SpotGraphs: Graph-based analysis of spatially resolved transcriptional data in R

Introduction Common spatial transcriptomic analysis pipelines in R focus on pre-processing and visualization, while providing limited and indirect methods to leverage true spatially resolved quantification of transcripts. Often, x,y-coordinates in spatial transcriptomics (ST) data are integrated into analysis via “spatially aware” normalization (Salim et al., 2024), clustering methods (Zhao et al., 2021), or the identification of spatially variable genes (Yan et al., 2025). Though useful, these methods do not provide any opportunity for analysts to adjust or interrogate the underlying graphs that define adjacencies between spots in their data. Here, we present SpotGraphs, a package that allows the user a more direct and flexible option to interact with the x,y-coordinates of their ST data in R through the existing igraph infrastructure (Antonov et al., 2023; Csárdi et al., 2025; Csardi & Nepusz, 2006). Similar functionality exists in Python through SquidPy’s graph API (Palla et al., 2022), and we compare results obtained from both packages, demonstrating similar performance. Additionally, we provide a set of tools that are useful for ST data analysis, including a toolkit to filter low quality spots laying on tissue debris, beyond arbitrary thresholds, edit spot-level adjacencies based on spatial clusters, and identify centers or boundaries of user-defined neighborhoods of interest.