Comethyl

Comethyl applies Weighted Gene Correlation Network Analysis (WGCNA) to DNA methylation data to identify modules of comethylated genomic regions and test their associations with sample metadata to discover biomarkers and molecular mechanisms in health and disease.

Key Features:

• Weighted Gene Correlation Network Analysis (WGCNA): Comethyl employs WGCNA to detect modules of comethylated regions from methylation data.
• User-Defined Genomic Regions: Regions can be specified based on CpG genomic location or regulatory annotation.
• Region Filtering Criteria: Regions are filtered by CpG count, sequencing depth, and variability to ensure data quality.
• Module Identification by Methylation Correlation: Modules are generated by correlating methylation values within selected regions to identify interconnected nodes.
• Complexity Reduction to Eigennode Values: Each module of comethylated regions is summarized as a single eigennode value for downstream analysis.
• Integration with Epigenomic Data and Noncoding Regulatory Regions: Comethyl covers noncoding regulatory regions and integrates with other epigenomic data to aid interpretation of GWAS.
• Association Testing with Experimental Metadata: Eigennode values are tested for correlations with experimental and sample metadata.

Scientific Applications:

• Health Disparities and Disease Etiologies: Used to dissect multivariate contributors to health disparities across common disorders by linking methylation modules to metadata.
• Neurodevelopmental Disorders (ASD): Applied to identify ASD-associated modules with gene regions linked to brain glial functions using a dataset of male cord blood samples.

Methodology:

Regions are defined and filtered based on CpG count, sequencing depth, and variability; correlation networks are constructed to identify interconnected nodes forming comethylated modules; modules are reduced to eigennode values for correlation testing with sample traits.