Spatial Analysis with SPIAT and spaSim to Characterize and Simulate Tissue Microenvironments
Here we present SPIAT (spatial image analysis of tissues), a spatial-platform agnostic toolkit with a suite of spatial analysis algorithms, and spaSim (spatial simulator), a simulator of tissue spatial data. SPIAT includes multiple colocalization, neighborhood and spatial heterogeneity metrics to characterize the spatial patterns of cells. Ten spatial metrics of SPIAT are benchmarked using simulated data generated with spaSim. We show how SPIAT can uncover cancer immune subtypes correlated with prognosis in cancer and characterize cell dysfunction in diabetes. Our results suggest SPIAT and spaSim as useful tools for quantifying spatial patterns, identifying and validating correlates of clinical outcomes and supporting method development.
Yuzhou Feng, Tianpei Yang, John Zhu, Mabel Li, Maria Doyle, Volkan Ozcoban, Greg T. Bass, Angela Pizzolla, Lachlan Cain, Sirui Weng, Anupama Pasam, Nikolce Kocovski, Yu-Kuan Huang, Simon P. Keam, Terence P. Speed, Paul J. Neeson, Richard B. Pearson, Shahneen Sandhu, David L. Goode & Anna S. Trigos
Peter MacCallum Cancer Centre, Melbourne, VIC, Australia, Research & Development, CSL Innovation, Parkville, VIC, Australia, The Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, VIC, Australia, Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Parkville, VIC, Australia
Source: Feng et al. Spatial Analysis with SPIAT and spaSim to Characterize and Simulate Tissue Microenvironments. Nature. 2023