Identification of the gene modules by weighted gene correlation network analysis (WGCNA).

<p>A. Determination of parameter <i>β</i> of the adjacency function in the WGCNA algorithm. The higher the square value of the correlation coefficient is, the closer the network is to the distribution without network scale. B. Hierarchical cluster analysis dendrogram of gene module...

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Bibliographic Details
Main Author:	Quan Du (58889) (author)
Other Authors:	Ren-Dong Xiao (12878506) (author), Rong-Gang Luo (12878509) (author), Jin-Bao Xie (12878512) (author), Zu-Dong Su (12878515) (author), Yu Wang (12152) (author)
Published:	2022
Subjects:	Biochemistry Cell Biology Genetics Molecular Biology Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified snhg12 – hsa glycan biosynthesis pathway functional enrichment analysis expression network analysis current study aimed cancer genome atlas 133a – st6gal1 1 – st6gal1 weighted gene co snhg3 – mir snhg12 – mir as1 – mir analyses using data identified biomarkers provide seven gene modules related esophageal cancer div >< p differentially expressed genes identified cerna networks gene modules clinical data turquoise modules survival rate silico </ prognostic rnas novel insight molecular mechanism mirtarbase databases key roles disease progression dels ), constructed based coding rnas closely related cerna networks as2 </ 906 degs 40 dels
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Identification of the gene modules by weighted gene correlation network analysis (WGCNA).

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