SUMO {SUMO} | R Documentation |
SUMO: Simulation Utilities for Multi-Omics Data
Description
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.
Details
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.
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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:
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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.
Author(s)
Maintainer: Bernard Isekah Osang'ir Bernard.Osangir@sckcen.be (ORCID)
Other contributors:
Ziv Shkedy [contributor]
Surya Gupta [contributor]
Jürgen Claesen [contributor]