ArticleBMC bioinformatics2018
Robust volcano plot: identification of differential metabolites in the presence of outliers.
Article in BMC bioinformatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.
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35 citing papers in PubMed.
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- AI-driven breath biopsy from a case-control study assists in the early detection of paediatric brain tumours.EBioMedicine · 2025Article
- Effects of Nicotine on SH-SY5Y Cells: An NMR-Based Metabolomic Study.Metabolites · 2025Article
- Global profiling of polyketide synthases in facultative multicellular eukaryotes.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Effect of Storage Time on the Fermentation Quality, Bacterial Community Structure, and Metabolic Profiles of Jinmu Grain Grass Silage.Microorganisms · 2025Article
- Impact of a Formulation Containing Chaga Extract, Coenzyme Q10, and Alpha-Lipoic Acid on Mitochondrial Dysfunction and Oxidative Stress: NMR Metabolomic Insights into Cellular Energy.Antioxidants (Basel, Switzerland) · 2025Article
- 1H-NMR-based metabolomics identifies disrupted betaine metabolism as distinct serum signature of pre-frailty.npj aging · 2025Article
- Algorithms and tools for data-driven omics integration to achieve multilayer biological insights: a narrative review.Journal of translational medicine · 2025Review
- Evaluation of Different Drying Methods on the Quality Parameters ofFoods (Basel, Switzerland) · 2025Article
- Effects of Lysozyme-Supplemented Diets on Muscle Texture and Metabolite Profiles in Yellowfin Seabream (Aquaculture nutrition · 2025Article
- MetaPhenotype: A Transferable Meta-Learning Model for Single-Cell Mass Spectrometry-Based Cell Phenotype Prediction Using Limited Number of Cells.Analytical chemistry · 2024Article
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Corrections and comments
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Authors and funding
3 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundThe identification of differential metabolites in metabolomics is still a big challenge and plays a prominent role in metabolomics data analyses. Metabolomics datasets often contain outliers because of analytical, experimental, and biological ambiguity, but the currently available differential metabolite identification techniques are sensitive to outliers.
resultsWe propose a kernel weight based outlier-robust volcano plot for identifying differential metabolites from noisy metabolomics datasets. Two numerical experiments are used to evaluate the performance of the proposed technique against nine existing techniques, including the t-test and the Kruskal-Wallis test. Artificially generated data with outliers reveal that the proposed method results in a lower misclassification error rate and a greater area under the receiver operating characteristic curve compared with existing methods. An experimentally measured breast cancer dataset to which outliers were artificially added reveals that our proposed method produces only two non-overlapping differential metabolites whereas the other nine methods produced between seven and 57 non-overlapping differential metabolites.
conclusionOur data analyses show that the performance of the proposed differential metabolite identification technique is better than that of existing methods. Thus, the proposed method can contribute to analysis of metabolomics data with outliers. The R package and user manual of the proposed method are available at https://github.com/nishithkumarpaul/Rvolcano .
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