New Upstream Release - r-bioc-demixt

QA Page
Maintainer email: r-pkg-team@alioth-lists.debian.net
Automatic publish policy: main: push-derived , pristine-tar: push-derived , upstream: push-derived
Last processed: 2023-06-15T03:59 (took 7m5s)
Branch URL: https://salsa.debian.org/r-pkg-team/r-bioc-demixt.git -b master (taken from version 1.18.0+dfsg-1)
Ready changes

Summary

Merged new upstream version: 1.16.0+dfsg (was: 1.14.0+dfsg).
Diff

diff --git a/DESCRIPTION b/DESCRIPTION
index 4d7d7eb..5485bac 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -2,7 +2,7 @@ Package: DeMixT
 Title: Cell type-specific deconvolution of heterogeneous tumor samples
         with two or three components using expression data from RNAseq
         or microarray platforms
-Version: 1.14.0
+Version: 1.16.0
 Date: 2022-10-04
 Author: Zeya Wang <zw17.rice@gmail.com>, Shaolong
         Cao<scao@mdanderson.org>, Wenyi Wang <wwang7@@mdanderson.org>
@@ -21,9 +21,9 @@ biocViews: Software, StatisticalMethod, Classification, GeneExpression,
         Sequencing, Microarray, TissueMicroarray, Coverage
 License: GPL-3
 RoxygenNote: 7.1.2
-Packaged: 2022-11-01 21:20:59 UTC; biocbuild
+Packaged: 2023-04-25 20:51:43 UTC; biocbuild
 git_url: https://git.bioconductor.org/packages/DeMixT
-git_branch: RELEASE_3_16
-git_last_commit: 8da5af8
-git_last_commit_date: 2022-11-01
-Date/Publication: 2022-11-01
+git_branch: RELEASE_3_17
+git_last_commit: 419f21b
+git_last_commit_date: 2023-04-25
+Date/Publication: 2023-04-25
diff --git a/build/vignette.rds b/build/vignette.rds
index 2acbfa7..6a01fbc 100644
Binary files a/build/vignette.rds and b/build/vignette.rds differ
diff --git a/debian/changelog b/debian/changelog
index d0f6d23..732926d 100644
--- a/debian/changelog
+++ b/debian/changelog
@@ -1,3 +1,9 @@
+r-bioc-demixt (1.16.0+dfsg-1) UNRELEASED; urgency=low
+
+  * New upstream release.
+
+ -- Debian Janitor <janitor@jelmer.uk>  Thu, 15 Jun 2023 03:52:47 -0000
+
 r-bioc-demixt (1.14.0+dfsg-1) unstable; urgency=medium
 
   * Team upload.
diff --git a/inst/doc/demixt.html b/inst/doc/demixt.html
index 90293a1..7d059dc 100644
--- a/inst/doc/demixt.html
+++ b/inst/doc/demixt.html
@@ -14,6 +14,22 @@
 
 <title>A Vignette for DeMixT</title>
 
+<script>// Hide empty <a> tag within highlighted CodeBlock for screen reader accessibility (see https://github.com/jgm/pandoc/issues/6352#issuecomment-626106786) -->
+// v0.0.1
+// Written by JooYoung Seo (jooyoung@psu.edu) and Atsushi Yasumoto on June 1st, 2020.
+
+document.addEventListener('DOMContentLoaded', function() {
+  const codeList = document.getElementsByClassName("sourceCode");
+  for (var i = 0; i < codeList.length; i++) {
+    var linkList = codeList[i].getElementsByTagName('a');
+    for (var j = 0; j < linkList.length; j++) {
+      if (linkList[j].innerHTML === "") {
+        linkList[j].setAttribute('aria-hidden', 'true');
+      }
+    }
+  }
+});
+</script>
 
 <style type="text/css">
   code{white-space: pre-wrap;}
@@ -35,9 +51,9 @@
   }
 </style>
 <style type="text/css" data-origin="pandoc">
-a.sourceLine { display: inline-block; line-height: 1.25; }
-a.sourceLine { pointer-events: none; color: inherit; text-decoration: inherit; }
-a.sourceLine:empty { height: 1.2em; }
+code.sourceCode > span { display: inline-block; line-height: 1.25; }
+code.sourceCode > span { color: inherit; text-decoration: inherit; }
+code.sourceCode > span:empty { height: 1.2em; }
 .sourceCode { overflow: visible; }
 code.sourceCode { white-space: pre; position: relative; }
 div.sourceCode { margin: 1em 0; }
@@ -47,14 +63,16 @@ div.sourceCode { overflow: auto; }
 }
 @media print {
 code.sourceCode { white-space: pre-wrap; }
-a.sourceLine { text-indent: -1em; padding-left: 1em; }
-}
-pre.numberSource a.sourceLine
-  { position: relative; left: -4em; }
-pre.numberSource a.sourceLine::before
-  { content: attr(title);
+code.sourceCode > span { text-indent: -5em; padding-left: 5em; }
+}
+pre.numberSource code
+  { counter-reset: source-line 0; }
+pre.numberSource code > span
+  { position: relative; left: -4em; counter-increment: source-line; }
+pre.numberSource code > span > a:first-child::before
+  { content: counter(source-line);
     position: relative; left: -1em; text-align: right; vertical-align: baseline;
-    border: none; pointer-events: all; display: inline-block;
+    border: none; display: inline-block;
     -webkit-touch-callout: none; -webkit-user-select: none;
     -khtml-user-select: none; -moz-user-select: none;
     -ms-user-select: none; user-select: none;
@@ -63,9 +81,9 @@ pre.numberSource a.sourceLine::before
   }
 pre.numberSource { margin-left: 3em; border-left: 1px solid #aaaaaa;  padding-left: 4px; }
 div.sourceCode
-  {  }
+  {   }
 @media screen {
-a.sourceLine::before { text-decoration: underline; }
+code.sourceCode > span > a:first-child::before { text-decoration: underline; }
 }
 code span.al { color: #ff0000; font-weight: bold; } /* Alert */
 code span.an { color: #60a0b0; font-weight: bold; font-style: italic; } /* Annotation */
@@ -323,7 +341,7 @@ code > span.er { color: #a61717; background-color: #e3d2d2; }
 
 
 <h1 class="title toc-ignore">A Vignette for DeMixT</h1>
-<h4 class="date">Last updated: 2022-11-01</h4>
+<h4 class="date">Last updated: 2023-04-25</h4>
 
 
 
@@ -430,15 +448,15 @@ library(DeMixT)</code></pre>
 <h1>6. Examples</h1>
 <div id="simulated-two-component-data" class="section level2">
 <h2>6.1 Simulated two-component data</h2>
-<div class="sourceCode" id="cb4"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb4-1" title="1"><span class="kw">data</span>(<span class="st">&quot;test.data.2comp&quot;</span>)</a>
-<a class="sourceLine" id="cb4-2" title="2"><span class="co"># res.GS = DeMixT_GS(data.Y = test.data.2comp$data.Y, </span></a>
-<a class="sourceLine" id="cb4-3" title="3"><span class="co">#                     data.N1 = test.data.2comp$data.N1,</span></a>
-<a class="sourceLine" id="cb4-4" title="4"><span class="co">#                     niter = 30, nbin = 50, nspikein = 50,</span></a>
-<a class="sourceLine" id="cb4-5" title="5"><span class="co">#                     if.filter = TRUE, ngene.Profile.selected = 150,</span></a>
-<a class="sourceLine" id="cb4-6" title="6"><span class="co">#                     mean.diff.in.CM = 0.25, ngene.selected.for.pi = 150,</span></a>
-<a class="sourceLine" id="cb4-7" title="7"><span class="co">#                     tol = 10^(-5))</span></a>
-<a class="sourceLine" id="cb4-8" title="8"><span class="kw">load</span>(<span class="st">&#39;Res_2comp/res.GS.RData&#39;</span>)</a></code></pre></div>
-<div class="sourceCode" id="cb5"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb5-1" title="1"><span class="kw">head</span>(<span class="kw">t</span>(res.GS<span class="op">$</span>pi))</a></code></pre></div>
+<div class="sourceCode" id="cb4"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb4-1"><a href="#cb4-1"></a><span class="kw">data</span>(<span class="st">&quot;test.data.2comp&quot;</span>)</span>
+<span id="cb4-2"><a href="#cb4-2"></a><span class="co"># res.GS = DeMixT_GS(data.Y = test.data.2comp$data.Y, </span></span>
+<span id="cb4-3"><a href="#cb4-3"></a><span class="co">#                     data.N1 = test.data.2comp$data.N1,</span></span>
+<span id="cb4-4"><a href="#cb4-4"></a><span class="co">#                     niter = 30, nbin = 50, nspikein = 50,</span></span>
+<span id="cb4-5"><a href="#cb4-5"></a><span class="co">#                     if.filter = TRUE, ngene.Profile.selected = 150,</span></span>
+<span id="cb4-6"><a href="#cb4-6"></a><span class="co">#                     mean.diff.in.CM = 0.25, ngene.selected.for.pi = 150,</span></span>
+<span id="cb4-7"><a href="#cb4-7"></a><span class="co">#                     tol = 10^(-5))</span></span>
+<span id="cb4-8"><a href="#cb4-8"></a><span class="kw">load</span>(<span class="st">&#39;Res_2comp/res.GS.RData&#39;</span>)</span></code></pre></div>
+<div class="sourceCode" id="cb5"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb5-1"><a href="#cb5-1"></a><span class="kw">head</span>(<span class="kw">t</span>(res.GS<span class="op">$</span>pi))</span></code></pre></div>
 <pre><code>##               PiN1       PiT
 ## Sample 1 0.5955120 0.4044880
 ## Sample 2 0.2759014 0.7240986
@@ -446,30 +464,30 @@ library(DeMixT)</code></pre>
 ## Sample 4 0.4497041 0.5502959
 ## Sample 5 0.6516980 0.3483020
 ## Sample 6 0.4365191 0.5634809</code></pre>
-<div class="sourceCode" id="cb7"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb7-1" title="1"><span class="kw">head</span>(res.GS<span class="op">$</span>gene.name)</a></code></pre></div>
+<div class="sourceCode" id="cb7"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb7-1"><a href="#cb7-1"></a><span class="kw">head</span>(res.GS<span class="op">$</span>gene.name)</span></code></pre></div>
 <pre><code>## [1] &quot;Gene 418&quot; &quot;Gene 452&quot; &quot;Gene 421&quot; &quot;Gene 112&quot; &quot;Gene 154&quot; &quot;Gene 143&quot;</code></pre>
-<div class="sourceCode" id="cb9"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb9-1" title="1"><span class="kw">data</span>(<span class="st">&quot;test.data.2comp&quot;</span>)</a>
-<a class="sourceLine" id="cb9-2" title="2"><span class="co"># res.S2 &lt;- DeMixT_S2(data.Y = test.data.2comp$data.Y, </span></a>
-<a class="sourceLine" id="cb9-3" title="3"><span class="co">#                     data.N1 = test.data.2comp$data.N1,</span></a>
-<a class="sourceLine" id="cb9-4" title="4"><span class="co">#                     data.N2 = NULL, </span></a>
-<a class="sourceLine" id="cb9-5" title="5"><span class="co">#                     givenpi = c(t(res.S1$pi[-nrow(res.GS$pi),])), nbin = 50)</span></a>
-<a class="sourceLine" id="cb9-6" title="6"><span class="kw">load</span>(<span class="st">&#39;Res_2comp/res.S2.RData&#39;</span>)</a></code></pre></div>
-<div class="sourceCode" id="cb10"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb10-1" title="1"><span class="kw">head</span>(res.S2<span class="op">$</span>decovExprT[,<span class="dv">1</span><span class="op">:</span><span class="dv">5</span>],<span class="dv">3</span>)</a></code></pre></div>
+<div class="sourceCode" id="cb9"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb9-1"><a href="#cb9-1"></a><span class="kw">data</span>(<span class="st">&quot;test.data.2comp&quot;</span>)</span>
+<span id="cb9-2"><a href="#cb9-2"></a><span class="co"># res.S2 &lt;- DeMixT_S2(data.Y = test.data.2comp$data.Y, </span></span>
+<span id="cb9-3"><a href="#cb9-3"></a><span class="co">#                     data.N1 = test.data.2comp$data.N1,</span></span>
+<span id="cb9-4"><a href="#cb9-4"></a><span class="co">#                     data.N2 = NULL, </span></span>
+<span id="cb9-5"><a href="#cb9-5"></a><span class="co">#                     givenpi = c(t(res.S1$pi[-nrow(res.GS$pi),])), nbin = 50)</span></span>
+<span id="cb9-6"><a href="#cb9-6"></a><span class="kw">load</span>(<span class="st">&#39;Res_2comp/res.S2.RData&#39;</span>)</span></code></pre></div>
+<div class="sourceCode" id="cb10"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb10-1"><a href="#cb10-1"></a><span class="kw">head</span>(res.S2<span class="op">$</span>decovExprT[,<span class="dv">1</span><span class="op">:</span><span class="dv">5</span>],<span class="dv">3</span>)</span></code></pre></div>
 <pre><code>##         Sample 1   Sample 2   Sample 3  Sample 4   Sample 5
 ## Gene 1 18.857446  60.727041 159.878946 92.031635  40.873852
 ## Gene 2  2.322481   3.390938   2.406093  2.558962   2.438189
 ## Gene 3 48.843631 208.166410  66.986239 38.107580 460.556751</code></pre>
-<div class="sourceCode" id="cb12"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb12-1" title="1"><span class="kw">head</span>(res.S2<span class="op">$</span>decovExprN1[,<span class="dv">1</span><span class="op">:</span><span class="dv">5</span>],<span class="dv">3</span>)</a></code></pre></div>
+<div class="sourceCode" id="cb12"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb12-1"><a href="#cb12-1"></a><span class="kw">head</span>(res.S2<span class="op">$</span>decovExprN1[,<span class="dv">1</span><span class="op">:</span><span class="dv">5</span>],<span class="dv">3</span>)</span></code></pre></div>
 <pre><code>##         Sample 1  Sample 2 Sample 3  Sample 4  Sample 5
 ## Gene 1  59.37087  71.80492  74.1755  73.55878  72.96267
 ## Gene 2 107.66874 131.20005 113.6376 120.35924 125.28224
 ## Gene 3 513.43184 669.79145 613.3042 491.09308 741.76507</code></pre>
-<div class="sourceCode" id="cb14"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb14-1" title="1"><span class="kw">head</span>(res.S2<span class="op">$</span>decovMu,<span class="dv">3</span>)</a></code></pre></div>
+<div class="sourceCode" id="cb14"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb14-1"><a href="#cb14-1"></a><span class="kw">head</span>(res.S2<span class="op">$</span>decovMu,<span class="dv">3</span>)</span></code></pre></div>
 <pre><code>##            MuN1      MuT
 ## Gene 1 6.166484 5.924321
 ## Gene 2 6.677594 2.974551
 ## Gene 3 9.329628 7.396647</code></pre>
-<div class="sourceCode" id="cb16"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb16-1" title="1"><span class="kw">head</span>(res.S2<span class="op">$</span>decovSigma,<span class="dv">3</span>)</a></code></pre></div>
+<div class="sourceCode" id="cb16"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb16-1"><a href="#cb16-1"></a><span class="kw">head</span>(res.S2<span class="op">$</span>decovSigma,<span class="dv">3</span>)</span></code></pre></div>
 <pre><code>##           SigmaN   SigmaT
 ## Gene 1 0.2222914 1.127726
 ## Gene 2 0.2319681 1.614169
@@ -478,149 +496,149 @@ library(DeMixT)</code></pre>
 <div id="simulated-two-component-data-1" class="section level2">
 <h2>6.2 Simulated two-component data</h2>
 <p>In the simulation,</p>
-<div class="sourceCode" id="cb18"><pre class="sourceCode markdown"><code class="sourceCode markdown"><a class="sourceLine" id="cb18-1" title="1"><span class="fu">## Simulate MuN and MuT for each gene</span></a>
-<a class="sourceLine" id="cb18-2" title="2">  MuN &lt;- rnorm(G, 7, 1.5)</a>
-<a class="sourceLine" id="cb18-3" title="3">  MuT &lt;- rnorm(G, 7, 1.5)</a>
-<a class="sourceLine" id="cb18-4" title="4">  Mu &lt;- cbind(MuN, MuT)</a>
-<a class="sourceLine" id="cb18-5" title="5"><span class="fu">## Simulate SigmaN and SigmaT for each gene</span></a>
-<a class="sourceLine" id="cb18-6" title="6">  SigmaN &lt;- runif(n = G, min = 0.1, max = 0.8)</a>
-<a class="sourceLine" id="cb18-7" title="7">  SigmaT &lt;- runif(n = G, min = 0.1, max = 0.8)</a>
-<a class="sourceLine" id="cb18-8" title="8"><span class="fu">## Simulate Tumor Proportion</span></a>
-<a class="sourceLine" id="cb18-9" title="9">  PiT = truncdist::rtrunc(n = My,</a>
-<a class="sourceLine" id="cb18-10" title="10"><span class="bn">                          spec = &#39;norm&#39;, </span></a>
-<a class="sourceLine" id="cb18-11" title="11"><span class="bn">                          mean = 0.55,</span></a>
-<a class="sourceLine" id="cb18-12" title="12"><span class="bn">                          sd = 0.2,</span></a>
-<a class="sourceLine" id="cb18-13" title="13"><span class="bn">                          a = 0.25,</span></a>
-<a class="sourceLine" id="cb18-14" title="14"><span class="bn">                          b = 0.95)</span></a>
-<a class="sourceLine" id="cb18-15" title="15"></a>
-<a class="sourceLine" id="cb18-16" title="16"><span class="fu">## Simulate Data</span></a>
-<a class="sourceLine" id="cb18-17" title="17">  for(k in 1:G){</a>
-<a class="sourceLine" id="cb18-18" title="18"><span class="bn">    </span></a>
-<a class="sourceLine" id="cb18-19" title="19"><span class="bn">    data.N1[k,] &lt;- 2^rnorm(M1, MuN[k], SigmaN[k]); # normal reference</span></a>
-<a class="sourceLine" id="cb18-20" title="20"><span class="bn">    </span></a>
-<a class="sourceLine" id="cb18-21" title="21"><span class="bn">    True.data.T[k,] &lt;- 2^rnorm(My, MuT[k], SigmaT[k]);  # True Tumor</span></a>
-<a class="sourceLine" id="cb18-22" title="22"><span class="bn">    </span></a>
-<a class="sourceLine" id="cb18-23" title="23"><span class="bn">    True.data.N1[k,] &lt;- 2^rnorm(My, MuN[k], SigmaN[k]);  # True Normal</span></a>
-<a class="sourceLine" id="cb18-24" title="24"><span class="bn">    </span></a>
-<a class="sourceLine" id="cb18-25" title="25"><span class="bn">    data.Y[k,] &lt;- pi[1,]*True.data.N1[k,] + pi[2,]*True.data.T[k,] # Mixture Tumor</span></a>
-<a class="sourceLine" id="cb18-26" title="26"><span class="bn">    </span></a>
-<a class="sourceLine" id="cb18-27" title="27">  }</a></code></pre></div>
+<div class="sourceCode" id="cb18"><pre class="sourceCode markdown"><code class="sourceCode markdown"><span id="cb18-1"><a href="#cb18-1"></a><span class="fu">## Simulate MuN and MuT for each gene</span></span>
+<span id="cb18-2"><a href="#cb18-2"></a>  MuN &lt;- rnorm(G, 7, 1.5)</span>
+<span id="cb18-3"><a href="#cb18-3"></a>  MuT &lt;- rnorm(G, 7, 1.5)</span>
+<span id="cb18-4"><a href="#cb18-4"></a>  Mu &lt;- cbind(MuN, MuT)</span>
+<span id="cb18-5"><a href="#cb18-5"></a><span class="fu">## Simulate SigmaN and SigmaT for each gene</span></span>
+<span id="cb18-6"><a href="#cb18-6"></a>  SigmaN &lt;- runif(n = G, min = 0.1, max = 0.8)</span>
+<span id="cb18-7"><a href="#cb18-7"></a>  SigmaT &lt;- runif(n = G, min = 0.1, max = 0.8)</span>
+<span id="cb18-8"><a href="#cb18-8"></a><span class="fu">## Simulate Tumor Proportion</span></span>
+<span id="cb18-9"><a href="#cb18-9"></a>  PiT = truncdist::rtrunc(n = My,</span>
+<span id="cb18-10"><a href="#cb18-10"></a><span class="bn">                          spec = &#39;norm&#39;, </span></span>
+<span id="cb18-11"><a href="#cb18-11"></a><span class="bn">                          mean = 0.55,</span></span>
+<span id="cb18-12"><a href="#cb18-12"></a><span class="bn">                          sd = 0.2,</span></span>
+<span id="cb18-13"><a href="#cb18-13"></a><span class="bn">                          a = 0.25,</span></span>
+<span id="cb18-14"><a href="#cb18-14"></a><span class="bn">                          b = 0.95)</span></span>
+<span id="cb18-15"><a href="#cb18-15"></a></span>
+<span id="cb18-16"><a href="#cb18-16"></a><span class="fu">## Simulate Data</span></span>
+<span id="cb18-17"><a href="#cb18-17"></a>  for(k in 1:G){</span>
+<span id="cb18-18"><a href="#cb18-18"></a><span class="bn">    </span></span>
+<span id="cb18-19"><a href="#cb18-19"></a><span class="bn">    data.N1[k,] &lt;- 2^rnorm(M1, MuN[k], SigmaN[k]); # normal reference</span></span>
+<span id="cb18-20"><a href="#cb18-20"></a><span class="bn">    </span></span>
+<span id="cb18-21"><a href="#cb18-21"></a><span class="bn">    True.data.T[k,] &lt;- 2^rnorm(My, MuT[k], SigmaT[k]);  # True Tumor</span></span>
+<span id="cb18-22"><a href="#cb18-22"></a><span class="bn">    </span></span>
+<span id="cb18-23"><a href="#cb18-23"></a><span class="bn">    True.data.N1[k,] &lt;- 2^rnorm(My, MuN[k], SigmaN[k]);  # True Normal</span></span>
+<span id="cb18-24"><a href="#cb18-24"></a><span class="bn">    </span></span>
+<span id="cb18-25"><a href="#cb18-25"></a><span class="bn">    data.Y[k,] &lt;- pi[1,]*True.data.N1[k,] + pi[2,]*True.data.T[k,] # Mixture Tumor</span></span>
+<span id="cb18-26"><a href="#cb18-26"></a><span class="bn">    </span></span>
+<span id="cb18-27"><a href="#cb18-27"></a>  }</span></code></pre></div>
 <p>where <span class="math inline">\(\pi_i \in (0.25, 0.95)\)</span> is from truncated normal distribution. In general, the true distribution of tumor proportion does not follow a uniform distribution between <span class="math inline">\([0,1]\)</span>, but instead skewed to the upper part of the interval.</p>
-<div class="sourceCode" id="cb19"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb19-1" title="1"><span class="co"># ## DeMixT_DE without Spike-in Normal</span></a>
-<a class="sourceLine" id="cb19-2" title="2"><span class="co"># res.S1 = DeMixT_DE(data.Y = test.data.2comp$data.Y, </span></a>
-<a class="sourceLine" id="cb19-3" title="3"><span class="co">#                    data.N1 = test.data.2comp$data.N1,</span></a>
-<a class="sourceLine" id="cb19-4" title="4"><span class="co">#                    niter = 30, nbin = 50, nspikein = 0,</span></a>
-<a class="sourceLine" id="cb19-5" title="5"><span class="co">#                    if.filter = TRUE, </span></a>
-<a class="sourceLine" id="cb19-6" title="6"><span class="co">#                    mean.diff.in.CM = 0.25, ngene.selected.for.pi = 150,</span></a>
-<a class="sourceLine" id="cb19-7" title="7"><span class="co">#                    tol = 10^(-5))</span></a>
-<a class="sourceLine" id="cb19-8" title="8"><span class="co"># ## DeMixT_DE with Spike-in Normal</span></a>
-<a class="sourceLine" id="cb19-9" title="9"><span class="co"># res.S1.SP = DeMixT_DE(data.Y = test.data.2comp$data.Y, </span></a>
-<a class="sourceLine" id="cb19-10" title="10"><span class="co">#                      data.N1 = test.data.2comp$data.N1,</span></a>
-<a class="sourceLine" id="cb19-11" title="11"><span class="co">#                      niter = 30, nbin = 50, nspikein = 50,</span></a>
-<a class="sourceLine" id="cb19-12" title="12"><span class="co">#                      if.filter = TRUE, </span></a>
-<a class="sourceLine" id="cb19-13" title="13"><span class="co">#                      mean.diff.in.CM = 0.25, ngene.selected.for.pi = 150,</span></a>
-<a class="sourceLine" id="cb19-14" title="14"><span class="co">#                      tol = 10^(-5))</span></a>
-<a class="sourceLine" id="cb19-15" title="15"><span class="co"># ## DeMixT_GS with Spike-in Normal</span></a>
-<a class="sourceLine" id="cb19-16" title="16"><span class="co"># res.GS.SP = DeMixT_GS(data.Y = test.data.2comp$data.Y,</span></a>
-<a class="sourceLine" id="cb19-17" title="17"><span class="co">#                      data.N1 = test.data.2comp$data.N1,</span></a>
-<a class="sourceLine" id="cb19-18" title="18"><span class="co">#                      niter = 30, nbin = 50, nspikein = 50,</span></a>
-<a class="sourceLine" id="cb19-19" title="19"><span class="co">#                      if.filter = TRUE, ngene.Profile.selected = 150,</span></a>
-<a class="sourceLine" id="cb19-20" title="20"><span class="co">#                      mean.diff.in.CM = 0.25, ngene.selected.for.pi = 150,</span></a>
-<a class="sourceLine" id="cb19-21" title="21"><span class="co">#                      tol = 10^(-5))</span></a>
-<a class="sourceLine" id="cb19-22" title="22"><span class="kw">load</span>(<span class="st">&#39;Res_2comp/res.S1.RData&#39;</span>); <span class="kw">load</span>(<span class="st">&#39;Res_2comp/res.S1.SP.RData&#39;</span>); </a>
-<a class="sourceLine" id="cb19-23" title="23"><span class="kw">load</span>(<span class="st">&#39;Res_2comp/res.GS.RData&#39;</span>); <span class="kw">load</span>(<span class="st">&#39;Res_2comp/res.GS.SP.RData&#39;</span>); </a></code></pre></div>
+<div class="sourceCode" id="cb19"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb19-1"><a href="#cb19-1"></a><span class="co"># ## DeMixT_DE without Spike-in Normal</span></span>
+<span id="cb19-2"><a href="#cb19-2"></a><span class="co"># res.S1 = DeMixT_DE(data.Y = test.data.2comp$data.Y, </span></span>
+<span id="cb19-3"><a href="#cb19-3"></a><span class="co">#                    data.N1 = test.data.2comp$data.N1,</span></span>
+<span id="cb19-4"><a href="#cb19-4"></a><span class="co">#                    niter = 30, nbin = 50, nspikein = 0,</span></span>
+<span id="cb19-5"><a href="#cb19-5"></a><span class="co">#                    if.filter = TRUE, </span></span>
+<span id="cb19-6"><a href="#cb19-6"></a><span class="co">#                    mean.diff.in.CM = 0.25, ngene.selected.for.pi = 150,</span></span>
+<span id="cb19-7"><a href="#cb19-7"></a><span class="co">#                    tol = 10^(-5))</span></span>
+<span id="cb19-8"><a href="#cb19-8"></a><span class="co"># ## DeMixT_DE with Spike-in Normal</span></span>
+<span id="cb19-9"><a href="#cb19-9"></a><span class="co"># res.S1.SP = DeMixT_DE(data.Y = test.data.2comp$data.Y, </span></span>
+<span id="cb19-10"><a href="#cb19-10"></a><span class="co">#                      data.N1 = test.data.2comp$data.N1,</span></span>
+<span id="cb19-11"><a href="#cb19-11"></a><span class="co">#                      niter = 30, nbin = 50, nspikein = 50,</span></span>
+<span id="cb19-12"><a href="#cb19-12"></a><span class="co">#                      if.filter = TRUE, </span></span>
+<span id="cb19-13"><a href="#cb19-13"></a><span class="co">#                      mean.diff.in.CM = 0.25, ngene.selected.for.pi = 150,</span></span>
+<span id="cb19-14"><a href="#cb19-14"></a><span class="co">#                      tol = 10^(-5))</span></span>
+<span id="cb19-15"><a href="#cb19-15"></a><span class="co"># ## DeMixT_GS with Spike-in Normal</span></span>
+<span id="cb19-16"><a href="#cb19-16"></a><span class="co"># res.GS.SP = DeMixT_GS(data.Y = test.data.2comp$data.Y,</span></span>
+<span id="cb19-17"><a href="#cb19-17"></a><span class="co">#                      data.N1 = test.data.2comp$data.N1,</span></span>
+<span id="cb19-18"><a href="#cb19-18"></a><span class="co">#                      niter = 30, nbin = 50, nspikein = 50,</span></span>
+<span id="cb19-19"><a href="#cb19-19"></a><span class="co">#                      if.filter = TRUE, ngene.Profile.selected = 150,</span></span>
+<span id="cb19-20"><a href="#cb19-20"></a><span class="co">#                      mean.diff.in.CM = 0.25, ngene.selected.for.pi = 150,</span></span>
+<span id="cb19-21"><a href="#cb19-21"></a><span class="co">#                      tol = 10^(-5))</span></span>
+<span id="cb19-22"><a href="#cb19-22"></a><span class="kw">load</span>(<span class="st">&#39;Res_2comp/res.S1.RData&#39;</span>); <span class="kw">load</span>(<span class="st">&#39;Res_2comp/res.S1.SP.RData&#39;</span>); </span>
+<span id="cb19-23"><a href="#cb19-23"></a><span class="kw">load</span>(<span class="st">&#39;Res_2comp/res.GS.RData&#39;</span>); <span class="kw">load</span>(<span class="st">&#39;Res_2comp/res.GS.SP.RData&#39;</span>); </span></code></pre></div>
 <p>This simulation was designed to compare previous DeMixT resutls with DeMixT spike-in results under both gene selection method.</p>
-<div class="sourceCode" id="cb20"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb20-1" title="1">res<span class="fl">.2</span>comp =<span class="st"> </span><span class="kw">as.data.frame</span>(<span class="kw">cbind</span>(<span class="kw">round</span>(<span class="kw">rep</span>(<span class="kw">t</span>(test.data<span class="fl">.2</span>comp<span class="op">$</span>pi[<span class="dv">2</span>,]),<span class="dv">3</span>),<span class="dv">2</span>), </a>
-<a class="sourceLine" id="cb20-2" title="2">                                <span class="kw">round</span>(<span class="kw">c</span>(<span class="kw">t</span>(res.S1<span class="op">$</span>pi[<span class="dv">2</span>,]),<span class="kw">t</span>(res.S1.SP<span class="op">$</span>pi[<span class="dv">2</span>,]), <span class="kw">t</span>(res.GS.SP<span class="op">$</span>pi[<span class="dv">2</span>,])),<span class="dv">2</span>),</a>
-<a class="sourceLine" id="cb20-3" title="3">                                <span class="kw">rep</span>(<span class="kw">c</span>(<span class="st">&#39;DE&#39;</span>,<span class="st">&#39;DE-SP&#39;</span>,<span class="st">&#39;GS-SP&#39;</span>), <span class="dt">each =</span> <span class="dv">100</span>)), <span class="dt">num =</span> <span class="dv">1</span><span class="op">:</span><span class="dv">2</span>)</a>
-<a class="sourceLine" id="cb20-4" title="4">res<span class="fl">.2</span>comp<span class="op">$</span>V1 &lt;-<span class="st"> </span><span class="kw">as.numeric</span>(<span class="kw">as.character</span>(res<span class="fl">.2</span>comp<span class="op">$</span>V1))</a>
-<a class="sourceLine" id="cb20-5" title="5">res<span class="fl">.2</span>comp<span class="op">$</span>V2 &lt;-<span class="st"> </span><span class="kw">as.numeric</span>(<span class="kw">as.character</span>(res<span class="fl">.2</span>comp<span class="op">$</span>V2))</a>
-<a class="sourceLine" id="cb20-6" title="6">res<span class="fl">.2</span>comp<span class="op">$</span>V3 =<span class="st"> </span><span class="kw">as.factor</span>(res<span class="fl">.2</span>comp<span class="op">$</span>V3)</a>
-<a class="sourceLine" id="cb20-7" title="7"><span class="kw">names</span>(res<span class="fl">.2</span>comp) =<span class="st"> </span><span class="kw">c</span>(<span class="st">&#39;True.Proportion&#39;</span>, <span class="st">&#39;Estimated.Proportion&#39;</span>, <span class="st">&#39;Method&#39;</span>)</a>
-<a class="sourceLine" id="cb20-8" title="8"><span class="co">## Plot</span></a>
-<a class="sourceLine" id="cb20-9" title="9"><span class="kw">ggplot</span>(res<span class="fl">.2</span>comp, <span class="kw">aes</span>(<span class="dt">x=</span>True.Proportion, <span class="dt">y=</span>Estimated.Proportion, <span class="dt">group =</span> Method, <span class="dt">color=</span>Method, <span class="dt">shape=</span>Method)) <span class="op">+</span></a>
-<a class="sourceLine" id="cb20-10" title="10"><span class="st">  </span><span class="kw">geom_point</span>() <span class="op">+</span><span class="st"> </span></a>
-<a class="sourceLine" id="cb20-11" title="11"><span class="st">  </span><span class="kw">geom_abline</span>(<span class="dt">intercept =</span> <span class="dv">0</span>, <span class="dt">slope =</span> <span class="dv">1</span>, <span class="dt">linetype =</span> <span class="st">&quot;dashed&quot;</span>, <span class="dt">color =</span> <span class="st">&quot;black&quot;</span>, <span class="dt">lwd =</span> <span class="fl">0.5</span>) <span class="op">+</span></a>
-<a class="sourceLine" id="cb20-12" title="12"><span class="st">  </span><span class="kw">xlim</span>(<span class="dv">0</span>,<span class="dv">1</span>) <span class="op">+</span><span class="st"> </span><span class="kw">ylim</span>(<span class="dv">0</span>,<span class="dv">1</span>)  <span class="op">+</span></a>
-<a class="sourceLine" id="cb20-13" title="13"><span class="st">  </span><span class="kw">scale_shape_manual</span>(<span class="dt">values=</span><span class="kw">c</span>(<span class="kw">seq</span>(<span class="dv">1</span><span class="op">:</span><span class="dv">3</span>))) <span class="op">+</span></a>
-<a class="sourceLine" id="cb20-14" title="14"><span class="st">  </span><span class="kw">labs</span>(<span class="dt">x =</span> <span class="st">&#39;True Proportion&#39;</span>, <span class="dt">y =</span> <span class="st">&#39;Estimated Proportion&#39;</span>)  </a></code></pre></div>
+<div class="sourceCode" id="cb20"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb20-1"><a href="#cb20-1"></a>res<span class="fl">.2</span>comp =<span class="st"> </span><span class="kw">as.data.frame</span>(<span class="kw">cbind</span>(<span class="kw">round</span>(<span class="kw">rep</span>(<span class="kw">t</span>(test.data<span class="fl">.2</span>comp<span class="op">$</span>pi[<span class="dv">2</span>,]),<span class="dv">3</span>),<span class="dv">2</span>), </span>
+<span id="cb20-2"><a href="#cb20-2"></a>                                <span class="kw">round</span>(<span class="kw">c</span>(<span class="kw">t</span>(res.S1<span class="op">$</span>pi[<span class="dv">2</span>,]),<span class="kw">t</span>(res.S1.SP<span class="op">$</span>pi[<span class="dv">2</span>,]), <span class="kw">t</span>(res.GS.SP<span class="op">$</span>pi[<span class="dv">2</span>,])),<span class="dv">2</span>),</span>
+<span id="cb20-3"><a href="#cb20-3"></a>                                <span class="kw">rep</span>(<span class="kw">c</span>(<span class="st">&#39;DE&#39;</span>,<span class="st">&#39;DE-SP&#39;</span>,<span class="st">&#39;GS-SP&#39;</span>), <span class="dt">each =</span> <span class="dv">100</span>)), <span class="dt">num =</span> <span class="dv">1</span><span class="op">:</span><span class="dv">2</span>)</span>
+<span id="cb20-4"><a href="#cb20-4"></a>res<span class="fl">.2</span>comp<span class="op">$</span>V1 &lt;-<span class="st"> </span><span class="kw">as.numeric</span>(<span class="kw">as.character</span>(res<span class="fl">.2</span>comp<span class="op">$</span>V1))</span>
+<span id="cb20-5"><a href="#cb20-5"></a>res<span class="fl">.2</span>comp<span class="op">$</span>V2 &lt;-<span class="st"> </span><span class="kw">as.numeric</span>(<span class="kw">as.character</span>(res<span class="fl">.2</span>comp<span class="op">$</span>V2))</span>
+<span id="cb20-6"><a href="#cb20-6"></a>res<span class="fl">.2</span>comp<span class="op">$</span>V3 =<span class="st"> </span><span class="kw">as.factor</span>(res<span class="fl">.2</span>comp<span class="op">$</span>V3)</span>
+<span id="cb20-7"><a href="#cb20-7"></a><span class="kw">names</span>(res<span class="fl">.2</span>comp) =<span class="st"> </span><span class="kw">c</span>(<span class="st">&#39;True.Proportion&#39;</span>, <span class="st">&#39;Estimated.Proportion&#39;</span>, <span class="st">&#39;Method&#39;</span>)</span>
+<span id="cb20-8"><a href="#cb20-8"></a><span class="co">## Plot</span></span>
+<span id="cb20-9"><a href="#cb20-9"></a><span class="kw">ggplot</span>(res<span class="fl">.2</span>comp, <span class="kw">aes</span>(<span class="dt">x=</span>True.Proportion, <span class="dt">y=</span>Estimated.Proportion, <span class="dt">group =</span> Method, <span class="dt">color=</span>Method, <span class="dt">shape=</span>Method)) <span class="op">+</span></span>
+<span id="cb20-10"><a href="#cb20-10"></a><span class="st">  </span><span class="kw">geom_point</span>() <span class="op">+</span><span class="st"> </span></span>
+<span id="cb20-11"><a href="#cb20-11"></a><span class="st">  </span><span class="kw">geom_abline</span>(<span class="dt">intercept =</span> <span class="dv">0</span>, <span class="dt">slope =</span> <span class="dv">1</span>, <span class="dt">linetype =</span> <span class="st">&quot;dashed&quot;</span>, <span class="dt">color =</span> <span class="st">&quot;black&quot;</span>, <span class="dt">lwd =</span> <span class="fl">0.5</span>) <span class="op">+</span></span>
+<span id="cb20-12"><a href="#cb20-12"></a><span class="st">  </span><span class="kw">xlim</span>(<span class="dv">0</span>,<span class="dv">1</span>) <span class="op">+</span><span class="st"> </span><span class="kw">ylim</span>(<span class="dv">0</span>,<span class="dv">1</span>)  <span class="op">+</span></span>
+<span id="cb20-13"><a href="#cb20-13"></a><span class="st">  </span><span class="kw">scale_shape_manual</span>(<span class="dt">values=</span><span class="kw">c</span>(<span class="kw">seq</span>(<span class="dv">1</span><span class="op">:</span><span class="dv">3</span>))) <span class="op">+</span></span>
