Anticlustering for sample allocation to minimize batch effects.

Area of Science:

• Genomics
• Bioinformatics
• Computational Biology

Background:

• High-throughput sequencing generates large datasets but is susceptible to batch effects that can confound biological signals.
• Batch effects arise from systematic variations during sample processing, potentially leading to inaccurate experimental conclusions.

Purpose of the Study:

• To introduce anticlustering as an automated method for creating balanced experimental batches.
• To minimize covariate imbalance and address user-defined constraints in batch assignment for high-throughput sequencing.

Main Methods:

• Anticlustering algorithm, specifically the Two-Phase Must-Link (2PML) variant, was employed for sample-to-batch assignment.
• Simulations were conducted to compare anticlustering performance against existing methods.
• A real-world case study from the UCSF-Stanford ENACT Center was used to demonstrate practical application.

Main Results:

• Anticlustering demonstrated superior performance in generating balanced batches compared to existing methods in simulations.
• The 2PML algorithm successfully incorporated 'must-link' constraints, balancing key covariates like disease stage and menstrual cycle phase.
• The method was validated using samples from the ENACT Center, where intra-individual sample batching was crucial.

Conclusions:

• Anticlustering provides an effective automated solution for balanced batch assignment in high-throughput sequencing.
• The anticlust R package and RShiny app offer accessible tools for implementing and visualizing these methods.
• This approach enhances the reliability of biological signal detection by mitigating confounding batch effects.