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Get Free AccessAbstract Spatial proteomics technologies have revealed an underappreciated link between the location of cells in tissue microenvironments and the underlying biology and clinical features, but there is significant lag in the development of downstream analysis methods and benchmarking tools. 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 Bass, Angela Pizzolla, Lachlan D. 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 Trigos (2023). Spatial analysis with SPIAT and spaSim to characterize and simulate tissue microenvironments. , 14(1), DOI: https://doi.org/10.1038/s41467-023-37822-0.
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Type
Article
Year
2023
Authors
20
Datasets
0
Total Files
0
Language
en
DOI
https://doi.org/10.1038/s41467-023-37822-0
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