| Contents |
Transformation, normalization and batch effect in the analysis of mass spectrometry data for omics studies / Bart J. A. Mertens -- Automated Alignment of Mass Spectrometry Data Using Functional Geometry / Anuj Srivastava -- The analysis of peptide-centric mass spectrometry data utilizing information about the expected isotope distribution / Tomasz Burzykowski, Jürgen Claesen, and Dirk Valkenborg -- Probabilistic and likelihood-based methods for protein identification from MS/MS data / Ryan Gill and Susmita Datta -- An MCMC-MRF Algorithm for Incorporating Spatial Information in IMS Data Processing / Lu Xiong and Don Hong -- Mass Spectrometry Analysis Using MALDIquant / Sebastian Gibb and Korbinian Strimmer -- Model-based analysis of quantitative proteomics data with data independent acquisition mass spectrometry / Gengbo Chen, Guo Shou Teo, Guo Ci Teo, and Hyungwon Choi -- The analysis of human serum albumin proteoforms using compositional framework / Shripad Sinari, Dobrin Nedelkov, Peter Reaven, and Dean Billheimer -- Variability Assessment of Label-Free LC-MS Experiments for Difference Detection / Yi Zhao, Tsung-Heng Tsai, Cristina Di Poto, Lewis K. Pannell, Mahlet G. Tadesse, and Habtom W. Ressom -- Statistical approach for biomarker discovery using label-free LC-MS data : an overview / Caroline Truntzer and Patrick Ducoroy -- Bayesian posterior integration for classification of mass spectrometry data / Bobbie-Jo M. Webb-Robertson, Thomas O. Metz, Katrina M. Waters, Qibin Zhang, and Marian Rewers -- Logistic regression modeling on mass spectrometry data in proteomics case-control discriminant studies / Bart J. A. Mertens -- Robust and confident predictor selection in metabolomics / J. A. Hageman, B. Engel, Ric C. H. De Vos, Roland Mumm, Robert D. Hall, H.Jwanro, D. Crouzillat, J.C. Spadone, and F. A. van Eeuwijk -- On the combination of omics data for prediction of binary outcomes / Mar Rodríguez-Girondo, Alexia Kakourou, Pertu Salo, Markus Perola, Wilma E. Mesker, Rob A. E. M. Tollenaar, Jeanine Houwing-Duistermaat, and Bart J. A. Mertens -- Statistical analysis of lipidomics data in a case-control study / Bart J. A. Mertens, Susmita Datta, Thomas Hankemeier, Marian Beekman, and Hae-Won Uh. |
| Abstract |
"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"--Page 4 of cover. |
