Title:	Generating Multi-Omics Datasets for Testing and Benchmarking
Version:	1.2.0
Description:	Provides tools to simulate multi-omics datasets with predefined signal structures. The generated data can be used for testing, validating, and benchmarking integrative analysis methods such as factor models and clustering approaches. This version includes enhanced signal customization, visualization tools (scatter, histogram, 3D), MOFA-based analysis pipelines, PowerPoint export, and statistical profiling of datasets. Designed for both method development and teaching, SUMO supports real and synthetic data pipelines with interpretable outputs. Tini, Giulia, et al (2019) <doi:10.1093/bib/bbx167>.
License:	CC BY 4.0
Encoding:	UTF-8
RoxygenNote:	7.3.2
Suggests:	testthat (≥ 3.0.0), MOFAdata, MOFA2, rvg, fabia, tidyverse, grid, basilisk, systemfonts,
Config/testthat/edition:	3
Imports:	ggplot2, gridExtra, rlang, stats, graphics, utils, dplyr, readr, readxl, stringr, data.table, magrittr, officer
Collate:	'SUMO.R' 'compute_means_vars.R' 'demo_multiomics_analysis.R' 'divide_vector.R' 'divide_features_one.R' 'divide_features_two.R' 'divide_samples.R' 'divide_samples_alternative.R' 'feature_selection_one.R' 'feature_selection_two.R' 'globals.R' 'plot_factor.R' 'plot_simData.R' 'plot_weights.R' 'simulateMultiOmics.R' 'simulate_twoOmicsData.R'
NeedsCompilation:	no
Packaged:	2025-05-29 10:55:14 UTC; bosangir
Author:	Bernard Isekah Osang'ir [aut, cre], Ziv Shkedy [ctb], Surya Gupta [ctb], Jürgen Claesen [ctb]
Maintainer:	Bernard Isekah Osang'ir <Bernard.Osangir@sckcen.be>
Repository:	CRAN
Date/Publication:	2025-05-29 11:10:02 UTC

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.

• 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.

Author(s)

Maintainer: Bernard Isekah Osang'ir Bernard.Osangir@sckcen.be (ORCID)

Other contributors:

• Ziv Shkedy [contributor]

• Surya Gupta [contributor]

• Jürgen Claesen [contributor]

Helper function to build histogram plot (internal use)

Description

Helper function to build histogram plot (internal use)

Usage

build_histogram_plot(df, title = "", show.legend = TRUE)

Arguments

Helper function to build scatter plot (internal use)

Description

Helper function to build scatter plot (internal use)

Usage

build_scatter_plot(df, title = "", show.legend = TRUE)

Arguments

Compute Summary Statistics for a List of Datasets

Description

Computes overall, row-wise, and column-wise means and standard deviations for each dataset in a list. Also provides average statistics across datasets.

Usage

compute_means_vars(data_list)

Arguments

Value

A named list containing:

• Overall mean and SD for each dataset.

• Average row-wise mean and SD.

• Average column-wise mean and SD.

• mean_smp: Average row-wise mean across all datasets.

• sd_smp: Average row-wise SD across all datasets.

Examples

# Example using simulated matrices
set.seed(123)
dataset1 <- matrix(rnorm(100, mean = 5, sd = 2), nrow = 10, ncol = 10)
dataset2 <- matrix(rnorm(100, mean = 10, sd = 3), nrow = 10, ncol = 10)
data_list <- list(dataset1, dataset2)
results <- compute_means_vars(data_list)
print(results)

## Not run: 
# Example using real experimental data (requires MOFAdata)
if (requireNamespace("MOFAdata", quietly = TRUE)) {
  utils::data("CLL_data", package = "MOFAdata")
  CLL_data2 <- CLL_data[c(2, 3)]
  results <- compute_means_vars(CLL_data2)
  print(results)
}

## End(Not run)

Demonstration of SUMO Utility in Multi-Omics Analysis using MOFA2

Description

Run a complete MOFA2-based analysis pipeline using either SUMO-generated or real-world CLL multi-omics data. This function includes preprocessing, MOFA model training, variance decomposition visualization, and optional PowerPoint report generation.

