SimOmics: A Simulation Toolkit for Multivariate and Multi-Omics Data

TL;DR

SimOmics is an R package that generates realistic synthetic multi-omics datasets to aid benchmarking, method development, and reproducibility in bioinformatics, especially for omics data integration tasks.

Contribution

It introduces a comprehensive simulation toolkit supporting complex data structures, noise models, and covariance modeling for multi-omics data.

Findings

Enables realistic simulation of multi-omics datasets

Supports complex covariance and sparsity structures

Facilitates benchmarking and reproducibility in bioinformatics

Abstract

SimOmics is an R package designed to generate realistic, multivariate, and multi-omics synthetic datasets. It is intended for use in benchmarking, method development, and reproducibility in bioinformatics, particularly in the context of omics integration tasks such as those encountered in transcriptomics, proteomics, and metabolomics. SimOmics supports latent factor simulation, sparsity structures, block-wise covariance modeling, and biologically inspired noise models and feature dimensions.