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

TL;DR

SUMO is an R package that generates customizable multi-omics datasets to test and develop new computational methods.

Contribution

SUMO introduces a flexible framework for simulating multi-omics data with controllable latent structures and noise.

Findings

SUMO allows users to define distinct and shared latent factors in multi-omics datasets.

The package supports reproducible testing of methods through controlled signal structures.

SUMO is freely available on CRAN and GitHub for open use and development.

Abstract

Insights from integrative multi-omics analyses have fueled demand for innovative computational methods and tools in multi-omics research. However, the scarcity of multi-omics datasets with user-defined signal structures hinders the evaluation of these newly developed tools. SUMO (SimUlating Multi-Omics), an open-source R package, was developed to address this gap by enabling the generation of high-quality factor analysis-based datasets with full control over the dataset’s structure such as latent structures, noise, and complexity. Users can configure datasets with distinct and/or shared non-overlapping latent factors, enabling flexible and precise control over the signal structures. Consequently, SUMO allows reproducible testing and validation of methods, fostering methodological innovation.

The SUMO R package is freely available and accessible on the Comprehensive R Archive Network https://doi.org/10.32614/CRAN.package.SUMO and on GitHub https://github.com/lucp12891/SUMO.git under CC-BY 4.0 license.

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