Download or read book Statistical Approaches to Analyze Mass Spectrometry Data written by Soyoung Ryu and published by . This book was released on 2011 with total page 236 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Computational and Statistical Methods for Protein Quantification by Mass Spectrometry written by Ingvar Eidhammer and published by John Wiley & Sons. This book was released on 2012-12-10 with total page 290 pages. Available in PDF, EPUB and Kindle. Book excerpt: The definitive introduction to data analysis in quantitative proteomics This book provides all the necessary knowledge about mass spectrometry based proteomics methods and computational and statistical approaches to pursue the planning, design and analysis of quantitative proteomics experiments. The author’s carefully constructed approach allows readers to easily make the transition into the field of quantitative proteomics. Through detailed descriptions of wet-lab methods, computational approaches and statistical tools, this book covers the full scope of a quantitative experiment, allowing readers to acquire new knowledge as well as acting as a useful reference work for more advanced readers. Computational and Statistical Methods for Protein Quantification by Mass Spectrometry: Introduces the use of mass spectrometry in protein quantification and how the bioinformatics challenges in this field can be solved using statistical methods and various software programs. Is illustrated by a large number of figures and examples as well as numerous exercises. Provides both clear and rigorous descriptions of methods and approaches. Is thoroughly indexed and cross-referenced, combining the strengths of a text book with the utility of a reference work. Features detailed discussions of both wet-lab approaches and statistical and computational methods. With clear and thorough descriptions of the various methods and approaches, this book is accessible to biologists, informaticians, and statisticians alike and is aimed at readers across the academic spectrum, from advanced undergraduate students to post doctorates entering the field.

Download or read book Statistical Analysis of Proteomics Metabolomics and Lipidomics Data Using Mass Spectrometry written by Susmita Datta and published by Springer. This book was released on 2016-12-15 with total page 294 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents an overview of computational and statistical design and analysis of mass spectrometry-based proteomics, metabolomics, and lipidomics data. This contributed volume provides an introduction to the special aspects of statistical design and analysis with mass spectrometry data for the new omic sciences. The text discusses common aspects of design and analysis between and across all (or most) forms of mass spectrometry, while also providing special examples of application with the most common forms of mass spectrometry. Also covered are applications of computational mass spectrometry not only in clinical study but also in the interpretation of omics data in plant biology studies. Omics research fields are expected to revolutionize biomolecular research by the ability to simultaneously profile many compounds within either patient blood, urine, tissue, or other biological samples. Mass spectrometry is one of the key analytical techniques used in these new omic sciences. Liquid chromatography mass spectrometry, time-of-flight data, and Fourier transform mass spectrometry are but a selection of the measurement platforms available to the modern analyst. Thus in practical proteomics or metabolomics, researchers will not only be confronted with new high dimensional data types—as opposed to the familiar data structures in more classical genomics—but also with great variation between distinct types of mass spectral measurements derived from different platforms, which may complicate analyses, comparison, and interpretation of results.

