Harnessing the potential of spatial statistics for spatial omics data with <i>pasta</i>

Spatial omics allow for the molecular characterization of cells in their spatial context. Notably, the two main technological streams, imaging-based and high-throughput sequencing-based, give rise to very different data modalities. The characteristics of the two data types are well known in spatial statistics as point patterns and lattice data. In this perspective, we show the versatility of spatial statistics to quantify biological phenomena from local gene expression to tissue organization. As an example, we describe how to use exploratory metrics to address scientific questions in breast cancer, including cellular co-localization and gene co-expression analysis. We discuss technical concepts like window sampling, homogeneity, and weight matrix construction and show their importance. We also provide pasta (https://robinsonlabuzh.github.io/pasta), an extensive analysis vignette for spatial statistics both using R and Python packages with further biology-driven applications.