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            <h1 id="seo-header">Seurat (六) 整合数据（二）</h1>
            
            
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                <h2 id="第一步-常规配置"><a href="#第一步-常规配置" class="headerlink" title="第一步 常规配置"></a>第一步 常规配置</h2><figure class="highlight gauss"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br><span class="line">15</span><br><span class="line">16</span><br><span class="line">17</span><br><span class="line">18</span><br><span class="line">19</span><br></pre></td><td class="code"><pre><code class="hljs gauss"><span class="hljs-keyword">library</span>(Matrix)<br><span class="hljs-keyword">library</span>(Seurat)<br><span class="hljs-keyword">library</span>(plyr)<br><span class="hljs-keyword">library</span>(dplyr)<br><span class="hljs-keyword">library</span>(patchwork)<br><span class="hljs-keyword">library</span>(purrr)<br><br><span class="hljs-built_in">rm</span>(list = <span class="hljs-built_in">ls</span>())<br><span class="hljs-meta"># 配置数据路径</span><br>root_path = <span class="hljs-string">&quot;~/zlliu/R_data/TCGA&quot;</span><br> <br><span class="hljs-meta"># 配置结果保存路径</span><br>output_path = root_path<br><span class="hljs-keyword">if</span> (!file.exists(output_path))&#123;dir.create(output_path)&#125;<br> <br><span class="hljs-meta"># 设置工作目录，输出文件将保存在此目录下</span><br><span class="hljs-built_in">setwd</span>(output_path)<br><span class="hljs-built_in">getwd</span>()<br><br></code></pre></td></tr></table></figure>

<figure class="highlight isbl"><table><tr><td class="gutter"><pre><span class="line">1</span><br></pre></td><td class="code"><pre><code class="hljs isbl"><span class="hljs-variable">scRNA</span> &lt;- <span class="hljs-function"><span class="hljs-title">f_read10x</span>(<span class="hljs-string">&#x27;CRPC-PRAD&#x27;</span>)</span><br></code></pre></td></tr></table></figure>

<h2 id="第二步-质量控制"><a href="#第二步-质量控制" class="headerlink" title="第二步 质量控制"></a>第二步 质量控制</h2><figure class="highlight less"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br></pre></td><td class="code"><pre><code class="hljs less"><span class="hljs-selector-tag">options</span>(repr.plot.width=<span class="hljs-number">12</span>, repr.plot.height=<span class="hljs-number">6</span>)<br><span class="hljs-selector-tag">options</span>(ggrepel.max.overlaps = Inf)<br><span class="hljs-selector-tag">library</span>(ggplot2)<br><span class="hljs-selector-tag">for</span> (scRNAo in scRNA)&#123;<br>    <span class="hljs-selector-tag">print</span>(<span class="hljs-built_in">VlnPlot</span>(scRNAo, features = <span class="hljs-built_in">c</span>(<span class="hljs-string">&quot;nFeature_RNA&quot;</span>, <span class="hljs-string">&quot;nCount_RNA&quot;</span>, <span class="hljs-string">&quot;percent.mt&quot;</span>), ncol = <span class="hljs-number">3</span>) + <span class="hljs-built_in">labs</span>(title = scRNAo<span class="hljs-variable">@project</span>.name))<br>&#125;<br><br></code></pre></td></tr></table></figure>

<h2 id="第三步-整合数据"><a href="#第三步-整合数据" class="headerlink" title="第三步 整合数据"></a>第三步 整合数据</h2><figure class="highlight lua"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br><span class="line">15</span><br></pre></td><td class="code"><pre><code class="hljs lua">f_go4pca &lt;- <span class="hljs-function"><span class="hljs-keyword">function</span><span class="hljs-params">(scRNA)</span></span>&#123;<br>    <span class="hljs-keyword">for</span> (lc_ba <span class="hljs-keyword">in</span> names(scRNA))&#123;<br>        scRNA<span class="hljs-string">[[lc_ba]]</span> &lt;- scRNA<span class="hljs-string">[[lc_ba]]</span> %&gt;% NormalizeData() %&gt;% FindVariableFeatures()<br>        all.genes &lt;- rownames(scRNA<span class="hljs-string">[[lc_ba]]</span>)<br>        scRNA<span class="hljs-string">[[lc_ba]]</span> &lt;- ScaleData(scRNA<span class="hljs-string">[[lc_ba]]</span>, features = all.genes)<br>        scRNA<span class="hljs-string">[[lc_ba]]</span> &lt;- RunPCA(scRNA<span class="hljs-string">[[lc_ba]]</span>, features = VariableFeatures(object = scRNA<span class="hljs-string">[[lc_ba]]</span>))<br>    &#125;<br>    scRNA<br>&#125;<br>f_go4group &lt;- <span class="hljs-function"><span class="hljs-keyword">function</span><span class="hljs-params">(scRNA, mdN, mdNo=&#x27;orig.ident&#x27;, s=1, e=4)</span></span>&#123;<br>    <span class="hljs-keyword">for</span> (lc_ba <span class="hljs-keyword">in</span> names(scRNA))&#123;<br>        scRNA<span class="hljs-string">[[lc_ba]]</span><span class="hljs-string">[[mdN]]</span> &lt;- substr(unlist(scRNA<span class="hljs-string">[[lc_ba]]</span><span class="hljs-string">[[mdNo]]</span>),s,e)<br>    &#125;<br>    scRNA<br>&#125;<br></code></pre></td></tr></table></figure>

