Hunan Engineering and Technology Research Center for Agricultural Big Data Analysis and Decision-Making, Hunan Agricultural University, Changsha, 410128, Hunan, China.
College of Science, Central South University of Forestry and Technology, Changsha, 410004, Hunan, China.
Interdiscip Sci. 2022 Mar;14(1):245-257. doi: 10.1007/s12539-021-00485-w. Epub 2021 Oct 25.
The weighted gene co-expression network analysis (WGCNA) method constructs co-expressed gene modules based on the linear similarity between paired gene expressions. Linear correlations are the main form of similarity between genes, however, nonlinear correlations still existed and had always been ignored. We proposed a modified network analysis method, WGCNA-P + M, which combines Pearson's correlation coefficient and the maximum information coefficient (MIC) as the similarity measures to assess the linear and nonlinear correlations between genes, respectively. Taking two real datasets, GSE44861 and liver hepatocellular carcinoma (TCGA-LIHC), as examples, we compared the gene modules constructed by WGCNA-P + M and WGCNA from four perspectives: the "Usefulness" score, GO enrichment analysis on genes in the gray module, prediction performance of the top hub gene, survival analysis and literature reports on different hub genes. The results showed that the modules obtained by WGCNA-P + M are more biological meaningful, the hub genes obtained from WGCNA-P + M have more potential cancer genes.
加权基因共表达网络分析（WGCNA）方法基于配对基因表达之间的线性相似性构建共表达基因模块。线性相关性是基因之间相似性的主要形式，但非线性相关性仍然存在且一直被忽视。我们提出了一种改进的网络分析方法，WGCNA-P+M，它结合了 Pearson 相关系数和最大信息系数（MIC）作为相似性度量，分别评估基因之间的线性和非线性相关性。以两个真实数据集 GSE44861 和肝肝细胞癌（TCGA-LIHC）为例，我们从四个方面比较了 WGCNA-P+M 和 WGCNA 构建的基因模块：“有用性”评分、灰色模块中基因的 GO 富集分析、顶级枢纽基因的预测性能、不同枢纽基因的生存分析和文献报道。结果表明，WGCNA-P+M 获得的模块更具有生物学意义，WGCNA-P+M 获得的枢纽基因更具有潜在的癌症基因。
