Precision and Accuracy in Quantitative Measurement of Gene Expression from Single-cell/nucleus RNA Sequencing Data

Area of Science:

• Genomics
• Bioinformatics
• Molecular Biology

Background:

• Single-cell and single-nucleus RNA sequencing (sc/snRNA-seq) are vital for cell type gene expression profiling.
• Lack of quantitative benchmarks hinders sc/snRNA-seq data quality assessment and reproducibility.

Purpose of the Study:

• To systematically evaluate quantitative precision and accuracy in sc/snRNA-seq expression measures.
• To establish data-driven guidelines for optimizing sc/snRNA-seq study design and analysis.
• To develop a tool for assessing sc/snRNA-seq data quality and differential expression reliability.

Main Methods:

• Evaluated 23 sc/snRNA-seq datasets (3,682,576 cells, 339 samples).
• Assessed precision using technical replicates and pseudo-bulks.
• Assessed accuracy using matched scRNA-seq and pooled-cell RNA sequencing data.

Main Results:

• Precision and accuracy of expression measures are generally low at the single-cell level.
• Reproducibility is significantly influenced by cell count and RNA quality.
• Recommended minimum of 500 cells per cell type per individual for reliable quantification.
• Signal-to-noise ratio is crucial for identifying reproducible differentially expressed genes.

Conclusions:

• Established practical, evidence-based guidelines to enhance sc/snRNA-seq reliability.
• Developed the Variability In single-Cell gene Expressions (VICE) tool to aid data quality evaluation and differential expression analysis.
• Findings aim to reduce variability and improve consistency in sc/snRNA-seq studies.