# 3.Function Analysis

## 1) Table of Contents

* [3.1 GO](/teaching/part-ii.-basic-analyses/3.function-analysis/3.1.go.md)
* [3.2 KEGG](/teaching/part-ii.-basic-analyses/3.function-analysis/3.2.kegg.md)
* [3.3 GSEA](/teaching/part-ii.-basic-analyses/3.function-analysis/3.3.gsea.md)
* 当我们找到一些感兴趣的基因后（比如在某种处理条件下，与对照相比，表达量有明显差异的基因），我们希望能从这些基因中提炼出生物学意义，即根据一些已有的知识判断这些基因和哪些生物学功能是有相关性的。

![Fig 1. Overview of existing pathway analysis methods using gene expression data as an example](/files/-LPVvEnyDhTOly_dNV7X)

* 人们可以根据已有的生物学知识把基因注释到不同的功能或通路中，这样每个通路或功能都会对应一个基因集合(gene set)，GO和KEGG等就是这样的例子。根据特定领域的专家知识，也可以自己定义一些基因集合。
* 本章中我们将介绍评估在某种处理后发生变化的基因和已知生物学功能的关系的两类方法:
  * 一类是所谓的over representation analysis，给定一个基因集合（如表达量显著上升的基因），通过评估该基因集合和已知对应各种功能/通路的基因集合的重叠程度，判断该基因集合富集到哪些功能。[3.1 GO](/teaching/part-ii.-basic-analyses/3.function-analysis/3.1.go.md)和[3.2 KEGG](/teaching/part-ii.-basic-analyses/3.function-analysis/3.2.kegg.md)就属于这类分析。
  * 另一类是以GSEA(gene set enrichment analysis)为代表的打分方法，按某种指标（如某种处理后基因表达的fold change）对所有基因进行排序，得到一个ranked list，再通过统计检验判断已知对应各种功能/通路的基因集合富集在ranked list前端，后端，还是没有富集，从而判断各个通路与实验处理产生的变化有正相关，负相关还是不相关。我们将在[3.3 GSEA](/teaching/part-ii.-basic-analyses/3.function-analysis/3.3.gsea.md)中进行介绍。
* 更多内容请参考 [Ten Years of Pathway Analysis: Current Approaches and Outstanding Challenges](https://doi.org/10.1371/journal.pcbi.1002375)

## 2) Files Needed <a href="#files" id="files"></a>

* 本章不需要使用docker，所用到的文件可以直接从 [**Files needed** ](https://courses.ncrnalab.org/files)中的**Files/** 路径下的相应文件夹中下载。

## 3) Teaching Videos

* see Videos in the [**Files needed** ](https://courses.ncrnalab.org/files)


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