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Ratio Level of Measurement

Ratio Level of Measurement

The way a set of data is measured is called its level of measurement. Correct statistical procedures depend on a researcher being familiar with levels of measurement. For analysis, data are classified into four levels of measurement—nominal, ordinal, interval, and ratio.
A set of data measured using the ratio scale takes care of the ratio problem and provides complete information. Ratio scale data are like interval scale data, except they have a zero point and ratios can be calculated. For...

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Measuring and managing ratio compression for accurate iTRAQ/TMT quantification.

Mikhail M Savitski¹, Toby Mathieson, Nico Zinn

¹Cellzome GmbH, Meyerhofstrasse 1, 69117 Heidelberg, Germany. Mikhail.M.Savitski@gsk.com

Journal of Proteome Research

|June 18, 2013

Summary

This summary is machine-generated.

Isobaric mass tagging, like TMT, offers precise protein quantification but suffers from cofragmentation errors. A new algorithm corrects these errors, improving accuracy while minimizing protein loss.

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Quantitative Magnetic Resonance Imaging of Skeletal Muscle Disease

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Area of Science:

Proteomics
Mass Spectrometry
Chemical Biology

Background:

Isobaric mass tagging (e.g., TMT, iTRAQ) is a sensitive multiplexed peptide/protein quantification method in mass spectrometry.
Cofragmentation of peptides in complex samples leads to systematic underestimation of quantitative ratios, impacting accuracy.
Label-free quantification avoids cofragmentation bias but lacks multiplexing capability and precision.

Purpose of the Study:

To compare protein quantification accuracy between isobaric mass tagging and label-free methods in a chemoproteomic competition binding experiment.
To evaluate spectrum purity measures for estimating cofragmentation impact on TMT ratios.
To develop and validate an algorithm for correcting TMT ratios based on peptide interference levels.

Main Methods:

Comparison of TMT and label-free quantification in a chemoproteomic competition binding assay.
Analysis of spectrum purity in survey spectra to assess cofragmentation.
Development of a novel algorithm to correct TMT ratios using peptide interference levels.
Validation of the correction algorithm using spiked chemoproteomics samples.

Main Results:

Stringent interference filters improved TMT quantification accuracy but reduced protein identification by 30%-60%.
The developed algorithm corrected experimental TMT ratios based on peptide interference levels.
The corrected TMT quantification achieved accuracy comparable to stringent filters with less than 10% loss in protein coverage.
The algorithm demonstrated broad applicability by successful validation on spiked samples.

Conclusions:

A novel algorithm effectively corrects cofragmentation-induced errors in TMT quantification.
This correction method significantly enhances proteomic data accuracy while preserving protein quantification coverage.
The developed algorithm offers a valuable tool for accurate quantitative proteomics in complex biological samples.