Feature visualization in large scale imaging mass spectrometry data

Abstract

Imaging mass spectrometry is an innovative technique that combines high-resolution microscopic imaging tools with analytical capabilities of spectrometry. It is a powerful tool to determine the spatial distribution of chemical compounds on complex surfaces, for example, for microscale analysis of cells and tissue in biological samples. The result is a large spectral datacube: a three-dimensional (3D) dataset in which surface position and mass spectral distribution are represented. Analysts try to discover ‘features’: correlations in spectral profiles with a recognizable spatial distribution. Techniques for feature extraction and visualization are developed to improve the exploratory analysis of spectral datacubes. The topic of this work is the design and implementation of feature extraction and visualization techniques in high-resolution imaging spectrometry data. Principal Component Analysis (PCA) is interactively used as a governing approach for feature detection. A wide range of visualization techniques are implemented based on extracted features. The thesis is organized as follows. In Chapter 2 (Spectral analysis: a survey), we provide a brief background survey on spectral analysis. The analysis in the proposed approach is divided into three stages: data acquisition, feature extraction and feature visualization. For each stage, a detailed description of currently applied methods is given. The methods most appropriate for this qualitative approach of analysis are chosen as a specific subset. PCA in combination with a binning function is most suited for extracting features from imaging mass spectral data. Both methods increase the signal-to-noise ratio and reduce the amount of data from imaging mass spectrometry. Chapter 3 (PCA-based feature extraction) compares the quality of three different PCA-based methods for detecting and extracting features from spectral datacubes. We discuss preprocessing of mass spectral data, PCA, additional rotational optimization by VARIMAX, and the PARAFAC method for factor regression. The results are compared quantitatively and qualitatively, together with some performance characteristics. For the quantitative comparison, we used a RMSE metric to compare the methods with ground truth spectra under various noise conditions. For the qualitative comparison, we used three criteria to judge the quality of features in the resulting visualizations. These criteria were applied to interpret the visualizations of features. In Chapter 4 (Feature-based registration), a robust method for automatic featurebased registration is developed. The reduction of uncorrelated noise provided by PCA allows high-resolution imaging mass spectrometry datasets to be automatically aligned and combined for high-resolution analysis of large areas. The results clearly show that the entropy-weighted, mean squared error landscape of chemically matched component images can be used to automatically align high-resolution imaging mass spectrometry datasets. Several spectral datacubes are combined to provide better detection and extraction of features. In Chapter 5 (Feature visualization), a visualization technique is described that utilizes principal components to create transfer functions for volume rendering of a spectral datacube. Two types of spectral datacubes are visualized in 3D by direct volume rendering with these transfer functions to control opacity and highlight extracted features. This enables us to visualize the link between the spectral and spatial characteristics of a feature within the spectral datacube. Applications demonstrate the additional value of these visualizations. Chapter 6 (Feature zooming) presents a technique for spectral and/or spatial zooming of extracted features. This technique is especially useful for spatially extended datasets. The combined spectral datasets are too large in size to be explored and visualized using commonly feature extraction and visualization techniques. Analysts are able to select important features or deselect unimportant features for further analysis on different levels of detail. Moreover, features with unwanted artifacts can be removed to reduce noise. Chapter 7 (High-resolution feature visualization) provides an approach to parametrically visualize features in 3D and at the highest resolution possible. Three parameters control the spectral contribution, the level of detail and the level of density on which an extracted feature is represented. This visualization has feature shapes with well-defined borders and provides more insight into the influences of noise on a mass spectral measurement. It is possible to distinguish different peaks according to their difference in density and spatial position, which would not be possible in a separate spectral or spatial view. An application shows how resulting features are visualized and interpreted. The developed tools generate new possibilities to handle, explore, and visualize the large imaging mass spectrometry datasets. A sensitive, selective, and robust approach for feature extraction enables detection and classification of features in different proteomics applications. Multiple feature shapes with high-resolution characteristics can be compared and examined on different levels of detail. These visualizations can provide more detailed molecular insight in the biochemistry of surfaces and improve classification of peptides and proteins.