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

ABSTRACT: 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.

ORGANISM(S): Homo sapiens

PROVIDER: GSE298265 | GEO | 2025/07/30

REPOSITORIES: GEO

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Benchmarking of Computational Demultiplexing Methods for Single-Nucleus RNA Sequencing Data [dataset 2]

Project description: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.

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