Application of Weighted Gene Co-Expression Network Analysis to Metabolomic Data from an ApoA-I Knockout Mouse Model.

ABSTRACT: As the ability to collect profiling data in metabolomics increases substantially with the advances in Liquid Chromatography-Mass Spectrometry (LC-MS) instruments, it is urgent to develop new and powerful data analysis approaches to match the big data collected and to extract as much meaningful information as possible from tens of thousands of molecular features. Here, we applied weighted gene co-expression network analysis (WGCNA), an algorithm popularly used in microarray or RNA sequencing, to plasma metabolomic data and demonstrated several advantages of WGCNA over conventional statistical approaches such as principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA). By using WGCNA, a large number of molecular features were clustered into a few modules to

SUBMITTER: Zhou Z 

PROVIDER: S-EPMC10856800 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature