Quantification and visualization of the tumor microenvironment heterogeneity from spatial transcriptomic experiments

Spatially-resolved transcriptomic technologies promise to increase our understanding of the tumor microenvironment, which will lead to better cancer prognosis and therapies. Several spatial transcriptomics technologies have been developed, as well as a few methods for data analysis. Nonetheless, analytical pipelines that take users from gene counts to association of tumor spatial heterogeneity with clinical data are not available. Here we present spatialGE, a software that provides visualizations and quantification of the tumor microenvironment heterogeneity using spatial transcriptomics. Our software includes: 1) generation of high-resolution gene expression surfaces via spatial interpolation; 2) quantification of spatial heterogeneity measures that can be compared against clinical information (e.g., patient survival); 3) cell deconvolution methods at the spot level; 4) spatially-informed clustering; and 5) a new data structure that allows storage and analysis of multiple ST samples simultaneously. We show the utility of spatialGE by studying the spatial heterogeneity of cutaneous malignant melanoma samples. SignificancespatialGE enables the analysis of spatial transcriptomics samples and exploration of the tumor microenvironment, as well as the association of clinical outcomes with the spatial variation in gene expression.