NormalyzerDE: Online Tool for Improved Normalization of Omics Expression Data and High-Sensitivity Differential Expression Analysis

Research output: Contribution to journalArticle


Technical biases are introduced in omics data sets during data generation and interfere with the ability to study biological mechanisms. Several normalization approaches have been proposed to minimize the effects of such biases, but fluctuations in the electrospray current during liquid chromatography-mass spectrometry gradients cause local and sample-specific bias not considered by most approaches. Here we introduce a software named NormalyzerDE that includes a generic retention time (RT)-segmented approach compatible with a wide range of global normalization approaches to reduce the effects of time-resolved bias. The software offers straightforward access to multiple normalization methods, allows for data set evaluation and normalization quality assessment as well as subsequent or independent differential expression analysis using the empirical Bayes Limma approach. When evaluated on two spike-in data sets the RT-segmented approaches outperformed conventional approaches by detecting more peptides (8-36%) without loss of precision. Furthermore, differential expression analysis using the Limma approach consistently increased recall (2-35%) compared to analysis of variance. The combination of RT-normalization and Limma was in one case able to distinguish 108% (2597 vs 1249) more spike-in peptides compared to traditional approaches. NormalyzerDE provides widely usable tools for performing normalization and evaluating the outcome and makes calculation of subsequent differential expression statistics straightforward. The program is available as a web server at


External organisations
  • Swedish University of Agricultural Sciences
Research areas and keywords

Subject classification (UKÄ) – MANDATORY

  • Biochemistry and Molecular Biology


  • data overview, differential expression analysis, limma, normalization, omics data, preprocessing, proteomics data, R-package, singularity
Original languageEnglish
Pages (from-to)732-740
JournalJournal of Proteome Research
Issue number2
Early online date2018 Oct 2
Publication statusPublished - 2019
Publication categoryResearch