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Statistical Inference from Multiple iTRAQ Experiments without Using Common Reference Standards

delete2013-01-16
delete108
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OA
AI
S
Shelley M. Herbrich
R
Robert N. Cole
K
Keith P. West
K
Kerry Schulze
J
James D. Yager
J
John D. Groopman
P
Parul Christian
L
Lee Wu
R
Robert N. O’Meally
D
Damon May
M
Martin McIntosh
I
Ingo Ruczinski *
DOI:10.1021/pr300624gdelete
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Abstract

Abstract

En 中文
Isobaric tags for relative and absolute quantitation (iTRAQ) is a prominent mass spectrometry technology for protein identification and quantification that is capable of analyzing multiple samples in a single experiment. Frequently, iTRAQ experiments are carried out using an aliquot from a pool of all samples, or masterpool, in one of the channels as a reference sample standard to estimate protein relative abundances in the biological samples and to combine abundance estimates from multiple experiments. In this manuscript, we show that using a masterpool is counterproductive. We obtain more precise estimates of protein relative abundance by using the available biological data instead of the masterpool and do not need to occupy a channel that could otherwise be used for another biological sample. In addition, we introduce a simple statistical method to associate proteomic data from multiple iTRAQ experiments with a numeric response and show that this approach is more powerful than the conventionally employed masterpool-based approach. We illustrate our methods using data from four replicate iTRAQ experiments on aliquots of the same pool of plasma samples and from a 406-sample project designed to identify plasma proteins that covary with nutrient concentrations in chronically undernourished children from South Asia.
Keywords:
Mass spectrometry
iTRAQ
statistical analysis
experimental design
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Journal

Journal of Proteome Research cover
Journal of Proteome Research
IF:
3.6
Papers:
9.4K
Citations:
2.3W

Organization

U
utmd anderson cancer center
Scholars:
3.0W
Papers: 2.4W
Citations: 27
J
Johns Hopkins University
Scholars:
10.2W
Papers: 8.8W
Citations: 13.0W
U
university of texas system
Scholars:
18.5W
Papers: 15.6W
Citations: 210
J
johns hopkins bloomberg school of public health
Scholars:
1.7W
Papers: 1.4W
Citations: 18
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