@article{cd6089dc27b2413389a2a8dabeb92101,
title = "Statistical methods for quantitative mass spectrometry proteomic experiments with labeling.",
abstract = "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.",
author = "Oberg, {Ann L.} and Mahoney, {Douglas W.}",
note = "Funding Information: The authors wish to thank Dr. Leslie T. Cooper of the Mayo Clinic for his permission to use the giant cell myocarditis data set; Dr. Manish Kohli of the Mayo Clinic for his permission to use the prostate cancer data set; Dr. LeeAnn Higgins of the University of Minnesota for providing step by step instructions for exporting data. The authors{\textquoteright} work was supported by the Kemper Foundation, the University of Minnesota Biomedical Informatics and Computational Biology Program, United States National Cancer Institute CA15083 (Mayo Clinic Cancer Center), United States National Institutes of Health Grant CA 136393 (Mayo Clinic SPORE in Ovarian Cancer) from the National Cancer Institute. In addition, the prostate cancer study was supported by National Institutes of Health Grant 1R21CA133536-01A1 and the GCM study was supported by Grant Number 1 UL1 RR024150-01 from the National Center for Research Resources (NCRR), a component of the National Institutes of Health (NIH), and the NIH Roadmap for Medical Research. The contents of this publication are solely the responsibility of the authors and do not necessarily represent the official view of NCRR, NCI or NIH. This article has been published as part of BMC Bioinformatics Volume 13 Supplement 16, 2012: Statistical mass spectrometry-based proteomics. The full contents of the supplement are available online at http://www. biomedcentral.com/bmcbioinformatics/supplements/13/S16.",
year = "2012",
doi = "10.1186/1471-2105-13-s16-s7",
language = "English (US)",
volume = "13 Suppl 16",
pages = "S7",
journal = "BMC bioinformatics",
issn = "1471-2105",
publisher = "BioMed Central",
}