Cun-Ha Dan

Spatially orthogonal factor models for spatial transcriptomics and remote sensing data

Principal component analyses are often applied to spatial data towards inference on latent modes of spatial variation. These analyses are widespread across domains including spatial transcriptomics and environmental sciences, where the modes of spatial variation are represented by corresponding factors of gene expressi...