In the age of big data, imaging techniques such as imaging mass spectrometry (IMS) stand out due to the combination of data size and spatial referencing. However, the data analytic tools readily accessible to investigators often ignore the spatial information or provide results with vague interpretations. We focus on imaging techniques like IMS that collect data along a regular grid and develop methods to automate the process of modeling spatially-referenced imaging data using a process convolution (PC) approach. The PC approach provides a flexible framework to model spatially-referenced geostatistical data, but to make it computationally efficient requires identification of model parameters. We perform simulation studies to define optimal methods for specifying PC parameters and then test those methods using simulations that spike in real spatial information. In doing so, we demonstrate that our methods concurrently account for the spatial information and provide clear interpretations of covariate effects, while maximizing power and maintaining type I error rates near the nominal level. To make these methods accessible, we detail the imagingPC R package. Our approach provides a framework that is flexible and scalable to the level required by many imaging techniques.

在大数据时代，由于数据大小和空间参考的结合，诸如成像质谱（IMS）等成像技术脱颖而出。但是，调查人员容易获得的数据分析工具通常会忽略空间信息，或者提供含糊不清的解释结果。我们专注于像IMS这样的成像技术，它们沿着规则的网格收集数据，并开发出使用过程卷积（PC）方法来自动化对空间参考成像数据进行建模的过程的方法。PC方法提供了一个灵活的框架，可以对空间参考的地统计数据进行建模，但是要使其具有较高的计算效率，则需要识别模型参数。我们进行仿真研究，以定义用于指定PC参数的最佳方法，然后使用在真实空间信息中突增的仿真来测试这些方法。通过这样做，我们证明了我们的方法同时考虑了空间信息并提供了协变量效应的清晰解释，同时使功效最大化并使I型错误率保持在名义水平附近。为了使这些方法易于使用，我们详细介绍了ImagingPC R软件包。我们的方法提供了一个灵活且可扩展到许多成像技术所需水平的框架。