Download or read book Statistical Methods for the Analysis of Mass Spectrometry Data written by Yuping Wu (Ph. D.) and published by . This book was released on 2006 with total page 312 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Statistical Methods for the Analysis of Mass Spectrometry based Proteomics Data written by Xuan Wang and published by . This book was released on 2012 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Proteomics serves an important role at the systems-level in understanding of biological functioning. Mass spectrometry proteomics has become the tool of choice for identifying and quantifying the proteome of an organism. In the most widely used bottom-up approach to MS-based high-throughput quantitative proteomics, complex mixtures of proteins are first subjected to enzymatic cleavage, the resulting peptide products are separated based on chemical or physical properties and then analyzed using a mass spectrometer. The three fundamental challenges in the analysis of bottom-up MS-based proteomics are as follows: (i) Identifying the proteins that are present in a sample, (ii) Aligning different samples on elution (retention) time, mass, peak area (intensity) and etc, (iii) Quantifying the abundance levels of the identified proteins after alignment. Each of these challenges requires knowledge of the biological and technological context that give rise to the observed data, as well as the application of sound statistical principles for estimation and inference. In this dissertation, we present a set of statistical methods in bottom-up proteomics towards protein identification, alignment and quantification. We describe a fully Bayesian hierarchical modeling approach to peptide and protein identification on the basis of MS/MS fragmentation patterns in a unified framework. Our major contribution is to allow for dependence among the list of top candidate PSMs, which we accomplish with a Bayesian multiple component mixture model incorporating decoy search results and joint estimation of the accuracy of a list of peptide identifications for each MS/MS fragmentation spectrum. We also propose an objective criteria for the evaluation of the False Discovery Rate (FDR) associated with a list of identifications at both peptide level, which results in more accurate FDR estimates than existing methods like PeptideProphet. Several alignment algorithms have been developed using different warping functions. However, all the existing alignment approaches suffer from a useful metric for scoring an alignment between two data sets and hence lack a quantitative score for how good an alignment is. Our alignment approach uses "Anchor points" found to align all the individual scan in the target sample and provides a framework to quantify the alignment, that is, assigning a p-value to a set of aligned LC-MS runs to assess the correctness of alignment. After alignment using our algorithm, the p-values from Wilcoxon signed-rank test on elution (retention) time, M/Z, peak area successfully turn into non-significant values. Quantitative mass spectrometry-based proteomics involves statistical inference on protein abundance, based on the intensities of each protein's associated spectral peaks. However, typical mass spectrometry-based proteomics data sets have substantial proportions of missing observations, due at least in part to censoring of low intensities. This complicates intensity-based differential expression analysis. We outline a statistical method for protein differential expression, based on a simple Binomial likelihood. By modeling peak intensities as binary, in terms of "presence / absence", we enable the selection of proteins not typically amendable to quantitative analysis; e.g., "one-state" proteins that are present in one condition but absent in another. In addition, we present an analysis protocol that combines quantitative and presence / absence analysis of a given data set in a principled way, resulting in a single list of selected proteins with a single associated FDR.

Download or read book Mass Spectrometry Data Analysis in Proteomics written by Rune Matthiesen and published by Springer Science & Business Media. This book was released on 2008-02-02 with total page 322 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is an in-depth guide to the theory and practice of analyzing raw mass spectrometry (MS) data in proteomics. The volume outlines available bioinformatics programs, algorithms, and databases available for MS data analysis. General guidelines for data analysis using search engines such as Mascot, Xtandem, and VEMS are provided, with specific attention to identifying poor quality data and optimizing search parameters.

Download or read book Statistical Analysis of Proteomic Data written by Thomas Burger and published by Springer Nature. This book was released on 2022-10-29 with total page 398 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book explores the most important processing steps of proteomics data analysis and presents practical guidelines, as well as software tools, that are both user-friendly and state-of-the-art in chemo- and biostatistics. Beginning with methods to control the false discovery rate (FDR), the volume continues with chapters devoted to software suites for constructing quantitation data tables, missing value related issues, differential analysis software, and more. Written for the highly successful Methods in Molecular Biology series, chapters include the kind of detail and implementation advice that leads to successful results. Authoritative and practical, Statistical Analysis of Proteomic Data: Methods and Tools serves as an ideal guide for proteomics researchers looking to extract the best of their data with state-of-the art tools while also deepening their understanding of data analysis.

Download or read book Statistical Treatment of Analytical Data written by Zeev B. Alfassi and published by John Wiley & Sons. This book was released on 2009-02-12 with total page 272 pages. Available in PDF, EPUB and Kindle. Book excerpt: Statistical techniques have assumed an integral role in both the interpretation and quality assessment of analytical results. In this book the range of statistical methods available for such tasks are described in detail, with the advantages and disadvantages of each technique clarified by use of examples. With a focus on the essential practical application of these techniques the book also includes sufficient theory to facilitate understanding of the statistical principles involved. Statistical Treatment of Analytical Data is written for professional analytical chemists in industry, government and research institutions who require a practical understanding of the application of statistics in day to day activities in the analytical laboratory. It is also for students who require further and detailed information that may not be available directly in a typical undergraduate course.

