Department of Physiology and Pharmacology, Biomedicum, Karolinska Institutet, Stockholm, Sweden.
Methods Mol Biol. 2022;2445:289-302. doi: 10.1007/978-1-0716-2071-7_18.
Gene coexpression network analysis is a commonly used approach in bioinformatics and biomedical research to construct coexpression networks and detect coexpressed genes. This type of analysis has proven valuable for gene function prediction and for disease biomarker discovery.Here, we introduce and guide researchers through a method of differential coexpression analysis focusing on key autophagy and metabolic genes. We utilized the open-source Cancer Cell Line Encyclopedia (CCLE ) project as this is one of the most comprehensive genomic and transcriptomic resources including more than 1000 cell lines of distinct origins. However, the coexpression analysis method described here can also be applied to any open-source dataset where the RNA expression are provided.We here provide detailed comprehensive practical instructions for investigators to successfully identify novel coexpression signatures.
基因共表达网络分析是生物信息学和生物医学研究中常用的方法，用于构建共表达网络和检测共表达基因。这种分析方法已被证明对基因功能预测和疾病生物标志物的发现非常有价值。在这里，我们介绍并指导研究人员使用一种专注于关键自噬和代谢基因的差异共表达分析方法。我们利用开源的癌症细胞系百科全书 (CCLE) 项目，因为它是最全面的基因组和转录组资源之一，其中包括超过 1000 种不同来源的细胞系。然而，这里描述的共表达分析方法也可以应用于任何提供 RNA 表达的开源数据集。我们在此为研究人员提供详细全面的实用说明，以成功识别新的共表达特征。
