Estimating genomic coexpression networks using first-order conditional independence

We describe a computationally efficient statistical framework for estimating networks of coexpressed genes. This framework exploits first-order conditional independence relationships among gene-expression measurements to estimate patterns of association. We use this approach to estimate a coexpression network from microarray gene-expression measurements from Saccharomyces cerevisiae. We demonstrate the biological utility of this approach by showing that a large number of metabolic pathways are coherently represented in the estimated network. We describe a complementary unsupervised graph search algorithm for discovering locally distinct subgraphs of a large weighted graph. We apply this algorithm to our coexpression network model and show that subgraphs found using this approach correspond to particular biological processes or contain representatives of distinct gene families.     

Identification of gene expression profiles and key genes in subchondral bone of osteoarthritis using weighted gene coexpression network analysis

Osteoarthritis (OA) significantly influences the quality life of people around the world. It is urgent to find an effective way to understand the genetic etiology of OA. We used weighted gene coexpression network analysis (WGCNA) to explore the key genes involved in the subchondral bone pathological process of OA. Fifty gene expression profiles of GSE51588 were downloaded from the Gene Expression Omnibus database. The OA‐associated genes and gene ontologies were acquired from JuniorDoc. Weighted gene coexpression network analysis was used to find disease‐related networks based on 21756 gene expression correlation coefficients, hub‐genes with the highest connectivity in each module were selected, and the correlation between module eigengene and clinical traits was calculated. The genes in the traits‐related gene coexpression modules were subject to functional annotation and pathway enrichment analysis using ClusterProfiler. A total of 73 gene modules were identified, of which, 12 modules were found with high connectivity with clinical traits. Five modules were found with enriched OA‐associated genes. Moreover, 310 OA‐associated genes were found, and 34 of them were among hub‐genes in each module. Consequently, enrichment results indicated some key metabolic pathways, such as extracellular matrix (ECM)‐receptor interaction (hsa04512), focal adhesion (hsa04510), the phosphatidylinositol 3'‐kinase (PI3K)‐Akt signaling pathway (PI3K‐AKT) (hsa04151), transforming growth factor beta pathway, and Wnt pathway. We intended to identify some core genes, collagen (COL)6A3, COL6A1, ITGA11, BAMBI, and HCK, which could influence downstream signaling pathways once they were activated. In this study, we identified important genes within key coexpression modules, which associate with a pathological process of subchondral bone in OA. Functional analysis results could provide important information to understand the mechanism of OA.     

MetaGSCA: A tool for meta-analysis of gene set differential coexpression

Analyses of gene set differential coexpression may shed light on molecular mechanisms underlying phenotypes and diseases. However, differential coexpression analyses of conceptually similar individual studies are often inconsistent and underpowered to provide definitive results. Researchers can greatly benefit from an open-source application facilitating the aggregation of evidence of differential coexpression across studies and the estimation of more robust common effects. We developed Meta Gene Set Coexpression Analysis (MetaGSCA), an analytical tool to systematically assess differential coexpression of an a priori defined gene set by aggregating evidence across studies to provide a definitive result. In the kernel, a nonparametric approach that accounts for the gene-gene correlation structure is used to test whether the gene set is differentially coexpressed between two comparative conditions, from which a permutation test p-statistic is computed for each individual study. A meta-analysis is then performed to combine individual study results with one of two options: a random-intercept logistic regression model or the inverse variance method. We demonstrated MetaGSCA in case studies investigating two human diseases and identified pathways highly relevant to each disease across studies. We further applied MetaGSCA in a pan-cancer analysis with hundreds of major cellular pathways in 11 cancer types. The results indicated that a majority of the pathways identified were dysregulated in the pan-cancer scenario, many of which have been previously reported in the cancer literature. Our analysis with randomly generated gene sets showed excellent specificity, indicating that the significant pathways/gene sets identified by MetaGSCA are unlikely false positives. MetaGSCA is a user-friendly tool implemented in both forms of a Web-based application and an R package “MetaGSCA”. It enables comprehensive meta-analyses of gene set differential coexpression data, with an optional module of post hoc pathway crosstalk network analysis to identify and visualize pathways having similar coexpression profiles.     

