SUMO: an R package for simulating multi-omics data for methods development and testing.

Area of Science:

• Computational Biology
• Bioinformatics
• Data Science

Background:

• Multi-omics research integrates diverse biological data types, driving demand for advanced computational tools.
• Evaluating new multi-omics computational methods is challenging due to the lack of standardized datasets with defined signal structures.

Purpose of the Study:

• To introduce SUMO (SimUlating Multi-Omics), an open-source R package designed for generating synthetic multi-omics datasets.
• To provide researchers with a flexible tool for creating datasets with controllable latent structures, noise levels, and complexity.

Main Methods:

• SUMO utilizes factor analysis-based approaches to simulate multi-omics data.
• The package allows users to specify distinct or shared non-overlapping latent factors, offering precise control over signal characteristics.

Main Results:

• SUMO enables the generation of high-quality, factor analysis-based multi-omics datasets.
• The package facilitates reproducible testing and validation of computational methods by providing customizable synthetic data.

Conclusions:

• SUMO addresses the critical need for benchmark datasets in multi-omics research.
• This tool supports methodological innovation by enabling rigorous evaluation of new computational approaches in the field.