title: WGCNA Application to Proteomic and Metabolomic Data Analysis.
abstract: Progresses in mass spectrometric instrumentation and bioinformatics identification algorithms made over the past decades allow quantitative measurements of relative or absolute protein/metabolite amounts in cells in a high-throughput manner, which has significantly expedited the exploration into functions and dynamics of complex biological systems. However, interpretation of high-throughput data is often restricted by the limited availability of suitable computational methods and enough statistical power. While many computational methodologies have been developed in the past decades to address the issue, it becomes clear that network-focused rather than individual gene/protein-focused strategies would be more appropriate to obtain a complete picture of cellular responses. Recently, an R analytical package named as weighted gene coexpression network analysis (WGCNA) was developed and applied to high-throughput microarray or RNA-seq datasets since it provides a systems-level insights, high sensitivity to low abundance, or small fold changes genes without any information loss. The approach was also recently applied to proteomic and metabolomic data analysis. However, due to the fact that low coverage of the current proteomic and metabolomic analytical technologies, causing the format of datasets are often incomplete, the method needs to be modified so that it can be properly utilized for meaningful biologically interpretation. In this chapter, we provide a detailed introduction of the modified protocol and its tutorials for applying the WGCNA approach in analyzing proteomic and metabolomic datasets.
authors.affiliation: Laboratory of Synthetic Microbiology, School of Chemical Engineering & Technology, Tianjin University, Tianjin, PR China; Key Laboratory of Systems Bioengineering (Ministry of Education), Tianjin University, Tianjin, PR China; SynBio Research Platform, Collaborative Innovation Center of Chemical Science and Engineering (Tianjin), Tianjin, PR China.
authors.forename: G
authors.initials: G
authors.lastname: Pei
authors.affiliation: Laboratory of Synthetic Microbiology, School of Chemical Engineering & Technology, Tianjin University, Tianjin, PR China; Key Laboratory of Systems Bioengineering (Ministry of Education), Tianjin University, Tianjin, PR China; SynBio Research Platform, Collaborative Innovation Center of Chemical Science and Engineering (Tianjin), Tianjin, PR China.
authors.forename: L
authors.initials: L
authors.lastname: Chen
authors.affiliation: Laboratory of Synthetic Microbiology, School of Chemical Engineering & Technology, Tianjin University, Tianjin, PR China; Key Laboratory of Systems Bioengineering (Ministry of Education), Tianjin University, Tianjin, PR China; SynBio Research Platform, Collaborative Innovation Center of Chemical Science and Engineering (Tianjin), Tianjin, PR China. Electronic address: wwzhang8@tju.edu.cn.
authors.forename: W
authors.initials: W
authors.lastname: Zhang
mesh_terms: D019295:Computational Biology; D030562:Databases, Protein; D055432:Metabolomics / Q000379:methods*; D011506:Proteins / Q000737:chemistry; D040901:Proteomics / Q000379:methods*
chemical_list: D011506:Proteins
keywords: Data analysis; Metabolomic; Proteomic; WGCNA