Giotto: a toolbox for integrative analysis and visualization of spatial expression data

Abstract

Spatial transcriptomic and proteomic technologies have provided new opportunities to investigate cells in their native microenvironment. Here we present Giotto, a comprehensive and open-source toolbox for spatial data analysis and visualization. The analysis module provides end-to-end analysis by implementing a wide range of algorithms for characterizing tissue composition, spatial expression patterns, and cellular interactions. Furthermore, single-cell RNAseq data can be integrated for spatial cell-type enrichment analysis. The visualization module allows users to interactively visualize analysis outputs and imaging features. To demonstrate its general applicability, we apply Giotto to a wide range of datasets encompassing diverse technologies and platforms.

Cell segmentation-free inference of cell types from in situ transcriptomics data

“…The package is available as an easily installable Python package, and can easily be extended with existing in situ transcriptomics pipelines, e.g. starfish ( https://github.com/spacetx/starfish ) or Giotto 36 . SSAM is accompanied with a notebook outlining all the steps presented in this paper.…”

Community-wide hackathons to identify central themes in single-cell multi-omics

“…The implementation of novel analysis tools requires further software ecosystems, including Bioconductor [61], Biopython, and toolkits such as Scanpy [62], Seurat [63], or Giotto [64], in which users can implement their analysis approaches, while anticipating stable and adaptive data structures that are applicable for these emerging technologies. The size of these emerging datasets, particularly in the context of their application to atlas projects (e.g., the Human Tumor Atlas Network [65], Human Cell Atlas [66], Allen Brain Initiative, Brain Initiative Cell Census Network, or ENCODE/Roadmap/4D [46]…”

Define and visualize pathological architectures of human tissues from spatially resolved transcriptomics using deep learning

“…1b and Supplementary Tables 1-2). Our results demonstrate RESEPT outperforms six existing tools, namely Seurat 5 , BayesSpace 7 , SpaGCN 9 , stLearn 8 , STUtility 12 , and Giotto 6 on tissue architecture identification in terms of Adjusted Rand Index (ARI) (Fig. 1c).…”

“…Recent advances in spatially resolved technologies such as 10x Genomics Visium provide spatial context together with high-throughput gene expression for exploring tissue domains, cell types, cell-cell communications, and their biological consequences 4 . Some graph-based clustering methods ( e.g ., Seurat 5 and Giotto 6 ), statistical methods ( e.g ., BayesSpace 7 ), or deep learning-based methods ( e.g. , stLearn 8 and SpaGCN 9 ) can identify spatial architecture and interpret spatial heterogeneity.…”