full documentation update

docs/bookdown/404.html

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 <meta name="author" content="John Doe" />
 
 
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docs/bookdown/actvity-based-analysis.html

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 <meta name="author" content="John Doe" />
 
 
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@@ -262,15 +262,15 @@ <h3><span class="header-section-number">10.1.1</span> Calculate activities per g
 <p><code>getActivity_individual()</code> is designed to calculate the activities per group. It takes the network files as the input:</p>
 <div class="sourceCode" id="cb124"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb124-1"><a href="actvity-based-analysis.html#cb124-1" tabindex="-1"></a><span class="do">## let&#39;s use B cell as an example</span></span>
 <span id="cb124-2"><a href="actvity-based-analysis.html#cb124-2" tabindex="-1"></a>activity_B.eset <span class="ot">&lt;-</span> <span class="fu">getActivity_individual</span>(<span class="at">input_eset =</span> pbmc14k_log2cpm.eset[, <span class="fu">pData</span>(pbmc14k_log2cpm.eset)<span class="sc">$</span>trueLabel <span class="sc">==</span> <span class="st">&quot;B&quot;</span>],</span>
-<span id="cb124-3"><a href="actvity-based-analysis.html#cb124-3" tabindex="-1"></a>                                          <span class="at">network_file.tf =</span> <span class="fu">system.file</span>(<span class="st">&quot;extdata/demo_pbmc14k/PBMC14k/SJARACNe/B/TF/bt100_pc001/consensus_network_ncol_.txt&quot;</span>, <span class="at">package =</span> <span class="st">&quot;scMINER&quot;</span>),</span>
-<span id="cb124-4"><a href="actvity-based-analysis.html#cb124-4" tabindex="-1"></a>                                          <span class="at">network_file.sig =</span> <span class="fu">system.file</span>(<span class="st">&quot;extdata/demo_pbmc14k/PBMC14k/SJARACNe/B/SIG/bt100_pc001/consensus_network_ncol_.txt&quot;</span>, <span class="at">package =</span> <span class="st">&quot;scMINER&quot;</span>),</span>
+<span id="cb124-3"><a href="actvity-based-analysis.html#cb124-3" tabindex="-1"></a>                                          <span class="at">network_file.tf =</span> <span class="fu">system.file</span>(<span class="st">&quot;extdata/demo_pbmc14k/SJARACNe/B/TF/bt100_pc001/consensus_network_ncol_.txt&quot;</span>, <span class="at">package =</span> <span class="st">&quot;scMINER&quot;</span>),</span>
+<span id="cb124-4"><a href="actvity-based-analysis.html#cb124-4" tabindex="-1"></a>                                          <span class="at">network_file.sig =</span> <span class="fu">system.file</span>(<span class="st">&quot;extdata/demo_pbmc14k/SJARACNe/B/SIG/bt100_pc001/consensus_network_ncol_.txt&quot;</span>, <span class="at">package =</span> <span class="st">&quot;scMINER&quot;</span>),</span>
 <span id="cb124-5"><a href="actvity-based-analysis.html#cb124-5" tabindex="-1"></a>                                          <span class="at">driver_type =</span> <span class="st">&quot;TF_SIG&quot;</span>)</span></code></pre></div>
 </div>
 <div id="calculate-activities-in-batch" class="section level3 hasAnchor" number="10.1.2">
 <h3><span class="header-section-number">10.1.2</span> Calculate activities in batch<a href="actvity-based-analysis.html#calculate-activities-in-batch" class="anchor-section" aria-label="Anchor link to header"></a></h3>
 <p>If you need to calculate the activity for multiple groups, this is usually the case, you can do it using <code>getActivity_individual()</code> as shown above one by one and merge the esets after that. Or, scMINER privides another function, <code>getActivity_inBatch()</code>, to calculate the activity in batch:</p>
 <div class="sourceCode" id="cb125"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb125-1"><a href="actvity-based-analysis.html#cb125-1" tabindex="-1"></a><span class="do">## let&#39;s use B cell as an example</span></span>
-<span id="cb125-2"><a href="actvity-based-analysis.html#cb125-2" tabindex="-1"></a>activity.eset <span class="ot">&lt;-</span> <span class="fu">getActivity_inBatch</span>(<span class="at">input_eset =</span> pbmc14k_log2cpm.eset, <span class="at">sjaracne_dir =</span> <span class="fu">system.file</span>(<span class="st">&quot;extdata/demo_pbmc14k/PBMC14k/SJARACNe&quot;</span>, <span class="at">package =</span> <span class="st">&quot;scMINER&quot;</span>),  <span class="at">group_name =</span> <span class="st">&quot;trueLabel&quot;</span>, <span class="at">driver_type =</span> <span class="st">&quot;TF_SIG&quot;</span>, <span class="at">activity_method =</span> <span class="st">&quot;mean&quot;</span>, <span class="at">do.z_normalization =</span> <span class="cn">TRUE</span>)</span></code></pre></div>
+<span id="cb125-2"><a href="actvity-based-analysis.html#cb125-2" tabindex="-1"></a>activity.eset <span class="ot">&lt;-</span> <span class="fu">getActivity_inBatch</span>(<span class="at">input_eset =</span> pbmc14k_log2cpm.eset, <span class="at">sjaracne_dir =</span> <span class="fu">system.file</span>(<span class="st">&quot;extdata/demo_pbmc14k/SJARACNe&quot;</span>, <span class="at">package =</span> <span class="st">&quot;scMINER&quot;</span>),  <span class="at">group_name =</span> <span class="st">&quot;trueLabel&quot;</span>, <span class="at">driver_type =</span> <span class="st">&quot;TF_SIG&quot;</span>, <span class="at">activity_method =</span> <span class="st">&quot;mean&quot;</span>, <span class="at">do.z_normalization =</span> <span class="cn">TRUE</span>)</span></code></pre></div>
 <pre><code>## 7 groups were found in trueLabel ...
 ## Checking network files for each group ...
 ##  Group 1 / 7 : Monocyte ...

