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            <h1 id="seo-header">SingleR (六) 切出指定群并检验</h1>
            
            
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                <h2 id="第一步-切出神经细胞"><a href="#第一步-切出神经细胞" class="headerlink" title="第一步 切出神经细胞"></a>第一步 切出神经细胞</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><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><span class="line">112</span><br><span class="line">113</span><br><span class="line">114</span><br><span class="line">115</span><br><span class="line">116</span><br><span class="line">117</span><br><span class="line">118</span><br><span class="line">119</span><br><span class="line">120</span><br><span class="line">121</span><br><span class="line">122</span><br><span class="line">123</span><br><span class="line">124</span><br><span class="line">125</span><br><span class="line">126</span><br><span class="line">127</span><br><span class="line">128</span><br><span class="line">129</span><br><span class="line">130</span><br><span class="line">131</span><br><span class="line">132</span><br><span class="line">133</span><br></pre></td><td class="code"><pre><code class="hljs lua">library(Matrix)<br>library(Seurat)<br>library(plyr)<br>library(dplyr)<br>library(patchwork)<br>library(purrr)<br><br>library(RColorBrewer)<br>library(ggplot2)<br>library(ggrepel)<br>blank_theme &lt;- theme_minimal()+<br>    theme(<br>        axis.title.x = element_blank(),<br>        axis.text.x=element_blank(),<br>        axis.title.y = element_blank(),<br>        axis.text.y=element_blank(),<br>        panel.border = element_blank(),<br>        panel.grid=element_blank(),<br>        axis.ticks = element_blank(),<br>        plot.title=element_text(size=<span class="hljs-number">14</span>, face=<span class="hljs-string">&quot;bold&quot;</span>,hjust = <span class="hljs-number">0.5</span>)<br>    )<br>col_Paired &lt;- colorRampPalette(brewer.pal(<span class="hljs-number">12</span>, <span class="hljs-string">&quot;Paired&quot;</span>))<br>f_pie &lt;- <span class="hljs-function"><span class="hljs-keyword">function</span><span class="hljs-params">(lc_x, lc_main, lc_x_p = 1.3, lc_r = T)</span></span>&#123;<br>    lc_cols &lt;- col_Paired(length(lc_x))<br>    lc_v &lt;- as.vector(<span class="hljs-number">100</span>*lc_x)<br>    lc_df &lt;- data.frame(<span class="hljs-built_in">type</span> = names(lc_x), nums = lc_v)<br>    lc_df &lt;- lc_df[order(lc_df$<span class="hljs-built_in">type</span>),]<br>    lc_percent = sprintf(<span class="hljs-string">&#x27;%0.2f%%&#x27;</span>,lc_df$nums)<br>    <span class="hljs-keyword">if</span>(lc_r)&#123;<br>        lc_df$pos &lt;- with(lc_df, <span class="hljs-number">100</span>-cumsum(nums)+nums/<span class="hljs-number">2</span>)<br>    &#125;<span class="hljs-keyword">else</span>&#123;<br>        lc_df$pos &lt;- with(lc_df, cumsum(nums)-nums/<span class="hljs-number">2</span>)<br>    &#125; <br>    lc_pie &lt;- ggplot(data = lc_df, mapping = aes(x = <span class="hljs-number">1</span>, y = nums, fill = <span class="hljs-built_in">type</span>)) + geom_bar(stat = <span class="hljs-string">&#x27;identity&#x27;</span>)<br>#     <span class="hljs-built_in">print</span>(lc_df)<br>#     <span class="hljs-built_in">print</span>(lc_pie)<br>    