Design and Statistical Analysis of Mass Spectrometry Imaging Experiments

The goal of mass spectrometry-based imaging (MSI) is to characterize the chemical composition of biological samples such as tissues in spatial resolution, and changes in the composition between conditions such as the status of a disease. A single mass spectrometry image is a collection of mass spectra at gridded locations on a tissue. When acquired over multiple biological individuals, tissues, and conditions, the resulting datasets are tremendously complex in both scale and in the stochastic structure of the quantified spectral features. Accurate characterization of the biological and technical variation in these experiments is therefore key for deriving sensitive and reproducible biological conclusions. This dissertation develops a rigorous statistical framework, based on hierarchical Bayesian spatial models, that accurately represents arbitrary complex experimental designs, in (1) structure of the conditions or stresses, (2) the origin of the biological tissues, and (3) the within-tissue spatial dependencies in the quantified spectral features. It makes a methodological contribution by expanding spatial statistics to a large-scale, highly multivariate setting. The performance of the proposed statistical and computational methods are evaluated on a series of synthetic datasets with known stochastic structure, and applied to real-life biomedical experiments. Finally, an open-source R package msiCompare is contributed to provide a practical solution for carrying out the statistical inference in MSI experiments