A realistic FastQ-based framework FastQDesign for ScRNA-seq study design issues.

Area of Science:

• Genomics
• Bioinformatics
• Computational Biology

Background:

• Single-cell RNA sequencing (scRNA-seq) enables transcriptomic profiling at single-cell resolution.
• Study design, including cell number and sequencing depth, is critical for scRNA-seq success.
• Current design methods often rely on simulations and lack direct connection to raw sequencing data (FastQ files).

Purpose of the Study:

• To develop the first FastQ-based framework for designing scRNA-seq experiments.
• To provide an optimal study design considering budget constraints.
• To offer practical guidance for cost-benefit trade-offs in scRNA-seq.

Main Methods:

• Developed "FastQDesign," a novel framework utilizing raw FastQ files from public datasets.
• Leveraged publicly available FastQ data as references for design optimization.
• Validated the framework using a synthetic dataset and nine real-world scRNA-seq datasets.

Main Results:

• Demonstrated the effectiveness of the FastQDesign framework on synthetic and real-world data.
• Showcased the framework's ability to suggest optimal designs within a fixed budget.
• Highlighted the importance of appropriate scRNA-seq design for studying cell heterogeneity.

Conclusions:

• FastQDesign offers a practical, FastQ-based approach to scRNA-seq study design.
• The framework aids researchers in making informed decisions regarding cell number and sequencing depth.
• Optimized study design is essential for robust analysis of complex biological systems using scRNA-seq.