lc_pie &lt;- lc_pie + coord_polar(<span class="hljs-string">&quot;y&quot;</span>, start=<span class="hljs-number">0</span>, direction = <span class="hljs-number">1</span>) + scale_fill_manual(values=lc_cols) + blank_theme <br>    lc_pie &lt;- lc_pie + geom_text_repel(aes(x = lc_x_p, y=pos),label= lc_percent, force = T, <br>                        arrow = arrow(length=unit(<span class="hljs-number">0.01</span>, <span class="hljs-string">&quot;npc&quot;</span>)), segment.color = <span class="hljs-string">&quot;#cccccc&quot;</span>, segment.size = <span class="hljs-number">0.5</span>)<br>    lc_pie &lt;- lc_pie + labs(title = lc_main)<br>    lc_pie<br>&#125;<br><br># 配置数据和mark基因表的路径<br>root_path = <span class="hljs-string">&quot;~/zlliu/R_data/hBLA&quot;</span><br> <br># 配置结果保存路径<br>output_path = <span class="hljs-string">&quot;~/zlliu/R_data/21.10.01.10x&quot;</span><br><span class="hljs-keyword">if</span> (!file.exists(output_path))&#123;dir.<span class="hljs-built_in">create</span>(output_path)&#125;<br> <br># 设置工作目录，输出文件将保存在此目录下<br>setwd(output_path)<br>getwd()<br><br>scRNA &lt;- readRDS(<span class="hljs-string">&#x27;~/zlliu/R_data/21.09.29.10x/scRNA.rds&#x27;</span>)<br><br>scRNA<br><br>f_UMAP_more &lt;- <span class="hljs-function"><span class="hljs-keyword">function</span><span class="hljs-params">(sObject, lc_group.by, lc_reduction=&quot;umap&quot;)</span></span>&#123;<br>    res &lt;- (DimPlot(sObject, reduction = lc_reduction, group.by = lc_group.by[<span class="hljs-number">1</span>], label = T, repel = T, label.size = <span class="hljs-number">6</span>) + <br>    labs(title = lc_group.by[<span class="hljs-number">1</span>]))<br>    <span class="hljs-keyword">for</span>(lc_i <span class="hljs-keyword">in</span> <span class="hljs-number">2</span>:length(lc_group.by))&#123;<br>        res &lt;- res/<br>        (DimPlot(sObject, reduction = lc_reduction, group.by = lc_group.by[lc_i], label = T, repel = T, label.size = <span class="hljs-number">6</span>) + <br>        labs(title = lc_group.by[lc_i]))<br>    &#125;<br>    res<br>&#125;<br><br>scRNA@meta.data<br><br>Idents(scRNA) &lt;- scRNA<span class="hljs-string">[[&quot;hM1_hmca_class&quot;]]</span><br><br>options(repr.plot.width=<span class="hljs-number">9</span>, repr.plot.height=<span class="hljs-number">18</span>)<br>options(ggrepel.<span class="hljs-built_in">max</span>.overlaps = Inf)<br>f_UMAP_more(scRNA, c(<span class="hljs-string">&#x27;hM1_hmca_class&#x27;</span>, <span class="hljs-string">&#x27;hM1_class&#x27;</span>, <span class="hljs-string">&#x27;hmca_class&#x27;</span>))<br><br>levels(scRNA)<br><br>unique(scRNA<span class="hljs-string">[[&quot;hmca_class&quot;]]</span>)<br><br>unique(scRNA<span class="hljs-string">[[&quot;hM1_class&quot;]]</span>)<br><br>n_ExN &lt;- c(<span class="hljs-string">&#x27;L4 IT&#x27;</span>,<span class="hljs-string">&#x27;L5 IT&#x27;</span>,<span class="hljs-string">&#x27;L5 ET&#x27;</span>,<span class="hljs-string">&#x27;IT&#x27;</span>,<span class="hljs-string">&#x27;L6b&#x27;</span>,<span class="hljs-string">&#x27;L5/6 IT Car3&#x27;</span>,<span class="hljs-string">&#x27;L6 IT&#x27;</span>,<span class="hljs-string">&#x27;L2/3 IT&#x27;</span>,<span class="hljs-string">&#x27;L5/6 NP&#x27;</span>,<span