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emObject: domain specific data abstraction for spatial omics

Baker, E. A. G.; Huang, M.-Y.; Lam, A.; Rahim, M. K.; Bieniosek, M. F.; Wang, B.; Zhang, N. R.; Mayer, A. T.; Trevino, A. E.

2023-06-09 bioinformatics
10.1101/2023.06.07.543950 bioRxiv
Show abstract

Recent advances in high-parameter spatial biology have yielded a rapidly growing new class of biological data, allowing researchers to more comprehensively characterize cellular state and morphology in native tissue context. However, spatial biology lacks a cohesive data abstraction on which to build novel computational tools and algorithms, making it difficult to fully leverage these emergent data. Here, we present emObject, a domain-specific data abstraction for spatial biology data and experiments. We demonstrate the simplicity, flexibility, and extensibility of emObject for a range of spatial omics data types, including the analysis of Visium, MIBI, and CODEX data, as well as for integrated spatial multiomic experiments. The development of emObject is an essential step towards building a unified data science ecosystem for spatial biology and accelerating the pace of scientific discovery.

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