Abstract：
Gene co-expression analysis is a data analysis technique that helps identify groups of genes with similar expression patterns across several different conditions. By means of these techniques, different groups have been able to assign putative metabolic pathways and functions to understudied genes and to identify novel metabolic regulation networks for different metabolites. Some groups have even used network comparative studies to understand the evolution of these networks from green algae to land plants. In this chapter, we will go over the basic definitions required to understand network topology and gene module identification. Additionally, we offer the reader a walk-through a standard analysis pipeline as implemented in the package WGCNA that takes as input raw fastq files and obtains co-expressed gene clusters and representative gene expression patterns from each module for downstream applications.
摘要：
基因共表达分析是一种数据分析技术，可帮助识别在几种不同条件下具有相似表达模式的基因群。通过这些技术，不同的群体已经能够将推定的代谢途径和功能分配给未被研究的基因，并为不同的代谢物鉴定新的代谢调节网络。一些小组甚至使用网络比较研究来了解这些网络从绿藻到陆地植物的演变。在这一章中,我们将介绍理解网络拓扑和基因模块识别所需的基本定义。此外,我们为读者提供了WGCNA包中实现的标准分析流程，该流程将原始fastq文件作为输入，并从每个模块获得共表达的基因簇和代表性基因表达模式，用于下游应用。
