ArticleBriefings in bioinformatics2020
A novel bioinformatics approach to identify the consistently well-performing normalization strategy for current metabolomic studies.
Article in Briefings in bioinformatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Gene signatures for cancer research: A 25-year retrospective and future avenues.PLoS computational biology · 2024Article
- DrugMAP: molecular atlas and pharma-information of all drugs.Nucleic acids research · 2023Article
- In-silico screening and in-vitro assay show the antiviral effect of Indomethacin against SARS-CoV-2.Computers in biology and medicine · 2022Article
- Prospects and challenges of cancer systems medicine: from genes to disease networks.Briefings in bioinformatics · 2022Review
- RNA-RNA interactions between SARS-CoV-2 and host benefit viral development and evolution during COVID-19 infection.Briefings in bioinformatics · 2022Article
- VARIDT 2.0: structural variability of drug transporter.Nucleic acids research · 2022Article
- Optimization of metabolomic data processing using NOREVA.Nature protocols · 2022Review
- The miRNA: a small but powerful RNA for COVID-19.Briefings in bioinformatics · 2021Review
- Recent Advances in Predicting Protein S-Nitrosylation Sites.BioMed research international · 2021Review
- Genomic Variation Prediction: A Summary From Different Views.Frontiers in cell and developmental biology · 2021Review
- Antioxidant Activity and the Potential Mechanism of the Fruit FromFrontiers in veterinary science · 2021Article
- Construction of the Classification Model Using Key Genes Identified Between Benign and Malignant Thyroid Nodules From Comprehensive Transcriptomic Data.Frontiers in genetics · 2021Article
- Article
- NOREVA: enhanced normalization and evaluation of time-course and multi-class metabolomic data.Nucleic acids research · 2020Article
- Computational advances of tumor marker selection and sample classification in cancer proteomics.Computational and structural biotechnology journal · 2020Review
- A Cancer Gene Module Mining Method Based on Bio-Network of Multi-Omics Gene Groups.Frontiers in oncology · 2020Article
- PSBP-SVM: A Machine Learning-Based Computational Identifier for Predicting Polystyrene Binding Peptides.Frontiers in bioengineering and biotechnology · 2020Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Unwanted experimental/biological variation and technical error are frequently encountered in current metabolomics, which requires the employment of normalization methods for removing undesired data fluctuations. To ensure the 'thorough' removal of unwanted variations, the collective consideration of multiple criteria ('intragroup variation', 'marker stability' and 'classification capability') was essential. However, due to the limited number of available normalization methods, it is extremely challenging to discover the appropriate one that can meet all these criteria. Herein, a novel approach was proposed to discover the normalization strategies that are consistently well performing (CWP) under all criteria. Based on various benchmarks, all normalization methods popular in current metabolomics were 'first' discovered to be non-CWP. 'Then', 21 new strategies that combined the 'sample'-based method with the 'metabolite'-based one were found to be CWP. 'Finally', a variety of currently available methods (such as cubic splines, range scaling, level scaling, EigenMS, cyclic loess and mean) were identified to be CWP when combining with other normalization. In conclusion, this study not only discovered several strategies that performed consistently well under all criteria, but also proposed a novel approach that could ensure the identification of CWP strategies for future biological problems.
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