Apr 18, 2020

Tail-Robust Quantile Normalization

BioRxiv : the Preprint Server for Biology
E. BrombacherClemens Kreutz

Abstract

High-throughput biological data -- such as mass spectrometry-based proteomics data -- suffer from systematic non-biological variance, which is introduced by systematic errors such as batch effects. This hinders the estimation of 'real' biological signals and, thus, decreases the power of statistical tests and biases the identification of differentially expressed sample classes. To remove such unintended variation, while retaining the biological signal of interest, the analysis workflows for mass spectrometry-based quantification typically comprises normalization steps prior to the statistical analysis of the data. Several normalization methods, such as quantile normalization, have originally been developed for microarray data. However, unlike microarray data, proteomics data may contain features, in the form of protein intensities, that are consistently highly abundant across experimental conditions and, hence, are encountered in the tails of the protein intensity distribution. If such proteins are present, statistical inferences of the intensity profiles of the normalized features are impeded through the increased number of false positive findings due to the biased estimation of the variance of the data. Thus, we developed a, ...Continue Reading

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Mentioned in this Paper

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