Willforss J, Chawade A, Levander F
J. Proteome Res. - (-) - [2018-10-02; online 2018-10-02]
Technical biases are introduced in omics datasets 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 LC-MS gradients causes 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 dataset 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 datasets 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 ANOVA. 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, and evaluating the outcome of, normalization and makes calculation of subsequent differential expression statistics straightforward. The program is available as a web server at http://quantitativeproteomics.org/normalyzerde.