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            <h1 id="seo-header">pyscenic (二) 转录因子分析</h1>
            
            
              <div class="markdown-body">
                
                <h2 id="前置要求"><a href="#前置要求" class="headerlink" title="前置要求"></a>前置要求</h2><p><a target="_blank" rel="noopener" href="https://limour.top/1063.html">pyscenic (一) 转录因子分析</a></p>
<p><a target="_blank" rel="noopener" href="https://pyscenic.readthedocs.io/en/latest/index.html">pySCENIC</a></p>
<h4 id="aertslab-pySCENIC"><a href="#aertslab-pySCENIC" class="headerlink" title="aertslab&#x2F;pySCENIC"></a><a target="_blank" rel="noopener" href="https://github.com/aertslab">aertslab</a>&#x2F;<strong><a target="_blank" rel="noopener" href="https://github.com/aertslab/pySCENIC">pySCENIC</a></strong></h4><h2 id="第一步-准备数据库"><a href="#第一步-准备数据库" class="headerlink" title="第一步 准备数据库"></a>第一步 准备数据库</h2><ul>
<li>设置工作目录</li>
</ul>
<figure class="highlight pf"><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 pf">import <span class="hljs-keyword">os</span><br><span class="hljs-comment"># 配置数据路径</span><br>root_path = <span class="hljs-keyword">os</span>.path.expanduser(<span class="hljs-string">&quot;~/zlliu/R_output/21.10.23.pyscenic&quot;</span>)<br> <br><span class="hljs-comment"># 配置结果保存路径</span><br>output_path = root_path<br>if not <span class="hljs-keyword">os</span>.path.isdir(output_path):<br>    <span class="hljs-keyword">os</span>.makedirs(output_path)<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><span class="hljs-keyword">os</span>.chdir(output_path) <br><span class="hljs-keyword">os</span>.getcwd()<br></code></pre></td></tr></table></figure>

<ul>
<li>下载智人的转录因子列表 <a target="_blank" rel="noopener" href="https://github.com/aertslab/pySCENIC/tree/master/resources">https://github.com/aertslab/pySCENIC/tree/master/resources</a></li>
</ul>
<figure class="highlight awk"><table><tr><td class="gutter"><pre><span class="line">1</span><br></pre></td><td class="code"><pre><code class="hljs awk">!wget https:<span class="hljs-regexp">//</span>raw.githubusercontent.com<span class="hljs-regexp">/aertslab/</span>pySCENIC<span class="hljs-regexp">/master/</span>resources/hs_hgnc_tfs.txt<br></code></pre></td></tr></table></figure>

<ul>
<li>下载 annotations file <a target="_blank" rel="noopener" href="https://pyscenic.readthedocs.io/en/latest/installation.html#auxiliary-datasets">https://pyscenic.readthedocs.io/en/latest/installation.html#auxiliary-datasets</a></li>
</ul>
<figure class="highlight awk"><table><tr><td class="gutter"><pre><span class="line">1</span><br></pre></td><td class="code"><pre><code class="hljs awk">!wget https:<span class="hljs-regexp">//</span>resources.aertslab.org<span class="hljs-regexp">/cistarget/m</span>otif2tf/motifs-v9-nr.hgnc-m0.<span class="hljs-number">001</span>-o0.<span class="hljs-number">0</span>.tbl<br></code></pre></td></tr></table></figure>

<ul>
<li>下载 Auxiliary datasets <a target="_blank" rel="noopener" href="https://resources.aertslab.org/cistarget/">https://resources.aertslab.org/cistarget/</a></li>
</ul>
<figure class="highlight awk"><table><tr><td class="gutter"><pre><span class="line">1</span><br></pre></td><td class="code"><pre><code class="hljs awk">!wget https:<span class="hljs-regexp">//</span>resources.aertslab.org<span class="hljs-regexp">/cistarget/</span>databases<span class="hljs-regexp">/homo_sapiens/</span>hg38<span class="hljs-regexp">/refseq_r80/m</span>c9nr<span class="hljs-regexp">/gene_based/</span>hg38__refseq-r80__10kb_up_and_down_tss.mc9nr.feather<br></code></pre></td></tr></table></figure>

