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 typesas opposed to the familiar data structures in more classical genomicsbut also with great variation between distinct types of mass spectral measurements derived from different platforms, which may complicate analyses, comparison, and interpretation of results.
Recenzijas
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. (Hsun-Hsien Chang, Computing Reviews, January, 17 , 2018)
Transformation, normalization and batch effect in the analysis of mass
spectrometry data for omics studies.- Automated Alignment of Mass
Spectrometry Data Using Functional Geometry.- The analysis of peptide-centric
mass spectrometry data utilizing information about the expected isotope
distribution.- Probabilistic and likelihood-based methods for protein
identification from MS/MS data.- An MCMC-MRF Algorithm for Incorporating
Spatial Information in IMS Data Processing.- Mass Spectrometry Analysis Using
MALDIquant.- Model-based analysis of quantitative proteomics data with data
independent acquisition mass spectrometry.- The analysis of human serum
albumin proteoforms using compositional framework.- Variability Assessment of
Label-Free LC-MS Experiments for Difference Detection.- Statistical approach
for biomarker discovery using label-free LC-MS data - an overview.- Bayesian
posterior integration for classification ofmass spectrometry data.- Logistic
regression modeling on mass spectrometry data in proteomics case-control
discriminant studies.- Robust and confident predictor selection in
metabolomics.- On the combination of omics data for prediction of binary
Outcomes.- Statistical analysis of lipidomics data in a case-control study.
Susmita Datta received her PhD in statistics from the University of Georgia. She is a tenured professor in the Department of Biostatistics at the University of Florida. Before joining the University of Florida she was a professor in the Department of Bioinformatics and Biostatistics and a distinguished university scholar at the University of Louisville. She is a Fellow of the American Association for the Advancement of Science (AAAS), American Statistical Association (ASA), and an elected member of the International Statistical Institute (ISI). Her research interests include bioinformatics, genomics, proteomics, clustering and classification techniques, infectious disease modeling, statistical issues in population biology, systems biology, survival analysis, and multi state models. She is past president of the Caucus for Women in Statistics, and she actively supports research and education for women in STEM fields.
Bart Mertens received his PhD in statistical sciences from University College London, Department of Statistical Sciences, on statistical analysis methods for spectrometry data. He is currently Associate Professor at the Department of Medical Statistics and Bioinformatics of the Leiden University Medical Centre, where he has been working in both research and consulting for statistical analysis methodology with mass spectrometry proteomic data for more than 10 years.
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