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<h1 class="title toc-ignore">A Vignette for DeMixT</h1>
<h4 class="date">Last updated: 2022-11-01</h4>



<div style="text-align: justify">



<div id="introduction" class="section level1">
<h1>1. Introduction</h1>
<p>Transcriptomic deconvolution in cancer and other heterogeneous tissues remains challenging. Available methods lack the ability to estimate both component-specific proportions and expression profiles for individual samples. We develop a three-component deconvolution model, DeMixT, for expression data from a mixture of cancerous tissues, infiltrating immune cells and tumor microenvironment. DeMixT is a software package that performs deconvolution on transcriptome data from a mixture of two or three components.</p>
<p>DeMixT is a frequentist-based method and fast in yielding accurate estimates of cell proportions and compart-ment-specific expression profiles for two-component three-component deconvolution problem. Our method promises to provide deeper insight into cancer biomarkers and assist in the development of novel prognostic markers and therapeutic strategies.</p>
<p>The function DeMixT is designed to finish the whole pipeline of deconvolution for two or three components. The newly added DeMixT_GS function is designed to estimates the proportions of mixed samples for each mixing component based on a new approach to select genes more effectively that utilizes profile likelihood. DeMixT_DE function is designed to estimate the proportions of all mixed samples for each mixing component based on the gene differential expressions to select genes. DeMixT_S2 function is designed to estimate the component-specific deconvolved expressions of individual mixed samples for a given set of genes.</p>
</div>
<div id="feature-description" class="section level1">
<h1>2 Feature Description</h1>
<p>The DeMixT R-package builds the transcriptomic deconvolution with a couple of novel features into R-based standard analysis pipeline through Bioconductor. DeMixT showed high accuracy and efficiency from our designed experiment. Hence, DeMixT can be considered as an important step towards linking tumor transcriptomic data with clinical outcomes.</p>
<p>Different from most previous computational deconvolution methods, DeMixT has integrated new features for the deconvolution with more than 2 components.</p>
<p><strong>Joint estimation</strong>: jointly estimate component proportions and expression profiles for individual samples by requiring reference samples instead of reference genes; For the three-component deconvolution considering immune infiltration, it provides a comprehensive view of tumor-stroma-immune transcriptional dynamics, as compared to methods that address only immune subtypes within the immune component, in each tumor sample.</p>
<p><strong>Efficient estimation</strong>: DeMixT adopts an approach of iterated conditional modes (ICM) to guarantee a rapid convergence to a local maximum. We also design a novel gene-set-based component merging approach to reduce the bias of proportion estimation for three-component deconvolutionthe.</p>
<p><strong>Parallel computing</strong>: OpenMP enable parallel computing on single computer by taking advantage of the multiple cores shipped on modern CPUs. The ICM framework further enables parallel computing, which helps compensate for the expensive computing time used in the repeated numerical double integrations.</p>
</div>
<div id="installation" class="section level1">
<h1>3. Installation</h1>
<p>The DeMixT package is compatible with Windows, Linux and MacOS. The user can install it from <code>Bioconductor</code>:</p>
<pre><code>if (!require(&quot;BiocManager&quot;, quietly = TRUE))
    install.packages(&quot;BiocManager&quot;)

BiocManager::install(&quot;DeMixT&quot;)</code></pre>
<p>For Linux and MacOS, the user can also install the latest DeMixT from GitHub:</p>
<pre><code>if (!require(&quot;devtools&quot;, quietly = TRUE))
    install.packages(&#39;devtools&#39;)

devtools::install_github(&quot;wwylab/DeMixT&quot;)</code></pre>
<p>Check if DeMixT is installed successfully:</p>
<pre><code># load package
library(DeMixT)</code></pre>
<p><strong>Note</strong>: DeMixT relies on OpenMP for parallel computing. Starting from R 4.00, R no longer supports OpenMP on MacOS, meaning the user can only run DeMixT with one core on MacOS. We therefore recommend the users to mainly use Linux system for running DeMixT to take advantage of the multi-core parallel computation.</p>
</div>
<div id="functions" class="section level1">
<h1>4. Functions</h1>
<p>The following table shows the functions included in DeMixT.</p>
<table>
<colgroup>
<col width="17%"></col>
<col width="82%"></col>
</colgroup>
<thead>
<tr class="header">
<th>Table Header</th>
<th>Second Header</th>
</tr>
</thead>
<tbody>
<tr class="odd">
<td>DeMixT</td>
<td>Deconvolution of tumor samples with two or three components.</td>
</tr>
<tr class="even">
<td>DeMixT_GS</td>
<td>Estimates the proportions of mixed samples for each mixing component based on a new approach to select genes that utilizes profile likelihood.</td>
</tr>
<tr class="odd">
<td>DeMixT_DE</td>
<td>Estimates the proportions of mixed samples for each mixing component.</td>
</tr>
<tr class="even">
<td>DeMixT_S2</td>
<td>Deconvolves expressions of each sample for unknown component.</td>
</tr>
<tr class="odd">
<td>Optimum_KernelC</td>
<td>Call the C function used for parameter estimation in DeMixT.</td>
</tr>
<tr class="even">
<td>DeMixT_Preprocessing</td>
<td>Preprocessing functions before running DeMixT.</td>
</tr>
</tbody>
</table>
</div>
<div id="methods" class="section level1">
<h1>5. Methods</h1>
<div id="model" class="section level2">
<h2>5.1 Model</h2>
<p>Let <span class="math inline">\(Y_{ig}\)</span> be the observed expression levels of the raw measured data from clinically derived malignant tumor samples for gene <span class="math inline">\(g, g = 1, \cdots, G\)</span> and sample <span class="math inline">\(i, i = 1, \cdots, My\)</span>. <span class="math inline">\(G\)</span> denotes the total number of probes/genes and <span class="math inline">\(My\)</span> denotes the number of samples. The observed expression levels for solid tumors can be modeled as a linear combination of raw expression levels from three components: <span class="math display">\[ {Y_{ig}} = \pi _{1,i}N_{1,ig} + \pi _{2,i}N_{2,ig} + 
(1 - \pi_{1,i} - \pi _{2,i}){T_{ig}} \label{eq:1} \]</span></p>
<p>Here <span class="math inline">\(N_{1,ig}\)</span>, <span class="math inline">\(N_{2,ig}\)</span> and <span class="math inline">\({T_{ig}}\)</span> are the unobserved raw expression levels from each of the three components. We call the two components for which we require reference samples the <span class="math inline">\(N_1\)</span>-component and the <span class="math inline">\(N_2\)</span>-component. We call the unknown component the T-component. We let <span class="math inline">\(\pi_{1,i}\)</span> denote the proportion of the <span class="math inline">\(N_1\)</span>-component, <span class="math inline">\(\pi_{2,i}\)</span> denote the proportion of the <span class="math inline">\(N_2\)</span>-component, and <span class="math inline">\(1 - \pi_{1,i}-\pi_{2,i}\)</span> denote the proportion of the T-component. We assume that the mixing proportions of one specific sample remain the same across all genes.</p>
<p>Our model allows for one component to be unknown, and therefore does not require reference profiles from all components. A set of samples for <span class="math inline">\(N_{1,ig}\)</span> and <span class="math inline">\(N_{2,ig}\)</span>, respectively, needs to be provided as input data. This three-component deconvolution model is applicable to the linear combination of any three components in any type of material. It can also be simplified to a two-component model, assuming there is just one <span class="math inline">\(N\)</span>-component. For application in this paper, we consider tumor (<span class="math inline">\(T\)</span>), stromal (<span class="math inline">\(N_1\)</span>) and immune components (<span class="math inline">\(N_2\)</span>) in an admixed sample (<span class="math inline">\(Y\)</span>).</p>
<p>Following the convention that <span class="math inline">\(\log_2\)</span>-transformed microarray gene expression data follow a normal distribution, we assume that the raw measures <span class="math inline">\(N_{1,ig} \sim LN({\mu _{{N_1}g}},\sigma _{{N_1}g}^2)\)</span>, <span class="math inline">\(N_{2,ig} \sim LN({\mu _{{N_2}g}},\sigma _{{N_2}g}^2)\)</span> and <span class="math inline">\({T_{ig}} \sim LN({\mu _{Tg}}, \sigma _{Tg}^2)\)</span>, where LN denotes a <span class="math inline">\(\log_2\)</span>-normal distribution and <span class="math inline">\(\sigma _{{N_1}g}^2\)</span>,<span class="math inline">\(\sigma _{{N_2}g}^2\)</span>, <span class="math inline">\(\sigma _{Tg}^2\)</span> reflect the variations under <span class="math inline">\(\log_2\)</span>-transformed data. Consequently, our model can be expressed as the convolution of the density function for three <span class="math inline">\(\log_2\)</span>-normal distributions. Because there is no closed form of this convolution, we use numerical integration to evaluate the complete likelihood function (see the full likelihood in the Supplementary Materials in [1]).</p>
