Gene Coexpression Network and Module Analysis across 52 Human Tissues.

Binsheng He,Pingping Bing,Bo Liao,Yingxiang Tian,Jidong Lang,Junlin Xu,Xiaofei Mo,Geng Tian,Qingqing Lu,Huixin Lin

doi:10.1155/2020/6782046

Abstract

Gene coexpression analysis is widely used to infer gene modules associated with diseases and other clinical traits. However, a systematic view and comparison of gene coexpression networks and modules across a cohort of tissues are more or less ignored. In this study, we first construct gene coexpression networks and modules of 52 GTEx tissues and cell lines. The network modules are enriched in many tissue-common functions like organelle membrane and tissue-specific functions. We then study the correlation of tissues from the network point of view. As a result, the network modules of most tissues are significantly correlated, indicating a general similar network pattern across tissues. However, the level of similarity among the tissues is different. The tissues closing in a physical location seem to be more similar in their coexpression networks. For example, the two adjacent tissues fallopian tube and bladder have the highest Fisher's exact test p value 8.54E-291 among all tissue pairs. It is known that immune-associated modules are frequently identified in coexperssion modules. In this study, we found immune modules in many tissues like liver, kidney cortex, lung, uterus, adipose subcutaneous, and adipose visceral omentum. However, not all tissues have immune-associated modules, for example, brain cerebellum. Finally, by the clique analysis, we identify the largest clique of modules, in which the genes in each module are significantly overlapped with those in other modules. As a result, we are able to find a clique of size 40 (out of 52 tissues), indicating a strong correlation of modules across tissues. It is not surprising that the 40 modules are most commonly enriched in immune-related functions.

Highlights

With the development of next-generation sequencing technologies, there are more and more sequencing data available, which provides a great opportunity to unravel the biological mechanisms behind them using bioinformatics and machine learning tools [1]
A network graph was created by weighted gene coexpression network analysis (WGCNA) for the spleen tissue, with a total of 18,648 genes and 20 modules
Gene coexpression networks have been extensively studied recently due to its ability in finding key regulatory mechanisms and critical modules involved in a function

Summary

Introduction

With the development of next-generation sequencing technologies, there are more and more sequencing data available, which provides a great opportunity to unravel the biological mechanisms behind them using bioinformatics and machine learning tools [1]. A gene is coexpressed with the other one if there are some correlations between the expression profiles of the two genes across the sample set, which might be caused by gene regulation and other biological mechanisms. Modularity is a very important feature of the gene coexpression network. A gene expression module is a set of genes, in which each pair of genes are highly coexpressed. The key driver genes in a module are usually highly associated with disease progression and patients’ survival, which are usually used as diagnostic biomarkers and drug targets [4]

Methods

Results

Discussion

Conclusion