A hybrid approach to protein differential expression in mass spectrometry-based proteomics

Handle URI:
http://hdl.handle.net/10754/597289
Title:
A hybrid approach to protein differential expression in mass spectrometry-based proteomics
Authors:
Wang, X.; Anderson, G. A.; Smith, R. D.; Dabney, A. R.
Abstract:
MOTIVATION: Quantitative mass spectrometry-based proteomics involves statistical inference on protein abundance, based on the intensities of each protein's associated spectral peaks. However, typical MS-based proteomics datasets have substantial proportions of missing observations, due at least in part to censoring of low intensities. This complicates intensity-based differential expression analysis. RESULTS: We outline a statistical method for protein differential expression, based on a simple Binomial likelihood. By modeling peak intensities as binary, in terms of 'presence/absence,' we enable the selection of proteins not typically amenable to quantitative analysis; e.g. 'one-state' proteins that are present in one condition but absent in another. In addition, we present an analysis protocol that combines quantitative and presence/absence analysis of a given dataset in a principled way, resulting in a single list of selected proteins with a single-associated false discovery rate. AVAILABILITY: All R code available here: http://www.stat.tamu.edu/~adabney/share/xuan_code.zip.
Citation:
Wang X, Anderson GA, Smith RD, Dabney AR (2012) A hybrid approach to protein differential expression in mass spectrometry-based proteomics. Bioinformatics 28: 1586–1591. Available: http://dx.doi.org/10.1093/bioinformatics/bts193.
Publisher:
Oxford University Press (OUP)
Journal:
Bioinformatics
KAUST Grant Number:
KUS-C1-016-04
Issue Date:
19-Apr-2012
DOI:
10.1093/bioinformatics/bts193
PubMed ID:
22522136
PubMed Central ID:
PMC3371829
Type:
Article
ISSN:
1367-4803; 1460-2059
Sponsors:
Funding: NIH National Center for Research Resources (RR18522), National Institute of Allergy and Infectious Diseases NIH/DHHS through Interagency agreement Y1-AI-8401 and Award No. U54AI081680, and in part on work supported by Award No. KUS-C1-016-04, made by King Abdullah University of Science and Technology (KAUST). Work was performed in the Environmental Molecular Science Laboratory, a national scientific user facility sponsored by the US Department of Energys Office of Biological and Environmental Research and located at Pacific Northwest National Laboratory in Richland, Washington. Pacific Northwest National Laboratory is operated by Battelle Memorial Institute for the US Department of Energy under contract DE-AC05-76RL0 1830.
Appears in Collections:
Publications Acknowledging KAUST Support

Full metadata record

DC FieldValue Language
dc.contributor.authorWang, X.en
dc.contributor.authorAnderson, G. A.en
dc.contributor.authorSmith, R. D.en
dc.contributor.authorDabney, A. R.en
dc.date.accessioned2016-02-25T12:29:55Zen
dc.date.available2016-02-25T12:29:55Zen
dc.date.issued2012-04-19en
dc.identifier.citationWang X, Anderson GA, Smith RD, Dabney AR (2012) A hybrid approach to protein differential expression in mass spectrometry-based proteomics. Bioinformatics 28: 1586–1591. Available: http://dx.doi.org/10.1093/bioinformatics/bts193.en
dc.identifier.issn1367-4803en
dc.identifier.issn1460-2059en
dc.identifier.pmid22522136en
dc.identifier.doi10.1093/bioinformatics/bts193en
dc.identifier.urihttp://hdl.handle.net/10754/597289en
dc.description.abstractMOTIVATION: Quantitative mass spectrometry-based proteomics involves statistical inference on protein abundance, based on the intensities of each protein's associated spectral peaks. However, typical MS-based proteomics datasets have substantial proportions of missing observations, due at least in part to censoring of low intensities. This complicates intensity-based differential expression analysis. RESULTS: We outline a statistical method for protein differential expression, based on a simple Binomial likelihood. By modeling peak intensities as binary, in terms of 'presence/absence,' we enable the selection of proteins not typically amenable to quantitative analysis; e.g. 'one-state' proteins that are present in one condition but absent in another. In addition, we present an analysis protocol that combines quantitative and presence/absence analysis of a given dataset in a principled way, resulting in a single list of selected proteins with a single-associated false discovery rate. AVAILABILITY: All R code available here: http://www.stat.tamu.edu/~adabney/share/xuan_code.zip.en
dc.description.sponsorshipFunding: NIH National Center for Research Resources (RR18522), National Institute of Allergy and Infectious Diseases NIH/DHHS through Interagency agreement Y1-AI-8401 and Award No. U54AI081680, and in part on work supported by Award No. KUS-C1-016-04, made by King Abdullah University of Science and Technology (KAUST). Work was performed in the Environmental Molecular Science Laboratory, a national scientific user facility sponsored by the US Department of Energys Office of Biological and Environmental Research and located at Pacific Northwest National Laboratory in Richland, Washington. Pacific Northwest National Laboratory is operated by Battelle Memorial Institute for the US Department of Energy under contract DE-AC05-76RL0 1830.en
dc.publisherOxford University Press (OUP)en
dc.titleA hybrid approach to protein differential expression in mass spectrometry-based proteomicsen
dc.typeArticleen
dc.identifier.journalBioinformaticsen
dc.identifier.pmcidPMC3371829en
dc.contributor.institutionTexas A and M University, College Station, United Statesen
dc.contributor.institutionPacific Northwest National Laboratory, Richland, United Statesen
kaust.grant.numberKUS-C1-016-04en

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