20               reshape2_1.4.4            

+##  [79] codetools_0.2-18           glue_1.4.2                

+##  [81] evaluate_0.14              data.table_1.14.0         

+##  [83] BiocManager_1.30.10        png_0.1-7                 

+##  [85] vctrs_0.3.6                foreach_1.5.1             

+##  [87] gtable_0.3.0               purrr_0.3.4               

+##  [89] clue_0.3-58                assertthat_0.2.1          

+##  [91] cachem_1.0.4               ggplot2_3.3.5             

+##  [93] xfun_0.22                  assertive.types_0.0-3     

+##  [95] RcppEigen_0.3.3.9.1        ragg_1.1.3                

+##  [97] tibble_3.1.0               iterators_1.0.13          

+##  [99] memoise_2.0.0              cluster_2.1.1             

+## [101] ellipsis_0.3.1</code></pre>

 </div>

   </div>

@@ -475,7 +462,7 @@

       <footer><div class="copyright">

-  <p>Developed by Joshua Campbell, Sean Corbett, Yusuke Koga, Shiyi Yang, Eric Reed, Zhe Wang.</p>

+  <p>Developed by Joshua Campbell, Shiyi Yang, Zhe Wang, Sean Corbett, Yusuke Koga.</p>

 </div>

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+<!DOCTYPE html>

+<!-- Generated by pkgdown: do not edit by hand --><html lang="en">

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+<meta http-equiv="Content-Type" content="text/html; charset=UTF-8">

+<meta charset="utf-8">

+<meta http-equiv="X-UA-Compatible" content="IE=edge">

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+      <h1 data-toc-skip>Decontamination of ambient RNA in single-cell genomic data with DecontX</h1>

+                        <h4 class="author">Shiyi (Iris) Yang</h4>

+            <address class="author_afil">

+      Boston University School of Medicine<br><h4 class="author">Zhe Wang</h4>

+            <address class="author_afil">

+      Boston University School of Medicine<br><h4 class="author">Yuan Yin</h4>

+            <address class="author_afil">

+      Boston University School of Medicine<br><h4 class="author">Joshua Campbell</h4>

+            <address class="author_afil">

+      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>

+      </address>

+                  

+            <h4 class="date">2021-09-29</h4>

+      

+      <small class="dont-index">Source: <a href="https://github.com/campbio/celda/blob/master/vignettes/decontX.Rmd"><code>vignettes/decontX.Rmd</code></a></small>

+      <div class="hidden name"><code>decontX.Rmd</code></div>

+

+    </address>

+</address>

+</address>

+</div>

+

+    

+    

+<div id="introduction" class="section level1">

+<h1 class="hasAnchor">

+<a href="#introduction" class="anchor"></a>Introduction</h1>

+<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>

+<p>The package can be loaded using the <code>library</code> command.</p>

+<div class="sourceCode" id="cb1"><pre class="downlit sourceCode r">

+<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>

+<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>

+</div>

+<div id="load-pbmc4k-data-from-10x" class="section level1">

+<h1 class="hasAnchor">

+<a href="#load-pbmc4k-data-from-10x" class="anchor"></a>Load PBMC4k data from 10X</h1>

+<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>

+<div class="sourceCode" id="cb2"><pre class="downlit sourceCode r">

+<code class="sourceCode R"><span class="co"># Install TENxPBMCData if is it not already</span>

+<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>

+  <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>

+    <span class="fu"><a href="https://rdrr.io/r/utils/install.packages.html">install.packages</a></span><span class="op">(</span><span class="st">"BiocManager"</span><span class="op">)</span>

+  <span class="op">}</span>

+  <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>

+<span class="op">}</span>

+

+<span class="co"># Load PBMC data</span>

+<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>

+<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>

+<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>

+<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>

+</div>

+<div id="running-decontx" class="section level1">

+<h1 class="hasAnchor">

+<a href="#running-decontx" class="anchor"></a>Running decontX</h1>

+<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>

+<div class="sourceCode" id="cb3"><pre class="downlit sourceCode r">

+<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>

+<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>

+<div class="sourceCode" id="cb4"><pre class="downlit sourceCode r">

+<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>

+<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>

+<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>

+</div>

+<div id="plotting-decontx-results" class="section level1">

+<h1 class="hasAnchor">

+<a href="#plotting-decontx-results" class="anchor"></a>Plotting DecontX results</h1>

+<div id="cluster-labels-on-umap" class="section level2">

+<h2 class="hasAnchor">

+<a href="#cluster-labels-on-umap" class="anchor"></a>Cluster labels on UMAP</h2>

+<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>

+<div class="sourceCode" id="cb5"><pre class="downlit sourceCode r">

+<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>

+<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>,

+    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>

+<p><img src="decontX_files/figure-html/UMAP_Clusters-1.png" width="700"></p>

+</div>

+<div id="contamination-on-umap" class="section level2">

+<h2 class="hasAnchor">

+<a href="#contamination-on-umap" class="anchor"></a>Contamination on UMAP</h2>

+<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>

+<div class="sourceCode" id="cb6"><pre class="downlit sourceCode r">

+<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>

+<p><img src="decontX_files/figure-html/plot_decon-1.png" width="700"></p>

+</div>

+<div id="expression-of-markers-on-umap" class="section level2">

+<h2 class="hasAnchor">

+<a href="#expression-of-markers-on-umap" class="anchor"></a>Expression of markers on UMAP</h2>

