SC-MEB: spatial clustering with hidden Markov random field using empirical Bayes.

ABSTRACT: Spatial transcriptomics has been emerging as a powerful technique for resolving gene expression profiles while retaining tissue spatial information. These spatially resolved transcriptomics make it feasible to examine the complex multicellular systems of different microenvironments. To answer scientific questions with spatial transcriptomics and expand our understanding of how cell types and states are regulated by microenvironment, the first step is to identify cell clusters by integrating the available spatial information. Here, we introduce SC-MEB, an empirical Bayes approach for spatial clustering analysis using a hidden Markov random field. We have also derived an efficient expectation-maximization algorithm based on an iterative conditional mode for SC-MEB. In contrast to BayesSpace,

SUBMITTER: Yang Y 

PROVIDER: S-EPMC8690176 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature