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  240.             <h1 id="seo-header">Seurat (五) 简单总结</h1>
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  243.               <div class="markdown-body">
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  244.                 
    245. 

  245.                 <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 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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><span class="line">134</span><br><span class="line">135</span><br><span class="line">136</span><br><span class="line">137</span><br><span class="line">138</span><br><span class="line">139</span><br><span class="line">140</span><br><span class="line">141</span><br><span class="line">142</span><br><span class="line">143</span><br><span class="line">144</span><br><span class="line">145</span><br><span class="line">146</span><br><span class="line">147</span><br><span class="line">148</span><br><span class="line">149</span><br><span class="line">150</span><br><span 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class="line">301</span><br><span class="line">302</span><br><span class="line">303</span><br><span class="line">304</span><br><span class="line">305</span><br><span class="line">306</span><br><span class="line">307</span><br></pre></td><td class="code"><pre><code class="hljs reasonml">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<span class="hljs-constructor">_minimal()</span>+<br>    theme(<br>        axis.title.x = element<span class="hljs-constructor">_blank()</span>,<br>        axis.text.x=element<span class="hljs-constructor">_blank()</span>,<br>        axis.title.y = element<span class="hljs-constructor">_blank()</span>,<br>        axis.text.y=element<span class="hljs-constructor">_blank()</span>,<br>        panel.border = element<span class="hljs-constructor">_blank()</span>,<br>        panel.grid=element<span class="hljs-constructor">_blank()</span>,<br>        axis.ticks = element<span class="hljs-constructor">_blank()</span>,<br>        plot.title=element<span class="hljs-constructor">_text(<span class="hljs-params">size</span>=14, <span class="hljs-params">face</span>=<span class="hljs-string">&quot;bold&quot;</span>,<span class="hljs-params">hjust</span> = 0.5)</span><br>    )<br>col_Paired &lt;- color<span class="hljs-constructor">RampPalette(<span class="hljs-params">brewer</span>.<span class="hljs-params">pal</span>(12, <span class="hljs-string">&quot;Paired&quot;</span>)</span>)<br>f_pie &lt;- <span class="hljs-keyword">function</span>(lc_x, lc_main, lc_x_p = <span class="hljs-number">1.3</span>, lc_r = T)&#123;<br>    lc_cols &lt;- col<span class="hljs-constructor">_Paired(<span class="hljs-params">length</span>(<span class="hljs-params">lc_x</span>)</span>)<br>    lc_v &lt;- <span class="hljs-keyword">as</span>.vector(<span class="hljs-number">100</span>*lc_x)<br>    lc_df &lt;- data.frame(<span class="hljs-keyword">type</span> = names(lc_x), nums = lc_v)<br>    lc_df &lt;- lc_df<span class="hljs-literal">[<span class="hljs-identifier">order</span>(<span class="hljs-identifier">lc_df$type</span>),]</span><br>    lc_percent = sprintf(&#x27;%<span class="hljs-number">0.2</span>f%%&#x27;,lc_df$nums)<br>    <span class="hljs-keyword">if</span>(lc_r)&#123;<br>        lc_df$pos &lt;- <span class="hljs-keyword">with</span>(lc_df, <span class="hljs-number">100</span>-cumsum(nums)+nums/<span class="hljs-number">2</span>)<br>    &#125;<span class="hljs-keyword">else</span>&#123;<br>        lc_df$pos &lt;- <span class="hljs-keyword">with</span>(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-keyword">type</span>)) + geom<span class="hljs-constructor">_bar(<span class="hljs-params">stat</span> = &#x27;<span class="hljs-params">identity</span>&#x27;)</span><br>#     print(lc_df)<br>#     print(lc_pie)<br>    lc_pie &lt;- lc_pie + coord<span class="hljs-constructor">_polar(<span class="hljs-string">&quot;y&quot;</span>, <span class="hljs-params">start</span>=0, <span class="hljs-params">direction</span> = 1)</span> + scale<span class="hljs-constructor">_fill_manual(<span class="hljs-params">values</span>=<span class="hljs-params">lc_cols</span>)</span> + blank_theme <br>    lc_pie &lt;- lc_pie + geom<span class="hljs-constructor">_text_repel(<span class="hljs-params">aes</span>(<span class="hljs-params">x</span> = <span class="hljs-params">lc_x_p</span>, <span class="hljs-params">y</span>=<span class="hljs-params">pos</span>)</span>,label= lc_percent, force = T, <br>                        arrow = arrow(length=<span class="hljs-built_in">unit</span>(<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>f_pie_metaN &lt;- <span class="hljs-keyword">function</span>(sObject, lc_group.by)&#123;<br>    tp_data &lt;- prop.table(table(sObject<span class="hljs-literal">[[<span class="hljs-identifier">lc_group</span>.<span class="hljs-identifier">by</span>]</span>]))<br>    f<span class="hljs-constructor">_pie(<span class="hljs-params">tp_data</span>, <span class="hljs-params">sprintf</span>(&#x27;Proportion <span class="hljs-params">of</span> %<span class="hljs-params">s</span>&#x27;, <span class="hljs-params">lc_group</span>.<span class="hljs-params">by</span>)</span>)<br>&#125;<br><br>f_UMAP_more &lt;- <span class="hljs-keyword">function</span>(sObject, lc_group.by, lc_reduction=<span class="hljs-string">&quot;umap&quot;</span>)&#123;<br>    res &lt;- (<span class="hljs-constructor">DimPlot(<span class="hljs-params">sObject</span>, <span class="hljs-params">reduction</span> = <span class="hljs-params">lc_reduction</span>, <span class="hljs-params">group</span>.<span class="hljs-params">by</span> = <span class="hljs-params">lc_group</span>.<span class="hljs-params">by</span>[1], <span class="hljs-params">label</span> = T, <span class="hljs-params">repel</span> = T, <span class="hljs-params">label</span>.<span class="hljs-params">size</span> = 6)</span> + <br>    labs(title = lc_group.by<span class="hljs-literal">[<span class="hljs-number">1</span>]</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>        (<span class="hljs-constructor">DimPlot(<span class="hljs-params">sObject</span>, <span class="hljs-params">reduction</span> = <span class="hljs-params">lc_reduction</span>, <span class="hljs-params">group</span>.<span class="hljs-params">by</span> = <span class="hljs-params">lc_group</span>.<span class="hljs-params">by</span>[<span class="hljs-params">lc_i</span>], <span class="hljs-params">label</span> = T, <span class="hljs-params">repel</span> = T, <span class="hljs-params">label</span>.<span class="hljs-params">size</span> = 6)</span> + <br>        labs(title = lc_group.by<span class="hljs-literal">[<span class="hljs-identifier">lc_i</span>]</span>))<br>    &#125;<br>    res<br>&#125;<br><br>f_br_cluster_f &lt;- <span class="hljs-keyword">function</span>(sObject, lc_groupN)&#123;<br>    lc_filter &lt;- unlist(unique(sObject<span class="hljs-literal">[[<span class="hljs-identifier">lc_groupN</span>]</span>]))<br>    lc_filter &lt;- lc_filter<span class="hljs-literal">[!<span class="hljs-identifier">is</span>.<span class="hljs-identifier">na</span>(<span class="hljs-identifier">lc_filter</span>)]</span><br>    lc_filter<br>&#125;<br><br>f_br_cluster &lt;- <span class="hljs-keyword">function</span>(sObject, lc_groupN, lc_labelN, lc_prop = F)&#123;<br>    lc_all &lt;- unique(sObject<span class="hljs-literal">[[<span class="hljs-identifier">lc_labelN</span>]</span>])<br>    rownames(lc_all) &lt;- lc_all<span class="hljs-literal">[[<span class="hljs-number">1</span>]</span>]<br>    colnames(lc_all) &lt;- <span class="hljs-string">&quot;CB&quot;</span><br>    lc_tp  &lt;- <span class="hljs-constructor">SplitObject(<span class="hljs-params">subset</span>(<span class="hljs-params">x</span> = <span class="hljs-params">sObject</span>, !!