WGCNA: An R Package for Weighted Correlation Network Analysis
Author Information
Author(s): Peter Langfelder, Steve Horvath
Primary Institution: University of California, Los Angeles
Conclusion
The WGCNA package provides R functions for weighted correlation network analysis, enabling the analysis of gene expression data.
Supporting Evidence
- The WGCNA package includes comprehensive functions for analyzing gene expression data.
- The package has been applied successfully in various biological contexts, including cancer and genetics.
- The methods allow for the identification of candidate biomarkers and therapeutic targets.
Takeaway
This study introduces a software package that helps scientists analyze how genes work together by looking at their expression patterns.
Methodology
The WGCNA package includes functions for network construction, module detection, gene selection, and visualization.
Potential Biases
Results can be biased or invalid due to technical artifacts, tissue contamination, or poor experimental design.
Limitations
The methods assume properly pre-processed data and may be biased by technical artifacts or poor experimental design.
Participant Demographics
Data from female mice was used, specifically focusing on liver expression data.
Statistical Information
P-Value
5 × 10-14
Statistical Significance
p<0.05
Digital Object Identifier (DOI)
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