Application of survival analysis methodology to the quantitative analysis of LC-MS proteomics data

Tekwe, CD; Carroll, RJ; Dabney, AR

Application of survival analysis methodology to the quantitative analysis of LC-MS proteomics data

Tekwe, CD Carroll, RJ Dabney, AR

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Publication Type:: Journal Article
Citation:: Bioinformatics, 2012, 28 (15), pp. 1998 - 2003
Issue Date:: 2012-08-01

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Field	Value	Language
dc.contributor.author	Tekwe, CD	en_US
dc.contributor.author	Carroll, RJ	en_US
dc.contributor.author	Dabney, AR	en_US
dc.date.issued	2012-08-01	en_US
dc.identifier.citation	Bioinformatics, 2012, 28 (15), pp. 1998 - 2003	en_US
dc.identifier.issn	1367-4803	en_US
dc.identifier.uri	http://hdl.handle.net/10453/114928
dc.description.abstract	Motivation: Protein abundance in quantitative proteomics is often based on observed spectral features derived from liquid chromatography mass spectrometry (LC-MS) or LC-MS/MS experiments. Peak intensities are largely non-normal in distribution. Furthermore, LC-MS-based proteomics data frequently have large proportions of missing peak intensities due to censoring mechanisms on low-abundance spectral features. Recognizing that the observed peak intensities detected with the LC-MS method are all positive, skewed and often left-censored, we propose using survival methodology to carry out differential expression analysis of proteins. Various standard statistical techniques including non-parametric tests such as the Kolmogorov-Smirnov and Wilcoxon-Mann-Whitney rank sum tests, and the parametric survival model and accelerated failure time-model with log-normal, log-logistic and Weibull distributions were used to detect any differentially expressed proteins. The statistical operating characteristics of each method are explored using both real and simulated datasets.Results: Survival methods generally have greater statistical power than standard differential expression methods when the proportion of missing protein level data is 5% or more. In particular, the AFT models we consider consistently achieve greater statistical power than standard testing procedures, with the discrepancy widening with increasing missingness in the proportions. © The Author 2012. Published by Oxford University Press. All rights reserved.	en_US
dc.relation.ispartof	Bioinformatics	en_US
dc.relation.isbasedon	10.1093/bioinformatics/bts306	en_US
dc.subject.classification	Bioinformatics	en_US
dc.subject.mesh	Humans	en_US
dc.subject.mesh	Diabetes Mellitus	en_US
dc.subject.mesh	Proteins	en_US
dc.subject.mesh	Chromatography, Liquid	en_US
dc.subject.mesh	Models, Statistical	en_US
dc.subject.mesh	Likelihood Functions	en_US
dc.subject.mesh	Statistics, Nonparametric	en_US
dc.subject.mesh	Proteomics	en_US
dc.subject.mesh	Computer Simulation	en_US
dc.subject.mesh	Software	en_US
dc.subject.mesh	Mass Spectrometry	en_US
dc.subject.mesh	Tandem Mass Spectrometry	en_US
dc.title	Application of survival analysis methodology to the quantitative analysis of LC-MS proteomics data	en_US
dc.type	Journal Article
utslib.citation.volume	15	en_US
utslib.citation.volume	28	en_US
utslib.for	01 Mathematical Sciences	en_US
utslib.for	06 Biological Sciences	en_US
utslib.for	08 Information and Computing Sciences	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Science
pubs.organisational-group	/University of Technology Sydney/Faculty of Science/School of Mathematical and Physical Sciences
utslib.copyright.status	closed_access
pubs.issue	15	en_US
pubs.publication-status	Published	en_US
pubs.volume	28	en_US

Abstract:

Motivation: Protein abundance in quantitative proteomics is often based on observed spectral features derived from liquid chromatography mass spectrometry (LC-MS) or LC-MS/MS experiments. Peak intensities are largely non-normal in distribution. Furthermore, LC-MS-based proteomics data frequently have large proportions of missing peak intensities due to censoring mechanisms on low-abundance spectral features. Recognizing that the observed peak intensities detected with the LC-MS method are all positive, skewed and often left-censored, we propose using survival methodology to carry out differential expression analysis of proteins. Various standard statistical techniques including non-parametric tests such as the Kolmogorov-Smirnov and Wilcoxon-Mann-Whitney rank sum tests, and the parametric survival model and accelerated failure time-model with log-normal, log-logistic and Weibull distributions were used to detect any differentially expressed proteins. The statistical operating characteristics of each method are explored using both real and simulated datasets.Results: Survival methods generally have greater statistical power than standard differential expression methods when the proportion of missing protein level data is 5% or more. In particular, the AFT models we consider consistently achieve greater statistical power than standard testing procedures, with the discrepancy widening with increasing missingness in the proportions. © The Author 2012. Published by Oxford University Press. All rights reserved.

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http://hdl.handle.net/10453/114928