Bayesian nonparametric models for peak identification in MALDI-TOF mass spectroscopy

We present a novel nonparametric Bayesian approach based on L\'{e}vy Adaptive Regression Kernels (LARK) to model spectral data arising from MALDI-TOF (Matrix Assisted Laser Desorption Ionization Time-of-Flight) mass spectrometry. This model-based approach provides identification and quantification of proteins through model parameters that are directly interpretable as the number of proteins, mass and abundance of proteins and peak resolution, while having the ability to adapt to unknown smoothness as in wavelet based methods. Informative prior distributions on resolution are key to distinguishing true peaks from background noise and resolving broad peaks into individual peaks for multiple protein species. Posterior distributions are obtained using a reversible jump Markov chain Monte Carlo algorithm and provide inference about the number of peaks (proteins), their masses and abundance. We show through simulation studies that the procedure has desirable true-positive and false-discovery rates. Finally, we illustrate the method on five example spectra: a blank spectrum, a spectrum with only the matrix of a low-molecular-weight substance used to embed target proteins, a spectrum with known proteins, and a single spectrum and average of ten spectra from an individual lung cancer patient.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS450 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Informed baseline subtraction of proteomic mass spectrometry data aided by a novel sliding window algorithm

Proteomic matrix-assisted laser desorption/ionisation (MALDI) linear time-of-flight (TOF) mass spectrometry (MS) may be used to produce protein profiles from biological samples with the aim of discovering biomarkers for disease. However, the raw protein profiles suffer from several sources of bias or systematic variation which need to be removed via pre-processing before meaningful downstream analysis of the data can be undertaken. Baseline subtraction, an early pre-processing step that removes the non-peptide signal from the spectra, is complicated by the following: (i) each spectrum has, on average, wider peaks for peptides with higher mass-to-charge ratios (m/z), and (ii) the time-consuming and error-prone trial-and-error process for optimising the baseline subtraction input arguments. With reference to the aforementioned complications, we present an automated pipeline that includes (i) a novel `continuous' line segment algorithm that efficiently operates over data with a transformed m/z-axis to remove the relationship between peptide mass and peak width, and (ii) an input-free algorithm to estimate peak widths on the transformed m/z scale. The automated baseline subtraction method was deployed on six publicly available proteomic MS datasets using six different m/z-axis transformations. Optimality of the automated baseline subtraction pipeline was assessed quantitatively using the mean absolute scaled error (MASE) when compared to a gold-standard baseline subtracted signal. Near-optimal baseline subtraction was achieved using the automated pipeline. The advantages of the proposed pipeline include informed and data specific input arguments for baseline subtraction methods, the avoidance of time-intensive and subjective piecewise baseline subtraction, and the ability to automate baseline subtraction completely. Moreover, individual steps can be adopted as stand-alone routines.Comment: 50 pages, 19 figure

A new peak detection algorithm for MALDI mass spectrometry data based on a modified Asymmetric Pseudo-Voigt model

Background Mass Spectrometry (MS) is a ubiquitous analytical tool in biological research and is used to measure the mass-to-charge ratio of bio-molecules. Peak detection is the essential first step in MS data analysis. Precise estimation of peak parameters such as peak summit location and peak area are critical to identify underlying bio-molecules and to estimate their abundances accurately. We propose a new method to detect and quantify peaks in mass spectra. It uses dual-tree complex wavelet transformation along with Stein's unbiased risk estimator for spectra smoothing. Then, a new method, based on the modified Asymmetric Pseudo-Voigt (mAPV) model and hierarchical particle swarm optimization, is used for peak parameter estimation. Results Using simulated data, we demonstrated the benefit of using the mAPV model over Gaussian, Lorentz and Bi-Gaussian functions for MS peak modelling. The proposed mAPV model achieved the best fitting accuracy for asymmetric peaks, with lower percentage errors in peak summit location estimation, which were 0.17% to 4.46% less than that of the other models. It also outperformed the other models in peak area estimation, delivering lower percentage errors, which were about 0.7% less than its closest competitor - the Bi-Gaussian model. In addition, using data generated from a MALDI-TOF computer model, we showed that the proposed overall algorithm outperformed the existing methods mainly in terms of sensitivity. It achieved a sensitivity of 85%, compared to 77% and 71% of the two benchmark algorithms, continuous wavelet transformation based method and Cromwell respectively. Conclusions The proposed algorithm is particularly useful for peak detection and parameter estimation in MS data with overlapping peak distributions and asymmetric peaks. The algorithm is implemented using MATLAB and the source code is freely available at http://mapv.sourceforge.net

