WGCNA分析在CKD研究中的应用

2025-05-13 MedSci xAi 发表于广东省
本文深入探讨WGCNA分析在慢性肾病(CKD)研究中的应用,基于GSE137570数据集,详解基因共表达网络构建、模块-性状相关性分析等关键步骤,提供最新技术解析与临床应用指南。
WGCNA analysis was primarily conducted online using BIC (39429882). In most cases, the soft threshold β = 0.85 was used, and the input data consisted of standardized whole-genome transcriptomes or differential gene transcriptomes. For cohort 1 of GSE137570, the selected clinical traits were "Gender," "GFP," "TIF (degree of renal tubulointerstitial fibrosis, %)," and "CKD staging." For cohort 2, the input trait was "CKD progression (0=stable, 1=progressive)." The main outcomes included "sample cluster," "module dendrogram," "module-traits correlation," and "module gene-trait correlation," among others.
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