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.
Highlights
Most tissues consist of multiple cell types that operate together to perform their functions
Recent studies have shown that identical cell types may have tissuespecific expression patterns [1, 2], indicating that the tissue environment plays an important role in mediating cell states
Giotto Analyzer can be used to perform common steps often similar to scRNAseq analysis, such as pre-processing, feature selection, dimension reduction, and unsupervised clustering; on the other hand, the main strength comes from its ability to integrate gene expression and spatial information in order to gain insights into the structural and functional organization of a tissue and its expression patterns
Summary
Most tissues consist of multiple cell types that operate together to perform their functions. A number of technological advances have enabled transcriptomic/proteomic profiling in a spatially resolved manner [3,4,5,6,7,8,9,10,11,12,13,14] such that cellular features (for example transcripts or proteins) can be assigned to single cells for which the original cell location information is retained (Fig. 1a, inset) Applications of these technologies have revealed distinct spatial patterns that previously are only inferred through indirect means [15, 16]. There is an urgent need for standardized spatial analysis tools that can facilitate comprehensive exploration of the current and upcoming spatial datasets [17, Dries et al Genome Biology (2021) 22:78
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