Package: dependentsimr 1.0.0.0

dependentsimr: Simulate Omics-Scale Data with Dependency

Using a Gaussian copula approach, this package generates simulated data mimicking a target real dataset. It supports normal, Poisson, empirical, and 'DESeq2' (negative binomial with size factors) marginal distributions. It uses an low-rank plus diagonal covariance matrix to efficiently generate omics-scale data. Methods are described in: Yang, Grant, and Brooks (2025) <doi:10.1101/2025.01.31.634335>.

Authors:Thomas Brooks [aut, cre, cph]

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manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
dependentsimr/json (API)

# Install 'dependentsimr' in R:

install.packages('dependentsimr', repos = c('https://tgbrooks.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/tgbrooks/dependentsimr/issues

Datasets:

read_counts - GSE151923: cortex from 6-month-old wildtype C57BL/6 mice

On CRAN:

4.08 score 2 stars 12 scripts 248 downloads 7 exports 1 dependencies

Last updated from:5c6ef0af47. Checks:7 NOTE, 2 OK. Indexed: yes.

Target	Result	Time
linux-devel	NOTE	187
source / vignettes	OK	411
linux-release	NOTE	359
macos-release	NOTE	145
macos-oldrel	NOTE	208
windows-devel	NOTE	95
windows-release	NOTE	335
windows-oldrel	NOTE	112
wasm-release	OK	288

Exports:draw_from_multivariate_corr get_random_structure match_with_spiked_wishart multi_sample_spiked_wishart remove_dependence sample_spiked_wishart sample_spiked_wishart_and_jac

Dependencies:rlang

Simulating data with dependentsimr

Rendered fromsimulate_data.Rmdusingknitr::rmarkdown

Last update: 2025-07-11
Started: 2025-01-28

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Draw random samples from the given random structure	draw_from_multivariate_corr
Compute structure of dependency from a given data	get_random_structure
Compute what spiked SD values will give you the desired top eigenvalues by iteratively solving	match_with_spiked_wishart
Compute means of each singular value and the mean Jacobian, see sample_spiked_wishart_and_jac	multi_sample_spiked_wishart
GSE151923: cortex from 6-month-old wildtype C57BL/6 mice	read_counts
Remove all dependence in a random structure	remove_dependence
Efficiently sample the singular values corresponding to a random Wishart matrix with spiked eigenvalues Specifically, if W = G G^T with each column of G drawn iid from N(0, Sigma), then W is a Wishart matrix and this function samples the singular values of G. The eigenvalues of W are just the squares of the singular values. Here, Sigma is diagonal with its leading entries from spiked_sd^2 and all remaining entries are population_sd^2.	sample_spiked_wishart
Efficiently sample the singular values corresponding to a random Wishart matrix with spiked eigenvalues and the Jacobian I.e., these are the singular values of G if GG^T is Wishart. The square of these give the eigenvalues of the random Wishart matrix.	sample_spiked_wishart_and_jac