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<title>第 34 章 GO/KEGG功能富集分析 | R编程与作图</title>
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<li><a class="" href="index.html">前言</a></li>
<li class="book-part">R编程基础</li>
<li><a class="" href="baseR-intro-ds.html"><span class="header-section-number">1</span> R语言介绍及资料推荐</a></li>
<li><a class="" href="baseR-install.html"><span class="header-section-number">2</span> 安装与环境配置</a></li>
<li><a class="" href="baseR-objects.html"><span class="header-section-number">3</span> 对象</a></li>
<li><a class="" href="baseR-vectors.html"><span class="header-section-number">4</span> 向量</a></li>
<li><a class="" href="baseR-data-structure.html"><span class="header-section-number">5</span> 数据结构</a></li>
<li><a class="" href="baseR-operators.html"><span class="header-section-number">6</span> 运算符及向量运算</a></li>
<li><a class="" href="baseR-functions.html"><span class="header-section-number">7</span> 函数</a></li>
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<li><a class="" href="tidyverse-dplyr-apply.html"><span class="header-section-number">12</span> dplyr进阶</a></li>
<li><a class="" href="tidyverse-tidyr.html"><span class="header-section-number">13</span> 数据规整1</a></li>
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<li><a class="" href="tidyverse-ggplot2-theme.html"><span class="header-section-number">21</span> ggplot2之主题设置</a></li>
<li><a class="" href="tidyverse-ggplot2-guides.html"><span class="header-section-number">22</span> ggplot2之图例系统</a></li>
<li><a class="" href="tidyverse-ggplot2-customize.html"><span class="header-section-number">23</span> ggplot2之扩展内容</a></li>
<li><a class="" href="tidyverse-ggplot2-stat-layer.html"><span class="header-section-number">24</span> ggplot2之统计图层</a></li>
<li><a class="" href="tidyverse-ggplot2-from-layer-to-geom.html"><span class="header-section-number">25</span> ggplot2之从图层到几何形状</a></li>
<li><a class="" href="tidyverse-ggplot2-colors.html"><span class="header-section-number">26</span> ggplot2之数据可视化中的配色</a></li>
<li><a class="" href="tidyverse-ggplot2-override-aes.html"><span class="header-section-number">27</span> ggplot2之控制图例的外观</a></li>
<li><a class="" href="tidyverse-ggplot2-aes-eval.html"><span class="header-section-number">28</span> ggplot2之延迟映射</a></li>
<li><a class="" href="tidyverse-ggplot2-academic.html"><span class="header-section-number">29</span> ggplot2之科研数据可视化</a></li>
<li><a class="" href="tidyverse-ggplot2-gganimate.html"><span class="header-section-number">30</span> ggplot2之让你的数据动起来</a></li>
<li><a class="" href="tidyverse-ggplot2-pass-function-as-parameters.html"><span class="header-section-number">31</span> ggplot2中传递函数作为参数值</a></li>
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<li><a class="" href="tidyverse-rmarkdown.html"><span class="header-section-number">32</span> 可重复性文档</a></li>
<li><a class="" href="eda-practice.html"><span class="header-section-number">33</span> 一天一练</a></li>
<li class="book-part">GO/KEGG富集分析</li>
<li><a class="active" href="GO-KEGG.html"><span class="header-section-number">34</span> GO/KEGG功能富集分析</a></li>
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</header><main class="col-sm-12 col-md-9 col-lg-7" id="content"><div id="GO-KEGG" class="section level1" number="34">
<h1>
<span class="header-section-number">第 34 章</span> GO/KEGG功能富集分析<a class="anchor" aria-label="anchor" href="#GO-KEGG"><i class="fas fa-link"></i></a>
</h1>
<div id="功能富集分析介绍" class="section level2" number="34.1">
<h2>
<span class="header-section-number">34.1</span> 功能富集分析介绍<a class="anchor" aria-label="anchor" href="#%E5%8A%9F%E8%83%BD%E5%AF%8C%E9%9B%86%E5%88%86%E6%9E%90%E4%BB%8B%E7%BB%8D"><i class="fas fa-link"></i></a>
</h2>
<p>功能富集分析对于解释转录组学数据至关重要。转录组鉴定了差异表达基因后，通常会进行GO或KEGG富集分析，识别这些差异基因的功能或参与调控的通路，来说明关键基因表达上调/或下调后可能会导致哪功能或通路被激活/或抑制，进而与表型进行联系。</p>
<p>目前能够进行GO、KEGG富集分析的工具有很多，不同的工具之间在算法、数据库上存在不同，因此结果也可能大相径庭。这里就以R包clusterProfiler的方法为例（该包基于超几何分布的原理），展示如何基于给定的差异基因列表进行GO、KEGG富集分析。</p>
</div>
<div id="使用clusterprofiler做功能富集分析" class="section level2" number="34.2">
<h2>
<span class="header-section-number">34.2</span> 使用clusterProfiler做功能富集分析<a class="anchor" aria-label="anchor" href="#%E4%BD%BF%E7%94%A8clusterprofiler%E5%81%9A%E5%8A%9F%E8%83%BD%E5%AF%8C%E9%9B%86%E5%88%86%E6%9E%90"><i class="fas fa-link"></i></a>
</h2>
<p>使用Bioconductor安装clusterProfiler</p>
<div class="sourceCode" id="cb2367"><pre class="downlit sourceCode r">
<code class="sourceCode R"><span><span class="fu">BiocManager</span><span class="fu">::</span><span class="fu"><a href="https://bioconductor.github.io/BiocManager/reference/install.html">install</a></span><span class="op">(</span><span class="st">'clusterProfiler'</span><span class="op">)</span></span></code></pre></div>
<p>详细的用法请参考<a href="https://yulab-smu.top/biomedical-knowledge-mining-book/enrichment-overview.html">Overview of enrichment analysis using clusterProfiler</a> 以及 <a href="https://zhuanlan.zhihu.com/p/561522453">使用R包clusterProfiler进行GO/KEGG富集分析（有参/无参）</a>.</p>
<div id="refs" class="references csl-bib-body hanging-indent">
<div id="ref-Wickham2017" class="csl-entry">
Grolemund, Garrett, and Hadley Wickham. 2017. <em>R for Data Science: Import, Tidy, Transform, Visualize, and Model Data</em>. 1 edition. Houston: O’Reilly Media. <a href="http://r4ds.had.co.nz/">http://r4ds.had.co.nz/</a>.
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<div class="prev"><a href="eda-practice.html"><span class="header-section-number">33</span> 一天一练</a></div>
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<li><a class="nav-link" href="#GO-KEGG"><span class="header-section-number">34</span> GO/KEGG功能富集分析</a></li>
<li><a class="nav-link" href="#%E5%8A%9F%E8%83%BD%E5%AF%8C%E9%9B%86%E5%88%86%E6%9E%90%E4%BB%8B%E7%BB%8D"><span class="header-section-number">34.1</span> 功能富集分析介绍</a></li>
<li><a class="nav-link" href="#%E4%BD%BF%E7%94%A8clusterprofiler%E5%81%9A%E5%8A%9F%E8%83%BD%E5%AF%8C%E9%9B%86%E5%88%86%E6%9E%90"><span class="header-section-number">34.2</span> 使用clusterProfiler做功能富集分析</a></li>
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