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

Area of Science:

• Proteomics
• Bioinformatics
• Data Analysis

Background:

• Omics data generation introduces technical biases, hindering biological mechanism studies.
• Existing normalization methods often fail to account for time-resolved, sample-specific biases from liquid chromatography-mass spectrometry gradients.

Purpose of the Study:

• To introduce NormalyzerDE, a software tool for retention time (RT)-segmented normalization to mitigate time-resolved biases in omics data.
• To provide a platform for data evaluation, normalization quality assessment, and differential expression analysis.

Main Methods:

• Development of NormalyzerDE software featuring a generic RT-segmented normalization approach.
• Integration of multiple global normalization methods and the empirical Bayes Limma approach for differential expression analysis.
• Evaluation using two spike-in liquid chromatography-mass spectrometry datasets.

Main Results:

• RT-segmented normalization outperformed conventional methods, increasing peptide detection by 8-36% without precision loss.
• Differential expression analysis with Limma consistently improved recall by 2-35% compared to analysis of variance.
• The combination of RT-normalization and Limma identified up to 108% more spike-in peptides compared to traditional methods.

Conclusions:

• NormalyzerDE effectively reduces time-resolved bias in omics data, improving quantitative accuracy.
• The software offers accessible tools for normalization, evaluation, and differential expression analysis, facilitating biological discovery.
• NormalyzerDE is available as a web server, promoting wider adoption in quantitative proteomics research.