<span class="hljs-params">sym</span>(<span class="hljs-params">lc_groupN</span>)</span>%<span class="hljs-keyword">in</span>%f<span class="hljs-constructor">_br_cluster_f(<span class="hljs-params">sObject</span>, <span class="hljs-params">lc_groupN</span>)</span>), split.by = lc_groupN)<br>    <span class="hljs-keyword">for</span>(lc_i <span class="hljs-keyword">in</span> <span class="hljs-number">1</span>:length(lc_tp))&#123;<br>        <span class="hljs-keyword">if</span>(lc_prop)&#123;<br>            lc_tp<span class="hljs-literal">[[<span class="hljs-identifier">lc_i</span>]</span>] &lt;- prop.table(table(lc_tp<span class="hljs-literal">[[<span class="hljs-identifier">lc_i</span>]</span>]<span class="hljs-literal">[[<span class="hljs-identifier">lc_labelN</span>]</span>]))<br>        &#125;<span class="hljs-keyword">else</span>&#123;<br>            lc_tp<span class="hljs-literal">[[<span class="hljs-identifier">lc_i</span>]</span>] &lt;- table(lc_tp<span class="hljs-literal">[[<span class="hljs-identifier">lc_i</span>]</span>]<span class="hljs-literal">[[<span class="hljs-identifier">lc_labelN</span>]</span>])<br>        &#125;<br>    &#125;<br>    <span class="hljs-keyword">for</span>(lc_name <span class="hljs-keyword">in</span> names(lc_tp))&#123;<br>        lc_all<span class="hljs-literal">[[<span class="hljs-identifier">lc_name</span>]</span>] = <span class="hljs-number">0</span><br>        lc_all<span class="hljs-literal">[<span class="hljs-identifier">names</span>(<span class="hljs-identifier">lc_tp</span>[[<span class="hljs-identifier">lc_name</span>]</span>]), lc_name] = lc_tp<span class="hljs-literal">[[<span class="hljs-identifier">lc_name</span>]</span>]<br>    &#125;<br>    lc_all<span class="hljs-literal">[,-<span class="hljs-number">1</span>]</span><br>&#125;<br> <br>f_q2l &lt;- <span class="hljs-keyword">function</span>(lc_q)&#123;<br>    res  &lt;- NULL<br>    <span class="hljs-keyword">for</span>(lc_c <span class="hljs-keyword">in</span> colnames(lc_q))&#123;<br>        <span class="hljs-keyword">for</span>(lc_r <span class="hljs-keyword">in</span> rownames(lc_q))&#123;<br>            res  &lt;- rbind(res, c(group=lc_c, label=lc_r, value=lc_q<span class="hljs-literal">[<span class="hljs-identifier">lc_r</span>,<span class="hljs-identifier">lc_c</span>]</span>))<br>        &#125;<br>    &#125;<br>    res &lt;- data.frame(res)<br>    res$value = <span class="hljs-keyword">as</span>.numeric(res$value)<br>    res<br>&#125;<br> <br>library(RColorBrewer)<br>library(ggplot2)<br>col_Paired &lt;- color<span class="hljs-constructor">RampPalette(<span class="hljs-params">brewer</span>.<span class="hljs-params">pal</span>(12, <span class="hljs-string">&quot;Paired&quot;</span>)</span>)<br>f_q_frequnency &lt;- <span class="hljs-keyword">function</span>(lc_q)&#123;<br>    ggplot(f<span class="hljs-constructor">_q2l(<span class="hljs-params">lc_q</span>)</span>,mapping = aes(group,value,fill=label))+<br>    geom<span class="hljs-constructor">_bar(<span class="hljs-params">stat</span>=&#x27;<span class="hljs-params">identity</span>&#x27;,<span class="hljs-params">position</span>=&#x27;<span class="hljs-params">fill</span>&#x27;)</span> + scale<span class="hljs-constructor">_fill_manual(<span class="hljs-params">values</span>= <span class="hljs-params">col_Paired</span>(<span class="hljs-params">nrow</span>(<span class="hljs-params">lc_q</span>)</span>))+<br>    labs(x = &#x27;group&#x27;,y = &#x27;frequnency&#x27;) +<br>    theme(axis.title =element<span class="hljs-constructor">_text(<span class="hljs-params">size</span> = 16)</span>,axis.text =element<span class="hljs-constructor">_text(<span class="hljs-params">size</span> = 14, <span class="hljs-params">color</span> = &#x27;<span class="hljs-params">black</span>&#x27;)</span>)+<br>    theme(axis.text.x = element<span class="hljs-constructor">_text(<span class="hljs-params">angle</span> = 45, <span class="hljs-params">hjust</span> = 1)</span>)+coord<span class="hljs-constructor">_flip()</span> <br>&#125;<br><br>f_DEG_Volcano &lt;- <span class="hljs-keyword">function</span>(lc_logFC, lc_p, lc_gene, Threshold_logFC = <span class="hljs-number">1</span>, Threshold_p = <span class="hljs-number">0.05</span>, lc_rep=<span class="hljs-number">1</span>:<span class="hljs-number">10</span>)&#123;<br>    col_vector = rep(rgb(<span class="hljs-number">108</span>, <span class="hljs-number">200</span>, <span class="hljs-number">228</span>, maxColorValue = <span class="hljs-number">255</span>), length(lc_logFC))<br>    col_vector<span class="hljs-literal">[<span class="hljs-identifier">lc_p</span> &lt; T<span class="hljs-identifier">hreshold_p</span> &amp; <span class="hljs-identifier">lc_logFC</span> &gt; T<span class="hljs-identifier">hreshold_logFC</span>]</span> = rgb(<span class="hljs-number">226</span>, <span class="hljs-number">61</span>, <span class="hljs-number">75</span>, maxColorValue = <span class="hljs-number">255</span>)<br>    col_vector<span class="hljs-literal">[<span class="hljs-identifier">lc_p</span> &lt; T<span class="hljs-identifier">hreshold_p</span> &amp; <span class="hljs-identifier">lc_logFC</span> &lt; -T<span class="hljs-identifier">hreshold_logFC</span>]</span> = rgb(<span class="hljs-number">232</span>, <span class="hljs-number">168</span>, <span class="hljs-number">71</span>, maxColorValue = <span class="hljs-number">255</span>)<br>    lc_p<span class="hljs-literal">[<span class="hljs-identifier">lc_p</span> &lt; <span class="hljs-number">1e-10</span>]</span> = <span class="hljs-number">1e-10</span><br>    lc_p<span class="hljs-literal">[<span class="hljs-identifier">lc_p</span> &gt; <span class="hljs-number">1</span>  <span class="hljs-identifier">is</span>.<span class="hljs-identifier">na</span>(<span class="hljs-identifier">lc_p</span>)]</span> = <span class="hljs-number">1</span><br>    df = data.frame(logFC &lt;- lc_logFC, `-log10(P)` &lt;- -log10(lc_p), col &lt;- col_vector, gene &lt;- lc_gene)<br>    colnames(df) &lt;- c(&#x27;logFC&#x27;, &#x27;-log10(P)&#x27;, <span class="hljs-string">&quot;col&quot;</span>, <span class="hljs-string">&quot;gene&quot;</span>)<br>    lc_tp_logFC &lt;- df$logFC<br>    lc_tp_logFC<span class="hljs-literal">[<span class="hljs-identifier">lc_p</span>&gt;=T<span class="hljs-identifier">hreshold_p</span>]</span> = <span class="hljs-number">0</span><br>    lc_idx &lt;- order(lc_tp_logFC)<span class="hljs-literal">[<span class="hljs-identifier">c</span>(<span class="hljs-identifier">lc_rep</span>, <span class="hljs-identifier">length</span>(<span class="hljs-identifier">lc_gene</span>)+<span class="hljs-number">1</span>-<span class="hljs-identifier">lc_rep</span>)]</span><br>    df$logFC<span class="hljs-literal">[<span class="hljs-identifier">df$logFC</span> &gt; <span class="hljs-number">10</span>]</span> = <span class="hljs-number">10</span><br>    df$logFC<span class="hljs-literal">[<span class="hljs-identifier">df$logFC</span> &lt; -<span class="hljs-number">10</span>]</span> = -<span class="hljs-number">10</span><br>    res &lt;- ggplot<span class="hljs-literal">()</span> + geom<span class="hljs-constructor">_point(<span class="hljs-params">aes</span>(<span class="hljs-params">logFC</span>, `-<span class="hljs-params">log10</span>(P)</span>`, col=<span class="hljs-constructor">I(<span class="hljs-params">col</span>)</span>), data = df) <br>    res &lt;- res + theme<span class="hljs-constructor">_bw()</span> + theme(panel.grid=element<span class="hljs-constructor">_line(<span class="hljs-params">colour</span>=NA)</span>)<br>    res &lt;- res + geom<span class="hljs-constructor">_hline(<span class="hljs-params">yintercept</span>=-<span class="hljs-params">log10</span>(Threshold_p)</span>, linetype=<span class="hljs-string">&quot;longdash&quot;</span>)<br>    res &lt;- res + geom<span class="hljs-constructor">_vline(<span class="hljs-params">xintercept</span>=<span class="hljs-params">c</span>(Threshold_logFC, -Threshold_logFC)</span>, linetype=<span class="hljs-string">&quot;longdash&quot;</span>)<br>    res &lt;- res + geom<span class="hljs-constructor">_text_repel(<span class="hljs-params">data</span>=<span class="hljs-params">df</span>[<span class="hljs-params">lc_idx</span>,],<span class="hljs-params">aes</span>(<span class="hljs-params">logFC</span>,`-<span class="hljs-params">log10</span>(P)</span>`,label=gene), force=T, max.overlaps=Inf)<br>    res<br>&#125;<br><br>f_cluster_averages &lt;- <span class="hljs-keyword">function</span>(lc_scRNA, lc_metaN=&#x27;ident&#x27;)&#123;<br>    # 切分出Clusters<br>    lc_clusters &lt;- <span class="hljs-constructor">SplitObject(<span class="hljs-params">lc_scRNA</span>, <span class="hljs-params">split</span>.