scRNA-seq

Recommended Analysis Workflows

Primary Recommendation: nf-core/scrnaseq + Seurat

For most single-cell RNA-seq projects, we recommend using nf-core/scrnaseq for initial processing followed by Seurat for downstream analysis.

Workflow Overview: 1. Raw data processing → Run nf-core/scrnaseq on HiperGator using SLURM configuration 2. Analysis & visualization → Use Seurat via RStudio Server on HiperGator 3. Interactive exploration → Generate .cloupe files with our CloupeRator tool and explore data in 10X Loupe Browser

Alternative: 10X CellRanger + Seurat

For certain assays not optimally supported by nf-core/scrnaseq (such as specific 10X protocols or custom library preparations), we recommend: - 10X CellRanger for initial processing - Seurat for downstream analysis

Python Alternative: scanpy

While scanpy is an excellent Python-based option for single-cell analysis, R/Seurat is our primary expertise and we can provide more comprehensive support for R-based workflows.

Best Practices & Considerations

Data Quality: - Always perform thorough quality control metrics evaluation - Consider batch effects early in experimental design - Plan for appropriate controls and replicates

Computational Resources: - Use HiperGator's SLURM scheduler for efficient resource allocation - Scale computational requests based on cell count and complexity - Consider memory requirements for large datasets (>50K cells)

Analysis Depth: - Define biological questions before beginning analysis - Plan integration strategies for multi-sample experiments
- Consider trajectory analysis needs for developmental studies

Getting Started

Submit a support request to discuss your specific scRNA-seq project needs and workflow requirements.