Tuning hyperparameters of doublet-detection methods for single-cell RNA sequencing data.

Doublet is a major confounder in single-cell RNA sequencing data analysis. Computational doublet-detection methods aim to remove doublets from scRNA-seq data. The performance of those methods relies on the appropriate setting of their hyperparameters. In this study, we explore the optimal hyperparameters for scDblFinder, a cutting-edge doublet-detection method. Our optimization utilizes a full factorial design, a response surface model, and 16 real scRNA-seq datasets. The optimal hyperparameters achieve top doublet-detection performance under a wide range of biological conditions. Our methodology is applicable to broader computational methods in scRNA-seq data analysis. Background: 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. Methods: 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. Results: 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. Conclusions: The hyperparameter configuration significantly impacts the performance of computational doublet-detection methods. Our study is the first attempt to systematically explore the optimal hyperparameters under various biological conditions and optimization objectives. Our study provides much-needed guidance for hyperparameter tuning in computational doublet-detection methods.