<span class="hljs-params">by</span> = <span class="hljs-params">lc_metaN</span>)</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-literal">[[<span class="hljs-identifier">lc_i</span>]</span>]  &lt;- lc_clusters<span class="hljs-literal">[[<span class="hljs-identifier">lc_i</span>]</span>]<span class="hljs-literal">[[<span class="hljs-identifier">lc_clusters</span>[[<span class="hljs-identifier">lc_i</span>]</span>]@active.assay]]@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-literal">[[<span class="hljs-identifier">lc_i</span>]</span>]  &lt;- apply(lc_clusters<span class="hljs-literal">[[<span class="hljs-identifier">lc_i</span>]</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(clusterProfiler)<br>library(pheatmap)<br>library(ggdendro)<br>f_DEG_hclust &lt;- <span class="hljs-keyword">function</span>(lc_counts)&#123;<br>    ggdendrogram(hclust(dist(t(lc_counts))), rotate = T, size = <span class="hljs-number">3</span>)+theme(axis.text = element<span class="hljs-constructor">_text(<span class="hljs-params">size</span>=14,<span class="hljs-params">face</span> = <span class="hljs-string">&quot;bold&quot;</span>)</span>)<br>&#125;<br><br>f_DEG_pheatmap_choose_matrix &lt;- <span class="hljs-keyword">function</span>(lc_tp_d, lc_significant_markers, lc_n = <span class="hljs-number">120</span>, Threshold_logFC = <span class="hljs-number">1</span>)&#123;<br>    res &lt;- subset(lc_significant_markers, abs(avg_log2FC) &gt; Threshold_logFC)<br>    res &lt;- res<span class="hljs-literal">[<span class="hljs-identifier">order</span>(<span class="hljs-identifier">abs</span>(<span class="hljs-identifier">res$avg_log2FC</span>), <span class="hljs-identifier">decreasing</span> = T),]</span><br>    res &lt;- head(unique(res$gene), n = lc_n)<br>    res &lt;- lc_tp_d<span class="hljs-literal">[<span class="hljs-identifier">res</span>,]</span><br>    res<br>&#125;<br><br>require(ggplotify)<br>f_DEG_pheatmap &lt;- <span class="hljs-keyword">function</span>(choose_matrix)&#123;<br>    choose_matrix = t(scale(t(choose_matrix)))<br>    <span class="hljs-keyword">as</span>.ggplot(pheatmap(choose_matrix))<br>&#125;<br><br>f_prepare4CSOmap &lt;- <span class="hljs-keyword">function</span>(lc_scRNA, lc_csomap_data_dir, lc_className)&#123;<br>    lc_csomap_data_dir &lt;- system(paste(<span class="hljs-string">&quot;echo&quot;</span>, lc_csomap_data_dir), intern = T)<br>    <span class="hljs-keyword">if</span>(!file.exists(lc_csomap_data_dir))&#123;dir.create(lc_csomap_data_dir)&#125;<br>    # 导出label.txt<br>    labels &lt;- lc_scRNA<span class="hljs-literal">[[<span class="hljs-identifier">lc_className</span>]</span>]<br>    labels$cells &lt;- gsub(<span class="hljs-string">&quot;-&quot;</span>, <span class="hljs-string">&quot;.&quot;</span> ,rownames(labels)) # TPM的colnames 不知为何导出时被替换了，这里也替换一下<br>    labels$labels &lt;- <span class="hljs-keyword">as</span>.character(labels<span class="hljs-literal">[[<span class="hljs-identifier">lc_className</span>]</span>])<br>    rownames(labels) &lt;- NULL<br>    labels = labels<span class="hljs-literal">[,<span class="hljs-identifier">c</span>(&quot;<span class="hljs-identifier">cells</span>&quot;, &quot;<span class="hljs-identifier">labels</span>&quot;)]</span><br>    write.table(labels, file.path(lc_csomap_data_dir, <span class="hljs-string">&quot;label.txt&quot;</span>), row.names = F, sep = <span class="hljs-string">&quot;\t&quot;</span>, quote = F) # 不要引号<br> <br>    # copy <span class="hljs-module-access"><span class="hljs-module"><span class="hljs-identifier">LR_pairs</span>.</span></span>txt<br>    file.copy(from = file.path(lc_csomap_data_dir,<span class="hljs-string">&quot;..&quot;</span>,<span class="hljs-string">&quot;demo&quot;</span>,<span class="hljs-string">&quot;LR_pairs.txt&quot;</span>), <span class="hljs-keyword">to</span> = file.path(lc_csomap_data_dir, <span class="hljs-string">&quot;LR_pairs.txt&quot;</span>))<br>    <br>    # 导出<span class="hljs-module-access"><span class="hljs-module"><span class="hljs-identifier">TPM</span>.</span></span>txt<br>    tpm &lt;- exp(lc_scRNA<span class="hljs-literal">[[&#x27;RNA&#x27;]</span>]@data)<br>    tpm &lt;- tpm - <span class="hljs-number">1</span><br>    tpm &lt;- tpm*<span class="hljs-number">100</span> # <span class="hljs-number">1E4</span> <span class="hljs-keyword">to</span> <span class="hljs-number">1E6</span><br>    colnames(tpm)<span class="hljs-literal">[<span class="hljs-number">1</span>]</span> = paste0(<span class="hljs-character">&#x27;T&#x27;</span>, colnames(tpm)<span class="hljs-literal">[<span class="hljs-number">1</span>]</span>) # 预留\t位置<br>    write.table(tpm, file.path(lc_csomap_data_dir, <span class="hljs-string">&quot;TPM.txt&quot;</span>), sep = <span class="hljs-string">&quot;\t&quot;</span>, quote = F) # 不要引号<br>    <br>    lc_fix &lt;- tempfile<span class="hljs-literal">()</span><br>    lc_py &lt;- sprintf(&#x27;<br>import mmap, os<br> <br>def mapfile(filename, *args, size=None, **kwargs):<br>    file = <span class="hljs-keyword">open</span>(filename, *args, **kwargs)<br>    <span class="hljs-keyword">if</span> size is None: size = os.path.getsize(filename)<br>    return mmap.mmap(file.fileno<span class="hljs-literal">()</span>, size)<br> <br>path = <span class="hljs-string">&quot;%s&quot;</span><br>print(path, <span class="hljs-string">&quot;%s&quot;</span>)<br>f = mapfile(path,<span class="hljs-string">&quot;r+&quot;</span>, size=<span class="hljs-number">10</span>)<br>f<span class="hljs-literal">[<span class="hljs-number">0</span>:<span class="hljs-number">1</span>]</span> = b<span class="hljs-string">&quot;\t&quot;</span><br>print(f<span class="hljs-literal">[:]</span>)<br>f.close<span class="hljs-literal">()</span><br>print(<span class="hljs-string">&quot;Done&quot;</span>)<br>&#x27;, file.path(lc_csomap_data_dir, <span class="hljs-string">&quot;TPM.txt&quot;</span>), lc_fix)<br>    print(lc_py)<br>    cat(file=lc_fix, lc_py)<br>    print(system(paste(<span class="hljs-string">&quot;python3&quot;</span>, lc_fix), intern = T))<br>&#125;<br><br>f_prepare4cellphoneDB &lt;- <span class="hljs-keyword">function</span>(lc_scRNA, lc_dir, lc_className)&#123;<br>    <span class="hljs-keyword">if</span> (!file.exists(lc_dir))&#123;dir.create(lc_dir)&#125;<br>    # 生成 count.txt <br>    write.table(<span class="hljs-keyword">as</span>.matrix(lc_scRNA@assays$RNA@data), file.path(lc_dir,&#x27;cellphonedb_count.txt&#x27;), sep=<span class="hljs-character">&#x27;\t&#x27;</span>, quote=F)<br>    # 生成 meta.txt<br>    lc_meta_data &lt;- cbind(rownames(lc_scRNA@meta.data), lc_scRNA@meta.data<span class="hljs-literal">[, <span class="hljs-identifier">lc_className</span>, <span class="hljs-identifier">drop</span>=F]</span>)<br>    lc_meta_data &lt;- <span class="hljs-keyword">as</span>.matrix(lc_meta_data)<br>    lc_meta_data<span class="hljs-literal">[<span class="hljs-identifier">is</span>.<span class="hljs-identifier">na</span>(<span class="hljs-identifier">lc_meta_data</span>)]</span> = <span class="hljs-string">&quot;Unkown&quot;</span> #  细胞类型中不能有NA<br>    write.table(lc_meta_data, file.path(lc_dir,&#x27;cellphonedb_meta.txt&#x27;), sep=<span class="hljs-character">&#x27;\t&#x27;</span>, quote=F, row.names=F)<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>, lc_pdf=T, lc_resolution=<span class="hljs-number">72</span>)&#123;<br>    <span class="hljs-keyword">if</span>(lc_pdf)&#123;<br>        width = width<span class="hljs-operator"> / </span>lc_resolution<br>        height = height<span class="hljs-operator"> / </span>lc_resolution<br>        pdf(paste(fileName, <span class="hljs-string">&quot;.pdf&quot;</span>, sep=<span class="hljs-string">&quot;&quot;</span>), width = width, height = height)<br>    &#125;<span class="hljs-keyword">else</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>    &#125;<br>    print(image)<br>    dev.off<span class="hljs-literal">()</span><br>&#125;<br> <br># Rearrange data column sequence<br>library(dplyr)<br>f_cDB_order_sequence &lt;- <span class="hljs-keyword">function</span>(lc_df)&#123;<br>    da &lt;- data.frame<span class="hljs-literal">()</span><br>    df &lt;- subset(lc_df, receptor_a<span class="hljs-operator"> == </span>&#x27;True&#x27; &amp; receptor_b<span class="hljs-operator"> == </span>&#x27;False&#x27;  receptor_a<span class="hljs-operator"> == </span>&#x27;False&#x27; &amp; receptor_b<span class="hljs-operator"> == </span>&#x27;True&#x27;)<br>    <span class="hljs-keyword">for</span>(i <span class="hljs-keyword">in</span> <span class="hljs-number">1</span>:length(df$gene_a))&#123;<br>    sub_data &lt;- df<span class="hljs-literal">[<span class="hljs-identifier">i</span>, ]</span><br>    <span class="hljs-keyword">if</span>(sub_data$receptor_b==&#x27;False&#x27;)&#123;<br>        <span class="hljs-keyword">if</span>(sub_data$receptor_a==&#x27;True&#x27;)&#123;<br>            old_names &lt;- colnames(sub_data)<br>            my_list &lt;- strsplit(old_names<span class="hljs-literal">[-<span class="hljs-identifier">c</span>(<span class="hljs-number">1</span>:<span class="hljs-number">11</span>)]</span>, split=<span class="hljs-string">&quot;\\&quot;</span>)<br>            my_character &lt;- paste(sapply(my_list, &#x27;<span class="hljs-literal">[[&#x27;, <span class="hljs-number">2L</span>), <span class="hljs-identifier">sapply</span>(<span class="hljs-identifier">my_list</span>, &#x27;[[&#x27;, <span class="hljs-number">1L</span>), <span class="hljs-identifier">sep</span>=&#x27;&#x27;)</span><br><span class="hljs-literal">            <span class="hljs-identifier">new_names</span> &lt;- <span class="hljs-identifier">c</span>(<span class="hljs-identifier">names</span>(<span class="hljs-identifier">sub_data</span>)[<span class="hljs-number">1</span>:<span class="hljs-number">4</span>]</span>, &#x27;gene_b&#x27;, &#x27;gene_a&#x27;, &#x27;secreted&#x27;, &#x27;receptor_b&#x27;, &#x27;receptor_a&#x27;, <span class="hljs-string">&quot;annotation_strategy&quot;</span>, <span class="hljs-string">&quot;is_integrin&quot;</span>, my_character)<br>            sub_data = dplyr::select(sub_data, new_names)<br>            # print(&#x27;Change sequence!!!