Weighted gene coexpression network analysis strategies applied to mouse weight

Systems-oriented genetic approaches that incorporate gene expression and genotype data are valuable in the quest for genetic regulatory loci underlying complex traits. Gene coexpression network analysis lends itself to identification of entire groups of differentially regulated genes—a highly relevant endeavor in finding the underpinnings of complex traits that are, by definition, polygenic in nature. Here we describe one such approach based on liver gene expression and genotype data from an F₂ mouse intercross utilizing weighted gene coexpression network analysis (WGCNA) of gene expression data to identify physiologically relevant modules. We describe two strategies: single-network analysis and differential network analysis. Single-network analysis reveals the presence of a physiologically interesting module that can be found in two distinct mouse crosses. Module quantitative trait loci (mQTLs) that perturb this module were discovered. In addition, we report a list of genetic drivers for this module. Differential network analysis reveals differences in connectivity and module structure between two networks based on the liver expression data of lean and obese mice. Functional annotation of these genes suggests a biological pathway involving epidermal growth factor (EGF). Our results demonstrate the utility of WGCNA in identifying genetic drivers and in finding genetic pathways represented by gene modules. These examples provide evidence that integration of network properties may well help chart the path across the gene–trait chasm. Electronic supplementary material The online version of this article (doi: ) contains supplementary material, which is available to authorized users. Tova F. Fuller, Anatole Ghazalpour contributed equally to this work.     

Gene coexpression analysis offers important modules and pathway of human lung adenocarcinomas

Lung adenocarcinomas injured greatly on the people worldwide. Although clinic experiments and gene profiling analyses had been well performed, to our knowledge, systemic coexpression analysis of human genes for this cancer is still limited to date. Here, using the published data GSE75037, we built the coexpression modules of genes by Weighted Gene Co-Expression Network Analysis (WGCNA), and investigated function and protein–protein interaction network of coexpression genes by Database for Annotation, visualization, and Integrated Discovery (DAVID) and String database, respectively. First, 11 coexpression modules were conducted for 5,000 genes in the 83 samples recently. Number of genes for each module ranged from 90 to 1,260, with the mean of 454. Second, interaction relationships of hub-genes between pairwise modules showed great differences, suggesting relatively high scale independence of the modules. Third, functional enrichment of the coexpression modules showed great differences. We found that genes in modules 8 significantly enriched in the biological process and/or pathways of cell adhesion, extracellular matrix (ECM)–receptor interaction, focal adhesion, and PI3K-Akt signaling pathway, and so forth. It was inferred as the key module underlying lung adenocarcinomas. Furthermore, PPI analysis revealed that the genes COL1A1, COL1A2, COL3A1, CTGF, and BGN owned the largest number of adjacency genes, unveiling that they may functioned importantly during the occurrence of lung adenocarcinomas. To summary, genes involved in cell adhesion, ECM–receptor interaction, focal adhesion, and PI3K-Akt signaling pathway play crucial roles in human lung adenocarcinomas.     

Key genes and functional coexpression modules involved in the pathogenesis of systemic lupus erythematosus

We performed a systematic review of genome‐wide gene expression datasets to identify key genes and functional modules involved in the pathogenesis of systemic lupus erythematosus (SLE) at a systems level. Genome‐wide gene expression datasets involving SLE patients were searched in Gene Expression Omnibus and ArrayExpress databases. Robust rank aggregation (RRA) analysis was used to integrate those public datasets and identify key genes associated with SLE. The weighted gene coexpression network analysis (WGCNA) was adapted to identify functional modules involved in SLE pathogenesis, and the gene ontology enrichment analysis was utilized to explore their functions. The aberrant expressions of several randomly selected key genes were further validated in SLE patients through quantitative real‐time polymerase chain reaction. Fifteen genome‐wide gene expression datasets were finally included, which involved a total of 1,778 SLE patients and 408 healthy controls. A large number of significantly upregulated or downregulated genes were identified through RRA analysis, and some of those genes were novel SLE gene signatures and their molecular roles in etiology of SLE remained vague. WGCNA further successfully identified six main functional modules involved in the pathogenesis of SLE. The most important functional module involved in SLE included 182 genes and mainly enriched in biological processes, including defense response to virus, interferon signaling pathway, and cytokine‐mediated signaling pathway. This study identifies a number of key genes and functional coexpression modules involved in SLE, which provides deepening insights into the molecular mechanism of SLE at a systems level and also provides some promising therapeutic targets.     