Usage

demo_multiomics_analysis(
  data_type = c("SUMO", "real_world"),
  export_pptx = TRUE,
  verbose = TRUE
)

Arguments

Details

PowerPoint generation is skipped if required packages (officer, rvg, and ⁠systemfonts >= 1.1.0⁠) are not available.

Value

Invisibly returns the trained MOFA model object.

See Also

simulate_twoOmicsData(), plot_factor(), plot_weights()

Examples

if (
  requireNamespace("MOFA2", quietly = TRUE) &&
  requireNamespace("MOFAdata", quietly = TRUE) &&
  requireNamespace("systemfonts", quietly = TRUE) &&
  utils::packageVersion("systemfonts") >= "1.1.0" &&
  identical(Sys.getenv("NOT_CRAN"), "true")
) {
  demo_multiomics_analysis("SUMO", export_pptx = FALSE)
  demo_multiomics_analysis("real_world", export_pptx = FALSE)
}

Dividing features to create vectors with signal in the first omic for single data

Description

Dividing features to create vectors with signal in the first omic for single data

Usage

divide_features_one(n_features_one, num.factor)

Arguments

Dividing features to create vectors with signal in the second omic for single data

Description

Dividing features to create vectors with signal in the second omic for single data

Usage

divide_features_two(n_features_two, num.factor)

Arguments

Global Variable

Description

A global variable used in multiple functions.

This utility function divides a sequence of sample indices into num segments ensuring that each segment meets a specified minimum size. It optionally extracts a subset of each segment based on predefined selection logic:

• For a single group (num = 1): selects a random contiguous sub-vector comprising between 10% and 55% of the total samples.

• For multiple groups (num > 1): selects a contiguous sub-vector comprising approximately 75% of each segment.

Usage

divide_samples(n_samples, num, min_size)

divide_samples(n_samples, num, min_size)

Arguments

Details

This function is primarily used for randomized simulation of sample blocks, useful in bootstrapping, subsampling, or simulating latent factor scores across multi-omics datasets.

Value

A list of integer vectors. Each vector contains a sequence of indices representing a subsample of the corresponding segment.

Examples

divide_samples(n_samples = 100, num = 3, min_size = 10)
divide_samples(n_samples = 50, num = 1, min_size = 5)

#' Global Variable #' #' A global variable used in multiple functions. #' #'

Description

#' Global Variable #' #' A global variable used in multiple functions. #' #'

Usage

divide_vector(n_samples, num, min_size)

Arguments

Dividing features to create vectors with signal in the first omic

Description

Dividing features to create vectors with signal in the first omic

Usage

feature_selection_one(n_features_one, num.factor, no_factor)

Arguments

Dividing features to create vectors with signal in the second omic

Description

Dividing features to create vectors with signal in the second omic

Usage

feature_selection_two(n_features_two, num.factor, no_factor)

Arguments

Visualization of factor scores (ground truth)

Description

Scatter or histogram plots of sample-level factor scores from simulated multi-omics data, using scores from list_alphas and list_gammas.

Usage

plot_factor(
  sim_object = NULL,
  factor_num = NULL,
  type = "scatter",
  show.legend = TRUE
)

Arguments

Examples

output_obj <- simulate_twoOmicsData(
  vector_features = c(4000,3000),
  n_samples = 100,
  n_factors = 2,
  snr = 2.5,
  num.factor = 'multiple',
  advanced_dist = 'mixed')

plot_factor(sim_object = output_obj, factor_num = 1)
plot_factor(sim_object = output_obj, factor_num = 'all', type = 'histogram')

Visualizing the simulated data using heatmap or 3D surface plot

Description

Generates a visual representation of the simulated omics data either as a heatmap or a 3D surface plot. You can select which dataset to visualize: the merged/concatenated matrix, or any individual omic (e.g., "omic1", "omic2", etc.).

