Omixer

Omixer randomizes and optimizes sample allocation across batches to mitigate batch effects in multivariate omics studies and prevent confounding of technical factors with biological variables.

Key Features:

• R implementation: Provided as an R package for computational sample randomization and allocation.
• Multivariate randomization: Balances samples using multivariate covariates to reduce correlations between technical factors and biological variables.
• Optimization across multiple batches and outcomes: Optimizes distribution of samples across multiple batches and outcomes to minimize batch-induced bias.
• Reproducible randomization: Produces reproducible randomization processes to ensure consistent experimental design across runs.
• Laboratory sample sheets: Generates sample sheets for laboratory use to link computational plans with wet-lab execution.
• Publication: Described in the literature (PMID: 33693546).

Scientific Applications:

• Batch-effect mitigation: Prevents technical confounding and reduces bias in omics experiments by randomizing and balancing sample allocation prior to measurement.
• Multi-batch experimental design: Supports experimental designs involving multiple batches and multiple outcomes by optimizing sample distribution.
• Improving downstream inference: Reduces risk of false positives and biased results in downstream omics analyses by minimizing batch-driven artifacts.

Methodology:

Computational steps include multivariate sample randomization prior to measurement, optimization of sample distribution across batches and outcomes, and reproducible generation of randomized allocations implemented in an R package.