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            <h1 id="seo-header">TCGA (一) 获取数据</h1>
            
            
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                <h2 id="第一步-安装程辑包并加载"><a href="#第一步-安装程辑包并加载" class="headerlink" title="第一步 安装程辑包并加载"></a>第一步 安装程辑包并加载</h2><figure class="highlight scss"><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></pre></td><td class="code"><pre><code class="hljs scss"><span class="hljs-built_in">options</span>(BioC_mirror=&quot;https://mirrors.tuna.tsinghua.edu.cn/bioconductor&quot;)<br>BiocManager::<span class="hljs-built_in">install</span>(<span class="hljs-string">&quot;TCGAbiolinks&quot;</span>)<br><br># 加载响应的包，默认已经安装好TCGAbiolinks包<br><span class="hljs-built_in">library</span>(TCGAbiolinks)<br><span class="hljs-built_in">library</span>(plyr)<br><span class="hljs-built_in">library</span>(limma)<br><span class="hljs-built_in">library</span>(biomaRt)<br><span class="hljs-built_in">library</span>(SummarizedExperiment)<br></code></pre></td></tr></table></figure>

<h2 id="第二步-查看癌症类型"><a href="#第二步-查看癌症类型" class="headerlink" title="第二步 查看癌症类型"></a>第二步 查看癌症类型</h2><figure class="highlight elixir"><table><tr><td class="gutter"><pre><span class="line">1</span><br></pre></td><td class="code"><pre><code class="hljs elixir"><span class="hljs-title class_">TCGAbiolinks</span>::<span class="hljs-symbol">:getGDCprojects</span>()<span class="hljs-variable">$project_id</span><br></code></pre></td></tr></table></figure>

<p><a target="_blank" rel="noopener" href="https://img-cdn.limour.top/blog_wp/2021/09/image-23.png"><img src="https://img-cdn.limour.top/blog_wp/2021/09/image-23.png" srcset="https://jscdn.limour.top/gh/Limour-dev/Sakurairo_Vision/load_svg/inload.svg" lazyload></a></p>
<p>癌症类型说明：<a target="_blank" rel="noopener" href="https://www.biowolf.cn/TCGA/tcga_sample.html">癌症类型和样本代号详解TCGA</a></p>
<h2 id="第三步-查看对应癌症的数据类型"><a href="#第三步-查看对应癌症的数据类型" class="headerlink" title="第三步 查看对应癌症的数据类型"></a>第三步 查看对应癌症的数据类型</h2><figure class="highlight elixir"><table><tr><td class="gutter"><pre><span class="line">1</span><br></pre></td><td class="code"><pre><code class="hljs elixir"><span class="hljs-title class_">TCGAbiolinks</span>::<span class="hljs-symbol">:getProjectSummary</span>(<span class="hljs-string">&#x27;TCGA-PRAD&#x27;</span>) <span class="hljs-comment"># 以前列腺癌为例</span><br></code></pre></td></tr></table></figure>

