WGCNA Application to Proteomic and Metabolomic Data Analysis

Area of Science:

• Biochemistry
• Bioinformatics
• Systems Biology

Background:

• High-throughput mass spectrometry enables quantitative protein and metabolite measurements.
• Interpreting complex biological data requires advanced computational and statistical methods.
• Network-focused analyses offer a more comprehensive view of cellular responses than individual gene/protein approaches.

Purpose of the Study:

• To adapt the Weighted Gene Coexpression Network Analysis (WGCNA) method for analyzing proteomic and metabolomic datasets.
• To address challenges posed by incomplete data common in current proteomic and metabolomic technologies.
• To provide a modified protocol and tutorials for biologically meaningful interpretation.

Main Methods:

• Application of the R package WGCNA.
• Modification of WGCNA for handling incomplete proteomic and metabolomic data.
• Development of tutorials for the modified WGCNA protocol.

Main Results:

• The modified WGCNA approach facilitates systems-level insights from proteomic and metabolomic data.
• The protocol is effective even with datasets characterized by low coverage and missing values.
• Enhanced sensitivity for detecting biologically relevant changes in low-abundance or small fold-change molecules.

Conclusions:

• A modified WGCNA protocol enables robust analysis of incomplete proteomic and metabolomic data.
• This adaptation enhances the biological interpretation of high-throughput omics datasets.
• The developed tutorials support the wider application of WGCNA in systems biology research.