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  135. <h1 id="seo-header">SingleR (五) 不同参考集与混合参考集的对比</h1>
  136. <div class="markdown-body">
  137. <h2 id="第一步-构建-21-09-21-SingleR-R"><a href="#第一步-构建-21-09-21-SingleR-R" class="headerlink" title="第一步 构建 21.09.21.SingleR.R"></a>第一步 构建 21.09.21.SingleR.R</h2><figure class="highlight routeros"><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><span class="line">20</span><br><span class="line">21</span><br><span class="line">22</span><br><span class="line">23</span><br><span class="line">24</span><br><span class="line">25</span><br><span class="line">26</span><br><span class="line">27</span><br><span class="line">28</span><br><span class="line">29</span><br><span class="line">30</span><br><span class="line">31</span><br><span class="line">32</span><br><span class="line">33</span><br><span class="line">34</span><br><span class="line">35</span><br><span class="line">36</span><br><span class="line">37</span><br><span class="line">38</span><br><span class="line">39</span><br><span class="line">40</span><br><span class="line">41</span><br><span class="line">42</span><br><span class="line">43</span><br><span class="line">44</span><br><span class="line">45</span><br><span class="line">46</span><br><span class="line">47</span><br><span class="line">48</span><br><span class="line">49</span><br><span class="line">50</span><br><span class="line">51</span><br><span class="line">52</span><br><span class="line">53</span><br><span class="line">54</span><br><span class="line">55</span><br><span class="line">56</span><br><span class="line">57</span><br><span class="line">58</span><br><span class="line">59</span><br><span class="line">60</span><br><span class="line">61</span><br><span class="line">62</span><br><span class="line">63</span><br><span class="line">64</span><br><span class="line">65</span><br><span class="line">66</span><br><span class="line">67</span><br><span class="line">68</span><br><span class="line">69</span><br><span class="line">70</span><br><span class="line">71</span><br><span class="line">72</span><br><span class="line">73</span><br><span class="line">74</span><br><span class="line">75</span><br><span class="line">76</span><br><span class="line">77</span><br><span class="line">78</span><br><span class="line">79</span><br><span class="line">80</span><br><span class="line">81</span><br><span class="line">82</span><br><span class="line">83</span><br><span class="line">84</span><br><span class="line">85</span><br><span class="line">86</span><br><span class="line">87</span><br><span class="line">88</span><br><span class="line">89</span><br><span class="line">90</span><br><span class="line">91</span><br><span class="line">92</span><br><span class="line">93</span><br><span class="line">94</span><br><span class="line">95</span><br><span class="line">96</span><br><span class="line">97</span><br><span class="line">98</span><br><span class="line">99</span><br><span class="line">100</span><br><span class="line">101</span><br><span class="line">102</span><br><span class="line">103</span><br><span class="line">104</span><br><span class="line">105</span><br><span class="line">106</span><br><span class="line">107</span><br><span class="line">108</span><br><span class="line">109</span><br><span class="line">110</span><br><span class="line">111</span><br></pre></td><td class="code"><pre><code class="hljs routeros"><span class="hljs-comment"># 0、加载程辑包</span><br>library(Seurat)<br>library(dplyr)<br>library(patchwork)<br> <br><span class="hljs-comment"># 加载 SingleR</span><br>library(SingleR)<br>library(SummarizedExperiment)<br>library(scater)<br>library(BiocParallel) # 并行计算加速<br> <br><span class="hljs-comment"># 配置数据路径</span><br>root_path = <span class="hljs-string">&quot;/home/rqzhang/rqzhang&quot;</span><br> <br><span class="hljs-comment"># 配置结果保存路径</span><br>output_path = <span class="hljs-string">&quot;~/zlliu/R_output/21.09.21.SingleR&quot;</span><br><span class="hljs-keyword">if</span> (!file.exists(output_path))&#123;dir.create(output_path)&#125;<br> <br><span class="hljs-comment"># 设置工作目录,输出文件将保存在此目录下</span><br>setwd(output_path)<br>getwd()<br><br><span class="hljs-comment"># 1、读取数据和参考数据集</span><br>scRNA = readRDS(<span class="hljs-string">&quot;/home/rqzhang/rqzhang/SC_gene_integrate.rds&quot;</span>)<br><br><span class="hljs-keyword">if</span> (!(<span class="hljs-string">&quot;hM1.se&quot;</span> %<span class="hljs-keyword">in</span>% ls()))&#123;<br> hM1.se &lt;- readRDS(<span class="hljs-string">&quot;/home/rqzhang/zlliu/R_data/human_M1_10x/hM1.se.rds&quot;</span>)<br>&#125;<br><span class="hljs-keyword">if</span> (!(<span class="hljs-string">&quot;hmca&quot;</span> %<span class="hljs-keyword">in</span>% ls()))&#123;<br> hmca &lt;- readRDS(<span class="hljs-string">&quot;/home/rqzhang/zlliu/R_data/Human_Multiple_Cortical_Areas_SMART-seq/hmca.rds&quot;</span>)<br>&#125;<br><br><span class="hljs-comment"># 1.1、裁剪数据集</span><br>f_noNull &lt;- function(lc_se, lc_col)&#123;<br> <span class="hljs-keyword">if</span>(any(lc_se<span class="hljs-variable">$meta</span>[<span class="hljs-string">&quot;sample_name&quot;</span>] != colnames(lc_se)))&#123;<br> return<br> &#125;<br> lc_idx = which(lc_se<span class="hljs-variable">$meta</span>[lc_col] == <span class="hljs-string">&quot;&quot;</span>)<br> lc_res = lc_se[, -lc_idx]<br> lc_res<span class="hljs-variable">$meta</span> = lc_se<span class="hljs-variable">$meta</span>[-lc_idx,]<br> lc_res<br>&#125;<br> <br>hmca &lt;- f_noNull(hmca, <span class="hljs-string">&quot;subclass_label&quot;</span>)<br><br><span class="hljs-comment"># 1.2、修订标签</span><br>f_listUpdateRe &lt;- function(lc_obj, lc_bool, lc_item)&#123;<br> lc_obj[lc_bool] &lt;- rep(lc_item,<span class="hljs-attribute">times</span>=sum(lc_bool))<br> lc_obj<br>&#125;<br><br>f_subSameName &lt;- function(lc_obj, lc_name_x, lc_name_y)&#123;<br> <span class="hljs-keyword">for</span> (lc_i <span class="hljs-keyword">in</span> 1:length(lc_name_x))&#123;<br> lc_x = lc_name_x[lc_i]<br> lc_y = lc_name_y[lc_i]<br> lc_idx = (lc_obj == lc_x)<br> lc_obj = f_listUpdateRe(lc_obj , lc_idx, lc_y)<br> &#125;<br> lc_obj<br>&#125;<br><br>hM1.se<span class="hljs-variable">$meta</span><span class="hljs-variable">$subclass_label</span> &lt;- f_subSameName(hM1.se<span class="hljs-variable">$meta</span><span class="hljs-variable">$subclass_label</span>,<br> c(<span class="hljs-string">&#x27;Astrocyte&#x27;</span>, <span class="hljs-string">&#x27;Endothelial&#x27;</span>, <span class="hljs-string">&#x27;LAMP5&#x27;</span>, <span class="hljs-string">&#x27;Microglia&#x27;</span>, <span class="hljs-string">&#x27;Oligodendrocyte&#x27;</span>, <span class="hljs-string">&#x27;PVALB&#x27;</span>, <span class="hljs-string">&#x27;SST&#x27;</span>, <span class="hljs-string">&#x27;VIP&#x27;</span>),<br> c(<span class="hljs-string">&#x27;Astro&#x27;</span>, <span class="hljs-string">&#x27;Endo&#x27;</span>, <span class="hljs-string">&#x27;Lamp5&#x27;</span>, <span class="hljs-string">&#x27;Micro-PVM&#x27;</span>, <span class="hljs-string">&#x27;Oligo&#x27;</span>, <span class="hljs-string">&#x27;Pvalb&#x27;</span>, <span class="hljs-string">&#x27;Sst&#x27;</span>, <span