</div>
<div id="the-demixt-algorithm-for-deconvolution" class="section level2">
<h2>5.2 The DeMixT algorithm for deconvolution</h2>
<p>DeMixT estimates all distribution parameters and cellular proportions and reconstitutes the expression profiles for all three components for each gene and each sample. The estimation procedure (summarized in Figure 1b) has two main steps as follows.</p>
<ol style="list-style-type: decimal">
<li><p>Obtain a set of parameters <span class="math inline">\(\{\pi_{1,i}, \pi_{2,i}\}_{i=1}^{My}\)</span>, <span class="math inline">\(\{\mu_T, \sigma_T\}_{g=1}^G\)</span> to maximize the complete likelihood function, for which <span class="math inline">\(\{\mu_{N_{1,g}}, \sigma_{N_{1,g}}, \mu_{N_{2,g}}, \sigma_{N_{2,g}}\}_{g=1}^G\)</span> were already estimated from the available unmatched samples of the <span class="math inline">\(N_1\)</span> and <span class="math inline">\(N_2\)</span> component tissues. (See further details in our paper.)</p></li>
<li><p>Reconstitute the expression profiles by searching each set of <span class="math inline">\(\{n_{1,ig}, n_{2,ig}\}\)</span> that maximizes the joint density of <span class="math inline">\(N_{1,ig}\)</span>, <span class="math inline">\(N_{2,ig}\)</span> and <span class="math inline">\(T_{ig}\)</span>. The value of <span class="math inline">\(t_{ig}\)</span> is solved as <span class="math inline">\({y_{ig}} - {{\hat \pi }_{1,i}}{n_{1,ig}} - {{\hat \pi }_{2,i}}{n_{2,ig}}\)</span>.</p></li>
</ol>
<p>These two steps can be separately implemented using the function DeMixT_DE or DeMixT_GS for the first step and DeMixT_S2 for the second, which are combined in the function DeMixT(Note: DeMixT_GS is the default function for first step).</p>
<p>Since version 1.8.2, DeMixT added simulated normal reference samples, i.e., spike-in, based on the observed normal reference samples. It has been shown to improve accuracy in proportion estimation for the scenario where a dataset consists of samples where true tumor proportions are skewed to the high end.</p>
<p><img 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" width="100%" /></p>
</div>
</div>
<div id="examples" class="section level1">
<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">$</span>pi))</a></code></pre></div>
<pre><code>##               PiN1       PiT
## Sample 1 0.5955120 0.4044880
## Sample 2 0.2759014 0.7240986
## Sample 3 0.5401655 0.4598345
## Sample 4 0.4497041 0.5502959
## Sample 5 0.6516980 0.3483020
## Sample 6 0.4365191 0.5634809</code></pre>
<div class="sourceCode" id="cb7"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb7-1" title="1"><span class="kw">head</span>(res.GS<span class="op">$</span>gene.name)</a></code></pre></div>
<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">$</span>decovExprT[,<span class="dv">1</span><span class="op">:</span><span class="dv">5</span>],<span class="dv">3</span>)</a></code></pre></div>
<pre><code>##         Sample 1   Sample 2   Sample 3  Sample 4   Sample 5
## Gene 1 18.857446  60.727041 159.878946 92.031635  40.873852
## Gene 2  2.322481   3.390938   2.406093  2.558962   2.438189
## Gene 3 48.843631 208.166410  66.986239 38.107580 460.556751</code></pre>
<div class="sourceCode" id="cb12"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb12-1" title="1"><span class="kw">head</span>(res.S2<span class="op">$</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>
<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">$</span>decovMu,<span class="dv">3</span>)</a></code></pre></div>
<pre><code>##            MuN1      MuT
## Gene 1 6.166484 5.924321
## Gene 2 6.677594 2.974551
## Gene 3 9.329628 7.396647</code></pre>
<div class="sourceCode" id="cb16"><pre class="sourceCode r"><code class="sourceCode r"><a class="sourceLine" id="cb16-1" title="1"><span class="kw">head</span>(res.S2<span class="op">$</span>decovSigma,<span class="dv">3</span>)</a></code></pre></div>
<pre><code>##           SigmaN   SigmaT
## Gene 1 0.2222914 1.127726
## Gene 2 0.2319681 1.614169
## Gene 3 0.1881647 1.320477</code></pre>
</div>
<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>
<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>
<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>
<p><img 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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>
<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>
<p><img 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" style="display: block; margin: auto;" /></p>
</div>
<div id="real-data-prad-in-tcga-dataset" class="section level2">
<h2>6.4 Real data: PRAD in TCGA dataset</h2>
<div id="preprocessing" class="section level3">
<h3>6.4.1 Preprocessing</h3>
