R Programming for Mass Spectrometry
Effective and Reproducible Data Analysis
Seiten
2025
John Wiley & Sons Inc (Verlag)
978-1-119-87235-1 (ISBN)
John Wiley & Sons Inc (Verlag)
978-1-119-87235-1 (ISBN)
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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.
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.
Randall K. Julian, Jr., PhD, is founder and CEO of Indigo BioAutomation where his team uses cloud computing and machine learning to automatically analyze millions of mass spectrometry samples for diagnostic and hospital labs. Indigo's technology is also used to drive advanced diagnostic instruments used globally. He is also currently Adjunct Professor of Chemistry at Purdue University where he developed and taught a course on R in Analytical Chemistry. He has developed short courses on the use of R for mass spectrometry which he has taught at international conferences.
| Erscheint lt. Verlag | 13.5.2025 |
|---|---|
| Verlagsort | New York |
| Sprache | englisch |
| Themenwelt | Mathematik / Informatik ► Mathematik |
| Naturwissenschaften ► Biologie ► Biochemie | |
| Naturwissenschaften ► Chemie ► Analytische Chemie | |
| ISBN-10 | 1-119-87235-9 / 1119872359 |
| ISBN-13 | 978-1-119-87235-1 / 9781119872351 |
| Zustand | Neuware |
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