Performance Evaluation and Online Realization of Data-driven Normalization Methods Used in LC/MS based Untargeted Metabolomics Analysis

Abstract

In untargeted metabolomics analysis, several factors (e.g., unwanted experimental & biological variations and technical errors) may hamper the identification of differential metabolic features, which requires the data-driven normalization approaches before feature selection. So far, ≥16 normalization methods have been widely applied for processing the LC/MS based metabolomics data. However, the performance and the sample size dependence of those methods have not yet been exhaustively compared and no online tool for comparatively and comprehensively evaluating the performance of all 16 normalization methods has been provided. In this study, a comprehensive comparison on these methods was conducted. As a result, 16 methods were categorized into three groups based on their normalization performances across various sample sizes. The VSN, the Log Transformation and the PQN were identified as methods of the best normalization performance, while the Contrast consistently underperformed across all sub-datasets of different benchmark data. Moreover, an interactive web tool comprehensively evaluating the performance of 16 methods specifically for normalizing LC/MS based metabolomics data was constructed and hosted at http://server.idrb.cqu.edu.cn/MetaPre/. In summary, this study could serve as a useful guidance to the selection of suitable normalization methods in analyzing the LC/MS based metabolomics data.

Charting the cross-functional map between transcription factors and cancer metabolism

“…Normalization of large-scale non-targeted metabolomics profiles is a fundamental aspect of data analysis 19 , which becomes particularly challenging when comparing mammalian cell lines with large differences in cell size. We propose a two-fold approach: (i) measure metabolome profiles from the same cell line at different cell densities, enabling a multiple regression approach to decouple cell linespecific metabolic signatures from differences in extracted cell amount, plate-to-plate variance and background noise (Supplementary Figure S2), (ii) image analysis of bright-field microscopy images to correct for differences in cell volume between cell lines (Supplementary Figures S1-2).…”

Systematic evaluation of normalization methods for glycomics data based on performance of network inference

“…Different normalization procedures have substantially different assumptions regarding the nature of the non-biological variation, which, however, is unknown in most practical cases. Systematic comparisons of commonly implemented preprocessing strategies for various omics technologies have been published in recent years, including transcriptomics 2 , proteomics 3 , as well as metabolomics [4][5][6] . An analogous study for glycomics data is, to the best of our knowledge, currently still unavailable.…”

“…Minimizing the variation between technical replicates 20,21 ; 2. Maximizing the variation across groups 6 . Consistency across technical replicates is a desirable outcome, but alone is not sufficient to guarantee good data quality, and technical replicates might not always be available.…”

Evaluation of intensity drift correction strategies using MetaboDrift, a normalization tool for multi-batch metabolomics data

“…The most widely-used tools for metabolomics data analysis such as XCMS Online and MetaboAnalyst offer basic methods such as total signal or median signal correction for automated normalization of metabolomics data [16, 17], but do not support QC-sample based intensity drift correction. Most QC-based drift correction of metabolomics data has been performed using command-line driven software, often using scripts written in individual research laboratories.…”