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Generating correlated data for omics simulation.

Jianing Yang1,2, Gregory R Grant1,3, Thomas G Brooks1

  • 1Institute for Translational Medicine and Therapeutics, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.

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|September 5, 2025
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Summary
This summary is machine-generated.

Simulating omics data with correlations is crucial for accurate computational pipeline benchmarking. Our Gaussian copula method efficiently generates realistic, dependent omics data, improving method evaluation.

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Area of Science:

  • Computational Biology
  • Bioinformatics
  • Statistical Modeling

Background:

  • Realistic omics data simulation is vital for benchmarking computational pipelines.
  • Omics data often exhibits correlations between measured features, which are frequently ignored in simulations due to computational challenges.
  • Existing simulation methods struggle to incorporate feature dependencies efficiently.

Purpose of the Study:

  • To introduce efficient methods for generating omics-scale data with correlated measures.
  • To demonstrate the impact of including correlations in benchmarking studies.
  • To provide a flexible R package for simulating dependent omics data.

Main Methods:

  • Developed three simulation approaches based on a Gaussian copula model.
  • Utilized a covariance matrix decomposition (diagonal and low-rank) for computational efficiency.
  • Implemented methods in the R package `dependentsimr` for various marginal distributions.

Main Results:

  • Showed increased variance in DESeq2 results when feature dependence was included.
  • Demonstrated performance improvements for CYCLOPS in inferring circadian time with gene-gene dependencies.
  • Validated the importance of correlation in omics data simulation for accurate benchmarking.

Conclusions:

  • Efficient simulation of correlated omics data is achievable and essential for robust benchmarking.
  • Incorporating feature dependencies can significantly impact the performance of omics analysis tools.
  • The `dependentsimr` package offers a practical solution for generating realistic, dependent omics data.