+<span id="cb20-14"><a href="#cb20-14"></a><span class="st">  </span><span class="kw">labs</span>(<span class="dt">x =</span> <span class="st">&#39;True Proportion&#39;</span>, <span class="dt">y =</span> <span class="st">&#39;Estimated Proportion&#39;</span>)  </span></code></pre></div>
 <p><img src="data:image/png;base64,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" style="display: block; margin: auto;" /></p>
 </div>
 <div id="simulated-three-component-data" class="section level2">
 <h2>6.3 Simulated three-component data</h2>
 <p>In this simulation,</p>
-<div class="sourceCode" id="cb21"><pre class="sourceCode markdown"><code class="sourceCode markdown"><a class="sourceLine" id="cb21-1" title="1">G &lt;- G1 + G2</a>
-<a class="sourceLine" id="cb21-2" title="2"><span class="fu">## Simulate MuN1, MuN2 and MuT for each gene</span></a>
-<a class="sourceLine" id="cb21-3" title="3">  MuN1 &lt;- rnorm(G, 7, 1.5)</a>
-<a class="sourceLine" id="cb21-4" title="4">  MuN2_1st &lt;- MuN1[1:G1] + truncdist::rtrunc(n = 1, </a>
-<a class="sourceLine" id="cb21-5" title="5"><span class="bn">                                             spec = &#39;norm&#39;,</span></a>
-<a class="sourceLine" id="cb21-6" title="6"><span class="bn">                                             mean = 0,</span></a>
-<a class="sourceLine" id="cb21-7" title="7"><span class="bn">                                             sd = 1.5,</span></a>
-<a class="sourceLine" id="cb21-8" title="8"><span class="bn">                                             a = -0.1, </span></a>
-<a class="sourceLine" id="cb21-9" title="9"><span class="bn">                                             b = 0.1)</span></a>
-<a class="sourceLine" id="cb21-10" title="10">  MuN2_2nd &lt;- c()</a>
-<a class="sourceLine" id="cb21-11" title="11">  for(l in (G1+1):G){</a>
-<a class="sourceLine" id="cb21-12" title="12"><span class="bn">    tmp &lt;- MuN1[l] + truncdist::rtrunc(n = 1, </span></a>
-<a class="sourceLine" id="cb21-13" title="13"><span class="bn">                                       spec = &#39;norm&#39;,</span></a>
-<a class="sourceLine" id="cb21-14" title="14"><span class="bn">                                       mean = 0,</span></a>
-<a class="sourceLine" id="cb21-15" title="15"><span class="bn">                                       sd = 1.5,</span></a>
-<a class="sourceLine" id="cb21-16" title="16"><span class="bn">                                       a = 0.1, </span></a>
-<a class="sourceLine" id="cb21-17" title="17"><span class="bn">                                       b = 3)^rbinom(1, size=1, prob=0.5)</span></a>
-<a class="sourceLine" id="cb21-18" title="18"><span class="bn">    while(tmp &lt;= 0) tmp &lt;- MuN1[l] + truncdist::rtrunc(n = 1, </span></a>
-<a class="sourceLine" id="cb21-19" title="19"><span class="bn">                                                       spec = &#39;norm&#39;,</span></a>
-<a class="sourceLine" id="cb21-20" title="20"><span class="bn">                                                       mean = 0,</span></a>
-<a class="sourceLine" id="cb21-21" title="21"><span class="bn">                                                       sd = 1.5,</span></a>
-<a class="sourceLine" id="cb21-22" title="22"><span class="bn">                                                       a = 0.1, </span></a>
-<a class="sourceLine" id="cb21-23" title="23"><span class="bn">                                                       b = 3)^rbinom(1, size=1, prob=0.5)</span></a>
-<a class="sourceLine" id="cb21-24" title="24"><span class="bn">    MuN2_2nd &lt;- c(MuN2_2nd, tmp)</span></a>
-<a class="sourceLine" id="cb21-25" title="25">  }</a>
-<a class="sourceLine" id="cb21-26" title="26"><span class="fu">## Simulate SigmaN1, SigmaN2 and SigmaT for each gene</span></a>
-<a class="sourceLine" id="cb21-27" title="27">  SigmaN1 &lt;- runif(n = G, min = 0.1, max = 0.8)</a>
-<a class="sourceLine" id="cb21-28" title="28">  SigmaN2 &lt;- runif(n = G, min = 0.1, max = 0.8)</a>
-<a class="sourceLine" id="cb21-29" title="29">  SigmaT &lt;- runif(n = G, min = 0.1, max = 0.8)</a>
-<a class="sourceLine" id="cb21-30" title="30"><span class="fu">## Simulate Tumor Proportion</span></a>
-<a class="sourceLine" id="cb21-31" title="31">  pi &lt;- matrix(0, 3, My)</a>
-<a class="sourceLine" id="cb21-32" title="32">  pi[1,] &lt;- runif(n = My, min = 0.01, max = 0.97)</a>
-<a class="sourceLine" id="cb21-33" title="33">  for(j in 1:My){</a>
-<a class="sourceLine" id="cb21-34" title="34"><span class="bn">    pi[2, j] &lt;- runif(n = 1, min = 0.01, max = 0.98 - pi[1,j])</span></a>
-<a class="sourceLine" id="cb21-35" title="35"><span class="bn">    pi[3, j] &lt;- 1 - sum(pi[,j])</span></a>
-<a class="sourceLine" id="cb21-36" title="36">  }</a>
-<a class="sourceLine" id="cb21-37" title="37"><span class="fu">## Simulate Data</span></a>
-<a class="sourceLine" id="cb21-38" title="38">  for(k in 1:G){</a>
-<a class="sourceLine" id="cb21-39" title="39"><span class="bn">    </span></a>
-<a class="sourceLine" id="cb21-40" title="40"><span class="bn">    data.N1[k,] &lt;- 2^rnorm(M1, MuN1[k], SigmaN1[k]); # normal reference 1</span></a>
-<a class="sourceLine" id="cb21-41" title="41"><span class="bn">    </span></a>
-<a class="sourceLine" id="cb21-42" title="42"><span class="bn">    data.N2[k,] &lt;- 2^rnorm(M2, MuN2[k], SigmaN2[k]); # normal reference 1</span></a>
-<a class="sourceLine" id="cb21-43" title="43"><span class="bn">    </span></a>
-<a class="sourceLine" id="cb21-44" title="44"><span class="bn">    True.data.T[k,] &lt;- 2^rnorm(My, MuT[k], SigmaT[k]);  # True Tumor</span></a>
-<a class="sourceLine" id="cb21-45" title="45"><span class="bn">    </span></a>
-<a class="sourceLine" id="cb21-46" title="46"><span class="bn">    True.data.N1[k,] &lt;- 2^rnorm(My, MuN1[k], SigmaN1[k]);  # True Normal 1</span></a>
-<a class="sourceLine" id="cb21-47" title="47"><span class="bn">    </span></a>
-<a class="sourceLine" id="cb21-48" title="48"><span class="bn">    True.data.N2[k,] &lt;- 2^rnorm(My, MuN2[k], SigmaN2[k]);  # True Normal 1</span></a>
-<a class="sourceLine" id="cb21-49" title="49"><span class="bn">    </span></a>
-<a class="sourceLine" id="cb21-50" title="50"><span class="bn">    data.Y[k,] &lt;- pi[1,]*True.data.N1[k,] + pi[2,]*True.data.N2[k,] +</span></a>
-<a class="sourceLine" id="cb21-51" title="51"><span class="bn">      pi[3,]*True.data.T[k,] # Mixture Tumor</span></a>
-<a class="sourceLine" id="cb21-52" title="52"><span class="bn">    </span></a>
-<a class="sourceLine" id="cb21-53" title="53">  }</a></code></pre></div>
+<div class="sourceCode" id="cb21"><pre class="sourceCode markdown"><code class="sourceCode markdown"><span id="cb21-1"><a href="#cb21-1"></a>G &lt;- G1 + G2</span>
+<span id="cb21-2"><a href="#cb21-2"></a><span class="fu">## Simulate MuN1, MuN2 and MuT for each gene</span></span>
+<span id="cb21-3"><a href="#cb21-3"></a>  MuN1 &lt;- rnorm(G, 7, 1.5)</span>
+<span id="cb21-4"><a href="#cb21-4"></a>  MuN2_1st &lt;- MuN1[1:G1] + truncdist::rtrunc(n = 1, </span>
+<span id="cb21-5"><a href="#cb21-5"></a><span class="bn">                                             spec = &#39;norm&#39;,</span></span>
+<span id="cb21-6"><a href="#cb21-6"></a><span class="bn">                                             mean = 0,</span></span>
+<span id="cb21-7"><a href="#cb21-7"></a><span class="bn">                                             sd = 1.5,</span></span>
+<span id="cb21-8"><a href="#cb21-8"></a><span class="bn">                                             a = -0.1, </span></span>
+<span id="cb21-9"><a href="#cb21-9"></a><span class="bn">                                             b = 0.1)</span></span>
+<span id="cb21-10"><a href="#cb21-10"></a>  MuN2_2nd &lt;- c()</span>
+<span id="cb21-11"><a href="#cb21-11"></a>  for(l in (G1+1):G){</span>
+<span id="cb21-12"><a href="#cb21-12"></a><span class="bn">    tmp &lt;- MuN1[l] + truncdist::rtrunc(n = 1, </span></span>
+<span id="cb21-13"><a href="#cb21-13"></a><span class="bn">                                       spec = &#39;norm&#39;,</span></span>
+<span id="cb21-14"><a href="#cb21-14"></a><span class="bn">                                       mean = 0,</span></span>
+<span id="cb21-15"><a href="#cb21-15"></a><span class="bn">                                       sd = 1.5,</span></span>
+<span id="cb21-16"><a href="#cb21-16"></a><span class="bn">                                       a = 0.1, </span></span>
+<span id="cb21-17"><a href="#cb21-17"></a><span class="bn">                                       b = 3)^rbinom(1, size=1, prob=0.5)</span></span>
+<span id="cb21-18"><a href="#cb21-18"></a><span class="bn">    while(tmp &lt;= 0) tmp &lt;- MuN1[l] + truncdist::rtrunc(n = 1, </span></span>
+<span id="cb21-19"><a href="#cb21-19"></a><span class="bn">                                                       spec = &#39;norm&#39;,</span></span>
+<span id="cb21-20"><a href="#cb21-20"></a><span class="bn">                                                       mean = 0,</span></span>
+<span id="cb21-21"><a href="#cb21-21"></a><span class="bn">                                                       sd = 1.5,</span></span>
+<span id="cb21-22"><a href="#cb21-22"></a><span class="bn">                                                       a = 0.1, </span></span>
+<span id="cb21-23"><a href="#cb21-23"></a><span class="bn">                                                       b = 3)^rbinom(1, size=1, prob=0.5)</span></span>
+<span id="cb21-24"><a href="#cb21-24"></a><span class="bn">    MuN2_2nd &lt;- c(MuN2_2nd, tmp)</span></span>
+<span id="cb21-25"><a href="#cb21-25"></a>  }</span>
+<span id="cb21-26"><a href="#cb21-26"></a><span class="fu">## Simulate SigmaN1, SigmaN2 and SigmaT for each gene</span></span>
+<span id="cb21-27"><a href="#cb21-27"></a>  SigmaN1 &lt;- runif(n = G, min = 0.1, max = 0.8)</span>
+<span id="cb21-28"><a href="#cb21-28"></a>  SigmaN2 &lt;- runif(n = G, min = 0.1, max = 0.8)</span>
+<span id="cb21-29"><a href="#cb21-29"></a>  SigmaT &lt;- runif(n = G, min = 0.1, max = 0.8)</span>
+<span id="cb21-30"><a href="#cb21-30"></a><span class="fu">## Simulate Tumor Proportion</span></span>
+<span id="cb21-31"><a href="#cb21-31"></a>  pi &lt;- matrix(0, 3, My)</span>
+<span id="cb21-32"><a href="#cb21-32"></a>  pi[1,] &lt;- runif(n = My, min = 0.01, max = 0.97)</span>
+<span id="cb21-33"><a href="#cb21-33"></a>  for(j in 1:My){</span>
+<span id="cb21-34"><a href="#cb21-34"></a><span class="bn">    pi[2, j] &lt;- runif(n = 1, min = 0.01, max = 0.98 - pi[1,j])</span></span>
+<span id="cb21-35"><a href="#cb21-35"></a><span class="bn">    pi[3, j] &lt;- 1 - sum(pi[,j])</span></span>
+<span id="cb21-36"><a href="#cb21-36"></a>  }</span>
+<span id="cb21-37"><a href="#cb21-37"></a><span class="fu">## Simulate Data</span></span>
+<span id="cb21-38"><a href="#cb21-38"></a>  for(k in 1:G){</span>
+<span id="cb21-39"><a href="#cb21-39"></a><span class="bn">    </span></span>
+<span id="cb21-40"><a href="#cb21-40"></a><span class="bn">    data.N1[k,] &lt;- 2^rnorm(M1, MuN1[k], SigmaN1[k]); # normal reference 1</span></span>
+<span id="cb21-41"><a href="#cb21-41"></a><span class="bn">    </span></span>
+<span id="cb21-42"><a href="#cb21-42"></a><span class="bn">    data.N2[k,] &lt;- 2^rnorm(M2, MuN2[k], SigmaN2[k]); # normal reference 1</span></span>
+<span id="cb21-43"><a href="#cb21-43"></a><span class="bn">    </span></span>
+<span id="cb21-44"><a href="#cb21-44"></a><span class="bn">    True.data.T[k,] &lt;- 2^rnorm(My, MuT[k], SigmaT[k]);  # True Tumor</span></span>
+<span id="cb21-45"><a href="#cb21-45"></a><span class="bn">    </span></span>
+<span id="cb21-46"><a href="#cb21-46"></a><span class="bn">    True.data.N1[k,] &lt;- 2^rnorm(My, MuN1[k], SigmaN1[k]);  # True Normal 1</span></span>
+<span id="cb21-47"><a href="#cb21-47"></a><span class="bn">    </span></span>
+<span id="cb21-48"><a href="#cb21-48"></a><span class="bn">    True.data.N2[k,] &lt;- 2^rnorm(My, MuN2[k], SigmaN2[k]);  # True Normal 1</span></span>
+<span id="cb21-49"><a href="#cb21-49"></a><span class="bn">    </span></span>
+<span id="cb21-50"><a href="#cb21-50"></a><span class="bn">    data.Y[k,] &lt;- pi[1,]*True.data.N1[k,] + pi[2,]*True.data.N2[k,] +</span></span>
+<span id="cb21-51"><a href="#cb21-51"></a><span class="bn">      pi[3,]*True.data.T[k,] # Mixture Tumor</span></span>
+<span id="cb21-52"><a href="#cb21-52"></a><span class="bn">    </span></span>
+<span id="cb21-53"><a href="#cb21-53"></a>  }</span></code></pre></div>
 <p>where <span class="math inline">\(G1\)</span> is the number of genes that <span class="math inline">\(\mu_{N1}\)</span> is close to <span class="math inline">\(\mu_{N2}\)</span>.</p>
-<div class="sourceCode" id="cb22"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb22-1" title="1"><span class="kw">data</span>(<span class="st">&quot;test.data.3comp&quot;</span>)</a>
-<a class="sourceLine" id="cb22-2" title="2"><span class="co"># res.S1 &lt;- DeMixT_DE(data.Y = test.data.3comp$data.Y, data.N1 = test.data.3comp$data.N1,</span></a>
-<a class="sourceLine" id="cb22-3" title="3"><span class="co">#                    data.N2 = test.data.3comp$data.N2, if.filter = TRUE)</span></a>
-<a class="sourceLine" id="cb22-4" title="4"><span class="kw">load</span>(<span class="st">&#39;Res_3comp/res.S1.RData&#39;</span>); </a></code></pre></div>
-<div class="sourceCode" id="cb23"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb23-1" title="1">res<span class="fl">.3</span>comp=<span class="st"> </span><span class="kw">as.data.frame</span>(<span class="kw">cbind</span>(<span class="kw">round</span>(<span class="kw">t</span>(<span class="kw">matrix</span>(<span class="kw">t</span>(test.data<span class="fl">.3</span>comp<span class="op">$</span>pi), <span class="dt">nrow =</span> <span class="dv">1</span>)),<span class="dv">2</span>), </a>
-<a class="sourceLine" id="cb23-2" title="2">                                <span class="kw">round</span>(<span class="kw">t</span>(<span class="kw">matrix</span>(<span class="kw">t</span>(res.S1<span class="op">$</span>pi), <span class="dt">nrow =</span> <span class="dv">1</span>)),<span class="dv">2</span>), </a>
-<a class="sourceLine" id="cb23-3" title="3">                                <span class="kw">rep</span>(<span class="kw">c</span>(<span class="st">&#39;N1&#39;</span>,<span class="st">&#39;N2&#39;</span>,<span class="st">&#39;T&#39;</span>), <span class="dt">each =</span> <span class="dv">20</span>)))</a>
-<a class="sourceLine" id="cb23-4" title="4">res<span class="fl">.3</span>comp<span class="op">$</span>V1 &lt;-<span class="st"> </span><span class="kw">as.numeric</span>(<span class="kw">as.character</span>(res<span class="fl">.3</span>comp<span class="op">$</span>V1))</a>
-<a class="sourceLine" id="cb23-5" title="5">res<span class="fl">.3</span>comp<span class="op">$</span>V2 &lt;-<span class="st"> </span><span class="kw">as.numeric</span>(<span class="kw">as.character</span>(res<span class="fl">.3</span>comp<span class="op">$</span>V2))</a>
-<a class="sourceLine" id="cb23-6" title="6">res<span class="fl">.3</span>comp<span class="op">$</span>V3 =<span class="st"> </span><span class="kw">as.factor</span>(res<span class="fl">.3</span>comp<span class="op">$</span>V3)</a>
-<a class="sourceLine" id="cb23-7" title="7"><span class="kw">names</span>(res<span class="fl">.3</span>comp) =<span class="st"> </span><span class="kw">c</span>(<span class="st">&#39;True.Proportion&#39;</span>, <span class="st">&#39;Estimated.Proportion&#39;</span>, <span class="st">&#39;Component&#39;</span>)</a>
-<a class="sourceLine" id="cb23-8" title="8"><span class="co">## Plot</span></a>
-<a class="sourceLine" id="cb23-9" title="9"><span class="kw">ggplot</span>(res<span class="fl">.3</span>comp, <span class="kw">aes</span>(<span class="dt">x=</span>True.Proportion, <span class="dt">y=</span>Estimated.Proportion, <span class="dt">group =</span> Component, <span class="dt">color=</span>Component, <span class="dt">shape=</span>Component)) <span class="op">+</span></a>
-<a class="sourceLine" id="cb23-10" title="10"><span class="st">  </span><span class="kw">geom_point</span>() <span class="op">+</span><span class="st"> </span></a>
-<a class="sourceLine" id="cb23-11" title="11"><span class="st">  </span><span class="kw">geom_abline</span>(<span class="dt">intercept =</span> <span class="dv">0</span>, <span class="dt">slope =</span> <span class="dv">1</span>, <span class="dt">linetype =</span> <span class="st">&quot;dashed&quot;</span>, <span class="dt">color =</span> <span class="st">&quot;black&quot;</span>, <span class="dt">lwd =</span> <span class="fl">0.5</span>) <span class="op">+</span></a>
-<a class="sourceLine" id="cb23-12" title="12"><span class="st">  </span><span class="kw">xlim</span>(<span class="dv">0</span>,<span class="dv">1</span>) <span class="op">+</span><span class="st"> </span><span class="kw">ylim</span>(<span class="dv">0</span>,<span class="dv">1</span>)  <span class="op">+</span></a>
-<a class="sourceLine" id="cb23-13" title="13"><span class="st">  </span><span class="kw">scale_shape_manual</span>(<span class="dt">values=</span><span class="kw">c</span>(<span class="kw">seq</span>(<span class="dv">1</span><span class="op">:</span><span class="dv">3</span>))) <span class="op">+</span></a>
-<a class="sourceLine" id="cb23-14" title="14"><span class="st">  </span><span class="kw">labs</span>(<span class="dt">x =</span> <span class="st">&#39;True Proportion&#39;</span>, <span class="dt">y =</span> <span class="st">&#39;Estimated Proportion&#39;</span>)  </a></code></pre></div>
+<div class="sourceCode" id="cb22"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb22-1"><a href="#cb22-1"></a><span class="kw">data</span>(<span class="st">&quot;test.data.3comp&quot;</span>)</span>
+<span id="cb22-2"><a href="#cb22-2"></a><span class="co"># res.S1 &lt;- DeMixT_DE(data.Y = test.data.3comp$data.Y, data.N1 = test.data.3comp$data.N1,</span></span>
+<span id="cb22-3"><a href="#cb22-3"></a><span class="co">#                    data.N2 = test.data.3comp$data.N2, if.filter = TRUE)</span></span>
+<span id="cb22-4"><a href="#cb22-4"></a><span class="kw">load</span>(<span class="st">&#39;Res_3comp/res.S1.RData&#39;</span>); </span></code></pre></div>
+<div class="sourceCode" id="cb23"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb23-1"><a href="#cb23-1"></a>res<span class="fl">.3</span>comp=<span class="st"> </span><span class="kw">as.data.frame</span>(<span class="kw">cbind</span>(<span class="kw">round</span>(<span class="kw">t</span>(<span class="kw">matrix</span>(<span class="kw">t</span>(test.data<span class="fl">.3</span>comp<span class="op">$</span>pi), <span class="dt">nrow =</span> <span class="dv">1</span>)),<span class="dv">2</span>), </span>
+<span id="cb23-2"><a href="#cb23-2"></a>                                <span class="kw">round</span>(<span class="kw">t</span>(<span class="kw">matrix</span>(<span class="kw">t</span>(res.S1<span class="op">$</span>pi), <span class="dt">nrow =</span> <span class="dv">1</span>)),<span class="dv">2</span>), </span>
+<span id="cb23-3"><a href="#cb23-3"></a>                                <span class="kw">rep</span>(<span class="kw">c</span>(<span class="st">&#39;N1&#39;</span>,<span class="st">&#39;N2&#39;</span>,<span class="st">&#39;T&#39;</span>), <span class="dt">each =</span> <span class="dv">20</span>)))</span>
+<span id="cb23-4"><a href="#cb23-4"></a>res<span class="fl">.3</span>comp<span class="op">$</span>V1 &lt;-<span class="st"> </span><span class="kw">as.numeric</span>(<span class="kw">as.character</span>(res<span class="fl">.3</span>comp<span class="op">$</span>V1))</span>
+<span id="cb23-5"><a href="#cb23-5"></a>res<span class="fl">.3</span>comp<span class="op">$</span>V2 &lt;-<span class="st"> </span><span class="kw">as.numeric</span>(<span class="kw">as.character</span>(res<span class="fl">.3</span>comp<span class="op">$</span>V2))</span>