Download or read book Computational and Statistical Methods for Mass Spectrometry Data Analysis written by Mateusz Łącki and published by . This book was released on 2017 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Słowa kluczowe: obliczeniowa spektrometria mas, modelowanie dysocjacji indukowanej transferem elektronu, obliczenia dokładnej struktury izotopowej, statystyczne modelowanie dekonwolucji sygnału w spektrometrii mas, computational mass spectrometry, modelling electron transfer dissociation, calculations of the isotopic fine structure, statistical model of signal deconvolution in mass spectrometry.

Download or read book Statistical Methods for Analyzing Biological Time Series Data written by Juliet Ndukum and published by . This book was released on 2012 with total page 234 pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation consists of three research works. The abstract is written in three main paragraphs to reflect the three components of the dissertation research. The first research work is focused on NMR-based systems biology. The second component researches on software develop for mass spectrometry data. The third and last research work is based on developing a statistical algorithm for identification of post translational modifications from tandem mass spectrometry data. Part 1: Comparing metabolic profiles under different biological perturbations has become a powerful approach to investigating the functioning of cells. The profiles can be taken as single snapshots of a system, but more information is gained if they are measured longitudinally over time. The results are short time series consisting of relatively sparse data that cannot be analyzed effectively with standard time series techniques, such as autocorrelation and frequency domain methods. In this work, we study longitudinal time series profiles of glucose consumption in the yeast Saccharomyces cerevisae under different temperatures and preconditioning regimens, which we obtained with methods of in vivo nuclear magnetic resonance (NMR) spectroscopy. For the statistical analysis we first fit several nonlinear mixed effect regression models to the longitudinal profiles and then used an ANOVA likelihood ratio method in order to test for significant differences between the profiles. The proposed methods are capable of distinguishing metabolic time trends resulting from different treatments and associate significance levels to these differences. Among several nonlinear mixed-effects regression models tested, a three-parameter logistic function represents the data with highest accuracy. ANOVA and likelihood ratio tests suggest that there are significant differences between the glucose consumption rate profiles for cells that had been-or had not been-preconditioned by heat during growth. Furthermore, pair-wise t-tests reveal significant differences in the longitudinal profiles for glucose consumption rates between optimal conditions and heat stress, optimal and recovery conditions, and heat stress and recovery conditions (p-values

Download or read book Quantitative Medical Data Analysis Using Mathematical Tools And Statistical Techniques written by Don Hong and published by World Scientific. This book was released on 2007-07-10 with total page 364 pages. Available in PDF, EPUB and Kindle. Book excerpt: Quantitative biomedical data analysis is a fast-growing interdisciplinary area of applied and computational mathematics, statistics, computer science, and biomedical science, leading to new fields such as bioinformatics, biomathematics, and biostatistics. In addition to traditional statistical techniques and mathematical models using differential equations, new developments with a very broad spectrum of applications, such as wavelets, spline functions, curve and surface subdivisions, sampling, and learning theory, have found their mathematical home in biomedical data analysis.This book gives a new and integrated introduction to quantitative medical data analysis from the viewpoint of biomathematicians, biostatisticians, and bioinformaticians. It offers a definitive resource to bridge the disciplines of mathematics, statistics, and biomedical sciences. Topics include mathematical models for cancer invasion and clinical sciences, data mining techniques and subset selection in data analysis, survival data analysis and survival models for cancer patients, statistical analysis and neural network techniques for genomic and proteomic data analysis, wavelet and spline applications for mass spectrometry data preprocessing and statistical computing.

