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Statistical analysis of proteomics, metabolomics, and lipidomics data using mass spectrometry
Datta S., Mertens B., Springer International Publishing, New York, NY, 2016. 295 pp. Type: Book (978-3-319458-07-6)
Date Reviewed: Jan 17 2018

This is a collection of technical papers dedicated to mass spectrometric data processing. Mass spectrometry is becoming a standard of studying structures and features of biomolecules in the genomic era. The ultimate goal is to fully investigate biological systems from various aspects, such as proteomics, metabolomics, and lipidomics.

The advent of mass spectrometry has led to various fields, such as time-of-flight mass spectrometry, liquid chromatography-mass spectrometry (LC-MS), and Fourier transform ion cyclotron resonance mass spectrometry (FTICRMS or FTMS). Different mass spectrometric measurements generate distinct data, and hence need strategic data analyses. Typically, the data has complex structures, and so far there is no standardized analysis pipeline for mass spectrometry. Thus, this book provides modern technologies of mass spectrometric analysis.

The book can be largely divided into three main parts. The first part is dedicated to signal processing. Raw signals from mass spectrometers do not directly reflect the biology of samples, so researchers have to transform and normalize physical signals to obtain meaningful information. Signals may show different functional features of the specimen, so technologies of aligning signals are important to avoid misinterpretation. Moreover, signals are usually noisy, so denoising algorithms are presented to teach the reader how to handle noise. Further, the detection of true signals is addressed in order to reconstruct the structural and functional features of biomolecules.

The second part of the book is dedicated to data analysis. Once signals are processed, extracting meaningful data is crucial for biology studies. The book presents techniques to perform data analysis, such as estimation, prediction, and classification. The techniques may involve advanced statistics, such as Bayesian statistics. Possible modeling methods are presented as well.

The third part is dedicated to statistically studying biological features from the analyzed data. Given analytic results, the researchers aim to relate analytics with biomedicine. For example, how the protein information provides clues of diseases is an important question for scientific studies. In some examples, biomarkers associating with biological conditions are identified.

In summary, this book provides a comprehensive overview on statistical analyses of mass spectrometric data. The book aggregates cutting-edge methods developed by established researchers and offers readers opportunities of utilizing mass spectrometry to advance biomedical studies. Statisticians, computer scientists, computational biologists, analytical chemists, and data scientists can benefit from reading this book.

Reviewer:  Hsun-Hsien Chang Review #: CR145780 (1803-0136)
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