Automated mass spectrometry-based metabolomics data processing by blind source separation methods

Una de les principals limitacions de la metabolòmica és la transformació de dades crues en informació biològica. A més, la metabolòmica basada en espectrometria de masses genera grans quantitats de dades complexes caracteritzades per la co-elució de compostos i artefactes experimentals. L'objectiu d'aquesta tesi és desenvolupar estratègies automatitzades basades en deconvolució cega del senyal per millorar les capacitats dels mètodes existents que tracten les limitacions de les diferents passes del processament de dades en metabolòmica. L'objectiu d'aquesta tesi és també desenvolupar eines capaces d'executar el flux de treball del processament de dades en metabolòmica, que inclou el preprocessament de dades, deconvolució espectral, alineament i identificació. Com a resultat, tres nous mètodes automàtics per deconvolució espectral basats en deconvolució cega del senyal van ser desenvolupats. Aquests mètodes van ser inclosos en dues eines computacionals que permeten convertir automàticament dades crues en informació biològica interpretable i per tant, permeten resoldre hipòtesis biològiques i adquirir nous coneixements biològics.Una de les principals limitacions de la metabolòmica és la transformació de dades crues en informació biològica. A més, la metabolòmica basada en espectrometria de masses genera grans quantitats de dades complexes caracteritzades per la co-elució de compostos i artefactes experimentals. L'objectiu d'aquesta tesi és desenvolupar estratègies automatitzades basades en deconvolució cega del senyal per millorar les capacitats dels mètodes existents que tracten les limitacions de les diferents passes del processament de dades en metabolòmica. L'objectiu d'aquesta tesi és també desenvolupar eines capaces d'executar el flux de treball del processament de dades en metabolòmica, que inclou el preprocessament de dades, deconvolució espectral, alineament i identificació. Com a resultat, tres nous mètodes automàtics per deconvolució espectral basats en deconvolució cega del senyal van ser desenvolupats. Aquests mètodes van ser inclosos en dues eines computacionals que permeten convertir automàticament dades crues en informació biològica interpretable i per tant, permeten resoldre hipòtesis biològiques i adquirir nous coneixements biològics.Una de las principales limitaciones de la metabolómica es la transformación de datos crudos en información biológica. Además, la metabolómica basada en espectrometría de masas genera grandes cantidades de datos complejos caracterizados por la co-elución de compuestos y artefactos experimentales. El objetivo de esta tesis es desarrollar estrategias automatizadas basadas en deconvolución ciega de la señal para mejorar las capacidades de los métodos existentes que tratan las limitaciones de los diferentes pasos del procesamiento de datos en metabolómica. El objetivo de esta tesis es también desarrollar herramientas capaces de ejecutar el flujo de trabajo del procesamiento de datos en metabolómica, que incluye el preprocessamiento de datos, deconvolución espectral, alineamiento e identificación. Como resultado, tres nuevos métodos automáticos para deconvolución espectral basados en deconvolución ciega de la señal fueron desarrollados. Estos métodos fueron incluidos en dos herramientas computacionales que permiten convertir automáticamente datos crudos en información biológica interpretable y por lo tanto, permiten resolver hipótesis biológicas y adquirir nuevos conocimientos biológicos.One of the major bottlenecks in metabolomics is to convert raw data samples into biological interpretable information. Moreover, mass spectrometry-based metabolomics generates large and complex datasets characterized by co-eluting compounds and with experimental artifacts. This thesis main objective is to develop automated strategies based on blind source separation to improve the capabilities of the current methods that tackle the different metabolomics data processing workflow steps limitations. Also, the objective of this thesis is to develop tools capable of performing the entire metabolomics workflow for GC--MS, including pre-processing, spectral deconvolution, alignment and identification. As a result, three new automated methods for spectral deconvolution based on blind source separation were developed. These methods were embedded into two computation tools able to automatedly convert raw data into biological interpretable information and thus, allow resolving biological answers and discovering new biological insights

Exploratory Data Analysis

In the Food research and production field, system complexity is increasing and several new challenges are emerging every day. This implies a urgent necessity to extract information and obtain models capable of inferring the underlying relationships that link all the variability sources which characterize food or its production process (e.g. compositional profile, processing conditions) to very general end-properties of foodstuff, such as the healthiness, the consumer perception, the link to a territory and the effect of the production chain itself on food. This makes a \u201cdeductive\u201d, theory-driven research approach inefficient, since it is often difficult to formulate hypotheses. Explorative Multivariate Data Analysis methods, together with the most recent analytical instrumentation, offer the possibility to come back to an \u201cinductive\u201d data-driven attitude with a minimum of a priori hypotheses, instead helping in formulating new ones from the direct observation of data. The aim of this Chapter is to offer the reader an overview of the most significant tools which can be used in a preliminary, exploratory phase, ranging from the most classical descriptive statistics methods, to Multivariate Analysis methods, with particular attention to Projection methods. For all techniques, examples are given so that the main advantage of this techniques, that is a direct, graphical representation of data and their characteristics, can be immediately experienced by the reader

Real-time Feedback of B0 Shimming at Ultra High Field MRI

Magnetic resonance imaging(MRI) is moving towards higher and higher field strengths. After 1.5T MRI scanners became commonplace, 3T scanners were introduced and once 3T scanners became commonplace, ultra high field (UHF) scanners were introduced. UHF scanners typically refer to scanners with a field strength of 7T or higher. The number of sites that utilise UHF scanners is slowly growing and the first 7T MRI scanners were recently CE certified for clinical use. Although UHF scanners have the benefit of higher signal-to-noise ratio (SNR), they come with their own challenges. One of the many challenges is the problem of inhomogeneity of the main static magnetic field(B0 field). This thesis addresses multiple aspects associated with the problem of B0 inhomogeneity. The process of homogenising the field is called "shimming". The focus of this thesis is on active shimming where extra shim coils drive DC currents to generate extra magnetic fields superimposed on the main magnetic field to correct for inhomogeneities. In particular, we looked at the following issues: algorithms for calculating optimal shim currents; global static shimming using very high order/degree spherical harmonic-based (VHOS) coils; dynamic slice-wise shimming using VHOS coils compared to a localised multi-coil array shim system; B0 field monitoring using an NMR field camera; characterisation of the shim system using a field camera; and designing a controller based on the shim system model for real-time feedback

Active Wavelength Selection for Chemical Identification Using Tunable Spectroscopy

Spectrometers are the cornerstone of analytical chemistry. Recent advances in microoptics manufacturing provide lightweight and portable alternatives to traditional spectrometers. In this dissertation, we developed a spectrometer based on Fabry-Perot interferometers (FPIs). A FPI is a tunable (it can only scan one wavelength at a time) optical filter. However, compared to its traditional counterparts such as FTIR (Fourier transform infrared spectroscopy), FPIs provide lower resolution and lower signal-noiseratio (SNR). Wavelength selection can help alleviate these drawbacks. Eliminating uninformative wavelengths not only speeds up the sensing process but also helps improve accuracy by avoiding nonlinearity and noise. Traditional wavelength selection algorithms follow a training-validation process, and thus they are only optimal for the target analyte. However, for chemical identification, the identities are unknown. To address the above issue, this dissertation proposes active sensing algorithms that select wavelengths online while sensing. These algorithms are able to generate analytedependent wavelengths. We envision this algorithm deployed on a portable chemical gas platform that has low-cost sensors and limited computation resources. We develop three algorithms focusing on three different aspects of the chemical identification problems. First, we consider the problem of single chemical identification. We formulate the problem as a typical classification problem where each chemical is considered as a distinct class. We use Bayesian risk as the utility function for wavelength selection, which calculates the misclassification cost between classes (chemicals), and we select the wavelength with the maximum reduction in the risk. We evaluate this approach on both synthesized and experimental data. The results suggest that active sensing outperforms the passive method, especially in a noisy environment. Second, we consider the problem of chemical mixture identification. Since the number of potential chemical mixtures grows exponentially as the number of components increases, it is intractable to formulate all potential mixtures as classes. To circumvent combinatorial explosion, we developed a multi-modal non-negative least squares (MMNNLS) method that searches multiple near-optimal solutions as an approximation of all the solutions. We project the solutions onto spectral space, calculate the variance of the projected spectra at each wavelength, and select the next wavelength using the variance as the guidance. We validate this approach on synthesized and experimental data. The results suggest that active approaches are superior to their passive counterparts especially when the condition number of the mixture grows larger (the analytes consist of more components, or the constituent spectra are very similar to each other). Third, we consider improving the computational speed for chemical mixture identification. MM-NNLS scales poorly as the chemical mixture becomes more complex. Therefore, we develop a wavelength selection method based on Gaussian process regression (GPR). GPR aims to reconstruct the spectrum rather than solving the mixture problem, thus, its computational cost is a function of the number of wavelengths. We evaluate the approach on both synthesized and experimental data. The results again demonstrate more accurate and robust performance in contrast to passive algorithms

Observations of Coherence in Oxygenic Photosynthesis.

The field of two dimensional electronic spectroscopy (2DES) is rapidly advancing, both in theory and implementation to tackle increasingly complex and delicate problems. In the past seven years, observations of coherent or wave-like dynamics in 2D spectra of photosynthetic antenna has captured the imagination of many practitioners in the field, from theorists to experimentalists. Two questions are being raised: what is the origin of coherent dynamics in photosynthesis and, more importantly, do they matter for the function of biological systems? For certain photosynthetic antenna systems there is now considerable evidence and theoretical backing to suggest that coherent dynamics have a positive functional impact on energy transfer. Less explored is how such dynamics may influence charge separation, the primary purpose of photosynthetic reaction centers. Coherent signals are typically weak and difficult to resolve from population dynamics. To address this issue, we developed a method to collect 2DES which has dramatically improved the signal to noise over previous implementations. The new method has been applied to the photosystem II reaction center (PSII RC). The PSII RC is the photosynthetic enzyme uniquely capable of using solar energy to split water. As such it is an important system both for basic plant science and renewable energy generation. With this technique, we find eight coherent modes in PSII RC in the first such report of coherent dynamics on this system. Most of the wave-like motions are assigned to be of vibrational character while four are assigned to a mixture of vibrational and electronic character. Based on supporting simulations it is shown that charge separation is enhanced by the inclusion of such mixed character modes.PhDBiophysicsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/109003/1/fullerf_1.pd