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Benchmarking of Computational Demultiplexing Methods for Single-Nucleus RNA Sequencing Data [dataset 1]

GSE298265 Homo sapiens Expression profiling by high throughput sequencing 6 samples Submitted 2025/07/30 Platform GPL24676
Summary
Single-nucleus RNA sequencing enables high-resolution profiling of complex tissues, but its high cost limits large-scale studies. Sample pooling with genetic demultiplexing is a scalable solution, yet comparative benchmarks are lacking. We benchmarked four widely used tools using variants from SNP arrays or extracted from matched bulk RNA-Seq. Performance, including accuracy, runtime, robustness, and scalability, was evaluated. Real-world application to 10x RNA-Seq from human and multi-species heart tissue demonstrates the tools' utility for demultiplexing and doublet removal.
Published in
Benchmarking of computational demultiplexing methods for single-nucleus RNA sequencing data
Fu Y, Youness M, Virzì A et al. · Briefings in bioinformatics 2025 · PMID 40702707 · doi:10.1093/bib/bbaf371
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Also filed as BioProject PRJNA1268586 and SRA study SRP588002. Searching any of these in the dataset finder brings you back here.

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