Emons Martin, Gunz Samuel, Crowell Helena L, Mallona Izaskun, Kuehl Malte, Furrer Reinhard, Robinson Mark D
Department of Molecular Life Sciences and SIB Swiss Institute of Bioinformatics, University of Zurich, 8057 Zurich, Switzerland.
Centro Nacional de Análisis Genómico (CNAG), 08028 Barcelona, Spain.
Nucleic Acids Res. 2025 Sep 5;53(17). doi: 10.1093/nar/gkaf870.
Spatial omics allow for the molecular characterization of cells in their spatial context. Notably, the two main technological streams, imaging-based and high-throughput sequencing-based, give rise to very different data modalities. The characteristics of the two data types are well known in spatial statistics as point patterns and lattice data. In this perspective, we show the versatility of spatial statistics to quantify biological phenomena from local gene expression to tissue organization. As an example, we describe how to use exploratory metrics to address scientific questions in breast cancer, including cellular co-localization and gene co-expression analysis. We discuss technical concepts like window sampling, homogeneity, and weight matrix construction and show their importance. We also provide pasta (https://robinsonlabuzh.github.io/pasta), an extensive analysis vignette for spatial statistics both using R and Python packages with further biology-driven applications.
空间组学能够在空间背景下对细胞进行分子特征分析。值得注意的是，两种主要的技术流派，即基于成像的和基于高通量测序的，产生了截然不同的数据模式。这两种数据类型的特征在空间统计学中作为点模式和格网数据广为人知。从这个角度来看，我们展示了空间统计学在量化从局部基因表达到组织组织的生物现象方面的多功能性。作为一个例子，我们描述了如何使用探索性指标来解决乳腺癌中的科学问题，包括细胞共定位和基因共表达分析。我们讨论了窗口采样、同质性和权重矩阵构建等技术概念，并展示了它们的重要性。我们还提供了pasta（https://robinsonlabuzh.github.io/pasta），这是一个广泛的空间统计分析示例，使用R和Python包，并带有进一步的生物学驱动应用。
