scPipe: an extended preprocessing pipeline for comprehensive single-cell ATAC-Seq data integration in R/Bioconductor

Area of Science:

• Genomics
• Bioinformatics
• Computational Biology

Background:

• Single-cell RNA sequencing (scRNA-Seq) analysis relies on flexible bioinformatics tools.
• Emerging single-cell technologies like ATAC-Seq require adaptable analytical pipelines.
• Existing tools may not fully support multi-modal single-cell data integration.

Purpose of the Study:

• To enhance the scPipe R/Bioconductor package for single-cell ATAC-Seq and multi-modal (RNA-Seq and ATAC-Seq) data analysis.
• To develop a comprehensive preprocessing pipeline for diverse single-cell genomics data.
• To facilitate downstream analyses of single-cell ATAC-Seq data within the R environment.

Main Methods:

• Extended scPipe to process single-cell ATAC-Seq and multi-modal data.
• Implemented data cleaning steps including removal of duplicated reads and low-quality cells/features.
• Generated a SingleCellExperiment object containing sparse count matrices and metadata for quality control and annotations.

Main Results:

• scPipe efficiently identifies true single cells through flexible quality control thresholds.
• The package generates a SingleCellExperiment object with comprehensive quality control metrics and feature annotations.
• Demonstrated the utility of scPipe for downstream single-cell ATAC-Seq analyses such as dimensionality reduction and motif enrichment.

Conclusions:

• The enhanced scPipe package provides a complete beginning-to-end pipeline for single-cell ATAC-Seq and RNA-Seq data analysis in R.
• scPipe offers flexibility for users to fine-tune quality control parameters based on various metrics.
• The tool empowers researchers to leverage Bioconductor's downstream analysis capabilities for single-cell genomics data.