Evaluating Batch Correction Methods for Large-Scale Mass Spectrometry Imaging of Heterogeneous Tissues

In mass spectrometry imaging (MSI), the fluctuation in detected ion intensities, which is associated with "technical factors" and not the variability of molecular composition of the sample itself, may be referred to as "batch effects". These batch effects are a major barrier to the more widespread uptake and use of MSI for larger clinical and preclinical studies. In other fields, such as metabolomics and transcriptomics, batch correction methods have been introduced and commonly adopted. These methods aim to mitigate systematic biases introduced by differences in experimental conditions, instruments, or processing batches in high-dimensional data, such as omics or imaging data sets. Mass spectrometry imaging poses additional challenges compared to these fields such as the need to ensure that expected intensity fluctuations throughout a sample, associated with expected spatial variability, are maintained and the inability to randomly introduce quality control spectra. To date, there is no widely adopted approach to the batch correction of mass spectrometry imaging data. In this work, we consider both stabilization of intensity variability and the usefulness of correction methods for spatially resolved data. We present a pixel-by-pixel evaluation of batch correction for mass spectrometry imaging data.