class="hljs-string">&#x27;L6 IT Car3&#x27;</span>,<span class="hljs-string">&#x27;L6 CT&#x27;</span>)<br><br>n_InN &lt;- c(<span class="hljs-string">&#x27;Lamp5&#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>,<span class="hljs-string">&#x27;Sncg&#x27;</span>)<br><br>n_NoN &lt;- c(<span class="hljs-string">&#x27;Astro&#x27;</span>,<span class="hljs-string">&#x27;PAX6&#x27;</span>,<span class="hljs-string">&#x27;Endo&#x27;</span>,<span class="hljs-string">&#x27;Micro-PVM&#x27;</span>,<span class="hljs-string">&#x27;OPC&#x27;</span>,<span class="hljs-string">&#x27;Oligo&#x27;</span>,<span class="hljs-string">&#x27;Pericyte&#x27;</span>,<span class="hljs-string">&#x27;VLMC&#x27;</span>)<br><br>n_groups &lt;- list(NoN=n_NoN, ExN=n_ExN, InN=n_InN)<br><br>f_listUpdateRe &lt;- <span class="hljs-function"><span class="hljs-keyword">function</span><span class="hljs-params">(lc_obj, lc_bool, lc_item)</span></span>&#123;<br>    lc_obj[lc_bool] &lt;- <span class="hljs-built_in">rep</span>(lc_item,times=sum(lc_bool))<br>    lc_obj<br>&#125;<br><br>f_grouplabel &lt;- <span class="hljs-function"><span class="hljs-keyword">function</span><span class="hljs-params">(lc_meta.data, lc_groups)</span></span>&#123;<br>    res &lt;- lc_meta.data<span class="hljs-string">[[1]]</span><br>    <span class="hljs-keyword">for</span>(lc_g <span class="hljs-keyword">in</span> names(lc_groups))&#123;<br>        lc_bool = (res %<span class="hljs-keyword">in</span>% lc_groups<span class="hljs-string">[[lc_g]]</span>)<br>        <span class="hljs-keyword">for</span>(c_n <span class="hljs-keyword">in</span> colnames(lc_meta.data))&#123;<br>            lc_bool = lc_bool  (lc_meta.data<span class="hljs-string">[[c_n]]</span> %<span class="hljs-keyword">in</span>% lc_groups<span class="hljs-string">[[lc_g]]</span>)<br>        &#125;<br>        res &lt;- f_listUpdateRe(res, lc_bool, lc_g)<br>    &#125;<br>    names(res) &lt;- rownames(lc_meta.data)<br>    res<br>&#125;<br><br>scRNA<span class="hljs-string">[[&#x27;n_groups&#x27;]]</span> &lt;- f_grouplabel(scRNA<span class="hljs-string">[[c(&quot;hM1_hmca_class&quot;,&quot;hM1_class&quot;,&quot;hmca_class&quot;)]]</span>, n_groups)<br><br>options(repr.plot.width=<span class="hljs-number">9</span>, repr.plot.height=<span class="hljs-number">12</span>)<br>options(ggrepel.<span class="hljs-built_in">max</span>.overlaps = Inf)<br>f_UMAP_more(scRNA, c(<span class="hljs-string">&#x27;hM1_hmca_class&#x27;</span>, <span class="hljs-string">&#x27;n_groups&#x27;</span>))<br><br>sc_Neuron &lt;- subset(x = scRNA, n_groups %<span class="hljs-keyword">in</span>% c(<span class="hljs-string">&quot;InN&quot;</span>, <span class="hljs-string">&quot;ExN&quot;</span>))<br>saveRDS(sc_Neuron, <span class="hljs-string">&quot;sc_Neuron.rds&quot;</span>)<br><br>f_UMAP_more(sc_Neuron, c(<span class="hljs-string">&#x27;hM1_hmca_class&#x27;</span>, <span class="hljs-string">&#x27;hM1_class&#x27;</span>, <span class="hljs-string">&#x27;hmca_class&#x27;</span>))<br><br>scRNA<span class="hljs-string">[[&#x27;hM1_hmca_groups&#x27;]]</span> &lt;- f_grouplabel(scRNA<span class="hljs-string">[[&quot;hM1_hmca_class&quot;]]</span>, n_groups)<br>scRNA<span class="hljs-string">[[&#x27;hmca_groups&#x27;]]</span> &lt;- f_grouplabel(scRNA<span