Statistical Inference from Multiple iTRAQ Experiments without Using Common Reference Standards

详细信息    查看全文

• 作者：Shelley M. Herbrich ; Robert N. Cole ; Keith P. West ; Jr. ; Kerry Schulze ; James D. Yager ; John D. Groopman ; Parul Christian ; Lee Wu ; Robert N. O鈥橫eally ; Damon H. May ; Martin W. McIntosh ; Ingo Ruczinski
• 刊名：Journal of Proteome Research
• 出版年：2013
• 出版时间：February 1, 2013
• 年：2013
• 卷：12
• 期：2
• 页码：594-604
• 全文大小：627K
• 年卷期：v.12,no.2(February 1, 2013)
• ISSN：1535-3907

文摘

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 鈥渕asterpool鈥? 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.
<h4>Keywords: h4> Mass spectrometry; iTRAQ; statistical analysis; experimental design