Generating Correlated Data for Omics Simulation

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.