A new peak detection algorithm for MALDI mass spectrometry data based on a modified Asymmetric Pseudo-Voigt model

Background: Mass Spectrometry (MS) is a ubiquitous analytical tool in biological research and is used to measure the mass-to-charge ratio of bio-molecules. Peak detection is the essential first step in MS data analysis. Precise estimation of peak parameters such as peak summit location and peak area are critical to identify underlying bio-molecules and to estimate their abundances accurately. We propose a new method to detect and quantify peaks in mass spectra. It uses dual-tree complex wavelet transformation along with Stein's unbiased risk estimator for spectra smoothing. Then, a new method, based on the modified Asymmetric Pseudo-Voigt (mAPV) model and hierarchical particle swarm optimization, is used for peak parameter estimation. Results: Using simulated data, we demonstrated the benefit of using the mAPV model over Gaussian, Lorentz and Bi-Gaussian functions for MS peak modelling. The proposed mAPV model achieved the best fitting accuracy for asymmetric peaks, with lower percentage errors in peak summit location estimation, which were 0.17% to 4.46% less than that of the other models. It also outperformed the other models in peak area estimation, delivering lower percentage errors, which were about 0.7% less than its closest competitor - the Bi-Gaussian model. In addition, using data generated from a MALDI-TOF computer model, we showed that the proposed overall algorithm outperformed the existing methods mainly in terms of sensitivity. It achieved a sensitivity of 85%, compared to 77% and 71% of the two benchmark algorithms, continuous wavelet transformation based method and Cromwell respectively. Conclusions: The proposed algorithm is particularly useful for peak detection and parameter estimation in MS data with overlapping peak distributions and asymmetric peaks. The algorithm is implemented using MATLAB and the source code is freely available at http://mapv.sourceforge.net

Peptide Identification: Refining a Bayesian Stochastic Model

Notwithstanding the challenges associated with different methods of peptide identification, other methods have been explored over the years. The complexity, size and computational challenges of peptide-based data sets calls for more intrusion into this sphere. By relying on the prior information about the average relative abundances of bond cleavages and the prior probability of any specific amino acid sequence, we refine an already developed Bayesian approach in identifying peptides. The likelihood function is improved by adding additional ions to the model and its size is driven by two overall goodness of fit measures. In the face of the complexities associated with our posterior density, a Markov chain Monte Carlo algorithm coupled with simulated annealing is used to simulate candidate choices from the posterior distribution of the peptide sequence, where the peptide with the largest posterior density is estimated as the true peptide

Recent trends in molecular diagnostics of yeast infections : from PCR to NGS

The incidence of opportunistic yeast infections in humans has been increasing over recent years. These infections are difficult to treat and diagnose, in part due to the large number and broad diversity of species that can underlie the infection. In addition, resistance to one or several antifungal drugs in infecting strains is increasingly being reported, severely limiting therapeutic options and showcasing the need for rapid detection of the infecting agent and its drug susceptibility profile. Current methods for species and resistance identification lack satisfactory sensitivity and specificity, and often require prior culturing of the infecting agent, which delays diagnosis. Recently developed high-throughput technologies such as next generation sequencing or proteomics are opening completely new avenues for more sensitive, accurate and fast diagnosis of yeast pathogens. These approaches are the focus of intensive research, but translation into the clinics requires overcoming important challenges. In this review, we provide an overview of existing and recently emerged approaches that can be used in the identification of yeast pathogens and their drug resistance profiles. Throughout the text we highlight the advantages and disadvantages of each methodology and discuss the most promising developments in their path from bench to bedside

Comparison of metaheuristic strategies for peakbin selection in proteomic mass spectrometry data

Mass spectrometry (MS) data provide a promising strategy for biomarker discovery. For this purpose, the detection of relevant peakbins in MS data is currently under intense research. Data from mass spectrometry are challenging to analyze because of their high dimensionality and the generally low number of samples available. To tackle this problem, the scientific community is becoming increasingly interested in applying feature subset selection techniques based on specialized machine learning algorithms. In this paper, we present a performance comparison of some metaheuristics: best first (BF), genetic algorithm (GA), scatter search (SS) and variable neighborhood search (VNS). Up to now, all the algorithms, except for GA, have been first applied to detect relevant peakbins in MS data. All these metaheuristic searches are embedded in two different filter and wrapper schemes coupled with Naive Bayes and SVM classifiers

Clinical microbiology with multi-view deep probabilistic models

Clinical microbiology is one of the critical topics of this century. Identification and discrimination of microorganisms is considered a global public health threat by the main international health organisations, such as World Health Organisation (WHO) or the European Centre for Disease Prevention and Control (ECDC). Rapid spread, high morbidity and mortality, as well as the economic burden associated with their treatment and control are the main causes of their impact. Discrimination of microorganisms is crucial for clinical applications, for instance, Clostridium difficile (C. diff ) increases the mortality and morbidity of healthcare-related infections. Furthermore, in the past two decades, other bacteria, including Klebsiella pneumoniae (K. pneumonia), have demonstrated a significant propensity to acquire antibiotic resistance mechanisms. Consequently, the use of an ineffective antibiotic may result in mortality. Machine Learning (ML) has the potential to be applied in the clinical microbiology field to automatise current methodologies and provide more efficient guided personalised treatments. However, microbiological data are challenging to exploit owing to the presence of a heterogeneous mix of data types, such as real-valued high-dimensional data, categorical indicators, multilabel epidemiological data, binary targets, or even time-series data representations. This problem, which in the field of ML is known as multi-view or multi-modal representation learning, has been studied in other application fields such as mental health monitoring or haematology. Multi-view learning combines different modalities or views representing the same data to extract richer insights and improve understanding. Each modality or view corresponds to a distinct encoding mechanism for the data, and this dissertation specifically addresses the issue of heterogeneity across multiple views. In the probabilistic ML field, the exploitation of multi-view learning is also known as Bayesian Factor Analysis (FA). Current solutions face limitations when handling high-dimensional data and non-linear associations. Recent research proposes deep probabilistic methods to learn hierarchical representations of the data, which can capture intricate non-linear relationships between features. However, some Deep Learning (DL) techniques rely on complicated representations, which can hinder the interpretation of the outcomes. In addition, some inference methods used in DL approaches can be computationally burdensome, which can hinder their practical application in real-world situations. Therefore, there is a demand for more interpretable, explainable, and computationally efficient techniques for highdimensional data. By combining multiple views representing the same information, such as genomic, proteomic, and epidemiologic data, multi-modal representation learning could provide a better understanding of the microbial world. Hence, in this dissertation, the development of two deep probabilistic models, that can handle current limitations in state-of-the-art of clinical microbiology, are proposed. Moreover, both models are also tested in two real scenarios regarding antibiotic resistance prediction in K. pneumoniae and automatic ribotyping of C. diff in collaboration with the Instituto de Investigación Sanitaria Gregorio Marañón (IISGM) and the Instituto Ramón y Cajal de Investigación Sanitaria (IRyCIS). The first presented algorithm is the Kernelised Sparse Semi-supervised Heterogeneous Interbattery Bayesian Analysis (SSHIBA). This algorithm uses a kernelised formulation to handle non-linear data relationships while providing compact representations through the automatic selection of relevant vectors. Additionally, it uses an Automatic Relevance Determination (ARD) over the kernel to determine the input feature relevance functionality. Then, it is tailored and applied to the microbiological laboratories of the IISGM and IRyCIS to predict antibiotic resistance in K. pneumoniae. To do so, specific kernels that handle Matrix-Assisted Laser Desorption Ionization (MALDI)-Time-Of-Flight (TOF) mass spectrometry of bacteria are used. Moreover, by exploiting the multi-modal learning between the spectra and epidemiological information, it outperforms other state-of-the-art algorithms. Presented results demonstrate the importance of heterogeneous models that can analyse epidemiological information and can automatically be adjusted for different data distributions. The implementation of this method in microbiological laboratories could significantly reduce the time required to obtain resistance results in 24-72 hours and, moreover, improve patient outcomes. The second algorithm is a hierarchical Variational AutoEncoder (VAE) for heterogeneous data using an explainable FA latent space, called FA-VAE. The FA-VAE model is built on the foundation of the successful KSSHIBA approach for dealing with semi-supervised heterogeneous multi-view problems. This approach further expands the range of data domains it can handle. With the ability to work with a wide range of data types, including multilabel, continuous, binary, categorical, and even image data, the FA-VAE model offers a versatile and powerful solution for real-world data sets, depending on the VAE