Fig. 2 | Scientific Reports

Fig. 2

From: Identification of UBE2N as a biomarker of Alzheimer’s disease by combining WGCNA with machine learning algorithms

Fig. 2

Construction of the WGCNA co-expression network. (A) Soft threshold (β = 7) and scale-free topological fit index (R2). (B) Original and combined modules using a clustering tree. The cluster dendrogram was the result before module cutting, while the Merged Dynamic was the result after module cutting. (C) Cluster tree plot of the module feature genes. (D) Heat map of module-trait correlations. The values inside the brackets represent the p-value of significance, whereas the values outside the brackets represent the values of the correlation between the Control and AD groups. (E) Venn diagram of key module genes vs. DEGs. The overlapping regions represented the same 109 genes in DEGs and WGCNA. AD, Alzheimer’s Disease; DEGs, differential genes; WGCNA: Weighted gene co-expression network analysis.

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