Geometric interpretation of gene coexpression network analysis

THE MERGING OF NETWORK THEORY AND MICROARRAY DATA ANALYSIS TECHNIQUES HAS SPAWNED A NEW FIELD: gene coexpression network analysis. While network methods are increasingly used in biology, the network vocabulary of computational biologists tends to be far more limited than that of, say, social network theorists. Here we review and propose several potentially useful network concepts. We take advantage of the relationship between network theory and the field of microarray data analysis to clarify the meaning of and the relationship among network concepts in gene coexpression networks. Network theory offers a wealth of intuitive concepts for describing the pairwise relationships among genes, which are depicted in cluster trees and heat maps. Conversely, microarray data analysis techniques (singular value decomposition, tests of differential expression) can also be used to address difficult problems in network theory. We describe conditions when a close relationship exists between network analysis and microarray data analysis techniques, and provide a rough dictionary for translating between the two fields. Using the angular interpretation of correlations, we provide a geometric interpretation of network theoretic concepts and derive unexpected relationships among them. We use the singular value decomposition of module expression data to characterize approximately factorizable gene coexpression networks, i.e., adjacency matrices that factor into node specific contributions. High and low level views of coexpression networks allow us to study the relationships among modules and among module genes, respectively. We characterize coexpression networks where hub genes are significant with respect to a microarray sample trait and show that the network concept of intramodular connectivity can be interpreted as a fuzzy measure of module membership. We illustrate our results using human, mouse, and yeast microarray gene expression data. The unification of coexpression network methods with traditional data mining methods can inform the application and development of systems biologic methods.     

Gene coexpression network alignment and conservation of gene modules between two grass species: maize and rice

One major objective for plant biology is the discovery of molecular subsystems underlying complex traits. The use of genetic and genomic resources combined in a systems genetics approach offers a means for approaching this goal. This study describes a maize (Zea mays) gene coexpression network built from publicly available expression arrays. The maize network consisted of 2,071 loci that were divided into 34 distinct modules that contained 1,928 enriched functional annotation terms and 35 cofunctional gene clusters. Of note, 391 maize genes of unknown function were found to be coexpressed within modules along with genes of known function. A global network alignment was made between this maize network and a previously described rice (Oryza sativa) coexpression network. The IsoRankN tool was used, which incorporates both gene homology and network topology for the alignment. A total of 1,173 aligned loci were detected between the two grass networks, which condensed into 154 conserved subgraphs that preserved 4,758 coexpression edges in rice and 6,105 coexpression edges in maize. This study provides an early view into maize coexpression space and provides an initial network-based framework for the translation of functional genomic and genetic information between these two vital agricultural species.     

Annotating genes of known and unknown function by large-scale coexpression analysis

About 40% of the proteins encoded in eukaryotic genomes are proteins of unknown function (PUFs). Their functional characterization remains one of the main challenges in modern biology. In this study we identified the PUF encoding genes from Arabidopsis (Arabidopsis thaliana) using a combination of sequence similarity, domain-based, and empirical approaches. Large-scale gene expression analyses of 1,310 publicly available Affymetrix chips were performed to associate the identified PUF genes with regulatory networks and biological processes of known function. To generate quality results, the study was restricted to expression sets with replicated samples. First, genome-wide clustering and gene function enrichment analysis of clusters allowed us to associate 1,541 PUF genes with tightly coexpressed genes for proteins of known function (PKFs). Over 70% of them could be assigned to more specific biological process annotations than the ones available in the current Gene Ontology release. The most highly overrepresented functional categories in the obtained clusters were ribosome assembly, photosynthesis, and cell wall pathways. Interestingly, the majority of the PUF genes appeared to be controlled by the same regulatory networks as most PKF genes, because clusters enriched in PUF genes were extremely rare. Second, large-scale analysis of differentially expressed genes was applied to identify a comprehensive set of abiotic stress-response genes. This analysis resulted in the identification of 269 PKF and 104 PUF genes that responded to a wide variety of abiotic stresses, whereas 608 PKF and 206 PUF genes responded predominantly to specific stress treatments. The provided coexpression and differentially expressed gene data represent an important resource for guiding future functional characterization experiments of PUF and PKF genes. Finally, the public Plant Gene Expression Database (http://bioweb.ucr.edu/PED) was developed as part of this project to provide efficient access and mining tools for the vast gene expression data of this study.     

Identification of key gene modules and pathways of human glioma through coexpression network

Glioma causes great harm to people worldwide. Systemic coexpression analysis of this disease could be beneficial for the identification and development of new prognostic and predictive markers in the clinical management of glioma. In this study, we extracted data sets from the Gene Expression Omnibus data set by using “glioma” as the keyword. Then, a coexpression module was constructed with the help of Weighted Gene Coexpression Network Analysis software. Besides, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed on the genes in these modules. As a result, the critical modules and target genes were identified. Eight coexpression modules were constructed using the 4,000 genes with a high expression value of the total 141 glioma samples. The result of the analysis of the interaction among these modules showed that there was a high scale independence degree among them. The GO and KEGG enrichment analyses showed that there was a significant difference in the enriched terms and degree among these eight modules, and module 5 was identified as the most important module. Besides, the pathways it was enriched in, hsa04510: Focal adhesion and hsa04610: Complement and coagulation cascades, were determined as the most important pathways. In summary, module 5 and the pathways it was enriched in, hsa04510: Focal adhesion and has 04610: Complement and coagulation cascades, have the potential to serve as biomarkers for patients with glioma.     

