In RubD/Giotto_site: Spatial Single-Cell Transcriptomics Toolbox

Description

Giotto provides a flexible framework for common single-cell processing steps such as:
- quality control
- normalization
- dimension reduction
- clustering and cell type annotation
To facilitate the analysis of recently emerging high-throughput, but lower-resolution spatial transcriptomic technologies, such as 10X Genomics Visium and Slide-seq, Giotto has 3 implemented algorithms for estimating the spatial enrichment of different cell types by integration of known gene signatures or single-cell RNAseq expression and annotation data.
Spatial information is retained through the formation of a spatial grid and/or a spatial proximity network, which is used to:
- identify spatial genes
- extract continuous spatial-expression patterns
- identify discrete spatial domains using HMRF
- explore cell-type/cell-type spatial interaction enrichment or depletion
- calculate spatially increased ligand-receptor expression in cells of interacting cell type pairs
- find interaction changed genes (ICG): genes that change expression in one cell type due to interaction with a neighboring cell type
Giotto provides a number of options to visualize both 2D and 3D data and the outcome of Giotto can be interactively explored using Giotto Viewer, which allows you to overlay the obtained results with raw or additional images of the profiled tissue section(s).

Make sure to check out the Datasets section to see examples of the Giotto workflow.

Workflow diagram

HOWTOs

Giotto provides a lot of analyses, visualizations and other options to facilitate your spatial dataset analysis. We are working on providing easy-to-understand examples or tutorials, but if anything is not clear or if there is something you would like to see in particular, then do not hesitate to contact us.