Omixer: multivariate and reproducible sample randomization to proactively counter batch effects in omics studies.

Area of Science:

• Omics
• Bioinformatics
• Computational Biology

Background:

• Batch effects significantly impact omics study results, introducing bias and leading to false positives.
• Existing methods for controlling batch effects through sample randomization are often inadequate, poorly documented, and lack the flexibility for complex experimental designs.

Purpose of the Study:

• To develop a robust software solution for preemptive control of batch effects in omics studies.
• To implement a multivariate and reproducible sample randomization strategy to mitigate bias in omics data.

Main Methods:

• Development of Omixer, a Bioconductor package.
• Implementation of multivariate sample randomization algorithms.
• Optimization of sample distribution across experimental batches to minimize technical factor correlations with biological variables.

Main Results:

• Omixer enables proactive management of batch effects by ensuring balanced sample distribution.
• The package facilitates reproducible randomization, crucial for the reliability of omics data.
• Demonstrated ability to counter correlations between technical factors and biological variables of interest.

Conclusions:

• Omixer provides a vital tool for researchers to improve the quality and reliability of omics studies.
• The software addresses the lack of effective preemptive strategies for controlling batch effects.
• Omixer enhances the validity of omics findings by minimizing bias and false positives.