15 Resources
Links to various useful resources.
Single cell best practicies : Comprehensive resource on single cell analysis
OSCA - (Orchestrating Single Cell Analayis with bioconductor ) : A detailed resource on single cell analyses using the bioconductor ecosystem of packages - still applicable to certain spatial analyses.
OSTA (Orchestrating Spatial Transcriptomics Analysis with Bioconductor : A detailed resource on how to perform spatial analyses using the bioconductor ecosystem of packages. Even if using Seurat, this is a useful resource for detailed explanations of analysis tasks.
Voyager Documentaion: A bioconductor package for spatial analyses, centred around SpatialFeatureExperiment objects
SCverse: An ecosystem for analysing single cell and spatial single cell data with python.
Seurat spatial vignette (imaging-based ) : How to load and plot spatial data with seurat - covers different technologies.
Spatial Sampler : Worked examples and code snippets to run various statistical tests on single cell spatial data.
Understanding UMAP : Exploring out UMAP works, and what affects it.
Propeller for testing celltype proportion changes: The speckle package has tools to test for changes in celltype proportions between groups
clustree : A useful toolkit for choosing cluster resolutions.
Biologists, stop putting UMAP plots in your papers: A discussion on how UMAPs can sometimes be misinterpreted
Other references
- Korsunsky et al 2019: Harmony paper
- Antonsson and Melsted (2025) : Review on batch correction methods in single cell data