NOREVA: normalization and evaluation of MS-based metabolomics data

Abstract

Diverse forms of unwanted signal variations in mass spectrometry-based metabolomics data adversely affect the accuracies of metabolic profiling. A variety of normalization methods have been developed for addressing this problem. However, their performances vary greatly and depend heavily on the nature of the studied data. Moreover, given the complexity of the actual data, it is not feasible to assess the performance of methods by single criterion. We therefore developed NOREVA to enable performance evaluation of various normalization methods from multiple perspectives. NOREVA integrated five well-established criteria (each with a distinct underlying theory) to ensure more comprehensive evaluation than any single criterion. It provided the most complete set of the available normalization methods, with unique features of removing overall unwanted variations based on quality control metabolites and allowing quality control samples based correction sequentially followed by data normalization. The originality of NOREVA and the reliability of its algorithms were extensively validated by case studies on five benchmark datasets. In sum, NOREVA is distinguished for its capability of identifying the well performed normalization method by taking multiple criteria into consideration and can be an indispensable complement to other available tools. NOREVA can be freely accessed at http://server.idrb.cqu.edu.cn/noreva/.

Normalizing Gas‐Chromatography–Mass Spectrometry Data: Method Choice can Alter Biological Inference

“…We also encourage researchers to assess the potential performance of different normalization methods on their empirical profiles, using simulated data that reflect the structure of empirical data in question (e.g., number of compounds, profile shapes, sample sizes, etc.) as in the present study, or via the open source tool NOREVA …”

“…Furthermore, because of this size effect, pre‐processing must be carried out before profiles can be compared statistically. A large number of pre‐processing techniques are routinely applied to GC‐MS data, but each is subject to inherent limitations that can lead to different, and sometimes misrepresentative results, making biological interpretation problematic. Despite highly variable performance, it remains an open question, which pre‐processing technique is the most appropriate, and many researchers are unaware of the biases associated with these methods.…”

Nutrimetabolomics: An Integrative Action for Metabolomic Analyses in Human Nutritional Studies

“…As a general recommendation, several normalization methods should be tried and critically assessed (see Boxes and ). Heuristics for the performance comparison of normalization strategies were recently suggested, based on statistical criteria such as the maximization of biological intergroup effects, the reduction of intragroup effects, and p ‐value distributions …”

Concepts and Software Package for Efficient Quality Control in Targeted Metabolomics Studies – MeTaQuaC

“…E.g. many batchcorrection algorithms exist which use data-driven, internal standards (IS)-based or quality control samples (QC)-based normalization 5,[25][26][27][28] . Random errors should be statistically quantified by analysis of suitably defined replicates throughout a study, batch or run.…”