Sampling and ranking spatial transcriptomics data embeddings to identify tissue architecture.

ABSTRACT: Spatial transcriptomics is an emerging technology widely applied to the analyses of tissue architecture and corresponding biological functions. Substantial computational methods have been developed for analyzing spatial transcriptomics data. These methods generate embeddings from gene expression and spatial locations for spot clustering or tissue architecture segmentation. Although the hyperparameters used to produce an embedding can be tuned for a given training set, a fixed embedding has variable performance from case to case due to data distributions. Therefore, selecting an effective embedding for new data in advance would be useful. For this purpose, we developed an embedding evaluation method named message passing-Moran's I with maximum filtering (MP-MIM), which combines message pass

SUBMITTER: Lin Y 

PROVIDER: S-EPMC9411666 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature