scRNAvigator: Interactive exploration, processing, and analysis of your scRNA-seq data

This collection of R notebooks has been designed to guide you through processing and analysing your single cell RNA (scRNA) sequencing data. They are designed to be worked through in the following order:

1. Quality control
2. Doublet detection
3. Dataset integration
4. Cell annotation
5. Pseudobulking and differential gene expression analysis
6. Pathway enrichment analyses.

Each notebook explains what is happening in each step, complete with code and rationales for the choices we have made in our approach.

It is important to note there is no one single way to pre-process scRNA data - there are as many ways as there are different software packages and libraries for scRNA analysis, and the limitless ways to use each of their tools and functions.

The workflow presented in these notebooks is the synthesis of best practices, studies, and discussions of how to analyse scRNA data with a focus on using Seurat in R. Footnotes and external links accompany the text throughout the document - please view these for useful additional information and rationale on why steps are done in certain ways.

This content primarily uses the Seurat R package, but the way and order things are run differs vastly from their tutorials. We like to note that the Satija lab Seurat tutorials are instructions on how to use the package, but not how to conduct robust scRNA pre-processing and analysis. This content leverages the flexibility of the Seurat package, but is supplemented by the practices outlined in existing resources. These resources are the most influential:

• Current best practices in single-cell RNA-seq analysis: a tutorial (Luecken and Theis, 2019)
• scRNAseq analysis in R with Seurat (Williams and Perlaza, 2024)
• Spatial Sampler (Williams, 2025)

Usage and platforms

Detailed installation scripts have been provided to allow running the workflow locally or on an RStudio instance on NCI Gadi's Australian Research Environment. We recommend the latter so you can utilise the compute resources required to process larger data sets.

More information

Please visit the GitHub repository for more information on the workflow, setting up, and usage.