spatialGE: Quantification and visualization of the tumor microenvironment heterogeneity using spatial transcriptomics

Summary Spatially-resolved transcriptomics promises to increase our understanding of the tumor microenvironment and improve cancer prognosis and therapies. Nonetheless, analytical methods to explore associations between the spatial heterogeneity of the tumor and clinical data are not available. Hence, we have developed spatialGE, a software that provides visualizations and quantification of the tumor microenvironment heterogeneity through gene expression surfaces, spatial heterogeneity statistics (SThet) that can be compared against clinical information, spot-level cell deconvolution, and spatially-informed clustering (STclust), all using a new data object to store data and resulting analyses simultaneously. Availability and implementation The R package and tutorial/vignette are available at https://github.com/FridleyLab/spatialGE . A script to reproduce the analyses in this manuscript is available in Supplementary information. Contact [email protected] or [email protected] Supplementary information Available at Bioinformatics online. Abstract Figure Graphical abstract Overview of spatialGE features. A . The STList data object from spatialGE can be creared from several sources, including comma- or tab-separated files containing gene counts and spatial coordinates. The object can also be created directly from Visium outputs, Seurat objects, or GeoMx outputs. B . Users can optionally provide a metadata file, containing information associated with each sample (one row per sample, or per ROI if GeoMx data). C . Methods for quality control of data are provided by spatialGE, including visualizations of counts and genes per spot, as well as filtering of spots or genes within user-determined thresholds. D . A novel method (STclust) performs spatially informed clustering of spots and tissue domain identification. E . spatialGE provides different types of data visualization, including gene expression at each spot (“quilt plots”), as well as adaptation of spatial interpolation (“kriging”) to spatial transcriptomics data (transcriptomic surface). F . spatialGE also leverages spatial statistics (Moran’s I, Geary’s C, Getis-Ord Gi) to quantitatively describe heterogeneity within the tumor microenvironment and to explore associations between spatial heterogeneity and clinical oucomes. G . Gene expression deconvolution can also be applied to each spot to detect immune cell types (xCell) and classification of spots as tumor or stroma (ESTIMATE).