Computational approach to evaluate scRNA-seq data quality and gene body coverage with SkewC

Area of Science:

• Bioinformatics
• Genomics
• Molecular Biology

Background:

• Single-cell RNA sequencing (scRNA-seq) is a powerful technology for analyzing cellular heterogeneity.
• Accurate quality control is crucial for reliable scRNA-seq data interpretation.
• Existing quality metrics may not capture all sources of technical variation.

Purpose of the Study:

• To introduce SkewC, a new computational tool for scRNA-seq data quality evaluation.
• To establish gene body coverage skewness as a robust metric for assessing individual cell quality.
• To differentiate between typical and skewed cells based on their coverage profiles.

Main Methods:

• SkewC calculates the skewness of gene body coverage for each single cell.
• This metric is used to classify cells into typical or skewed categories.
• The method is independent of the specific scRNA-seq technology used.

Main Results:

• SkewC effectively quantifies gene body coverage skewness as a quality indicator.
• The tool successfully distinguishes cells with prototypical coverage profiles from those with skewed profiles.
• SkewC provides a technology-independent approach for scRNA-seq data quality control.

Conclusions:

• SkewC offers a novel and reliable method for scRNA-seq data quality assessment.
• Gene body coverage skewness is a valuable metric for identifying low-quality cells.
• This tool enhances the accuracy and interpretability of single-cell gene expression studies.