It provides tools for simulating complex multi-omics datasets, enabling researchers to generate data that mirrors the biological intricacies observed in real-world omics studies. This package addresses a critical gap in current bioinformatics by offering flexible and customizable methods for synthetic multi-omics data generation, supporting method development, validation, and benchmarking.
Maintainer: Bernard Isekah Osang'ir Bernard.Osangir@sckcen.be (ORCID)
Other contributors:
Ziv Shkedy [contributor]
Surya Gupta [contributor]
Jürgen Claesen [contributor]
Key Features:
Multi-Omics Simulation: Generate multi-layered datasets with shared and modality-specific structures.
Flexible Generation Engine: Fine control over samples, noise levels, signal distributions, and latent factor structures.
Pipeline-Friendly Design: Seamlessly integrates with existing multi-omics analysis workflows and packages (e.g., SummarizedExperiment, MultiAssayExperiment).
Visualization Tools: Built-in plotting functions for exploring synthetic signals, factor structures, and noise.
Main Functions:
simulateMultiOmics(): Simulates multiple (> two) high-dimensional multi-omics datasets.
simulate_twoOmicsData(): Simulates two high-dimensional multi-omics datasets.
plot_simData(): Visualizes generated data at different levels.
plot_factor(): Displays factor scores across samples for signal inspection.
plot_weights(): Visualizes feature loadings to assess signal versus noise.
demo_multiomics_analysis(): Full demo function for applying MOFA on SUMO-generated or real-world data.
compute_means_vars(): Estimate parameters from the real experimental dataset.