论文标题
调整单细胞RNA测序数据的Doublet检测方法的高参数
Tuning hyperparameters of doublet-detection methods for single-cell RNA sequencing data
论文作者
论文摘要
单细胞RNA测序（SCRNA-SEQ）中的双重峰值在下游数据分析中构成了巨大挑战。已经开发了计算双线检测方法，以从SCRNA-SEQ数据中删除双重球。但是，这些方法的默认超参数设置可能无法提供最佳性能。在这里，我们提出了一种调整超级参数的策略，以进行尖端的双线检测方法。我们利用完整的阶乘设计来探索16个真实SCRNA-SEQ数据集上的超参数和检测准确性之间的关系。最佳的超参数是通过响应表面模型和凸优化获得的。我们表明，在各种生物学条件下，最佳的超参数可在SCRNA-Seq数据集中提供最高的性能。我们的调整策略可以应用于其他计算双线检测方法。它还为SCRNA-SEQ数据分析中的更广泛的计算方法提供了对超参数调整的见解。
The existence of doublets in single-cell RNA sequencing (scRNA-seq) data poses a great challenge in downstream data analysis. Computational doublet-detection methods have been developed to remove doublets from scRNA-seq data. Yet, the default hyperparameter settings of those methods may not provide optimal performance. Here, we propose a strategy to tune hyperparameters for a cutting-edge doublet-detection method. We utilize a full factorial design to explore the relationship between hyperparameters and detection accuracy on 16 real scRNA-seq datasets. The optimal hyperparameters are obtained by a response surface model and convex optimization. We show that the optimal hyperparameters provide top performance across scRNA-seq datasets under various biological conditions. Our tuning strategy can be applied to other computational doublet-detection methods. It also offers insights into hyperparameter tuning for broader computational methods in scRNA-seq data analysis.
