14 Resources

Links to various useful resources.

General guides & Vignettes

• Single cell best practices : 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.
• Spatial Sampler : Worked examples and code snippets to run various statistical tests on single cell spatial data.
• 10X analysis guides
• Basic cell histology
• Introduction to imaging-based spatial transcriptomics analysis: Workshop walking through a spatial analysis of cosmx data.

Analysis ecosytems

• Seurat: Seurat package. Originally for single cell transcriptomics, it now supports some analyses in spatial technologies.
• Bioconductor: The bioconductor repository is much broader than just spatial, but various toolks centre around the SpatialExperiment and SpatialFeatureExperiment (derived from SingleCellExperiment objects). The OSTA book is a good overview about whats possible. With the Voyager Documentaion covering SpatialFeatureExperiment methods.
• SCverse: An ecosystem for analysing single cell and spatial single cell data with python.
• Giotto Suite: Another R based ecosystem for spatial analyses.
• Cell Profiler : One toolkit often used in traditional imaging for various tasks, e.g. cell morphology quantifications.

Specific analyses / tools

• Seurat spatial vignette (imaging-based ) : How to load and plot spatial data with seurat - covers different technologies.

• [Seurat visum HD vignette][https://satijalab.org/seurat/articles/visiumhd_analysis_vignette] : Analysis of spot-based visium HD data.

• Seurat cheat-sheet : Essential utility commands for seurat analysis, e.g merging seurat objects from different samples, subsetting, managing feilds of view, assays and layer data.

• ‘Introduction to scRNA-seq integration’ vignette: To merge data from different experiments (including scRNAseq)

• Seurat sketch based analysis : For very large datasets.

• Clustree : A useful toolkit for choosing cluster resolutions.

• Propeller for testing celltype proportion changes: The speckle package has tools to test for changes in celltype proportions between groups

• Statial package and Kontextual paper: Toolkit for quanitifying spatial differences between