+<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>

+<div class="sourceCode" id="cb7"><pre class="downlit sourceCode r">

+<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>

+<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>

+<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>,

+    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>,

+    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>,

+        <span class="st">"LYZ"</span>, <span class="st">"S100A8"</span>, <span class="st">"S100A9"</span>,

+        <span class="st">"CD79A"</span>, <span class="st">"CD79B"</span>, <span class="st">"MS4A1"</span><span class="op">)</span>,

+    exactMatch <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></code></pre></div>

+<p><img src="decontX_files/figure-html/plot_feature-1.png" width="700"></p>

+</div>

+<div id="barplot-of-markers-detected-in-cell-clusters" class="section level2">

+<h2 class="hasAnchor">

+<a href="#barplot-of-markers-detected-in-cell-clusters" class="anchor"></a>Barplot of markers detected in cell clusters</h2>

+<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>

+<div class="sourceCode" id="cb8"><pre class="downlit sourceCode r">

+<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>,

+    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>,

+    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>,

+    NKcell_Markers <span class="op">=</span> <span class="st">"GNLY"</span><span class="op">)</span>

+<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>

+<span class="fu"><a href="../reference/plotDecontXMarkerPercentage.html">plotDecontXMarkerPercentage</a></span><span class="op">(</span><span class="va">sce</span>,

+    markers <span class="op">=</span> <span class="va">markers</span>,

+    groupClusters <span class="op">=</span> <span class="va">cellTypeMappings</span>,

+    assayName <span class="op">=</span> <span class="st">"counts"</span><span class="op">)</span></code></pre></div>

+<p><img src="decontX_files/figure-html/barplotCounts-1.png" width="700"></p>

+<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>

+<div class="sourceCode" id="cb9"><pre class="downlit sourceCode r">

+<code class="sourceCode R"><span class="fu"><a href="../reference/plotDecontXMarkerPercentage.html">plotDecontXMarkerPercentage</a></span><span class="op">(</span><span class="va">sce</span>,

+    markers <span class="op">=</span> <span class="va">markers</span>,

+    groupClusters <span class="op">=</span> <span class="va">cellTypeMappings</span>,

+    assayName <span class="op">=</span> <span class="st">"decontXcounts"</span><span class="op">)</span></code></pre></div>

+<p><img src="decontX_files/figure-html/barplotDecontCounts-1.png" width="700"></p>

+<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>

+<div class="sourceCode" id="cb10"><pre class="downlit sourceCode r">

+<code class="sourceCode R"><span class="fu"><a href="../reference/plotDecontXMarkerPercentage.html">plotDecontXMarkerPercentage</a></span><span class="op">(</span><span class="va">sce</span>,

+    markers <span class="op">=</span> <span class="va">markers</span>,

+    groupClusters <span class="op">=</span> <span class="va">cellTypeMappings</span>,

+    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>

+<p><img src="decontX_files/figure-html/barplotBoth-1.png" width="700"></p>

+<p>Some helpful hints when using <code>plotDecontXMarkerPercentage</code>:</p>

+<ol style="list-style-type: decimal">

+<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>

+</li>

+<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>

+<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>

+</ol>

+</div>

+<div id="violin-plot-to-compare-the-distributions-of-original-and-decontaminated-counts" class="section level2">

+<h2 class="hasAnchor">

+<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>

+<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>

+<div class="sourceCode" id="cb11"><pre class="downlit sourceCode r">

+<code class="sourceCode R"><span class="fu"><a href="../reference/plotDecontXMarkerExpression.html">plotDecontXMarkerExpression</a></span><span class="op">(</span><span class="va">sce</span>,

+    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>,

+    groupClusters <span class="op">=</span> <span class="va">cellTypeMappings</span>,

+    ncol <span class="op">=</span> <span class="fl">3</span><span class="op">)</span></code></pre></div>

+<p><img src="decontX_files/figure-html/plotDecontXMarkerExpression-1.png" width="700"></p>

+<p>Some helpful hints when using <code>plotDecontXMarkerExpression</code>:</p>

+<ol style="list-style-type: decimal">

+<li>

+<code>groupClusters</code> works the same way as in <code>plotDecontXMarkerPercentage</code>.</li>

+<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>

+<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>

+<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>

+</ol>

+<div class="sourceCode" id="cb12"><pre class="downlit sourceCode r">

+<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>,

+    exprs_values <span class="op">=</span> <span class="st">"decontXcounts"</span>,

+    name <span class="op">=</span> <span class="st">"dlogcounts"</span><span class="op">)</span>

+

+<span class="fu"><a href="../reference/plotDecontXMarkerExpression.html">plotDecontXMarkerExpression</a></span><span class="op">(</span><span class="va">sce</span>,

+    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>,

+    groupClusters <span class="op">=</span> <span class="va">cellTypeMappings</span>,

+    ncol <span class="op">=</span> <span class="fl">3</span>,

+    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>

+</div>

+</div>

+<div id="other-important-notes" class="section level1">

+<h1 class="hasAnchor">

+<a href="#other-important-notes" class="anchor"></a>Other important notes</h1>

+<div id="choosing-appropriate-cell-clusters" class="section level2">

+<h2 class="hasAnchor">