SpatialFeatureExperiment: An S4 Class Bringing Geospatial Tools To Spatial Omics
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Lambda Moses, with the California Institute of Technology, gave a workshop at the BioConductor Conference 2022. Moses’ workshop was onSpatialFeatureExperiment: An S4 Class Bringing Geospatial Tools To Spatial Omics.
From the Bioconductor Conference 2022
Author(s): Lambda Moses, Lior Pachter
Affiliation(s): California Institute of Technology
Spatial localization of gene expression and histological images in spatial transcriptomics present many opportunities unavailable to non-spatial single cell RNA-seq (scRNA-seq). Some existing data analysis packages for spatial transcriptomics have been inspired by spatial statistics originally developed for geospatial data, such as Moran's I, Ripley's K, and Gaussian process regression. However, as existing tools generally regard cells or Visium spots as points and non-spatial methods are still widely used, many such opportunities have been missed. Here we present the SpatialFeatureExperiment (SFE) package, which implements an S4 class extending SpatialExperiment (SPE), to better take advantage of such opportunities. SF is a popular and well-established geospatial package that provides an R interface to the standard Simple Features representation of geometries and the GEOS C++ library for geometry operations.
We demonstrate the opportunities presented by geospatial tools in an analysis of a Visium mouse skeletal muscle regeneration dataset using SFE. We performed spatial autocorrelation, hot spot analyses, and geographically weighted principal component analysis (GWPCA) of myofiber and nuclei morphology and identified histological regions based on these analyses. Spatial autocorrelation of canonical scRNA-seq quality control (QC) metrics such as library size and number of genes detected per spot is also examined. In addition, we relate the morphology and number of nuclei and myofibers in each Visium spot to gene expression. We plan to submit the SpatialFeatureExperiment to Bioconductor; the development version can be accessed on GitHub: https://github.com/pachterlab/Spatial....
Package demo details
https://lambdamoses.github.io/SFEWork...
Source code
https://github.com/lambdamoses/SFEWor...
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