{"product_id":"r-programming-for-mass-spectrometry-isbn-9781119872351","title":"R Programming for Mass Spectrometry","description":"\u003cp\u003e\u003cb\u003eA practical guide to reproducible and high impact mass spectrometry data analysis\u003c\/b\u003e \u003c\/p\u003e\u003cp\u003e\u003ci\u003eR Programming for Mass Spectrometry\u003c\/i\u003e teaches a rigorous and detailed approach to analyzing mass spectrometry data using the R programming language. It emphasizes reproducible research practices and transparent data workflows and is designed for analytical chemists, biostatisticians, and data scientists working with mass spectrometry. \u003c\/p\u003e\u003cp\u003eReaders will find specific algorithms and reproducible examples that address common challenges in mass spectrometry alongside example code and outputs. Each chapter provides practical guidance on statistical summaries, spectral search, chromatographic data processing, and machine learning for mass spectrometry. \u003c\/p\u003e\u003cp\u003e\u003cb\u003eKey topics include: \u003c\/b\u003e \u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eComprehensive data analysis using the Tidyverse in combination with Bioconductor, a widely used software project for the analysis of biological data\u003c\/li\u003e\n\u003cli\u003eProcessing chromatographic peaks, peak detection, and quality control in mass spectrometry data\u003c\/li\u003e\n\u003cli\u003eApplying machine learning techniques, using Tidymodels for supervised and unsupervised learning, as well as for feature engineering and selection, providing modern approaches to data-driven insights\u003c\/li\u003e\n\u003cli\u003eMethods for producing reproducible, publication-ready reports and web pages using RMarkdown\u003c\/li\u003e\n\u003c\/ul\u003e \u003cp\u003e\u003ci\u003eR Programming for Mass Spectrometry\u003c\/i\u003e is an indispensable guide for researchers, instructors, and students. It provides modern tools and methodologies for comprehensive data analysis. With a companion website that includes code and example datasets, it serves as both a practical guide and a valuable resource for promoting reproducible research in mass spectrometry. \u003c\/p\u003e\u003cp\u003eForeword ix\u003c\/p\u003e \u003cp\u003ePreface xi\u003c\/p\u003e \u003cp\u003eAcknowledgments xv\u003c\/p\u003e \u003cp\u003eAbout the Companion Website xvii\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Data Analysis with R 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e1.1 Introduction 1\u003c\/p\u003e \u003cp\u003e1.2 Modern R Programming 2\u003c\/p\u003e \u003cp\u003e1.3 Bioconductor 17\u003c\/p\u003e \u003cp\u003e1.4 Reproducible Data Analysis 18\u003c\/p\u003e \u003cp\u003e1.5 Summary 20\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Introduction to Mass Spectrometry Data Analysis 21\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e2.1 An Example of Mass Spectrometry Data Analysis 21\u003c\/p\u003e \u003cp\u003e2.2 Using the Tidyverse in Mass Spectrometry 25\u003c\/p\u003e \u003cp\u003e2.3 Dynamic Reports with R Markdown 39\u003c\/p\u003e \u003cp\u003e2.4 Summary 40\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Wrangling Mass Spectrometry Data 41\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction 41\u003c\/p\u003e \u003cp\u003e3.2 Accessing Mass Spectrometry Data 41\u003c\/p\u003e \u003cp\u003e3.3 Types of Mass Spectrometry Data 44\u003c\/p\u003e \u003cp\u003e3.4 Result Data 58\u003c\/p\u003e \u003cp\u003e3.5 Example of Wrangling Data: Identification Data 60\u003c\/p\u003e \u003cp\u003e3.6 Wrangling Multiple Data Sources 63\u003c\/p\u003e \u003cp\u003e3.7 Summary 74\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 Exploratory Data Analysis 75\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction 75\u003c\/p\u003e \u003cp\u003e4.2 Exploring Tabular Data 75\u003c\/p\u003e \u003cp\u003e4.3 Exploring Raw Mass Spectrometry Data 83\u003c\/p\u003e \u003cp\u003e4.4 Chromatograms and Other Chemical Separations 101\u003c\/p\u003e \u003cp\u003e4.5 Summary 112\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Data Analysis of Mass Spectra 113\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction 113\u003c\/p\u003e \u003cp\u003e5.2 Molecular Weight Calculations 114\u003c\/p\u003e \u003cp\u003e5.3 