Statistical Inference from Multiple iTRAQ Experiments without Using Common Reference Standards

Abstract

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.

CONSTANd : A Normalization Method for Isobaric Labeled Spectra by Constrained Optimization

“…The CONSTANd method employs constrained optimization to achieve this task in a most optimal manner. As a result, peptides from isobaric labeled data can be efficiently compared within and between a multiplexed experiment that are normalized by the CONSTANd method without the requirement of a reference sample similar in nature as Herbrich et al (24). Latter property is especially useful when more than six or eight (TMT/iTRAQ) biological samples are required in a quantitative proteomics experiment to detect differential peptides with sufficient statistical power (44).…”

“…Second, by dividing the intensity of reporter ions by a stochastic variable, i.e. the reference sample, you will inflate the variability (52). Also, reference protein sets, spiked-in standards or intermediate control runs are not required to infer the CONSTANd normalization factors.…”

“…The authors performed normalization on the estimated peak areas, and in their model, used a percentage contribution of the reporter channels. A few landmark publications combine data normalization in a statistical framework based on ANOVA models or linear mixed models which allows simultaneous normalization and statistical analysis of multiple isobaric experiments (23)(24)(25)(26)(27). On the other hand, for a quality control of the samples, data visualization or downstream analysis with machine learning techniques, a simple and correct data normalization, is sufficient.…”

“…Further, CONSTANd scales well with large data sets. Here, we employ the CONSTANd method to a quantitative TMT experiment and illustrate its superiority over quantile normalization and median sweep normalization (24).…”

MS-PyCloud: An open-source, cloud computing-based pipeline for LC-MS/MS data analysis

“…The peptide and protein quantification are calculated based on iTRAQ reporter ion intensity at PSM level and the corresponding FDR is estimated at both peptide and protein level. The relative protein abundance is calculated as the median of relative abundances of peptides belonging to same protein, which is calculated as the median of log2 ratio of reporter ion intensities of PSMs belonging to the same peptides 3,16,17 . Graphical User Interface.…”