Extended Overview of Weighted Gene Co-Expression Network Analysis (WGCNA)

Extended Overview of Weighted Gene Co-Expression Network Analysis (WGCNA). Steve Horvath University of California, Los Angeles. Webpage where the material can be found. http:// www.genetics.ucla.edu /labs/ horvath / CoexpressionNetwork /WORKSHOP/

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Extended Overview of Weighted Gene Co-Expression Network Analysis (WGCNA)

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Extended Overview of Weighted Gene Co-Expression Network Analysis (WGCNA) Steve Horvath University of California, Los Angeles
Webpage where the material can be found • http://www.genetics.ucla.edu/labs/horvath/CoexpressionNetwork/WORKSHOP/ • R software tutorials from S. H, see corrected tutorial for chapter 12 at the following link: http://www.genetics.ucla.edu/labs/horvath/CoexpressionNetwork/Book/
Contents • How to construct a weighted gene co-expression network? • Why use soft thresholding? • How to detect network modules? • How to relate modules to an external clinical trait? • What is intramodular connectivity? • How to use networks for gene screening? • How to integrate networks with genetic marker data? • What is weighted gene co-expression network analysis (WGCNA)?
Control Experimental Standard microarray analyses seek to identify ‘differentially expressed’ genes • Each gene is treated as an individual entity • Often misses the forest for the trees: Fails to recognize that thousands of genes can be organized into relatively few modules
Philosophy of Weighted Gene Co-Expression Network Analysis • Understand the “system” instead of reporting a list of individual parts • Describe the functioning of the engine instead of enumerating individual nuts and bolts • Focus on modules as opposed to individual genes • this greatly alleviates multiple testing problem • Network terminology is intuitive to biologists
What is weighted gene co-expression network analysis?
Construct a network Rationale: make use of interaction patterns between genes Identify modules Rationale: module (pathway) based analysis Relate modules to external information Array Information: Clinical data, SNPs, proteomics Gene Information: gene ontology, EASE, IPA Rationale: find biologically interesting modules • Study Module Preservation across different data • Rationale: • Same data: to check robustness of module definition • Different data: to find interesting modules. Find the key drivers in interesting modules Tools: intramodular connectivity, causality testing Rationale: experimental validation, therapeutics, biomarkers
Weighted correlation networks are valuable for a biologically meaningful… • reduction of high dimensional data • expression: microarray, RNA-seq • gene methylation data, fMRI data, etc. • integration of multiscale data: • expression data from multiple tissues • SNPs (module QTL analysis) • Complex phenotypes

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