Abstract
Single-cell RNA sequencing (scRNA-seq) has emerged as a powerful technology for characterizing transcriptomic profiles at single-cell resolution. It is crucial to consider both the number of cells and sequencing depth during library preparation. The existing methods are primarily simulation-based, rely on Unique Molecular Identifier (UMI) matrix, and have little context in the actual FastQ reads. Here we propose the first FastQ-based study design framework, named "FastQDesign," which leverages raw FastQ files from publicly available datasets as references and suggests an optimal design within a fixed budget. We demonstrate our framework through a synthetic dataset and applications to nine real-world datasets. Our study underscores the importance of an appropriate design to investigate the biology of heterogeneous cell populations and offers practical guidance considering cost-benefit trade-offs. A high-efficiency software suite is available at https://github.com/yuw444/FastQDesign .