Statistical analysis of relative labeled mass spectrometry data from complex samples using ANOVA

Ann L. Oberg; Douglas W. Mahoney; Jeanette E. Eckel-Passow; Christopher J. Malone; Russell D. Wolfinger; Elizabeth G. Hill; Leslie T. Cooper; Oyere K. Onuma; Craig Spiro; Terry M. Therneau; H. Robert Bergen

doi:10.1021/pr700734f

Statistical analysis of relative labeled mass spectrometry data from complex samples using ANOVA

Ann L. Oberg, Douglas W. Mahoney, Jeanette E. Eckel-Passow, Christopher J. Malone, Russell D. Wolfinger, Elizabeth G. Hill, Leslie T. Cooper, Oyere K. Onuma, Craig Spiro, Terry M. Therneau, H. Robert Bergen

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

125 Scopus citations

Abstract

Statistical tools enable unified analysis of data from multiple global proteomic experiments, producing unbiased estimates of normalization terms despite the missing data problem inherent in these studies. The modeling approach, implementation, and useful visualization tools are demonstrated via a case study of complex biological samples assessed using the iTRAQ relative labeling protocol.

Original language	English (US)
Pages (from-to)	225-233
Number of pages	9
Journal	Journal of Proteome Research
Volume	7
Issue number	1
DOIs	https://doi.org/10.1021/pr700734f
State	Published - Jan 2008

Keywords

ANOVA
Backfitting
Fixed effects model
Gauss-Siedel
Missing data
Mixed effects model
Normalization
Proteomics
Relative labeling protocol
iTRAQ

ASJC Scopus subject areas

General Chemistry
Biochemistry

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10.1021/pr700734f

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title = "Statistical analysis of relative labeled mass spectrometry data from complex samples using ANOVA",

abstract = "Statistical tools enable unified analysis of data from multiple global proteomic experiments, producing unbiased estimates of normalization terms despite the missing data problem inherent in these studies. The modeling approach, implementation, and useful visualization tools are demonstrated via a case study of complex biological samples assessed using the iTRAQ relative labeling protocol.",

keywords = "ANOVA, Backfitting, Fixed effects model, Gauss-Siedel, Missing data, Mixed effects model, Normalization, Proteomics, Relative labeling protocol, iTRAQ",

author = "Oberg, {Ann L.} and Mahoney, {Douglas W.} and Eckel-Passow, {Jeanette E.} and Malone, {Christopher J.} and Wolfinger, {Russell D.} and Hill, {Elizabeth G.} and Cooper, {Leslie T.} and Onuma, {Oyere K.} and Craig Spiro and Therneau, {Terry M.} and Bergen, {H. Robert}",

year = "2008",

month = jan,

doi = "10.1021/pr700734f",

language = "English (US)",

volume = "7",

pages = "225--233",

journal = "Journal of Proteome Research",

issn = "1535-3893",

publisher = "American Chemical Society",

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T1 - Statistical analysis of relative labeled mass spectrometry data from complex samples using ANOVA

AU - Oberg, Ann L.

AU - Mahoney, Douglas W.

AU - Eckel-Passow, Jeanette E.

AU - Malone, Christopher J.

AU - Wolfinger, Russell D.

AU - Hill, Elizabeth G.

AU - Cooper, Leslie T.

AU - Onuma, Oyere K.

AU - Spiro, Craig

AU - Therneau, Terry M.

AU - Bergen, H. Robert

PY - 2008/1

Y1 - 2008/1

N2 - Statistical tools enable unified analysis of data from multiple global proteomic experiments, producing unbiased estimates of normalization terms despite the missing data problem inherent in these studies. The modeling approach, implementation, and useful visualization tools are demonstrated via a case study of complex biological samples assessed using the iTRAQ relative labeling protocol.

AB - Statistical tools enable unified analysis of data from multiple global proteomic experiments, producing unbiased estimates of normalization terms despite the missing data problem inherent in these studies. The modeling approach, implementation, and useful visualization tools are demonstrated via a case study of complex biological samples assessed using the iTRAQ relative labeling protocol.

KW - ANOVA

KW - Backfitting

KW - Fixed effects model

KW - Gauss-Siedel

KW - Missing data

KW - Mixed effects model

KW - Normalization

KW - Proteomics

KW - Relative labeling protocol

KW - iTRAQ

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DO - 10.1021/pr700734f

M3 - Article

C2 - 18173221

AN - SCOPUS:38649099066

SN - 1535-3893

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SP - 225

EP - 233

JO - Journal of Proteome Research

JF - Journal of Proteome Research

IS - 1

ER -

Statistical analysis of relative labeled mass spectrometry data from complex samples using ANOVA

Abstract

Keywords

ASJC Scopus subject areas

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