WGCNA analysis of GSE21374.

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posted on 2024-11-27, 18:29 authored by Xihao Shen, Jiyue Wu, Feilong Zhang, Qing Bi, Zejia Sun, Wei Wang

A. Cluster Dendrogram: Hierarchical clustering of genes, with branches representing gene modules, color-coded below the dendrogram. These modules reflect co-expressed genes potentially involved in transplant rejection. B. Scale Independence and Mean Connectivity: Left: Scale-free topology model fit (R^2) versus soft-thresholding power, ensuring network adherence to a scale-free topology. Right: Mean connectivity as a function of soft-thresholding power, used to determine the optimal network parameters. C. Network Heatmap: Topological overlap matrix (TOM) heatmap showing the strength of connections between genes, with dendrograms and module colors corresponding to (A).

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weighted gene co two clusters underscored multiple established databases machine learning algorithms integrative machine learning gene expression omnibus demographic shift towards consensus clustering algorithm b >(aged donor kidneys term graft outcomes expression network analysis differential expression analysis comprehensive analysis underscores worse graft survival reduced graft survival predicting graft survival two rejection clusters rejection remains elusive optimal acute rejection delayed graft function ktx rejection poses ktx rejection occurrence identify predictive srgs invasive diagnostic model kidney transplant rejection graft survival ktx rejection kidney graft diagnostic model kidney rejection gsva analysis cluster analysis allograft function rejection samples diagnosing rejection prognostic model kidney transplantation xlink ">wgcna ),significant threat related genes provided microarray positive correlation level landscape ischemic damage detailed cellular conducted using conclusions drawn cluster c1 c2 ).

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WGCNA analysis of GSE21374.

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