scDAPP: a comprehensive single-cell transcriptomics analysis pipeline optimized for cross-group comparison.

Area of Science:

• Bioinformatics
• Computational Biology
• Genomics

Background:

• Single-cell transcriptomics (scRNA-seq) enables cross-group comparisons of cell populations and gene expression.
• Large scRNA-seq datasets and complex designs necessitate advanced, standardized bioinformatics analysis.
• Evolving software and analytic methods highlight the need for efficient, flexible, and automated pipelines.

Approach:

• Developed the single-cell Differential Analysis and Processing Pipeline (scDAPP), an R-based workflow.
• Automates scRNA-seq data pre-processing using data-learned parameters and integrates benchmarked software.
• Facilitates comparative analysis of single-cell or pseudobulked transcriptomic data across multiple groups.

Key Points:

• scDAPP automates pre-processing, integrates validated tools, and supports both single-cell and pseudobulk analyses.
• Generates comprehensive intermediate data and results, enhancing transparency with extensive visualizations.
• Provides a seamless transition from raw sequencing data to biological interpretation for users of all expertise levels.

Conclusions:

• scDAPP offers an automated, standardized, and flexible solution for comparative scRNA-seq analysis.
• The pipeline enhances transparency and facilitates biological interpretation of complex transcriptomic data.
• Freely available as an R package with comprehensive documentation and sample data on GitHub.