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Statistical analysis of proteomics, ...

Datta, Susmita.

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Statistical analysis of proteomics, metabolomics, and lipidomics data using mass spectrometry /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Statistical analysis of proteomics, metabolomics, and lipidomics data using mass spectrometry // Susmita Datta, Bart J.A. Mertens, editors.
其他作者:	Datta, Susmita.
出版者:	Cham, Switzerland :Springer, : 2017.,
面頁冊數:	viii, 295 p. :ill. (some col.) ;24 cm.
標題:	Lipids - Statistical methods. -
ISBN:	9783319458076

Statistical analysis of proteomics, metabolomics, and lipidomics data using mass spectrometry /
Statistical analysis of proteomics, metabolomics, and lipidomics data using mass spectrometry /Susmita Datta, Bart J.A. Mertens, editors. - Cham, Switzerland :Springer,2017. - viii, 295 p. :ill. (some col.) ;24 cm. - Frontiers in probability and the statistical sciences. - Frontiers in probability and the statistical sciences..

Includes bibliographical references.

Transformation, normalization and batch effect in the analysis of mass spectrometry data for omics studies /Bart J. A. Mertens --

"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. Alsocovered 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 expectedto 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-flightdata, and Fourier transform mass spectrometry are but a selectionof 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 ofmass spectral measurements derived from different platforms, which may complicate analyses, comparison, and interpretation of results"--Page 4 of cover.

ISBN: 9783319458076EUR109.99

LCCN: 2016960566Subjects--Topical Terms:

3197768
Lipids
--Statistical methods.

LC Class. No.: QP519.9.M3 / S73 2017

Statistical analysis of proteomics, metabolomics, and lipidomics data using mass spectrometry /
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020 $a 9783319458076 $q (hbk.) : $c EUR109.99
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050 # 4 $a QP519.9.M3 $b S73 2017
245 1 0 $a Statistical analysis of proteomics, metabolomics, and lipidomics data using mass spectrometry / $c Susmita Datta, Bart J.A. Mertens, editors.
260 # $a Cham, Switzerland : $b Springer, $c 2017.
300 $a viii, 295 p. : $b ill. (some col.) ; $c 24 cm.
490 1 $a Frontiers in probability and the statistical sciences
504 $a Includes bibliographical references.
505 0 # $t Transformation, normalization and batch effect in the analysis of mass spectrometry data for omics studies / $r Bart J. A. Mertens -- $t Automated Alignment of Mass Spectrometry Data Using Functional Geometry / $r Anuj Srivastava -- $t The analysis of peptide-centric mass spectrometry data utilizing information about the expected isotope distribution / $r Tomasz Burzykowski, Jürgen Claesen, and Dirk Valkenborg -- $t Probabilistic and likelihood-based methods for protein identification from MS/MS data / $r Ryan Gill and Susmita Datta -- $t An MCMC-MRF Algorithm forIncorporating Spatial Information in IMS Data Processing / $r Lu Xiong and Don Hong -- $t Mass Spectrometry Analysis Using MALDIquant / $r Sebastian Gibb and Korbinian Strimmer -- $t Model-based analysis of quantitative proteomics data with data independent acquisition mass spectrometry / $r Gengbo Chen, Guo Shou Teo, Guo Ci Teo, and Hyungwon Choi -- $t The analysis of humanserum albumin proteoforms using compositional framework / $r Shripad Sinari, Dobrin Nedelkov, Peter Reaven, and Dean Billheimer -- $t Variability Assessment of Label-Free LC-MS Experiments for Difference Detection / $r Yi Zhao, Tsung-Heng Tsai,Cristina Di Poto, Lewis K. Pannell, Mahlet G. Tadesse, and HabtomW. Ressom -- $t Statistical approach for biomarker discovery using label-free LC-MS data : an overview / $r Caroline Truntzer and Patrick Ducoroy -- $t Bayesian posterior integration for classification of mass spectrometry data / $r Bobbie-Jo M. Webb-Robertson, Thomas O. Metz, Katrina M. Waters, Qibin Zhang, and Marian Rewers -- $t Logistic regression modeling on mass spectrometry data in proteomics case-control discriminant studies/ $r Bart J. A. Mertens -- $t Robust and confident predictor selection in metabolomics / $r 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 -- $t On the combination of omics data for prediction of binary outcomes / $r 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 -- $t Statistical analysis of lipidomics data ina case-control study / $r Bart J. A. Mertens, Susmita Datta, ThomasHankemeier, Marian Beekman, and Hae-Won Uh.
520 # $a "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. Alsocovered 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 expectedto 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-flightdata, and Fourier transform mass spectrometry are but a selectionof 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 ofmass spectral measurements derived from different platforms, which may complicate analyses, comparison, and interpretation of results"--Page 4 of cover.
650 # 0 $a Lipids $x Statistical methods. $3 3197768
650 # 0 $a Mass spectrometry. $3 551172
650 # 0 $a Proteomics $x Statistical methods. $3 1559579
700 1 # $a Datta, Susmita. $3 3197766
700 1 # $a Mertens, Bart J. A. $3 3197767
830 0 $a Frontiers in probability and the statistical sciences. $3 2072820