PT  - JOURNAL ARTICLE
AU  - Carter Allen
AU  - Yuzhou Chang
AU  - Brian Neelon
AU  - Won Chang
AU  - Hang J. Kim
AU  - Zihai Li
AU  - Qin Ma
AU  - Dongjun Chung
TI  - A Bayesian Multivariate Mixture Model for Spatial Transcriptomics Data
AID  - 10.1101/2021.06.23.449615
DP  - 2021 Jan 01
TA  - bioRxiv
PG  - 2021.06.23.449615
4099  - http://biorxiv.org/content/early/2021/06/24/2021.06.23.449615.short
4100  - http://biorxiv.org/content/early/2021/06/24/2021.06.23.449615.full
AB  - High throughput spatial transcriptomics (HST) is a rapidly emerging class of experimental technologies that allow for profiling gene expression in tissue samples at or near single-cell resolution while retaining the spatial location of each sequencing unit within the tissue sample. Through analyzing HST data, we seek to identify sub-populations within a tissue sample that reflect distinct cell types or states. Existing methods either ignore the spatial heterogeneity in gene expression profiles, fail to account for important statistical features such as skewness, or are heuristic-based network clustering methods that lack the inferential benefits of statistical modeling. To address this gap, we develop SPRUCE: a Bayesian spatial multivariate finite mixture model based on multivariate skew-normal distributions, which is capable of identifying distinct cellular sub-populations in HST data. We further implement a novel combination of Pólya–Gamma data augmentation and spatial random effects to infer spatially correlated mixture component membership probabilities without relying on approximate inference techniques. Via a simulation study, we demonstrate the detrimental inferential effects of ignoring skewness or spatial correlation in HST data. Using publicly available human brain HST data, SPRUCE outperforms existing methods in recovering expertly annotated brain layers. Finally, our application of SPRUCE to human breast cancer HST data indicates that SPRUCE can distinguish distinct cell populations within the tumor microenvironment.Competing Interest StatementThe authors have declared no competing interest.