Single-Cell RNA-Seq Analysis Reports

A collection of code templates and training materials for performing downstream analysis following single-cell RNA preprocessing. This repository provides standardized workflows for researchers working with scRNA-seq data, from quality assessment to differential expression.

View Documentation → | GitHub Repository →

Key Features

• Quality Assessment: Templates for evaluating scRNA and scATAC data quality
• Integration Analysis: Workflows for combining multiple samples with batch effect correction
• Differential Expression: Multiple approaches including pseudobulk analysis via DESeq2 and MAST methodology
• Compositional Analysis: Methods for examining cell-type abundance changes across conditions
• Gene Imputation: Techniques using ALRA and MAGIC algorithms to address data sparsity
• Pipeline Integration: Support for nf-core scrnaseq pipeline outputs and standalone Cell Ranger processing

Technical Stack

Built primarily in R with supporting shell scripts:

• Seurat for single-cell data handling and analysis
• DESeq2 for differential expression analysis
• Harmony for batch effect correction
• nf-core scrnaseq pipeline integration
• Quarto and R Markdown for reproducible reports

What It Provides

The repository includes:

• Ready-to-use analysis templates for common scRNA-seq tasks
• Training materials and documentation
• Standardized workflows following best practices
• Integration with popular preprocessing pipelines
• Examples for both scRNA-seq and scATAC-seq data

Development Status

Templates are labeled with revision tiers:

• Stable: Fully tested and production-ready
• Alpha: Functional but requires additional testing
• Draft: Under active development, may need manual parameter tuning

Why This Matters

Single-cell RNA-seq analysis requires specialized knowledge and tools. This project:

• Reduces the learning curve for new single-cell researchers
• Provides validated workflows following community best practices
• Enables reproducible analysis through standardized templates
• Supports multiple experimental designs and research questions
• Integrates seamlessly with popular preprocessing pipelines

For more information or to contribute, visit the GitHub repository or explore the documentation.