&#x27;)<br>            names(sub_data) &lt;- old_names<br>            da = rbind(da, sub_data) <br>        &#125;<br>    &#125;<span class="hljs-keyword">else</span>&#123;<br>            da = rbind(da, sub_data)<br>    &#125;<br>    &#125;<br>    return(da)<br>&#125;<br> <br>f_cDB_mergePandM &lt;- <span class="hljs-keyword">function</span>(means_order, pvals_order)&#123;<br>    means_sub &lt;- means_order<span class="hljs-literal">[, <span class="hljs-identifier">c</span>(&#x27;<span class="hljs-identifier">interacting_pair</span>&#x27;, <span class="hljs-identifier">colnames</span>(<span class="hljs-identifier">means_order</span>)[-<span class="hljs-identifier">c</span>(<span class="hljs-number">1</span>:<span class="hljs-number">11</span>)]</span>)]<br>    pvals_sub &lt;- pvals_order<span class="hljs-literal">[, <span class="hljs-identifier">c</span>(&#x27;<span class="hljs-identifier">interacting_pair</span>&#x27;, <span class="hljs-identifier">colnames</span>(<span class="hljs-identifier">means_order</span>)[-<span class="hljs-identifier">c</span>(<span class="hljs-number">1</span>:<span class="hljs-number">11</span>)]</span>)]<br>    means_gather &lt;- tidyr::gather(means_sub, celltype, mean_expression, names(means_sub)<span class="hljs-literal">[-<span class="hljs-number">1</span>]</span>)<br>    pvals_gather &lt;- tidyr::gather(pvals_sub, celltype, pval, names(pvals_sub)<span class="hljs-literal">[-<span class="hljs-number">1</span>]</span>)<br>    mean_pval &lt;- dplyr::left<span class="hljs-constructor">_join(<span class="hljs-params">means_gather</span>, <span class="hljs-params">pvals_gather</span>, <span class="hljs-params">by</span> = <span class="hljs-params">c</span>(&#x27;<span class="hljs-params">interacting_pair</span>&#x27;, &#x27;<span class="hljs-params">celltype</span>&#x27;)</span>)<br>    mean_pval<br>&#125;<br> <br>f_readcellphoneDB &lt;- <span class="hljs-keyword">function</span>(lc_dir)&#123;<br>    res = <span class="hljs-built_in">list</span><span class="hljs-literal">()</span><br>    res$pvals &lt;- f<span class="hljs-constructor">_cDB_order_sequence(<span class="hljs-params">read</span>.<span class="hljs-params">delim</span>(<span class="hljs-params">file</span>.<span class="hljs-params">path</span>(<span class="hljs-params">lc_dir</span>, <span class="hljs-string">&quot;out&quot;</span>,<span class="hljs-string">&quot;pvalues.txt&quot;</span>)</span>, check.names = FALSE))<br>    res$means &lt;- f<span class="hljs-constructor">_cDB_order_sequence(<span class="hljs-params">read</span>.<span class="hljs-params">delim</span>(<span class="hljs-params">file</span>.<span class="hljs-params">path</span>(<span class="hljs-params">lc_dir</span>, <span class="hljs-string">&quot;out&quot;</span>, <span class="hljs-string">&quot;means.txt&quot;</span>)</span>, check.names = FALSE))<br>    res$s_means &lt;- read.delim(file.path(lc_dir, <span class="hljs-string">&quot;out&quot;</span>, <span class="hljs-string">&quot;significant_means.txt&quot;</span>), check.names = FALSE)<br>    res$m_p &lt;- f<span class="hljs-constructor">_cDB_mergePandM(<span class="hljs-params">res$means</span>, <span class="hljs-params">res$pvals</span>)</span><br>    lc_tp &lt;- res$m_p %&gt;% dplyr::select(interacting_pair, celltype, pval) %&gt;% tidyr::spread(key=celltype, value=pval)<br>    lc_sig_pairs &lt;- lc_tp<span class="hljs-literal">[<span class="hljs-identifier">which</span>(<span class="hljs-identifier">rowSums</span>(<span class="hljs-identifier">lc_tp</span>&lt;=<span class="hljs-number">0.05</span>)!=<span class="hljs-number">0</span>), ]</span><br>    res$s_m_p &lt;- subset(res$m_p, interacting_pair %<span class="hljs-keyword">in</span>% lc_sig_pairs$interacting_pair)<br>    res<br>&#125;<br> <br>f_cDB_dotplot &lt;- <span class="hljs-keyword">function</span>(lc_m_p)&#123;<br>    lc_m_p %&gt;% ggplot(aes(x=interacting_pair, y=celltype)) +<br>    # geom<span class="hljs-constructor">_point(<span class="hljs-params">aes</span>(<span class="hljs-params">color</span>=<span class="hljs-params">log2</span>(<span class="hljs-params">mean_expression</span>)</span>, size=pval)) +<br>    # scale<span class="hljs-constructor">_size(<span class="hljs-params">trans</span> = &#x27;<span class="hljs-params">reverse</span>&#x27;)</span> +<br>    geom<span class="hljs-constructor">_point(<span class="hljs-params">aes</span>(<span class="hljs-params">color</span>=<span class="hljs-params">log2</span>(<span class="hljs-params">mean_expression</span>)</span>, size=-log10(pval+<span class="hljs-number">1</span>*<span class="hljs-number">10</span>^-<span class="hljs-number">3</span>)) ) +<br>    guides(colour = guide<span class="hljs-constructor">_colourbar(<span class="hljs-params">order</span> = 1)</span>,size = guide<span class="hljs-constructor">_legend(<span class="hljs-params">order</span> = 2)</span>) +<br>    labs(x=&#x27;&#x27;, y=&#x27;&#x27;) +<br>    scale<span class="hljs-constructor">_color_gradientn(<span class="hljs-params">name</span>=&#x27;Expression <span class="hljs-params">level</span> \<span class="hljs-params">n</span>(<span class="hljs-params">log2</span> <span class="hljs-params">mean</span> <span class="hljs-params">expression</span> \<span class="hljs-params">nmolecule1</span>, <span class="hljs-params">molecule2</span>)</span>&#x27;, colours = terrain.colors(<span class="hljs-number">100</span>)) +<br>    # scale<span class="hljs-constructor">_color_gradient2(&#x27;Expression <span class="hljs-params">level</span> \<span class="hljs-params">n</span>(<span class="hljs-params">log2</span> <span class="hljs-params">mean</span> <span class="hljs-params">expression</span> \<span class="hljs-params">nmolecule1</span>, <span class="hljs-params">molecule2</span>)</span>&#x27;, low = &#x27;blue&#x27;, mid = &#x27;yellow&#x27;, high = &#x27;red&#x27;) +<br>    theme(axis.text.x= element<span class="hljs-constructor">_text(<span class="hljs-params">angle</span>=45, <span class="hljs-params">hjust</span>=1)</span>) +<br>    # coord<span class="hljs-constructor">_flip()</span> +<br>    theme(<br>    panel.border = element<span class="hljs-constructor">_rect(<span class="hljs-params">color</span> = &#x27;<span class="hljs-params">black</span>&#x27;, <span class="hljs-params">fill</span> = NA)</span>,<br>    panel.grid.major.x = element<span class="hljs-constructor">_blank()</span>,<br>    panel.grid.major.y = element<span class="hljs-constructor">_blank()</span>,<br>    panel.grid.minor.x = element<span class="hljs-constructor">_blank()</span>,<br>    panel.grid.minor.y = element<span class="hljs-constructor">_blank()</span>,<br>    panel.background   = element<span class="hljs-constructor">_blank()</span>,<br>    axis.title.x       = element<span class="hljs-constructor">_blank()</span>,<br>    axis.title.y       = element<span class="hljs-constructor">_blank()</span>,<br>    axis.ticks         = element<span class="hljs-constructor">_blank()</span><br>    # plot.title         = element<span class="hljs-constructor">_text(<span class="hljs-params">hjust</span> = 0.5)</span>,<br>    # legend.position = &#x27;bottom&#x27; # guides(fill = guide<span class="hljs-constructor">_legend(<span class="hljs-params">label</span>.<span class="hljs-params">position</span> = <span class="hljs-string">&quot;bottom&quot;</span>)</span>)<br>    # legend.position    = <span class="hljs-string">&quot;bottom&quot;</span><br>    # axis.text.y.right  = element<span class="hljs-constructor">_text(<span class="hljs-params">angle</span>=270, <span class="hljs-params">hjust</span>=0.5)</span><br>    ) +<br>    theme(legend.key.size = <span class="hljs-built_in">unit</span>(<span class="hljs-number">0.4</span>, &#x27;cm&#x27;), #change legend key size<br>    # legend.key.height = <span class="hljs-built_in">unit</span>(<span class="hljs-number">1</span>, &#x27;cm&#x27;), #change legend key height<br>    # legend.key.width = <span class="hljs-built_in">unit</span>(<span class="hljs-number">1</span>, &#x27;cm&#x27;), #change legend key width<br>    legend.title = element<span class="hljs-constructor">_text(<span class="hljs-params">size</span>=9)</span>, #change legend title font size<br>    legend.text = element<span class="hljs-constructor">_text(<span class="hljs-params">size</span>=8)</span>) #change legend text font size<br>&#125;<br><br></code></pre></td></tr></table></figure>
    246. 

  246. 

  247. <figure class="highlight cmake"><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></pre></td><td class="code"><pre><code class="hljs cmake"><span class="hljs-comment"># 配置数据和mark基因表的路径</span><br>root_path = <span class="hljs-string">&quot;~/zlliu/R_data/hBLA&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_data/21.10.04.split&quot;</span><br><span class="hljs-keyword">if</span> (!<span class="hljs-keyword">file</span>.<span class="hljs-keyword">exists</span>(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></code></pre></td></tr></table></figure>
    248. 

  248. 

  249. <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></pre></td><td class="code"><pre><code class="hljs mipsasm"><span class="hljs-comment"># 1、读取数据</span><br><span class="hljs-keyword">scRNA </span>= readRDS(<span class="hljs-string">&quot;~/zlliu/R_output/21.09.21.SingleR/scRNA.rds&quot;</span>)<br><span class="hljs-keyword">scRNA </span>&lt;- <span class="hljs-keyword">subset(x </span>= <span class="hljs-keyword">scRNA, </span>!!sym(<span class="hljs-string">&quot;Region&quot;</span>)%in%f_br_cluster_f(<span class="hljs-keyword">scRNA, </span><span class="hljs-string">&quot;Region&quot;</span>))<br><span class="hljs-keyword">scRNA@meta.data</span><br></code></pre></td></tr></table></figure>
    250. 

  250. 

  251. <figure class="highlight stylus"><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 stylus"><span class="hljs-function"><span class="hljs-title">options</span><span class="hljs-params">(repr.plot.width=<span class="hljs-number">12</span>, repr.plot.height=<span class="hljs-number">6</span>)</span></span><br><span class="hljs-function"><span class="hljs-title">options</span><span class="hljs-params">(ggrepel.max.overlaps = Inf)</span></span><br><span class="hljs-function"><span class="hljs-title">f_pie_metaN</span><span class="hljs-params">(scRNA, <span class="hljs-string">&quot;Region&quot;</span>)</span></span> + <span class="hljs-built_in">f_pie_metaN</span>(scRNA, <span class="hljs-string">&quot;hM1_hmca_class&quot;</span>)<br></code></pre></td></tr></table></figure>
    252. 

  252. 

  253. <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></pre></td><td class="code"><pre><code class="hljs lua">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></code></pre></td></tr></table></figure>
    254. 

  254. 

  255. <figure class="highlight stylus"><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></pre></td><td class="code"><pre><code class="hljs stylus"><span class="hljs-function"><span class="hljs-title">options</span><span class="hljs-params">(repr.plot.width=<span class="hljs-number">9</span>, repr.plot.height=<span class="hljs-number">5</span>)</span></span><br>tp_test &lt;- <span class="hljs-built_in">f_br_cluster</span>(scRNA, <span class="hljs-string">&#x27;Region&#x27;</span>, <span class="hljs-string">&#x27;hM1_hmca_class&#x27;</span>)<br><span class="hljs-function"><span class="hljs-title">f_q_frequnency</span><span class="hljs-params">(tp_test)</span></span><br>friedman<span class="hljs-selector-class">.test</span>(as<span class="hljs-selector-class">.matrix</span>(tp_test))<br>chisq<span class="hljs-selector-class">.test</span>(as<span class="hljs-selector-class">.matrix</span>(tp_test), simulate<span class="hljs-selector-class">.p</span><span class="hljs-selector-class">.value</span> = TRUE)<br>fisher<span class="hljs-selector-class">.test</span>(as<span class="hljs-selector-class">.matrix</span>(tp_test), simulate<span class="hljs-selector-class">.p</span><span class="hljs-selector-class">.value</span> = TRUE)<br>tp_test<br><span class="hljs-function"><span class="hljs-title">options</span><span class="hljs-params">(repr.plot.width=<span class="hljs-number">9</span>, repr.plot.height=<span class="hljs-number">9</span>)</span></span><br><span class="hljs-function"><span class="hljs-title">f_UMAP_more</span><span class="hljs-params">(scRNA, c(<span class="hljs-string">&#x27;hM1_class&#x27;</span>, <span class="hljs-string">&#x27;hmca_class&#x27;</span>)</span></span>)<br><span class="hljs-function"><span class="hljs-title">f_UMAP_more</span><span class="hljs-params">(scRNA, c(<span class="hljs-string">&#x27;hM1_hmca_class&#x27;</span>, <span class="hljs-string">&#x27;Region&#x27;</span>)</span></span>)<br></code></pre></td></tr></table></figure>
    256. 

  256. 

  257. <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></pre></td><td class="code"><pre><code class="hljs lua">scRNA<span class="hljs-string">[[&#x27;n_groups&#x27;]]</span> &lt;- f_grouplabel(scRNA<span class="hljs-string">[[c(&quot;hM1_hmca_class&quot;)]]</span>, n_groups)<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>sc_Neuron &lt;- subset(x = sc_Neuron, !!sym(<span class="hljs-string">&quot;Region&quot;</span>)%<span class="hljs-keyword">in</span>%f_br_cluster_f(sc_Neuron, <span class="hljs-string">&quot;Region&quot;</span>))<br></code></pre></td></tr></table></figure>
    258. 

  258. 

  259. <figure class="highlight stylus"><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></pre></td><td class="code"><pre><code class="hljs stylus"><span class="hljs-function"><span class="hljs-title">options</span><span class="hljs-params">(repr.plot.width=<span class="hljs-number">12</span>, repr.plot.height=<span class="hljs-number">6</span>)</span></span><br><span class="hljs-function"><span class="hljs-title">options</span><span class="hljs-params">(ggrepel.max.overlaps = Inf)</span></span><br><span class="hljs-function"><span class="hljs-title">f_pie_metaN</span><span class="hljs-params">(sc_Neuron, <span class="hljs-string">&quot;Region&quot;</span>)</span></span> + <span class="hljs-built_in">f_pie_metaN</span>(sc_Neuron, <span class="hljs-string">&quot;hM1_hmca_class&quot;</span>)<br><br><span class="hljs-function"><span class="hljs-title">options</span><span class="hljs-params">(repr.plot.width=<span class="hljs-number">9</span>, repr.plot.height=<span class="hljs-number">5</span>)</span></span><br>tp_test &lt;- <span class="hljs-built_in">f_br_cluster</span>(sc_Neuron, <span class="hljs-string">&#x27;Region&#x27;</span>, <span class="hljs-string">&#x27;hM1_hmca_class&#x27;</span>)<br><span class="hljs-function"><span class="hljs-title">f_q_frequnency</span><span class="hljs-params">(tp_test)</span></span><br>friedman<span class="hljs-selector-class">.test</span>(as<span class="hljs-selector-class">.matrix</span>(tp_test))<br>chisq<span class="hljs-selector-class">.test</span>(as<span class="hljs-selector-class">.matrix</span>(tp_test), simulate<span class="hljs-selector-class">.p</span><span class="hljs-selector-class">.value</span> = TRUE)<br>fisher<span class="hljs-selector-class">.test</span>(as<span class="hljs-selector-class">.matrix</span>(tp_test), simulate<span class="hljs-selector-class">.p</span><span class="hljs-selector-class">.value</span> = TRUE)<br>tp_test<br><br></code></pre></td></tr></table></figure>
    260. 

  260. 

  261. <figure class="highlight stylus"><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></pre></td><td class="code"><pre><code class="hljs stylus">sc_Neuron_ExN &lt;- <span class="hljs-built_in">subset</span>(x = sc_Neuron, n_groups == <span class="hljs-string">&quot;ExN&quot;</span>)<br><br><span class="hljs-function"><span class="hljs-title">options</span><span class="hljs-params">(repr.plot.width=<span class="hljs-number">12</span>, repr.plot.height=<span class="hljs-number">6</span>)</span></span><br><span class="hljs-function"><span class="hljs-title">options</span><span class="hljs-params">(ggrepel.max.overlaps = Inf)</span></span><br><span class="hljs-function"><span class="hljs-title">f_pie_metaN</span><span class="hljs-params">(sc_Neuron_ExN, <span class="hljs-string">&quot;Region&quot;</span>)</span></span> + <span class="hljs-built_in">f_pie_metaN</span>(sc_Neuron_ExN, <span class="hljs-string">&quot;hM1_hmca_class&quot;</span>)<br><br><span class="hljs-function"><span class="hljs-title">options</span><span class="hljs-params">(repr.plot.width=<span class="hljs-number">9</span>, repr.plot.height=<span class="hljs-number">5</span>)</span></span><br>tp_test &lt;- <span class="hljs-built_in">f_br_cluster</span>(sc_Neuron_ExN, <span class="hljs-string">&#x27;Region&#x27;</span>, <span class="hljs-string">&#x27;hM1_hmca_class&#x27;</span>)<br><span class="hljs-function"><span class="hljs-title">f_q_frequnency</span><span class="hljs-params">(tp_test)</span></span><br>friedman<span class="hljs-selector-class">.test</span>(as<span class="hljs-selector-class">.matrix</span>(tp_test))<br>chisq<span class="hljs-selector-class">.test</span>(as<span class="hljs-selector-class">.matrix</span>(tp_test), simulate<span class="hljs-selector-class">.p</span><span class="hljs-selector-class">.value</span> = TRUE)<br>fisher<span class="hljs-selector-class">.test</span>(as<span class="hljs-selector-class">.matrix</span>(tp_test), simulate<span class="hljs-selector-class">.p</span><span class="hljs-selector-class">.value</span> = TRUE)<br>tp_test<br><br><span class="hljs-function"><span class="hljs-title">options</span><span class="hljs-params">(repr.plot.width=<span class="hljs-number">9</span>, repr.plot.height=<span class="hljs-number">9</span>)</span></span><br><span class="hljs-function"><span class="hljs-title">f_UMAP_more</span><span class="hljs-params">(sc_Neuron_ExN, c(<span class="hljs-string">&#x27;hM1_class&#x27;</span>, <span class="hljs-string">&#x27;hmca_class&#x27;</span>)</span></span>)<br><span class="hljs-function"><span class="hljs-title">f_UMAP_more</span><span class="hljs-params">(sc_Neuron_ExN, c(<span class="hljs-string">&#x27;hM1_hmca_class&#x27;</span>, <span class="hljs-string">&#x27;Region&#x27;</span>)</span></span>)<br><br></code></pre></td></tr></table></figure>
    262. 

  262. 

  263. <figure class="highlight stylus"><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></pre></td><td class="code"><pre><code class="hljs stylus">sc_Neuron_InN &lt;- <span class="hljs-built_in">subset</span>(x = sc_Neuron, n_groups == <span class="hljs-string">&quot;InN&quot;</span>)<br><br><span class="hljs-function"><span class="hljs-title">options</span><span class="hljs-params">(repr.plot.width=<span class="hljs-number">12</span>, repr.plot.height=<span class="hljs-number">6</span>)</span></span><br><span class="hljs-function"><span class="hljs-title">options</span><span class="hljs-params">(ggrepel.max.overlaps = Inf)</span></span><br><span class="hljs-function"><span class="hljs-title">f_pie_metaN</span><span class="hljs-params">(sc_Neuron_InN, <span class="hljs-string">&quot;Region&quot;</span>)</span></span> + <span class="hljs-built_in">f_pie_metaN</span>(sc_Neuron_InN, <span class="hljs-string">&quot;hM1_hmca_class&quot;</span>)<br><br><span class="hljs-function"><span class="hljs-title">options</span><span class="hljs-params">(repr.plot.width=<span class="hljs-number">9</span>, repr.plot.height=<span class="hljs-number">5</span>)</span></span><br>tp_test &lt;- <span class="hljs-built_in">f_br_cluster</span>(sc_Neuron_InN, <span class="hljs-string">&#x27;Region&#x27;</span>, <span class="hljs-string">&#x27;hM1_hmca_class&#x27;</span>)<br><span class="hljs-function"><span class="hljs-title">f_q_frequnency</span><span class="hljs-params">(tp_test)</span></span><br>friedman<span class="hljs-selector-class">.test</span>(as<span class="hljs-selector-class">.matrix</span>(tp_test))<br>chisq<span class="hljs-selector-class">.test</span>(as<span class="hljs-selector-class">.matrix</span>(tp_test), simulate<span class="hljs-selector-class">.p</span><span class="hljs-selector-class">.value</span> = TRUE)<br>fisher<span class="hljs-selector-class">.test</span>(as<span class="hljs-selector-class">.matrix</span>(tp_test), simulate<span class="hljs-selector-class">.p</span><span class="hljs-selector-class">.value</span> = TRUE)<br>tp_test<br><br><span class="hljs-function"><span class="hljs-title">options</span><span class="hljs-params">(repr.plot.width=<span class="hljs-number">9</span>, repr.plot.height=<span class="hljs-number">9</span>)</span></span><br><span class="hljs-function"><span class="hljs-title">f_UMAP_more</span><span class="hljs-params">(sc_Neuron_InN, c(<span class="hljs-string">&#x27;hM1_class&#x27;</span>, <span class="hljs-string">&#x27;hmca_class&#x27;</span>)</span></span>)<br><span class="hljs-function"><span class="hljs-title">f_UMAP_more</span><span class="hljs-params">(sc_Neuron_InN, c(<span class="hljs-string">&#x27;hM1_hmca_class&#x27;</span>, <span class="hljs-string">&#x27;Region&#x27;</span>)</span></span>)<br><br></code></pre></td></tr></table></figure>
    264. 

  264. 

  265. <figure class="highlight stylus"><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 stylus"><span class="hljs-function"><span class="hljs-title">Idents</span><span class="hljs-params">(sc_Neuron)</span></span> &lt;- sc_Neuron<span class="hljs-selector-attr">[[<span class="hljs-string">&#x27;hM1_hmca_class&#x27;</span>]</span>]<br>all_markers &lt;- <span class="hljs-built_in">FindAllMarkers</span>(sc_Neuron, min<span class="hljs-selector-class">.pct</span> = <span class="hljs-number">0.25</span>, logfc<span class="hljs-selector-class">.threshold</span> = <span class="hljs-number">0.25</span>)<br>significant_markers &lt;- <span class="hljs-built_in">subset</span>(all_markers, subset = p_val_adj&lt;<span class="hljs-number">0.05</span>)<br><br></code></pre></td></tr></table></figure>
    266. 

  266. 

  267. <figure class="highlight stylus"><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 stylus"><span class="hljs-function"><span class="hljs-title">options</span><span class="hljs-params">(repr.plot.width=<span class="hljs-number">12</span>, repr.plot.height=<span class="hljs-number">6</span>)</span></span><br>tp_d &lt;- <span class="hljs-built_in">f_cluster_averages</span>(sc_Neuron, <span class="hljs-string">&quot;hM1_hmca_class&quot;</span>)<br>tp_d_br &lt;- <span class="hljs-built_in">f_cluster_averages</span>(sc_Neuron, <span class="hljs-string">&quot;Region&quot;</span>)<br><span class="hljs-function"><span class="hljs-title">f_DEG_hclust</span><span class="hljs-params">(tp_d)</span></span> + <span class="hljs-built_in">f_DEG_hclust</span>(tp_d_br)<br></code></pre></td></tr></table></figure>
    268. 

  268. 

  269. <figure class="highlight stylus"><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 stylus"><span class="hljs-function"><span class="hljs-title">Idents</span><span class="hljs-params">(sc_Neuron)</span></span> &lt;- sc_Neuron<span class="hljs-selector-attr">[[<span class="hljs-string">&#x27;hM1_hmca_class&#x27;</span>]</span>]<br>all_markers &lt;- <span class="hljs-built_in">FindAllMarkers</span>(sc_Neuron, min<span class="hljs-selector-class">.pct</span> = <span class="hljs-number">0.25</span>, logfc<span class="hljs-selector-class">.threshold</span> = <span class="hljs-number">0.25</span>)<br>significant_markers &lt;- <span class="hljs-built_in">subset</span>(all_markers, subset = p_val_adj&lt;<span class="hljs-number">0.05</span>)<br><br></code></pre></td></tr></table></figure>
    270. 

  270. 

  271. <figure class="highlight stylus"><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 stylus"><span class="hljs-function"><span class="hljs-title">options</span><span class="hljs-params">(repr.plot.width=<span class="hljs-number">9</span>, repr.plot.height=<span class="hljs-number">30</span>)</span></span><br>p1 &lt;- <span class="hljs-built_in">f_DEG_pheatmap</span>(<span class="hljs-built_in">f_DEG_pheatmap_choose_matrix</span>(tp_d, significant_markers))<br><span class="hljs-function"><span class="hljs-title">options</span><span class="hljs-params">(repr.plot.width=<span class="hljs-number">9</span>, repr.plot.height=<span class="hljs-number">30</span>)</span></span><br>p2 &lt;- <span class="hljs-built_in">f_DEG_pheatmap</span>(<span class="hljs-built_in">f_DEG_pheatmap_choose_matrix</span>(tp_d_br, significant_markers_br, Threshold_logFC = <span class="hljs-number">0.1</span>))<br><span class="hljs-function"><span class="hljs-title">options</span><span class="hljs-params">(repr.plot.width=<span class="hljs-number">18</span>, repr.plot.height=<span class="hljs-number">30</span>)</span></span><br>p1 + p2<br></code></pre></td></tr></table></figure>
    272. 

  272. 

  273. <figure class="highlight stylus"><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 stylus"><span class="hljs-function"><span class="hljs-title">f_prepare4cellphoneDB</span><span class="hljs-params">(sc_Neuron,<span class="hljs-string">&quot;Neuron&quot;</span>, <span class="hljs-string">&quot;hM1_hmca_class&quot;</span>)</span></span><br><span class="hljs-function"><span class="hljs-title">f_prepare4cellphoneDB</span><span class="hljs-params">(sc_Neuron,<span class="hljs-string">&quot;Neuron_br&quot;</span>, <span class="hljs-string">&quot;Region&quot;</span>)</span></span><br><span class="hljs-function"><span class="hljs-title">f_prepare4CSOmap</span><span class="hljs-params">(sc_Neuron, <span class="hljs-string">&quot;~/CSOmap/data/zlliu_s_Neuron&quot;</span>, <span class="hljs-string">&quot;hM1_hmca_class&quot;</span>)</span></span><br><span class="hljs-function"><span class="hljs-title">f_prepare4CSOmap</span><span class="hljs-params">(sc_Neuron, <span class="hljs-string">&quot;~/CSOmap/data/zlliu_s_Neuron_br&quot;</span>, <span class="hljs-string">&quot;Region&quot;</span>)</span></span><br></code></pre></td></tr></table></figure>
    274. 

  274. 

  275. <figure class="highlight bash"><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></pre></td><td class="code"><pre><code class="hljs bash"><span class="hljs-meta">#!/bin/bash</span><br><span class="hljs-comment">#PBS -q batch</span><br><span class="hljs-comment">#PBS -V</span><br><span class="hljs-comment">#PBS -o /home/rqzhang/cellphonedb.out</span><br><span class="hljs-comment">#PBS -e /home/rqzhang/cellphonedb.err</span><br><span class="hljs-comment">#PBS -l nodes=1:ppn=32</span><br><span class="hljs-comment">#PBS -r y</span><br> <br><span class="hljs-built_in">cd</span> /home/rqzhang/zlliu/R_data/21.10.04.<span class="hljs-built_in">split</span>/Neuron<br>cellphonedb method statistical_analysis  cellphonedb_meta.txt  cellphonedb_count.txt --counts-data=gene_name  --threads=32<br>cellphonedb plot dot_plot<br>cellphonedb plot heatmap_plot cellphonedb_meta.txt<br><br><span class="hljs-built_in">cd</span> /home/rqzhang/zlliu/R_data/21.10.04.<span class="hljs-built_in">split</span>/Neuron_br<br>cellphonedb method statistical_analysis  cellphonedb_meta.txt  cellphonedb_count.txt --counts-data=gene_name  --threads=32<br>cellphonedb plot dot_plot<br>cellphonedb plot heatmap_plot cellphonedb_meta.txt<br><br></code></pre></td></tr></table></figure>
    276. 

