Harnessing the Potential of Spatial Statistics for Spatial Omics Data with pasta

TL;DR

This paper explores how spatial statistics can be applied to spatial omics data, highlighting advantages and challenges, and introduces the R package 'pasta' to demonstrate practical applications in biology.

Contribution

It presents a comprehensive discussion on applying spatial statistical methods to spatial omics data and introduces the 'pasta' R package for practical analysis.

Findings

Spatial statistics provide valuable insights into spatial omics data.

The 'pasta' package facilitates the application of spatial statistical tools in biology.

Challenges include data heterogeneity and methodological nuances.

Abstract

Spatial omics assays allow for the molecular characterisation of cells in their spatial context. Notably, the two main technological streams, imaging-based and high-throughput sequencing-based, can give rise to very different data modalities. The characteristics of the two data types are well known in adjacent fields such as spatial statistics as point patterns and lattice data, and there is a wide range of tools available. This paper discusses the application of spatial statistics to spatially-resolved omics data and in particular, discusses various advantages, challenges, and nuances. This work is accompanied by a vignette, pasta, that showcases the usefulness of spatial statistics in biology using several R packages.