+<span id="cb23-6"><a href="#cb23-6"></a>res<span class="fl">.3</span>comp<span class="op">$</span>V3 =<span class="st"> </span><span class="kw">as.factor</span>(res<span class="fl">.3</span>comp<span class="op">$</span>V3)</span>
+<span id="cb23-7"><a href="#cb23-7"></a><span class="kw">names</span>(res<span class="fl">.3</span>comp) =<span class="st"> </span><span class="kw">c</span>(<span class="st">&#39;True.Proportion&#39;</span>, <span class="st">&#39;Estimated.Proportion&#39;</span>, <span class="st">&#39;Component&#39;</span>)</span>
+<span id="cb23-8"><a href="#cb23-8"></a><span class="co">## Plot</span></span>
+<span id="cb23-9"><a href="#cb23-9"></a><span class="kw">ggplot</span>(res<span class="fl">.3</span>comp, <span class="kw">aes</span>(<span class="dt">x=</span>True.Proportion, <span class="dt">y=</span>Estimated.Proportion, <span class="dt">group =</span> Component, <span class="dt">color=</span>Component, <span class="dt">shape=</span>Component)) <span class="op">+</span></span>
+<span id="cb23-10"><a href="#cb23-10"></a><span class="st">  </span><span class="kw">geom_point</span>() <span class="op">+</span><span class="st"> </span></span>
+<span id="cb23-11"><a href="#cb23-11"></a><span class="st">  </span><span class="kw">geom_abline</span>(<span class="dt">intercept =</span> <span class="dv">0</span>, <span class="dt">slope =</span> <span class="dv">1</span>, <span class="dt">linetype =</span> <span class="st">&quot;dashed&quot;</span>, <span class="dt">color =</span> <span class="st">&quot;black&quot;</span>, <span class="dt">lwd =</span> <span class="fl">0.5</span>) <span class="op">+</span></span>
+<span id="cb23-12"><a href="#cb23-12"></a><span class="st">  </span><span class="kw">xlim</span>(<span class="dv">0</span>,<span class="dv">1</span>) <span class="op">+</span><span class="st"> </span><span class="kw">ylim</span>(<span class="dv">0</span>,<span class="dv">1</span>)  <span class="op">+</span></span>
+<span id="cb23-13"><a href="#cb23-13"></a><span class="st">  </span><span class="kw">scale_shape_manual</span>(<span class="dt">values=</span><span class="kw">c</span>(<span class="kw">seq</span>(<span class="dv">1</span><span class="op">:</span><span class="dv">3</span>))) <span class="op">+</span></span>
+<span id="cb23-14"><a href="#cb23-14"></a><span class="st">  </span><span class="kw">labs</span>(<span class="dt">x =</span> <span class="st">&#39;True Proportion&#39;</span>, <span class="dt">y =</span> <span class="st">&#39;Estimated Proportion&#39;</span>)  </span></code></pre></div>
 <p><img src="data:image/png;base64,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" style="display: block; margin: auto;" /></p>
 </div>
 <div id="real-data-prad-in-tcga-dataset" class="section level2">
@@ -636,9 +654,9 @@ library(DeMixT)</code></pre>
 <p>to visually inspect the separation of tumor and normal samples based on the hierarchical clustering of their expressions, in which <code>count.matrix</code> is the raw count matrix; <code>normal.id</code> and <code>tumor.id</code> are the vectors of normal and tumor sample ids, respectively. Generally, one cluster contains tumor samples and the other contains normal samples. Any samples that are clustered outside of its own group label, e.g., tumor samples clustered within the normal sample cluster or normal samples in the tumor cluster, are considered as mislabelled samples and filtered out.</p>
 <p><img src="data:image/png;base64,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" width="85%" style="display: block; margin: auto;" /></p>
 <p><code>count.matrix</code>, <code>normal.id</code> and <code>tumor.id</code> are updated by</p>
-<div class="sourceCode" id="cb25"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb25-1" title="1"><span class="co"># normal.id &lt;- setdiff(normal.id, names(hc_labels$cluster[hc_labels$cluster == 1]))</span></a>
-<a class="sourceLine" id="cb25-2" title="2"><span class="co"># tumor.id &lt;- setdiff(tumor.id, names(hc_labels$cluster[hc_labels$cluster == 2]))</span></a>
-<a class="sourceLine" id="cb25-3" title="3"><span class="co"># count.matrix &lt;- count.matrix[, c(normal.id, tumor.id)]</span></a></code></pre></div>
+<div class="sourceCode" id="cb25"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb25-1"><a href="#cb25-1"></a><span class="co"># normal.id &lt;- setdiff(normal.id, names(hc_labels$cluster[hc_labels$cluster == 1]))</span></span>
+<span id="cb25-2"><a href="#cb25-2"></a><span class="co"># tumor.id &lt;- setdiff(tumor.id, names(hc_labels$cluster[hc_labels$cluster == 2]))</span></span>
+<span id="cb25-3"><a href="#cb25-3"></a><span class="co"># count.matrix &lt;- count.matrix[, c(normal.id, tumor.id)]</span></span></code></pre></div>
 <ol start="2" style="list-style-type: decimal">
 <li><strong>Select genes with small variation in gene expression across samples</strong></li>
 </ol>
@@ -713,20 +731,20 @@ library(DeMixT)</code></pre>
 <div id="deconvolution-using-normal-reference-samples-from-gtex" class="section level2">
 <h2>6.5 Deconvolution using normal reference samples from GTEx</h2>
 <p>We conducted experiments across cancer types to evaluate the impact of technical artifacts such as batch effects to the proportion estimation when using a different cohort. We applied GTEx expression data from normal prostate samples as the normal reference to deconvolute the TCGA prostate cancer samples, where normal tissues were selected without significant pathology. The estimated proportions showed a reasonable correlation (Spearman correlation coefficient = 0.65) with those generated using TCGA normal prostate samples as the normal reference.</p>
-<div class="sourceCode" id="cb35"><pre class="sourceCode markdown"><code class="sourceCode markdown"><a class="sourceLine" id="cb35-1" title="1"><span class="fu">## Deconvolute TCGA prostate cancer samples from GTEx normal samples</span></a>
-<a class="sourceLine" id="cb35-2" title="2">res.GS.GTEx = DeMixT_GS(data.Y = TCGA_PRAD_Tumor,</a>
-<a class="sourceLine" id="cb35-3" title="3"><span class="bn">                        data.N1 = GTEx_PRAD_Normal,</span></a>
-<a class="sourceLine" id="cb35-4" title="4"><span class="bn">                        niter = 50, nbin = 50, nspikein = 49, filter.sd = 0.6,</span></a>
-<a class="sourceLine" id="cb35-5" title="5"><span class="bn">                        if.filter = TRUE, ngene.Profile.selected = 1500,</span></a>
-<a class="sourceLine" id="cb35-6" title="6"><span class="bn">                        mean.diff.in.CM = 0.25, ngene.selected.for.pi = 1500,</span></a>
-<a class="sourceLine" id="cb35-7" title="7"><span class="bn">                        tol = 10^(-5))</span></a>
-<a class="sourceLine" id="cb35-8" title="8"><span class="fu">## Deconvolute TCGA prostate cancer samples from TCGA normal samples</span></a>
-<a class="sourceLine" id="cb35-9" title="9">res.GS.TCGA = DeMixT_GS(data.Y = TCGA_PRAD_Tumor,</a>
-<a class="sourceLine" id="cb35-10" title="10"><span class="bn">                        data.N1 = TCGA_PRAD_Normal,</span></a>
-<a class="sourceLine" id="cb35-11" title="11"><span class="bn">                        niter = 50, nbin = 50, nspikein = 49, filter.sd = 0.6,</span></a>
-<a class="sourceLine" id="cb35-12" title="12"><span class="bn">                        if.filter = TRUE, ngene.Profile.selected = 1500,</span></a>
-<a class="sourceLine" id="cb35-13" title="13"><span class="bn">                        mean.diff.in.CM = 0.25, ngene.selected.for.pi = 1500,</span></a>
-<a class="sourceLine" id="cb35-14" title="14"><span class="bn">                        tol = 10^(-5))                 </span></a></code></pre></div>
+<div class="sourceCode" id="cb35"><pre class="sourceCode markdown"><code class="sourceCode markdown"><span id="cb35-1"><a href="#cb35-1"></a><span class="fu">## Deconvolute TCGA prostate cancer samples from GTEx normal samples</span></span>
+<span id="cb35-2"><a href="#cb35-2"></a>res.GS.GTEx = DeMixT_GS(data.Y = TCGA_PRAD_Tumor,</span>
+<span id="cb35-3"><a href="#cb35-3"></a><span class="bn">                        data.N1 = GTEx_PRAD_Normal,</span></span>
+<span id="cb35-4"><a href="#cb35-4"></a><span class="bn">                        niter = 50, nbin = 50, nspikein = 49, filter.sd = 0.6,</span></span>
+<span id="cb35-5"><a href="#cb35-5"></a><span class="bn">                        if.filter = TRUE, ngene.Profile.selected = 1500,</span></span>
+<span id="cb35-6"><a href="#cb35-6"></a><span class="bn">                        mean.diff.in.CM = 0.25, ngene.selected.for.pi = 1500,</span></span>
+<span id="cb35-7"><a href="#cb35-7"></a><span class="bn">                        tol = 10^(-5))</span></span>
+<span id="cb35-8"><a href="#cb35-8"></a><span class="fu">## Deconvolute TCGA prostate cancer samples from TCGA normal samples</span></span>
+<span id="cb35-9"><a href="#cb35-9"></a>res.GS.TCGA = DeMixT_GS(data.Y = TCGA_PRAD_Tumor,</span>
+<span id="cb35-10"><a href="#cb35-10"></a><span class="bn">                        data.N1 = TCGA_PRAD_Normal,</span></span>
+<span id="cb35-11"><a href="#cb35-11"></a><span class="bn">                        niter = 50, nbin = 50, nspikein = 49, filter.sd = 0.6,</span></span>
+<span id="cb35-12"><a href="#cb35-12"></a><span class="bn">                        if.filter = TRUE, ngene.Profile.selected = 1500,</span></span>
+<span id="cb35-13"><a href="#cb35-13"></a><span class="bn">                        mean.diff.in.CM = 0.25, ngene.selected.for.pi = 1500,</span></span>
+<span id="cb35-14"><a href="#cb35-14"></a><span class="bn">                        tol = 10^(-5))                 </span></span></code></pre></div>
 <p><img src="data:image/png;base64,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" width="65%" style="display: block; margin: auto;" /></p>
 </div>
 </div>
@@ -736,14 +754,14 @@ library(DeMixT)</code></pre>
 </div>