docs/bookdown/cell-type-annotation.html

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docs/bookdown/create-sparseeset-object.html

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 <meta name="author" content="John Doe" />
 
 
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docs/bookdown/data-filtration.html

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docs/bookdown/data-normalization.html

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 <meta name="author" content="John Doe" />
 
 
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docs/bookdown/generate-gene-expresion-matrix.html

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 <meta name="author" content="John Doe" />
 
 
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docs/bookdown/get-started.html

@@ -23,7 +23,7 @@
 <meta name="author" content="John Doe" />
 
 
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@@ -262,7 +262,7 @@ <h2><span class="header-section-number">2.1</span> Installation<a href="get-star
 <h3>Install scMINER R package<a href="get-started.html#install-scminer-r-package" class="anchor-section" aria-label="Anchor link to header"></a></h3>
 <p>The scMINER R package requires R 4.2.3 or newer, and can be installed from <a href="https://github.com/">GitHub</a> with:</p>
 <div class="sourceCode" id="cb1"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb1-1"><a href="get-started.html#cb1-1" tabindex="-1"></a><span class="co"># install.packages(&quot;devtools&quot;)</span></span>
-<span id="cb1-2"><a href="get-started.html#cb1-2" tabindex="-1"></a>devtools<span class="sc">::</span><span class="fu">install_github</span>(<span class="st">&quot;https://github.com/jyyulab/scMINER.git@dev-qpan&quot;</span>)</span></code></pre></div>
+<span id="cb1-2"><a href="get-started.html#cb1-2" tabindex="-1"></a>devtools<span class="sc">::</span><span class="fu">install_github</span>(<span class="st">&quot;https://github.com/jyyulab/scMINER.git&quot;</span>)</span></code></pre></div>
 </div>
 <div id="install-mica-and-sjaracne" class="section level3 unnumbered hasAnchor">
 <h3>Install MICA and SJARACNe<a href="get-started.html#install-mica-and-sjaracne" class="anchor-section" aria-label="Anchor link to header"></a></h3>

docs/bookdown/index.html

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 <meta name="author" content="John Doe" />
 
 
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@@ -254,7 +254,7 @@ <h1>
 <div id="header">
 <h1 class="title">scMINER: a mutual information-based framework for identifying hidden drivers from single-cell omics data</h1>
 <p class="author"><em>John Doe</em></p>
-<p class="date"><em>2024-08-04</em></p>
+<p class="date"><em>2024-08-05</em></p>
 </div>
 <div id="section" class="section level1 unnumbered hasAnchor">
 <h1><img src="images/scMINER_logo.png" alt="" width="250px"><a href="index.html#section" class="anchor-section" aria-label="Anchor link to header"></a></h1>

docs/bookdown/introduction.html

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docs/bookdown/mi-based-clustering-analysis.html

