SPICEMIX: Integrative single-cell spatial modeling of cell identity

Abstract

Spatial transcriptomics technologies promise to reveal spatial relationships of cell-type composition in complex tissues. However, the development of computational methods that can utilize the unique properties of spatial transcriptome data to unveil cell identities remains a challenge. Here, we introduce SO_SCPLOWPICEC_SCPLOWMO_SCPLOWIXC_SCPLOW, a new interpretable method based on probabilistic, latent variable modeling for effective joint analysis of spatial information and gene expression from spatial transcriptome data. Both simulation and real data evaluations demonstrate that SO_SCPLOWPICEC_SCPLOWMO_SCPLOWIXC_SCPLOW markedly improves upon 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 SO_SCPLOWPICEC_SCPLOWMO_SCPLOWIXC_SCPLOW can enhance the inference of complex cell identities, reveal interpretable spatial metagenes, and uncover differentiation trajectories. SO_SCPLOWPICEC_SCPLOWMO_SCPLOWIXC_SCPLOW 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.