A Six-Gene Signature Predicts Survival of Adenocarcinoma Type of Non-Small-Cell Lung Cancer Patients: A Comprehensive Study Based on Integrated Analysis and Weighted Gene Coexpression Network

<div>Determination of parameter <i>β</i> of the adjacency function in the weighted gene correlation network analysis (WGCNA) algorithm. (a) Analysis of the scale-free fit index for various soft thresholding powers <i>β</i>. (b) Analysis of the mean connectivity for various soft thresholding powers. (c) Histogram of connectivity distribution when <i>β</i> = 19. (d) Checking the scale-free topology when <i>β</i> = 19. Figure 4 was reproduced from Sun et al. [<a href="/journals/bmri/2019/4250613/#B16">16</a>].</div>

BioMed Research International

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Figure 4: A Six-Gene Signature Predicts Survival of Adenocarcinoma Type of Non-Small-Cell Lung Cancer Patients: A Comprehensive Study Based on Integrated Analysis and Weighted Gene Coexpression Network 

Figure 4 | A Six-Gene Signature Predicts Survival of Adenocarcinoma Type of Non-Small-Cell Lung Cancer Patients: A Comprehensive Study Based on Integrated Analysis and Weighted Gene Coexpression Network 