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This article is part of the supplement: Statistical mass spectrometry-based proteomics

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Statistical methods for quantitative mass spectrometry proteomic experiments with labeling

Ann L Oberg* and Douglas W Mahoney

Author Affiliations

Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA

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BMC Bioinformatics 2012, 13(Suppl 16):S7  doi:10.1186/1471-2105-13-S16-S7

Published: 5 November 2012


Mass Spectrometry utilizing labeling allows multiple specimens to be subjected to mass spectrometry simultaneously. As a result, between-experiment variability is reduced. Here we describe use of fundamental concepts of statistical experimental design in the labeling framework in order to minimize variability and avoid biases. We demonstrate how to export data in the format that is most efficient for statistical analysis. We demonstrate how to assess the need for normalization, perform normalization, and check whether it worked. We describe how to build a model explaining the observed values and test for differential protein abundance along with descriptive statistics and measures of reliability of the findings. Concepts are illustrated through the use of three case studies utilizing the iTRAQ 4-plex labeling protocol.

mass spectrometry; experimental design; quality control; normalization; variance structure; labeling; iTRAQ; relative quantification