@@ -23,7 +23,7 @@
 <meta name="author" content="John Doe" />
 
 
-<meta name="date" content="2024-08-04" />
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@@ -291,7 +291,7 @@ <h2><span class="header-section-number">7.3</span> Run MICA<a href="mi-based-clu
 <div id="integrate-mica-outputs-into-sparseeset-object" class="section level2 hasAnchor" number="7.4">
 <h2><span class="header-section-number">7.4</span> Integrate MICA outputs into SparseEset object<a href="mi-based-clustering-analysis.html#integrate-mica-outputs-into-sparseeset-object" class="anchor-section" aria-label="Anchor link to header"></a></h2>
 <p>MICA generates several files and save all of them in the output directory specified by the user with <code>-o</code> argument. The core, and only, output file we need for subsequent analysis is the clustering label file named in the format of <code>ProjectName_clustering_VisualizeMethod_euclidean_NumberOfDimensions_Resolution.txt</code>. In this case, since we used a range of resolutions, there are several clustering label files generated, one for each resolution. Based on the knowledge about PBMC14k dataset, we compared the results of different resolutions and picked <code>clustering_UMAP_euclidean_20_2.05.txt</code> for subsequent analysis.</p>
-<div class="sourceCode" id="cb77"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb77-1"><a href="mi-based-clustering-analysis.html#cb77-1" tabindex="-1"></a>micaOutput <span class="ot">&lt;-</span> <span class="fu">read.table</span>(<span class="fu">system.file</span>(<span class="st">&quot;extdata/demo_pbmc14k/PBMC14k/MICA/clustering_UMAP_euclidean_20_2.05.txt&quot;</span>, <span class="at">package =</span> <span class="st">&quot;scMINER&quot;</span>), <span class="at">header =</span> <span class="cn">TRUE</span>, <span class="at">sep =</span> <span class="st">&quot;</span><span class="sc">\t</span><span class="st">&quot;</span>, <span class="at">quote =</span> <span class="st">&quot;&quot;</span>, <span class="at">stringsAsFactors =</span> F)</span>
+<div class="sourceCode" id="cb77"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb77-1"><a href="mi-based-clustering-analysis.html#cb77-1" tabindex="-1"></a>micaOutput <span class="ot">&lt;-</span> <span class="fu">read.table</span>(<span class="fu">system.file</span>(<span class="st">&quot;extdata/demo_pbmc14k/MICA/clustering_UMAP_euclidean_20_2.05.txt&quot;</span>, <span class="at">package =</span> <span class="st">&quot;scMINER&quot;</span>), <span class="at">header =</span> <span class="cn">TRUE</span>, <span class="at">sep =</span> <span class="st">&quot;</span><span class="sc">\t</span><span class="st">&quot;</span>, <span class="at">quote =</span> <span class="st">&quot;&quot;</span>, <span class="at">stringsAsFactors =</span> F)</span>
 <span id="cb77-2"><a href="mi-based-clustering-analysis.html#cb77-2" tabindex="-1"></a><span class="fu">head</span>(micaOutput)</span></code></pre></div>
 <pre><code>##               ID        X        Y label
 ## 1 CACTTTGACGCAAT 14.91650 13.04096     6
@@ -308,7 +308,7 @@ <h2><span class="header-section-number">7.4</span> Integrate MICA outputs into S
 <li><code>label</code>: labels of predicted clusters.</li>
 </ul>
 <p>The clustering result can be easily easily added to the SparseExpressionSet object by <code>addMICAoutput()</code>:</p>
-<div class="sourceCode" id="cb79"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb79-1"><a href="mi-based-clustering-analysis.html#cb79-1" tabindex="-1"></a>pbmc14k_log2cpm.eset <span class="ot">&lt;-</span> <span class="fu">addMICAoutput</span>(pbmc14k_log2cpm.eset, <span class="at">mica_output_file =</span> <span class="fu">system.file</span>(<span class="st">&quot;extdata/demo_pbmc14k/PBMC14k/MICA/clustering_UMAP_euclidean_20_2.05.txt&quot;</span>, <span class="at">package =</span> <span class="st">&quot;scMINER&quot;</span>), <span class="at">visual_method =</span> <span class="st">&quot;umap&quot;</span>)</span>
+<div class="sourceCode" id="cb79"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb79-1"><a href="mi-based-clustering-analysis.html#cb79-1" tabindex="-1"></a>pbmc14k_log2cpm.eset <span class="ot">&lt;-</span> <span class="fu">addMICAoutput</span>(pbmc14k_log2cpm.eset, <span class="at">mica_output_file =</span> <span class="fu">system.file</span>(<span class="st">&quot;extdata/demo_pbmc14k/MICA/clustering_UMAP_euclidean_20_2.05.txt&quot;</span>, <span class="at">package =</span> <span class="st">&quot;scMINER&quot;</span>), <span class="at">visual_method =</span> <span class="st">&quot;umap&quot;</span>)</span>
 <span id="cb79-2"><a href="mi-based-clustering-analysis.html#cb79-2" tabindex="-1"></a><span class="fu">head</span>(<span class="fu">pData</span>(pbmc14k_log2cpm.eset))</span></code></pre></div>
 <pre><code>##                trueLabel_full trueLabel projectID nUMI nFeature    pctMito
 ## CACTTTGACGCAAT CD14+ Monocyte  Monocyte   PBMC14k  764      354 0.01832461