scrnaseq2 is an open-source pipeline designed to simplify and automate the analysis of single-cell sequencing data. Hosted on GitHub, this versatile pipeline integrates a series of widely-used bioinformatics tools and best practices, making it easier for researchers to process, analyze, and interpret complex single-cell datasets.

Key Features

Automation: With scrnaseq2, users can quickly generate reproducible results by following a streamlined, automated workflow, reducing the need for extensive bioinformatics expertise.

Customizability: Although designed to be easy to use, scrnaseq2 provides extensive customization options for advanced users who want to fine-tune their analyses.

Documentation: scrnaseq2 generates an extensive HTML report that contains additional visualisations, tables and documentation for a better understanding.

High scalability: The workflow is optimized for both small and large datasets, making it suitable for single-cell studies of varying sizes.

Modular design: The workflow is flexible and can be adapted to various research needs, supporting multiple stages of single-cell data analysis, including data pre-processing, quality control, normalization, clustering, and marker detection.

Solid basis: The workflow is written in R and is largely based on the Seurat v5 R package.

Versatility: The workflow supports single-cell and single-nuclei RNA and protein sequencing data from one or more samples processed with 10X Genomics, SmartSeq, Parse Biosciences and Scale Bio.

For more information, please visit the GitHub repository.