Normalization of mass spectrometry data (NOMAD)

Carl Murie, Brian Sandri, Ann Sofi Sandberg, Timothy J. Griffin, Janne Lehtiö, Christine Wendt, Ola Larsson

Research output: Contribution to journalArticlepeer-review

8 Scopus citations

Abstract

iTRAQ and TMT reagent-based mass spectrometry (MS) are commonly used technologies for quantitative proteomics in biological samples. Such studies are often performed over multiple MS runs, potentially resulting in introduction of MS run bias that could affect downstream analysis. Such MS data have therefore commonly been normalized using a reference sample which is included in each MS run. We show, however, that reference normalization does not effectively remove systematic MS run bias. A linear model approach was previously proposed to improve on the reference normalization approach but does not computationally scale to larger data sets. Here we describe the NOMAD (normalization of mass spectrometry data) R package which implements a computationally efficient ANOVA normalization approach with protein assembly functionality. NOMAD provides the same advantages as the linear regression solution but is more computationally efficient which allows superior scaling to larger sample sizes. Moreover, NOMAD effectively removes bias which improves valid across MS run comparisons.

Original languageEnglish (US)
Pages (from-to)128-133
Number of pages6
JournalAdvances in Biological Regulation
Volume67
DOIs
StatePublished - Jan 2018

Bibliographical note

Funding Information:
We would like to acknowledge Sue Van Riper (University of Minnesota Center for Mass Spectrometry and Proteomics, CMSP) for critical review of this manuscript and Pratik Jagtap (University of Minnesota, CMSP) for technical advice. This research was supported by grants from the Swedish Research Council , the Swedish Cancer foundation and the Wallenberg Academy Fellows program (O.L); Swedish Foundation for Strategic Research (SSF; to J.L.) and NIH R01 HL107612 (C.W.). T.J.G was supported in part by grant 1147079 from the U.S. National Science Foundation .

Publisher Copyright:
© 2017 The Authors

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