Moderated designs can balance between batch-effect mitigation and cell loss due to hashtag-assisted pooling in...

Area of Science:

• Single-cell omics
• Genomics
• Bioinformatics

Background:

• Minimizing experimental noise is essential for reliable single-cell omics data.
• Current methods use hashtag-assisted pooling to reduce batch effects, but can cause cell loss during demultiplexing.

Purpose of the Study:

• To evaluate four alternative experimental designs (compound, reference, chain, confounded) against the standard single-pool approach.
• To quantify batch effects and cell loss for each design in single-cell omics.
• To identify optimal designs for mitigating batch effects while minimizing cell loss.

Main Methods:

• Computational analysis of four experimental designs: compound, reference, chain, and confounded.
• Quantification of batch effects and cell loss associated with each design.
• Comparison of alternative designs to the standard single-pool hashtagging method.

Main Results:

• A linear relationship was observed: cell loss percentage is double the number of hashtags used.
• Alternative designs were assessed for their ability to mitigate batch effects and reduce cell loss.
• The reference design demonstrated superior overall performance in balancing batch effect mitigation and cell retention.

Conclusions:

• The choice of experimental design significantly impacts batch effects and cell loss in single-cell omics.
• Alternative designs, particularly the reference design, offer improved strategies for data generation.
• This study provides guidance for selecting appropriate experimental approaches based on specific research needs.