  276. 

  277. <figure class="highlight bash"><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></pre></td><td class="code"><pre><code class="hljs bash"><span class="hljs-meta">#!/bin/bash</span><br><span class="hljs-comment">#PBS -q batch</span><br><span class="hljs-comment">#PBS -V</span><br><span class="hljs-comment">#PBS -o /home/rqzhang/zlliu/PBS/CSOmap/CSOmap.out</span><br><span class="hljs-comment">#PBS -e /home/rqzhang/zlliu/PBS/CSOmap/CSOmap.err</span><br><span class="hljs-comment">#PBS -l nodes=1:ppn=1</span><br><span class="hljs-comment">#PBS -r y</span><br><span class="hljs-built_in">cd</span> /home/rqzhang/CSOmap/code<br>matlab -nodisplay -r <span class="hljs-string">&quot;runme(&#x27;zlliu_s_Neuron&#x27;);exit&quot;</span><br>matlab -nodisplay -r <span class="hljs-string">&quot;runme(&#x27;zlliu_s_Neuron_br&#x27;);exit&quot;</span><br><span class="hljs-built_in">echo</span> <span class="hljs-variable">$HOME</span><br></code></pre></td></tr></table></figure>
    278. 

  278. 

  279. <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">n_d &lt;- f<span class="hljs-constructor">_readcellphoneDB(&#x27;Neuron&#x27;)</span><br>tp_img &lt;- f<span class="hljs-constructor">_cDB_dotplot(<span class="hljs-params">subset</span>(<span class="hljs-params">n_d$s_m_p</span>, <span class="hljs-params">pval</span>&lt;0.05)</span>)<br>f<span class="hljs-constructor">_image_output(&#x27;Neuron&#x27;,<span class="hljs-params">tp_img</span>, <span class="hljs-params">width</span> = 1080,<span class="hljs-params">height</span> = 1080)</span><br><br></code></pre></td></tr></table></figure>
    280. 

  280. 

  281. <h2 id="更新"><a href="#更新" class="headerlink" title="更新"></a>更新</h2><figure class="highlight stylus"><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></pre></td><td class="code"><pre><code class="hljs stylus">f_prepare4cellphoneDB_sp &lt;- <span class="hljs-built_in">function</span>(lc_scRNA, lc_dir, lc_className, lc_groupN)&#123;<br>    <span class="hljs-keyword">if</span> (!file<span class="hljs-selector-class">.exists</span>(lc_dir))&#123;dir<span class="hljs-selector-class">.create</span>(lc_dir)&#125;<br>    lc_clusters &lt;- <span class="hljs-built_in">SplitObject</span>(lc_scRNA, split<span class="hljs-selector-class">.by</span> = lc_groupN)<br>    <span class="hljs-built_in">for</span>(lc_g <span class="hljs-keyword">in</span> <span class="hljs-built_in">names</span>(lc_clusters))&#123;<br>        c_dir = file<span class="hljs-selector-class">.path</span>(lc_dir,lc_g)<br>        <span class="hljs-keyword">if</span> (!file<span class="hljs-selector-class">.exists</span>(c_dir))&#123;dir<span class="hljs-selector-class">.create</span>(c_dir)&#125;<br>        <span class="hljs-built_in">f_prepare4cellphoneDB</span>(lc_clusters<span class="hljs-selector-attr">[[lc_g]</span>], c_dir, lc_className)<br>    &#125;<br>&#125;<br><br></code></pre></td></tr></table></figure>
    282. 

  282. 

  283. <figure class="highlight stylus"><table><tr><td class="gutter"><pre><span class="line">1</span><br></pre></td><td class="code"><pre><code class="hljs stylus"><span class="hljs-function"><span class="hljs-title">f_prepare4cellphoneDB_sp</span><span class="hljs-params">(sc_Neuron,<span class="hljs-string">&quot;Neuron_br_sp&quot;</span>, <span class="hljs-string">&quot;hM1_hmca_class&quot;</span>, <span class="hljs-string">&quot;Region&quot;</span>)</span></span><br></code></pre></td></tr></table></figure>
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  616.         var toc = jQuery('#toc');
    617. 

  617.         if (toc.length === 0 || !tocbot) {
    618. 

  618.           return;
    619. 

  619.         }
    620. 

  620.         if (toc.find('.toc-list-item').length > 0) {
    621. 

  621.           toc.css('visibility', 'visible');
    622. 

  622.         }
    623. 

  623.       }
    624. 

  624.     });
    625. 

  625.   });
    626. 

  626. </script>
    627. 

  627. 

  628. 

  629.   <script src=https://lib.baomitu.com/clipboard.js/2.0.11/clipboard.min.js></script>
    630. 

  630. 

  631.   <script>Fluid.plugins.codeWidget();</script>
    632. 

  632. 

  633. 

  634.   
    635. 

  635. <script>
    636. 

  636.   Fluid.utils.createScript('https://jscdn.limour.top/npm/anchor-js@4.3.1/anchor.min.js', function() {
    637. 

  637.     window.anchors.options = {
    638. 

  638.       placement: CONFIG.anchorjs.placement,
    639. 

  639.       visible  : CONFIG.anchorjs.visible
    640. 

  640.     };
    641. 

  641.     if (CONFIG.anchorjs.icon) {
    642. 

  642.       window.anchors.options.icon = CONFIG.anchorjs.icon;
    643. 

  643.     }
    644. 

  644.     var el = (CONFIG.anchorjs.element || 'h1,h2,h3,h4,h5,h6').split(',');
    645. 

  645.     var res = [];
    646. 

  646.     for (var item of el) {
    647. 

  647.       res.push('.markdown-body > ' + item.trim());
    648. 

  648.     }
    649. 

  649.     if (CONFIG.anchorjs.placement === 'left') {
    650. 

  650.       window.anchors.options.class = 'anchorjs-link-left';
    651. 

  651.     }
    652. 

  652.     window.anchors.add(res.join(', '));
    653. 

  653. 

  654.     Fluid.events.registerRefreshCallback(function() {
    655. 

  655.       if ('anchors' in window) {
    656. 

  656.         anchors.removeAll();
    657. 

  657.         var el = (CONFIG.anchorjs.element || 'h1,h2,h3,h4,h5,h6').split(',');
    658. 

  658.         var res = [];
    659. 

  659.         for (var item of el) {
    660. 

  660.           res.push('.markdown-body > ' + item.trim());
    661. 

  661.         }
    662. 

  662.         if (CONFIG.anchorjs.placement === 'left') {
    663. 

  663.           anchors.options.class = 'anchorjs-link-left';
    664. 

  664.         }
    665. 

  665.         anchors.add(res.join(', '));
    666. 

  666.       }
    667. 

  667.     });
    668. 

  668.   });
    669. 

  669. </script>
    670. 

  670. 

  671. 

  672.   
    673. 

  673. <script>
    674. 

  674.   Fluid.utils.createScript('https://lib.baomitu.com/fancybox/3.5.7/jquery.fancybox.min.js', function() {
    675. 

  675.     Fluid.plugins.fancyBox();
    676. 

  676.   });
    677. 

  677. </script>
    678. 

  678. 

  679. 

  680.   <script>Fluid.plugins.imageCaption();</script>
    681. 

  681. 

  682.   <script  src="/js/local-search.js" ></script>
    683. 

  683. 

  684. 

  685. 

  686. 

  687. 

  688. <!-- 主题的启动项，将它保持在最底部 -->
    689. 

  689. <!-- the boot of the theme, keep it at the bottom -->
    690. 

  690. <script  src="/js/boot.js" ></script>
    691. 

  691. 

  692. 

  693.   
    694. 

  694. 

  695.   <noscript>
    696. 

  696.     <div class="noscript-warning">Blog works best with JavaScript enabled</div>
    697. 

  697.   </noscript>
    698. 

  698. <!-- hexo injector body_end start -->
    699. 

  699.   <script defer src="/theme-inject/timeliness.js"></script>
    700. 

  700. <!-- hexo injector body_end end --></body>
    701. 

  701. </html>
    702.