  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.
