decorate: differential epigenetic correlation test.

Area of Science:

• Genomics
• Epigenetics
• Computational Biology

Background:

• Correlated epigenetic features offer insights into disease biology.
• Previous analysis frameworks for gene expression data have not scaled well for epigenetic data due to computational costs and lack of robust statistical tests.

Purpose of the Study:

• To develop a scalable computational framework for identifying differential epigenetic correlations.
• To provide a robust statistical test for analyzing epigenetic data across multiple sample groups.

Main Methods:

• Developed the Decorate (differential epigenetic correlation test) R package.
• Designed Decorate to identify clusters of epigenetically correlated features that differ between data subsets.
• Ensured the software scales to genome-wide epigenetic datasets involving hundreds of individuals.

Main Results:

• Decorate successfully identifies correlated epigenetic features and detects differential correlations between groups.
• The software demonstrates scalability for large-scale epigenome-wide association studies (EWAS).
• Applied Decorate to analyze DNA methylation, ATAC-seq, and ChIP-seq data from four large datasets.

Conclusions:

• Decorate provides a scalable and robust method for analyzing differential epigenetic correlations.
• This tool facilitates novel insights into disease biology by comparing epigenetic patterns between healthy and diseased individuals.
• The Decorate R package is publicly available for broader research application.