Automating a Process Convolution Approach to Account for Spatial Information in Imaging Mass Spectrometry Data.

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.