 <div id="session-info" class="section level1">
 <h1>8. Session Info</h1>
-<div class="sourceCode" id="cb36"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb36-1" title="1"><span class="kw">sessionInfo</span>(<span class="dt">package =</span> <span class="st">&quot;DeMixT&quot;</span>)</a></code></pre></div>
-<pre><code>## R version 4.2.1 (2022-06-23)
+<div class="sourceCode" id="cb36"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb36-1"><a href="#cb36-1"></a><span class="kw">sessionInfo</span>(<span class="dt">package =</span> <span class="st">&quot;DeMixT&quot;</span>)</span></code></pre></div>
+<pre><code>## R version 4.3.0 RC (2023-04-13 r84269)
 ## Platform: x86_64-pc-linux-gnu (64-bit)
-## Running under: Ubuntu 20.04.5 LTS
+## Running under: Ubuntu 22.04.2 LTS
 ## 
 ## Matrix products: default
-## BLAS:   /home/biocbuild/bbs-3.16-bioc/R/lib/libRblas.so
-## LAPACK: /home/biocbuild/bbs-3.16-bioc/R/lib/libRlapack.so
+## BLAS:   /home/biocbuild/bbs-3.17-bioc/R/lib/libRblas.so 
+## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
 ## 
 ## locale:
 ##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
@@ -753,76 +771,78 @@ library(DeMixT)</code></pre>
 ##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
 ## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
 ## 
+## time zone: America/New_York
+## tzcode source: system (glibc)
+## 
 ## attached base packages:
 ## character(0)
 ## 
 ## other attached packages:
-## [1] DeMixT_1.14.0
+## [1] DeMixT_1.16.0
 ## 
 ## loaded via a namespace (and not attached):
-##   [1] colorspace_2.0-3            bsseq_1.34.0               
-##   [3] rjson_0.2.21                XVector_0.38.0             
-##   [5] GenomicRanges_1.50.0        base64enc_0.1-3            
-##   [7] farver_2.1.1                stats_4.2.1                
-##   [9] bit64_4.0.5                 AnnotationDbi_1.60.0       
-##  [11] fansi_1.0.3                 codetools_0.2-18           
-##  [13] splines_4.2.1               R.methodsS3_1.8.2          
-##  [15] sparseMatrixStats_1.10.0    mnormt_2.1.1               
-##  [17] cachem_1.0.6                knitr_1.40                 
-##  [19] jsonlite_1.8.3              Rsamtools_2.14.0           
-##  [21] annotate_1.76.0             base_4.2.1                 
-##  [23] png_0.1-7                   R.oo_1.25.0                
-##  [25] DSS_2.46.0                  HDF5Array_1.26.0           
-##  [27] compiler_4.2.1              httr_1.4.4                 
-##  [29] assertthat_0.2.1            Matrix_1.5-1               
-##  [31] fastmap_1.1.0               limma_3.54.0               
-##  [33] cli_3.4.1                   htmltools_0.5.3            
-##  [35] tools_4.2.1                 gtable_0.3.1               
-##  [37] glue_1.6.2                  GenomeInfoDbData_1.2.9     
-##  [39] dplyr_1.0.10                grDevices_4.2.1            
-##  [41] Rcpp_1.0.9                  Biobase_2.58.0             
-##  [43] jquerylib_0.1.4             vctrs_0.5.0                
-##  [45] Biostrings_2.66.0           rhdf5filters_1.10.0        
-##  [47] nlme_3.1-160                rtracklayer_1.58.0         
-##  [49] DelayedMatrixStats_1.20.0   psych_2.2.9                
-##  [51] xfun_0.34                   stringr_1.4.1              
-##  [53] lifecycle_1.0.3             restfulr_0.0.15            
-##  [55] gtools_3.9.3                XML_3.99-0.12              
-##  [57] dendextend_1.16.0           edgeR_3.40.0               
-##  [59] zlibbioc_1.44.0             scales_1.2.1               
-##  [61] BSgenome_1.66.0             graphics_4.2.1             
-##  [63] MatrixGenerics_1.10.0       parallel_4.2.1             
-##  [65] SummarizedExperiment_1.28.0 rhdf5_2.42.0               
-##  [67] utils_4.2.1                 yaml_2.3.6                 
-##  [69] memoise_2.0.1               gridExtra_2.3              
-##  [71] ggplot2_3.3.6               sass_0.4.2                 
-##  [73] datasets_4.2.1              stringi_1.7.8              
-##  [75] RSQLite_2.2.18              highr_0.9                  
-##  [77] genefilter_1.80.0           S4Vectors_0.36.0           
-##  [79] BiocIO_1.8.0                permute_0.9-7              
-##  [81] BiocGenerics_0.44.0         BiocParallel_1.32.0        
-##  [83] GenomeInfoDb_1.34.0         rlang_1.0.6                
-##  [85] pkgconfig_2.0.3             matrixStats_0.62.0         
-##  [87] bitops_1.0-7                evaluate_0.17              
-##  [89] lattice_0.20-45             Rhdf5lib_1.20.0            
-##  [91] GenomicAlignments_1.34.0    labeling_0.4.2             
-##  [93] bit_4.0.4                   tidyselect_1.2.0           
-##  [95] magrittr_2.0.3              R6_2.5.1                   
-##  [97] IRanges_2.32.0              generics_0.1.3             
-##  [99] DelayedArray_0.24.0         DBI_1.1.3                  
-## [101] pillar_1.8.1                withr_2.5.0                
-## [103] mgcv_1.8-41                 survival_3.4-0             
-## [105] KEGGREST_1.38.0             RCurl_1.98-1.9             
-## [107] tibble_3.1.8                crayon_1.5.2               
-## [109] KernSmooth_2.23-20          utf8_1.2.2                 
-## [111] rmarkdown_2.17              viridis_0.6.2              
-## [113] locfit_1.5-9.6              grid_4.2.1                 
-## [115] sva_3.46.0                  data.table_1.14.4          
-## [117] blob_1.2.3                  methods_4.2.1              
-## [119] matrixcalc_1.0-6            digest_0.6.30              
-## [121] xtable_1.8-4                R.utils_2.12.1             
-## [123] stats4_4.2.1                munsell_0.5.0              
-## [125] viridisLite_0.4.1           bslib_0.4.0</code></pre>
+##   [1] DBI_1.1.3                   mnormt_2.1.1               
+##   [3] bitops_1.0-7                gridExtra_2.3              
+##   [5] bsseq_1.36.0                permute_0.9-7              
+##   [7] rlang_1.1.0                 magrittr_2.0.3             
+##   [9] matrixStats_0.63.0          compiler_4.3.0             
+##  [11] RSQLite_2.3.1               mgcv_1.8-42                
+##  [13] DelayedMatrixStats_1.22.0   png_0.1-8                  
+##  [15] vctrs_0.6.2                 sva_3.48.0                 
+##  [17] pkgconfig_2.0.3             crayon_1.5.2               
+##  [19] fastmap_1.1.1               XVector_0.40.0             
+##  [21] labeling_0.4.2              utf8_1.2.3                 
+##  [23] Rsamtools_2.16.0            rmarkdown_2.21             
+##  [25] grDevices_4.3.0             bit_4.0.5                  
+##  [27] xfun_0.39                   zlibbioc_1.46.0            
+##  [29] cachem_1.0.7                graphics_4.3.0             
+##  [31] GenomeInfoDb_1.36.0         jsonlite_1.8.4             
+##  [33] blob_1.2.4                  highr_0.10                 
+##  [35] rhdf5filters_1.12.0         DelayedArray_0.26.0        
+##  [37] Rhdf5lib_1.22.0             BiocParallel_1.34.0        
+##  [39] psych_2.3.3                 parallel_4.3.0             
+##  [41] R6_2.5.1                    bslib_0.4.2                
+##  [43] limma_3.56.0                rtracklayer_1.60.0         
+##  [45] genefilter_1.82.0           GenomicRanges_1.52.0       
+##  [47] jquerylib_0.1.4             Rcpp_1.0.10                
+##  [49] SummarizedExperiment_1.30.0 knitr_1.42                 
+##  [51] base64enc_0.1-3             R.utils_2.12.2             
+##  [53] IRanges_2.34.0              Matrix_1.5-4               
+##  [55] splines_4.3.0               tidyselect_1.2.0           
+##  [57] yaml_2.3.7                  viridis_0.6.2              
+##  [59] codetools_0.2-19            lattice_0.21-8             
+##  [61] tibble_3.2.1                Biobase_2.60.0             
+##  [63] withr_2.5.0                 KEGGREST_1.40.0            
+##  [65] evaluate_0.20               base_4.3.0                 
+##  [67] survival_3.5-5              Biostrings_2.68.0          
+##  [69] pillar_1.9.0                MatrixGenerics_1.12.0      
+##  [71] DSS_2.48.0                  KernSmooth_2.23-20         
+##  [73] stats4_4.3.0                generics_0.1.3             
+##  [75] RCurl_1.98-1.12             S4Vectors_0.38.0           
+##  [77] ggplot2_3.4.2               sparseMatrixStats_1.12.0   
+##  [79] munsell_0.5.0               scales_1.2.1               
+##  [81] stats_4.3.0                 gtools_3.9.4               
+##  [83] xtable_1.8-4                glue_1.6.2                 
+##  [85] tools_4.3.0                 dendextend_1.17.1          
+##  [87] datasets_4.3.0              BiocIO_1.10.0              
+##  [89] data.table_1.14.8           BSgenome_1.68.0            
+##  [91] annotate_1.78.0             locfit_1.5-9.7             
+##  [93] GenomicAlignments_1.36.0    XML_3.99-0.14              
+##  [95] rhdf5_2.44.0                grid_4.3.0                 
+##  [97] utils_4.3.0                 matrixcalc_1.0-6           
+##  [99] methods_4.3.0               edgeR_3.42.0               
+## [101] AnnotationDbi_1.62.0        colorspace_2.1-0           
+## [103] nlme_3.1-162                GenomeInfoDbData_1.2.10    
+## [105] HDF5Array_1.28.0            restfulr_0.0.15            
+## [107] cli_3.6.1                   fansi_1.0.4                
+## [109] viridisLite_0.4.1           dplyr_1.1.2                
+## [111] gtable_0.3.3                R.methodsS3_1.8.2          
+## [113] sass_0.4.5                  digest_0.6.31              
+## [115] BiocGenerics_0.46.0         farver_2.1.1               
+## [117] rjson_0.2.21                memoise_2.0.1              
+## [119] htmltools_0.5.5             R.oo_1.25.0                
+## [121] lifecycle_1.0.3             httr_1.4.5                 
+## [123] bit64_4.0.5</code></pre>
 </div>
More details

Full run details
The Debian Janitor

New Upstream Release - r-bioc-demixt

Ready changes

Summary

Diff

More details

Historical runs
