  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.