Statistical Analysis of Spectra 124\u003c\/p\u003e \u003cp\u003e5.4 Summary 150\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Analysis of Chromatographic Data from Mass Spectrometers 151\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 151\u003c\/p\u003e \u003cp\u003e6.2 Chromatographic Peak Basics 151\u003c\/p\u003e \u003cp\u003e6.3 Fundamentals of Peak Detection 160\u003c\/p\u003e \u003cp\u003e6.4 Frequency Analysis 188\u003c\/p\u003e \u003cp\u003e6.5 Quantification 207\u003c\/p\u003e \u003cp\u003e6.6 Quality Control 226\u003c\/p\u003e \u003cp\u003e6.7 Summary 229\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Machine Learning in Mass Spectrometry 231\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction 231\u003c\/p\u003e \u003cp\u003e7.2 Tidymodels 232\u003c\/p\u003e \u003cp\u003e7.3 Feature Conditioning, Engineering, and Selection 233\u003c\/p\u003e \u003cp\u003e7.4 Unsupervised Learning 244\u003c\/p\u003e \u003cp\u003e7.5 Using Unsupervised Methods with Mass Spectra 247\u003c\/p\u003e \u003cp\u003e7.6 Supervised Learning 256\u003c\/p\u003e \u003cp\u003e7.7 Explaining Machine Learning Models 283\u003c\/p\u003e \u003cp\u003e7.8 Summary 287\u003c\/p\u003e \u003cp\u003eReferences 289\u003c\/p\u003e \u003cp\u003eIndex 301\u003c\/p\u003e  \u003cp\u003e\u003cb\u003eRandall K. Julian, Jr., PhD,\u003c\/b\u003e is the founder and CEO of Indigo BioAutomation, where his team uses cloud computing, signal processing, and advanced algorithms to automatically analyze millions of mass spectrometry samples for diagnostic and hospital labs. Indigo’s technology powers advanced diagnostic instruments worldwide. Dr. Julian also leads Indigo’s AI\/ML research team and is an Adjunct Professor of Chemistry at Purdue University. He co-developed several short courses on using R for mass spectrometry, which he teaches at international scientific conferences.   \u003c\/p\u003e\u003cp\u003e\u003cb\u003eA practical guide to reproducible and high impact mass spectrometry data analysis\u003c\/b\u003e \u003c\/p\u003e\u003cp\u003e\u003ci\u003eR Programming for Mass Spectrometry\u003c\/i\u003e teaches a rigorous and detailed approach to analyzing mass spectrometry data using the R programming language. It emphasizes reproducible research practices and transparent data workflows and is designed for analytical chemists, biostatisticians, and data scientists working with mass spectrometry. \u003c\/p\u003e\u003cp\u003eReaders will find specific algorithms and reproducible examples that address common challenges in mass spectrometry alongside example code and outputs. Each chapter provides practical guidance on statistical summaries, spectral search, chromatographic data processing, and machine learning for mass spectrometry. \u003c\/p\u003e\u003cp\u003e\u003cb\u003eKey topics include: \u003c\/b\u003e \u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eComprehensive data analysis using the Tidyverse in combination with Bioconductor, a widely used software project for the analysis of biological data\u003c\/li\u003e\n\u003cli\u003eProcessing chromatographic peaks, peak detection, and quality control in mass spectrometry data\u003c\/li\u003e\n\u003cli\u003eApplying machine learning techniques, using Tidymodels for supervised and unsupervised learning, as well as for feature engineering and selection, providing modern approaches to data-driven insights\u003c\/li\u003e\n\u003cli\u003eMethods for producing reproducible, publication-ready reports and web pages using RMarkdown\u003c\/li\u003e\n\u003c\/ul\u003e \u003cp\u003e\u003ci\u003eR Programming for Mass Spectrometry\u003c\/i\u003e is an indispensable guide for researchers, instructors, and students. It provides modern tools and methodologies for comprehensive data analysis. With a companion website that includes code and example datasets, it serves as both a practical guide and a valuable resource for promoting reproducible research in mass spectrometry.\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47989902344421,"sku":"NP9781119872351","price":145.0,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781119872351.jpg?v=1761785853","url":"https:\/\/k12savings.com\/products\/r-programming-for-mass-spectrometry-isbn-9781119872351","provider":"K12savings","version":"1.0","type":"link"}