A Bayesian modified Ising model for identifying spatially variable genes from spatial transcriptomics data. (24th July 2022)

• Record Type: Journal Article
• Authors: Jiang, Xi; Xiao, Guanghua; Li, Qiwei
• Is Part Of: Statistics in medicine. Volume 41:Number 23(2022)
• Page Start: 4647
• Page End: 4665
• Publication Date: 2022-07-24
• Subjects: dichotomization -- double Metropolis‐Hastings -- multitype point pattern -- spatial correlation -- spatial molecular profiling; Medical statistics -- Periodicals; Statistique médicale -- Périodiques; Statistiques médicales -- Périodiques; 610.727
• DOI: 10.1002/sim.9530
• Languages: English
• ISSNs: 0277-6715

Abstract

Abstract : A recent technology breakthrough in spatial molecular profiling (SMP) has enabled the comprehensive molecular characterizations of single cells while preserving spatial information. It provides new opportunities to delineate how cells from different origins form tissues with distinctive structures and functions. One immediate question in SMP data analysis is to identify genes whose expressions exhibit spatially correlated patterns, called spatially variable (SV) genes. Most current methods to identify SV genes are built upon the geostatistical model with Gaussian process to capture the spatial patterns. However, the Gaussian process models rely on ad hoc kernels that could limit the models' ability to identify complex spatial patterns. In order to overcome this challenge and capture more types of spatial patterns, we introduce a Bayesian approach to identify SV genes via a modified Ising model. The key idea is to use the energy interaction parameter of the Ising model to characterize spatial expression patterns. We use auxiliary variable Markov chain Monte Carlo algorithms to sample from the posterior distribution with an intractable normalizing constant in the model. Simulation studies using both simulated and synthetic data showed that the energy‐based modeling approach led to higher accuracy in detecting SV genes than those kernel‐based methods. When applied to two real spatial transcriptomics (ST) datasets, the proposed method discovered novel spatial patterns that shed light on the biological mechanisms. In summary, the proposed method presents a new perspective for analyzing ST data.