A multi-center cross-platform single-cell RNA sequencing reference dataset.

Area of Science:

• Genomics
• Bioinformatics
• Computational Biology

Background:

• Single-cell RNA sequencing (scRNA-seq) technology is rapidly advancing, presenting researchers with numerous experimental and analytical choices.
• A critical need exists for standardized reference datasets to benchmark the performance of various scRNA-seq platforms and bioinformatics pipelines.

Purpose of the Study:

• To establish a comprehensive, multi-platform benchmark single-cell RNA sequencing dataset.
• To facilitate the rigorous evaluation and comparison of different scRNA-seq technologies and computational methods.

Main Methods:

• Generated 20 scRNA-seq datasets from two distinct cell lines, processed individually and as mixtures.
• Acquired data across multiple popular scRNA-seq platforms and four independent sequencing centers.
• Leveraged extensive multi-platform whole genome sequencing data for the chosen cell lines.

Main Results:

• The benchmark dataset comprises scRNA-seq data from diverse platforms and centers, enabling cross-platform comparisons.
• The datasets are derived from well-characterized cell lines, ensuring biological relevance and reproducibility.
• Associated whole genome sequencing data provide a valuable reference for scRNA-seq analysis validation.

Conclusions:

• The presented benchmark dataset serves as a crucial resource for the scRNA-seq research community.
• It will enable robust evaluation of bioinformatics methods for preprocessing, normalization, clustering, batch correction, and differential analysis.
• This work supports the advancement of reliable and reproducible single-cell genomics research.