Local coexpression domains of two to four genes in the genome of Arabidopsis

Expression of genes in eukaryotic genomes is known to cluster, but cluster size is generally loosely defined and highly variable. We have here taken a very strict definition of cluster as sets of physically adjacent genes that are highly coexpressed and form so-called local coexpression domains. The Arabidopsis (Arabidopsis thaliana) genome was analyzed for the presence of such local coexpression domains to elucidate its functional characteristics. We used expression data sets that cover different experimental conditions, organs, tissues, and cells from the Massively Parallel Signature Sequencing repository and microarray data (Affymetrix) from a detailed root analysis. With these expression data, we identified 689 and 1,481 local coexpression domains, respectively, consisting of two to four genes with a pairwise Pearson's correlation coefficient larger than 0.7. This number is approximately 1- to 5-fold higher than the numbers expected by chance. A small (5%-10%) yet significant fraction of genes in the Arabidopsis genome is therefore organized into local coexpression domains. These local coexpression domains were distributed over the genome. Genes in such local domains were for the major part not categorized in the same functional category (GOslim). Neither tandemly duplicated genes nor shared promoter sequence nor gene distance explained the occurrence of coexpression of genes in such chromosomal domains. This indicates that other parameters in genes or gene positions are important to establish coexpression in local domains of Arabidopsis chromosomes.     

Differential coexpression networks in bronchiolitis and emphysema phenotypes reveal heterogeneous mechanisms of chronic obstructive pulmonary disease

Chronic obstructive pulmonary disease (COPD) is a heterogeneous disease with multiple molecular mechanisms. To investigate and contrast the molecular processes differing between bronchiolitis and emphysema phenotypes of COPD, we downloaded the GSE69818 microarray data set from the Gene Expression Omnibus (GEO), which based on lung tissues from 38 patients with emphysema and 32 patients with bronchiolitis. Then, weighted gene coexpression network analysis (WGCNA) and differential coexpression (DiffCoEx) analysis were performed, followed by gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes enrichment analysis (KEGG) analysis. Modules and hub genes for bronchiolitis and emphysema were identified, and we found that genes in modules linked to neutrophil degranulation, Rho protein signal transduction and B cell receptor signalling were coexpressed in emphysema. DiffCoEx analysis showed that four hub genes (IFT88, CCDC103, MMP10 and Bik) were consistently expressed in emphysema patients; these hub genes were enriched, respectively, for functions of cilium assembly and movement, proteolysis and apoptotic mitochondrial changes. In our re‐analysis of GSE69818, gene expression networks in relation to emphysema deepen insights into the molecular mechanism of COPD and also identify some promising therapeutic targets.     

Beyond modules and hubs: the potential of gene coexpression networks for investigating molecular mechanisms of complex brain disorders

In a research environment dominated by reductionist approaches to brain disease mechanisms, gene network analysis provides a complementary framework in which to tackle the complex dysregulations that occur in neuropsychiatric and other neurological disorders. Gene–gene expression correlations are a common source of molecular networks because they can be extracted from high‐dimensional disease data and encapsulate the activity of multiple regulatory systems. However, the analysis of gene coexpression patterns is often treated as a mechanistic black box, in which looming ‘hub genes’ direct cellular networks, and where other features are obscured. By examining the biophysical bases of coexpression and gene regulatory changes that occur in disease, recent studies suggest it is possible to use coexpression networks as a multi‐omic screening procedure to generate novel hypotheses for disease mechanisms. Because technical processing steps can affect the outcome and interpretation of coexpression networks, we examine the assumptions and alternatives to common patterns of coexpression analysis and discuss additional topics such as acceptable datasets for coexpression analysis, the robust identification of modules, disease‐related prioritization of genes and molecular systems and network meta‐analysis. To accelerate coexpression research beyond modules and hubs, we highlight some emerging directions for coexpression network research that are especially relevant to complex brain disease, including the centrality–lethality relationship, integration with machine learning approaches and network pharmacology .     

利用基因共表达网络分析蛋白质的相互作用

基于Hom in的基因共表达网络的比较分析,发现人类基因共表达网络和蛋白质相互作用数据之间存在一定的相关性。采用基因本体论对这两个网络重叠区域进行基因分类后发现,这些编码的蛋白质主要集中在对刺激物的应答途径之中。通过对该途径中的蛋白质相互作用网络作图,获得了两个独立的功能模块。通过对模块中的基因分类和关键基因分析得出两者分别对应于内外源刺激物的应答功能。本研究对于利用不断丰富的核酸公共数据信息挖掘蛋白质相互作用的研究具有积极的促进作用。