architecture. Additionally, this model is adapted and used in the microbiological laboratory of IISGM, resulting in an innovative technique for automatic ribotyping of C. diff, using MALDI-TOF data. To the best of our knowledge, this is the first demonstration of using any kind of ML for C. diff ribotyping. Experiments have been conducted on strains of Hospital General Universitario Gregorio Marañón (HGUGM) to evaluate the viability of the proposed approach. The results have demonstrated high accuracy rates where KSSHIBA even achieved perfect accuracy in the first data collection. These models have also been tested in a real-life outbreak scenario at the HGUGM, where successful classification of all outbreak samples has been achieved by FAVAE. The presented results have not only shown high accuracy in predicting each strain’s ribotype but also revealed an explainable latent space. Furthermore, traditional ribotyping methods, which rely on PCR, required 7 days while FA-VAE has predicted equal results on the same day. This improvement has significantly reduced the time response by helping in the decision-making of isolating patients with hyper-virulent ribotypes of C. diff on the same day of infection. The promising results, obtained in a real outbreak, have provided a solid foundation for further advancements in the field. This study has been a crucial stepping stone towards realising the full potential of MALDI-TOF for bacterial ribotyping and advancing our ability to tackle bacterial outbreaks. In conclusion, this doctoral thesis has significantly contributed to the field of Bayesian FA by addressing its drawbacks in handling various data types through the creation of novel models, namely KSSHIBA and FA-VAE. Additionally, a comprehensive analysis of the limitations of automating laboratory procedures in the microbiology field has been carried out. The shown effectiveness of the newly developed models has been demonstrated through their successful implementation in critical problems, such as predicting antibiotic resistance and automating ribotyping. As a result, KSSHIBA and FA-VAE, both in terms of their technical and practical contributions, signify noteworthy progress both in the clinical and the Bayesian statistics fields. This dissertation opens up possibilities for future advancements in automating microbiological laboratories.La microbiología clínica es uno de los temas críticos de este siglo. La identificación y discriminación de microorganismos se considera una amenaza mundial para la salud pública por parte de las principales organizaciones internacionales de salud, como la Organización Mundial de la Salud (OMS) o el Centro Europeo para la Prevención y Control de Enfermedades (ECDC). La rápida propagación, alta morbilidad y mortalidad, así como la carga económica asociada con su tratamiento y control, son las principales causas de su impacto. La discriminación de microorganismos es crucial para aplicaciones clínicas, como el caso de Clostridium difficile (C. diff ), el cual aumenta la mortalidad y morbilidad de las infecciones relacionadas con la atención médica. Además, en las últimas dos décadas, otros tipos de bacterias, incluyendo Klebsiella pneumoniae (K. pneumonia), han demostrado una propensión significativa a adquirir mecanismos de resistencia a los antibióticos. En consecuencia, el uso de un antibiótico ineficaz puede resultar en un aumento de la mortalidad. El aprendizaje automático (ML) tiene el potencial de ser aplicado en el campo de la microbiología clínica para automatizar las metodologías actuales y proporcionar tratamientos personalizados más eficientes y guiados. Sin embargo, los datos microbiológicos son difíciles de explotar debido a la presencia de una mezcla heterogénea de tipos de datos, tales como datos reales de alta dimensionalidad, indicadores categóricos, datos epidemiológicos multietiqueta, objetivos binarios o incluso series temporales. Este problema, conocido en el campo del aprendizaje automático (ML) como aprendizaje multimodal o multivista, ha sido estudiado en otras áreas de aplicación, como en el monitoreo de la salud mental o la hematología. El aprendizaje multivista combina diferentes modalidades o vistas que representan los mismos datos para extraer conocimientos más ricos y mejorar la comprensión. Cada vista corresponde a un mecanismo de codificación distinto para los datos, y esta tesis aborda particularmente el problema de la heterogeneidad multivista. En el campo del aprendizaje automático probabilístico, la explotación del aprendizaje multivista también se conoce como Análisis de Factores (FA) Bayesianos. Las soluciones actuales enfrentan limitaciones al manejar datos de alta dimensionalidad y correlaciones no lineales. Investigaciones recientes proponen métodos probabilísticos profundos para aprender representaciones jerárquicas de los datos, que pueden capturar relaciones no lineales intrincadas entre características. Sin embargo, algunas técnicas de aprendizaje profundo (DL) se basan en representaciones complejas, dificultando así la interpretación de los resultados. Además, algunos métodos de inferencia utilizados en DL pueden ser computacionalmente costosos, obstaculizando su aplicación práctica. Por lo tanto, existe una demanda de técnicas más interpretables, explicables y computacionalmente eficientes para datos de