Simulation data 1.

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.本研究针对单细胞RNA测序（scRNA-seq）数据中技术因素引入的噪声与偏倚展开分析，并对六种广泛应用的标准化方法开展了全面的基准测试研究。本次评估从细胞聚类、差异表达分析以及计算资源需求三个维度进行，共采用7个真实数据集与4个模拟数据集完成测试。研究结果显示，Dino在10×数据集及细胞数量较多的数据集的聚类任务中表现突出，而scTransform在基于全长文库制备流程生成的数据集上展现出优异性能。此外，SCnorm被证实适用于小规模数据集。本研究为科研人员选择合适的标准化分析工具提供了重要参考，有助于提升后续scRNA-seq数据分析的准确性与可靠性。