Interactive Visualization of Spatial Omics Neighborhoods

TL;DR

This paper introduces an interactive visualization method for spatial omics data that leverages neighborhood-based features to produce spatially consistent low-dimensional embeddings, aiding in the exploration of spatial gene expression patterns.

Contribution

It presents novel neighborhood-based spatial features and an interactive visualization tool, NBFvis, for improved dimensionality reduction of spatial omic data.

Findings

Neighborhood features improve embedding spatial consistency

Simulation shows advantages over non-neighborhood approaches

Re-analysis demonstrates workflow effectiveness

Abstract

Dimensionality reduction of spatial omic data can reveal shared, spatially structured patterns of expression across a collection of genomic features. We study strategies for discovering and interactively visualizing low-dimensional structure in spatial omic data based on the construction of neighborhood features. We design quantile and network-based spatial features that result in spatially consistent embeddings. A simulation compares embeddings made with and without neighborhood-based featurization, and a re-analysis of [Keren et al., 2019] illustrates the overall workflow. We provide an R package, NBFvis, to support computation and interactive visualization for the proposed dimensionality reduction approach. Code and data for reproducing experiments and analysis is available at https://github.com/XTH1114/NBFvis.