[Retracted] High-Throughput Screen of Natural Compounds and Biomarkers for NSCLC Treatment by Differential Expression and Weighted Gene Coexpression Network Analysis (WGCNA)

<div>The gene set enrichment analysis and the identification of modules. (a) Dendrogram of dissimilarity between all filtered gene sets, which enriched according to a dissimilarity measure (1-TOM), and the cluster modules were marked as different colors. Dynamic tree cut algorithm generated the first set, then merged the correlated modules together. Each gene was represented by a branch, and each module was represented by one color. (b) Heat map of the correlation between the pharmaceutical effect of TCM fractions and MEs in NSCLC. The dark degree of the module color represented the significance of their relationship.</div>

BioMed Research International

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Figure 4

Figure 4: [Retracted] High-Throughput Screen of Natural Compounds and Biomarkers for NSCLC Treatment by Differential Expression and Weighted Gene Coexpression Network Analysis (WGCNA) 

Figure 4 | [Retracted] High-Throughput Screen of Natural Compounds and Biomarkers for NSCLC Treatment by Differential Expression and Weighted Gene Coexpression Network Analysis (WGCNA) 