<p>For the deconvolution of real data with DeMixT, the user may apply the following preprocessing steps first. Here, we use the PRAD (prostate adenocarcinoma) from TCGA as an example.</p>
<ol style="list-style-type: decimal">
<li><strong>(Optional) Remove suspicious samples by hierarchical clustering</strong></li>
</ol>
<p>It is possible that the some of the tumor and normal samples are mislabelled. We use the function</p>
<pre><code># hc_labels &lt;- detect_suspicious_sample_by_hierarchical_clustering_2comp(count.matrix, normal.id, tumor.id)</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 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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>
<ol start="2" style="list-style-type: decimal">
<li><strong>Select genes with small variation in gene expression across samples</strong></li>
</ol>
<p>In this step, we select a subset of ~9000 genes from the original gene set (&gt;50,000) before running DeMixT with the GS (Gene Selection) method so that our model-based gene selection maintains good statistical properties. We use the function</p>
<pre><code># plot_sd(count.matrix, normal.id, tumor.id)</code></pre>
<p>to visualize the distribution of standard deviation of log2 expressions space of normal and tumor samples, <img src="data:image/png;base64,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" width="85%" style="display: block; margin: auto;" /> and use the function</p>
<pre><code># num_gene_remaining_different_cutoffs &lt;- subset_sd_gene_remaining(count.matrix, normal.id, tumor.id, cutoff_normal_range = c(0.1, 1.0),  cutoff_tumor_range = c(0, 2.5), cutoff_step = 0.1)</code></pre>
<p>to find he a range of variance in genes from normal samples <code>(cutoff_normal_range)</code> and from tumor samples <code>(cutoff_tumor_range)</code> which results in roughly 9,000 genes.</p>
<p>We then use the function</p>
<pre><code># count.matrix &lt;- subset_sd(count.matrix, normal.id, tumor.id, cutoff_normal = cutoff_normal_range, cutoff_tumor = cutoff_tumor_range)</code></pre>
<p>to update the <code>count.matrix</code> such that it only includes the selected genes.</p>
<ol start="3" style="list-style-type: decimal">
<li><strong>Scale normalization</strong></li>
</ol>
<p>We apply a scale normalization at the 75th percentile across all the tumor and normal samples using the function</p>
<pre><code># count.matrix &lt;- scale_normalization_75th_percentile(count.matrix)</code></pre>
<p>to adjust the expression levels in the samples.</p>
<p><strong>Note</strong>: The user may also use the function</p>
<pre><code># count.matrix &lt;- DeMixT_preprocessing(count.matrix, normal.id, tumor.id, cutoff_normal_range = c(0.1, 1.0),  cutoff_tumor_range = c(0, 2.5), cutoff_step = 0.1)</code></pre>
<p>to perform the preprocessing steps of 2) and 3) in one go.</p>
<ol start="4" style="list-style-type: decimal">
<li><strong>(Optional) Batch effect correction for tumor samples from different batches by ComBat</strong></li>
</ol>
<p>If the tumor samples are from different batches, we recommend the user to inspect the batch effect using the function before running DeMixT</p>
<pre><code># plot_dim(count.matrix.tumor, labels, legend.position = &#39;bottomleft&#39;, legend.cex = 1.2)</code></pre>
<p>This function will generate a PCA plot, in which the samples are colored by the <code>labels</code> indicating different batches of tumor samples, as well as normal samples. If there is a clear separation between different batches of tumor samples, there is likely batch effects. We use the function</p>
<pre><code># count.matrix.tumor &lt;- batch_correction(count.matrix.tumor, labels)</code></pre>
<p>to reduce this effect, where <code>count.matrix.tumor</code> is the raw count matrix only from tumor samples and <code>labels</code> is the factor of tumor batches. The user may choose other batch effect correction methods at this step.</p>
<p>The user may inspect the batch effect again after the above step using the mentioned function</p>
<pre><code># plot_dim(count.matrix.tumor, labels, legend.position = &#39;bottomleft&#39;, legend.cex = 1.2)</code></pre>
</div>
<div id="deconvolution-using-demixt" class="section level3">
<h3>6.4.2 Deconvolution using DeMixT</h3>
<p>To optimize the DeMixT parameter setting for the input data, we recommend testing an array of combinations of the number of spike-ins and the number of selected genes.</p>
<p>The number of CPU cores used by the DeMixT function for parallel computing is specified by the parameter <code>nthread</code>. By default (such as in the code block below), <code>nthread = total_number_of_cores_on_the_machine - 1</code>. The user can change <code>nthread</code> to a number between 0 and the total number of cores on the machine.</p>
<pre><code>## Because of the random initial values and the spike-in samples within the DeMixT function, 
## we would like to remind the user to set seeds to keep track. This seed setting will be 
## internalized in DeMixT in the next update.

