A realistic FastQ-based framework FastQDesign for ScRNA-seq study design issues. Commun Biol 2025 Apr 02;8(1):547
Date
04/03/2025Pubmed ID
40175506Pubmed Central ID
PMC11965523DOI
10.1038/s42003-025-07938-8Scopus ID
2-s2.0-105001734248 (requires institutional sign-in at Scopus site) 1 CitationAbstract
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 .
Author List
Wang Y, Chen YG, Ahn KW, Lin CWAuthors
Kwang Woo Ahn PhD Director, Professor in the Data Science Institute department at Medical College of WisconsinYi-Guang Chen PhD Professor in the Pediatrics department at Medical College of Wisconsin
Chien-Wei Lin PhD Associate Professor in the Data Science Institute department at Medical College of Wisconsin
MESH terms used to index this publication - Major topics in bold
Computational BiologyGene Expression Profiling
Humans
Sequence Analysis, RNA
Single-Cell Analysis
Software
