Analyzing Spatial Transcriptomics Data Using Giotto. Issue 4 (5th April 2022)

• Record Type: Journal Article
• Authors: Del Rossi, Natalie; Chen, Jiaji G.; Yuan, Guo‐Cheng; Dries, Ruben
• Is Part Of: Current protocols. Volume 2:Issue 4(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-04-05
• Subjects: cell‐cell interaction -- cell type -- deconvolution -- single cell -- software -- spatial transcriptomics; Life sciences -- Laboratory manuals -- Periodicals; Biology -- Laboratory manuals -- Periodicals; Life sciences -- Technique -- Periodicals; Biology -- Technique -- Periodicals; 570.028
• DOI: 10.1002/cpz1.405
• Languages: English
• ISSNs: 2691-1299

Abstract

Abstract: Spatial transcriptomic technologies have been developed rapidly in recent years. The addition of spatial context to expression data holds the potential to revolutionize many fields in biology. However, the lack of computational tools remains a bottleneck that is preventing the broader utilization of these technologies. Recently, we have developed Giotto as a comprehensive, generally applicable, and user‐friendly toolbox for spatial transcriptomic data analysis and visualization. Giotto implements a rich set of algorithms to enable robust spatial data analysis. To help users get familiar with the Giotto environment and apply it effectively in analyzing new datasets, we will describe the detailed protocols for applying Giotto without any advanced programming skills. © 2022 Wiley Periodicals LLC. Basic Protocol 1 : Getting Giotto set up for use Basic Protocol 2 : Pre‐processing Basic Protocol 3 : Clustering and cell‐type identification Basic Protocol 4 : Cell‐type enrichment and deconvolution analyses Basic Protocol 5 : Spatial structure analysis tools Basic Protocol 6 : Spatial domain detection by using a hidden Markov random field model Support Protocol 1 : Spatial proximity–associated cell‐cell interactions Support Protocol 2 : Assembly of a registered 3D Giotto object from 2D slices