Interpolation of Imaging Mass Spectrometry Data by a Window-Based Adversarial Autoencoder Method

Imaging mass spectrometry (IMS) is a technique for simultaneously acquiring the expression and distribution of molecules on the surface of a sample, and it plays a crucial role in spatial omics research. In IMS, the time cost and instrument load required for large data sets must be considered, as IMS typically involves tens of thousands of pixels or more. In this study, we developed a high-resolution method for IMS data reconstruction using a window-based Adversarial Autoencoder (AAE) method. We acquired IMS data from partial cerebellum regions of mice with a pitch size of 75 μm and then down-sampled the data to a pitch size of 150 μm, selecting 22