An R package for divergence analysis of omics data.

Area of Science:

• Bioinformatics
• Computational Biology
• Genomics

Background:

• High-dimensional omics data presents significant analytical challenges.
• Existing methods may not effectively capture complex biological variations.
• Need for accessible and versatile omics data analysis tools.

Purpose of the Study:

• Introduce a novel divergence analysis framework for omics data.
• Demonstrate the utility of the R 'divergence' package.
• Facilitate simplified analysis of complex, high-dimensional omics datasets.

Main Methods:

• Divergence analysis transforms omics data entries into binary or ternary codes.
• Encoding is based on deviation from a defined baseline population.
• The 'divergence' R package provides functions for this transformation.

Main Results:

• The divergence framework enables digitization of continuous omics data.
• Univariate and multivariate analyses are supported.
• The method is applicable across diverse omics platforms, including The Cancer Genome Atlas data.

Conclusions:

• Divergence analysis provides a powerful and flexible approach to omics data interpretation.
• The R package simplifies the implementation of this novel analytical method.
• This framework enhances the ability to derive insights from complex biological datasets.