[PDF] Computational Methods For Mass Spectrometry Data Analysis And Imaging eBook

Author : Purva Kulkarni
Publisher :
Page : pages
File Size : 15,76 MB
Release : 2018
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ISBN :

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A powerful enhancement to MS-based detection is the addition of spatial information to the chemical data; an approach called mass spectrometry imaging (MSI). MSI enables two- and three-dimensional overviews of hundreds of molecular species over a wide mass range in complex biological samples. In this work, we present two computational methods and a workflow that address three different aspects of MSI data analysis: correction of mass shifts, unsupervised exploration of the data and importance of preprocessing and chemometrics to extract meaningful information from the data. We introduce a new lock mass-free recalibration procedure that enables to significantly reduce these mass shift effects in MSI data. Our method exploits similarities amongst peaklist pairs and takes advantage of the spatial context in three different ways, to perform mass correction in an iterative manner. As an extension of this work, we also present a Java-based tool, MSICorrect, that implements our recalibration approach and also allows data visualization. In the next part, an unsupervised approach to rank ion intensity maps based on the abundance of their spatial pattern is presented. Our method provides a score to every ion intensity map based on the abundance of spatial pattern present in it and then ranks all the maps using it. To know which masses exhibit similar spatial distribution, our method uses spatial-similarity based grouping to provide lists of masses that exhibit similar distribution patterns. In the last part, we demonstrate the application of a data preprocessing and multivariate analysis pipeline to a real-world biological dataset. We demonstrate this by applying the full pipeline to a high-resolution MSI dataset acquired from the leaf surface of Black cottonwood (Populus trichocarpa). Application of the pipeline helped in highlighting and visualizing the chemical specificity on the leaf surface.

Author : Alexander Dexter
Publisher :
Page : 0 pages
File Size : 16,53 MB
Release : 2018
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ISBN :

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MSI is a suite of powerful imaging tools that can be used to perform untargeted unlabelled analysis into the distribution of a wide range of molecules from a variety of different sample types. Despite widespread use in numerous different research areas, many aspects of MSI fundamentals remain unknown. Not only are experimental aspects such as desorption and ionisation not always fully understood, but the success (or failure) of many of the computational methods used to mine these data cannot yet be easily evaluated. In this thesis, multivariate analysis methods are used to investigate fundamentals of laser parameters in raster mode MALDI imaging, and DF and CF variables in LESA coupled to FAIMS. Following this, novel methods to evaluate clustering algorithms are described, including multivariate normality testing for distance metric evaluation, and means to generate synthetic data based on multivariate normal distribution sampling. These synthetic data are then used to evaluate a variety of different clustering algorithms used previously in MSI and other fields, and a new, more efficient algorithm using graph based clustering and a two phase subset sampling approach is described. This is then demonstrated on large synthetic and real MSI datasets producing extremely accurate and informative segmentation.

Author : Dong Xu
Publisher : Springer
Page : 214 pages
File Size : 37,5 MB
Release : 2017-09-08
Category : Medical
ISBN : 3319449818

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This book provides a comprehensive overview of different biomedical data types, including both clinical and genomic data. Thorough explanations enable readers to explore key topics ranging from electrocardiograms to Big Data health mining and EEG analysis techniques. Each chapter offers a summary of the field and a sample analysis. Also covered are telehealth infrastructure, healthcare information association rules, methods for mass spectrometry imaging, environmental biodiversity, and the global nonlinear fitness function for protein structures. Diseases are addressed in chapters on functional annotation of lncRNAs in human disease, metabolomics characterization of human diseases, disease risk factors using SNP data and Bayesian methods, and imaging informatics for diagnostic imaging marker selection. With the exploding accumulation of Electronic Health Records (EHRs), there is an urgent need for computer-aided analysis of heterogeneous biomedical datasets. Biomedical data is notorious for its diversified scales, dimensions, and volumes, and requires interdisciplinary technologies for visual illustration and digital characterization. Various computer programs and servers have been developed for these purposes by both theoreticians and engineers. This book is an essential reference for investigating the tools available for analyzing heterogeneous biomedical data. It is designed for professionals, researchers, and practitioners in biomedical engineering, diagnostics, medical electronics, and related industries.

Author : Fei Gao
Publisher : Springer
Page : 203 pages
File Size : 48,73 MB
Release : 2015-06-11
Category : Technology & Engineering
ISBN : 3319184318

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This volume contains original submissions on the development and application of molecular imaging computing. The editors invited authors to submit high-quality contributions on a wide range of topics including, but not limited to: • Image Synthesis & Reconstruction of Emission Tomography (PET, SPECT) and other Molecular Imaging Modalities • Molecular Imaging Enhancement • Data Analysis of Clinical & Pre-clinical Molecular Imaging • Multi-Modal Image Processing (PET/CT, PET/MR, SPECT/CT, etc.) • Machine Learning and Data Mining in Molecular Imaging. Molecular imaging is an evolving clinical and research discipline enabling the visualization, characterization and quantification of biological processes taking place at the cellular and subcellular levels within intact living subjects. Computational methods play an important role in the development of molecular imaging, from image synthesis to data analysis and from clinical diagnosis to therapy individualization. This work will bring readers from academia and industry up to date on the most recent developments in this field.

Author : Laura M Cole
Publisher : Springer Nature
Page : 217 pages
File Size : 39,80 MB
Release : 2023-07-06
Category : Science
ISBN : 1071633198

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This second edition details new and updated chapters on key methodologies and breakthroughs in the mass spectrometry imaging (MSI) field. Chapters guide readers through nano-Desorption Electrospray Ionisation (nDESI), Matrix Assisted Laser Desorption Ionisation-2 (MALDI-2), Laser Ablation - Inductively Coupled Plasma-Mass Spectrometry (LA-ICP-MS) ,Imaging Mass Cytometry (IMC) with a variety of diverse samples including eye tissue, crop analysis, 3D cell culture models, and counterfeit goods analysis. Written in the format of the highly successful Methods in Molecular Biology series, each chapter includes an introduction to the topic, lists necessary materials and reagents, includes tips on troubleshooting and known pitfalls, and step-by-step, readily reproducible protocols. Authoritative and cutting-edge, Imaging Mass Spectrometry: Methods and Protocols, Second Edition aims to be a useful and practical guide to new researchers and experts looking to expand their knowledge.

Author : Fahad Saeed
Publisher : Springer Nature
Page : 146 pages
File Size : 41,7 MB
Release : 2022-09-02
Category : Science
ISBN : 3031019601

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To date, processing of high-throughput Mass Spectrometry (MS) data is accomplished using serial algorithms. Developing new methods to process MS data is an active area of research but there is no single strategy that focuses on scalability of MS based methods. Mass spectrometry is a diverse and versatile technology for high-throughput functional characterization of proteins, small molecules and metabolites in complex biological mixtures. In the recent years the technology has rapidly evolved and is now capable of generating increasingly large (multiple tera-bytes per experiment) and complex (multiple species/microbiome/high-dimensional) data sets. This rapid advance in MS instrumentation must be matched by equally fast and rapid evolution of scalable methods developed for analysis of these complex data sets. Ideally, the new methods should leverage the rich heterogeneous computational resources available in a ubiquitous fashion in the form of multicore, manycore, CPU-GPU, CPU-FPGA, and IntelPhi architectures. The absence of these high-performance computing algorithms now hinders scientific advancements for mass spectrometry research. In this book we illustrate the need for high-performance computing algorithms for MS based proteomics, and proteogenomics and showcase our progress in developing these high-performance algorithms.