Abstract Spatial technologies that query the location of cells in tissues at single-cell resolution are gaining popularity and are likely to become commonplace. The resulting data includes the X, Y coordinates of millions of cells, cell phenotypes and marker or gene expression levels. However, to date, the tools for the analysis of this data are largely underdeveloped, making us severely underpowered in our ability to extract quantifiable information. We have developed SPIAT ( Sp atial I mage A nalysis of T issues), an R package with a suite of data processing, quality control, visualization, data handling and data analysis tools. SPIAT includes our novel algorithms for the identification of cell clusters, cell margins and cell gradients, the calculation of neighbourhood proportions, and algorithms for the prediction of cell phenotypes. SPIAT also includes speedy implementations of the calculation of cell distances and detection of cell communities. This version of SPIAT is directly compatible with Opal multiplex immunohistochemistry images analysed through the HALO and InForm analysis software, but its intuitive implementation allows use with a diversity of platforms. We expect SPIAT to become a user-friendly and speedy go-to package for the spatial analysis of cells in tissues. SPIAT is available on Github: https://github.com/cancer-evolution/SPIAT

SPIAT: An R package for the Spatial Image Analysis of Cells in Tissues