<p><a target="_blank" rel="noopener" href="https://img-cdn.limour.top/blog_wp/2021/09/image-24.png"><img src="https://img-cdn.limour.top/blog_wp/2021/09/image-24.png" srcset="https://jscdn.limour.top/gh/Limour-dev/Sakurairo_Vision/load_svg/inload.svg" lazyload></a></p>
<p>case_count为病人数，file_count为对应的文件数，”Transcriptome Profiling”表示表达谱</p>
<blockquote>
<p><a target="_blank" rel="noopener" href="http://blog.sciencenet.cn/blog-3291578-1066017.html#:~:text=%E5%9C%A8TCGA%E4%B8%AD%EF%BC%8C%E4%B8%BB%E8%A6%81%E6%9C%89%E4%B8%8B%E9%9D%A2%E7%9A%84%E6%95%B0%E6%8D%AE%E7%B1%BB%E5%9E%8B%EF%BC%9A%201%EF%BC%89%E8%BD%AC%E5%BD%95%E7%BB%84%E6%95%B0%E6%8D%AE%EF%BC%88Transcriptome%EF%BC%89,2%EF%BC%89%E7%94%B2%E5%9F%BA%E5%8C%96%E6%95%B0%E6%8D%AE%EF%BC%88Methylation%EF%BC%89%203%EF%BC%89%E5%9F%BA%E5%9B%A0%E7%AA%81%E5%8F%98%E6%95%B0%E6%8D%AE%EF%BC%88Mutation%EF%BC%89">“如何玩转生物大数据”系列：TCGA的样本注释信息和数据类型统计</a></p>
<p>1）转录组数据（Transcriptome）<br>2）甲基化数据（Methylation）<br>3）基因突变数据（Mutation）<br>4）拷贝数变化数据 （CNV）</p>
</blockquote>
<blockquote>
<p><a target="_blank" rel="noopener" href="http://paper.dxy.cn/article/511878">手把手教你 TCGA 数据库使用：以肝癌为例</a></p>
<p><img src="http://img.dxycdn.com/cms/upload/userfiles/image/2016/12/19/B1482129119_small.jpg" srcset="https://jscdn.limour.top/gh/Limour-dev/Sakurairo_Vision/load_svg/inload.svg" lazyload alt="wxid_7vqx3f6qn62f12_1482131772510_79.png"><br><img src="http://img.dxycdn.com/cms/upload/userfiles/image/2016/12/19/B1482129120_small.jpg" srcset="https://jscdn.limour.top/gh/Limour-dev/Sakurairo_Vision/load_svg/inload.svg" lazyload alt="wxid_7vqx3f6qn62f12_1482131772670_15.png"></p>
</blockquote>
<blockquote>
<p><a target="_blank" rel="noopener" href="https://cloud.tencent.com/developer/article/1481904">TCGAbiolinks包下载TCGA数据进行表达差异分析-乳腺癌案例</a></p>
<p>（4）workflow.type<br>该数据类型有很多种，根据data.type的不同而不同，不同的数据类型，有其对应的参数可供选择。比如Gene Expression Quantification数据类型下workflow.type 有4种类型分别为：<br>HTSeq - FPKM-UQ：FPKM上四分位数标准化值<br>HTSeq - FPKM：FPKM值&#x2F;表达量值<br>HTSeq - Counts：原始count数<br>STAR - Counts<br>具体可在GDC官网查看<br>（5）legacy<br>这个参数主要是因为TCGA数据有两个入口可以下载，GDC Legacy Archive 和 GDC Data Portal，区别主要是注释参考基因组版本不同分别是：GDC Legacy Archive（hg19和GDC Data Portal（hg38）。参数默认为FALSE，下载GDC Data Portal（hg38）。这里建议是，下载转录组层面的数据使用hg38，下载DNA层面的数据使用hg19，因为比如做SNP分析的时候很多数据库没有hg38版本的数据，都是hg19的。</p>
</blockquote>
<h2 id="第四步-下载对应数据"><a href="#第四步-下载对应数据" class="headerlink" title="第四步 下载对应数据"></a>第四步 下载对应数据</h2><figure class="highlight ada"><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 ada">query &lt;- GDCquery(project = <span class="hljs-symbol">&#x27;TCGA</span>-PRAD&#x27;,<br>                  data.category = <span class="hljs-string">&quot;Transcriptome Profiling&quot;</span>,<br>                  data.<span class="hljs-keyword">type</span> <span class="hljs-type">= </span><span class="hljs-string">&quot;Gene Expression Quantification&quot;</span>, <br>                  workflow.<span class="hljs-keyword">type</span> <span class="hljs-type">= </span><span class="hljs-string">&quot;HTSeq - Counts&quot;</span>)<br></code></pre></td></tr></table></figure>

<p><a target="_blank" rel="noopener" href="https://img-cdn.limour.top/blog_wp/2021/09/image-25.png"><img src="https://img-cdn.limour.top/blog_wp/2021/09/image-25.png" srcset="https://jscdn.limour.top/gh/Limour-dev/Sakurairo_Vision/load_svg/inload.svg" lazyload></a></p>
<h2 id="第五步-保存对应数据"><a href="#第五步-保存对应数据" class="headerlink" title="第五步 保存对应数据"></a>第五步 保存对应数据</h2><figure class="highlight reasonml"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br></pre></td><td class="code"><pre><code class="hljs reasonml"># 配置数据路径<br>root_path = <span class="hljs-string">&quot;~/zlliu/R_data/TCGA&quot;</span><br> <br># 配置结果保存路径<br>output_path = root_path<br><span class="hljs-keyword">if</span> (!file.exists(output_path))&#123;dir.create(output_path)&#125;<br> <br># 设置工作目录，输出文件将保存在此目录下<br>setwd(output_path)<br>getwd<span class="hljs-literal">()</span><br><br><span class="hljs-constructor">GDCdownload(<span class="hljs-params">query</span> = <span class="hljs-params">query</span>)</span><br>save<span class="hljs-constructor">RDS(<span class="hljs-params">query</span>,&#x27;TCGA-PRAD.<span class="hljs-params">rds</span>&#x27;)</span><br></code></pre></td></tr></table></figure>
                
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