class="hljs-string">&#x27;Vip&#x27;</span>))<br>hmca<span class="hljs-variable">$meta</span><span class="hljs-variable">$subclass_label</span> &lt;- f_subSameName(hmca<span class="hljs-variable">$meta</span><span class="hljs-variable">$subclass_label</span>,<br> c(<span class="hljs-string">&#x27;Astrocyte&#x27;</span>, <span class="hljs-string">&#x27;Endothelial&#x27;</span>, <span class="hljs-string">&#x27;LAMP5&#x27;</span>, <span class="hljs-string">&#x27;Microglia&#x27;</span>, <span class="hljs-string">&#x27;Oligodendrocyte&#x27;</span>, <span class="hljs-string">&#x27;PVALB&#x27;</span>, <span class="hljs-string">&#x27;SST&#x27;</span>, <span class="hljs-string">&#x27;VIP&#x27;</span>),<br> c(<span class="hljs-string">&#x27;Astro&#x27;</span>, <span class="hljs-string">&#x27;Endo&#x27;</span>, <span class="hljs-string">&#x27;Lamp5&#x27;</span>, <span class="hljs-string">&#x27;Micro-PVM&#x27;</span>, <span class="hljs-string">&#x27;Oligo&#x27;</span>, <span class="hljs-string">&#x27;Pvalb&#x27;</span>, <span class="hljs-string">&#x27;Sst&#x27;</span>, <span class="hljs-string">&#x27;Vip&#x27;</span>))<br><br><br><span class="hljs-comment"># 2、进行预测</span><br>data_for_SingleR = scRNA[[<span class="hljs-string">&quot;integrated&quot;</span>]]@data<br><br><br><span class="hljs-comment"># 保留共同基因</span><br>common_gene &lt;- intersect(rownames(data_for_SingleR), c(rownames(hM1.se), rownames(hmca)))<br>data_for_SingleR &lt;- data_for_SingleR[common_gene,]<br><br>tp_idx = na.omit(match(common_gene, rownames(hM1.se)))<br>lc_hM1.se &lt;- hM1.se[rownames(hM1.se)[tp_idx], ]<br>tp_idx = na.omit(match(common_gene, rownames(hmca)))<br>lc_hmca &lt;- hmca[rownames(hmca)[tp_idx], ]<br><br><span class="hljs-comment"># 进行分类预测</span><br>pred_1 &lt;- SingleR(test = data_for_SingleR, ref = list(<span class="hljs-attribute">m1</span>=lc_hM1.se, <span class="hljs-attribute">mca</span>=lc_hmca),<br> labels = list(hM1.se<span class="hljs-variable">$meta</span><span class="hljs-variable">$subclass_label</span>, hmca<span class="hljs-variable">$meta</span><span class="hljs-variable">$subclass_label</span>),<br> <span class="hljs-attribute">BPPARAM</span>=MulticoreParam(32)) # 32CPUs<br><br>pred_2 &lt;- SingleR(test = data_for_SingleR, ref = lc_hM1.se,<br> labels = hM1.se<span class="hljs-variable">$meta</span><span class="hljs-variable">$subclass_label</span>,<br> <span class="hljs-attribute">BPPARAM</span>=MulticoreParam(32)) # 32CPUs<br><br>pred_3 &lt;- SingleR(test = data_for_SingleR, ref = lc_hmca,<br> labels = hmca<span class="hljs-variable">$meta</span><span class="hljs-variable">$subclass_label</span>,<br> <span class="hljs-attribute">BPPARAM</span>=MulticoreParam(32)) # 32CPUs<br><br><span class="hljs-comment"># 保存结果</span><br>f_merge &lt;- function(lc_mergedList)&#123;<br> Reduce(function(<span class="hljs-built_in">..</span>.) merge(<span class="hljs-built_in">..</span>., <span class="hljs-attribute">by</span>=<span class="hljs-string">&quot;CB&quot;</span>), lc_mergedList)<br>&#125;<br><br>f_pred2meta &lt;- function(lc_pred, lc_colname)&#123;<br> lc_result &lt;- as.data.frame(lc_pred<span class="hljs-variable">$labels</span>)<br> lc_result<span class="hljs-variable">$CB</span> &lt;- rownames(lc_pred)<br> colnames(lc_result) &lt;- c(lc_colname, <span class="hljs-string">&#x27;CB&#x27;</span>)<br> lc_result<br>&#125;<br><br>pred_1 &lt;- f_pred2meta(pred_1, <span class="hljs-string">&quot;hM1_hmca_class&quot;</span>)<br>pred_2 &lt;- f_pred2meta(pred_2, <span class="hljs-string">&quot;hM1_class&quot;</span>)<br>pred_3 &lt;- f_pred2meta(pred_3, <span class="hljs-string">&quot;hmca_class&quot;</span>)<br><br></code></pre></td></tr></table></figure>
