Statistical Analysis of Proteomics, Metabolomics, and Lipidomics Data Using Mass Spectrometry

Editors: Susmita Datta, Bart J. A. Mertens



Presents an overview of distinct forms of mass spectrometry for different objectives and their associated statistical analysis from an internationally represented field of interdisciplinary contributors and chapter authors

Covers common aspects of data processing with (mass) spectrometry data, shared between most forms of mass spectrometry platforms—essential knowledge for any computational and statistical analyst seeking to gain expertise in the analysis of such data

Aids biologists and mass-spectrometry data analysts in gaining a basic understanding of key statistical principles and issues of study design

Provides the resources available for comprehensive open-source data analysis programs, such as R and MATLAB, for analyzing mass spectrometry data

This book presents an overview of computational and statistical design and analysis of mass spectrometry-based proteomics, metabolomics, and lipidomics data. This contributed volume provides an introduction to the special aspects of statistical design and analysis with mass spectrometry data for the new omic sciences. The text discusses common aspects of design and analysis between and across all (or most) forms of mass spectrometry, while also providing special examples of application with the most common forms of mass spectrometry. Also covered are applications of computational mass spectrometry not only in clinical study but also in the interpretation of omics data in plant biology studies.

Omics research fields are expected to revolutionize biomolecular research by the ability to simultaneously profile many compounds within either patient blood, urine, tissue, or other biological samples. Mass spectrometry is one of the key analytical techniques used in these new omic sciences. Liquid chromatography mass spectrometry, time-of-flight data, and Fourier transform mass spectrometry are but a selection of the measurement platforms available to the modern analyst. Thus in practical proteomics or metabolomics,  researchers will not only be confronted with new high dimensional data types—as opposed to the familiar data structures in more classical genomics—but also with great variation between distinct types of mass spectral measurements derived from different platforms, which may complicate analyses, comparison, and interpretation of results.

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