Robust mapping of spatiotemporal trajectories and cell-cell interactions in healthy and diseased tissues.

ABSTRACT: Spatial transcriptomics (ST) technologies generate multiple data types from biological samples, namely gene expression, physical distance between data points, and/or tissue morphology. Here we developed three computational-statistical algorithms that integrate all three data types to advance understanding of cellular processes. First, we present a spatial graph-based method, pseudo-time-space (PSTS), to model and uncover relationships between transcriptional states of cells across tissues undergoing dynamic change (e.g. neurodevelopment, brain injury and/or microglia activation, and cancer progression). We further developed a spatially-constrained two-level permutation (SCTP) test to study cell-cell interaction, finding highly interactive tissue regions across thousands of ligand-receptor

SUBMITTER: Pham D 

PROVIDER: S-EPMC10676408 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature