Computational Methods for the Analysis of Mass Spectrometry Imaging Data PDF Download
Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Computational Methods for the Analysis of Mass Spectrometry Imaging Data PDF full book. Access full book title Computational Methods for the Analysis of Mass Spectrometry Imaging Data by Purva Kulkarni. Download full books in PDF and EPUB format.
Author: Purva Kulkarni
Publisher:
ISBN:
Category :
Languages : en
Pages :
Get Book Here
Book Description
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: Purva Kulkarni
Publisher:
ISBN:
Category :
Languages : en
Pages :
Get Book Here
Book Description
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: Robin Schmid
Publisher:
ISBN:
Category :
Languages : en
Pages :
Get Book Here
Book Description
Author: Alexander Dexter
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
Get Book Here
Book Description
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
ISBN: 3319449818
Category : Medical
Languages : en
Pages : 214
Get Book Here
Book Description
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: Karl Michael Stefan Hanselmann
Publisher:
ISBN:
Category :
Languages : en
Pages : 172
Get Book Here
Book Description
Author: Jocelyne Bruand
Publisher:
ISBN: 9781267434678
Category :
Languages : en
Pages : 129
Get Book Here
Book Description
Imaging to assess the presence and localization of specific molecules in tissues and cells is central to the study of biological systems. However, most imaging technologies focus on specific molecules of interest. An exciting recent advance is the development of Mass Spectrometry Imaging (MSI), which allows for the generation of topographic 2D maps for various endogenous and some exogenous molecules (e.g., drugs and their metabolites) without prior specification. Advances in MSI have transformative potential, allowing us to answer questions about the localization of proteins, peptides, lipids, metabolites and other molecules. To help MSI realize its potential, we describe several algorithms for the analysis of MSI data from different angles. In a first problem, we start with the premise that we are given a pre-defined region of interest (ROI) based on the morphology of the tissue or organism. We aim to find and identify molecules that are specifically expressed in the ROI. We solve this problem by using a statistics for localization specificity and a novel pipeline for identification. Next, we extend the approach above to segment the MSI dataset into consistent regions of interest, and for each segment, we identify a molecular signature: a collection of peaks that are preferentially expressed in that segment. Our implementation, called AMASS (Algorithm for MSI Analysis by Semi-supervised Segmentation), relies on the discriminating power of a molecular signal instead of its intensity as a key feature, uses an internal consistency measure for validation, and allows significant user interaction and supervision as options. A third problem examines the comparative analysis of many MSI datasets. We describe a new method which, given a set of pertinent query molecules, finds, in each dataset, all molecules that have a similar spatial distribution and clusters the datasets based on the resulting molecular signatures. The approach has the potential to identify unknown relationships between multiple data acquisitions. Finally, we briefly touch on the peptide identification from on-tissue MS/MS data using a spectral library specific to MALDI imaging peptide identification. Our preliminary results highlight the potential of this approach.
Author: Fei Gao
Publisher: Springer
ISBN: 3319184318
Category : Technology & Engineering
Languages : en
Pages : 203
Get Book Here
Book Description
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: Sven Hans-Joachim Giese
Publisher:
ISBN:
Category :
Languages : en
Pages :
Get Book Here
Book Description
Author: Francisco A. Gómez Vela
Publisher: MDPI
ISBN: 3039437712
Category : Medical
Languages : en
Pages : 222
Get Book Here
Book Description
In recent decades, new technologies have made remarkable progress in helping to understand biological systems. Rapid advances in genomic profiling techniques such as microarrays or high-performance sequencing have brought new opportunities and challenges in the fields of computational biology and bioinformatics. Such genetic sequencing techniques allow large amounts of data to be produced, whose analysis and cross-integration could provide a complete view of organisms. As a result, it is necessary to develop new techniques and algorithms that carry out an analysis of these data with reliability and efficiency. This Special Issue collected the latest advances in the field of computational methods for the analysis of gene expression data, and, in particular, the modeling of biological processes. Here we present eleven works selected to be published in this Special Issue due to their interest, quality, and originality.
Author: Laura M Cole
Publisher: Springer Nature
ISBN: 1071633198
Category : Science
Languages : en
Pages : 217
Get Book Here
Book Description
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.
