Workshop: Exploratory spatial data analysis from single molecules to multiple samples

Imaging based spatial transcriptomics technologies with single molecule resolution such as MERFISH and Xenium are commercialized and increasingly adopted. Meanwhile, many studies have collected spatial data from multiple subjects across biological conditions, sometimes across multiple modalities. While many data analysis tools have been written to integrate data across tissue sections and modalities, fewer spatial data analysis tools go below the single cell resolution to analyze subcellular transcript localization, or go above adjacent sections to compare spatial characteristics of different biological conditions. Different spatial phenomena can be observed at different scales in the same region. Here we present a new version of Bioconductor packages SpatialFeatureExperiment (SFE) and Voyager to perform exploratory spatial data analysis from single molecules to multiple samples and scales in between. We demonstrate new functions to read output from commercial imaging based technologies, coupled with a language agnostic serialization of the SFE object to reformat the transcript spot data for spatial operations and faster reading. The transcript spots are used in spatial point process analyses. Functionalities on images have also been expanded, to support OME-TIFF with BioFormats and to extract data from raster images with vector geometries to relate gene expression data to histology. Between single molecules to tissue sections, we demonstrate spatial binning at different scales at molecular or cellular levels as an exploratory spatial data analysis tool on length scales. For example, Moran’s I can flip signs from single cell resolution to coarser bins. Above the tissue section level, we spatially align a Visium and a MALDI-MSI lipidomics dataset, and find genes that are spatially autocorrelated in wild type but not mutant mice on a high fat diet in a mouse adipose dataset.

Lambda Moses