Usage

plot_simData(sim_object, data = "merged", type = "heatmap")

Arguments

Examples

output_obj <- simulateMultiOmics(
  vector_features = c(3000, 2500, 2000),
  n_samples = 100,
  n_factors = 3,
  snr = 3,
  signal.samples = c(5, 1),
  signal.features = list(
    c(3, 0.3),
    c(2.5, 0.25),
    c(2, 0.2)
  ),
  factor_structure = "mixed",
  num.factor = "multiple",
  seed = 123
)

# Visualize merged heatmap
plot_simData(sim_object = output_obj, data = "merged", type = "heatmap")

# Visualize omic2 in 3D
plot_simData(sim_object = output_obj, data = "omic2", type = "heatmap")

Visualizing the raw loading/weights of the features

Description

Generates scatter or histogram plots of feature loadings (weights) from simulated multi-omics data. Supports plotting for omic.one, omic.two, or integrated views.

Usage

plot_weights(
  sim_object,
  factor_num = 1,
  data = "omic.one",
  type = "scatter",
  show.legend = TRUE
)

Arguments

Value

A ggplot object or a combined grid of plots.

Examples

output_obj <- simulate_twoOmicsData(
  vector_features = c(4000, 3000),
  n_samples = 100,
  n_factors = 2,
  signal.samples = NULL,
  signal.features.one = NULL,
  signal.features.two = NULL,
  snr = 2.5,
  num.factor = 'multiple',
  advanced_dist = 'mixed'
  )

plot_weights(
   sim_object = output_obj,
   factor_num = 1,
   data = 'omic.one',
   type = 'scatter',
   show.legend = FALSE
   )

plot_weights(
  sim_object = output_obj,
  factor_num = 2,
  data = 'omic.two',
  type = 'histogram'
  )

Simulation of omics with predefined single or multiple latent factors in multi-omics

Description

Simulate multiple omics (>2) datasets with non-overlapping sample and feature signal blocks.

Usage

simulateMultiOmics(
  vector_features,
  n_samples,
  n_factors,
  snr = 2,
  signal.samples = c(5, 1),
  signal.features = NULL,
  factor_structure = "mixed",
  num.factor = "multiple",
  seed = NULL
)

Arguments

Details

This function generates synthetic omics data where each omic layer has its own feature space and noise characteristics. The sample signal blocks for each latent factor are non-overlapping and sequential with random gaps. Feature signal blocks are generated per omic with sequential non-overlapping segments.

Value

A list containing:

• omic.list: List of simulated omic datasets.

• signal_annotation: Signal sample indices per factor.

• list_alphas, list_betas: Latent factor loading vectors.

Examples

sim_object1 <- simulateMultiOmics(
  vector_features = c(3000, 2500, 2000),
  n_samples = 100,
  n_factors = 3,
  snr = 3,
  signal.samples = c(5, 1),
  signal.features = list(
    c(3, 0.3),   # omic1 signal mean/sd
    c(2.5, 0.25),# omic2 signal mean/sd
    c(2, 0.2)    # omic3 signal mean/sd
  ),
  factor_structure = "mixed",
  num.factor = "multiple",
  seed = 123
)

# View available elements
names(sim_object1)

# Visualize the simulated data
plot_simData(sim_object = sim_object1, data = "merged", type = "heatmap")

sim_object2 <- simulateMultiOmics(
  vector_features = c(3000, 2500),
  n_samples = 100,
  n_factors = 1,
  snr = 0.5,
  signal.samples = c(3, 1),
  signal.features = list(
    c(3.5, 0.3),   # omic1 signal mean/sd
    c(4, 0.2)    # omic3 signal mean/sd
  ),
  factor_structure = "shared",
  num.factor = "single",
  seed = NULL
)

# Visualize the simulated data
plot_simData(sim_object = sim_object2, data = "merged", type = "heatmap")

Simulation of omics with predefined single or multiple latent factors in multi-omics

Description

Simulates two high-dimensional omics datasets with customizable latent factor structures. Users can control the number and type of factors (shared, unique, mixed), the signal-to-noise ratio, and the distribution of signal-carrying samples and features. The function is flexible for benchmarking multi-omics integration methods under various controlled scenarios.

Usage

simulate_twoOmicsData(
  vector_features = c(2000, 2000),
  n_samples = 50,
  n_factors = 3,
  signal.samples = NULL,
  signal.features.one = NULL,
  signal.features.two = NULL,
  num.factor = "multiple",
  snr = 1,
  advanced_dist = NULL,
  ...
)

Arguments