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

Abstract

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.BackgroundThe 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.MethodsWe 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.ResultsWe 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.ConclusionsThe 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.