alta dimensionalidad. Al combinar múltiples vistas que representan la misma información, como datos genómicos, proteómicos y epidemiológicos, el aprendizaje multimodal podría proporcionar una mejor comprensión del mundo microbiano. Dicho lo cual, en esta tesis se proponen el desarrollo de dos modelos probabilísticos profundos que pueden manejar las limitaciones actuales en el estado del arte de la microbiología clínica. Además, ambos modelos también se someten a prueba en dos escenarios reales relacionados con la predicción de resistencia a los antibióticos en K. pneumoniae y el ribotipado automático de C. diff en colaboración con el IISGM y el IRyCIS. El primer algoritmo presentado es Kernelised Sparse Semi-supervised Heterogeneous Interbattery Bayesian Analysis (SSHIBA). Este algoritmo utiliza una formulación kernelizada para manejar correlaciones no lineales proporcionando representaciones compactas a través de la selección automática de vectores relevantes. Además, utiliza un Automatic Relevance Determination (ARD) sobre el kernel para determinar la relevancia de las características de entrada. Luego, se adapta y aplica a los laboratorios microbiológicos del IISGM y IRyCIS para predecir la resistencia a antibióticos en K. pneumoniae. Para ello, se utilizan kernels específicos que manejan la espectrometría de masas Matrix-Assisted Laser Desorption Ionization (MALDI)-Time-Of-Flight (TOF) de bacterias. Además, al aprovechar el aprendizaje multimodal entre los espectros y la información epidemiológica, supera a otros algoritmos de última generación. Los resultados presentados demuestran la importancia de los modelos heterogéneos ya que pueden analizar la información epidemiológica y ajustarse automáticamente para diferentes distribuciones de datos. La implementación de este método en laboratorios microbiológicos podría reducir significativamente el tiempo requerido para obtener resultados de resistencia en 24-72 horas y, además, mejorar los resultados para los pacientes. El segundo algoritmo es un modelo jerárquico de Variational AutoEncoder (VAE) para datos heterogéneos que utiliza un espacio latente con un FA explicativo, llamado FA-VAE. El modelo FA-VAE se construye sobre la base del enfoque de KSSHIBA para tratar problemas semi-supervisados multivista. Esta propuesta amplía aún más el rango de dominios que puede manejar incluyendo multietiqueta, continuos, binarios, categóricos e incluso imágenes. De esta forma, el modelo FA-VAE ofrece una solución versátil y potente para conjuntos de datos realistas, dependiendo de la arquitectura del VAE. Además, este modelo es adaptado y utilizado en el laboratorio microbiológico del IISGM, lo que resulta en una técnica innovadora para el ribotipado automático de C. diff utilizando datos MALDI-TOF. Hasta donde sabemos, esta es la primera demostración del uso de cualquier tipo de ML para el ribotipado de C. diff. Se han realizado experimentos en cepas del Hospital General Universitario Gregorio Marañón (HGUGM) para evaluar la viabilidad de la técnica propuesta. Los resultados han demostrado altas tasas de precisión donde KSSHIBA incluso logró una clasificación perfecta en la primera colección de datos. Estos modelos también se han probado en un brote real en el HGUGM, donde FA-VAE logró clasificar con éxito todas las muestras del mismo. Los resultados presentados no solo han demostrado una alta precisión en la predicción del ribotipo de cada cepa, sino que también han revelado un espacio latente explicativo. Además, los métodos tradicionales de ribotipado, que dependen de PCR, requieren 7 días para obtener resultados mientras que FA-VAE ha predicho resultados correctos el mismo día del brote. Esta mejora ha reducido significativamente el tiempo de respuesta ayudando así en la toma de decisiones para aislar a los pacientes con ribotipos hipervirulentos de C. diff el mismo día de la infección. Los resultados prometedores, obtenidos en un brote real, han sentado las bases para nuevos avances en el campo. Este estudio ha sido un paso crucial hacia el despliegue del pleno potencial de MALDI-TOF para el ribotipado bacteriana avanzado así nuestra capacidad para abordar brotes bacterianos. En conclusión, esta tesis doctoral ha contribuido significativamente al campo del FA Bayesiano al abordar sus limitaciones en el manejo de tipos de datos heterogéneos a través de la creación de modelos noveles, concretamente, KSSHIBA y FA-VAE. Además, se ha llevado a cabo un análisis exhaustivo de las limitaciones de la automatización de procedimientos de laboratorio en el campo de la microbiología. La efectividad de los nuevos modelos, en este campo, se ha demostrado a través de su implementación exitosa en problemas críticos, como la predicción de resistencia a los antibióticos y la automatización del ribotipado. Como resultado, KSSHIBA y FAVAE, tanto en términos de sus contribuciones técnicas como prácticas, representan un progreso notable tanto en los campos clínicos como en la estadística Bayesiana. Esta disertación abre posibilidades para futuros avances en la automatización de laboratorios microbiológicos.Programa de Doctorado en Multimedia y Comunicaciones por la Universidad Carlos III de Madrid y la Universidad Rey Juan CarlosPresidente: Juan José Murillo Fuentes.- Secretario: Jerónimo Arenas García.- Vocal: María de las Mercedes Marín Arriaz