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

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…

• AG
  Australian Government
• UO
  University of Queensland
• MR
  Medical Research Council
• NH
  National Health and Medical Research Council

  Awards: 2001514, GNT2008928, 1163835, 1124503
• QB
  Queensland Brain Institute