Spatial analysis with SPIAT and spaSim to characterize and simulate tissue microenvironments
- Author(s)
- Feng, Y; Yang, T; Zhu, J; Li, M; Doyle, M; Ozcoban, V; Bass, GT; Pizzolla, A; Cain, L; Weng, S; Pasam, A; Kocovski, N; Huang, YK; Keam, SP; Speed, TP; Neeson, PJ; Pearson, RB; Sandhu, S; Goode, DL; Trigos, AS;
- Details
- Publication Year 2023-05-15,Volume 14,Issue #1,Page 2697
- Journal Title
- Nature Communications
- Publication Type
- Research article
- Abstract
- 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.
- Publisher
- Springer Nature
- Keywords
- Humans; *Neoplasms; Algorithms; Image Processing, Computer-Assisted/methods; Proteomics; Tumor Microenvironment
- Department(s)
- Laboratory Research
- PubMed ID
- 37188662
- Publisher's Version
- https://doi.org/10.1038/s41467-023-37822-0
- Open Access at Publisher's Site
https://doi.org/10.1038/s41467-023-37822-0- Terms of Use/Rights Notice
- Refer to copyright notice on published article.
Creation Date: 2023-08-15 07:31:24
Last Modified: 2023-08-15 07:32:14