A Bayesian modified Ising model for identifying spatially variable genes from spatial transcriptomics data
A Bayesian modified Ising model for identifying spatially variable genes from spatial transcriptomics data
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DOI：
10.1002/sim.9530
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发表时间：
2022-07-24
影响因子：
2
通讯作者：
Li, Qiwei
中科院分区：
文献类型：
--
作者：
Jiang, Xi;Xiao, Guanghua;Li, Qiwei
空间分子谱（SMP）技术的最新突破使单细胞的全面分子表征成为可能，同时保留了空间信息。它提供了新的机会来描绘来自不同来源的细胞如何形成具有独特结构和功能的组织。在SMP数据分析中，一个直接的问题是确定其表达表现出空间相关模式的基因，称为空间变量（SV）基因。目前的SV基因识别方法大多是建立在高斯过程的地统计模型上，以捕捉SV基因的空间分布模式。然而，高斯过程模型依赖于特别的核，这可能会限制模型识别复杂空间模式的能力。为了克服这一挑战并捕获更多类型的空间模式，我们通过改进的Ising模型引入贝叶斯方法来识别SV基因。关键思想是利用Ising模型的能量相互作用参数来表征空间表达模式。我们使用辅助变量马尔可夫链蒙特卡罗算法从模型中具有难以处理的归一化常数的后验分布中采样。利用模拟数据和合成数据进行的仿真研究表明，基于能量的建模方法比基于核的方法检测SV基因的准确性更高。当应用于两个真实的空间转录组学（ST）数据集时，所提出的方法发现了新的空间模式，揭示了生物学机制。总之，该方法为分析ST数据提供了一个新的视角。
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.