Comprehensive generation, visualization, and reporting of quality control metrics for single-cell RNA sequencing data

Area of Science:

• Genomics
• Bioinformatics
• Computational Biology

Background:

• Single-cell RNA sequencing (scRNA-seq) is crucial for understanding cellular heterogeneity.
• scRNA-seq data often contains technical artifacts requiring rigorous quality control (QC).
• Existing QC tools are fragmented across different software packages and programming environments.

Purpose of the Study:

• To develop a streamlined and integrated pipeline for scRNA-seq data quality control.
• To provide a standardized workflow for generating and visualizing QC metrics.
• To enhance the accessibility and usability of QC for scRNA-seq analysis.

Main Methods:

• Development of the SCTK-QC pipeline within the singleCellTK R package.
• Integration of data import from various single-cell platforms and preprocessing tools.
• Inclusion of modules for empty droplet detection, standard QC metric generation, doublet prediction, and ambient RNA estimation.
• Implementation of multiple execution modes: command line, R console, cloud platform, and graphical user interface.

Main Results:

• The SCTK-QC pipeline successfully integrates diverse QC tasks into a single workflow.
• The pipeline supports data from multiple single-cell platforms and preprocessing tools.
• SCTK-QC provides a comprehensive suite of QC metrics and visualizations.
• The tool is accessible via various interfaces, catering to different user preferences and computational environments.

Conclusions:

• The SCTK-QC pipeline significantly streamlines and standardizes the quality control process for scRNA-seq data.
• This integrated approach facilitates more reliable downstream analyses of cellular heterogeneity.
• The pipeline enhances the efficiency and reproducibility of scRNA-seq data QC.