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

15 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

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© 2017 The Authors

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