Download or read book Novel Bioinformatical and Statistical Methods for the Analysis of Mass Spectrometry based Phosphoproteomic Data written by Martin Klammer and published by . This book was released on 2015 with total page 318 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Statistical Methods for Interpretation of High Resolution Mass Spectra written by Parminder Kaur and published by . This book was released on 2007 with total page 157 pages. Available in PDF, EPUB and Kindle. Book excerpt: Protein study experiments generate thousands of mass spectra, generating an overload of data that necessitates the development of sophisticated data analysis methods. Our work aims at developing the following methods that allow for extraction of biochemically relevant information from mass spectra.

Download or read book Statistical Methods in Analyzing Mass Spectrometry Dataset written by Baolin Wu and published by . This book was released on 2004 with total page 310 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Statistical Inference and Classification for Mass Spectrometry MS Data written by Mourad Atlas and published by . This book was released on 2009 with total page 222 pages. Available in PDF, EPUB and Kindle. Book excerpt: Mass spectrometry has emerged as a core technology for high throughput proteomics profiling in biomedical research. However, the complexity of the data poses new statistical challenges for the analysis. Statistical methods and software developments for analyzing proteomics data are likely to continue as a major area of research in the coming years. In this dissertation, we develop a novel statistical method for analyzing MS data. We propose to use the chemical knowledge regarding isotopic distribution of the peptide molecules along with quantitative modeling to detect chemically valuable peaks from each spectrum. More specifically, a mixture of location-shifted Poisson distribution is fitted to the deamidated isotopic distribution of a peptide molecule in low to moderate molecular weight of the mass spectrum. Maximum likelihood estimation by the expectation-maximization (EM) technique is used to estimate the parameters of the distribution. We then identify the monoisotopic peaks of the spectrum through formal statistical hypotheses testing procedures. Unlike low to moderate range MS data a Poisson distribution is not suitable for high mass ranges of the spectrum data due to symmetric nature of the isotopic distribution. Also, due to preprocessing and pronounced effect of the additional sources of variability, a Poisson approximation to the binomial model to the isotopic distribution may not hold. Therefore, a mixture of location-shifted Normal model is fitted to model each of the deamidated (possibly) isotopic distribution of a mass spectrum. A nonlinear optimization method to maximize the observed data likelihood is applied instead of EM algorithm to estimate the parameters of the distribution. Similar statistical testing procedures are applied for the peak detection method. A study of the effectiveness of our features selection method compared to some other relatively new feature selection methods in classifying case and control samples is explored. Superiority of our method is established in terms of the overall classification accuracy, sensitivity, specificity and area under the receptor operative curve (ROC) curve.

Download or read book Mass Spectrometry Based Metabolomics in Clinical and Herbal Medicines written by Aihua Zhang and published by John Wiley & Sons. This book was released on 2021-08-20 with total page 292 pages. Available in PDF, EPUB and Kindle. Book excerpt: Highlights the importance and benefit of mass spectrometry-based metabolomics for identifying biomarkers that accurately screen for potential biomarkers of diseases Mass spectrometry-based metabolomics offer new opportunities for biomarker discovery in complex diseases and may provide pathological understanding of diseases beyond traditional technologies. It is the systematic analysis of low-molecular-weight metabolites in biological samples and has been applied to discovering and identifying the perturbed pathways. Currently, mass spectrometry-based metabolomics has become an important tool in clinical research and the diagnosis of human disease. Mass Spectrometry-Based Metabolomics in Clinical and Herbal Medicines comprehensively presents the current state, challenges, and applications of high-throughput mass spectrometry-based metabolomics such as metabolites analysis, biomarker discovery, technical challenges, discovery of natural product, mechanism interpretation of action, discovery of active ingredients, clinical application and precision medicine, and enhancing their biomedical value in a real world of biomedicine, shedding light on the potential for spectrometry-based metabolomics. It highlights the value of mass spectrometry-based metabolomics and metabolism to address the complexity of herbal medicines in systems pharmacology, especially, to link phytochemical analysis with the assessment of pharmacological effect and therapeutic potential. Each chapter has been laid out with introduction, method, up-to-date literature, identification of biomarker, and applications Covers the current state, challenges, and applications of high-throughput mass spectrometry-based metabolomics in the discovery of biomarker, active ingredients, natural product, etc. Constitutes a unique and indispensable practical guide for any phytochemistry or related laboratory, and provides hands-on description of new techniques Provides a guide for new practitioners of pharmacologists, pharmacological scholars, drug developers, botanist, researchers of traditional medicines. Mass Spectrometry-Based Metabolomics in Clinical and Herbal Medicines provides a landmark of mass spectrometry-based metabolomics research and a beneficial guideline to graduate students and researchers in academia, industry, and technology transfer organizations in all biomedical science fields.

