Ethan A. G. Baker

This work introduces SCGP, a method for unsupervised identification of tissue structures from spatial omics data, and demonstrates its application to spatial transcriptomics and proteomics datasets.

The paper introduces emObject, a domain-specific data abstraction for spatial biology, demonstrating its utility for various spatial omics data types.

The paper develops a computational framework for spatial omics power analysis using in silico tissue generation and applies it across diverse spatial data modalities.

SiLiCO simulates long read sequencing data from PacBio and Oxford Nanopore to assess sequencing failure mechanics and provide standardized reference datasets.