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

Chatterjee, B.; Gorga, K.; Blair, C.; Ohta, Y.; Hill, E. M.; Boughter, C. T.; Meier-Schellersheim, M.; Singh, N. J.

2025-10-17 bioinformatics
10.1101/2025.10.16.682943 bioRxiv
Show abstract

Minimizing experimental noise is integral to robust data generation in single-cell science. Experimental processing of different samples as a single pool, made possible by hashtag-assisted pooling, helps minimize batch-effects, but the computational demultiplexing of the data can also lead to loss of cells whose hashtags cannot be resolved accurately. Here, we examine four alternate experimental designs that could be used instead of a single-pool approach and quantify the batch effects as well as cell loss in each case. While a reference design offers the best performance, this study can help individual investigators choose one suited for their biological questions.

Published in Genome Research (predicted rank #28) · training set

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"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.