To fill this important gap, we present the first comprehensive, standardized, and user-friendly toolbox, called Giotto, that allows researchers to process, (re-)analyze, and interactively visualize spatial transcriptomic and proteomic datasets. There is an urgent need for standardized spatial analysis tools that can facilitate comprehensive exploration of the current and upcoming spatial datasets. Applications of these technologies have revealed distinct spatial patterns that previously are only inferred through indirect means. Recently, a number of technological advances have enabled transcriptomic/proteomic profiling in a spatially resolved manner 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. Since spatial information is lost during the process of tissue dissociation and cell isolation, the scRNAseq technology is intrinsically limited for studying the structural organization of a complex tissue and interactions between cells and their tissue environment. However, recent studies have also shown that identical cell types may have tissue-specific expression patterns, indicating that the tissue environment plays an important role in mediating cell states. With the rapid development of single-cell RNAseq (scRNAseq) technologies in the last decade, most attention has gone to unraveling the composition of cell types with each tissue. The behavior of each cell is in turn mediated by its tissue environment. Most tissues consist of multiple cell types that operate together to perform their functions.