Variance function estimation in quantitative mass spectrometry with application to iTRAQ labeling

TL;DR

This paper introduces and compares two methods for estimating the variance function in quantitative mass spectrometry with iTRAQ labeling, crucial for accurate proteomics analysis, and applies these methods to biological experiments involving cancer mutations.

Contribution

The paper presents novel approaches for variance function estimation in iTRAQ proteomics, addressing challenges of nuisance parameters and small replicate numbers, with practical applications to cancer research.

Findings

Estimated variance function is stable over time.

Methods enable construction of conservative p-values.

Application to cancer cell signaling experiments.

Abstract

This paper describes and compares two methods for estimating the variance function associated with iTRAQ (isobaric tag for relative and absolute quantitation) isotopic labeling in quantitative mass spectrometry based proteomics. Measurements generated by the mass spectrometer are proportional to the concentration of peptides present in the biological sample. However, the iTRAQ reporter signals are subject to errors that depend on the peptide amounts. The variance function of the errors is therefore an essential parameter for evaluating the results, but estimating it is complicated, as the number of nuisance parameters increases with sample size while the number of replicates for each peptide remains small. Two experiments that were conducted with the sole goal of estimating the variance function and its stability over time are analyzed, and the resulting estimated variance function is used to analyze an experiment targeting aberrant signaling cascades in cells harboring distinct oncogenic mutations. Methods for constructing conservative $p$-values and confidence intervals are discussed.