BioC2024

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. Meanwhile, from a data point of view, one significant question is how we make use of the spatial coordinates of each measurement unit (cells or spots). 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 description for the local neighborhood on the cellular microenvironment for the cell type of interest. In addition, scider takes advantage of 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 differential expression analysis within each cell type across diverse tissue contexts, while accounting for the localization of other cell types. scider is publicly available at https://bioconductor.org/packages/release/bioc/html/scider.html.