Spatial transcriptomics has revolutionized molecular biology by enabling the capture of gene expression within intact tissue architecture, thereby preserving crucial spatial context often lost in traditional single-cell RNA-sequencing. This breakthrough allows for the study of cellular organization, neighborhood interactions, and functional niches, offering critical insights into complex biological processes. To effectively analyze this complex data, specialized data containers are employed, integrating spatial coordinates with molecular expression. Before extracting biological insights, rigorous preprocessing steps are essential to characterize the tissue accurately. These foundational steps are followed by downstream analyses, including deconvolution, spatial-domain identification, and advanced methods for cell-type-specific differential expression, cell-cell communication, and neighborhood analysis. This chapter provides detailed descriptions of major R and Python object classes tailored for spatial transcriptomics analysis, supporting comprehensive workflows from data management to the derivation of profound biological insights.
Elhossiny et al. (Thu,) studied this question.