A systematic evaluation of normalization methods in quantitative label-free proteomics. (2nd October 2016)

• Record Type: Journal Article
• Authors: Välikangas, Tommi; Suomi, Tomi; Elo, Laura L
• Is Part Of: Briefings in bioinformatics. Volume 19:Number 1(2018:Jan.)
• Page Start: 1
• Page End: 11
• Publication Date: 2016-10-02
• Subjects: proteomics -- normalization -- label free -- bias -- differential expression -- logarithmic fold change -- quantitation -- intragroup variation -- reproducibility -- mass spectrometry; Genetics -- Data processing -- Periodicals; Molecular biology -- Data processing -- Periodicals; Genomes -- Data processing -- Periodicals; 572.80285
• DOI: 10.1093/bib/bbw095
• Languages: English
• ISSNs: 1467-5463

Abstract

Abstract: To date, mass spectrometry (MS) data remain inherently biased as a result of reasons ranging from sample handling to differences caused by the instrumentation. Normalization is the process that aims to account for the bias and make samples more comparable. The selection of a proper normalization method is a pivotal task for the reliability of the downstream analysis and results. Many normalization methods commonly used in proteomics have been adapted from the DNA microarray techniques. Previous studies comparing normalization methods in proteomics have focused mainly on intragroup variation. In this study, several popular and widely used normalization methods representing different strategies in normalization are evaluated using three spike-in and one experimental mouse label-free proteomic data sets. The normalization methods are evaluated in terms of their ability to reduce variation between technical replicates, their effect on differential expression analysis and their effect on the estimation of logarithmic fold changes. Additionally, we examined whether normalizing the whole data globally or in segments for the differential expression analysis has an effect on the performance of the normalization methods. We found that variance stabilization normalization (Vsn) reduced variation the most between technical replicates in all examined data sets. Vsn also performed consistently well in the differential expression analysis. Linear regression normalization and local regression normalization performed also systematically well. Finally, we discuss the choice of a normalization method and some qualities of a suitable normalization method in the light of the results of our evaluation.