Abstract
Spatial transcriptomics technologies promise to reveal spatial relationships of cell-type composition in complex tissues. However, the development of computational methods that capture the unique properties of single-cell spatial transcriptome data to unveil cell identities remains a challenge. Here, we report SPICEMIX, a new method based on probabilistic, latent variable modeling that enables effective joint analysis of spatial information and gene expression of single cells from spatial transcrip-tome data. Both simulation and real data evaluations demonstrate that SPICEMIX markedly improves upon the inference of cell types compared with existing approaches. Applications of SPICEMIX to single-cell spatial transcriptome data of the mouse primary visual cortex acquired by seqFISH+ and STARmap show that SPICEMIX can enhance the inference of cell identities and uncover potentially new cell subtypes with important biological processes. SPICEMIX is a generalizable framework for analyzing spatial transcriptome data to provide critical insights into the cell-type composition and spatial organization of cells in complex tissues.
Competing Interest Statement
The authors have declared no competing interest.