class="hljs-string">[[&quot;hmca_class&quot;]]</span>, n_groups)<br>scRNA<span class="hljs-string">[[&#x27;hM1_groups&#x27;]]</span> &lt;- f_grouplabel(scRNA<span class="hljs-string">[[&quot;hM1_class&quot;]]</span>, n_groups)<br><br>sc_Neuron &lt;- subset(x = scRNA, hmca_groups %<span class="hljs-keyword">in</span>% c(<span class="hljs-string">&quot;InN&quot;</span>, <span class="hljs-string">&quot;ExN&quot;</span>))<br>saveRDS(sc_Neuron, <span class="hljs-string">&quot;sc_Neuron.rds&quot;</span>)<br>Idents(sc_Neuron) &lt;- sc_Neuron<span class="hljs-string">[[&#x27;hmca_class&#x27;]]</span><br><br>f_pie_metaN &lt;- <span class="hljs-function"><span class="hljs-keyword">function</span><span class="hljs-params">(sObject, lc_group.by)</span></span>&#123;<br>    tp_data &lt;- prop.<span class="hljs-built_in">table</span>(<span class="hljs-built_in">table</span>(sObject<span class="hljs-string">[[lc_group.by]]</span>))<br>    f_pie(tp_data, sprintf(<span class="hljs-string">&#x27;Proportion of %s&#x27;</span>, lc_group.by))<br>&#125;<br><br></code></pre></td></tr></table></figure>

<p><a target="_blank" rel="noopener" href="https://img-cdn.limour.top/blog_wp/2021/10/p.png"><img src="https://img-cdn.limour.top/blog_wp/2021/10/p.png" srcset="https://jscdn.limour.top/gh/Limour-dev/Sakurairo_Vision/load_svg/inload.svg" lazyload></a></p>
<h2 id="第二步-画图查看相关性"><a href="#第二步-画图查看相关性" class="headerlink" title="第二步 画图查看相关性"></a>第二步 画图查看相关性</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><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></pre></td><td class="code"><pre><code class="hljs lua">f_cluster_averages &lt;- <span class="hljs-function"><span class="hljs-keyword">function</span><span class="hljs-params">(lc_scRNA)</span></span>&#123;<br>    # 切分出Clusters<br>    lc_clusters &lt;- SplitObject(lc_scRNA, split.by = <span class="hljs-string">&#x27;ident&#x27;</span>)<br>    <span class="hljs-keyword">for</span> (lc_i <span class="hljs-keyword">in</span> <span class="hljs-number">1</span>:length(lc_clusters))&#123;<br>        lc_clusters<span class="hljs-string">[[lc_i]]</span>  &lt;- lc_clusters<span class="hljs-string">[[lc_i]]</span><span class="hljs-string">[[lc_clusters[[lc_i]]@active.assay]]</span>@scale.data<br>    &#125;<br>    <span class="hljs-keyword">for</span> (lc_i <span class="hljs-keyword">in</span> <span class="hljs-number">1</span>:length(lc_clusters))&#123;<br>        lc_clusters<span class="hljs-string">[[lc_i]]</span>  &lt;- apply(lc_clusters<span class="hljs-string">[[lc_i]]</span>,<span class="hljs-number">1</span>,mean)<br>    &#125;<br>    lc_clusters &lt;- data.frame(lc_clusters)<br>    scale(lc_clusters)<br>&#125;<br><br>library(Hmisc)<br>library(corrplot)<br>f_corrplot &lt;- <span class="hljs-function"><span class="hljs-keyword">function</span><span class="hljs-params">(lc_scdata)</span></span>&#123;<br>    lc_cor &lt;- rcorr(lc_scdata)<br>    lc_cor$P[is.na(lc_cor$P)]=<span class="hljs-number">0</span><br>    lc_cor$P &lt;- -<span class="hljs-built_in">log10</span>(lc_cor$P)<br>    lc_cor$P[is.infinite(lc_cor$P)] = <span class="hljs-built_in">max</span>(lc_cor$P[!is.infinite(lc_cor$P)]) + <span class="hljs-number">1</span><br>    corrplot(lc_cor$r, <span class="hljs-built_in">type</span>=<span class="hljs-string">&quot;upper&quot;</span>,tl.pos = <span class="hljs-string">&quot;t&quot;</span>, order = <span class="hljs-string">&quot;hclust&quot;</span>, <br>             p.mat = lc_cor$P, insig = <span class="hljs-string">&quot;p-value&quot;</span>)<br>    corrplot(lc_cor$r, add=TRUE, <span class="hljs-built_in">type</span>=<span class="hljs-string">&quot;lower&quot;</span>, method=<span class="hljs-string">&quot;number&quot;</span>,col=<span class="hljs-string">&quot;black&quot;</span>,<br>             order = <span class="hljs-string">&quot;hclust&quot;</span>, tl.pos=<span class="hljs-string">&quot;l&quot;</span>, cl.pos=<span class="hljs-string">&quot;n&quot;</span>,addCoefasPercent = T)<br>&#125;<br>tp_d &lt;- f_cluster_averages(sc_Neuron)<br><br>library(Hmisc)<br>library(ggcorrplot)<br>f_corrplot &lt;- <span class="hljs-function"><span class="hljs-keyword">function</span><span class="hljs-params">(lc_scdata)</span></span>&#123;<br>    lc_cor &lt;- rcorr(lc_scdata)<br>    lc_cor$P[is.na(lc_cor$P)]=<span class="hljs-number">0</span><br>    ggcorrplot(lc_cor$r, <span class="hljs-built_in">type</span>=<span class="hljs-string">&quot;full&quot;</span>,hc.order = T, lab = T, p.mat = lc_cor$P)<br>&#125;<br><br></code></pre></td></tr></table></figure>

<p><a target="_blank" rel="noopener" href="https://img-cdn.limour.top/blog_wp/2021/10/cor.png"><img src="https://img-cdn.limour.top/blog_wp/2021/10/cor.png" srcset="https://jscdn.limour.top/gh/Limour-dev/Sakurairo_Vision/load_svg/inload.svg" lazyload></a></p>
<h2 id="第三步-Cluster差异基因"><a href="#第三步-Cluster差异基因" class="headerlink" title="第三步 Cluster差异基因"></a>第三步 Cluster差异基因</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><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></pre></td><td class="code"><pre><code class="hljs reasonml"><span class="hljs-constructor">Idents(<span class="hljs-params">sc_Neuron</span>)</span> &lt;- sc_Neuron<span class="hljs-literal">[[&#x27;<span class="hljs-identifier">hmca_class</span>&#x27;]</span>]<br>all_markers &lt;- <span class="hljs-constructor">FindAllMarkers(<span class="hljs-params">sc_Neuron</span>, <span class="hljs-params">only</span>.<span class="hljs-params">pos</span> = TRUE, <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>write.csv(significant_markers, <span class="hljs-string">&quot;significant_markers.csv&quot;</span>)<br><br>significant_markers %&gt;%<br>    group<span class="hljs-constructor">_by(<span class="hljs-params">cluster</span>)</span> %&gt;%<br>    top<span class="hljs-constructor">_n(<span class="hljs-params">n</span> = 10, <span class="hljs-params">wt</span> = <span class="hljs-params">avg_log2FC</span>)</span> -&gt; top10<br><span class="hljs-constructor">DoHeatmap(<span class="hljs-params">sc_Neuron</span>, <span class="hljs-params">features</span> = <span class="hljs-params">top10$gene</span>)</span><br><br>gl_Markers_ExN &lt;- read.table(file.path(root_path, <span class="hljs-string">&quot;ExN.csv&quot;</span>), header = F)<br>colnames(gl_Markers_ExN) &lt;- c(&#x27;marker<span class="hljs-character">&#x27;,&#x27;</span>celltype&#x27;)<br> <br>gl_Markers_InN &lt;- read.table(file.path(root_path, <span class="hljs-string">&quot;InN.csv&quot;</span>), header = F)<br>colnames(gl_Markers_InN) &lt;- c(&#x27;marker<span class="hljs-character">&#x27;,&#x27;</span>celltype&#x27;)<br><br>f_setRowName &lt;- <span class="hljs-keyword">function</span>(lc_df, lc_colName)&#123;<br>    lc_df &lt;- lc_df<span class="hljs-literal">[<span class="hljs-identifier">order</span>(<span class="hljs-identifier">lc_df</span>[[<span class="hljs-identifier">lc_colName</span>]</span>]),]<br>    tp_index &lt;- duplicated(lc_df<span class="hljs-literal">[[<span class="hljs-identifier">lc_colName</span>]</span>])<br>    lc_df &lt;- lc_df<span class="hljs-literal">[!<span class="hljs-identifier">tp_index</span>,]</span><br>    rownames(lc_df) &lt;- lc_df<span class="hljs-literal">[[<span class="hljs-identifier">lc_colName</span>]</span>]<br>    lc_df<br>&#125;<br><br>gl_Markers_ExN &lt;- f<span class="hljs-constructor">_setRowName(<span class="hljs-params">gl_Markers_ExN</span>, &#x27;<span class="hljs-params">marker</span>&#x27;)</span><br>gl_Markers_InN &lt;- f<span class="hljs-constructor">_setRowName(<span class="hljs-params">gl_Markers_InN</span>, &#x27;<span class="hljs-params">marker</span>&#x27;)</span><br><br>gl_ExN_InN &lt;- rbind(gl_Markers_ExN, gl_Markers_InN)<br><br># <span class="hljs-number">18</span>、绘制已知基因在不同cluster内的Violin plots<br># <span class="hljs-number">18.1</span> 定义Violin plots绘制方法<br>f_get_cell_markers &lt;- <span class="hljs-keyword">function</span>(lc_cellType, lc_significant_markers, lc_MarkerGene)&#123;<br>    lc_features &lt;- rownames(subset(lc_MarkerGene, subset = celltype<span class="hljs-operator"> == </span>lc_cellType))<br>    lc_features &lt;- lc_features<span class="hljs-literal">[<span class="hljs-identifier">lc_features</span> %<span class="hljs-identifier">in</span>% <span class="hljs-identifier">lc_significant_markers$gene</span>]</span><br>    lc_features<br>&#125;<br>f_VlnPlot &lt;- <span class="hljs-keyword">function</span>(sObject, lc_cellType, lc_significant_markers, lc_MarkerGene) &#123;<br>    lc_features &lt;- f<span class="hljs-constructor">_get_cell_markers(<span class="hljs-params">lc_cellType</span>, <span class="hljs-params">lc_significant_markers</span>, <span class="hljs-params">lc_MarkerGene</span>)</span><br>    <span class="hljs-keyword">if</span> (length(lc_features)!=<span class="hljs-number">0</span>)&#123;<span class="hljs-constructor">VlnPlot(<span class="hljs-params">sObject</span>, <span class="hljs-params">features</span> = <span class="hljs-params">lc_features</span>)</span>&#125;<br>    <span class="hljs-keyword">else</span>&#123; F &#125; <br>&#125;<br> <br>f_get_m_p &lt;- <span class="hljs-keyword">function</span>(sObject, lc_cellType, lc_significant_markers, lc_MarkerGene)&#123;<br>    lc_features &lt;- f<span class="hljs-constructor">_get_cell_markers(<span class="hljs-params">lc_cellType</span>, <span class="hljs-params">lc_significant_markers</span>, <span class="hljs-params">lc_MarkerGene</span>)</span><br>    <span class="hljs-keyword">if</span> (length(lc_features)==<span class="hljs-number">0</span>)&#123;return(NULL)&#125;<br>    <span class="hljs-constructor">DotPlot(<span class="hljs-params">sObject</span>, <span class="hljs-params">features</span> = <span class="hljs-params">lc_features</span>)</span> + <span class="hljs-constructor">RotatedAxis()</span> + <br>    ggtitle(sprintf(<span class="hljs-string">&quot;%s markers in %s&quot;</span>, lc_cellType, sObject@project.name)) + <br>    theme(plot.title = element<span class="hljs-constructor">_text(<span class="hljs-params">hjust</span> = 0.5)</span>)<br>&#125;<br> <br># <span class="hljs-number">18.2</span> 绘制已知细胞类型的marker genes在本数据集中的pattern<br>f_get_all_celltype &lt;- <span class="hljs-keyword">function</span>(lc_MarkerGene)&#123;<br>    tp_cellTypes &lt;- lc_MarkerGene$celltype # 获取所有细胞类型<br>    tp_cellTypes &lt;- sort(tp_cellTypes) # 排序<br>    tp_cellTypes &lt;- tp_cellTypes<span class="hljs-literal">[!<span class="hljs-identifier">duplicated</span>(<span class="hljs-identifier">tp_cellTypes</span>)]</span> #去重<br>    tp_cellTypes<br>&#125;<br> <br>f_get_m_p_a &lt;- <span class="hljs-keyword">function</span>(sObject, lc_significant_markers, lc_MarkerGene)&#123;<br>    lc_cellTypes &lt;- f<span class="hljs-constructor">_get_all_celltype(<span class="hljs-params">lc_MarkerGene</span>)</span><br>    tp_emm &lt;- <span class="hljs-number">2</span><br>    <span class="hljs-keyword">for</span> (lc_j <span class="hljs-keyword">in</span> <span class="hljs-number">1</span>:length(lc_cellTypes))&#123;<br>        lc_image &lt;- f<span class="hljs-constructor">_get_m_p(<span class="hljs-params">sObject</span>, <span class="hljs-params">lc_cellTypes</span>[<span class="hljs-params">lc_j</span>], <span class="hljs-params">lc_significant_markers</span>, <span class="hljs-params">lc_MarkerGene</span>)</span><br>        <span class="hljs-keyword">if</span> (!is.null(lc_image))&#123; break &#125;<br>        tp_emm  &lt;- tp_emm + <span class="hljs-number">1</span><br>    &#125;<br>    <br>    <span class="hljs-keyword">if</span> (tp_emm &gt; length(lc_cellTypes))&#123;return(NULL)&#125;<br>    <br>    <span class="hljs-keyword">for</span>(lc_j <span class="hljs-keyword">in</span> tp_emm:length(lc_cellTypes))&#123;<br>        lc_image &lt;- lc_image + f<span class="hljs-constructor">_get_m_p(<span class="hljs-params">sObject</span>, <span class="hljs-params">lc_cellTypes</span>[[<span class="hljs-params">lc_j</span>]], <span class="hljs-params">lc_significant_markers</span>, <span class="hljs-params">lc_MarkerGene</span>)</span><br>    &#125;<br>    lc_image &lt;- lc_image + plot<span class="hljs-constructor">_layout(<span class="hljs-params">ncol</span> = 3)</span><br>    lc_image<br>&#125;<br><br>f_image_output &lt;- <span class="hljs-keyword">function</span>(fileName, image, width=<span class="hljs-number">1920</span>, height=<span class="hljs-number">1080</span>)&#123;<br>  png(paste(fileName, <span class="hljs-string">&quot;.png&quot;</span>, sep=<span class="hljs-string">&quot;&quot;</span>), width = width, height = height)<br>  print(image)<br>  dev.off<span class="hljs-literal">()</span><br>&#125;<br><br></code></pre></td></tr></table></figure>

<p><a target="_blank" rel="noopener" href="https://img-cdn.limour.top/blog_wp/2021/10/gl_ExN_InN-1.png"><img src="https://img-cdn.limour.top/blog_wp/2021/10/gl_ExN_InN-1.png" srcset="https://jscdn.limour.top/gh/Limour-dev/Sakurairo_Vision/load_svg/inload.svg" lazyload></a></p>

                
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