Mahlich Yannick, Sohi Harkirat, Piehowski Paul, McDermott Jason E, Gosline Sara J
Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA, USA.
Environmental and Molecular Sciences Division, Pacific Northwest National Laboratory, Richland, WA, USA.
bioRxiv. 2025 Aug 28:2025.08.26.672472. doi: 10.1101/2025.08.26.672472.
Spatial omics is a young and evolving field and as such shows rapid development of novel technologies and analysis methods to measure transcripts, proteins, metabolites, and post-translational modifications at high spatial resolution. These advances in technology have enabled the simultaneous generation of abundance profiles for multiple different omics types and associated microscopy imaging data, as well as their analysis in a spatial context. However, most analytical tools are designed for spatial transcriptomics platforms and are challenging to use in other contexts such as mass spectrometry-based measurements or metagenomics. To this end we present spammR (spatial analysis of multi-omics measurements in R), an R package that enables end-to-end analysis with a specific focus on mass-spectrometry derived spatial omics datasets with (1) smaller sample sizes and spatial sparsity of samples, (2) considerable missingness, and (3) no a-priori knowledge about proteins or genes of interest, relying on a fully data-driven approach.
空间组学是一个年轻且不断发展的领域，因此展现出新技术和分析方法的快速发展，这些技术和方法能够在高空间分辨率下测量转录本、蛋白质、代谢物和翻译后修饰。技术上的这些进步使得能够同时生成多种不同组学类型的丰度图谱以及相关的显微镜成像数据，并在空间背景下对其进行分析。然而，大多数分析工具是为空间转录组学平台设计的，在其他背景下（如基于质谱的测量或宏基因组学）使用具有挑战性。为此，我们展示了spammR（R语言中的多组学测量空间分析），这是一个R包，它能够进行端到端分析，特别关注源自质谱的空间组学数据集，这些数据集具有以下特点：（1）样本量较小且样本空间稀疏；（2）存在大量缺失值；（3）对感兴趣的蛋白质或基因没有先验知识，依靠完全数据驱动的方法。