<h2 id="第二步-输出-ex-matrix"><a href="#第二步-输出-ex-matrix" class="headerlink" title="第二步 输出 ex_matrix"></a>第二步 输出 ex_matrix</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></pre></td><td class="code"><pre><code class="hljs lua">%%R<br><span class="hljs-built_in">require</span>(Seurat)<br>SeuratObject = subset(SeuratObject, CellType == <span class="hljs-string">&#x27;fibroblast&#x27;</span>)<br><span class="hljs-built_in">write</span>.csv(as.matrix(SeuratObject<span class="hljs-string">[[&#x27;RNA&#x27;]]</span>@counts), file = <span class="hljs-string">&quot;ex_matrix.csv&quot;</span>)<br>cellInfo = SeuratObject@metadata<br></code></pre></td></tr></table></figure>

<h2 id="第三步-计算"><a href="#第三步-计算" class="headerlink" title="第三步 计算"></a>第三步 计算</h2><ul>
<li>导入包和数据</li>
</ul>
<figure class="highlight elm"><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></pre></td><td class="code"><pre><code class="hljs elm"><span class="hljs-keyword">import</span> glob<br><span class="hljs-keyword">import</span> pickle<br><span class="hljs-keyword">import</span> pandas <span class="hljs-keyword">as</span> pd<br><span class="hljs-keyword">import</span> numpy <span class="hljs-keyword">as</span> np<br> <br><span class="hljs-title">from</span> dask.diagnostics <span class="hljs-keyword">import</span> ProgressBar<br> <br><span class="hljs-title">from</span> arboreto.utils <span class="hljs-keyword">import</span> load_tf_names<br><span class="hljs-title">from</span> arboreto.algo <span class="hljs-keyword">import</span> grnboost2<br> <br><span class="hljs-title">from</span> ctxcore.rnkdb <span class="hljs-keyword">import</span> FeatherRankingDatabase <span class="hljs-keyword">as</span> RankingDatabase<br><span class="hljs-title">from</span> pyscenic.utils <span class="hljs-keyword">import</span> modules_from_adjacencies, load_motifs<br><span class="hljs-title">from</span> pyscenic.prune <span class="hljs-keyword">import</span> prune2df, df2regulons<br><span class="hljs-title">from</span> pyscenic.aucell <span class="hljs-keyword">import</span> aucell<br> <br><span class="hljs-keyword">import</span> seaborn <span class="hljs-keyword">as</span> sns<br></code></pre></td></tr></table></figure>

<figure class="highlight routeros"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br><span class="line">15</span><br></pre></td><td class="code"><pre><code class="hljs routeros">SC_EXP_FNAME = <span class="hljs-string">&#x27;ex_matrix.csv&#x27;</span><br>TFS_FNAME = <span class="hljs-string">&#x27;hs_hgnc_tfs.txt&#x27;</span><br>DATABASES_GLOB = <span class="hljs-string">&quot;hg38*.mc9nr.feather&quot;</span><br>MOTIF_ANNOTATIONS_FNAME = <span class="hljs-string">&#x27;motifs-v9-nr.hgnc-m0.001-o0.0.tbl&#x27;</span><br><br>ex_matrix = pd.read_csv(SC_EXP_FNAME, <span class="hljs-attribute">sep</span>=<span class="hljs-string">&#x27;,&#x27;</span>, <span class="hljs-attribute">header</span>=0, <span class="hljs-attribute">index_col</span>=0).T<br>ex_matrix.shape<br><br>tf_names = load_tf_names(TFS_FNAME)<br><br>db_fnames = glob.glob(DATABASES_GLOB)<br>def name(fname):<br>    return os.path.splitext(os.path.basename(fname))[0]<br>dbs = [RankingDatabase(<span class="hljs-attribute">fname</span>=fname, <span class="hljs-attribute">name</span>=name(fname)) <span class="hljs-keyword">for</span> fname <span class="hljs-keyword">in</span> db_fnames]<br>dbs<br></code></pre></td></tr></table></figure>

<ul>
<li>计算 grn</li>
</ul>
<figure class="highlight routeros"><table><tr><td class="gutter"><pre><span class="line">1</span><br></pre></td><td class="code"><pre><code class="hljs routeros">adjacencies = grnboost2(ex_matrix, <span class="hljs-attribute">tf_names</span>=tf_names, <span class="hljs-attribute">verbose</span>=<span class="hljs-literal">True</span>)<br></code></pre></td></tr></table></figure>

<figure class="highlight sml"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br></pre></td><td class="code"><pre><code class="hljs sml"><span class="hljs-keyword">with</span> <span class="hljs-keyword">open</span>(<span class="hljs-symbol">&#x27;adjacencies</span>.pydata&#x27;,<span class="hljs-symbol">&#x27;wb&#x27;</span>) <span class="hljs-keyword">as</span> f:<br>    pickle.dump(adjacencies,f)<br></code></pre></td></tr></table></figure>

