2019
DOI: 10.3389/fphys.2019.00278
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Weighted Gene Co-expression Network Analysis for RNA-Sequencing Data of the Varicose Veins Transcriptome

Abstract: ObjectiveVaricose veins are a common problem worldwide and can cause significant impairments in health-related quality of life, but the etiology and pathogenesis remain not well defined. This study aims to elucidate transcriptomic regulations of varicose veins by detecting differentially expressed genes, pathways and regulator genes.MethodsWe harvested great saphenous veins (GSV) from patients who underwent coronary artery bypass grafting (CABG) and varicose veins from conventional stripping surgery. RNA-Seque… Show more

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Cited by 16 publications
(11 citation statements)
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References 41 publications
(43 reference statements)
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“…We then used dynamic tree cutting to divide the gene co-expression modules according to the common gene expression file. The clearest gene coexpression module distribution obtained at early post-SMI period using the Dynamic Merge tool included 24 modules comprising 23 meaningful modules and one undefined gray module (Figure 1D) (Zhang et al, 2019b).…”
Section: Gene Co-expression Modules During the Early Period Post-smimentioning
confidence: 99%
“…We then used dynamic tree cutting to divide the gene co-expression modules according to the common gene expression file. The clearest gene coexpression module distribution obtained at early post-SMI period using the Dynamic Merge tool included 24 modules comprising 23 meaningful modules and one undefined gray module (Figure 1D) (Zhang et al, 2019b).…”
Section: Gene Co-expression Modules During the Early Period Post-smimentioning
confidence: 99%
“…WGCNA [17] is a powerful method to identify co-expressed groups of genes from large RNA expression datasets [21], and is widely used to explore the correlation among transcriptomic datasets, identify hub genes and discover new pathways in both model and non-model species [22][23][24]. WGCNA has proven its superiority over partial correlation methods and provides a powerful tool for identifying higher-order correlation in complex traits of interest, by presenting a simplified network on the integrated function of gene modules [25,26].…”
Section: Discussionmentioning
confidence: 99%
“…after relating modules to clinical traits, biologically relevant modules can be identified in any given disease. compared with standard comparative analysis, WGcna can identify critical genes with key roles in the phenotype and development of a disease from interesting modules associated with important clinical traits (11). despite the strengths and popularity of network analysis, WGcna has not been employed to analyse lncrna microarray data in HcM.…”
Section: Identification Of Circulating Hub Long Noncoding Rnas Associmentioning
confidence: 99%