scruff: an R/Bioconductor package for preprocessing single-cell RNA-sequencing data.

Area of Science:

• Genomics
• Bioinformatics
• Computational Biology

Background:

• Single-cell RNA sequencing (scRNA-seq) provides high-throughput transcriptional profiling.
• scRNA-seq requires specialized preprocessing, including cell barcode identification and unique molecular identifier (UMI) deconvolution.
• Existing R packages lack comprehensive tools for rapid preprocessing and visualization of scRNA-seq data quality metrics across multiple samples.

Purpose of the Study:

• To introduce scruff, an R/Bioconductor package for streamlined scRNA-seq data preprocessing.
• To provide comprehensive quality metrics and visualizations for scRNA-seq data.
• To support data from CEL-Seq, CEL-Seq2, and 10X Genomics Cell Ranger pipelines.

Main Methods:

• scruff demultiplexes, aligns, and counts reads with UMI deduplication.
• The package offers extensive visualization of pre- and post-alignment data quality metrics for individual cells.
• It supports visualization of alignment quality metrics for multiple experiments from 10X Genomics.

Main Results:

• scruff enables rapid preprocessing of scRNA-seq data from CEL-Seq and CEL-Seq2 protocols.
• Comprehensive quality metrics and visualizations are generated for cells across multiple experiments.
• Isoform usage differences can be visualized at specific genomic coordinates with UMI information.
• Quality metrics for 10X Genomics Cell Ranger alignment files are also supported.

Conclusions:

• scruff simplifies scRNA-seq data preprocessing with a few R commands.
• The package systematically performs demultiplexing, alignment, counting, and quality reporting.
• scruff ensures reproducible and reliable analysis of scRNA-seq data through comprehensive quality control and visualization.