A practical guide to reproducible and high impact mass spectrometry data analysis
R Programming for Mass Spectrometry 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.
Readers 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.
Key topics include:
Comprehensive data analysis using the Tidyverse in combination with Bioconductor, a widely used software project for the analysis of biological data
Processing chromatographic peaks, peak detection, and quality control in mass spectrometry data
Applying 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
Methods for producing reproducible, publication-ready reports and web pages using RMarkdown
R Programming for Mass Spectrometry 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.
Sprache
Verlagsort
Zielgruppe
Für höhere Schule und Studium
Für Beruf und Forschung
Produkt-Hinweis
Fadenheftung
Gewebe-Einband
Maße
Höhe: 254 mm
Breite: 206 mm
Dicke: 20 mm
Gewicht
ISBN-13
978-1-119-87235-1 (9781119872351)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Klassifikation
Randall K. Julian, Jr., PhD, 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.
Autor*in
Indigo BioAutomation; Purdue University
Foreword ix
Preface xi
Acknowledgments xv
About the Companion Website xvii
1 Data Analysis with R 1
1.1 Introduction 1
1.2 Modern R Programming 2
1.3 Bioconductor 17
1.4 Reproducible Data Analysis 18
1.5 Summary 20
2 Introduction to Mass Spectrometry Data Analysis 21
2.1 An Example of Mass Spectrometry Data Analysis 21
2.2 Using the Tidyverse in Mass Spectrometry 25
2.3 Dynamic Reports with R Markdown 39
2.4 Summary 40
3 Wrangling Mass Spectrometry Data 41
3.1 Introduction 41
3.2 Accessing Mass Spectrometry Data 41
3.3 Types of Mass Spectrometry Data 44
3.4 Result Data 58
3.5 Example of Wrangling Data: Identification Data 60
3.6 Wrangling Multiple Data Sources 63
3.7 Summary 74
4 Exploratory Data Analysis 75
4.1 Introduction 75
4.2 Exploring Tabular Data 75
4.3 Exploring Raw Mass Spectrometry Data 83
4.4 Chromatograms and Other Chemical Separations 101
4.5 Summary 112
5 Data Analysis of Mass Spectra 113
5.1 Introduction 113
5.2 Molecular Weight Calculations 114
5.3 Statistical Analysis of Spectra 124
5.4 Summary 150
6 Analysis of Chromatographic Data from Mass Spectrometers 151
6.1 Introduction 151
6.2 Chromatographic Peak Basics 151
6.3 Fundamentals of Peak Detection 160
6.4 Frequency Analysis 188
6.5 Quantification 207
6.6 Quality Control 226
6.7 Summary 229
7 Machine Learning in Mass Spectrometry 231
7.1 Introduction 231
7.2 Tidymodels 232
7.3 Feature Conditioning, Engineering, and Selection 233
7.4 Unsupervised Learning 244
7.5 Using Unsupervised Methods with Mass Spectra 247
7.6 Supervised Learning 256
7.7 Explaining Machine Learning Models 283
7.8 Summary 287
References 289
Index 301