  138. <h2 id="第二步-构建PBS文件并提交"><a href="#第二步-构建PBS文件并提交" class="headerlink" title="第二步 构建PBS文件并提交"></a>第二步 构建PBS文件并提交</h2><h2 id="第三步-可视化"><a href="#第三步-可视化" class="headerlink" title="第三步 可视化"></a>第三步 可视化</h2><figure class="highlight mipsasm"><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><span class="line">20</span><br><span class="line">21</span><br><span class="line">22</span><br><span class="line">23</span><br><span class="line">24</span><br><span class="line">25</span><br><span class="line">26</span><br><span class="line">27</span><br><span class="line">28</span><br><span class="line">29</span><br><span class="line">30</span><br><span class="line">31</span><br><span class="line">32</span><br><span class="line">33</span><br><span class="line">34</span><br><span class="line">35</span><br><span class="line">36</span><br><span class="line">37</span><br><span class="line">38</span><br><span class="line">39</span><br><span class="line">40</span><br></pre></td><td class="code"><pre><code class="hljs mipsasm"><span class="hljs-comment"># 0、加载程辑包</span><br>library(Seurat)<br>library(dplyr)<br>library(patchwork)<br>library(ggplot2)<br><br><span class="hljs-comment"># 配置数据路径</span><br>root_path = <span class="hljs-string">&quot;~/zlliu/R_output/21.09.21.SingleR&quot;</span><br><br><span class="hljs-comment"># 配置结果保存路径</span><br>output_path = <span class="hljs-string">&quot;~/zlliu/R_output/21.09.21.SingleR&quot;</span><br>if (!file.exists(output_path))&#123;<span class="hljs-keyword">dir.create(output_path)&#125;</span><br><span class="hljs-keyword"></span><br><span class="hljs-comment"># 设置工作目录,输出文件将保存在此目录下</span><br>setwd(output_path)<br>getwd()<br><br><span class="hljs-comment"># 1、读取数据</span><br><span class="hljs-keyword">scRNA </span>= readRDS(<span class="hljs-string">&quot;/home/rqzhang/rqzhang/SC_gene_integrate.rds&quot;</span>)<br><br><span class="hljs-comment"># 2、读取预测信息</span><br>result &lt;- read.table(<span class="hljs-string">&quot;hM1_hmca_class.txt&quot;</span>, stringsAsFactors = F)<br><br><span class="hljs-comment"># 3、合并</span><br><span class="hljs-keyword">scRNA@meta.data$CB </span>&lt;- rownames(<span class="hljs-keyword">scRNA@meta.data)</span><br><span class="hljs-keyword"></span><span class="hljs-keyword">scRNA@meta.data=merge(scRNA@meta.data, </span>result, <span class="hljs-keyword">by=&quot;CB&quot;)</span><br><span class="hljs-keyword"></span>rownames(<span class="hljs-keyword">scRNA@meta.data) </span>&lt;- <span class="hljs-keyword">scRNA@meta.data$CB</span><br><span class="hljs-keyword"></span><br><span class="hljs-comment"># 11、进行PCA降维</span><br><span class="hljs-keyword">scRNA </span>&lt;- RunPCA(<span class="hljs-keyword">scRNA, </span>features = VariableFeatures(object = <span class="hljs-keyword">scRNA))</span><br><span class="hljs-keyword"></span><br><span class="hljs-comment"># 12、决定主维度</span><br>options(repr.plot.width=<span class="hljs-number">18</span>, repr.plot.height=<span class="hljs-number">6</span>)<br>ElbowPlot(<span class="hljs-keyword">scRNA, </span>ndims = <span class="hljs-number">40</span>)<br><br>pca_dim &lt;- <span class="hljs-number">24</span><br><br><span class="hljs-comment"># 13、UMAP降维可视化</span><br><span class="hljs-keyword">scRNA </span>&lt;- RunUMAP(<span class="hljs-keyword">scRNA, </span><span class="hljs-keyword">dims </span>= <span class="hljs-number">1</span>:pca_dim)<br><br></code></pre></td></tr></table></figure>
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