Statistical inference from multiple iTRAQ experiments without using common reference standards.

Area of Science:

• Proteomics
• Mass Spectrometry
• Quantitative Biology

Background:

• Isobaric tags for relative and absolute quantitation (iTRAQ) is a key mass spectrometry technique for protein analysis.
• iTRAQ experiments commonly use a masterpool reference sample for quantification and multi-experiment data integration.

Purpose of the Study:

• To demonstrate that employing a masterpool in iTRAQ experiments is suboptimal.
• To introduce a more precise and efficient alternative to the masterpool approach.
• To present a novel statistical method for integrating proteomic data from multiple iTRAQ experiments.

Main Methods:

• Comparative analysis of protein relative abundance estimation accuracy with and without a masterpool.
• Development and application of a statistical method for associating multi-experiment proteomic data with a numeric response.
• Validation using replicate iTRAQ experiments on plasma samples and a large-scale study of undernourished children.

Main Results:

• Using available biological data yields more precise protein relative abundance estimates than using a masterpool.
• The proposed statistical method offers greater statistical power than the conventional masterpool-based approach.
• The masterpool approach occupies a valuable experimental channel unnecessarily.

Conclusions:

• The masterpool strategy in iTRAQ experiments is detrimental to data precision and efficiency.
• A novel statistical method provides a more powerful approach for analyzing multi-experiment iTRAQ data.
• Alternative methods enhance the utility of iTRAQ for biological discovery.