Single-cell RNA-seq data normalization: A benchmarking study

Qinyu Ge, Yuqi Sheng, Junru Lu, Yuwei Yang and Min Pan

PLOS ONE, 2025, vol. 20, issue 12, 1-15

Abstract: This study examines the noise and biases introduced by technical factors in single-cell RNA sequencing (scRNA-seq) data, presenting a thorough benchmarking analysis of six widely utilized normalization methods. The evaluation of these methods is conducted from three perspectives: cell clustering, differential expression analysis, and computational resource requirements, utilizing seven real datasets alongside four simulated datasets. The findings indicate that Dino excels in clustering 10 × datasets and those with a substantial number of cells, while scTransform demonstrates strong performance with datasets produced through full-length library preparation protocols. Additionally, SCnorm is identified as suitable for small-scale datasets. This research serves as a significant reference for scholars in selecting appropriate normalization tools, thereby enhancing the accuracy and reliability of subsequent analyses of scRNA-seq data.

Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0335102

DOI: 10.1371/journal.pone.0335102

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