SPICEMIX enables integrative single-cell spatial modeling of cell identity.

ABSTRACT: Spatial transcriptomics can reveal spatially resolved gene expression of diverse cells in complex tissues. However, the development of computational methods that can use the unique properties of spatial transcriptome data to unveil cell identities remains a challenge. Here we introduce SPICEMIX, an interpretable method based on probabilistic, latent variable modeling for joint analysis of spatial information and gene expression from spatial transcriptome data. Both simulation and real data evaluations demonstrate that SPICEMIX markedly improves on the inference of cell types and their spatial patterns compared with existing approaches. By applying to spatial transcriptome data of brain regions in human and mouse acquired by seqFISH+, STARmap and Visium, we show that SPICEMIX can enhance th

SUBMITTER: Chidester B 

PROVIDER: S-EPMC9840703 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature