EpiVisR

EpiVisR provides visualization and exploratory analysis of epigenome-wide DNA methylation data (EWAS), enabling selection and integrated visualization of traits (e.g., concentrations of chemical compounds) with differentially methylated probes and regions from microarray datasets.

Key Features:

• Trait and methylation visualization: Selects and visualizes combinations of traits, such as concentrations of chemical compounds, alongside differentially methylated probes and regions.
• Enriched Manhattan plot: Identifies and visualizes significant probes and regions with notable methylation differences in a Manhattan-style plot.
• Enriched Volcano plot: Visualizes effect size against p-value to highlight probes with both statistical significance and biological relevance.
• Trait-methylation plot: Plots selected trait values against corresponding methylation levels to reveal correlations or associations.
• Methylation profile plot: Displays ranges of probes against selected trait values to visualize methylation patterns across conditions.
• Correlation profile plot: Selects and visualizes probes correlated with a chosen probe to identify related epigenetic changes.
• Annotation and linking: Annotates enriched plots and associated tables and links results to external data sources for integrated analysis of trait and epigenetic data from the same individuals.
• Data export and integration: Exports selected data for downstream analysis in external tools, including network analysis platforms.

Scientific Applications:

• Exploratory EWAS analysis: Facilitates exploratory analysis of epigenome-wide methylation datasets from microarray technology to detect differentially methylated probes and regions.
• Trait–methylation association studies: Supports investigation of relationships between environmental or biological traits, including chemical compound concentrations, and DNA methylation patterns.
• Quality control and hypothesis generation: Enables visualization-driven quality control and generation of hypotheses from complex methylation datasets.
• Cohort data integration and network preparation: Merges and visualizes data from multiple sources within the same cohort and prepares exported data for network or other downstream analyses.

Methodology:

Selection and visualization of trait versus methylation values, enriched Manhattan and volcano plotting, trait-methylation, methylation profile and correlation profile plotting, annotation of plots and tables, linking to external data sources, and exporting selected data for downstream analyses such as network analysis.