<ul>
<li>计算 ctx</li>
</ul>
<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">modules = <span class="hljs-built_in">list</span>(modules<span class="hljs-constructor">_from_adjacencies(<span class="hljs-params">adjacencies</span>, <span class="hljs-params">ex_matrix</span>)</span>)<br><span class="hljs-keyword">with</span> <span class="hljs-constructor">ProgressBar()</span>:<br>    df = prune2df(dbs, modules, MOTIF_ANNOTATIONS_FNAME)<br>regulons = df2regulons(df)<br></code></pre></td></tr></table></figure>

<figure class="highlight sml"><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 sml"><span class="hljs-keyword">with</span> <span class="hljs-keyword">open</span>(<span class="hljs-symbol">&#x27;modules</span>.pydata&#x27;,<span class="hljs-symbol">&#x27;wb&#x27;</span>) <span class="hljs-keyword">as</span> f:<br>    pickle.dump(modules,f)<br><span class="hljs-keyword">with</span> <span class="hljs-keyword">open</span>(<span class="hljs-symbol">&#x27;df</span>.pydata&#x27;,<span class="hljs-symbol">&#x27;wb&#x27;</span>) <span class="hljs-keyword">as</span> f:<br>    pickle.dump(df,f)<br><span class="hljs-keyword">with</span> <span class="hljs-keyword">open</span>(<span class="hljs-symbol">&#x27;regulons</span>.pydata&#x27;,<span class="hljs-symbol">&#x27;wb&#x27;</span>) <span class="hljs-keyword">as</span> f:<br>    pickle.dump(regulons,f)<br></code></pre></td></tr></table></figure>

<ul>
<li>计算 AUCell</li>
</ul>
<figure class="highlight ini"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br></pre></td><td class="code"><pre><code class="hljs ini"><span class="hljs-attr">regulons</span> = [r.rename(r.name.replace(<span class="hljs-string">&#x27;(+)&#x27;</span>,<span class="hljs-string">&#x27; (&#x27;</span>+str(len(r))+<span class="hljs-string">&#x27;g)&#x27;</span>)) for r in regulons] <span class="hljs-comment"># 第四步 导入方法二</span><br><span class="hljs-attr">auc_mtx</span> = aucell(ex_matrix, regulons, num_workers=<span class="hljs-number">4</span>)<br></code></pre></td></tr></table></figure>

<figure class="highlight sml"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br></pre></td><td class="code"><pre><code class="hljs sml"><span class="hljs-keyword">with</span> <span class="hljs-keyword">open</span>(<span class="hljs-symbol">&#x27;auc_mtx</span>.pydata&#x27;,<span class="hljs-symbol">&#x27;wb&#x27;</span>) <span class="hljs-keyword">as</span> f:<br>    pickle.dump(auc_mtx,f)<br></code></pre></td></tr></table></figure>

<h2 id="第四步-导入R中出图"><a href="#第四步-导入R中出图" class="headerlink" title="第四步 导入R中出图"></a>第四步 导入R中出图</h2><p><a target="_blank" rel="noopener" href="https://www.jianshu.com/p/eccfe2d1b2c7">pySCENIC</a></p>
<ul>
<li>导入方法一 （不推荐）</li>
</ul>
<figure class="highlight pgsql"><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 pgsql"><span class="hljs-keyword">from</span> pyscenic.export <span class="hljs-keyword">import</span> export2loom<br>#  regulons = [r.<span class="hljs-keyword">rename</span>(r.name.replace(<span class="hljs-string">&#x27;(+)&#x27;</span>,<span class="hljs-string">&#x27; (&#x27;</span>+str(len(r))+<span class="hljs-string">&#x27;g)&#x27;</span>)) <span class="hljs-keyword">for</span> r <span class="hljs-keyword">in</span> regulons]<br>export2loom(ex_mtx = ex_matrix, auc_mtx = auc_mtx, regulons=regulons, out_fname = &quot;results.loom&quot;)<br></code></pre></td></tr></table></figure>

