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

X. Wang; G. A. Anderson; R. D. Smith; A. R. Dabney

doi:10.1093/bioinformatics/bts193

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

X. Wang, G. A. Anderson, R. D. Smith, A. R. Dabney

Research output: Contribution to journal › Article › peer-review

20 Scopus citations

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.

Original language	English (US)
Pages (from-to)	1586-1591
Number of pages	6
Journal	Bioinformatics
Volume	28
Issue number	12
DOIs	https://doi.org/10.1093/bioinformatics/bts193
State	Published - Apr 19 2012
Externally published	Yes

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledged KAUST grant number(s): KUS-C1-016-04
Acknowledgements: 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.
This publication acknowledges KAUST support, but has no KAUST affiliated authors.

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10.1093/bioinformatics/bts193

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title = "A hybrid approach to protein differential expression in mass spectrometry-based proteomics",

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.",

author = "X. Wang and Anderson, {G. A.} and Smith, {R. D.} and Dabney, {A. R.}",

note = "KAUST Repository Item: Exported on 2020-10-01 Acknowledged KAUST grant number(s): KUS-C1-016-04 Acknowledgements: 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. This publication acknowledges KAUST support, but has no KAUST affiliated authors.",

year = "2012",

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doi = "10.1093/bioinformatics/bts193",

language = "English (US)",

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AU - Wang, X.

AU - Anderson, G. A.

AU - Smith, R. D.

AU - Dabney, A. R.

N1 - KAUST Repository Item: Exported on 2020-10-01 Acknowledged KAUST grant number(s): KUS-C1-016-04 Acknowledgements: 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. This publication acknowledges KAUST support, but has no KAUST affiliated authors.

PY - 2012/4/19

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N2 - 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.

AB - 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.

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

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