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

被引：148

作者：

Oberg, Ann L. ^{[1
]}

Mahoney, Douglas W. ^{[1
]}

Eckel-Passow, Jeanette E. ^{[1
]}

Malone, Christopher J. ^{[2
]}

Wolfinger, Russell D. ^{[3
]}

Hill, Elizabeth G. ^{[4
]}

Cooper, Leslie T. ^{[5
]}

Onuma, Oyere K. ^{[6
]}

Spiro, Craig ^{[7
]}

Therneau, Terry A. ^{[1
]}

Bergen, H. Robert, III ^{[8
]}

机构：

[1] Mayo Clin & Mayo Fdn, Canc Ctr Stat, Dept Hlth Sci Res, Div Biostat, Rochester, MN 55905 USA

[2] Winona State Univ, Dept Math & Stat, Winona, MN 55987 USA

[3] SAS Inst Inc, Cary, NC 27513 USA

[4] Med Univ S Carolina, Dept Biostat Bioinformat & Epidemiol, Charleston, SC 29425 USA

[5] Mayo Clin & Mayo Fdn, Div Cardiol, Rochester, MN 55905 USA

[6] Massachusetts Gen Hosp, Dept Med, Boston, MA 02114 USA

[7] Mayo Clin & Mayo Fdn, Rochester, MN 55905 USA

[8] Mayo Clin & Mayo Fdn, Mayo Proteom Res Ctr, Rochester, MN 55905 USA

来源：

JOURNAL OF PROTEOME RESEARCH | 2008年 / 7卷 / 01期

关键词：

proteomics; ANOVA; iTRAQ; normalization; relative labeling protocol; missing data; Gauss-Siedel; backfitting; fixed effects model; mixed effects model;

D O I：

10.1021/pr700734f

中图分类号：

Q5 [生物化学];

学科分类号：

071010 ; 081704 ;

摘要：

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.

引用

页码：225 / 233

页数：9

共 26 条

[1]

*APPL CORP MDS INC, 2004, APPL BIOS US PRO GRO

[2] Faster cyclic loess: normalizing RNA arrays via linear models [J].

Ballman, KV ;

Grill, DE ;

Oberg, AL ;

Therneau, TM .

BIOINFORMATICS, 2004, 20 (16) :2778-2786

[3] CONTROLLING THE FALSE DISCOVERY RATE - A PRACTICAL AND POWERFUL APPROACH TO MULTIPLE TESTING [J].

BENJAMINI, Y ;

HOCHBERG, Y .

JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY, 1995, 57 (01) :289-300

[4] Proteomic analysis of microvesicles derived from human colorectal cancer cells [J].

Choi, Dong-Sic ;

Lee, Jae-Min ;

Park, Gun Wook ;

Lim, Hyeon-Woo ;

Bang, Joo Young ;

Kim, Yoon-Keun ;

Kwon, Kyung-Hoon ;

Kwon, Ho Jeong ;

Kim, Kwang Pyo ;

Gho, Yong Song .

JOURNAL OF PROTEOME RESEARCH, 2007, 6 (12) :4646-4655

[5] Regression analysis for comparing protein samples with 16O/18O stable-isotope labeled mass spectrometry [J].

Eckel-Passow, J. E. ;

Oberg, A. L. ;

Therneau, T. M. ;

Mason, C. J. ;

Mahoney, D. W. ;

Johnson, K. L. ;

Olson, J. E. ;

Bergen, H. R., III .

BIOINFORMATICS, 2006, 22 (22) :2739-2745

[6] Experimental design and analysis of antibody microarrays: Applying methods from cDNA arrays [J].

Eckel-Passow, JE ;

Hoering, A ;

Therneau, TM ;

Ghobrial, I .

CANCER RESEARCH, 2005, 65 (08) :2985-2989

[7]

FISHER R. A., 1937, The design of experiments.

[8] Quantitative analysis of complex protein mixtures using isotope-coded affinity tags [J].

Gygi, SP ;

Rist, B ;

Gerber, SA ;

Turecek, F ;

Gelb, MH ;

Aebersold, R .

NATURE BIOTECHNOLOGY, 1999, 17 (10) :994-999

[9]

HASTIE TJ, 1990, GEN ADDITIVE MODELS, P105

[10]

HILL EG, 2008, UNPUB J PROTEOME RES, DOI DOI 10.1021/PR070502U

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