Department of Biostatistics & Bioinformatics, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, USA.
Biostatistics and Bioinformatics Shared Resource, Moffitt Cancer Center, Tampa, FL, USA.
Sci Rep. 2024 May 14;14(1):10967. doi: 10.1038/s41598-024-61758-0.
Spatial transcriptomics (ST) assays represent a revolution in how the architecture of tissues is studied by allowing for the exploration of cells in their spatial context. A common element in the analysis is delineating tissue domains or "niches" followed by detecting differentially expressed genes to infer the biological identity of the tissue domains or cell types. However, many studies approach differential expression analysis by using statistical approaches often applied in the analysis of non-spatial scRNA data (e.g., two-sample t-tests, Wilcoxon's rank sum test), hence neglecting the spatial dependency observed in ST data. In this study, we show that applying linear mixed models with spatial correlation structures using spatial random effects effectively accounts for the spatial autocorrelation and reduces inflation of type-I error rate observed in non-spatial based differential expression testing. We also show that spatial linear models with an exponential correlation structure provide a better fit to the ST data as compared to non-spatial models, particularly for spatially resolved technologies that quantify expression at finer scales (i.e., single-cell resolution).
空间转录组学（ST）分析代表了组织架构研究的一场革命，它允许在空间背景下探索细胞。分析中的一个常见元素是描绘组织域或“小生境”，然后检测差异表达的基因，以推断组织域或细胞类型的生物学特征。然而，许多研究通过使用通常应用于非空间 scRNA 数据分析的统计方法（例如，双样本 t 检验、Wilcoxon 秩和检验）来进行差异表达分析，因此忽略了 ST 数据中观察到的空间依赖性。在这项研究中，我们表明，使用具有空间相关结构的线性混合模型和空间随机效应可以有效地解释空间自相关，并减少非空间基础差异表达测试中观察到的Ⅰ型错误率膨胀。我们还表明，与非空间模型相比，具有指数相关结构的空间线性模型更适合 ST 数据，特别是对于以更精细的尺度（即单细胞分辨率）定量表达的空间分辨技术。
