UF Health Cancer Center Bioinformatics Hub
Analysis of Omics Data by Assay
scRNA-seq

scRNA-seq

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:

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.