Overview
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.
