Abstract

High-throughput omics data often contain systematic biases introduced during various steps of sample processing and data generation. As the source of these biases is usually unknown, it is difficult to select an optimal normalization method for a given data set. To facilitate this process, we introduce the open-source tool "Normalyzer". It normalizes the data with 12 different normalization methods and generates a report with several quantitative and qualitative plots for comparative evaluation of different methods. The usefulness of Normalyzer is demonstrated with three different case studies from quantitative proteomics and transcriptomics. The results from these case studies show that the choice of normalization method strongly influences the outcome of downstream quantitative comparisons. Normalyzer is an R package and can be used locally or through the online implementation at http://quantitativeproteomics.org/normalyzer.

Keywords

normalization; preprocessing; label-free; mass spectrometry; microarray; proteomics; transcriptomics

Published in

Journal of Proteome Research
2014, Volume: 13, number: 6, pages: 3114-3120
Publisher: AMER CHEMICAL SOC

SLU Authors

• Alexandersson, Erik

  • Department of Plant Protection Biology, Swedish University of Agricultural Sciences
    

UKÄ Subject classification

Biochemistry and Molecular Biology


Publication identifier

DOI: https://doi.org/10.1021/pr401264n

Permanent link to this page (URI)

https://res.slu.se/id/publ/66078