# set.seed(1234)

# data.Y = SummarizedExperiment(assays = list(counts = count.matrix[, tumor.id]))
# data.N1 &lt;- SummarizedExperiment(assays = list(counts = count.matrix[, normal.id]))

## In practice, we set the maximum number of spike-in as min(n/3, 200), 
## where n is the number of samples. 
# nspikesin_list = c(0, 50, 100, 150)
## One may set a wider range than provided below for studies other than TCGA.
# ngene.selected_list = c(500, 1000, 1500, 2500)

#for(nspikesin in nspikesin_list){
#    for(ngene.selected in ngene.selected_list){
#        name = paste(&quot;PRAD_demixt_GS_res_nspikesin&quot;, nspikesin, &quot;ngene.selected&quot;, 
#                      ngene.selected,  sep = &quot;_&quot;);
#        name = paste(name, &quot;.RData&quot;, sep = &quot;&quot;);
#        res = DeMixT(data.Y = data.Y,
#                     data.N1 = data.N1,
#                     ngene.selected.for.pi = ngene.selected,
#                     ngene.Profile.selected = ngene.selected,
#                     filter.sd = 0.7, # same upper bound of gene expression standard deviation 
#                     # for normal reference. i.e., preprocessed_data$sd_cutoff_normal[2]
#                     gene.selection.method = &quot;GS&quot;,
#                     nspikein = nspikesin)
#        save(res, file = name)
#    }
#}</code></pre>
<p>We suggest selecting the optimal parameter combination that produces the largest average correlation of estimated tumor propotions with those produced by other combinations. The location of the mode of the Pi estimation may also be considered. The mode located too high or too low may suggest biased estimation.</p>
<p>Instead of selecting using the parameter combination with the highest correlation, one can also select the parameter combination that produces estimated tumor proportions that are most biologically meaningful.</p>
<p>A comprehensive tutorial of using DeMixT for real data deconvolution can be found at <a href="https://wwylab.github.io/DeMixT/tutorial.html">https://wwylab.github.io/DeMixT/tutorial.html</a>.</p>
</div>
</div>
<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>
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" width="65%" style="display: block; margin: auto;" /></p>
</div>
</div>
<div id="reference" class="section level1">
<h1>7. Reference</h1>
<p>[1]. Wang, Z. et al. Transcriptome Deconvolution of Heterogeneous Tumor Samples with Immune Infiltration. iScience 9, 451–460 (2018).</p>
</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)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.5 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
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=en_GB              LC_COLLATE=C              
##  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
## 
## attached base packages:
## character(0)
## 
## other attached packages:
## [1] DeMixT_1.14.0
## 
## 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>
</div>



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