Oral Presentation 46th Lorne Genome Conference 2025

Cell type co-localization and cell type-specific microenvironment analysis for spatial transcriptomics data (114893)

Mengbo Li 1 , Ning Liu 2 , Yunshun Chen 1
  1. WEHI, Parkville, VIC, Australia
  2. South Australian immunoGENomics Cancer Institute, Adelaide

Spatial transcriptomics technologies are now able to measure spatially resolved gene expression at unprecedented high throughput and resolution. A central aim of analyzing spatial transcriptomics data is to decipher the organization of cells and tissues, spatially and temporally. Main tasks include definition of anatomical regions such as tumors, capture of cell-to-cell co-localization patterns, analysis of cellular microenvironment across different regions, and detection of spatially variable genes, to name but a few. One significant question to consider in data analysis is how we make use of the spatial coordinates of each measurement unit (cells or spots). Hereby we present scider, an R package for cell type co-localization and cell type-specific microenvironment analysis on spatial transcriptomics data. For each cell type, cell coordinates are summarized by the kernel-smoothed spatial density function, whereby cell type-specific regions of interest (ROIs) can be defined at each density level. We are then able to examine cell type co-localization and composition patterns within each ROI, which provides a local description on the cellular microenvironment for the cell type of interest. In addition, scider brings in geometric operations from geospatial analysis, based on which ROIs can also be defined by areas between every two contour lines of the spatial density function. This allows us to perform spatial context-specific differential expression analysis for each cell type, while accounting for the localization of other cell types. We applied scider to a range of spatial technologies and identified several genes and pathways underpinning intra- and inter-tumoral heterogeneity in breast and brain cancer samples.