Abstract
Simulation of realistic omics data is a key input for benchmarking studies that help users obtain optimal computational pipelines. Omics data involves large numbers of measured features on each samples and these measures are generally correlated with each other. However, simulation too often ignores these correlations, perhaps due to the inconvenience and computational hurdles of doing so. To alleviate this, we describe in detail three approaches for quickly generating omics-scale data with correlated measures which mimic real data sets. These approaches all are based on a Gaussian copula approach with a covariance matrix that decomposes into a diagonal part and a low-rank part. We use these approaches to demonstrate the importance of including correlation in two benchmarking applications. First, we show that variance of results from the popular DESeq2 method increases when dependence is included. Second, we demonstrate that CYCLOPS, a method for inferring circadian time of collection from transcriptomics, improves in performance when given gene-gene dependencies in some circumstances. We provide an R package, dependentsimr, that has efficient implementations of these methods and can generate dependent data with arbitrary distributions, including discrete (binary, ordered categorical, Poisson, negative binomial), continuous (normal), or with an empirical distribution.