Unsupervised and semi-supervised analyses of mass spectrometric images

Abstract

Mass spectrometry imaging (MSI) provides an untargeted characterization of the chemical composition of samples at spatial resolution. With the ability to quantify analytes from small metabolites to proteins in high throughput, and the applicability to various samples, such as tissues, plants, and microbiomes, MSI has become an important tool in spatial metabolomics and proteomics. In the spatial domain, the chemical composition varies across locations in the sample, e.g., due to different morphological structures, different pathological types, and different conditions. In the spectral domain, molecules can have similar spatial distributions, due to technical reasons, e.g., fragment ions, isotopic ions, and sodium adducts of a same analyte, or due to biological reasons, e.g., molecules being specific to a tissue compartment or cell type. The main objectives of MSI data analyses are 1) identify the spatial structure of the tissues defined by chemical compositions; 2) understand the associations of molecules in terms of spatial distribution. The first objective is achieved in part by segmenting MSI images into regions of homogeneous chemical compositions. To overcome the limitations of multivariate segmentation, I developed a method called spatial-DGMM for single ion image segmentation. It performs ion-specific tissue segmentation that accounts for spatial dependence between pixels and generates spatial-structure preserved summaries that are useful for downstream analyses. I evaluated this method on a simulated dataset and two experimental datasets from different ionization sources. In addition, I applied this method to an MSI investigation of metabolic perturbations in rat fibrotic liver tissues, induced by the dose of the combination of nevirapine and galactosamine. The first objective can also be achieved by distinguishing various sample types, such as tumor and non-tumor tissues, by supervised classification of mass spectra. Supervised classification methods require ground truth labels. In practice, while ground-truth labels are available at the whole tissue level, accessing such labels at the sub-tissue level is challenging. To address this issue, I developed mi-CNN, a multiple instance learning-based algorithm that classifies sub-tissue locations under weak supervision from tissue-level annotations. This method uses a convolutional neural network to capture the dependencies between spectral features. I extensively evaluated the proposed methods on datasets of diverse MSI workflows and biological samples and provided in-depth discussions on potential applications in MSI data analyses. The second objective of MSI data analysis can be achieved in part by unsupervised clustering of ion images. I contributed a deep clustering approach for ion images that accounts for both spatial contextual features and noise. This approach improved the interpretability of MSI data in the spectral domain by grouping ions from the same source into a same cluster more frequently than existing methods. In addition, it also improved the downstream interpretation in the spatial domain by using ions in representative cluster profiles as input for segmentation methods. These methods are implemented as a module CardinalNN, which is a part of Cardinal open-source software. The implementations are tested on multiple simulated and experimental datasets. The source codes, the documentation, and the vignettes with case studies are available on Bioconductor. Altogether, my work presents methods and workflows for MSI data analysis in both spatial and spectral domains. It complements the manual explorations and overcomes the limitations of current computational tools. --Author's abstract