<figure class="highlight reasonml"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br><span class="line">15</span><br><span class="line">16</span><br><span class="line">17</span><br><span class="line">18</span><br><span class="line">19</span><br></pre></td><td class="code"><pre><code class="hljs reasonml">%%R<br>require(ggplot2)<br>require(SCENIC)<br>require(SCopeLoomR)<br><br>loom &lt;- <span class="hljs-keyword">open</span><span class="hljs-constructor">_loom(<span class="hljs-string">&quot;results.loom&quot;</span>, <span class="hljs-params">mode</span>=<span class="hljs-string">&quot;r&quot;</span>)</span><br><br># Read information from loom file:<br>regulons_incidMat &lt;- get<span class="hljs-constructor">_regulons(<span class="hljs-params">loom</span>)</span><br>regulons &lt;- regulons<span class="hljs-constructor">ToGeneLists(<span class="hljs-params">regulons_incidMat</span>)</span><br>regulonsAUC &lt;- get<span class="hljs-constructor">_regulonsAuc(<span class="hljs-params">loom</span>)</span><br>regulonsAucThresholds &lt;- get<span class="hljs-constructor">_regulonThresholds(<span class="hljs-params">loom</span>)</span><br>embeddings &lt;- get<span class="hljs-constructor">_embeddings(<span class="hljs-params">loom</span>)</span><br><br>exprMat &lt;- get<span class="hljs-constructor">_dgem(<span class="hljs-params">loom</span>)</span><br>cellInfo &lt;- get<span class="hljs-constructor">_cellAnnotation(<span class="hljs-params">loom</span>)</span><br>clusterings &lt;- get<span class="hljs-constructor">_clusterings_withName(<span class="hljs-params">loom</span>)</span><br><br>close<span class="hljs-constructor">_loom(<span class="hljs-params">loom</span>)</span><br></code></pre></td></tr></table></figure>

<ul>
<li>导入方法二 （推荐）</li>
</ul>
<figure class="highlight reasonml"><table><tr><td class="gutter"><pre><span class="line">1</span><br></pre></td><td class="code"><pre><code class="hljs reasonml">auc_mtx.<span class="hljs-keyword">to</span><span class="hljs-constructor">_csv(&#x27;<span class="hljs-params">auc_mtx</span>.<span class="hljs-params">tsv</span>&#x27;)</span><br></code></pre></td></tr></table></figure>

<figure class="highlight zephir"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br><span class="line">15</span><br></pre></td><td class="code"><pre><code class="hljs zephir">%%R<br><span class="hljs-keyword">require</span>(ggplot2)<br><span class="hljs-keyword">require</span>(SCENIC)<br><span class="hljs-keyword">require</span>(SCopeLoomR)<br><span class="hljs-keyword">require</span>(AUCell)<br><br>regulonAUC &lt;- importAUCfromText(<span class="hljs-string">&#x27;auc_mtx.tsv&#x27;</span>)<br>regulonAUC &lt;- regulonAUC[onlyNonDuplicatedExtended(rownames(regulonAUC)),]<br># cellInfo = SeuratObject@metadata<br>regulonActivity_byCellType &lt;- sapply(split(rownames(cellInfo), cellInfo$CellType),<br>                                     <span class="hljs-function"><span class="hljs-keyword">function</span><span class="hljs-params">(cells)</span> <span class="hljs-title">rowMeans</span><span class="hljs-params">(getAUC<span class="hljs-params">(regulonAUC)</span>[,cells])</span>)</span><br><span class="hljs-function"><span class="hljs-title">regulonActivity_byCellType_Scaled</span> &lt;- <span class="hljs-title">t</span><span class="hljs-params">(scale<span class="hljs-params">(t<span class="hljs-params">(regulonActivity_byCellType)</span>, center = T, scale=T)</span>)</span></span><br><span class="hljs-function"><span class="hljs-title">pheatmap</span>::<span class="hljs-title">pheatmap</span><span class="hljs-params">(regulonActivity_byCellType_Scaled, #fontsize_row=<span class="hljs-number">3</span>, </span></span><br><span class="hljs-params"><span class="hljs-function">                   color=colorRampPalette<span class="hljs-params">(c<span class="hljs-params">(<span class="hljs-string">&quot;blue&quot;</span>,<span class="hljs-string">&quot;white&quot;</span>,<span class="hljs-string">&quot;red&quot;</span>)</span>)</span><span class="hljs-params">(<span class="hljs-number">100</span>)</span>, breaks=seq<span class="hljs-params">(<span class="hljs-number">-3</span>, <span class="hljs-number">3</span>, length.out = <span class="hljs-number">100</span>)</span>,</span></span><br><span class="hljs-params"><span class="hljs-function">                   treeheight_row=<span class="hljs-number">10</span>, treeheight_col=<span class="hljs-number">10</span>, border_color=NA)</span></span><br></code></pre></td></tr></table></figure>

<p><a target="_blank" rel="noopener" href="https://www.jianshu.com/p/b0fd795ad05c">SCENIC—Single Cell rEgulatory Network Inference and Clustering</a></p>

                
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