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Generating Correlated Data for Omics Simulation.

Jianing Yang^1,2, Gregory R Grant¹, Thomas G Brooks¹

¹Institute for Translational Medicine and Therapeutics, University of Pennsylvania.

Biorxiv : the Preprint Server for Biology

|February 20, 2025

Summary

This summary is machine-generated.

Simulating omics data with correlations is crucial for accurate computational pipeline benchmarking. Our Gaussian copula methods generate realistic, correlated omics data, improving analysis accuracy for tools like DESeq2 and CYCLOPS.

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Area of Science:

Computational biology
Bioinformatics
Statistical genetics

Background:

Realistic omics data simulation is vital for benchmarking computational pipelines.
Omics datasets often exhibit correlations between measured features, which are frequently ignored in simulations.
Ignoring correlations can lead to inaccurate benchmarking and suboptimal pipeline selection.

Purpose of the Study:

To present efficient methods for generating omics-scale data with correlated measures.
To highlight the importance of incorporating correlations in omics data simulation for benchmarking.
To provide a user-friendly R package for generating such data.

Main Methods:

Utilized a Gaussian copula approach with a covariance matrix decomposing into diagonal and low-rank components.
Developed three distinct methods for rapid generation of correlated omics data.
Implemented these methods in the R package 'dependentsimr'.

Main Results:

Demonstrated that including correlations increases the variance of results from the DESeq2 method.
Showed that the CYCLOPS method improves performance under certain conditions when gene-gene dependencies are considered.
The 'dependentsimr' package supports various data distributions, including discrete and continuous.

Conclusions:

Incorporating correlations in omics data simulation is essential for robust benchmarking.
The developed methods and package facilitate the creation of more realistic omics datasets.
Accurate simulation improves the reliability and performance assessment of bioinformatics tools.