<figure class="highlight actionscript"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br></pre></td><td class="code"><pre><code class="hljs actionscript">scRNA &lt;- f_go4group(scRNA, <span class="hljs-string">&#x27;group&#x27;</span>)<br>scRNA &lt;- f_go4group(scRNA, <span class="hljs-string">&#x27;batch&#x27;</span>, s=<span class="hljs-number">5</span>, e=<span class="hljs-number">5</span>)<br>scRNA &lt;- Reduce(<span class="hljs-keyword">function</span><span class="hljs-params">(<span class="hljs-rest_arg">...) merge</span>(<span class="hljs-rest_arg">...), scRNA</span>)</span><br></code></pre></td></tr></table></figure>

<figure class="highlight reasonml"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br></pre></td><td class="code"><pre><code class="hljs reasonml">scRNA &lt;- scRNA %&gt;% <span class="hljs-constructor">NormalizeData()</span> %&gt;% <span class="hljs-constructor">FindVariableFeatures()</span><br>all.genes &lt;- rownames(scRNA)<br>scRNA &lt;- <span class="hljs-constructor">ScaleData(<span class="hljs-params">scRNA</span>, <span class="hljs-params">features</span> = <span class="hljs-params">all</span>.<span class="hljs-params">genes</span>)</span><br>scRNA &lt;- <span class="hljs-constructor">RunPCA(<span class="hljs-params">scRNA</span>, <span class="hljs-params">features</span> = VariableFeatures(<span class="hljs-params">object</span> = <span class="hljs-params">scRNA</span>)</span>)<br></code></pre></td></tr></table></figure>

<figure class="highlight erlang"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br></pre></td><td class="code"><pre><code class="hljs erlang"><span class="hljs-function"><span class="hljs-title">library</span><span class="hljs-params">(harmony)</span></span><br><span class="hljs-function"><span class="hljs-title">scRNA</span> = <span class="hljs-title">scRNA</span> %&gt;% R<span class="hljs-title">unHarmony</span><span class="hljs-params">(<span class="hljs-string">&quot;batch&quot;</span>, plot_convergence = TRUE)</span></span><br></code></pre></td></tr></table></figure>

<h2 id="第四步-基于-Harmony-进行分群"><a href="#第四步-基于-Harmony-进行分群" class="headerlink" title="第四步 基于 Harmony 进行分群"></a>第四步 基于 Harmony 进行分群</h2><figure class="highlight reasonml"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br></pre></td><td class="code"><pre><code class="hljs reasonml">scRNA &lt;- scRNA %&gt;% <span class="hljs-constructor">RunUMAP(<span class="hljs-params">reduction</span> = <span class="hljs-string">&quot;harmony&quot;</span>, <span class="hljs-params">dims</span> = 1:30)</span><br>scRNA &lt;- scRNA %&gt;% <span class="hljs-constructor">FindNeighbors(<span class="hljs-params">reduction</span> = <span class="hljs-string">&quot;harmony&quot;</span>)</span> %&gt;%  <span class="hljs-constructor">FindClusters(<span class="hljs-params">resolution</span> = 0.1)</span><br>scRNA<span class="hljs-literal">[[&#x27;<span class="hljs-identifier">t_RNA_snn_res</span>.<span class="hljs-number">0.1</span>&#x27;]</span>] &lt;- <span class="hljs-constructor">Idents(<span class="hljs-params">scRNA</span>)</span><br><span class="hljs-constructor">Idents(<span class="hljs-params">scRNA</span>)</span> &lt;- scRNA<span class="hljs-literal">[[&#x27;<span class="hljs-identifier">t_RNA_snn_res</span>.<span class="hljs-number">0.1</span>&#x27;]</span>]<br>all_markers &lt;- <span class="hljs-constructor">FindAllMarkers(<span class="hljs-params">scRNA</span>, <span class="hljs-params">min</span>.<span class="hljs-params">pct</span> = 0.25, <span class="hljs-params">logfc</span>.<span class="hljs-params">threshold</span> = 0.25)</span><br>significant_markers &lt;- subset(all_markers, subset = p_val_adj&lt;<span class="hljs-number">0.05</span>)<br></code></pre></td></tr></table></figure>
                
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