Literature DB >> 33950479

Important Issues in Planning a Proteomics Experiment: Statistical Considerations of Quantitative Proteomic Data.

Karin Schork1,2, Katharina Podwojski1,3, Michael Turewicz1,2, Christian Stephan4,5, Martin Eisenacher6,7.   

Abstract

Mass spectrometry is frequently used in quantitative proteomics to detect differentially regulated proteins. A very important but unfortunately oftentimes neglected part in detecting differential proteins is the statistical analysis. Data from proteomics experiments are usually high-dimensional and hence require profound statistical methods. It is especially important to already correctly design a proteomic experiment before it is conducted in the laboratory. Only this can ensure that the statistical analysis is capable of detecting truly differential proteins afterward. This chapter thus covers aspects of both statistical planning as well as the actual analysis of quantitative proteomic experiments.

Keywords:  Data preprocessing; Experimental design; Fold change; Multiple testing; Normalization; Sample size calculation; Statistical hypothesis test; Volcano plot

Year:  2021        PMID: 33950479     DOI: 10.1007/978-1-0716-1024-4_1

Source DB:  PubMed          Journal:  Methods Mol Biol        ISSN: 1064-3745


  25 in total

Review 1.  Combining results of multiple search engines in proteomics.

Authors:  David Shteynberg; Alexey I Nesvizhskii; Robert L Moritz; Eric W Deutsch
Journal:  Mol Cell Proteomics       Date:  2013-05-29       Impact factor: 5.911

Review 2.  Less label, more free: approaches in label-free quantitative mass spectrometry.

Authors:  Karlie A Neilson; Naveid A Ali; Sridevi Muralidharan; Mehdi Mirzaei; Michael Mariani; Gariné Assadourian; Albert Lee; Steven C van Sluyter; Paul A Haynes
Journal:  Proteomics       Date:  2011-01-17       Impact factor: 3.984

Review 3.  Stable isotope labelling methods in mass spectrometry-based quantitative proteomics.

Authors:  Osama Chahrour; Diego Cobice; John Malone
Journal:  J Pharm Biomed Anal       Date:  2015-04-25       Impact factor: 3.935

Review 4.  Label-free quantification in clinical proteomics.

Authors:  Dominik A Megger; Thilo Bracht; Helmut E Meyer; Barbara Sitek
Journal:  Biochim Biophys Acta       Date:  2013-04-06

Review 5.  Statistical issues in quality control of proteomic analyses: good experimental design and planning.

Authors:  David A Cairns
Journal:  Proteomics       Date:  2011-02-07       Impact factor: 3.984

Review 6.  Biomarker discovery in mass spectrometry-based urinary proteomics.

Authors:  Samuel Thomas; Ling Hao; William A Ricke; Lingjun Li
Journal:  Proteomics Clin Appl       Date:  2016-02-11       Impact factor: 3.494

7.  Design and analysis issues in quantitative proteomics studies.

Authors:  Natasha A Karp; Kathryn S Lilley
Journal:  Proteomics       Date:  2007-09       Impact factor: 3.984

8.  Determination of variation parameters as a crucial step in designing TMT-based clinical proteomics experiments.

Authors:  Evelyne Maes; Dirk Valkenborg; Geert Baggerman; Hanny Willems; Bart Landuyt; Liliane Schoofs; Inge Mertens
Journal:  PLoS One       Date:  2015-03-16       Impact factor: 3.240

Review 9.  Advances in mass spectrometry-based clinical biomarker discovery.

Authors:  Christopher A Crutchfield; Stefani N Thomas; Lori J Sokoll; Daniel W Chan
Journal:  Clin Proteomics       Date:  2016-01-07       Impact factor: 3.988

10.  Mass spectrometry-based proteomic techniques to identify cerebrospinal fluid biomarkers for diagnosing suspected central nervous system infections. A systematic review.

Authors:  Tehmina Bharucha; Bevin Gangadharan; Abhinav Kumar; Xavier de Lamballerie; Paul N Newton; Markus Winterberg; Audrey Dubot-Pérès; Nicole Zitzmann
Journal:  J Infect       Date:  2019-08-09       Impact factor: 6.072

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