Название: R Programming for Mass Spectrometry: Effective and Reproducible Data AnalysisАвтор: Randall K. Julian
Издательство: Wiley
Год: 2025
Страниц: 336
Язык: английский
Формат: epub (true)
Размер: 11.8 MB
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.
The main goal of this book is to show how to analyze mass spectrometry data effectively and reproducibly using the R programming language. Any mass spectrometrist can learn to go beyond spreadsheets and build data analysis solutions using R in a reasonable amount of time. My approach will be like climbing a ladder. Through the lens of mass spectrometry, I will start by introducing native features of the R language. On the next rung are the packages that simplify data storage and retrieval, data manipulation, statistics, and visualization. The next step uses modules originally created to help with molecular biology tasks that also work with data from mass spectrometers. Further up the ladder are mass spectrometry-specific modules used to perform data manipulation and analysis for data generated specifically by mass spectrometers. Beyond that, the ladder goes on, but this book will end on the Machine Learning rung, far from the top.
Because the intended audience for this book is relatively broad, different sections will be of more value to some readers than others, so hopefully, familiar parts can be skipped. The example code is intended to show techniques and methods for analyzing mass spectrometry data that are effective and reproducible. However, within the example code, I hope you will find solutions to common problems that repeatedly appear in the analysis of mass spectrometry data. A word of warning: this is a code-heavy book, and the code is meant to be read. If some of the syntax is unfamiliar, please refer to some of the amazing books on R data analysis available. Along the way, I will provide pointers on where to find more information outside the scope of this book. I hope that some of the references will provide additional reading in areas of interest.
You will learn to analyze mass spectrometry data using R in a way that is widely accepted and supported by the data science community. In addition, you will learn to use various packages beyond the main R program to organize data, programs, and reports. Using examples from mass spectrometry research, you will learn how to understand your data, wrangle it into easy-to-manage structures, perform exploratory data analysis, visualize, and then analyze it to produce reproducible findings. You will also learn how to integrate description and discussion with data and code so you can build web pages and manuscripts about your analysis that other researchers can reproduce.
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.
Contents:
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