Download or read book Statistical Methods in Analytical Chemistry written by Peter C. Meier and published by John Wiley & Sons. This book was released on 2005-03-04 with total page 452 pages. Available in PDF, EPUB and Kindle. Book excerpt: This new edition of a successful, bestselling book continues to provide you with practical information on the use of statistical methods for solving real-world problems in complex industrial environments. Complete with examples from the chemical and pharmaceutical laboratory and manufacturing areas, this thoroughly updated book clearly demonstrates how to obtain reliable results by choosing the most appropriate experimental design and data evaluation methods. Unlike other books on the subject, Statistical Methods in Analytical Chemistry, Second Edition presents and solves problems in the context of a comprehensive decision-making process under GMP rules: Would you recommend the destruction of a $100,000 batch of product if one of four repeat determinations barely fails the specification limit? How would you prevent this from happening in the first place? Are you sure the calculator you are using is telling the truth? To help you control these situations, the new edition: * Covers univariate, bivariate, and multivariate data * Features case studies from the pharmaceutical and chemical industries demonstrating typical problems analysts encounter and the techniques used to solve them * Offers information on ancillary techniques, including a short introduction to optimization, exploratory data analysis, smoothing and computer simulation, and recapitulation of error propagation * Boasts numerous Excel files and compiled Visual Basic programs-no statistical table lookups required! * Uses Monte Carlo simulation to illustrate the variability inherent in statistically indistinguishable data sets Statistical Methods in Analytical Chemistry, Second Edition is an excellent, one-of-a-kind resource for laboratory scientists and engineers and project managers who need to assess data reliability; QC staff, regulators, and customers who want to frame realistic requirements and specifications; as well as educators looking for real-life experiments and advanced students in chemistry and pharmaceutical science. From the reviews of Statistical Methods in Analytical Chemistry, First Edition: "This book is extremely valuable. The authors supply many very useful programs along with their source code. Thus, the user can check the authenticity of the result and gain a greater understanding of the algorithm from the code. It should be on the bookshelf of every analytical chemist."-Applied Spectroscopy "The authors have compiled an interesting collection of data to illustrate the application of statistical methods . . . including calibrating, setting detection limits, analyzing ANOVA data, analyzing stability data, and determining the influence of error propagation."-Clinical Chemistry "The examples are taken from a chemical/pharmaceutical environment, but serve as convenient vehicles for the discussion of when to use which test, and how to make sense out of the results. While practical use of statistics is the major concern, it is put into perspective, and the reader is urged to use plausibility checks."-Journal of Chemical Education "The discussion of univariate statistical tests is one of the more thorough I have seen in this type of book . . . The treatment of linear regression is also thorough, and a complete set of equations for uncertainty in the results is presented . . . The bibliography is extensive and will serve as a valuable resource for those seeking more information on virtually any topic covered in the book."-Journal of American Chemical Society "This book treats the application of statistics to analytical chemistry in a very practical manner. [It] integrates PC computing power, testing programs, and analytical know-how in the context of good manufacturing practice/good laboratory practice (GMP/GLP) . . .The book is of value in many fields of analytical chemistry and should be available in all relevant libraries."-Chemometrics and Intelligent Laboratory Systems